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Wyświetlanie 1-15 z 118
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
Artykuł
Tytuł:
A Comparative Study between CS-LBP/SVM and CS-LBP/PCA in Facial Expression Recognition
Autorzy:
Gaur, Sheena
Powiązania:
https://bibliotekanauki.pl/articles/1075570.pdf
Data publikacji:
2019
Wydawca:
Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
Tematy:
Center symmetric local binary patterns (CS-LBP)
Facial Expression Analysis
Principal Component Analysis 9PCA)
Support Vector Machine (SVM)
Opis:
Face plays significant role in social communication. This is a 'window' to human personality, emotions and thoughts. Due to this, face is a subject of study in many areas of science such as psychology, behavioral science, medicine and computer science etc. In this paper, a comparative study is suggested between CS-LBP/SVM and CS-LBP/PCA. These algorithms are used in emotive facial expression recognition. Finally, a comparison is shown between PCA & SVM in terms of Dimension Reduction. The proposed system uses grayscale frontal face images of a Japanese female to classify six basic emotions namely happiness, sadness, disgust, fear, surprise and anger.
Źródło:
World Scientific News; 2019, 121; 83-89
2392-2192
Pojawia się w:
World Scientific News
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A comparative study on performance of basic and ensemble classifiers with various datasets
Autorzy:
Gunakala, Archana
Shahid, Afzal Hussain
Powiązania:
https://bibliotekanauki.pl/articles/30148255.pdf
Data publikacji:
2023
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
classification
Naïve Bayes
neural network
Support Vector Machine
Decision Tree
ensemble learning
Random Forest
Opis:
Classification plays a critical role in machine learning (ML) systems for processing images, text and high -dimensional data. Predicting class labels from training data is the primary goal of classification. An optimal model for a particular classification problem is chosen based on the model's performance and execution time. This paper compares and analyzes the performance of basic as well as ensemble classifiers utilizing 10-fold cross validation and also discusses their essential concepts, advantages, and disadvantages. In this study five basic classifiers namely Naïve Bayes (NB), Multi-layer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) and the ensemble of all the five classifiers along with few more combinations are compared with five University of California Irvine (UCI) ML Repository datasets and a Diabetes Health Indicators dataset from Kaggle repository. To analyze and compare the performance of classifiers, evaluation metrics like Accuracy, Recall, Precision, Area Under Curve (AUC) and F-Score are used. Experimental results showed that SVM performs best on two out of the six datasets (Diabetes Health Indicators and waveform), RF performs best for Arrhythmia, Sonar, Tic-tac-toe datasets, and the best ensemble combination is found to be DT+SVM+RF on Ionosphere dataset having respective accuracies 72.58%, 90.38%, 81.63%, 73.59%, 94.78% and 94.01%. The proposed ensemble combinations outperformed the conven¬tional models for few datasets.
Źródło:
Applied Computer Science; 2023, 19, 1; 107-132
1895-3735
2353-6977
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A linear Support Vector Machine solver for a large number of training examples
Autorzy:
Białoń, P.
Powiązania:
https://bibliotekanauki.pl/articles/970794.pdf
Data publikacji:
2009
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
support vector machine (SVM)
analytic center cutting plane method
RAM volume required
Opis:
A new linear Support Vector Machine algorithm and solver are presented. The algorithm is in a twofold way well-suited for problems with a large number of training examples. First, unlike many optimization algorithms, it does not simultaneously keep all the examples in RAM and thus does not exhaust the memory (moreover, it smartly passes through disk files storing the data: two mechanisms reduce the computation time by disregarding some input data without a loss in solution quality). Second, it uses the analytical center cutting plane scheme, appearing as more efficient for hard parameter settings than the Kelley's scheme used in other solvers, like SVM_perf. The experiments with both real-life and artificial examples are described. In one of them the solver proved to be capable of solving a problem with one billion training examples. A critical analysis of the complexity of SVM_perf is given.
Źródło:
Control and Cybernetics; 2009, 38, 1; 281-300
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Machine Learning Model for Improving Building Detection in Informal Areas: A Case Study of Greater Cairo
Autorzy:
Taha, Lamyaa Gamal El-deen
Ibrahim, Rania Elsayed
Powiązania:
https://bibliotekanauki.pl/articles/2055780.pdf
Data publikacji:
2022
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
multi-source image fusion
random forest
support vector machine
DEM extraction
unplanned unsafe areas
remote sensing
Opis:
Building detection in Ashwa’iyyat is a fundamental yet challenging problem, mainly because it requires the correct recovery of building footprints from images with high-object density and scene complexity. A classification model was proposed to integrate spectral, height and textural features. It was developed for the automatic detection of the rectangular, irregular structure and quite small size buildings or buildings which are close to each other but not adjoined. It is intended to improve the precision with which buildings are classified using scikit learn Python libraries and QGIS. WorldView-2 and Spot-5 imagery were combined using three image fusion techniques. The Grey-Level Co-occurrence Matrix was applied to determine which attributes are important in detecting and extracting buildings. The Normalized Digital Surface Model was also generated with 0.5-m resolution. The results demonstrated that when textural features of colour images were introduced as classifier input, the overall accuracy was improved in most cases. The results show that the proposed model was more accurate and efficient than the state-of-the-art methods and can be used effectively to extract the boundaries of small size buildings. The use of a classifier ensample is recommended for the extraction of buildings.
Źródło:
Geomatics and Environmental Engineering; 2022, 16, 2; 39--58
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A novel approach for automatic detection and classification of suspicious lesions in breast ultrasound images
Autorzy:
Karimi, B.
Krzyżak, A.
Powiązania:
https://bibliotekanauki.pl/articles/91890.pdf
Data publikacji:
2013
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
automatic detection
classification
breast cancer
cancer lesions
ultrasound images
AdaBoost
artificial neural network
Fuzzy Support Vector Machine
Opis:
In this research, a new method for automatic detection and classification of suspected breast cancer lesions using ultrasound images is proposed. In this fully automated method, de-noising using fuzzy logic and correlation among ultrasound images taken from different angles is used. Feature selection using combination of sequential backward search, sequential forward search and distance-based methods is obtained. A new segmentation method based on automatic selection of seed points and region growing is proposed and classification of lesions into two malignant and benign classes using combination of AdaBoost, Artificial Neural Network and Fuzzy Support Vector Machine classifiers and majority voting is implemented.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 4; 265-276
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Novel Approach To Diagnosis Of Analog Circuit Incipient Faults Based On KECA And OAO LSSVM
Autorzy:
Zhang, C.
He, Y.
Zuo, L.
Wang, J.
He, W.
Powiązania:
https://bibliotekanauki.pl/articles/221378.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
analog circuits
incipient fault diagnosis
wavelet transform
kernel entropy component analysis
least squares support vector machine
Opis:
Correct incipient identification of an analog circuit fault is conducive to the health of the analog circuit, yet very difficult. In this paper, a novel approach to analog circuit incipient fault identification is presented. Time responses are acquired by sampling outputs of the circuits under test, and then the responses are decomposed by the wavelet transform in order to generate energy features. Afterwards, lower-dimensional features are produced through the kernel entropy component analysis as samples for training and testing a one-against-one least squares support vector machine. Simulations of the incipient fault diagnosis for a Sallen-Key band-pass filter and a two-stage four-op-amp bi-quad low-pass filter demonstrate the diagnosing procedure of the proposed approach, and also reveal that the proposed approach has higher diagnosis accuracy than the referenced methods.
Źródło:
Metrology and Measurement Systems; 2015, 22, 2; 251-262
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A primal sub-gradient method for structured classification with the averaged sum loss
Autorzy:
Mančev, D.
Todorović, B.
Powiązania:
https://bibliotekanauki.pl/articles/331050.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
structured classification
support vector machine (SVM)
subgradient method
sequence labeling
klasyfikacja strukturalna
maszyna wektorów nośnych
rozpoznawanie wzorca
Opis:
We present a primal sub-gradient method for structured SVM optimization defined with the averaged sum of hinge losses inside each example. Compared with the mini-batch version of the Pegasos algorithm for the structured case, which deals with a single structure from each of multiple examples, our algorithm considers multiple structures from a single example in one update. This approach should increase the amount of information learned from the example. We show that the proposed version with the averaged sum loss has at least the same guarantees in terms of the prediction loss as the stochastic version. Experiments are conducted on two sequence labeling problems, shallow parsing and part-of-speech tagging, and also include a comparison with other popular sequential structured learning algorithms.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2014, 24, 4; 917-930
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A real-valued genetic algorithm to optimize the parameters of support vector machine for classification of multiple faults in NPP
Autorzy:
Amer, F. Z.
El-Garhy, A. M.
Awadalla, M. H.
Rashad, S. M.
Abdien, A. K.
Powiązania:
https://bibliotekanauki.pl/articles/147652.pdf
Data publikacji:
2011
Wydawca:
Instytut Chemii i Techniki Jądrowej
Tematy:
support vector machine (SVM)
fault classification
multi fault classification
genetic algorithm (GA)
machine learning
Opis:
Two parameters, regularization parameter c, which determines the trade off cost between minimizing the training error and minimizing the complexity of the model and parameter sigma (σ) of the kernel function which defines the non-linear mapping from the input space to some high-dimensional feature space, which constructs a non-linear decision hyper surface in an input space, must be carefully predetermined in establishing an efficient support vector machine (SVM) model. Therefore, the purpose of this study is to develop a genetic-based SVM (GASVM) model that can automatically determine the optimal parameters, c and sigma, of SVM with the highest predictive accuracy and generalization ability simultaneously. The GASVM scheme is applied on observed monitored data of a pressurized water reactor nuclear power plant (PWRNPP) to classify its associated faults. Compared to the standard SVM model, simulation of GASVM indicates its superiority when applied on the dataset with unbalanced classes. GASVM scheme can gain higher classification with accurate and faster learning speed.
Źródło:
Nukleonika; 2011, 56, 4; 323-332
0029-5922
1508-5791
Pojawia się w:
Nukleonika
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A smart fault identification system for ball bearing using simulation-driven vibration analysis
Autorzy:
Khaire, Pallavi
Phalle, Vikas
Powiązania:
https://bibliotekanauki.pl/articles/27309884.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
condition monitoring
bearing defect
FFT analyzer
BPFI
BPFO
multiclass support
vector machine
monitorowanie stanu
wada łożyska
analizator FFT
maszyna wektorowa
Opis:
Bearings are one of the pivotal parts of rotating machines. The health of a bearing is responsible for the hassle-free operation of a machine. As vibration signatures give intimations of machine failure at an earlier stage, mostly vibration-based condition monitoring is used to monitor bearing’s health for avoiding the risk of failure. In this work, a simulation-based approach is adopted to identify surface defects at ball bearing raceways. The vibration data in time and frequency domain is captured by FFT analyzer from an experimental setup. The time frequency domain conversion of a raw time domain data was carried out by wavelet packet transform, as it takes into account the transients and spectral frequencies. The rotor bearing model is simulated in Ansys. Finally, most influencing statistical features were extracted by employing Principal Component Analysis (PCA), and fed to Multiclass Support Vector Machine (MSVM). To train the algorithm, the simulated data is used whereas the data acquired from FFT analyzer is used for testing. It can be concluded that the defects are characterized by Ball Pass Frequency (BPF) at inner race and outer raceway as indicated in the literature. The developed model is capable to monitor bearing’s health which gives an average accuracy of 99%.
Źródło:
Archive of Mechanical Engineering; 2023, LXX, 2; 247--270
0004-0738
Pojawia się w:
Archive of Mechanical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A sorting method for coal and gangue based on surface grayness and glossiness
Metoda sortowania węgla i skały płonnej na podstawie szarości i połysku powierzchni
Autorzy:
Cheng, Gang
Wei, Yifan
Chen, Jie
Pan, Zeye
Powiązania:
https://bibliotekanauki.pl/articles/27311660.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Instytut Gospodarki Surowcami Mineralnymi i Energią PAN
Tematy:
surface glossiness
gangue recognition
image recognition
supervised classification
grey wolf algorithm
support vector machine
połysk powierzchni
rozpoznawanie skały płonnej
rozpoznawanie obrazu
klasyfikacja nadzorowana
algorytm szarych wilków
maszyna wektorów nośnych
Opis:
Sorting coal and gangue is important in raw coal production; accurately identifying coal and gangue is a prerequisite for effectively separating coal and gangue. The method of extracting coal and gangue using image grayscale information can effectively identify coal and gangue, but the recognition rate of the sorting process based on image grayscale information needs to substantially higher than that which is needed to meet production requirements. A sorting method of coal and gangue using object surface grayscale-gloss characteristics is proposed to improve the recognition rate of coal and gangue. Using different comparative experiments, bituminous coal from the Huainan area was used as the experimental object. It was found that the number of pixel points corresponding to the highest level grey value of the grayscale moment and illumination component of the coal and gangue images were combined into a total discriminant value and used as input for the best classification of coal and gangue using the GWO-SVM classification model. The recognition rate could reach up to 98.14%. This method sorts coal and gangue by combining surface greyness and glossiness features, optimizes the traditional greyness-based recognition method, improves the recognition rate, makes the model generalizable, enriches the research on coal and gangue recognition, and has theoretical and practical significance in enterprise production operations.
Sortowanie węgla i skały płonnej jest ważne w produkcji węgla surowego; dokładna identyfikacja węgla i skały płonnej jest warunkiem wstępnym skutecznego oddzielenia tych surowców. Metoda rozdzielenia węgla i skały płonnej przy użyciu informacji w skali szarości obrazu może skutecznie identyfikować węgiel i skałę płonną, ale stopień rozpoznawania procesu sortowania w oparciu o te informacje być znacznie wyższy niż wymagany do spełnienia wymagań produkcyjnych. W artykule zaproponowano metodę sortowania węgla i skały płonnej wykorzystującą charakterystykę połysku i skali szarości powierzchni obiektu w celu poprawy szybkości rozpoznawania węgla i skały płonnej. W badaniach wykorzystano próbki węgla kamiennego z obszaru Huainan. Stwierdzono, że liczbę punktów pikseli odpowiadającą najwyższemu poziomowi szarości momentu w skali szarości i składowej oświetlenia obrazów węgla i skały płonnej połączono w całkowitą wartość dyskryminującą i wykorzystano jako dane wejściowe dla najlepszej klasyfikacji węgla i skały płonnej przy użyciu modelu klasyfikacji GWO-SVM. Wskaźnik rozpoznawalności może osiągnąć nawet 98,14%. Ta metoda sortowania węgla i skały płonnej poprzez połączenie cech szarości i połysku powierzchni, optymalizuje tradycyjną metodę rozpoznawania w oparciu o szarość, poprawia współczynnik rozpoznawania, umożliwia uogólnienie modelu, wzbogaca badania nad rozpoznawaniem węgla i skały płonnej, ma znaczenie teoretyczne i praktyczne w operacjach produkcyjnych przedsiębiorstwa.
Źródło:
Gospodarka Surowcami Mineralnymi; 2023, 39, 3; 173--198
0860-0953
Pojawia się w:
Gospodarka Surowcami Mineralnymi
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A study on the influence of particle size on the identification accuracy of coal and gangue
Badanie wpływu wielkości cząstek na dokładność identyfikacji węgla i skały płonnej
Autorzy:
Li, Xin
Wang, Shuang
He, Lei
Luo, Qisheng
Powiązania:
https://bibliotekanauki.pl/articles/2203300.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Instytut Gospodarki Surowcami Mineralnymi i Energią PAN
Tematy:
particle size
gray feature
texture feature
support vector machine
coal identification
gangue identification
wielkość cząstek
cecha szarości
cecha tekstury
maszyna wektorów pomocniczych
identyfikacja węgla
identyfikacja skały płonnej
Opis:
In order to explore the impact of coal and gangue particle size changes on recognition accuracy and to improve the single particle size of coal and gangue identification accuracy of sorting equipment, this study established a database of different particle sizes of coal and gangue through image gray and texture feature extraction, using a relief feature selection algorithm to compare different particle size of coal and gangue optimal features of the combination, and to identify the points and particle size of coal and gangue. The results show that the optimal features and number of coal and gangue are different with different particle sizes. Based on visible-light coal and gangue separation technology, the change of coal and gangue particle size cause fluctuations in the recognition accuracy, and the fluctuation of recognition accuracy will gradually decrease with increases in the number of features. In the process of particle size classification, if the training model has a single particle size range, the recognition accuracy of each particle size range is low, with the highest recognition accuracy being 98% and the average recognition rate being only 97.2%. The method proposed in this paper can effectively improve the recognition accuracy of each particle size range. The maximum recognition accuracy is 100%, the maximum increase is 4%, and the average recognition accuracy is 99.2%. Therefore, this method has a high practical application value for the separation of coal and gangue with single particle size.
W celu zbadania wpływu zmian wielkości cząstek węgla i skały płonnej na dokładność rozpoznawania oraz poprawienia dokładności identyfikacji pojedynczych cząstek węgla i skały płonnej przez urządzenia sortujące, w ramach tej pracy utworzono bazę danych różnych rozmiarów cząstek węgla i skały płonnej za pomocą obrazów szarych i ekstrakcję cech tekstury przy użyciu algorytmu wyboru cech reliefowych w celu porównania różnych rozmiarów cząstek węgla i skały płonnej przy optymalnych cechach kombinacji oraz identyfikacji punktów i wielkości cząstek węgla i skały płonnej. Wyniki pokazują, że optymalne liczby cech węgla i skały płonnej są różne dla różnych rozmiarów cząstek. W oparciu o technologię separacji węgla i skały płonnej w świetle widzialnym, zmiana wielkości cząstek węgla i skały płonnej powoduje fluktuacje dokładności rozpoznawania, a te z kolei będą stopniowo zmniejszać się wraz ze wzrostem liczby cech. W procesie klasyfikacji wielkości cząstek, jeśli model uczący ma jeden zakres wielkości cząstek, dokładność rozpoznawania każdego zakresu wielkości cząstek jest niska, przy czym najwyższa dokładność rozpoznawania wynosi 98%, a średni wskaźnik rozpoznawania wynosi tylko 97,2%. Metoda zaproponowana w tym artykule może skutecznie poprawić dokładność rozpoznawania każdego zakresu wielkości cząstek. Maksymalna dokładność rozpoznawania wynosi 100%, maksymalny wzrost to 4%, a średnia dokładność rozpoznawania to 99,2%. Dlatego ta metoda ma dużą praktyczną wartość użytkową do oddzielania węgla i skały płonnej według rozmiaru pojedynczej cząstki.
Źródło:
Gospodarka Surowcami Mineralnymi; 2023, 39, 1; 109--129
0860-0953
Pojawia się w:
Gospodarka Surowcami Mineralnymi
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A support vector machine with the tabu search algorithm for freeway incident detection
Autorzy:
Yao, B.
Hu, P.
Zhang, M.
Jin, M.
Powiązania:
https://bibliotekanauki.pl/articles/329943.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
automated incident detection
support vector machine (SVM)
tabu search
freeway
maszyna wektorów wspierających
odcinek swobodny trasy
algorytm tabu search
Opis:
Automated Incident Detection (AID) is an important part of Advanced Traffic Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the influence of the incident. To accurately detect incidents in expressways, a Support Vector Machine (SVM) is used in this paper. Since the selection of optimal parameters for the SVM can improve prediction accuracy, the tabu search algorithm is employed to optimize the SVM parameters. The proposed model is evaluated with data for two freeways in China. The results show that the tabu search algorithm can effectively provide better parameter values for the SVM, and SVM models outperform Artificial Neural Networks (ANNs) in freeway incident detection.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2014, 24, 2; 397-404
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Adaptive control scheme based on the least squares support vector machine network
Autorzy:
Mahmoud, T. K.
Powiązania:
https://bibliotekanauki.pl/articles/930155.pdf
Data publikacji:
2011
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
modelowanie systemu
system nieliniowy
system sterowania
sieć neuronowa
maszyna wektorów wspierających
support vector machine (SVM)
neural network
nonlinear system modeling
nonlinear system control
pH control
Opis:
Recently, a new type of neural networks called Least Squares Support Vector Machines (LS-SVMs) has been receiving increasing attention in nonlinear system identification and control due to its generalization performance. This paper develops a stable adaptive control scheme using the LS-SVM network. The developed control scheme includes two parts: the identification part that uses a modified structure of LS-SVM neural networks called the multi-resolution wavelet least squares support vector machine network (MRWLS-SVM) as a predictor model, and the controller part that is developed to track a reference trajectory. By means of the Lyapunov stability criterion, stability analysis for the tracking errors is performed. Finally, simulation studies are performed to demonstrate the capability of the developed approach in controlling a pH process.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2011, 21, 4; 685-696
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An Accuracy Analysis Comparison of Supervised Classification Methods for Mapping Land Cover Using Sentinel 2 Images in the Al‑Hawizeh Marsh Area, Southern Iraq
Autorzy:
Alwan, Imzahim A.
Aziz, Nadia A.
Powiązania:
https://bibliotekanauki.pl/articles/1838006.pdf
Data publikacji:
2021
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
land cover mapping
Sentinel 2
supervised classification
maximum likelihood
Support Vector Machine (SVM)
confusion matrix
Opis:
Land cover mapping of marshland areas from satellite images data is not a simple process, due to the similarity of the spectral characteristics of the land cover. This leads to challenges being encountered with some land covers classes, especially in wetlands classes. In this study, satellite images from the Sentinel 2B by ESA (European Space Agency) were used to classify the land cover of Al Hawizeh marsh/Iraq Iran border. Three classification methods were used aimed at comparing their accuracy, using multispectral satellite images with a spatial resolution of 10 m. The classification process was performed using three different algorithms, namely: Maximum Likelihood Classification (MLC), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). The classification algorithms were carried out using ENVI 5.1 software to detect six land cover classes: deep water marsh, shallow water marsh, marsh vegetation (aquatic vegetation), urban area (built up area), agriculture area, and barren soil. The results showed that the MLC method applied to Sentinel 2B images provides a higher overall accuracy and the kappa coefficient compared to the ANN and SVM methods. Overall accuracy values for MLC, ANN, and SVM methods were 85.32%, 70.64%, and 77.01% respectively.
Źródło:
Geomatics and Environmental Engineering; 2021, 15, 1; 5-21
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 118

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