In recent day’s power distribution system is distress from acute power quality issues.In this work,for compensating Power Quality(PQ)disturbances a seven level cascaded H-bridge inverter is implemented in distributio...In recent day’s power distribution system is distress from acute power quality issues.In this work,for compensating Power Quality(PQ)disturbances a seven level cascaded H-bridge inverter is implemented in distribution static com-pensator which protects power quality problems in currents.Distribution Static Compensator(DSTATCOM)aid to enhances power factor and removes total har-monic distortion which is drawn from non-linear load.The D–Q reference theory based hysteresis current controller is employed to generate reference current for compensation of harmonics and reactive power,additionally Probabilistic Neural Network(PNN)classifier is used which easily separates exact harmonics.In the meantime fuzzy logic controller is also used to maintain capacitor DC-link poten-tial.When comparing to PI controller it decreases steady state time and reduces maximum peak overshoot.Cascaded H-bridge multilevel inverter converts direct current to Alternating current,through inductor opposite harmonics are injected in Power Control Centre reduces source current harmonics and reactive power.The implementation of CHBMLI in distribution STATic COMpensator simulation model is simulated by means of MATLAB.展开更多
The applications of wireless sensor network(WSN)exhibits a significant rise in recent days since it is enveloped with various advantageous benefits.In the medical field,the emergence of WSN has created marvelous chang...The applications of wireless sensor network(WSN)exhibits a significant rise in recent days since it is enveloped with various advantageous benefits.In the medical field,the emergence of WSN has created marvelous changes in monitoring the health conditions of the patients and so it is attracted by doctors and physicians.WSN assists in providing health care services without any delay and so it plays predominant role in saving the life of human.The data of different persons,time,places and networks have been linked with certain devices,which are collectively known as Internet of Things(IOT);it is regarded as the essential requirement of people in recent days.In the health care monitoring system,IOT plays a magnificent role,which has produced the real time monitoring of patient’s condition.However the medical data transmission is accomplished quickly with high security by the routing and key management.When the data from the digital record system(cloud)is accessed by the patients or doctors,the medical data is transferred quickly through WSN by performing routing.The Probabilistic Neural Network(PNN)is utilized,which authenticates the shortest path to reach the destination and its performance is identified by comparing it with the Dynamic Source Routing(DSR)protocol and Energy aware and Stable Routing(ESR)protocol.While performing routing,the secured transmission is achieved by key management,for which the Diffie Hellman key exchange is utilized,which performs encryption and decryption to secure the medical data.This enables the quick and secured transmission of data from source to destination with improved throughput and delivery ratio.展开更多
Big data is a term that refers to a set of data that,due to its largeness or complexity,cannot be stored or processed with one of the usual tools or applications for data management,and it has become a prominent word...Big data is a term that refers to a set of data that,due to its largeness or complexity,cannot be stored or processed with one of the usual tools or applications for data management,and it has become a prominent word in recent years for the massive development of technology.Almost immediately thereafter,the term“big data mining”emerged,i.e.,mining from big data even as an emerging and interconnected field of research.Classification is an important stage in data mining since it helps people make better decisions in a variety of situations,including scientific endeavors,biomedical research,and industrial applications.The probabilistic neural network(PNN)is a commonly used and successful method for handling classification and pattern recognition issues.In this study,the authors proposed to combine the probabilistic neural network(PPN),which is one of the data mining techniques,with the vibrating particles system(VPS),which is one of the metaheuristic algorithms named“VPS-PNN”,to solve classi-fication problems more effectively.The data set is eleven common benchmark medical datasets from the machine-learning library,the suggested method was tested.The suggested VPS-PNN mechanism outperforms the PNN,biogeography-based optimization,enhanced-water cycle algorithm(E-WCA)and the firefly algorithm(FA)in terms of convergence speed and classification accuracy.展开更多
Due to the presence of non-stationarities and discontinuities in the audio signal, segmentation and classification of audio signal is a really challenging task. Automatic music classification and annotation is still c...Due to the presence of non-stationarities and discontinuities in the audio signal, segmentation and classification of audio signal is a really challenging task. Automatic music classification and annotation is still considered as a challenging task due to the difficulty of extracting and selecting the optimal audio features. Hence, this paper proposes an efficient approach for segmentation, feature extraction and classification of audio signals. Enhanced Mel Frequency Cepstral Coefficient (EMFCC)-Enhanced Power Normalized Cepstral Coefficients (EPNCC) based feature extraction is applied for the extraction of features from the audio signal. Then, multi-level classification is done to classify the audio signal as a musical or non-musical signal. The proposed approach achieves better performance in terms of precision, Normalized Mutual Information (NMI), F-score and entropy. The PNN classifier shows high False Rejection Rate (FRR), False Acceptance Rate (FAR), Genuine Acceptance rate (GAR), sensitivity, specificity and accuracy with respect to the number of classes.展开更多
文摘In recent day’s power distribution system is distress from acute power quality issues.In this work,for compensating Power Quality(PQ)disturbances a seven level cascaded H-bridge inverter is implemented in distribution static com-pensator which protects power quality problems in currents.Distribution Static Compensator(DSTATCOM)aid to enhances power factor and removes total har-monic distortion which is drawn from non-linear load.The D–Q reference theory based hysteresis current controller is employed to generate reference current for compensation of harmonics and reactive power,additionally Probabilistic Neural Network(PNN)classifier is used which easily separates exact harmonics.In the meantime fuzzy logic controller is also used to maintain capacitor DC-link poten-tial.When comparing to PI controller it decreases steady state time and reduces maximum peak overshoot.Cascaded H-bridge multilevel inverter converts direct current to Alternating current,through inductor opposite harmonics are injected in Power Control Centre reduces source current harmonics and reactive power.The implementation of CHBMLI in distribution STATic COMpensator simulation model is simulated by means of MATLAB.
文摘The applications of wireless sensor network(WSN)exhibits a significant rise in recent days since it is enveloped with various advantageous benefits.In the medical field,the emergence of WSN has created marvelous changes in monitoring the health conditions of the patients and so it is attracted by doctors and physicians.WSN assists in providing health care services without any delay and so it plays predominant role in saving the life of human.The data of different persons,time,places and networks have been linked with certain devices,which are collectively known as Internet of Things(IOT);it is regarded as the essential requirement of people in recent days.In the health care monitoring system,IOT plays a magnificent role,which has produced the real time monitoring of patient’s condition.However the medical data transmission is accomplished quickly with high security by the routing and key management.When the data from the digital record system(cloud)is accessed by the patients or doctors,the medical data is transferred quickly through WSN by performing routing.The Probabilistic Neural Network(PNN)is utilized,which authenticates the shortest path to reach the destination and its performance is identified by comparing it with the Dynamic Source Routing(DSR)protocol and Energy aware and Stable Routing(ESR)protocol.While performing routing,the secured transmission is achieved by key management,for which the Diffie Hellman key exchange is utilized,which performs encryption and decryption to secure the medical data.This enables the quick and secured transmission of data from source to destination with improved throughput and delivery ratio.
文摘Big data is a term that refers to a set of data that,due to its largeness or complexity,cannot be stored or processed with one of the usual tools or applications for data management,and it has become a prominent word in recent years for the massive development of technology.Almost immediately thereafter,the term“big data mining”emerged,i.e.,mining from big data even as an emerging and interconnected field of research.Classification is an important stage in data mining since it helps people make better decisions in a variety of situations,including scientific endeavors,biomedical research,and industrial applications.The probabilistic neural network(PNN)is a commonly used and successful method for handling classification and pattern recognition issues.In this study,the authors proposed to combine the probabilistic neural network(PPN),which is one of the data mining techniques,with the vibrating particles system(VPS),which is one of the metaheuristic algorithms named“VPS-PNN”,to solve classi-fication problems more effectively.The data set is eleven common benchmark medical datasets from the machine-learning library,the suggested method was tested.The suggested VPS-PNN mechanism outperforms the PNN,biogeography-based optimization,enhanced-water cycle algorithm(E-WCA)and the firefly algorithm(FA)in terms of convergence speed and classification accuracy.
文摘Due to the presence of non-stationarities and discontinuities in the audio signal, segmentation and classification of audio signal is a really challenging task. Automatic music classification and annotation is still considered as a challenging task due to the difficulty of extracting and selecting the optimal audio features. Hence, this paper proposes an efficient approach for segmentation, feature extraction and classification of audio signals. Enhanced Mel Frequency Cepstral Coefficient (EMFCC)-Enhanced Power Normalized Cepstral Coefficients (EPNCC) based feature extraction is applied for the extraction of features from the audio signal. Then, multi-level classification is done to classify the audio signal as a musical or non-musical signal. The proposed approach achieves better performance in terms of precision, Normalized Mutual Information (NMI), F-score and entropy. The PNN classifier shows high False Rejection Rate (FRR), False Acceptance Rate (FAR), Genuine Acceptance rate (GAR), sensitivity, specificity and accuracy with respect to the number of classes.
