This article presents a metaheuristic approach, the binary whale optimization algorithm (BWOA), to solve complex, constrained, non-convex, binary-nature profit-based unit commitment (PBUC) optimiza...

Binary whale optimization algorithm: a new metaheuristic approach for profit-based unit commitment problems in competitive electricity markets

Fuzzy C-Means (FCM) plays a major role in brain tissue segmentation. The proposed method aims to implements rapid brain tissue segmentation from MRI human head scans using FCM in CPU and GPU. This method is known as FCM-GENIUS. This paper presents three novel steps to enrich the performance of conventional FCM algorithm in CPU. There are region of interest (ROI) selection, knowledge based initialization and knowledge based optimization. The ROI selection is a preprocessing step contains brain extraction and bounding box processes. An automatic knowledge based initialization to FCM algorithm using histogram smoothing for centroids selection from middle slice of the given MRI brain volume. Optimization helps to improve the computation speed up of FCM algorithm using MRI slice adjacency property. The materials used for the proposed work are gathering from internet brain segmentation repository (IBSR). The accuracy of segmentation also compared with traditional and existing methods. The proposed method yield equal segmentation accuracy compared with existing methods but reduces the segmentation time considerably up to seven times and average number of iterations up to three times. In addition, parallel FCM implements in GPU machine and the performance was compared with the conventional FCM in CPU. The single instruction multiple data (SIMD) model was used with the hybrid CPU–GPU implementation in the GPU machine to accelerate the medical image segmentation.

Knowledge based fuzzy c-means method for rapid brain tissues segmentation of magnetic resonance imaging scans with CUDA enabled GPU machine

The profit-based unit commitment (PBUC) procedure is a binary natured operational planning problem of generation company (GENCO) in competitive electricity market. The GENCO solves PBUC problem in day ahead market of energy and reserve markets by commitment and scheduling of thermal units with an objective of profit maximization for the set of given price and load forecasts. Therefore, the solution quality and efficacy of PBUC optimization problem play a vital role in ensuring maximum returns to the GENCO. This paper presents a mathematical model-inspired heuristic approach called binary sine–cosine algorithm (BSCA) for solving PBUC problem. The SCA algorithm is a heuristic approach that uses the fluctuating nature of individual candidates/search agents in the search space around the global solution. The fluctuating mechanism is realized by using mathematical functions, i.e. sine and cosine functions. The proposed binary approach of SCA algorithm uses modified sigmoidal transformation function for binary mapping of continuous real-valued search space to binary counterpart. The efficacy of proposed approach in terms of solution quality and convergence is demonstrated using test system with different market participation policies. The results demonstrate the effectiveness of proposed BSCA approach over existing approaches for solving PBUC.

A New Binary Variant of Sine–Cosine Algorithm: Development and Application to Solve Profit-Based Unit Commitment Problem

Binary fireworks algorithm for profit based unit commitment (PBUC) problem

https://hal.archives-ouvertes.fr/hal-01928357/document

Environmental impact assessment of chicken meat production via an integrated methodology based on LCA, simulation and genetic algorithms

\n\nIdentifying user suitability plays a vital role in various modalities like\nneuromuscular system research, rehabilitation engineering and movement biomechanics. This paper\nanalysis the user suitability based on neural networks (NN), subjects, age groups and gender\nfor surface electromyogram (sEMG) pattern recognition system to control the myoelectric hand.\nSix parametric feature extraction algorithms are used to extract the features from sEMG signals\nsuch as AR (Autoregressive) Burg, AR Yule Walker, AR Covariance, AR Modified Covariance,\nLevinson Durbin Recursion and Linear Prediction Coefficient. The sEMG signals are modeled using\nCascade Forward Back propagation Neural Network (CFBNN) and Pattern Recognition Neural\nNetwork.\n\n\n\nsEMG signals generated from forearm muscles of the participants are collected through\nan sEMG acquisition system. Based on the sEMG signals, the type of movement attempted by the\nuser is identified in the sEMG recognition module using signal processing, feature extraction and\nmachine learning techniques. The information about the identified movement is passed to microcontroller\nwherein a control is developed to command the prosthetic hand to emulate the identified\nmovement.\n\n\n\nFrom the six feature extraction algorithms and two neural network models used in the\nstudy, the maximum classification accuracy of 95.13% was obtained using AR Burg with Pattern\nRecognition Neural Network. This justifies that the Pattern Recognition Neural Network is best\nsuited for this study as the neural network model is specially designed for pattern matching problem.\nMoreover, it has simple architecture and low computational complexity. AR Burg is found to\nbe the best feature extraction technique in this study due to its high resolution for short data records\nand its ability to always produce a stable model. In all the neural network models, the\nmaximum classification accuracy is obtained for subject 10 as a result of his better muscle fitness\nand his maximum involvement in training sessions. Subjects in the age group of 26-30 years are\nbest suited for the study due to their better muscle contractions. Better muscle fatigue resistance\nhas contributed for better performance of female subjects as compared to male subjects. From the\nsingle trial analysis, it can be observed that the hand close movement has achieved best recognition\nrate for all neural network models.\n\n\n\nIn this paper a study was conducted to identify user suitability for designing hand\nprosthesis. Data were collected from ten subjects for twelve tasks related to finger movements.\nThe suitability of the user was identified using two neural networks with six parametric features.\nFrom the result, it was concluded thatfit women doing regular physical exercises aged between\n26-30 years are best suitable for developing HMI for designing a prosthetic hand. Pattern Recognition\nNeural Network with AR Burg extraction features using extension movements will be a better\nway to design the HMI. However, Signal acquisition based on wireless method is worth considering\nfor the future.\n

Identifying User Suitability in sEMG Based Hand Prosthesis Using Neural Networks

Objectives: Study of sEMG signals of the hand gestures is important in designing hand prosthesis. Designing a sEMG pattern recognition system to control a myoelectric hand using neural networks is the objective of this study. Methods/ Statictical Analysis: The sEMG signal was acquired from flexor digitorumsuperficialis and extensor digitorum muscle of the ten healthy subjects by performing 12 different hand gestures. Six parametric feature extraction algorithms were applied to derive the prominent information from sEMG such as AR (Autoregressive) Burg, AR Yule Walker, AR Covariance, AR Modified Covariance, Levinson Durbin Recursion and Linear Prediction Coefficient. Recognition of the 12 gestures is accomplished using General Regression Neural Network, Probabilistic Neural Network and Radial Basis Function Neural Network. Findings: From the empirical results it was observed that the AR Burg and RBFNN combination had the highest recognition accuracy rate of 94.04%. Investigation also proved that recognition accuracy of sEMG signals were better for females when compare to males. It was also observed from the results that subjects in the age of 26-30 years had better muscle flexion compared to the other age groups studied. Application/ Improvements: In this paper the feasibility of recognizing twelve hand gestures from the sEMG of the hand and finger movements using neural networks was investigated. Six feature extraction algorithms and three neural networks were used to design algorithms for recognizing the twelve hand gestures

sEMG based Classification of Hand Gestures using Artificial Neural Network

The requirement for astute cultivating particularly in creating nations like Indiahas developed to a more noteworthy degree. Additionally, investigate in IoT based remotesensor organizing agribusiness, for example, observing of ecological conditions liketemperature, Humidity of encompassing and soil dampness and so on. The reason forproposed framework is to enhance the water system arrangement of Indian horticultureand furthermore to give sufficient to water system to specific regionnow a days eachframework iscomputerized keeping in mind the end goal to confront new difficulties in thepresent days mechanized framework have less manual operation, adaptability, unwaveringquality and exactness. Because of this request each field incline toward mechanized controlframeworks.

A. Amudha

Papers

Identifying User Suitability in sEMG Based Hand Prosthesis Using Neural Networks

sEMG based Classification of Hand Gestures using Artificial Neural Network

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