Fuzzy entropy design for non convex fuzzy set and application to mutual information

Fuzzy entropy was designed for non convex fuzzy membership function using well known Hamming distance measure.The proposed fuzzy entropy had the same structure as that of convex fuzzy membership case.Design procedure of fuzzy entropy was proposed by considering fuzzy membership through distance measure,and the obtained results contained more flexibility than the general fuzzy membership function.Furthermore,characteristic analyses for non convex function were also illustrated.Analyses on the mutual information were carried out through the proposed fuzzy entropy and similarity measure,which was also dual structure of fuzzy entropy.By the illustrative example,mutual information was discussed.

Power interconnected system clustering with advanced fuzzy C-mean algorithm

An advanced fuzzy C-mean(FCM) algorithm was proposed for the efficient regional clustering of multi-nodes interconnected systems.Due to various locational prices and regional coherencies for each node and point,modified similarity measure was considered to gather nodes having similar characteristics.The similarity measure was needed to contain locational prices as well as regional coherency.In order to consider the two properties simultaneously,distance measure of fuzzy C-mean algorithm had to be modified.Regional clustering algorithm for interconnected power systems was designed based on the modified fuzzy C-mean algorithm.The proposed algorithm produces proper classification for the interconnected power system and the results are demonstrated in the example of IEEE 39-bus interconnected electricity system.

A speech enhancement algorithm to reduce noise and compensate for partial masking effect

To enhance the speech quality that is degraded by environmental noise,an algorithm was proposed to reduce the noise and reinforce the speech.The minima controlled recursive averaging(MCRA) algorithm was used to estimate the noise spectrum and the partial masking effect which is one of the psychoacoustic properties was introduced to reinforce speech.The performance evaluation was performed by comparing the PESQ(perceptual evaluation of speech quality) and segSNR(segmental signal to noise ratio) by the proposed algorithm with the conventional algorithm.As a result,average PESQ by the proposed algorithm was higher than the average PESQ by the conventional noise reduction algorithm and segSNR was higher as much as 3.2 dB in average than that of the noise reduction algorithm.

Speech enhancement through voice activity detection using speech absence probability based on Teager energy

In this work, a novel voice activity detection (VAD) algorithm that uses speech absence probability (SAP) based on Teager energy (TE) was proposed for speech enhancement. The proposed method employs local SAP (LSAP) based on the TE of noisy speech as a feature parameter for voice activity detection (VAD) in each frequency subband, rather than conventional LSAP. Results show that the TE operator can enhance the ability to discriminate speech and noise and further suppress noise components. Therefore, TE-based LSAP provides a better representation of LSAP, resulting in improved VAD for estimating noise power in a speech enhancement algorithm. In addition, the presented method utilizes TE-based global SAP (GSAP) derived in each frame as the weighting parameter for modifying the adopted TE operator and improving its performance. The proposed algorithm was evaluated by objective and subjective quality tests under various environments, and was shown to produce better results than the conventional method.

Improved speech absence probability estimation based on environmental noise classification

An improved speech absence probability estimation was proposed using environmental noise classification for speech enhancement.A relevant noise estimation approach,known as the speech presence uncertainty tracking method,requires seeking the "a priori" probability of speech absence that is derived by applying microphone input signal and the noise signal based on the estimated value of the "a posteriori" signal-to-noise ratio(SNR).To overcome this problem,first,the optimal values in terms of the perceived speech quality of a variety of noise types are derived.Second,the estimated optimal values are assigned according to the determined noise type which is classified by a real-time noise classification algorithm based on the Gaussian mixture model(GMM).The proposed algorithm estimates the speech absence probability using a noise classification algorithm which is based on GMM to apply the optimal parameter of each noise type,unlike the conventional approach which uses a fixed threshold and smoothing parameter.The performance of the proposed method was evaluated by objective tests,such as the perceptual evaluation of speech quality(PESQ) and composite measure.Performance was then evaluated by a subjective test,namely,mean opinion scores(MOS) under various noise environments.The proposed method show better results than existing methods.

Comparison of wrist motion classification methods using surface electromyogram

The Gaussian mixture model (GMM), k-nearest neighbor (k-NN), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA) were compared to classify wrist motions using surface electromyogram (EMG). Effect of feature selection in EMG signal processing was also verified by comparing classification accuracy of each feature, and the enhancement of classification accuracy by normalization was confirmed. EMG signals were acquired from two electrodes placed on the forearm of twenty eight healthy subjects and used for recognition of wrist motion. Features were extracted from the obtained EMG signals in the time domain and were applied to classification methods. The difference absolute mean value (DAMV), difference absolute standard deviation value (DASDV), mean absolute value (MAV), root mean square (RMS) were used for composing 16 double features which were combined of two channels. In the classification methods, the highest accuracy of classification showed in the GMM. The most effective combination of classification method and double feature was (MAV, DAMV) of GMM and its classification accuracy was 96.85%. The results of normalization were better than those of non-normalization in GMM, k-NN, and LDA.

Design of similarity measure for discrete data and application to multi-dimension

Similarity measure design for discrete data group was proposed. Similarity measure design for continuous membership function was also carried out. Proposed similarity measures were designed based on fuzzy number and distance measure, and were proved. To calculate the degree of similarity of discrete data, relative degree between data and total distribution was obtained. Discrete data similarity measure was completed with combination of mentioned relative degrees. Power interconnected system with multi characteristics was considered to apply discrete similarity measure. Naturally, similarity measure was extended to multi-dimensional similarity measure case, and applied to bus clustering problem.

Designing similarity measurement with distance measure and application on laterally directional mode flight test

Similarity measure construction has been proposed as fault detection of flight test method in order to obtain the primary control surface stuck and the combination stuck of primary control.Similarity measures were obtained through analyzing the certainty and uncertainty of fuzzy membership functions,which were designed based on well-known Hamming distance.It was applied to the fault detection of primary control surface stuck of uninhabited aerial vehicle(UAV).At post-failure control surface,if the UAV is controllable and trimmable using other control surfaces,the UAV is able to fly or return to the safety region through reconfiguration of flight control system.To detect the fault,similarity measure computations were carried out.This result could be applicable with the real-time parameter estimation method.By monitoring the value of coefficients due to the control surface deviation,it becomes aware that the control surface fault occurs or not.The control surface stuck position and value were separated by comparing the trim value with the reference value.This is the advantage of increasing in reliability without adding sensors or with additional low cost.

Sound transducer calibration of ambulatory audiometric system utilizing delta learning rule

An efficient calibration algorithm for an ambulatory audiometric test system is proposed. This system utilizes a personal digital assistant (PDA) device to generate the correct sound pressure level (SPL) from an audiometric transducer such as an earphone. The calibrated sound intensities for an audio-logical examination can be obtained in terms of the sound pressure levels of pure-tonal sinusoidal signals in eight-banded frequency ranges (250, 500, 1 000, 2 000, 3 000, 4 000, 6 000 and 8 000 Hz), and with mapping of the input sound pressure levels by the weight coefficients that are tuned by the delta learning rule. With this scheme, the sound intensities, which evoke eight-banded sound pressure levels by 5 dB steps from a minimum of 25 dB to a maximum of 80 dB, can be generated without volume displacement. Consequently, these sound intensities can be utilized to accurately determine the hearing threshold of a subject in the ambulatory audiometric testing environment.