Among several ways of communication, the voice remains the fastest natural tool for human-to-human and human-to-machine communication. That is why the research in automatic voice pathology detection and classification area has gained much interest in the recent years. Indeed, these automatic systems may be considered as assistive tools for the physicians during the assessment stage. This latter may help them to make decision, whether the voice signal belongs to a healthy or unhealthy subject and identifies the nature of pathology. In this context, this paper provides a voice pathology detection and classification system based on wavelet analysis and Teager Energy Operator (TEO). First, we used the input voice signal that we taken from Saarbrucken Voice Database (SVD) [1], to extract a set of features. These feature vectors are fed into a Gaussian Mixture Model (GMM) [2] for the sake of classification. The obtained results are 96.66% for the detection task and 92.5 % using TEO. These results show that our proposal outperforms some state-of-art methods used in voice pathology identification.
{"title":"Classification of Psychogenic and Laryngeal Voice Diseases Based on Teager Energy Operator","authors":"I. Hammami","doi":"10.18100/IJAMEC.458230","DOIUrl":"https://doi.org/10.18100/IJAMEC.458230","url":null,"abstract":"Among several ways of communication, the voice remains the fastest natural tool for human-to-human and human-to-machine communication. That is why the research in automatic voice pathology detection and classification area has gained much interest in the recent years. Indeed, these automatic systems may be considered as assistive tools for the physicians during the assessment stage. This latter may help them to make decision, whether the voice signal belongs to a healthy or unhealthy subject and identifies the nature of pathology. In this context, this paper provides a voice pathology detection and classification system based on wavelet analysis and Teager Energy Operator (TEO). First, we used the input voice signal that we taken from Saarbrucken Voice Database (SVD) [1], to extract a set of features. These feature vectors are fed into a Gaussian Mixture Model (GMM) [2] for the sake of classification. The obtained results are 96.66% for the detection task and 92.5 % using TEO. These results show that our proposal outperforms some state-of-art methods used in voice pathology identification.","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124082648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we propose the selection of most suitable mother wavelet function for Turkish phonemes using discrete wavelet transform. The determination of most similar mother wavelet function to the signal has been a challenge in speech processing. The optimum mother wavelet function for Turkish phonemes have been determined by using quantitative measures which are energy and Shannon entropy, information theoretic measures which are joint entropy, conditional entropy, mutual information, and relative entropy from wavelet coefficients of the phonemes. In this study, 101 potential functions were investigated to determine the most appropriate mother wavelet. Experimental results show that the most appropriate wavelet functions for /c/ and /s/ phonemes which are unvoiced fricatives have been found as Bi-orthogonal 3.9 and Bi-orthogonal 5.5, respectively. By considering all the results, it is seen that the Bi-orthogonal 3.1 and Discrete Meyer wavelet functions are the most suitable mother wavelets for all other phonemes.
{"title":"Selection of Optimum Mother Wavelet Function for Turkish Phonemes","authors":"E. Z. Engin, Özkan Arslan","doi":"10.18100/ijamec.556850","DOIUrl":"https://doi.org/10.18100/ijamec.556850","url":null,"abstract":"In this paper, we propose the selection of most suitable mother wavelet function for Turkish phonemes using discrete wavelet transform. The determination of most similar mother wavelet function to the signal has been a challenge in speech processing. The optimum mother wavelet function for Turkish phonemes have been determined by using quantitative measures which are energy and Shannon entropy, information theoretic measures which are joint entropy, conditional entropy, mutual information, and relative entropy from wavelet coefficients of the phonemes. In this study, 101 potential functions were investigated to determine the most appropriate mother wavelet. Experimental results show that the most appropriate wavelet functions for /c/ and /s/ phonemes which are unvoiced fricatives have been found as Bi-orthogonal 3.9 and Bi-orthogonal 5.5, respectively. By considering all the results, it is seen that the Bi-orthogonal 3.1 and Discrete Meyer wavelet functions are the most suitable mother wavelets for all other phonemes.","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133487727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: Solar energy is one of the main sources of renewable energy. In photovoltaic systems, energy is obtained from photovoltaic batteries In photovoltaic batteries, the current and voltage do not increase linearly. This causes the power to take different values. There is a point where power reaches its peak. Detecting this point called the maximum power point and transferring the energy to the load at this point, reduces the losses and increases the efficiency. Many microcontroller controlled circuits have been designed and algorithms have been developed for maximum power point trackingPerturb and Observe algorithm is the most widely used algorithm. In this study; Maximum Power Point Tracker Algorithm Based Solar Charge Controller is designed. Arduino microcontroller card was used as it has easy programmability and rapid prototype development. Developed charge regulator determines the maximum power point by measuring the current voltage values and charge the battery at this point. The current, voltage, battery charge rate and charge status information obtained from the charge controller can be monitored by android mobile devices. The developed charge control circuit has been tested using a 45 Watt photovoltaic panel under different weather conditions. It was observed that it followed the maximum power point.
{"title":"Design and Implementation of P&O Maximum Power Point Tracker Algorithm Based Solar Charge Controller","authors":"H. Akdeniz, A. Yönetken","doi":"10.18100/IJAMEC.534336","DOIUrl":"https://doi.org/10.18100/IJAMEC.534336","url":null,"abstract":": Solar energy is one of the main sources of renewable energy. In photovoltaic systems, energy is obtained from photovoltaic batteries In photovoltaic batteries, the current and voltage do not increase linearly. This causes the power to take different values. There is a point where power reaches its peak. Detecting this point called the maximum power point and transferring the energy to the load at this point, reduces the losses and increases the efficiency. Many microcontroller controlled circuits have been designed and algorithms have been developed for maximum power point trackingPerturb and Observe algorithm is the most widely used algorithm. In this study; Maximum Power Point Tracker Algorithm Based Solar Charge Controller is designed. Arduino microcontroller card was used as it has easy programmability and rapid prototype development. Developed charge regulator determines the maximum power point by measuring the current voltage values and charge the battery at this point. The current, voltage, battery charge rate and charge status information obtained from the charge controller can be monitored by android mobile devices. The developed charge control circuit has been tested using a 45 Watt photovoltaic panel under different weather conditions. It was observed that it followed the maximum power point.","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116331689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thrombosis on the valve that prevents the movement of mechanical heart valves is a fatal disease requiring urgent intervention. Thrombosis is detected by echocardiographic findings and/or CT images. In this study, it has been tried to determine the formation of thrombosis by listening method which has been used for controlling the functionality of the heart valves for years. For this firstly heart sounds of patients with thrombosis and normal mechanical heart valves were recorded. Then the first and second heart sounds (S1 and S2) were separated from the recorded sounds. After the frequency spectrum of S1 and S2 were found using autoregressive spectrum estimation methods, six features were obtained regarding the frequency components. Then the features obtained are classified by support vector machine methods. The accuracy value was found to be 100% by using the 3 fold cross-validation. The average accuracy is 95.18% as a result of running the classifier 500 times using 3 fold-cross validation.
{"title":"Detection of Mechanical Heart Valve Thrombosis Using Support Vector Machine","authors":"S. Altunkaya, Onur Inan","doi":"10.18100/IJAMEC.569835","DOIUrl":"https://doi.org/10.18100/IJAMEC.569835","url":null,"abstract":"Thrombosis on the valve that prevents the movement of mechanical heart valves is a fatal disease requiring urgent intervention. Thrombosis is detected by echocardiographic findings and/or CT images. In this study, it has been tried to determine the formation of thrombosis by listening method which has been used for controlling the functionality of the heart valves for years. For this firstly heart sounds of patients with thrombosis and normal mechanical heart valves were recorded. Then the first and second heart sounds (S1 and S2) were separated from the recorded sounds. After the frequency spectrum of S1 and S2 were found using autoregressive spectrum estimation methods, six features were obtained regarding the frequency components. Then the features obtained are classified by support vector machine methods. The accuracy value was found to be 100% by using the 3 fold cross-validation. The average accuracy is 95.18% as a result of running the classifier 500 times using 3 fold-cross validation.","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122241318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In industrial applications, modulation strategies play a significant role to provide the effective voltage generation at the outputs of inverter structures. Also, the modulation strategies are important to generate output voltages with lower harmonic distortions. For this purpose, there are several modulation techniques to produce stepped voltage waveforms by reducing harmonics for high voltage levels. Therefore, this work introduces the comparative study of multi-carrier based pulse width modulation (PWM) methods used in high power rated inverters. In this regard, phase disposition (PD), phase opposition disposition (POD) and alternative phase opposition disposition (APOD) PWM strategies are tested in five-level cascaded H-bridge inverter. The performance consequences are received for different carrier frequencies, and total harmonic distortions are evaluated for tested methods. The results show that total harmonic distortion in APOD-PWM controlled inverter is less than other methods. In addition, total harmonic distortion values are performed for frequency values, which are from 0.5 kHz to 4 kHz.
{"title":"Performance Evaluation of Multi-carrier PWM Techniques: PD, POD and APOD","authors":"Mustafa Inci","doi":"10.18100/IJAMEC.569660","DOIUrl":"https://doi.org/10.18100/IJAMEC.569660","url":null,"abstract":"In industrial applications, modulation strategies play a significant role to provide the effective voltage generation at the outputs of inverter structures. Also, the modulation strategies are important to generate output voltages with lower harmonic distortions. For this purpose, there are several modulation techniques to produce stepped voltage waveforms by reducing harmonics for high voltage levels. Therefore, this work introduces the comparative study of multi-carrier based pulse width modulation (PWM) methods used in high power rated inverters. In this regard, phase disposition (PD), phase opposition disposition (POD) and alternative phase opposition disposition (APOD) PWM strategies are tested in five-level cascaded H-bridge inverter. The performance consequences are received for different carrier frequencies, and total harmonic distortions are evaluated for tested methods. The results show that total harmonic distortion in APOD-PWM controlled inverter is less than other methods. In addition, total harmonic distortion values are performed for frequency values, which are from 0.5 kHz to 4 kHz.","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133237908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artificial Neural Networks (ANNs) that are the ability to learn from theirs environment in order to improve their performance are widely used in numerous applications. The Backpropagation (BP) Algorithm is one of the most popular and effective model of ANNs. However, since it uses gradient descent algorithm which attempts to minimize the error of the network by moving gradient of the error curve, easily get trapped at local minima. To avoid this problem, we proposed an ANNs and Swarm Intelligence (SI) method, where Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) algorithms were operated for the Multilayer Perceptron Neural Network (MLPNN) weights update. Two Electroencephalogram (EEG) datasets were used to test the success of all algorithms including ABC-MLPNN, PSO-MLPNN and conventional-MLPNN. Compared to conventional-MLPNN, higher success values were obtained on each dataset with the proposed methods. Experimental results demonstrate that combined SI and MLPNN algorithm has been increased the success of BP algorithm by avoiding local minima.
{"title":"Research on Brain Signals via Artificial Neural Network and Swarm Intelligence Algorithms","authors":"S. Yildirim, H. Koçer, A. Ekmekci","doi":"10.18100/IJAMEC.475090","DOIUrl":"https://doi.org/10.18100/IJAMEC.475090","url":null,"abstract":"Artificial Neural Networks (ANNs) that are the ability to learn from theirs environment in order to improve their performance are widely used in numerous applications. The Backpropagation (BP) Algorithm is one of the most popular and effective model of ANNs. However, since it uses gradient descent algorithm which attempts to minimize the error of the network by moving gradient of the error curve, easily get trapped at local minima. To avoid this problem, we proposed an ANNs and Swarm Intelligence (SI) method, where Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) algorithms were operated for the Multilayer Perceptron Neural Network (MLPNN) weights update. Two Electroencephalogram (EEG) datasets were used to test the success of all algorithms including ABC-MLPNN, PSO-MLPNN and conventional-MLPNN. Compared to conventional-MLPNN, higher success values were obtained on each dataset with the proposed methods. Experimental results demonstrate that combined SI and MLPNN algorithm has been increased the success of BP algorithm by avoiding local minima.","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126704358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Yetgin, M. Turan, Barış Cevher, A. Çanakoğlu, Ayhan Gün
3-phase induction motors are widely used in industrial applications such as pumps, blowers, compressors, conveyors, etc. In addition, single-phase motors also have domestic use. For this reason, the design of the induction motor is of great importance. During the design, according to the parameters given as input, there are many situations that are contradictory to each other in order to achieve the goal. For example, if high efficiency is required, the motor size and the price will increase, and if lower price is required, other performance characteristics should be ignored. In the practical design of an induction motor, the designer has to comply with a number of constraints because of the large number of input variables. For this reason manual calculation becomes a very long and difficult process. To overcome this tiresome process, it is necessary to create a computer aided program or simulation model to determine the induction motor design parameters. In induction motor design, many parameters directly affect motor performance. Therefore, the selection of these parameters is of great importance. The C 0 coefficient, which takes place in the first steps of the design process, also has a great influence on the motor design. In this paper, a program for optimizing design of 3-phase induction motors written in MATLAB is presented. The structure of the program is modular and therefore can be modified for the various requirements of the induction motor design. C 0 constant (output coefficient), one of the design parameters, also varies with some other parameters. The most important ones are the specific electrical loading coefficient ( A 1 ) and the specific magnetic loading coefficient ( B av ), making dramatic effect on the output coefficient. The effect of the variation of the C 0 constant on the performance of the induction motor was investigated in the study. Stator inner and outer diameter, stack length, stator and rotor slot parameters, equivalent circuit parameters, losses and efficiency was obtained according to the change of B av and A 1 values. The obtained results are given and interpreted in tables and graphs.
{"title":"Squirrel Cage Induction Motor Design and the Effect of Specific Magnetic and Electrical Loading Coefficient","authors":"A. Yetgin, M. Turan, Barış Cevher, A. Çanakoğlu, Ayhan Gün","doi":"10.18100/IJAMEC.461795","DOIUrl":"https://doi.org/10.18100/IJAMEC.461795","url":null,"abstract":"3-phase induction motors are widely used in industrial applications such as pumps, blowers, compressors, conveyors, etc. In addition, single-phase motors also have domestic use. For this reason, the design of the induction motor is of great importance. During the design, according to the parameters given as input, there are many situations that are contradictory to each other in order to achieve the goal. For example, if high efficiency is required, the motor size and the price will increase, and if lower price is required, other performance characteristics should be ignored. In the practical design of an induction motor, the designer has to comply with a number of constraints because of the large number of input variables. For this reason manual calculation becomes a very long and difficult process. To overcome this tiresome process, it is necessary to create a computer aided program or simulation model to determine the induction motor design parameters. In induction motor design, many parameters directly affect motor performance. Therefore, the selection of these parameters is of great importance. The C 0 coefficient, which takes place in the first steps of the design process, also has a great influence on the motor design. In this paper, a program for optimizing design of 3-phase induction motors written in MATLAB is presented. The structure of the program is modular and therefore can be modified for the various requirements of the induction motor design. C 0 constant (output coefficient), one of the design parameters, also varies with some other parameters. The most important ones are the specific electrical loading coefficient ( A 1 ) and the specific magnetic loading coefficient ( B av ), making dramatic effect on the output coefficient. The effect of the variation of the C 0 constant on the performance of the induction motor was investigated in the study. Stator inner and outer diameter, stack length, stator and rotor slot parameters, equivalent circuit parameters, losses and efficiency was obtained according to the change of B av and A 1 values. The obtained results are given and interpreted in tables and graphs.","PeriodicalId":120305,"journal":{"name":"International Journal of Applied Mathematics Electronics and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129728015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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