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Tytuł:
Wybór reprezentatywnej struktury zbiorów uczących dla potrzeb neuronowych modeli identyfikacyjnych wykorzystywanych w inżynierii rolniczej
Selection of representative structure of learning sets for purpose of neuron identification models used in agricultural engineering
Autorzy:
Nowakowski, K.
Boniecki, P.
Weres, J.
Mueller, W.
Powiązania:
https://bibliotekanauki.pl/articles/287545.pdf
Data publikacji:
2007
Wydawca:
Polskie Towarzystwo Inżynierii Rolniczej
Tematy:
reprezentatywne dane uczące
sieć neuronowa
analiza obrazu
inżynieria rolnicza
agricultural engineering
representative learning data
neural network
image analysis
Opis:
Wykorzystanie sztucznych sieci neuronowych do identyfikacji mechanicznych uszkodzeń ziarniaków na podstawie ich fotografii wymaga doboru odpowiednich cech charakterystycznych na podstawie, których zostanie przeprowadzony proces rozpoznawania. Ponieważ stosowanie sieci neuronowych do bezpośredniego mapowania zbiorów graficznych jest nieefektywne, wskazane jest użycie bloku przetwarzania wstępnego, tzw. preprocesora. Zaprojektowanie i wytworzenie właściwego systemu informatycznego dla tak sformułowanego celu pozwoliło na dokonanie transformacji danych pierwotnych (zdjęcia fotograficzne) do reprezentacji danych, która będzie odpowiednia dla wykorzystania w procesie uczenia sieci neuronowej.
Use of artificial neural networks for identification of the mechanical damages to grains based on photographs requires a selection of appropriate characteristic features in order to conduct a recognition process. Since the application of neural networks for direct mapping of graphic sets is not really effective, it is recommended to use the initial processing block, so called preprocessor. Design and creation of a proper information system for this particular purpose allowed to transform raw data (photographic images) for data representation, appropriate to be used in the learning process of neural network.
Źródło:
Inżynieria Rolnicza; 2007, R. 11, nr 6 (94), 6 (94); 183-188
1429-7264
Pojawia się w:
Inżynieria Rolnicza
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Predictive modelling of turbofan engine components condition using machine and deep learning methods
Autorzy:
Matuszczak, Michał
Żbikowski, Mateusz
Teodorczyk, Andrzej
Powiązania:
https://bibliotekanauki.pl/articles/1841686.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
reliability
prognostics
deep learning
machine learning
gas turbine
turbofan engine
neural network
condition-based maintenance
Opis:
The article proposes an approach based on deep and machine learning models to predict a component failure as an enhancement of condition based maintenance scheme of a turbofan engine and reviews currently used prognostics approaches in the aviation industry. Component degradation scale representing its life consumption is proposed and such collected condition data are combined with engines sensors and environmental data. With use of data manipulation techniques, a framework for models training is created and models' hyperparameters obtained through Bayesian optimization. Models predict the continuous variable representing condition based on the input. Best performed model is identified by detemining its score on the holdout set. Deep learning models achieved 0.71 MSE score (ensemble meta-model of neural networks) and outperformed significantly machine learning models with their best score at 1.75. The deep learning models shown their feasibility to predict the component condition within less than 1 unit of the error in the rank scale.
Źródło:
Eksploatacja i Niezawodność; 2021, 23, 2; 359-370
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deep Learning Can Improve Early Skin Cancer Detection
Autorzy:
Mohamed, Abeer
Mohamed, Wael A.
Zekry, Abdel Halim
Powiązania:
https://bibliotekanauki.pl/articles/963798.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
technology
dermoscopic lesions
convolutional
neural network
ISIC dataset
deep learning
neural networks
Opis:
Skin cancer is the most common form of cancer affecting humans. Melanoma is the most dangerous type of skin cancer; and early diagnosis is extremely vital in curing the disease. So far, the human knowledge in this field is very limited, thus, developing a mechanism capable of identifying the disease early on can save lives, reduce intervention and cut unnecessary costs. In this paper, the researchers developed a new learning technique to classify skin lesions, with the purpose of observing and identifying the presence of melanoma. This new technique is based on a convolutional neural network solution with multiple configurations; where the researchers employed an International Skin Imaging Collaboration (ISIC) dataset. Optimal results are achieved through a convolutional neural network composed of 14 layers. This proposed system can successfully and reliably predict the correct classification of dermoscopic lesions with 97.78% accuracy.
Źródło:
International Journal of Electronics and Telecommunications; 2019, 65, 3; 507-512
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hospitalization patient forecasting based on multi-task deep learning
Autorzy:
Zhou, Min
Huang, Xiaoxiao
Liu, Haipeng
Zheng, Dingchang
Powiązania:
https://bibliotekanauki.pl/articles/2201025.pdf
Data publikacji:
2023
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
hospitalization patient
neural network
multitask learning
pacjent hospitalizowany
sieć neuronowa
nauka wielozadaniowa
Opis:
Forecasting the number of hospitalization patients is important for hospital management. The number of hospitalization patients depends on three types of patients, namely, admission patients, discharged patients, and inpatients. However, previous works focused on one type of patients rather than the three types of patients together. In this paper, we propose a multi-task forecasting model to forecast the three types of patients simultaneously. We integrate three neural network modules into a unified model for forecasting. Besides, we extract date features of admission and discharged patient flows to improve forecasting accuracy. The algorithm is trained and evaluated on a real-world data set of a one-year daily observation of patient numbers in a hospital. We compare the performance of our model with eight baselines over two real-word data sets. The experimental results show that our approach outperforms other baseline algorithms significantly.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2023, 33, 1; 151--162
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Building computer vision systems using machine learning algorithms
Autorzy:
Boyko, N.
Sokil, N.
Powiązania:
https://bibliotekanauki.pl/articles/410768.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Oddział w Lublinie PAN
Tematy:
algorithm
information system
neural network
machine learning
client-server architecture
script
artificial system
machine learning algorithms
algorytm
systemy informacyjne
sieci neuronowe
systemy uczące
architektura klient-serwer
skrypt
Opis:
In this paper theoretic aspects of machine learning system in the field of computer vision is considered. There are presented methods of behavior analysis. There are offered tasks and problems associated with building systems using machine learning algorithm. The paper provides signs of problems that can be solved by using machine learning algorithms There is demonstrated step by step construction of computer vision system. The paper provides the algorithm of solving the problem of binary (two classes) classification for demonstration the machine learning algorithm possibilities in image recognition field, which can recognize the gender of the person on the photo. Aspects related to the search of data processing are also considered. There is analyzed the search of optimal parameters for algorithms. An interpretation of results in machine learning algorithm is provided. Binarization methods in machine learning algorithm are offered. There is analyzed the technology for improving the accuracy of machine learning algorithm. There are proposed ways to improve computer vision system in neural systems. Also there are analyzed large software modules that work using machine learning systems. The article provides prospects of powerful information technologies, which are necessary for the proper data selection in learning and configuration of feature extraction algorithm to create a computer vision system.
Źródło:
ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2017, 6, 2; 15-20
2084-5715
Pojawia się w:
ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Detection of Monocrystalline Silicon Wafer Defects Using Deep Transfer Learning
Autorzy:
Ganum, Adriana
Iskandar, D. N. F. Awang
Chin, Lim Phei
Fauzi, Ahmad Hadinata
Powiązania:
https://bibliotekanauki.pl/articles/2058502.pdf
Data publikacji:
2022
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
automated optical inspection
machine learning
neural network
wafer imperfection identification
Opis:
Defect detection is an important step in industrial production of monocrystalline silicon. Through the study of deep learning, this work proposes a framework for classifying monocrystalline silicon wafer defects using deep transfer learning (DTL). An existing pre-trained deep learning model was used as the starting point for building a new model. We studied the use of DTL and the potential adaptation of Mo bileNetV2 that was pre-trained using ImageNet for extracting monocrystalline silicon wafer defect features. This has led to speeding up the training process and to improving performance of the DTL-MobileNetV2 model in detecting and classifying six types of monocrystalline silicon wafer defects (crack, double contrast, hole, microcrack, saw-mark and stain). The process of training the DTL-MobileNetV2 model was optimized by relying on the dense block layer and global average pooling (GAP) method which had accelerated the convergence rate and improved generalization of the classification network. The monocrystalline silicon wafer defect classification technique relying on the DTL-MobileNetV2 model achieved the accuracy rate of 98.99% when evaluated against the testing set. This shows that DTL is an effective way of detecting different types of defects in monocrystalline silicon wafers, thus being suitable for minimizing misclassification and maximizing the overall production capacities.
Źródło:
Journal of Telecommunications and Information Technology; 2022, 1; 34--42
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Development of an automated assembly process supported with an artificial neural network
Autorzy:
Bobka, P.
Heyn, J.
Henningson, J.-O.
Römer, M.
Engbers, T.
Dietrich, F.
Dröder, K.
Powiązania:
https://bibliotekanauki.pl/articles/99408.pdf
Data publikacji:
2018
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
assembly
machine learning
neural network
industrial robot
Opis:
A central problem in automated assembly is the ramp-up phase. In order to achieve the required tolerances and cycle times, assembly parameters must be determined by extensive manual parameter variations. Therefore, the duration of the ramp-up phase represents a planning uncertainty and a financial risk, especially when high demands are placed on dynamics and precision. To complete this phase as efficiently as possible, comprehensive planning and experienced personnel are necessary. In this paper, we examine the use of machine learning techniques for the ramp-up of an automated assembly process. Specifically we use a deep artificial neural network to learn process parameters for pick-and-place operations of planar objects. We describe how the handling parameters of an industrial robot can be adjusted and optimized automatically by artificial neural networks and examine this approach in laboratory experiments. Furthermore, we test whether an artificial neural network can be used to optimize assembly parameters in process as an adaptive process controller. Finally, we discuss the advantages and disadvantages of the described approach for the determination of optimal assembly parameters in the ramp-up phase and during the utilization phase.
Źródło:
Journal of Machine Engineering; 2018, 18, 3; 28-41
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An autoencoder-enhanced stacking neural network model for increasing the performance of intrusion detection
Autorzy:
Brunner, Csaba
Kő, Andrea
Fodor, Szabina
Powiązania:
https://bibliotekanauki.pl/articles/2147134.pdf
Data publikacji:
2022
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
intrusion detection
neural network
ensemble classifiers
hyperparameter optimization
sparse autoencoder
NSL-KDD
machine learning
Opis:
Security threats, among other intrusions affecting the availability, confidentiality and integrity of IT resources and services, are spreading fast and can cause serious harm to organizations. Intrusion detection has a key role in capturing intrusions. In particular, the application of machine learning methods in this area can enrich the intrusion detection efficiency. Various methods, such as pattern recognition from event logs, can be applied in intrusion detection. The main goal of our research is to present a possible intrusion detection approach using recent machine learning techniques. In this paper, we suggest and evaluate the usage of stacked ensembles consisting of neural network (SNN) and autoencoder (AE) models augmented with a tree-structured Parzen estimator hyperparameter optimization approach for intrusion detection. The main contribution of our work is the application of advanced hyperparameter optimization and stacked ensembles together. We conducted several experiments to check the effectiveness of our approach. We used the NSL-KDD dataset, a common benchmark dataset in intrusion detection, to train our models. The comparative results demonstrate that our proposed models can compete with and, in some cases, outperform existing models.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 2; 149--163
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Subpixel localization of optical vortices using artificial neural networks
Autorzy:
Popiołek-Masajada, Agnieszka
Frączek, Ewa
Burnecka, Emilia
Powiązania:
https://bibliotekanauki.pl/articles/1849005.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
optical vortex
spiral phase map
pseudo phase
deep learning
neural network
Opis:
Optical vortices are getting attention in modern optical metrology. Because of their unique features, they can be used as precise position markers. In this paper, we show that an artificial neural network can be used to improve vortex localization. A deep neural network with several hidden layers was trained to find subpixel vortex positions on the spiral phase maps. Several thousand training samples, differing by spiral density, its orientation, and vortex position, were generated numerically for teaching purposes. As a result, Best Validation Performance of the order of 10-5 pixel has been reached. To verify the usefulness of the proposed method, a related experiment in the setup of an optical vortex scanning microscope has been reported. It is shown that the vortex can be localized with subpixel accuracy also on experimental phase maps.
Źródło:
Metrology and Measurement Systems; 2021, 28, 3; 497-508
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Subpixel localization of optical vortices using artificial neural networks
Autorzy:
Popiołek-Masajada, Agnieszka
Frączek, Ewa
Burnecka, Emilia
Powiązania:
https://bibliotekanauki.pl/articles/1849096.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
optical vortex
spiral phase map
pseudo phase
deep learning
neural network
Opis:
Optical vortices are getting attention in modern optical metrology. Because of their unique features, they can be used as precise position markers. In this paper, we show that an artificial neural network can be used to improve vortex localization. A deep neural network with several hidden layers was trained to find subpixel vortex positions on the spiral phase maps. Several thousand training samples, differing by spiral density, its orientation, and vortex position, were generated numerically for teaching purposes. As a result, Best Validation Performance of the order of 10-5 pixel has been reached. To verify the usefulness of the proposed method, a related experiment in the setup of an optical vortex scanning microscope has been reported. It is shown that the vortex can be localized with subpixel accuracy also on experimental phase maps.
Źródło:
Metrology and Measurement Systems; 2021, 28, 3; 497-508
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Breast cancer diagnosis using wrapper-based feature selection and artificial neural network
Autorzy:
Naveed, Nawazish
Madhloom, Hayan T.
Husain, Mohd Shahid
Powiązania:
https://bibliotekanauki.pl/articles/1956040.pdf
Data publikacji:
2021
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
breast cancer diagnosis
feature selection
neural network
grid search
machine learning
diagnostyka raka piersi
dobór cech
sieć neuronowa
przeszukiwanie sieci
uczenie maszynowe
Opis:
Breast cancer is commonest type of cancers among women. Early diagnosis plays a significant role in reducing the fatality rate. The main objective of this study is to propose an efficient approach to classify breast cancer tumor into either benign or malignant based on digitized image of a fine needle aspirate (FNA) of a breast mass represented by the Wisconsin Breast Cancer Dataset. Two wrapper-based feature selection methods, namely, sequential forward selection(SFS) and sequential backward selection (SBS) are used to identify the most discriminant features which can contribute to improve the classification performance. The feed forward neural network (FFNN) is used as a classification algorithm. The learning algorithm hyper-parameters are optimized using the grid search process. After selecting the optimal classification model, the data is divided into training set and testing set and the performance was evaluated. The feature space is reduced from nine feature to seven and six features using SFS and SBS respectively. The highest classification accuracy recorded was 99.03% with FFNN using the seven SFS selected features. While accuracy recorded with the six SBS selected features was 98.54%. The obtained results indicate that the proposed approach is effective in terms of feature space reduction leading to better accuracy and efficient classification model.
Źródło:
Applied Computer Science; 2021, 17, 3; 19-30
1895-3735
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A learning paradigm for motion control of mobile manipulators
Autorzy:
Abdessemed, F.
Monacelli, E.
Benmahammed, K.
Powiązania:
https://bibliotekanauki.pl/articles/908373.pdf
Data publikacji:
2006
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
manipulator ruchomy
sieć neuronowa
unikanie przeszkód
mobile manipulator
neural network
backpropagation
obstacle avoidance
Opis:
Motion control of a mobile manipulator is discussed. The objective is to allow the end-effector to track a given trajectory in a fixed world frame. The motion of the platform and that of the manipulator are coordinated by a neural network which is a kind of graph designed from the kinematic model of the system. A learning paradigm is used to produce the required reference variables for each of the mobile platform and the robot manipulator for an overall coordinate behavior. Simulation results are presented to show the effectiveness of the proposed scheme.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2006, 16, 4; 475-484
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Self-learning control algorithms used to manage the operating of an internal combustion engine
Autorzy:
Graba, Mariusz
Mamala, Jarosław
Bieniek, Andrzej
Powiązania:
https://bibliotekanauki.pl/articles/242942.pdf
Data publikacji:
2019
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
transport
simulation
combustion engines
environmental protection
neural network
Opis:
The article presents the possibility of using self-learning control algorithms to manage subassemblies of an internal combustion engine in order to reduce exhaust emissions to the natural environment. In compression ignition (CI) engines, the issue of emissions mainly concerns two components: particulate matter (PM) and nitrogen oxides (NOx). The work focuses mainly on the possibility of reducing the emission of nitrogen oxides. It is assumed that the particularly problematic points when it comes to excessive emission of harmful substances are the dynamic states in which combustion engines operate constantly. In dynamically changing operating points, it is very difficult to choose the right setting of actuators such as the exhaust gas recirculation (EGR) valve to ensure the correct operation of the unit and the minimum emission of these substances. In the light of the above, an attempt was made to develop a selflearning mathematical model, which can predict estimated emission levels of selected substance basing on current measurement signals (e.g. air, pressure, crankshaft rotational speed, etc.). The article presents the results of the estimation of nitrogen oxides by the trained neural network in comparison to the values measured with the use of a sensor installed in the exhaust system. The presented levels of estimated and measured results are very similar to each other and shifted over time in favour of neural networks, where the information about the emission level appears much earlier. On the basis of the estimated level, it shall be possible to make an appropriate decision about specific settings of recirculation system components, such as the EGR valve. It is estimated that by using the chosen control method it is possible significantly to reduce the emission of harmful substances into the natural environment while maintaining dynamic properties of the engine.
Źródło:
Journal of KONES; 2019, 26, 4; 75-82
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Zastosowanie uczenia maszynowego w budowie interfejsu sterowanego głosem na przykładzie odtwarzacza muzyki
Applying of machine learning in the construction of a voice-controlled interface on the example of a music player
Autorzy:
Basiakowski, Jakub
Powiązania:
https://bibliotekanauki.pl/articles/98114.pdf
Data publikacji:
2019
Wydawca:
Politechnika Lubelska. Instytut Informatyki
Tematy:
uczenie maszynowe
sieć neuronowa
rozpoznawanie głosu
machine learning
neural network
speech recognition
Opis:
Poniższy artykuł przedstawia wyniki badań wpływu zastosowania uczenia maszynowego w budowie interfejsu sterowanego głosem. Do analizy wykorzystane zostały dwa różne modele: jednokierunkowa sieć neuronowa zawierająca jedną warstwę ukrytą oraz bardziej skomplikowana konwolucyjna sieć neuronowa. Dodatkowo wykonane zostało porównanie modeli użytych w celu realizacji badań pod względem jakości oraz przebiegu treningu.
The following paper presents the results of research on the impact of machine learning in the construction of a voice-controlled interface. Two different models were used for the analysys: a feedforward neural network containing one hidden layer and a more complicated convolutional neural network. What is more, a comparison of the applied models was presented. This comparison was performed in terms of quality and the course of training.
Źródło:
Journal of Computer Sciences Institute; 2019, 13; 302-309
2544-0764
Pojawia się w:
Journal of Computer Sciences Institute
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms
Autorzy:
Trawiński, B.
Smętek, M.
Telec, Z.
Lasota, T.
Powiązania:
https://bibliotekanauki.pl/articles/331296.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
maszyna ucząca się
test statystyczny nieparametryczny
regresja statystyczna
sieć neuronowa
wielokrotne testy porównawcze
machine learning
nonparametric statistical tests
statistical regression
neural network
multiple comparison tests
Opis:
In the paper we present some guidelines for the application of nonparametric statistical tests and post-hoc procedures devised to perform multiple comparisons of machine learning algorithms. We emphasize that it is necessary to distinguish between pairwise and multiple comparison tests. We show that the pairwise Wilcoxon test, when employed to multiple comparisons, will lead to overoptimistic conclusions. We carry out intensive normality examination employing ten different tests showing that the output of machine learning algorithms for regression problems does not satisfy normality requirements. We conduct experiments on nonparametric statistical tests and post-hoc procedures designed for multiple 1 x N and N x N comparisons with six different neural regression algorithms over 29 benchmark regression data sets. Our investigation proves the usefulness and strength of multiple comparison statistical procedures to analyse and select machine learning algorithms.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 867-881
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
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