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Tytuł:
Navigation of autonomous mobile robot using different activation functions of wavelet neural network
Autorzy:
Panigrahi, P. K.
Ghosh, S.
Parhi, D. R.
Powiązania:
https://bibliotekanauki.pl/articles/229613.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
autonomous mobile robot
activation functions
obstacle avoidance
path planning
wavelet neural network
Opis:
An autonomous mobile robot is a robot which can move and act autonomously without the help of human assistance. Navigation problem of mobile robot in unknown environment is an interesting research area. This is a problem of deducing a path for the robot from its initial position to a given goal position without collision with the obstacles. Different methods such as fuzzy logic, neural networks etc. are used to find collision free path for mobile robot. This paper examines behavior of path planning of mobile robot using three activation functions of wavelet neural network i.e. Mexican Hat, Gaussian and Morlet wavelet functions by MATLAB. The simulation result shows that WNN has faster learning speed with respect to traditional artificial neural network.
Źródło:
Archives of Control Sciences; 2015, 25, 1; 21-34
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Research on electric vehicle charging load prediction and charging mode optimization
Autorzy:
Zhang, Zhiyan
Shi, Hang
Zhu, Ruihong
Zhao, Hongfei
Zhu, Yingjie
Powiązania:
https://bibliotekanauki.pl/articles/1841299.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
electric vehicles
Monte Carlo
wavelet neural network
charging load
pojazdy elektryczne
sieć neuronowa falkowa
Opis:
To reduce the influence of the disorderly charging of electric vehicles (EVs) on the grid load, the EV charging load and charging mode are studied in this paper. First, the distribution of EV charging capacity and state of charge (SOC) feature quantity are analyzed, and their probability density function is solved. It is verified that both EV charging capacity and SOC obey the skew-normal distribution. Second, considering the space-time distribution characteristics of the EV charging load, a method for charging load prediction based on a wavelet neural network is proposed, and compared with the traditional BP neural network, the prediction results show that the error of the wavelet neural network is smaller, and the effectiveness of the wavelet neural network prediction is verified. The optimization objective function with the lowest user costs is established, and the constraint conditions are determined, so the orderly charging behavior is simulated by the Monte Carlo method. Finally, the influence of charging mode optimization on power grid operation is analyzed, and the result shows that the effectiveness of the charging optimization model is verified.
Źródło:
Archives of Electrical Engineering; 2021, 70, 2; 399-414
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The soft rock socketed monopile with creep effects – a reliability approach based on wavelet neural networks
Pal osadzony w miękkiej skale z wpływem pełzania – podejście niezawodnościowe bazujące na sieciach falkowo-neuronowych
Autorzy:
Kozubal, J.
Tomanovic, Z.
Zivaljevic, S.
Powiązania:
https://bibliotekanauki.pl/articles/219798.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
niezawodność
pal
margiel
pełzanie
sieci falkowo-neuronowe
powierzchnia odpowiedzi
reliability
pile
marl
creep
wavelet neural network
response surface
Opis:
In the present study the numerical model of the pile embedded in marl described by a time dependent model, based on laboratory tests, is proposed. The solutions complement the state of knowledge of the monopile loaded by horizontal force in its head with respect to its random variability values in time function. The investigated reliability problem is defined by the union of failure events defined by the excessive horizontal maximal displacement of the pile head in each periods of loads. Abaqus has been used for modeling of the presented task with a two layered viscoplastic model for marl. The mechanical parameters for both parts of model: plastic and rheological were calibrated based on the creep laboratory test results. The important aspect of the problem is reliability analysis of a monopile in complex environment under random sequences of loads which help understanding the role of viscosity in nature of rock basis constructions. Due to the lack of analytical solutions the computations were done by the method of response surface in conjunction with wavelet neural network as a method recommended for time sequences process and description of nonlinear phenomenon.
W niniejszym studium zaprezentowany jest problem pojedynczego pala osadzonego w miękkiej skale, zastosowano wiskoplastyczny model materiału bazujący na wynikach badań laboratoryjnych zespołu z Uniwersytetu Montenegro. Rozwiązanie uzupełnia stan wiedzy dla pali obciążonych poziomą siłą w głowicy zmienną w sposób losowy w czasie. Badany problem niezawodności został określony przez sumę zdarzeń – awarii – zdefiniowanych jako przekroczenie maksymalnie dopuszczalnego poziomego przemieszczenia głowicy pala niezależnie w wszystkich stanach obciążenia. Zastosowano program metody elementów skończonych, ABAQUS, do budowy trójwymiarowego modelu z dwuwarstwowym wiskoplastycznym modelem dla margla. Parametry mechaniczne modelu zarówno w części plastycznej i reologicznej zostały skalibrowane na podstawie wyników badań laboratoryjnych wykonanych na przestrzeni ostatnich czterech lat na próbkach z jednorodnego złoża margla w Montenegro. Ważnym aspektem problemu jest analiza niezawodności pojedynczego pala dla złożonego mechanicznie środowiska w ramach sekwencji losowych obciążeń. Przedstawione zadanie pozwala dostrzec istotę lepkiej części modelu. Ze względu na brak rozwiązań analitycznych oraz długotrwałość procesu obliczeniowego obliczenia niezawodnościowe przeprowadzono metodą powierzchni odpowiedzi bazując na sieciach falkowo-neuronowych. Sieć poprzez nadanie jej struktury rejestru została dostosowana do opisu procesu o nieliniowym charakterze zjawiska i dla obciążeń zmiennych w czasie.
Źródło:
Archives of Mining Sciences; 2016, 61, 3; 571-585
0860-7001
Pojawia się w:
Archives of Mining Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Soft computing-based technique as a predictive tool to estimate blast-induced ground vibration
Autorzy:
Arthur, Clement Kweku
Temeng, Victor Amoako
Ziggah, Yao Yevenyo
Powiązania:
https://bibliotekanauki.pl/articles/1839011.pdf
Data publikacji:
2019
Wydawca:
Główny Instytut Górnictwa
Tematy:
radial basis function neural network
back propagation neural network
generalized regression neural network
wavelet neural network
group method of data handling
ground vibration
radialna funkcja bazowa
sieć neuronowa
GRNN
sieć falkowo-neuronowa
grupowa metoda przetwarzania danych
drgania gruntu
Opis:
The safety of workers, the environment and the communities surrounding a mine are primary concerns for the mining industry. Therefore, implementing a blast-induced ground vibration monitoring system to monitor the vibrations emitted due to blasting operations is a logical approach that addresses these concerns. Empirical and soft computing models have been proposed to estimate blast-induced ground vibrations. This paper tests the efficiency of the Wavelet Neural Network (WNN). The motive is to ascertain whether the WNN can be used as an alternative to other widely used techniques. For the purpose of comparison, four empirical techniques (the Indian Standard, the United State Bureau of Mines, Ambrasey-Hendron, and Langefors and Kilhstrom) and four standard artificial neural networks of backpropagation (BPNN), radial basis (RBFNN), generalised regression (GRNN) and the group method of data handling (GMDH) were employed. According to the results obtained from the testing dataset, the WNN with a single hidden layer and three wavelons produced highly satisfactory and comparable results to the benchmark methods of BPNN and RBFNN. This was revealed in the statistical results where the tested WNN had minor deviations of approximately 0.0024 mm/s, 0.0035 mm/s, 0.0043 mm/s, 0.0099 and 0.0168 from the best performing model of BPNN when statistical indicators of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Root Mean Square Error (RRMSE), Correlation Coefficient (R) and Coefficient of determination (R2) were considered.
Źródło:
Journal of Sustainable Mining; 2019, 18, 4; 287-296
2300-1364
2300-3960
Pojawia się w:
Journal of Sustainable Mining
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Plant classification based on leaf edges and leaf morphological veins using wavelet convolutional neural network
Autorzy:
Dewi, Wulan
Utomo, Wiranto Herry
Powiązania:
https://bibliotekanauki.pl/articles/1837797.pdf
Data publikacji:
2021
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
classification
leaf edges
leaf veins morphological
wavelet convolutional neural network
klasyfikacja
brzegi liści
budowa morfologiczna liści
splotowa sieć neuronowa
Opis:
The leaf is one of the plant organs, contains chlorophyll, and functions as a catcher of energy from sunlight which is used for photosynthesis. Perfect leaves are composed of three parts, namely midrib, stalk, and leaf blade. The way to identify the type of plant is to look at the shape of the leaf edges. The shape, color, and texture of a plant's leaf margins may influence its leaf veins, which in this vein morphology carry information useful for plant classification when shape, color, and texture are not noticeable. Humans, on the other hand, may fail to recognize this feature because they prefer to see plants solely based on leaf form rather than leaf margins and veins. This research uses the Wavelet method to denoise existing images in the dataset and the Convolutional Neural Network classifies through images. The results obtained using the Wavelet Convolutional Neural Network method are equal to 97.13%.
Źródło:
Applied Computer Science; 2021, 17, 1; 81-89
1895-3735
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fault diagnosis of analog circuit based on wavelet transform and neural network
Autorzy:
Wang, Hui
Powiązania:
https://bibliotekanauki.pl/articles/141368.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
analog circuit
fault diagnosis
neural network
wavelet transform
Opis:
Analog circuits need more effective fault diagnosis methods. In this study, the fault diagnosis method of analog circuits was studied. The fault feature vectors were extracted by a wavelet transform and then classified by a generalized regression neural network (GRNN). In order to improve the classification performance, a wolf pack algorithm (WPA) was used to optimize the GRNN, and a WPA-GRNN diagnosis algorithm was obtained. Then a simulation experiment was carried out taking a Sallen–Key bandpass filter as an example. It was found from the experimental results that the WPA could achieve the preset accuracy in the eighth iteration and had a good optimization effect. In the comparison between the GRNN, genetic algorithm (GA)-GRNN and WPA-GRNN, the WPA-GRNN had the highest diagnostic accuracy, and moreover it had high accuracy in diagnosing a single fault than multiple faults, short training time, smaller error, and an average accuracy rate of 91%. The experimental results prove the effectiveness of the WPA-GRNN in fault diagnosis of analog circuits, which can make some contributions to the further development of the fault diagnosis of analog circuits.
Źródło:
Archives of Electrical Engineering; 2020, 69, 1; 175-185
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Journal Bearing Fault Detection Based on Daubechies Wavelet
Autorzy:
Narendiranath, B. T.
Himamshu, H. S.
Prabin, K. N.
Rama, P. D.
Nishant, C.
Powiązania:
https://bibliotekanauki.pl/articles/176955.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
journal bearing
fault diagnosis
Debauchies wavelet
artificial neural network
Opis:
Journal bearings are widely used to support the shafts in industrial machinery involving heavy loads, such as compressors, turbines and centrifugal pumps. The major problem that could arise in journal bearings is catastrophic failure due to corrosion or erosion and fatigue, which results in economic loss and creates major safety risks. Thus, it is necessary to provide suitable condition monitoring technique to detect and diagnose failures, and achieve cost savings to the industry. Therefore, this paper focuses on fault diagnosis on journal bearing using Debauchies Wavelet-02 (DB-02). Nowadays, wavelet transformation is one of the most popular technique of the time-frequency-transformations. An experimental setup was used to diagnose the faults in the journal bearing. The accelerometer is used to collect vibration data, from the journal bearing in the form of time domain. This was then used as input for a MATLAB code that could plot the time domain signal. This signal was then decomposed based on the wavelet transform. The fast Fourier transform is then used to obtain the frequency domain, which gives us the frequency having the highest amplitude. To diagnose the faults various operating conditions are used in the journal bearing such as Full oil, half loose, half oil, fault 1, fault 2, fault 3 and full loose. Then the Artificial Neural Networks (ANN) is used to classify faults. The network is trained based on data already collected and then it is tested based on random data points. ANN was able to classify the faults with the classification rate of 85.7%. Thus, the test process for unseen vibration data of the trained ANN combined with ideal output target values indicates high success rate for automated bearing fault detection.
Źródło:
Archives of Acoustics; 2017, 42, 3; 401-414
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hybrid wavelet transform – MLR and ANN models for river flow prediction: Case study of Brahmaputra river (Pancharatna station)
Autorzy:
Khandekar, Sachin Dadu
Aswar, Dinesh Shrikrishna
Sabale, Pandurang Digamber
Khandekar, Varsha Sachin
Bajad, Mohankumar Namdeorao
Powiązania:
https://bibliotekanauki.pl/articles/36074310.pdf
Data publikacji:
2024
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
Tematy:
wavelet transform
artificial neural network
multiple linear regression
streamflow
Daubechies wavelet
time series
Opis:
In this research, discrete wavelet transform (DWT) is combined with MLR and ANN to develop WMLR and WANN hybrid models, respectively, for the Brahmaputra river (Pancharatna station) flow forecasting. Daily flow data for the period of 10 year were decomposed (up to fifth level) into detailed and approximation coefficients (using Daubechies wavelets db1, db2, db3, db8 and db10) which were fed as input to MLR and ANN to get the predicted discharge values two days, four days, seven days and 14 days ahead. For all lead times, the WMLR-db10 model was found to be superior as compared to WANN-db1, WANN-db2, WANN-db3, WANN-db8, WMLR-db1, WMLR-db2, WMLR-db3, WMLR-db8 and single MLR and ANN models. During testing period, the values of determination coefficient (R2) and RMSE for WMLR-db10 model for two-, four-, seven- and 14-day lead time were found to be, respectively, 0.996 (751.87 m3·s–1), 0.991 (1,174.80 m3·s–1), 0.984 (1,585.02 m3·s–1), and 0.968 (2,196.46 m3·s–1). Also, it was observed that for lower order wavelets (db1, db2, db3) WANN’s performance was better, and for higher order wavelets (db8, db10) WMLR’s performance was better. Correspondingly, it was observed that all hybrid models’ efficiency increased with increase in the decomposition level.
Źródło:
Scientific Review Engineering and Environmental Sciences; 2024, 33, 1; 69-94
1732-9353
Pojawia się w:
Scientific Review Engineering and Environmental Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Automatic driving comfort analysis and intelligent identification of uncomfortable manoeuvres based on vehicle-following scenario
Autorzy:
Feng, Jiacheng
Powiązania:
https://bibliotekanauki.pl/articles/2086975.pdf
Data publikacji:
2022
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
automatic driving
driving comfort
evaluation algorithm
wavelet filtering
neural network
Opis:
Driving comfort and performance is of vital importance to evaluate the control quality of an automatic driving system. The control quality and calibration of the automatic driving system not only affects comfort but also psychological load and tension. Therefore, this paper proposed an analysis method of driving comfort combined with subjective and objec- tive factors, including multidimensional analysis based on the velocity domain, acceleration energy and power analysis, perceived risk and deviation analysis. Moreover, the feature of typical uncomfortable manoeuvres is analysed and generates an intelligent identification al- gorithm. It has been found that the uncomfortable identification performance is excellent (the accuracy reached 99%).
Źródło:
Journal of Theoretical and Applied Mechanics; 2022, 60, 2; 303--316
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Research on islanding detection of solar distributed generation based on best wavelet packet and neural network
Autorzy:
Xi, Zhongmei
Zhao, Faqi
Zhao, Xiangyang
Peng, Hong
Xi, Chuanxin
Powiązania:
https://bibliotekanauki.pl/articles/141157.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
active distribution network
islanding detection
neural network
solar distributed generation
wavelet pocket transform
Opis:
The active distribution network (ADN) represents the future development of distribution networks, whether the islanding phenomenon occurs or not determines the control strategy adopted by the ADN. The best wavelet packet has a better time-frequency characteristic than traditional wavelet analysis in the different signal processing, because it can extract better and more information from the signal effectively. Based on wavelet packet energy and the neural network, the islanding phenomenon of the ADN can be detected. Firstly, the wavelet packet is used to decompose current and voltage signals of the public coupling point between the distributed photovoltaic (PV) system and power grid, and calculate the energy value of each decomposed frequency band. Secondly, the network is trained using the constructed energy characteristic matrix as a neural network learning sample. At last, in order to achieve the function of identification for islanding detection, lots of samples are trained in the neural network. Based on the actual circumstance of PV operation in the ADN, the MATLAB/SIMULINK simulation model of the ADN is established. After the simulation, there are good output results, which show that the method has the characteristics of high identification accuracy and strong generalization ability.
Źródło:
Archives of Electrical Engineering; 2019, 68, 4; 703-717
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prognozowanie wskaźników makroekonomicznychz uwzględnieniem transformaty falkowejna przykładzie wskaźnika inflacji
Forecasting macroeconomic indicators including wavelet transform. The example of inflation rate
Autorzy:
Hadaś-Dyduch, Monika
Powiązania:
https://bibliotekanauki.pl/articles/541181.pdf
Data publikacji:
2013
Wydawca:
Wyższa Szkoła Bankowa we Wrocławiu
Tematy:
inflacja
analiza falkowa
sztuczna sieć neuronowa
inflation
wavelet analysis
artificial neural network
Opis:
Celem artykułu jest przedstawienie niekonwencjonalnego sposobu predykcji wskaźników makroekonomicznych, tzn. predykcji na podstawie prostego autorskiego modelu integrującego analizę falkową oraz sztuczne sieci neuronowe. Przykładową predykcję proponowanego modelu przedstawiono dla wskaźnika inflacji. Zasadniczo proponowaną metodę predykcji wskaźników makroekonomicznych oparto w przeważającym stopniu na transformacie falkowej, ponieważ funkcje falkowe charakteryzują dobre własności lokalizacyjne zarówno względem czasu, jak i częstotliwości.
The purpose of this article is present an unconventional method of prediction of macroeconomic indicators, which is based on a simple model that integrates proprietary wavelet analysis and artificial neural networks. An example of prediction of the proposed model shows the rate of inflation. Basically, the proposed method of predicting macroeconomic indicators are to a large degree based levels of wavelet transform, since wavelet functions are characterized by good localization properties both in time and frequency.
Źródło:
Zeszyty Naukowe Wyższej Szkoły Bankowej we Wrocławiu; 2013, 2(34); 175-186
1643-7772
Pojawia się w:
Zeszyty Naukowe Wyższej Szkoły Bankowej we Wrocławiu
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prediction of Psychoacoustic Metrics Using Combination of Wavelet Packet Transform and an Optimized Artificial Neural Network
Autorzy:
Pourseiedrezaei, Mehdi
Loghmani, Ali
Keshmiri, Mehdi
Powiązania:
https://bibliotekanauki.pl/articles/177762.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
sound quality measurement
psychoacoustic metrics
wavelet packet transform
optimized artificial neural network
Opis:
In this paper, a modified sound quality evaluation (SQE) model is developed based on combination of an optimized artificial neural network (ANN) and the wavelet packet transform (WPT). The presented SQE model is a signal processing technique, which can be implemented in current microphones for predicting the sound quality. The proposed method extracts objective psychoacoustic metrics including loudness, sharpness, roughness, and tonality from sound samples, by using a special selection of multi-level nodes of the WPT combined with a trained ANN. The model is optimized using the particle swarm optimization (PSO) and the back propagation (BP) algorithms. The obtained results reveal that the proposed model shows the lowest mean square error and the highest correlation with human perception while it has the lowest computational cost compared to those of the other models and software.
Źródło:
Archives of Acoustics; 2019, 44, 3; 561-573
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Intelligent fault diagnosis of rolling bearings based on continuous wavelet transform-multiscale feature fusion and improved channel attention mechanism
Autorzy:
Zhang, Jiqiang
Kong, Xiangwei
Cheng, Liu
Qi, Haochen
Yu, Mingzhu
Powiązania:
https://bibliotekanauki.pl/articles/24200817.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
deep learning
continuous wavelet transform
improved channel attention mechanism
multi-conditions
convolutional neural network
Opis:
Accurate fault diagnosis is critical to operating rotating machinery safely and efficiently. Traditional fault information description methods rely on experts to extract statistical features, which inevitably leads to the problem of information loss. As a result, this paper proposes an intelligent fault diagnosis of rolling bearings based on a continuous wavelet transform(CWT)-multiscale feature fusion and an improved channel attention mechanism. Different from traditional CNNs, CWT can convert the 1-D signals into 2-D images, and extract the wavelet power spectrum, which is conducive to model recognition. In this case, the multiscale feature fusion was implemented by the parallel 2-D convolutional neural networks to accomplish deeper feature fusion. Meanwhile, the channel attention mechanism is improved by converting from compressed to extended ways in the excitation block to better obtain the evaluation score of the channel. The proposed model has been validated using two bearing datasets, and the results show that it has excellent accuracy compared to existing methods.
Źródło:
Eksploatacja i Niezawodność; 2023, 25, 1; art. no. 16
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Forecasting Stock Price using Wavelet Neural Network Optimized by Directed Artificial Bee Colony Algorithm
Autorzy:
Khuat, T. T.
Le, Q. C.
Nguyen, B. L.
Le, M. H.
Powiązania:
https://bibliotekanauki.pl/articles/308651.pdf
Data publikacji:
2016
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
Artificial Bee Colony algorithm
Artificial Neural Network
back-propagation algorithm
stock price forecasting
wavelet transform
Opis:
Stock prediction with data mining techniques is one of the most important issues in finance. This field has attracted great scientific interest and has become a crucial research area to provide a more precise prediction process. This study proposes an integrated approach where Haar wavelet transform and Artificial Neural Network optimized by Directed Artificial Bee Colony algorithm are combined for the stock price prediction. The proposed approach was tested on the historical price data collected from Yahoo Finance with different companies. Furthermore, the prediction result was found satisfactorily enough as a guide for traders and investors in making qualitative decisions.
Źródło:
Journal of Telecommunications and Information Technology; 2016, 2; 43-52
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Development of a Sound Quality Evaluation Model Based on an Optimal Analytic Wavelet Transform and an Artificial Neural Network
Autorzy:
Pourseiedrezaei, Mehdi
Loghmani, Ali
Keshmiri, Mehdi
Powiązania:
https://bibliotekanauki.pl/articles/1953511.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
analytic wavelet transform
AWT
sound quality evaluation
SQE
psychoacoustic metrics
back propagation neural network
BPNN
Opis:
The purpose of this study was to develop a sound quality model for real time active sound quality control systems. The model is based on an optimal analytic wavelet transform (OAWT) used along with a back propagation neural network (BPNN) in which the initial weights and thresholds are determined by particle swarm optimisation (PSO). In the model the input signal is decomposed into 24 critical bands to extract a feature matrix, based on energy, mean, and standard deviation indices of the sub signal scalogram obtained by OAWT. The feature matrix is fed into the neural network input to determine the psychoacoustic parameters used for sound quality evaluation. The results of the study show that the present model is in good agreement with psychoacoustic models of sound quality metrics and enables evaluation of the quality of sound at a lower computational cost than the existing models.
Źródło:
Archives of Acoustics; 2021, 46, 1; 55-65
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 27

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