发布求助
文献互助 智能选刊 最新文献

2015 Symposium on Recent Advances in Electrical Engineering (RAEE)最新文献

筛选
英文 中文
Gait generation and terrain navigation algorithm design for a self-reconfigurable robot 自重构机器人步态生成与地形导航算法设计
2015 Symposium on Recent Advances in Electrical Engineering (RAEE) Pub Date : 2015-12-17 DOI: 10.1109/RAEE.2015.7352753
Fatima Ahsan, K. M. Hasan
{"title":"Gait generation and terrain navigation algorithm design for a self-reconfigurable robot","authors":"Fatima Ahsan, K. M. Hasan","doi":"10.1109/RAEE.2015.7352753","DOIUrl":"https://doi.org/10.1109/RAEE.2015.7352753","url":null,"abstract":"This paper presents the gait generation and navigation algorithms of an autonomous self-reconfiguring mobile robot platform, Chaser, which is capable of changing its configuration according to its surroundings. This robot attains selftransforming capability due to multiple degrees of freedom in its structure and the on-board range-sensing ability. The proposed gait generation and navigation algorithms enable Chaser to reconfigure itself to a shape that is best suited to pass through, over or under the obstacles presented to it. Moreover, the robot has the capability to traverse through various type of terrains by moving on wheels, walking like a quadruple and swimming like humans respectively. These multiple kinds of gaits have been coupled with a terrain navigation algorithm so that robot could identify different kinds of terrains and obstacles and transform itself to navigate seamlessly through them. The performance of Chaser is experientially tested with various real-world obstacles. Experimental results validate its performance.","PeriodicalId":424263,"journal":{"name":"2015 Symposium on Recent Advances in Electrical Engineering (RAEE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126761395","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: 0
Data-driven technique for robust fault detection in generators 发电机鲁棒故障检测的数据驱动技术
2015 Symposium on Recent Advances in Electrical Engineering (RAEE) Pub Date : 2015-12-17 DOI: 10.1109/RAEE.2015.7352759
Abeer Fatima, Abdul Qayyum Khan
{"title":"Data-driven technique for robust fault detection in generators","authors":"Abeer Fatima, Abdul Qayyum Khan","doi":"10.1109/RAEE.2015.7352759","DOIUrl":"https://doi.org/10.1109/RAEE.2015.7352759","url":null,"abstract":"Protection of a synchronous generator presents a very challenging problem because of its simultaneous system connections on three different sides; the prime mover, grid and the source of DC excitation. Generator Model is a very extensive and complex model and model-based fault detection techniques are difficult to implement. For this data-driven techniques can be applied which need only the process data to establish FDD systems. This paper presents application of subspace aided system identification method and robust residual evaluation using the process data directly, to detect actuator faults occuring in synchronous generators.","PeriodicalId":424263,"journal":{"name":"2015 Symposium on Recent Advances in Electrical Engineering (RAEE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122725970","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: 1
Implementation of nonlinear classifiers for adaptive autoregressive EEG features classification 非线性分类器自适应自回归脑电特征分类的实现
2015 Symposium on Recent Advances in Electrical Engineering (RAEE) Pub Date : 2015-12-17 DOI: 10.1109/RAEE.2015.7352749
Muddasir Ahmad, M. Aqil
{"title":"Implementation of nonlinear classifiers for adaptive autoregressive EEG features classification","authors":"Muddasir Ahmad, M. Aqil","doi":"10.1109/RAEE.2015.7352749","DOIUrl":"https://doi.org/10.1109/RAEE.2015.7352749","url":null,"abstract":"The objective of this work is to realize two nonlinear classifiers for the adaptive autoregressive Electroencephalography (EEG) features. The EEG features are modeled as adaptive autoregressive model and estimated using recurring least square algorithm. Nonlinear classification is performed using multilayer perceptron (MLP) and radial basal function neural network to classify extracted features for a two classes experiment. For validation, hands movement imaginations based experiments are conducted using low price EEG EPOC headset. A comparative study, carried out amongst the nonlinear classifiers and with a linear discriminant analysis, demonstrates the dominance of the MLP as nonlinear classifier.","PeriodicalId":424263,"journal":{"name":"2015 Symposium on Recent Advances in Electrical Engineering (RAEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130927718","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
首页 上一页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信