Skocz do pozycji: 1.

Tytuł:

A new approach to image-based recommender systems with the application of heatmaps maps

Autorzy:

Woldan, Piotr
Duda, Piotr
Cader, Andrzej
Laktionov, Ivan

Powiązania:

https://bibliotekanauki.pl/articles/2201330.pdf

Data publikacji:

2023

Wydawca:

Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych

Tematy:

feature extraction
recommender system
heatmap

Opis:

One of the fundamental issues of modern society is access to interesting and useful content. As the amount of available content increases, this task becomes more and more challenging. Our needs are not always formulated in words; sometimes we have to use complex data types like images. In this paper, we consider the three approaches to creating recommender systems based on image data. The proposed systems are evaluated on a real-world dataset. Two case studies are presented. The first one presents the case of an item with many similar objects in a database, and the second one with only a few similar items

Źródło:

Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 2; 63--72
2083-2567
2449-6499

Pojawia się w:

Journal of Artificial Intelligence and Soft Computing Research

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 2.

Tytuł:

Klasyfikacja stanów przedkrytycznych
Classification of pre-critical states

Autorzy:

Topczewska, M.
Frischmuth, K.

Powiązania:

https://bibliotekanauki.pl/articles/154431.pdf

Data publikacji:

2012

Wydawca:

Stowarzyszenie Inżynierów i Techników Mechaników Polskich

Tematy:

klasyfikacja
ekstrakcja cech
classification
feature extraction

Opis:

Praca zawiera przykład klasyfikacji danych rzeczywistych opisujących sygnały niekrytyczne, przedkrytyczne i krytyczne. Celem jest rozpoznanie stanów niebezpiecznych tak wcześnie jak to możliwe. Ze względu na brak separowalności liniowej danych w celu separacji klas użyto klasyfikacji hierarchicznej z cięciami za pomocą klasyfikatorów liniowych oraz z podejściem one-versus-rest z wyróżnioną klasą sygnałów bezpiecznych. W wyniku ośmiu cięć uzyskano ostateczny podział przestrzeni skutkujący odseparowaniem klasy sygnałów bezpiecznych od podejrzanych, tj. przedkrytycznych i krytycznych oraz dający najmniejszą liczbę błędnie sklasyfikowanych obiektów z klasy sygnałów niekrytycznych.
The paper presents an application of classification methods to time-continuous signals (1). Signals with values that exceed a certain critical maximum are called dangerous or critical, otherwise we speak about normal or routine operation of the system under consideration, Fig. 1. The problem is to recognize pre-critical states, i.e. states preceding the actual dangerous ones, and that as early as possible. False negative classifications may have very serious consequences, while false positive verdicts cause expensive but unnecessary counter-measures. As pre-processing, the input signals are characterized by a number of features, which form sequences of vector data, indexed by the cycle number (2). In a first stage, suspicious feature vectors are selected, from which in a second sweep unlikely candidates are removed. The focus of the present paper is this second stage, i.e. the distinction between actual pre-critical and the harmless routine states among the suspicious states, indicated in the first stage by a certain preliminary test. The choice of features and the logic behind the preliminary test are beyond our present scope. Let it suffice to say that the first step is a combination of Principal Component Analysis and some statistical test, and that it is very effective but unspecific in the application at hand.For the real-world data we used to develop the method, it turned out that the obtained feature vectors were linearly non-separable. For that reason a hierarchical approach was applied, where in several steps linear cuts (4,5) of the one-versus-rest type were performed in order to single out the true pre-critical states. For the example under consideration, in eight iterations separation between pre-critical and non-pre-critical ones was achieved. We succeeded to keep the number of wrong negatives at zero while reducing the number of wrong positives to a fraction of the starting value, established by the preliminary test, Fig. 3, 4, 5. The final sensitivity, for the given data set, is 100%, and the achieved specificity is at 93.15%. Numerical experiments, using nonlinear classifiers on much larger data sets, are under way. The present aim is to find an optimal set of features and a one-step criterion which further improves the quality of the classification.

Źródło:

Pomiary Automatyka Kontrola; 2012, R. 58, nr 10, 10; 872-875
0032-4140

Pojawia się w:

Pomiary Automatyka Kontrola

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 3.

Tytuł:

Human Detection in Thermal Images Using Low-level Features

Autorzy:

Budzan, S.

Powiązania:

https://bibliotekanauki.pl/articles/114333.pdf

Data publikacji:

2015

Wydawca:

Stowarzyszenie Inżynierów i Techników Mechaników Polskich

Tematy:

human detection
infrared
feature extraction
HOG

Opis:

In this work the human detection method in infrared has been presented. The proposed solution focuses on the use low-level features and detecting parts of the human body. Low–level processing is based on modified HOG (Histogram of Oriented Gradients) algorithm. First, the only squared cells have been used, also calculation of the gradient has been improved. Next, the model of the head from the dataset IR (Infra Red) images has been created, also the model of the human body. Finally, the probability matrix has been examined using minimal distance classifier. The novelty of the proposed solution focuses on the combination of the pixel-gradient and body parts processing, also three stage classification process (head modelling, human modelling and classifier), which has been proposed to reduce the false detection. The experiments were performed on self-created IR images database, which contains images with most of the possible difficult situations such as overlapped people, different pose, small and high resolution of the people. The performance of the proposed algorithm was evaluated using Precision and Recall quality measure.

Źródło:

Measurement Automation Monitoring; 2015, 61, 6; 191-194
2450-2855

Pojawia się w:

Measurement Automation Monitoring

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 4.

Tytuł:

A survey of methods for 3D model feature extraction

Autorzy:

Hlavaty, T.
Skala, V.

Powiązania:

https://bibliotekanauki.pl/articles/119225.pdf

Data publikacji:

2003

Wydawca:

Polskie Towarzystwo Geometrii i Grafiki Inżynierskiej

Tematy:

feature extraction
retrieval systems
ekstrakcja cech
systemy wyszukiwania

Opis:

This paper deals with problems that are related to a feature extraction from 3D objects. The main aim of the feature extraction is to describe a shape of 3D object by a feature vector. Then the elements of this feature vector characterize the shape of the own 3D objects and they can serve as a key in searching for similar models. In this paper are introduced current methods for the feature extraction of 3D models and their classification. These methods are based on different mathematical background and according to that they are separated into several groups.

Źródło:

Journal Biuletyn of Polish Society for Geometry and Engineering Graphics; 2003, 13; 5-8
1644-9363

Pojawia się w:

Journal Biuletyn of Polish Society for Geometry and Engineering Graphics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 5.

Tytuł:

Overcoming Overfitting Challenges with HOG Feature Extraction and XGBoost-Based Classification for Concrete Crack Monitoring

Autorzy:

Barkiah, Ida
Sari, Yuslena

Powiązania:

https://bibliotekanauki.pl/articles/27311909.pdf

Data publikacji:

2023

Wydawca:

Polska Akademia Nauk. Czasopisma i Monografie PAN

Tematy:

HOG
XGBoost
classification
feature extraction
concrete crack monitoring

Opis:

This study proposes a method that combines Histogram of Oriented Gradients (HOG) feature extraction and Extreme Gradient Boosting (XGBoost) classification to resolve the challenges of concrete crack monitoring. The purpose of the study is to address the common issue of overfitting in machine learning models. The research uses a dataset of 40,000 images of concrete cracks and HOG feature extraction to identify relevant patterns. Classification is performed using the ensemble method XGBoost, with a focus on optimizing its hyperparameters. This study evaluates the efficacy of XGBoost in comparison to other ensemble methods, such as Random Forest and AdaBoost. XGBoost outperforms the other algorithms in terms of accuracy, precision, recall, and F1-score, as demonstrated by the results. The proposed method obtains an accuracy of 96.95% with optimized hyperparameters, a recall of 96.10%, a precision of 97.90%, and an F1-score of 97%. By optimizing the number of trees hyperparameter, 1200 trees yield the greatest performance. The results demonstrate the efficacy of HOG-based feature extraction and XGBoost for accurate and dependable classification of concrete fractures, overcoming the overfitting issues that are typically encountered in such tasks.

Źródło:

International Journal of Electronics and Telecommunications; 2023, 69, 3; 571--577
2300-1933

Pojawia się w:

International Journal of Electronics and Telecommunications

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 6.

Tytuł:

A comparative study of CNN, LSTM, BiLSTM, AND GRU architectures for tool wear prediction in milling processes

Autorzy:

Zegarra, Fabio C.
Vargas-Machuca, Juan
Coronado, Alberto M.

Powiązania:

https://bibliotekanauki.pl/articles/28407329.pdf

Data publikacji:

2023

Wydawca:

Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych

Tematy:

tool wear
feature extraction
preprocessing
recurrent neural network

Opis:

Accurately predicting machine tool wear requires models capable of capturing complex, nonlinear interactions in multivariate time series inputs. Recurrent neural networks (RNNs) are well-suited to this task, owing to their memory mechanisms and capacity to construct highly complex models. In particular, LSTM, BiLSTM, and GRU architectures have shown promise in wear prediction. This study demonstrates that RNNs can automatically extract relevant information from time series data, resulting in highly precise wear models with minimal feature engineering. Notably, this approach avoids the need for excessively large window sizes of data points during model training, which would increase model complexity and processing time. Instead, this study proposes a procedure that achieves low prediction errors with window sizes as small as 100 data points. By employing Bayesian hyperparameter optimization and two preprocessing techniques (detrend and offset), RMSE errors consistently fall below 10. A key difference in this study is the use of boxplots to provide a better representation of result variability, as opposed to solely reporting the best values. The proposed approach matches more complex state of-the-art. methods and offers a powerful tool for wear prediction in engineering applications.

Źródło:

Journal of Machine Engineering; 2023, 23, 4; 122--136
1895-7595
2391-8071

Pojawia się w:

Journal of Machine Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 7.

Tytuł:

Phase Autocorrelation Bark Wavelet Transform (PACWT) Features for Robust Speech Recognition

Autorzy:

Majeed, S. A.
Husain, H.
Samad, S. A.

Powiązania:

https://bibliotekanauki.pl/articles/177326.pdf

Data publikacji:

2015

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

speech recognition
feature extraction
phase autocorrelation
wavelet transform

Opis:

In this paper, a new feature-extraction method is proposed to achieve robustness of speech recognition systems. This method combines the benefits of phase autocorrelation (PAC) with bark wavelet transform. PAC uses the angle to measure correlation instead of the traditional autocorrelation measure, whereas the bark wavelet transform is a special type of wavelet transform that is particularly designed for speech signals. The extracted features from this combined method are called phase autocorrelation bark wavelet transform (PACWT) features. The speech recognition performance of the PACWT features is evaluated and compared to the conventional feature extraction method mel frequency cepstrum coefficients (MFCC) using TI-Digits database under different types of noise and noise levels. This database has been divided into male and female data. The result shows that the word recognition rate using the PACWT features for noisy male data (white noise at 0 dB SNR) is 60%, whereas it is 41.35% for the MFCC features under identical conditions.

Źródło:

Archives of Acoustics; 2015, 40, 1; 25-31
0137-5075

Pojawia się w:

Archives of Acoustics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 8.

Tytuł:

An improved algorithm for feature extraction from a fingerprint fuzzy image

Autorzy:

Surmacz, K.
Saeed, K.
Rapta, P.

Powiązania:

https://bibliotekanauki.pl/articles/174451.pdf

Data publikacji:

2013

Wydawca:

Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej

Tematy:

biometrics
feature extraction
fingerprints
minutiae
Gabor filter
feature vector
image processing

Opis:

Proper fingerprint feature extraction is crucial in fingerprint-matching algorithms. For good results, different pieces of information about a fingerprint image, such as ridge orientation and frequency, must be considered. It is often necessary to improve the quality of a fingerprint image in order for the feature extraction process to work correctly. In this paper we present a complete (fully implemented) improved algorithm for fingerprint feature extraction, based on numerous papers on this topic. The paper describes a fingerprint recognition system consisting of image preprocessing, filtration, feature extraction and matching for recognition. The image preprocessing includes normalization based on mean value and variation. The orientation field is extracted and Gabor filter is used to prepare the fingerprint image for further processing. For singular point detection, the Poincaré index with a partitioning method is used. The ridgeline thinning is presented and so is the minutia extraction by CN algorithm. The paper contains the comparison of obtained results to the other algorithms.

Źródło:

Optica Applicata; 2013, 43, 3; 515-527
0078-5466
1899-7015

Pojawia się w:

Optica Applicata

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 9.

Tytuł:

Determination of the Optimal Threshold Value and Number of Keypoints in Scale Invariant Feature Transform-based Copy-Move Forgery Detection

Autorzy:

Isnanto, R. Rizal
Zahra, Ajub Ajulian
Santoso, Imam
Lubis, Muhammad Salman

Powiązania:

https://bibliotekanauki.pl/articles/227299.pdf

Data publikacji:

2020

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

forgery
Gaussian noise
feature extraction
pattern matching
Euclidean distance

Opis:

The copy-move forgery detection (CMFD) begins with the preprocessing until the image is ready to process. Then, the image features are extracted using a feature-transform-based extraction called the scale-invariant feature transform (SIFT). The last step is features matching using Generalized 2 Nearest-Neighbor (G2NN) method with threshold values variation. The problem is what is the optimal threshold value and number of keypoints so that copy-move detection has the highest accuracy. The optimal threshold value and number of keypoints had determined so that the detection n has the highest accuracy. The research was carried out on images without noise and with Gaussian noise.

Źródło:

International Journal of Electronics and Telecommunications; 2020, 66, 3; 561-569
2300-1933

Pojawia się w:

International Journal of Electronics and Telecommunications

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 10.

Tytuł:

A Classification Method Related to Respiratory Disorder Events Based on Acoustical Analysis of Snoring

Autorzy:

Wang, Can
Peng, Jianxin
Zhang, Xiaowen

Powiązania:

https://bibliotekanauki.pl/articles/176601.pdf

Data publikacji:

2020

Wydawca:

Polska Akademia Nauk. Czasopisma i Monografie PAN

Tematy:

acoustical analysis
feature extraction
support vector machine
snoring sound

Opis:

Acoustical analysis of snoring provides a new approach for the diagnosis of obstructive sleep apnea hypopnea syndrome (OSAHS). A classification method is presented based on respiratory disorder events to predict the apnea-hypopnea index (AHI) of OSAHS patients. The acoustical features of snoring were extracted from a full night’s recording of 6 OSAHS patients, and regular snoring sounds and snoring sounds related to respiratory disorder events were classified using a support vector machine (SVM) method. The mean recognition rate for simple snoring sounds and snoring sounds related to respiratory disorder events is more than 91.14% by using the grid search, a genetic algorithm and particle swarm optimization methods. The predicted AHI from the present study has a high correlation with the AHI from polysomnography and the correlation coefficient is 0.976. These results demonstrate that the proposed method can classify the snoring sounds of OSAHS patients and can be used to provide guidance for diagnosis of OSAHS.

Źródło:

Archives of Acoustics; 2020, 45, 1; 141-151
0137-5075

Pojawia się w:

Archives of Acoustics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 11.

Tytuł:

Brain-computer interface as measurement and control system The review paper

Autorzy:

Rak, R. J.
Kołodziej, M.
Majkowski, A.

Powiązania:

https://bibliotekanauki.pl/articles/221747.pdf

Data publikacji:

2012

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

EEG
brain-computer interface
feature extraction
feature selection
measurement and control

Opis:

In the last decade of the XX-th century, several academic centers have launched intensive research programs on the brain-computer interface (BCI). The current state of research allows to use certain properties of electromagnetic waves (brain activity) produced by brain neurons, measured using electroencephalographic techniques (EEG recording involves reading from electrodes attached to the scalp - the non-invasive method - or with electrodes implanted directly into the cerebral cortex - the invasive method). A BCI system reads the user's "intentions" by decoding certain features of the EEG signal. Those features are then classified and "translated" (on-line) into commands used to control a computer, prosthesis, wheelchair or other device. In this article, the authors try to show that the BCI is a typical example of a measurement and control unit.

Źródło:

Metrology and Measurement Systems; 2012, 19, 3; 427-444
0860-8229

Pojawia się w:

Metrology and Measurement Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 12.

Tytuł:

An application of machine learning methods to cutting tool path clustering and rul estimation in machining

Autorzy:

Zegarra, Fabio C.
Vargas-Machuca, Juan
Roman-Gonzalez, Avid
Coronado, Alberto M.

Powiązania:

https://bibliotekanauki.pl/articles/28407324.pdf

Data publikacji:

2023

Wydawca:

Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych

Tematy:

feature extraction
k-means clustering
time series
unsupervised learning

Opis:

Machine learning has been widely used in manufacturing, leading to significant advances in diverse problems, including the prediction of wear and remaining useful life (RUL) of machine tools. However, the data used in many cases correspond to simple and stable processes that differ from practical applications. In this work, a novel dataset consisting of eight cutting tools with complex tool paths is used. The time series of the tool paths, corresponding to the three-dimensional position of the cutting tool, are grouped according to their shape. Three unsupervised clustering techniques are applied, resulting in the identification of DBA-k-means as the most appropriate technique for this case. The clustering process helps to identify training and testing data with similar tool paths, which is then applied to build a simple two-feature prediction model with the same level of precision for RUL prediction as a more complex four-feature prediction model. This work demonstrates that by properly selecting the methodology and number of clusters, tool paths can be effectively classified, which can later be used in prediction problems in more complex settings.

Źródło:

Journal of Machine Engineering; 2023, 23, 4; 5--17
1895-7595
2391-8071

Pojawia się w:

Journal of Machine Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 13.

Tytuł:

An improved feature extraction method for rolling bearing fault diagnosis based on MEMD and PE

Autorzy:

Zhang, H.
Zhao, L.
Liu, Q.
Luo, J.
Wei, Q.
Zhou, Z.
Qu, Y.

Powiązania:

https://bibliotekanauki.pl/articles/259770.pdf

Data publikacji:

2018

Wydawca:

Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa

Tematy:

improved feature extraction method
rolling bearing fault diagnosis
MEMD
PE

Opis:

The health condition of rolling bearing can directly influence to the efficiency and lifecycle of rotating machinery, thus monitoring and diagnosing the faults of rolling bearing is of great importance. Unfortunately, vibration signals of rolling bearing are usually overwhelmed by external noise, so the fault frequencies of rolling bearing cannot be readily obtained. In this paper, an improved feature extraction method called IMFs_PE, which combines the multivariate empirical mode decomposition with the permutation entropy, is proposed to extract fault frequencies from the noisy bearing vibration signals. First, the raw bearing vibration signals are filtered by an optimal band-pass filter determined by SK to remove the irrelative noise which is not in the same frequency band of fault frequencies. Then the filtered signals are processed by the IMFs_PE to get rid of the relative noise which is in the same frequency band of fault frequencies. Finally, a frequency domain condition indicator FFR(Fault Frequency Ratio), which measures the magnitude of fault frequencies in frequency domain, is calculated to compare the effectiveness of the feature extraction methods. The feature extraction method proposed in this paper has advantages of removing both irrelative noise and relative noise over other feature extraction methods. The effectiveness of the proposed method is validated by simulated and experimental bearing signals. And the results are shown that the proposed method outperforms other state of the art algorithms with regards to fault feature extraction of rolling bearing.

Źródło:

Polish Maritime Research; 2018, S 2; 98-106
1233-2585

Pojawia się w:

Polish Maritime Research

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 14.

Tytuł:

A Multistage Procedure of Mobile Vehicle Acoustic Identification for Single-Sensor Embedded Device

Autorzy:

Astapov, S.
Riid, A.

Powiązania:

https://bibliotekanauki.pl/articles/227146.pdf

Data publikacji:

2013

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

vehicle identification
acoustic signal analysis
feature extraction
classification
fuzzy logic

Opis:

Mobile vehicle identification has a wide application field for both civilian and military uses. Vehicle identification may be achieved by incorporating single or multiple sensor solutions and through data fusion. This paper considers a single-sensor multistage hierarchical algorithm of acoustic signal analysis and pattern recognition for the identification of mobile vehicles in an open environment. The algorithm applies several standalone techniques to enable complex decision-making during event identification. Computationally inexpensive procedures are specifically chosen in order to provide real-time operation capability. The algorithm is tested on pre-recorded audio signals of civilian vehicles passing the measurement point and shows promising classification accuracy. Implementation on a specific embedded device is also presented and the capability of real-time operation on this device is demonstrated.

Źródło:

International Journal of Electronics and Telecommunications; 2013, 59, 2; 151-160
2300-1933

Pojawia się w:

International Journal of Electronics and Telecommunications

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 15.

Tytuł:

Vein Biometric Recognition Methods and Systems: A Review

Autorzy:

Al-Khafaji, Ruaa S.S.
Al-Tamimi, Mohammed S.H.

Powiązania:

https://bibliotekanauki.pl/articles/2022496.pdf

Data publikacji:

2022

Wydawca:

Stowarzyszenie Inżynierów i Techników Mechaników Polskich

Tematy:

biometric technology
finger vein recognition
pre-processing
feature extraction
matching

Opis:

The Finger-vein recognition (FVR) method has received increasing attention in recent years. It is a new method of personal identification and biometric technology that identifies individuals using unique finger-vein patterns, which is the first reliable and suitable area to be recognized. It was discovered for the first time with a home imaging system; it is characterized by high accuracy and high processing speed. Also, the presence of patterns of veins inside one’s body makes it almost difficult to repeat and difficult to steal. Based on the increased focus on protecting privacy, that also produces vein biometrics safer alternatives without forgery, damage, or alteration over time. Fingerprint recognition is beneficial because it includes the use of low-cost, small devices which are difficult to counterfeit. This paper discusses preceding finger-vein recognition approaches systems with the methodologies taken from other researchers’ work about image acquisition, pretreatment, vein extraction, and matching. It is reviewing the latest algorithms; continues to critically review the strengths and weaknesses of these methods, and it states the modern results following a key comparative analysis of methods.

Źródło:

Advances in Science and Technology. Research Journal; 2022, 16, 1; 36-46
2299-8624

Pojawia się w:

Advances in Science and Technology. Research Journal

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 16.

Tytuł:

Learning-free deep features for multispectral palm-print classification

Autorzy:

Aounallah, Asma
Meraoumia, Abdallah
Bendjenna, Hakim

Powiązania:

https://bibliotekanauki.pl/articles/27312870.pdf

Data publikacji:

2023

Wydawca:

Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH

Tematy:

feature extraction
biometrics
multispectral imaging
deep learning
DCTNet
data fusion

Opis:

The feature-extraction step is a major and crucial step in analyzing and understanding raw data, as it has a considerable impact on system accuracy. Despite the very acceptable results that have been obtained by many handcrafted methods, these can unfortunately have difficulty representing features in the cases of large databases or with strongly correlated samples. In this context, we attempt to examine the discriminability of texture features by proposing a novel, simple, and lightweight method for deep feature extraction to characterize the discriminative power of different textures. We evaluated the performance of our method by using a palm print-based biometric system, and the experimental results (using the CASIA multispectral palm--print database) demonstrate the superiority of the proposed method over the latest handcrafted and deep methods.

Źródło:

Computer Science; 2023, 24 (2); 243--271
1508-2806
2300-7036

Pojawia się w:

Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 17.

Tytuł:

On the predictive power of meta-features in OpenML

Autorzy:

Bilalli, B.
Abelló, A.
Aluja-Banet, T.

Powiązania:

https://bibliotekanauki.pl/articles/331086.pdf

Data publikacji:

2017

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

feature extraction
feature selection
meta learning
ekstrakcja danych
selekcja danych
uczenie maszynowe

Opis:

The demand for performing data analysis is steadily rising. As a consequence, people of different profiles (i.e., nonexperienced users) have started to analyze their data. However, this is challenging for them. A key step that poses difficulties and determines the success of the analysis is data mining (model/algorithm selection problem). Meta-learning is a technique used for assisting non-expert users in this step. The effectiveness of meta-learning is, however, largely dependent on the description/characterization of datasets (i.e., meta-features used for meta-learning). There is a need for improving the effectiveness of meta-learning by identifying and designing more predictive meta-features. In this work, we use a method from exploratory factor analysis to study the predictive power of different meta-features collected in OpenML, which is a collaborative machine learning platform that is designed to store and organize meta-data about datasets, data mining algorithms, models and their evaluations. We first use the method to extract latent features, which are abstract concepts that group together meta-features with common characteristics. Then, we study and visualize the relationship of the latent features with three different performance measures of four classification algorithms on hundreds of datasets available in OpenML, and we select the latent features with the highest predictive power. Finally, we use the selected latent features to perform meta-learning and we show that our method improves the meta-learning process. Furthermore, we design an easy to use application for retrieving different meta-data from OpenML as the biggest source of data in this domain.

Źródło:

International Journal of Applied Mathematics and Computer Science; 2017, 27, 4; 697-712
1641-876X
2083-8492

Pojawia się w:

International Journal of Applied Mathematics and Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 18.

Tytuł:

Hybrid methodology of degradation feature extraction for bearing prognostics
Metodyka hybrydowa ekstrakcji cech degradacji do zastosowań w prognozowaniu czasu życia łożysk

Autorzy:

Gu, H.
Zhao, J.
Zhang, X.

Powiązania:

https://bibliotekanauki.pl/articles/302055.pdf

Data publikacji:

2013

Wydawca:

Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN

Tematy:

ekstrakcja cech
degradacja
sygnał
łożysko
feature extraction
degradation
signal
bearing

Opis:

Hybrid methodology of degradation feature extraction was presented which may enable prediction of remaining useful life of a product. In this methodology, firstly, the signal was de-noised by wavelet analysis. Then the autoregressive model was used to remove the discrete frequencies from de-noised signal. Further, the residual signal which mainly contained impulsive fault signal was enhanced by minimum entropy deconvolution filter. The kurtosis was extracted which was taken as the feature for prognostics. At last, the empirical mode decomposition was used to reduce fluctuation of feature value and to extract the trend content. A case study was presented to verify the effectiveness of the proposed method.
Przedstawiono hybrydową metodę ekstrakcji cech degradacji, która umożliwia przewidywanie pozostałego okresu użytkowania produktu. W tej metodyce, sygnał został najpierw odfiltrowany z wykorzystaniem analizy falkowej. Następnie, za pomocą modelu autoregresyjnego usunięto z pozbawionego szumów sygnału częstotliwości dyskretne. W dalszej kolejności, sygnał resztkowy, który zawierał głównie impulsowy sygnał uszkodzenia został wzmocniony z zastosowaniem filtru dekonwolucji minimum entropii. Obliczono kurtozę, którą przyjęto jako cechę w procesie prognozowania. Na koniec, zastosowano empiryczną dekompozycję sygnału (EMD) w celu zmniejszenia wahań wartości cechy oraz w celu ekstrakcji trendu. Studium przypadku demonstruje efektywność proponowanej metody.

Źródło:

Eksploatacja i Niezawodność; 2013, 15, 2; 195-201
1507-2711

Pojawia się w:

Eksploatacja i Niezawodność

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 19.

Tytuł:

A Fast Method of Feature Extraction for Lowering Vehicle Pass-By Noise Based on Nonnegative Tucker3 Decomposition

Autorzy:

Wang, H.
Cheng, G.
Deng, G.
Li, X.
Li, H.
Huang, Y.

Powiązania:

https://bibliotekanauki.pl/articles/177883.pdf

Data publikacji:

2017

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

vehicle pass-by noise
NTD
feature extraction
sound pressure level

Opis:

Usually, the judgement of one type fault of vehicle pass-by noise is difficult for engineers, especially when some significant features are disturbed by other interference noise, such as the squealing noise is almost simultaneous with the whistle in the exhaust system. In order to cope with this problem, a new method, with the antinoise ability of the algorithm on the condition by which the features are entangled, is developed to extract clear features for the fault analysis. In the proposed method, the nonnegative Tucker3 decomposition (NTD) with fast updating algorithm, signed as NTD_FUP, can find out the natural frequency of the parts/components from the exhaust system. Not only does the NTD_FUP extract clear features from the confused noise, but also it is superior to the traditional methods in practice. Then, an aluminium-foil alloy material, which is used for the heat shield for its lower noise radiation, replaces the aluminium alloy alone. Extensive experiments show that the sound pressure level of the vehicle pass-by noise is reduced 0.9 dB(A) by the improved heat shield, which is also considered as a more lightweight design for the exhaust system of an automobile.

Źródło:

Archives of Acoustics; 2017, 42, 4; 619-629
0137-5075

Pojawia się w:

Archives of Acoustics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 20.

Tytuł:

Classification of the indoor environment of a mobile robot using principal component analysis

Autorzy:

Yaqub, T.
Katupitya, J.

Powiązania:

https://bibliotekanauki.pl/articles/384275.pdf

Data publikacji:

2008

Wydawca:

Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów

Tematy:

mobile robot environment
PCA
classification
feature extraction
training data
bootstrap method

Opis:

Large indoor environments of a mobile robot usually consist of different types of areas connected together. The structure of a corridor differs from a room, a main hall or laboratory. A method for online classification of these areas using a laser scanner is presented in this paper. This classification can reduce the search space of localization module to a great extent making the navigation system efficient. The intention was to make the classification of a sensor observation in a fast and real-time fashion and immediately on its arrival in the sensor frame. Our approach combines both the feature based and statistical approaches. We extract some vital features of lines and corners with attributes such as average length of lines and distance between corners from the raw laser data and classify the observation based on these features. Bootstrap method is used to get a robust correlation of features from training data and finally Principal Component Analysis (PCA) is used to model the environment. In PCA, the underlying assumption is that data is coming from a multivariate normal distribution. The use of bootstrap method makes it possible to use the observations data set which set, which is not necessarily normally distributed. This technique lifts up the normality assumption and reduces the computational cost further as compared to the PCA techniques based on raw sensor data and can be easily implemented in moderately complex indoor environment. The knowledge of the environment can also be up-dated in an adaptive fashion. Results of experimentation in a simulated hospital building under varying environmental conditions using a real-time robotic software Player/Stage are shown.

Źródło:

Journal of Automation Mobile Robotics and Intelligent Systems; 2008, 2, 2; 44-53
1897-8649
2080-2145

Pojawia się w:

Journal of Automation Mobile Robotics and Intelligent Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 21.

Tytuł:

Domain specific key feature extraction using knowledge graph mining

Autorzy:

Barai, Mohit Kumar
Sanyal, Subhasis

Powiązania:

https://bibliotekanauki.pl/articles/2027771.pdf

Data publikacji:

2020

Wydawca:

Uniwersytet Ekonomiczny w Katowicach

Tematy:

Feature extraction
Knowledge graph
Natural language processing
Product review
Text processing

Opis:

In the field of text mining, many novel feature extraction approaches have been propounded. The following research paper is based on a novel feature extraction algorithm. In this paper, to formulate this approach, a weighted graph mining has been used to ensure the effectiveness of the feature extraction and computational efficiency; only the most effective graphs representing the maximum number of triangles based on a predefined relational criterion have been considered. The proposed novel technique is an amalgamation of the relation between words surrounding an aspect of the product and the lexicon-based connection among those words, which creates a relational triangle. A maximum number of a triangle covering an element has been accounted as a prime feature. The proposed algorithm performs more than three times better than TF-IDF within a limited set of data in analysis based on domain-specific data.

Źródło:

Multiple Criteria Decision Making; 2020, 15; 1-22
2084-1531

Pojawia się w:

Multiple Criteria Decision Making

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 22.

Tytuł:

Feature selection for EEG-based discrimination between imagination of left and right hand movements

Autorzy:

Binias, B.
Palus, H.

Powiązania:

https://bibliotekanauki.pl/articles/114144.pdf

Data publikacji:

2015

Wydawca:

Stowarzyszenie Inżynierów i Techników Mechaników Polskich

Tematy:

EEG signal
brain-computer interfaces
feature extraction
classification
lateralized brain activity

Opis:

: In this article was analyzed an influence of selected features on the accuracy of discrimination between imagination of right and left hand movements based on recorded EEG waveforms. The study showed a significant advantage that individual selection of features and a classification algorithm for analyzed data holds over the more general approach. The results were compared with the results obtained by the participants of the "BCI competition IV" and placed in the top three.

Źródło:

Measurement Automation Monitoring; 2015, 61, 4; 94-97
2450-2855

Pojawia się w:

Measurement Automation Monitoring

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 23.

Tytuł:

Przydatność algorytmów podpikselowej detekcji cech w wybranych zagadnieniach fotogrametrycznych
The usefulness of sub-pixel feature extraction algorithms in selected photogrammetric cases

Autorzy:

Mikrut, S.

Powiązania:

https://bibliotekanauki.pl/articles/130666.pdf

Data publikacji:

2009

Wydawca:

Stowarzyszenie Geodetów Polskich

Tematy:

fotogrametria cyfrowa
dokładność podpikselowa
detekcja cech
digital photogrammetry
subpixel precision
feature extraction

Opis:

Celem artykułu było zaprezentowanie wyników badań nad przydatnością algorytmów podpikselowej dokładności detekcji cech w wybranych zagadnieniach fotogrametrycznych. Pojęcie detekcji cech obejmuje wszystkie metody mające na celu wydobycie z obrazu cyfrowego właściwości takich jak: pojedyncze obiekty, krawędzie, geometryczne środki obiektów czy inne. W niniejszej publikacji autor skupił się na zagadnieniu detekcji cech prowadzących do zdefiniowania krawędzi wybranych obiektów celem ich lokalizacji z bardzo wysoką precyzją. Do tego celu wykorzystano autorskie algorytmy bazujące na wykrywaniu krawędzi z podpikselową dokładnością w oparciu o badanie pierwszej i drugiej pochodnej obrazu cyfrowego. Znajdowane z taką precyzją punkty na odpowiednim przekroju krawędzi (wiersz lub kolumna obrazu cyfrowego) są następnie aproksymowane wybraną funkcją matematyczną (w najprostszym przypadku będzie to linia prosta) i w efekcie końcowym dochodzimy do postaci wektorowej krawędzi. Algorytmy te zostały przetestowane w wielu pracach fotogrametrycznych. Niniejsza publikacja przybliża ich wykorzystanie w jednym z zadań fotogrametrii inżynierskiej jakim jest badanie imperfekcji kształtu wysmukłych budowli. Opracowane procedury pozwalające na wykonanie automatycznych pomiarów na obrazie cyfrowym, dały podstawy na zaprojektowania systemu, który docelowo miał być zintegrowany z notebookiem, aby umożliwić prace w czasie rzeczywistym. Algorytmy do badania imperfekcji kształtu na obrazie cyfrowym bazują na detekcji pojedynczych punktów przekroi krawędziowych, a następnie na wpasowaniu linii prostej reprezentującej badany kształt obiektu. Algorytmy zostały oprogramowanie i zaimplementowane do systemu powstającego w ramach projektu badawczego.
The purpose of the article was to present the results of research on the usefulness of algorithms of sub-pixel accuracy of feature extraction in selected photogrammetric cases. The concept of feature extraction includes all methods aiming at the extraction from a digital image of such selected properties as single objects, edges, geometric centres of objects, or other properties. In this publication, the author focuses on the problem of feature extraction, leading to the definition of edges of selected objects in order to be able to locate them with a very high precision. For this purpose, own algorithms based on edge detection with a sub-pixel accuracy based on the examination of the first and the second derivatives of the digital image were applied. Points, which have been found with such a precision at the appropriate section of the edge (row or column of the digital image) are later approximated, using a selected mathematical function (in the simplest case this will be a straight line), and finally, a vector form of the edge is obtained. Those algorithms have been tested in many photogrammetric works. This publication brings closer their utilization in one of the photogrammetric engineering tasks, namely the investigation of the imperfection of shape of lofty buildings and structures. The developed procedures for the implementation of automated measurements on a digital image have helped to design a system that eventually was to be integrated with a notebook, allowing for work to be performed in a real time. Algorithms for testing the shape imperfection on a digital image are based on detecting single points of edge sections, and then fitting a straight line, which represents the shape of the object under investigation. The software and algorithms have been implemented to the system being created under a research project. The whole is written in C + +.

Źródło:

Archiwum Fotogrametrii, Kartografii i Teledetekcji; 2009, 19; 299-308
2083-2214
2391-9477

Pojawia się w:

Archiwum Fotogrametrii, Kartografii i Teledetekcji

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 24.

Tytuł:

Texture and gene expression analysis of the MRI brain in detection of Alzheimer’s disease

Autorzy:

Bustamam, A.
Sarwinda, D.
Ardenaswari, G.

Powiązania:

https://bibliotekanauki.pl/articles/91834.pdf

Data publikacji:

2018

Wydawca:

Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych

Tematy:

Alzheimer’s disease
MRI
Feature Extraction
Bi-Clustering
Local Binary Pattern
LBP

Opis:

Alzheimer’s disease is a type of dementia that can cause problems with human memory, thinking and behavior. This disease causes cell death and nerve tissue damage in the brain. The brain damage can be detected using brain volume, whole brain form, and genetic testing. In this research, we propose texture analysis of the brain and genomic analysis to detect Alzheimer’s disease. 3D MRI images were chosen to analyze the texture of the brain, and microarray data were chosen to analyze gene expression. We classified Alzheimer’s disease into three types: Alzheimer’s, Mild Cognitive Impairment (MCI), and Normal. In this study, texture analysis was carried out by using the Advanced Local Binary Pattern (ALBP) and the Gray Level Co-occurrence Matrix (GLCM). We also propose the bi-clustering method to analyze microarray data. The experimental results from texture analysis show that ALBP had better performance than GLCM in classification of Alzheimer’s disease. The ALBP method achieved an average value of accuracy of between 75% - 100% for binary classification of the whole brain data. Furthermore, Biclustering method with microarray data shows good performance gene expression, where this information show influence Alzheimer’s disease with total of bi-cluster is 6.

Źródło:

Journal of Artificial Intelligence and Soft Computing Research; 2018, 8, 2; 111-120
2083-2567
2449-6499

Pojawia się w:

Journal of Artificial Intelligence and Soft Computing Research

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 25.

Tytuł:

Hydrogen Detection With a Gas Sensor Array – Processing and Recognition of Dynamic Responses Using Neural Networks

Autorzy:

Gwiżdż, P.
Brudnik, A.
Zakrzewska, K.

Powiązania:

https://bibliotekanauki.pl/articles/221723.pdf

Data publikacji:

2015

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

gas sensor
sensor array
temperature modulation
dynamic response
feature extraction
neural networks

Opis:

An array consisting of four commercial gas sensors with target specifications for hydrocarbons, ammonia, alcohol, explosive gases has been constructed and tested. The sensors in the array operate in the dynamic mode upon the temperature modulation from 350°C to 500°C. Changes in the sensor operating temperature lead to distinct resistance responses affected by the gas type, its concentration and the humidity level. The measurements are performed upon various hydrogen (17-3000 ppm), methane (167-3000 ppm) and propane (167-3000 ppm) concentrations at relative humidity levels of 0-75%RH. The measured dynamic response signals are further processed with the Discrete Fourier Transform. Absolute values of the dc component and the first five harmonics of each sensor are analysed by a feed-forward back-propagation neural network. The ultimate aim of this research is to achieve a reliable hydrogen detection despite an interference of the humidity and residual gases.

Źródło:

Metrology and Measurement Systems; 2015, 22, 1; 3-12
0860-8229

Pojawia się w:

Metrology and Measurement Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 26.

Tytuł:

Fault diagnosis method for an aerospace generator rotating rectifier based on dynamic FFT technology

Autorzy:

Feng, Sai
Cui, Jiang
Zhang, Zhuoran

Powiązania:

https://bibliotekanauki.pl/articles/1849142.pdf

Data publikacji:

2021

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

aerospace generator
rotating rectifier
fault diagnosis
dynamic Fast Fourier Transform
feature extraction

Opis:

A fault diagnosis method for the rotating rectifier of a brushless three-phase synchronous aerospace generator is proposed in this article. The proposed diagnostic system includes three steps: data acquisition, feature extraction and fault diagnosis. Based on a dynamic Fast Fourier Transform (FFT), this method processes the output voltages of aerospace generator continuously and monitors the continuous change trend of the main frequency in the spectrum before and after the fault. The trend can be used to perform fault diagnosis task. The fault features of the rotating rectifier proposed in this paper can quickly and effectively distinguish single and double faulty diodes. In order to verify the proposed diagnosis system, simulation and practical experiments are carried out in this paper, and good results can be achieved.

Źródło:

Metrology and Measurement Systems; 2021, 28, 2; 269-288
0860-8229

Pojawia się w:

Metrology and Measurement Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 27.

Tytuł:

k-centroids clustering for asymmetric dissimilarities

Autorzy:

Olszewski, D.

Powiązania:

https://bibliotekanauki.pl/articles/206422.pdf

Data publikacji:

2011

Wydawca:

Polska Akademia Nauk. Instytut Badań Systemowych PAN

Tematy:

k-centroids clustering
asymmetric dissimilarity
sound recognition
heart rhythm recognition
feature extraction

Opis:

In this paper, an asymmetric version of the kcentroids clustering algorithm is proposed. The asymmetry arises from the use of the asymmetric dissimilarities in the k-centroids algorithm. Application of the asymmetric measures of dissimilarity is motivated by the basic nature of the k-centroids algorithm, which uses dissimilarities in the asymmetric manner. It finds the minimal dissimilarity between an object being currently allocated, and one of the clusters centroids. Clusters centroids are treated as the dominant points governing the asymmetric relationships in the entire cluster analysis. The results of the experimental study on real and simulated data have shown the superiority of the asymmetric dissimilarities employed for the k-centroids method over their symmetric counterparts.

Źródło:

Control and Cybernetics; 2011, 40, 2; 554-574
0324-8569

Pojawia się w:

Control and Cybernetics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 28.

Tytuł:

Accurate identification on individual similar communication emitters by using HVG-NTE feature

Autorzy:

Li, Ke
Ge, Wei
Yang, Xiaoya
Xu, Zhengrong

Powiązania:

https://bibliotekanauki.pl/articles/2128146.pdf

Data publikacji:

2021

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

communication emitter
identification
feature extraction
HVG
NTE
emiter komunikacji
identyfikacja
wyodrębnianie cech

Opis:

Individual identification of similar communication emitters in the complex electromagnetic environment has great research value and significance in both military and civilian fields. In this paper, a feature extraction method called HVG-NTE is proposed based on the idea of system nonlinearity. The shape of the degree distribution, based on the extraction of HVG degree distribution, is quantified with NTE to improve the anti-noise performance. Then XGBoost is used to build a classifier for communication emitter identification. Our method achieves better recognition performance than the state-of-the-art technology of the transient signal data set of radio stations with the same plant, batch, and model, and is suitable for a small sample size.

Źródło:

Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 2; e136741, 1--6
0239-7528

Pojawia się w:

Bulletin of the Polish Academy of Sciences. Technical Sciences

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 29.

Tytuł:

Accurate identification on individual similar communication emitters by using HVG-NTE feature

Autorzy:

Li, Ke
Ge, Wei
Yang, Xiaoya
Xu, Zhengrong

Powiązania:

https://bibliotekanauki.pl/articles/2173613.pdf

Data publikacji:

2021

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

communication emitter
identification
feature extraction
HVG
NTE
emiter komunikacji
identyfikacja
wyodrębnianie cech

Opis:

Individual identification of similar communication emitters in the complex electromagnetic environment has great research value and significance in both military and civilian fields. In this paper, a feature extraction method called HVG-NTE is proposed based on the idea of system nonlinearity. The shape of the degree distribution, based on the extraction of HVG degree distribution, is quantified with NTE to improve the anti-noise performance. Then XGBoost is used to build a classifier for communication emitter identification. Our method achieves better recognition performance than the state-of-the-art technology of the transient signal data set of radio stations with the same plant, batch, and model, and is suitable for a small sample size.

Źródło:

Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 2; art. no. e136741
0239-7528

Pojawia się w:

Bulletin of the Polish Academy of Sciences. Technical Sciences

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 30.

Tytuł:

Enhancement in Bearing Fault Classification Parameters Using Gaussian Mixture Models and Mel Frequency Cepstral Coefficients Features

Autorzy:

Atmani, Youcef
Rechak, Said
Mesloub, Ammar
Hemmouche, Larbi

Powiązania:

https://bibliotekanauki.pl/articles/177335.pdf

Data publikacji:

2020

Wydawca:

Polska Akademia Nauk. Czasopisma i Monografie PAN

Tematy:

bearing faults
Gaussian mixture models
Mel frequency cepstral coefficients
feature extraction
diagnosis

Opis:

Last decades, rolling bearing faults assessment and their evolution with time have been receiving much interest due to their crucial role as part of the Conditional Based Maintenance (CBM) of rotating machinery. This paper investigates bearing faults diagnosis based on classification approach using Gaussian Mixture Model (GMM) and the Mel Frequency Cepstral Coefficients (MFCC) features. Throughout, only one criterion is defined for the evaluation of the performance during all the cycle of the classification process. This is the Average Classification Rate (ACR) obtained from the confusion matrix. In every test performed, the generated features vectors are considered along to discriminate between four fault conditions as normal bearings, bearings with inner and outer race faults and ball faults. Many configurations were tested in order to determinate the optimal values of input parameters, as the frame analysis length, the order of model, and others. The experimental application of the proposed method was based on vibration signals taken from the bearing datacenter website of Case Western Reserve University (CWRU). Results show that proposed method can reliably classify different fault conditions and have a highest classification performance under some conditions.

Źródło:

Archives of Acoustics; 2020, 45, 2; 283-295
0137-5075

Pojawia się w:

Archives of Acoustics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 31.

Tytuł:

Intelligent prediction of milling strategy using neural networks

Autorzy:

Klancnik, S.
Balic, J.
Cus, F.

Powiązania:

https://bibliotekanauki.pl/articles/971013.pdf

Data publikacji:

2010

Wydawca:

Polska Akademia Nauk. Instytut Badań Systemowych PAN

Tematy:

SOM neural networks
CAD/CAM system
feature extraction
milling strategy
CAD segmentation
STL model

Opis:

This paper presents the prediction of milling tool-path strategy using Artificial Neural Network (ANN), by taking the predefined technological objectives into account. In the presented case, the best possible surface quality of a machined surface was taken as the primary technological aim. This paper shows how feature extraction from a 3D CAD model, and classification using a self-organizing neural network, are done. The experimental results presented in this paper suggest that the prediction of milling strategy using the self-organizing neural network (SOM) is effective.

Źródło:

Control and Cybernetics; 2010, 39, 1; 9-24
0324-8569

Pojawia się w:

Control and Cybernetics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 32.

Tytuł:

Recognition of acoustic signals of induction motor using fft, smofs-10 and ISVM
Rozpoznawanie sygnałów akustycznich silnika indukcyjnego z zastosowaniem fft, smofs-10 i ISVM

Autorzy:

Głowacz, A.

Powiązania:

https://bibliotekanauki.pl/articles/1365918.pdf

Data publikacji:

2015

Wydawca:

Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN

Tematy:

acoustic signal
induction motor
feature extraction
classification
sygnał akustyczny
silnik indukcyjny
ekstrakcja cech
klasyfikacja

Opis:

A correct diagnosis of electrical circuits is very essential in industrial plants. An article deals with a recognition method of early fault detection of induction motor. The described approach is based on patterns recognition. Acoustic signals of specific induction motor are analyzed patterns. Acoustic signals include information about motor state. The analysis of the patterns was conducted for three states of induction motor using Fast Fourier Transform (FFT), shortened method of frequencies selection (SMoFS-10) and Linear Support Vector Machine (LSVM). The results of calculations suggest that the method is efficient and can be also used for diagnostic purposes.
Prawidłowa diagnostyka obwodów elektrycznych jest bardzo istotna w zakładach przemysłowych. Artykuł zajmuje się metodą rozpoznawania stanów przedawaryjnych silnika indukcyjnego. Opisane podejście jest oparte na rozpoznawaniu wzorców. Sygnały akustyczne określonego silnika indukcyjnego są badanymi wzorcami. Sygnały akustyczne zawierają informację o stanie silnika. Analiza wzorców została przeprowadzona dla trzech stanów silnika indukcyjnego używając FFT, skróconej metody wyboru częstotliwości (SMoFS-10) i liniowej maszyny wektorów wspierających (LSVM). Wyniki obliczeń sugerują, że metoda jest skuteczna i może być również zastosowana dla celów diagnostycznych.

Źródło:

Eksploatacja i Niezawodność; 2015, 17, 4; 569-574
1507-2711

Pojawia się w:

Eksploatacja i Niezawodność

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 33.

Tytuł:

Critical exponent analysis applied to surface EMG signals for multifunction myoelectric control

Autorzy:

Phinyomark, A.
Phothisonothai, M.
Phukpattaranont, P.
Limsakul, C.

Powiązania:

https://bibliotekanauki.pl/articles/220544.pdf

Data publikacji:

2011

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

biomedical signal processing
electromyography signal
feature extraction
fractal analysis
human machine interface
pattern classification

Opis:

Based on recent advances in non-linear analysis, the surface electromyography (sEMG) signal has been studied from the viewpoints of self-affinity and complexity. In this study, we examine usage of critical exponent analysis (CE) method, a fractal dimension (FD) estimator, to study properties of the sEMG signal and to deploy these properties to characterize different movements for gesture recognition. SEMG signals were recorded from thirty subjects with seven hand movements and eight muscle channels. Mean values and coefficient of variations of the CE from all experiments show that there are larger variations between hand movement types but there is small variation within the same type. It also shows that the CE feature related to the self-affine property for the sEMG signal extracted from different activities is in the range of 1.855∼2.754. These results have also been evaluated by analysis-of-variance (p-value). Results show that the CE feature is more suitable to use as a learning parameter for a classifier compared with other representative features including root mean square, median frequency and Higuchi's method. Most p-values of the CE feature were less than 0.0001. Thus the FD that is computed by the CE method can be applied to be used as a feature for a wide variety of sEMG applications.

Źródło:

Metrology and Measurement Systems; 2011, 18, 4; 645-658
0860-8229

Pojawia się w:

Metrology and Measurement Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 34.

Tytuł:

Semantic Segmentation of Diseases in Mushrooms using Enhanced Random Forest

Autorzy:

Yacharam, Rakesh Kumar
Sekhar, Dr. V. Chandra

Powiązania:

https://bibliotekanauki.pl/articles/31339414.pdf

Data publikacji:

2023

Wydawca:

Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Instytut Informatyki Technicznej

Tematy:

mushroom diseases
semantic segmentation
computer aided
Machine Learning
significant feature extraction
Random Forest classifier

Opis:

Mushrooms are a rich source of antioxidants and nutritional values. Edible mushrooms, however, are susceptible to various diseases such as dry bubble, wet bubble, cobweb, bacterial blotches, and mites. Farmers face significant production losses due to these diseases affecting mushrooms. The manual detection of these diseases relies on expertise, knowledge of diseases, and human effort. Therefore, there is a need for computer-aided methods, which serve as optimal substitutes for detecting and segmenting diseases. In this paper, we propose a semantic segmentation approach based on the Random Forest machine learning technique for the detection and segmentation of mushroom diseases. Our focus lies in extracting a combination of different features, including Gabor, Bouda, Kayyali, Gaussian, Canny edge, Roberts, Sobel, Scharr, Prewitt, Median, and Variance. We employ constant mean-variance thresholding and the Pearson correlation coefficient to extract significant features, aiming to enhance computational speed and reduce complexity in training the Random Forest classifier. Our results indicate that semantic segmentation based on Random Forest outperforms other methods such as Support Vector Machine (SVM), Naïve Bayes, K-means, and Region of Interest in terms of accuracy. Additionally, it exhibits superior precision, recall, and F1 score compared to SVM. It is worth noting that deep learning-based semantic segmentation methods were not considered due to the limited availability of diseased mushroom images.

Źródło:

Machine Graphics & Vision; 2023, 32, 2; 129-146
1230-0535
2720-250X

Pojawia się w:

Machine Graphics & Vision

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 35.

Tytuł:

Fusing laser and vision data for a perceptually rich environment description
Opis otoczenia na podstawie danych z sensorów laserowych i wizyjnych

Autorzy:

Skrzypczyński, P.

Powiązania:

https://bibliotekanauki.pl/articles/257108.pdf

Data publikacji:

2008

Wydawca:

Sieć Badawcza Łukasiewicz - Instytut Technologii Eksploatacji - Państwowy Instytut Badawczy

Tematy:

robot mobilny
nawigacja
system wizyjny
ekstrakcja cech
mobile robot
navigation
computer vision
feature extraction

Opis:

In this paper we, discuss methods to increase the discriminative properties of the laser-based geometric landmarks used in simultaneous localisation and mapping by employing monocular vision data. Vertical edges extracted from images help to estimate the length of the line segments, which are only partially observed. Salient visual features, which defy simple geometric interpretation, are handled by the scale invariant feature transform method. These different types of photometric features are aggregated together with the basic 2D line segments extracted from the laser scanner data into the Perceptually Rich Segments.
W pracy przedstawiono metody poprawiające rozróżnialność obiektów geometrycznych wyodrębnionych z danych uzyskanych ze skanera laserowego i wykorzystywanych w systemie jednoczesnej samolokalizacji i budowy mapy otoczenia robota. Założono, że robot porusza się w środowisku zbudowanym przez człowieka, w którym dominują pionowe płaszczyzny (ściany). Poprawę rozróżnialności obiektów uzyskano dzięki wykorzystaniu danych z monookularowego systemu wizyjnego robota. Krawędzie pionowe wyodrębnione z obrazów umożliwiają prawidłową estymację długości odcinków 2D odtworzonych uprzednio na podstawie danych ze skanera laserowego. Fotometryczne cechy znaczące wyodrębniane są z obrazów i opisywane za pomocą metody Scale Invariant Feature Transform (SIFT). Uzyskane wektory parametrów osadzane są następnie w "ramach" tworzonych przez odcinki poziome oraz krawędzie pionowe. Powstają w ten sposób obiekty nowego typu - PRS (ang. Perceptually Rich Segment). Zaprezentowano wyniki eksperymentów dotyczących wyodrębniania i dopasowywania do siebie wektorów SIFT oraz wstępne wyniki dotyczące budowy modelu otoczenia z użyciem obiektów PRS.

Źródło:

Problemy Eksploatacji; 2008, 3; 57-67
1232-9312

Pojawia się w:

Problemy Eksploatacji

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 36.

Tytuł:

Personal identification using retina

Autorzy:

Choraś, R.S.

Powiązania:

https://bibliotekanauki.pl/articles/333033.pdf

Data publikacji:

2009

Wydawca:

Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych

Tematy:

siatkówka
biometria siatkówki
wybór cech obrazu
retina
retina biometrics
vessel pattern
feature extraction
Gabor transform
feature vector
matching

Opis:

This paper proposes a biometric system for authentication that uses the retina blood vessel pattern. The retina biometric analyzes the layer of blood vessels located at the back of the eye. The blood vessels at the back of the eye have a unique pattern, from eye to eye and person to person. The retina, a layer of blood vessels located at the back of the eye, forms an identity card for the individual under investigation. In particular retinal recognition creates an ”eye signature” from its vascular configuration and its artificial duplication is thought to be virtually impossible.

Źródło:

Journal of Medical Informatics & Technologies; 2009, 13; 53-58
1642-6037

Pojawia się w:

Journal of Medical Informatics & Technologies

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 37.

Tytuł:

Research on bispectrum analysis of secondary feature for vehicle exterior noise based on nonnegative tucker3 decomposition
Badania nad analizą bispektrum cech drugorzędnych hałasu zewnętrznego pojazdów w oparciu o nieujemną dekompozycję Tuckera3

Autorzy:

Wang, H.
Deng, G.
Li, Q.
Kang, Q.

Powiązania:

https://bibliotekanauki.pl/articles/301586.pdf

Data publikacji:

2016

Wydawca:

Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN

Tematy:

feature extraction
vehicle exterior noise
NTD
updating algorithm
ekstrakcja cech
hałas zewnętrzny pojazdu
algorytm aktualizacyjny

Opis:

Nowadays, analysis of external vehicle noise has become more and more difficult for NVH (noise vibration and harshness) engineer to find out the fault among the exhaust system when some significant features are masked by the jamming signals, especially in the case of the vibration noise associating to the bodywork. New method is necessary to be explored and applied to decompose a high-order tensor and extract the useful features (also known as secondary features in this paper). Nonnegative Tucker3 decomposition (NTD) is proposed and applied into secondary feature extraction for its high efficiency of decomposition and well property of physical architecture, which serves as fault diagnosis of exhaust system for an automobile car. Furthermore, updating algorithm conjugating with Newton-Gaussian gradient decent is utilized to solve the problem of overfitting, which occurs abnormally on traditional iterative method of NTD. Extensive experimen results show the bispectrum of secondary features can not only exceedingly interpret the state of vehicle exterior noise, but also be benefit to observe the abnormal frequency of some important features masked before. Meanwhile, the overwhelming performance of NTD algorithm is verified more effective under the same condition, comparing with other traditional methods both at the deviation of successive relative error and the computation time.
Obecnie inżynierowie NVH (zajmujący się problematyką hałasu, drgań i uciążliwości akustycznych) napotykają na coraz większe trudności przy analizie hałasu zewnętrznego pojazdów wynikające z faktu, że istotne cechy związane z nieprawidłowościami układu wydechowego są maskowane przez sygnały zakłócające, szczególnie hałas wibracyjny związany z pracą nadwozia. Niezbędna jest zatem nowa metoda, która pozwoli rozkładać tensory wysokiego rzędu i wyodrębniać przydatne cechy (zwane w tym artykule także cechami drugorzędnymi). Do ekstrakcji cech drugorzędnych wykorzystano w prezentowanej pracy metodę nieujemnej faktoryzacji tensorów znaną także jako nieujemna dekompozycja Tuckera 3 (NTD) , która cechuje się wysoką efektywnością dekompozycji i może być wykorzystywana w diagnostyce uszkodzeń układu wydechowego samochodów. Problem nadmiernego dopasowania, który występuje w tradycyjnej metodzie iteracyjnej NTD rozwiązano przy pomocy algorytmu aktualizacyjnego sprzężonego z gradientem prostym Newtona-Gaussa. Wyniki doświadczeń pokazują, że bispektrum cech drugorzędnych nie tylko pozwala doskonale interpretować stan hałasu zewnętrznego pojazdu, ale również umożliwia wykrywanie wcześniej maskowanych nieprawidłowych częstotliwości odpowiadających niektórym ważnym cechom. Badania potwierdzają, że algorytmu NTD jest bardziej efektywny, w tych samych warunkach, w porównaniu z innymi tradycyjnymi metodami zarówno w zakresie odchyleń błędu względnego jak i czasu obliczeń.

Źródło:

Eksploatacja i Niezawodność; 2016, 18, 2; 291-298
1507-2711

Pojawia się w:

Eksploatacja i Niezawodność

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 38.

Tytuł:

Hybridisation of Mel Frequency Cepstral Coefficient and Higher Order Spectral Features for Musical Instruments Classification

Autorzy:

Bhalke, D. G.
Rama Rao, C. B.
Bormane, D.

Powiązania:

https://bibliotekanauki.pl/articles/176497.pdf

Data publikacji:

2016

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

feature extraction
MFCC
HOS
bispectrum
bicoherence
non-linearity
non-Gaussianity
CPNN
zero crossing rate (ZCR)

Opis:

This paper presents the classification of musical instruments using Mel Frequency Cepstral Coefficients (MFCC) and Higher Order Spectral features. MFCC, cepstral, temporal, spectral, and timbral features have been widely used in the task of musical instrument classification. As music sound signal is generated using non-linear dynamics, non-linearity and non-Gaussianity of the musical instruments are important features which have not been considered in the past. In this paper, hybridisation of MFCC and Higher Order Spectral (HOS) based features have been used in the task of musical instrument classification. HOS-based features have been used to provide instrument specific information such as non-Gaussianity and non-linearity of the musical instruments. The extracted features have been presented to Counter Propagation Neural Network (CPNN) to identify the instruments and their family. For experimentation, isolated sounds of 19 musical instruments have been used from McGill University Master Sample (MUMS) sound database. The proposed features show the significant improvement in the classification accuracy of the system.

Źródło:

Archives of Acoustics; 2016, 41, 3; 427-436
0137-5075

Pojawia się w:

Archives of Acoustics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 39.

Tytuł:

A diagnostic algorithm diagnosing the failure of railway signal equipment

Autorzy:

Wu, Yongcheng
Cao, Dejin

Powiązania:

https://bibliotekanauki.pl/articles/1955227.pdf

Data publikacji:

2021

Wydawca:

Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN

Tematy:

failure diagnosis
railway signal equipment
denoising
feature extraction
diagnostyka uszkodzeń
sygnalizacja kolejowa
odszumianie
ekstrakcja cech

Opis:

Failure of railway signal equipment can cause an impact on its normal operation, and it is necessary to make a timely diagnosis of the failure. In this study, the data of a railway bureau from 2016 to 2020 were studied as an example. Firstly, denoising and feature extraction were performed on the data; then the Adaptive Comprehensive Oversampling (ADASYN) method was used to synthesize minority class samples; finally, three algorithms, back-propagation neural network (BPNN), support vector machine (SVM) and C4.5 algorithms, were used for failure diagnosis. It was found that the three algorithms performed poorly in diagnosing the original data but performed significantly better in diagnosing the synthesized samples, among which the BPNN algorithm had the best performance. The average precision, recall rate and F1 score of the BPNN algorithm were 0.94, 0.92 and 0.93, respectively. The results verify the effectiveness of the BPNN algorithm for failure diagnosis, and the algorithm can be further promoted and applied in practice.

Źródło:

Diagnostyka; 2021, 22, 4; 33-38
1641-6414
2449-5220

Pojawia się w:

Diagnostyka

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 40.

Tytuł:

Research on ship trajectory extraction based on multiattribute DBSCAN optimisation algorithm

Autorzy:

Xu, Xiaofeng
Cui, Deqaing
Li, Yun
Xiao, Yingjie

Powiązania:

https://bibliotekanauki.pl/articles/1551877.pdf

Data publikacji:

2021

Wydawca:

Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa

Tematy:

clustering algorithm
abnormal route
DBSCAN
feature trajectory extraction
fitting analysis

Opis:

With the vigorous development of maritime traffic, the importance of maritime navigation safety is increasing day by day. Ship trajectory extraction and analysis play an important role in ensuring navigation safety. At present, the DBSCAN (density-based spatial clustering of applications with noise) algorithm is the most common method in the research of ship trajectory extraction, but it has shortcomings such as missing ship trajectories in the process of trajectory division. The improved multi-attribute DBSCAN algorithm avoids trajectory division and greatly reduces the probability of missing sub-trajectories. By introducing the position, speed and heading of the ship track point, dividing the complex water area and vectorising the ship track, the function of guaranteeing the track integrity can be achieved and the ship clustering effect can be better realised. The result shows that the cluster fitting effect reaches up to 99.83%, which proves that the multi-attribute DBSCAN algorithm and cluster analysis algorithm have higher reliability and provide better theoretical guidance for the analysis of ship abnormal behaviour.

Źródło:

Polish Maritime Research; 2021, 1; 136-148
1233-2585

Pojawia się w:

Polish Maritime Research

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 41.

Tytuł:

Dimensionality Reduction for Probabilistic Neural Network in Medical Data Classification Problems

Autorzy:

Kusy, M.

Powiązania:

https://bibliotekanauki.pl/articles/226697.pdf

Data publikacji:

2015

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

probabilistic neural network
dimensionality reduction
feature selection
feature extraction
single decision tree
random forest
principal component analysis
prediction ability

Opis:

This article presents the study regarding the problem of dimensionality reduction in training data sets used for classification tasks performed by the probabilistic neural network (PNN). Two methods for this purpose are proposed. The first solution is based on the feature selection approach where a single decision tree and a random forest algorithm are adopted to select data features. The second solution relies on applying the feature extraction procedure which utilizes the principal component analysis algorithm. Depending on the form of the smoothing parameter, different types of PNN models are explored. The prediction ability of PNNs trained on original and reduced data sets is determined with the use of a 10-fold cross validation procedure.

Źródło:

International Journal of Electronics and Telecommunications; 2015, 61, 3; 289-300
2300-1933

Pojawia się w:

International Journal of Electronics and Telecommunications

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 42.

Tytuł:

Optical character recognition using artifical inelligence technologies
Optyczne rozpoznawanie znaków z użyciem sztucznej inteligencji

Autorzy:

Musiał, A.
Szczepaniak, P.

Powiązania:

https://bibliotekanauki.pl/articles/408862.pdf

Data publikacji:

2014

Wydawca:

Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej

Tematy:

character recognition
artificial intelligence
feature extraction
clustering algorithms
rozpoznawanie znaków
sztuczna inteligencja
ekstrakcja cech
algorytmy klastrowania

Opis:

The article represents results of the research of an Optical Character Recognition system. Proposed OCR system is able to convert a raster image into the text string, which represents the text shown on the input image. The main innovation is the fact that the system was created without following any strict rules. It was more an innovative research rather than simple programming using ready guidelines.
Celem projektu opisywanego w artykule było przygotowanie działającego systemu do optycznego rozpoznawania znaków, tj. zdolnego przekształcić rastrowy obraz wejściowy w łańcuch znaków odpowiadający zapisanemu tekstowi na obrazie. Nowością jest m.in. fakt wykonania tego systemu bez podążania za z góry znaną architekturą aplikacji, a przygotowanie go w sposób bardziej doświadczalny, czyli wykorzystując podejście nowatorskie.

Źródło:

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska; 2014, 2; 41-44
2083-0157
2391-6761

Pojawia się w:

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 43.

Tytuł:

An AI & ML based detection & identification in remote imagery: state-of-the-art

Autorzy:

Hashmi, Hina
Dwivedi, Rakesh
Kumar, Anil

Powiązania:

https://bibliotekanauki.pl/articles/2141786.pdf

Data publikacji:

2021

Wydawca:

Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów

Tematy:

convolutional neural network
remote sensed imagery
object detection
artificial intelligence
feature extraction
deep learning
machine learning

Opis:

Remotely sensed images and their allied areas of application have been the charm for a long time among researchers. Remote imagery has a vast area in which it is serving and achieving milestones. From the past, after the advent of AL, ML, and DL-based computing, remote imagery is related techniques for processing and analyzing are continuously growing and offering countless services like traffic surveillance, earth observation, land surveying, and other agricultural areas. As Artificial intelligence has become the charm of researchers, machine learning and deep learning have been proven as the most commonly used and highly effective techniques for object detection. AI & ML-based object segmentation & detection makes this area hot and fond to the researchers again with the opportunities of enhanced accuracy in the same. Several researchers have been proposed their works in the form of research papers to highlight the effectiveness of using remotely sensed imagery for commercial purposes. In this article, we have discussed the concept of remote imagery with some preprocessing techniques to extract hidden and fruitful information from them. Deep learning techniques applied by various researchers along with object detection, object recognition are also discussed here. This literature survey is also included a chronological review of work done related to detection and recognition using deep learning techniques.

Źródło:

Journal of Automation Mobile Robotics and Intelligent Systems; 2021, 15, 4; 3-17
1897-8649
2080-2145

Pojawia się w:

Journal of Automation Mobile Robotics and Intelligent Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 44.

Tytuł:

Marine Mammals Classification using Acoustic Binary Patterns

Autorzy:

Nadir, Maheen
Adnan, Syed M.
Aziz, Sumair
Khan, Muhammad Umar

Powiązania:

https://bibliotekanauki.pl/articles/1953520.pdf

Data publikacji:

2020

Wydawca:

Polska Akademia Nauk. Czasopisma i Monografie PAN

Tematy:

marine mammals
1D Local Binary Patterns
Mel frequency cepstral coefficients
feature extraction
passive acoustic monitoring

Opis:

Marine mammal identification and classification for passive acoustic monitoring remain a challenging task. Mainly the interspecific and intraspecific variations in calls within species and among different individuals of single species make it more challenging. Varieties of species along with geographical diversity induce more complications towards an accurate analysis of marine mammal classification using acoustic signatures. Prior methods for classification focused on spectral features which result in increasing bias for contour base classifiers in automatic detection algorithms. In this study, acoustic marine mammal classification is performed through the fusion of 1D Local Binary Pattern (1D-LBP) and Mel Frequency Cepstral Coefficient (MFCC) based features. Multi-class Support Vector Machines (SVM) classifier is employed to identify different classes of mammal sounds. Classification of six species named Tursiops truncatus, Delphinus delphis, Peponocephala electra, Grampus griseus, Stenella longirostris, and Stenella attenuate are targeted in this research. The proposed model achieved 90.4% accuracy on 70-30% training testing and 89.6% on 5-fold cross-validation experiments.

Źródło:

Archives of Acoustics; 2020, 45, 4; 721-731
0137-5075

Pojawia się w:

Archives of Acoustics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 45.

Tytuł:

Enhanced algorithm for energy optimization and improvised synchronization in knee exoskeleton system

Autorzy:

Arunamithra, J.
Saravanan, R.
Venkatesh Babu, S.

Powiązania:

https://bibliotekanauki.pl/articles/24200592.pdf

Data publikacji:

2022

Wydawca:

Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach

Tematy:

knee exoskeleton
feature extraction
data classification
ANN algorithm
egzoszkielet kolana
ekstrakcja cech
klasyfikacja danych
algorytm ANN

Opis:

Purpose: The purpose of the study is to develop an augmented algorithm with optimised energy and improvised synchronisation to assist the knee exoskeleton design. This enhanced algorithm is used to estimate the accurate left and right movement signals from the brain and accordingly moves the lower-limb exoskeleton with the help of motors. Design/methodology/approach: An optimised deep learning algorithm is developed to differentiate the right and left leg movements from the acquired brain signals. The obtained test signals are then compared with the signals obtained from the conventional algorithm to find the accuracy of the algorithm. Findings: The obtained average accuracy rate of about 63% illustrates the improvised differentiation in identifying the right and left leg movement. Research limitations/implications: The future work involves the comparative study of the proposed algorithm with other classification technologies to extract more reliable results. A comparative analysis of the replaceable and rechargeable battery will be done in the future study to exhibit the effectiveness of the proposed model. Originality/value: This study involves the extended study of five frequency regions namely alpha, beta, gamma, delta and theta, to handle the real-time EEG signal processing exoskeleton, model.

Źródło:

Archives of Materials Science and Engineering; 2022, 117, 2; 79--85
1897-2764

Pojawia się w:

Archives of Materials Science and Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 46.

Tytuł:

Analysis of suspicious lesions in digital mammograms

Autorzy:

Choraś, R. S.

Powiązania:

https://bibliotekanauki.pl/articles/333369.pdf

Data publikacji:

2011

Wydawca:

Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych

Tematy:

sterowane filtry
mammografie
wychwytywanie cech
klasyfikacja
steerable filters
mammograms
moment and texture features
feature extraction
classification

Opis:

The system using steerable filters for analysis suspicious lesions in mammograms is proposed. This system is based on moments and texture features. The set of well defined and classified suspicious lesions regions from mammograms database are used as a reference pattern. The similarity measure for reference pattern image and patient mammogram is found by computing the distance between their corresponding feature vectors. The Euclidean distance metric is used to finding the nearest class to patient feature vector what in result mark the automatically classify this mammograms.

Źródło:

Journal of Medical Informatics & Technologies; 2011, 17; 151-158
1642-6037

Pojawia się w:

Journal of Medical Informatics & Technologies

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 47.

Tytuł:

A simultaneous localization and tracking method for a worm tracking system

Autorzy:

Kowalski, M.
Kaczmarek, P.
Kabaciński, R.
Matuszczak, M.
Tranbowicz, K.
Sobkowiak, R.

Powiązania:

https://bibliotekanauki.pl/articles/330526.pdf

Data publikacji:

2014

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

Caenorhabditis elegans behavior
worm tracking
computer vision
image processing
feature extraction
wizja komputerowa
przetwarzanie obrazu
ekstrakcja cech

Opis:

The idea of worm tracking refers to the path analysis of Caenorhabditis elegans nematodes and is an important tool in neurobiology which helps to describe their behavior. Knowledge about nematode behavior can be applied as a model to study the physiological addiction process or other nervous system processes in animals and humans. Tracking is performed by using a special manipulator positioning a microscope with a camera over a dish with an observed individual. In the paper, the accuracy of a nematode’s trajectory reconstruction is investigated. Special attention is paid to analyzing errors that occurred during the microscope displacements. Two sources of errors in the trajectory reconstruction are shown. One is due to the difficulty in accurately measuring the microscope shift, the other is due to a nematode displacement during the microscope movement. A new method that increases path reconstruction accuracy based only on the registered sequence of images is proposed. The method Simultaneously Localizes And Tracks (SLAT) the nematodes, and is robust to the positioning system displacement errors. The proposed method predicts the nematode position by using NonParametric Regression (NPR). In addition, two other methods of the SLAT problem are implemented to evaluate the NPR method. The first consists in ignoring the nematode displacement during microscope movement, and the second is based on a Kalman filter. The results suggest that the SLAT method based on nonparametric regression gives the most promising results and decreases the error of trajectory reconstruction by 25% compared with reconstruction based on data from the positioning system.

Źródło:

International Journal of Applied Mathematics and Computer Science; 2014, 24, 3; 599-609
1641-876X
2083-8492

Pojawia się w:

International Journal of Applied Mathematics and Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 48.

Tytuł:

Image retrieval based on hierarchical Gabor filters

Autorzy:

Andrysiak, T.
Choraś, M.

Powiązania:

https://bibliotekanauki.pl/articles/908448.pdf

Data publikacji:

2005

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

filtr Gabora
pobieranie obrazu
ekstrakcja cech
reprezentacja hierarchiczna
Gabor filters
image retrieval
texture feature extraction
hierarchical representation

Opis:

Content Based Image Retrieval (CBIR) is now a widely investigated issue that aims at allowing users of multimedia information systems to automatically retrieve images coherent with a sample image. A way to achieve this goal is the computation of image features such as the color, texture, shape, and position of objects within images, and the use of those features as query terms. We propose to use Gabor filtration properties in order to find such appropriate features. The article presents multichannel Gabor filtering and a hierarchical image representation. Then a salient (characteristic) point detection algorithm is presented so that texture parameters are computed only in a neighborhood of salient points. We use Gabor texture features as image content descriptors and efficiently emply them to retrieve images.

Źródło:

International Journal of Applied Mathematics and Computer Science; 2005, 15, 4; 471-480
1641-876X
2083-8492

Pojawia się w:

International Journal of Applied Mathematics and Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 49.

Tytuł:

Hardware acceleration of data classifiers for multimedia processing tasks
Sprzętowe przyspieszenie klasyfikacji danych multimedialnych

Autorzy:

Dziurzański, P.
Mąka, T.
Forczmański, P.

Powiązania:

https://bibliotekanauki.pl/articles/153826.pdf

Data publikacji:

2014

Wydawca:

Stowarzyszenie Inżynierów i Techników Mechaników Polskich

Tematy:

feature extraction
multimedia data classification
Network on Chip (NoC)
ImpulseC
ekstrakcja cech
klasyfikacja danych multimedialnych
sieci wewnątrzukładowe

Opis:

In this paper, experimental results of a proposed hardware acceleration of feature extraction and data classifiers for multimedia are presented. This hardware is based on multi-core architecture connected with a mesh Network on Chip (NoC). The cores in the system execute both data classifiers and feature extraction for audio and image data. Using various meta heuristics the system is optimized with regards to different data communication criteria. The system was implemented on an FPGA platform with use of ImpulseC hardware description language.
W artykule zostały zeprezentowane wyniki eksperymentalne dotyczące sprzętowego przyspieszania ekstrakcji cech i klasyfikacji danych multimedialnych. Opracowane rozwiązanie sprzętowe bazuje na architekturze wielordzeniowej, w której każdy blok realizuje przypisaną mu statycznie funkcjonalność. Rdzenie połączone są ze sobą za pomocą sieci wewnątrzukładowej (ang. Network on Chip, NoC) o architekturze siatki. W artykule opisano pokrótce autorskie oprogramowanie służące do generowania kodu sieci wewnątrzukładowej. Graficzny interfejs użytkownika został zaprezentowany na rys. 1. Narzędzie ma za zadanie dokonywać odwzorowania wybranych funkcjonalności do poszczególnych rdzeni z wykorzystaniem takich meta-heurystyk jak algorytmy genetyczne, symulowane wyżarzanie, poszukiwanie losowe czy algorytmu gradientowego. Jako kryterium optymalizacji można wybrać minimalizację całkowitego przesyłu danych, minimalizację maksymalnej liczby danych transmitowanych przez pojedyncze łącze, a także minimalizację odchylenia standardowego rozmiaru strumieni transmitowanych przez poszczególne łącza. Przykładowe wyniki optymalizacji losowej dla sieci wewnątrzukładowej zostały przedstawione w tab. 1, natomiast wyniki optymalizacji dla sieci wewnątrzukładowej wykorzystującej inne podejścia - w tab. 2. Dla systemu zoptymalizowanego w ten sposób został wygnerowany opisujący go kod w języku ImpulseC, który następnie posłużył do syntezy sprzętowej na układzie FPGA z rodziny Xilinx Virtex 5. Zajętość układu XC5VSX50T dla trzech wykorzystanych klasyfikatorów została przedstawiona na rys. 3. Z kolei tab. 3 przedstawia liczbę zasobów wykorzystanych przez narzędzie syntezy wysokiego poziomu dla tych klasyfikatorów. Technika przedstawiona w publikacji umożliwia określenie warunków i ograniczeń implementacji sprzętowej systemu służącego klasyfikacji danych multimedialnych.

Źródło:

Pomiary Automatyka Kontrola; 2014, R. 60, nr 6, 6; 382-384
0032-4140

Pojawia się w:

Pomiary Automatyka Kontrola

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 50.

Tytuł:

Grain size determination and classification using adaptive image segmentation with grain shape information for milling quality evaluation
Określenie rozmiaru ziarna i klasyfikacja z użyciem adaptacyjnej segmentacji obrazu i informacji o kształcie dla oceny jakości mielenia

Autorzy:

Budzan, S.
Pawełczyk, M.

Powiązania:

https://bibliotekanauki.pl/articles/328384.pdf

Data publikacji:

2018

Wydawca:

Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN

Tematy:

grain classification
particle analysis
image segmentation
feature extraction
klasyfikacja ziaren
analiza
wielkość ziaren
segmentacja obrazów
ekstrakcja cech

Opis:

In this paper, authors described methods of material granularity evaluation and a novel method for grain size determination with inline electromagnetic mill device diagnostics. The milling process quality evaluation can be carried out with vibration measurements, analysis of the milling material images or well-known screening machines. The method proposed in this paper is developed to the online examination of the milled product during the milling process using real-time digital images. In this paper, authors concentrated their work on copper ore milling process. Determination of the total number of the grain, the size of each grain, also the classification of the grains are the main goal of the developed method. In the proposed method the vision camera with lightning mounted at two assumed angles has been used. The detection of the grains has been based on an adaptive segmentation algorithm, improved with distance transform to enhance grains detection. The information about particles shape and context is used to optimize the grain classification process in the next step. The final classification is based on the rule-based method with defined particle shape and size parameters.
W pracy autorzy opisali metody oceny uziarnienia materiału i nową metodę określania wielkości ziaren z jednoczesną diagnostyką pracy młyna elektromagnetycznego. Ocena jakości mielenia może być realizowana na kilka sposobów, tj. poprzez pomiar drgań, analizę obrazów materiału zmielonego, lub wykorzystanie matryc przesiewowych. Proces mielenia jest procesem obciążonym znacznym zużyciem energii, dlatego proces diagnostyki powinien być wykonywany z dużą efektywnością. Metoda zaproponowana w niniejszym artykule opiera się na badaniu mielonego produktu podczas procesu mielenia przy użyciu analizy obrazów cyfrowych w czasie rzeczywistym. Głównym celem opracowanej metody jest określenie całkowitej liczby ziaren, wielkości ziaren, jak i klasyfikacja ziaren. W zaproponowanej metodzie wykorzystano akwizycję obrazów z kamery przy oświetlaniu badanych próbek pod kątem, co pozwala zwiększyć liczbę wykrywanych ziaren. Detekcja ziaren bazuje na metodzie segmentacji adaptacyjnej rozszerzonej o analizę map odległościowych w celu poprawienia jakości i liczby wykrytych ziaren. Informacje na temat kształtu ziaren są wykorzystywane w celu optymalizacji procesu klasyfikacji ziaren. Ostateczna klasyfikacja opiera się na metodzie bazującej na regułach, w których określono zależności dla różnych parametrów kształtu i rozmiaru ziaren.

Źródło:

Diagnostyka; 2018, 19, 1; 41-48
1641-6414
2449-5220

Pojawia się w:

Diagnostyka

Dostawca treści:

Biblioteka Nauki

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