2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE)最新文献_第5页

Diabetes Predicting mHealth Application Using Machine Learning 利用机器学习预测移动医疗应用中的糖尿病

2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE) Pub Date : 2017-12-01 DOI: 10.1109/WIECON-ECE.2017.8468885

Nabila Shahnaz Khan, Mehedi Hasan Muaz, A. Kabir, M. Islam

引用次数: 23

Price Aware Residential Demand Response With Renewable Sources and Electric Vehicle 价格敏感的住宅需求响应与可再生能源和电动汽车

2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE) Pub Date : 2017-12-01 DOI: 10.1109/WIECON-ECE.2017.8468915

Shalini Pal, Mukesh Kumar, R. Kumar

{"title":"Price Aware Residential Demand Response With Renewable Sources and Electric Vehicle","authors":"Shalini Pal, Mukesh Kumar, R. Kumar","doi":"10.1109/WIECON-ECE.2017.8468915","DOIUrl":"https://doi.org/10.1109/WIECON-ECE.2017.8468915","url":null,"abstract":"The demand response approach has become an essential regulatory option to drive the future smart grid. As per the conventional power system with thermal generation, it is not able to meet the future growing energy demand which leads the application of smart grid with an incorporation of renewable energy sources as a solution. The demand response programs offer the inclusion of renewable sources and electric vehicle as a solution to meet the demand at the peak periods when the electricity grid charges are very high. The load demand of user can be managed by employing the electric vehicle for households. This paper presents a demand response framework to build an economic and appropriate approach to meet the user demand in the smart grid structure. The residential user comprises different energy consuming loads in terms of various appliances such as base load, shiftable appliances, EV load, and storage system. The framework present here build a optimization structure where each user gets freedom and privacy to schedule their appliances. The simulation results demonstrate the economic benefits reaped by the customers are highly motivational to execute such approach in practical scenarios.","PeriodicalId":188031,"journal":{"name":"2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE)","volume":"34 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114032159","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}

引用次数: 10

Graphene Planted Organic Gas Sensor 石墨烯种植有机气体传感器

2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE) Pub Date : 2017-12-01 DOI: 10.1109/WIECON-ECE.2017.8468918

H. Pathak, B. Kumar

引用次数: 0

A Rectangular Monopole UWB Patch Antenna with CSRR slots having dual band notch Characteristics 具有双带陷波特性的CSRR槽的矩形单极UWB贴片天线

2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE) Pub Date : 2017-12-01 DOI: 10.1109/wiecon-ece.2017.8468931

Divya Atolia, S. Yadav

引用次数: 2

A New Graph Theoretic Power Flow Framework for Microgrid 一种新的微电网图论潮流框架

2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE) Pub Date : 2017-12-01 DOI: 10.1109/WIECON-ECE.2017.8468900

Dibya Bharti, M. De

引用次数: 1

ID-PAPC: Identity based Public Auditing Protocol for Cloud Computing 基于身份的云计算公共审计协议

2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE) Pub Date : 2017-12-01 DOI: 10.1109/WIECON-ECE.2017.8468932

N. Garg, S. Bawa

引用次数: 5

Optimization of Neuropsychological Scores at the Baseline Visit Using Evolutionary Technique 利用进化技术优化基线访视时的神经心理学评分

2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE) Pub Date : 2017-12-01 DOI: 10.1109/WIECON-ECE.2017.8468891

N. Vinutha, Sonu Sharma, P. D. Shenoy, K. Venugopal

{"title":"Optimization of Neuropsychological Scores at the Baseline Visit Using Evolutionary Technique","authors":"N. Vinutha, Sonu Sharma, P. D. Shenoy, K. Venugopal","doi":"10.1109/WIECON-ECE.2017.8468891","DOIUrl":"https://doi.org/10.1109/WIECON-ECE.2017.8468891","url":null,"abstract":"The neuropsychological battery of scores, are the measures of cognitive domains of human brain, that are considered as important features to distinguish healthy subjects from the subjects, suffering from Mild Cognitive Impairment (MCI). The instances of about 5542, with four time visits are separated from the total collected instances of the National Alzheimer’s Coordinating Center (NACC) database. The analysis of the selected data shows that the large number of subjects is identified for 66–75 and 76–85 age groups. The Genetic Algorithms (GA) applied on the neuropsychological scores at the baseline visit, selects the best subset of scores required for the clinical diagnosis, and these scores are evaluated by the logistic regression model using Area Under Curve (AUC), accuracy and Mean Squared Error (MSE) as the metric. Simulations result show that a highest classification accuracy of 0.9427, AUC of 0.9713 and less error rate of 0.041 is achieved for the combination of four neuropsychological scores Global Staging of Clinical Dementia Rating (CDRGLOB), Geriatric Depression Scale (GDS), Logical Memory Delayed (MEMUNITS), Digit Span Forward Length (DIGIFLEN). These scores are predominantly selected by the GA across many runs and thus have significant role for screening MCI subjects at the baseline visit.","PeriodicalId":188031,"journal":{"name":"2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131179976","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}

引用次数: 3

Airborne Radar Signal Processor Realisation 机载雷达信号处理器实现

2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE) Pub Date : 2017-12-01 DOI: 10.1109/WIECON-ECE.2017.8468930

Reena Mamgain, Rashi Jain

引用次数: 0

Automatic Focal Eplileptic Seizure Detection in EEG Signals 脑电信号中的自动局灶性癫痫发作检测

2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE) Pub Date : 2017-12-01 DOI: 10.1109/WIECON-ECE.2017.8468906

Satyajit Anand, Sandeep Jaiswal, P. K. Ghosh

{"title":"Automatic Focal Eplileptic Seizure Detection in EEG Signals","authors":"Satyajit Anand, Sandeep Jaiswal, P. K. Ghosh","doi":"10.1109/WIECON-ECE.2017.8468906","DOIUrl":"https://doi.org/10.1109/WIECON-ECE.2017.8468906","url":null,"abstract":"In this paper, we propose an automatic epilepsy diagnosis based on the statistical feature extraction. At outset, EEG signals are recorded from the patient and pre-processed to remove the unwanted signals: Dc drift elimination, high pass and low pass filter techniques are applied to preprocess the EEG signals. The noise is diminished from the signal by the method of Hilbert-Huang Transform (HHT). Empirical mode decomposition is the portion of HHT by which intrinsic mode functions (IMFs) are separated from the signal. In Hilbert spectral analysis, the instant frequency of IMFs is executed using Hilbert transform, which allows the finding of localized features. Empirical wavelet transform (EWT) is applied to acquire EWT components from the EEG signals. These features are further extracted in to five frequency subbands based on clinical interest. Genetic algorithm is structured for displaying the best features from the localized features. Based on the optimized features, support vector machine is applied to classify and evaluated the signals as epileptic seizure and seizure-free EEG signals. An experimental result shows that the proposed method can attain a very high accuracy.","PeriodicalId":188031,"journal":{"name":"2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131337587","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}

引用次数: 4

Audio Visual Emotion Recognition Using Cross Correlation and Wavelet Packet Domain Features 基于相互关联和小波包域特征的视听情感识别

2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE) Pub Date : 2017-12-01 DOI: 10.1109/WIECON-ECE.2017.8468871

Shamman Noor, Ehsan Ahmed Dhrubo, A. T. Minhaz, C. Shahnaz, S. Fattah

{"title":"Audio Visual Emotion Recognition Using Cross Correlation and Wavelet Packet Domain Features","authors":"Shamman Noor, Ehsan Ahmed Dhrubo, A. T. Minhaz, C. Shahnaz, S. Fattah","doi":"10.1109/WIECON-ECE.2017.8468871","DOIUrl":"https://doi.org/10.1109/WIECON-ECE.2017.8468871","url":null,"abstract":"The better a machine realizes non-verbal ways of communication, such as emotion, better levels of human machine interrelation is achieved. This paper describes a method for recognizing emotions from human Speech and visual data for machine to understand. For extraction of features, videos consisting 6 classes of emotions (Happy, Sad, Fear, Disgust, Angry, and Surprise) of 44 different subjects from eNTERFACE05 database are used. As video feature, Horizontal and Vertical Cross Correlation (HCCR and VCCR) signals, extracted from regions-eye and mouth, are used. As Speech feature, Perceptual Linear Predictive Coefficients (PLPC) and Mel-frequency Cepstral Coefficients (MFCC), extracted from Wavelet Packet Coefficients, are used in conjunction with PLPC and MFCC extracted from original signal. For both types of feature, K-Nearest Neighbour (KNN) multiclass classification method is applied separately for identifying emotions expressed in speech and through facial movement. Emotion expressed in a video file is identified by concatenating the Speech and video features and applying KNN classification method.","PeriodicalId":188031,"journal":{"name":"2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124904240","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}

引用次数: 2