2017 IEEE International Conference on Mechatronics and Automation (ICMA)最新文献_第7页

Dynamic modeling and analysis of laparoscope arm for Minimally Invasive Surgical Robot 微创手术机器人腹腔镜臂动力学建模与分析

2017 IEEE International Conference on Mechatronics and Automation (ICMA) Pub Date : 2017-08-01 DOI: 10.1109/ICMA.2017.8015875

Jing Yang, Lingtao Yu, Ning Zhang, Lan Wang, Jiliang Zhao

引用次数: 2

Design of experimental setups for evaluating hover performance of a Martian coaxial rotorcraft 火星型同轴旋翼机悬停性能评估实验装置设计

2017 IEEE International Conference on Mechatronics and Automation (ICMA) Pub Date : 2017-08-01 DOI: 10.1109/ICMA.2017.8016026

Pengyue Zhao, Zhijun Zhao, Shuitian Chen, Q. Quan, He Li, Deen Bai, Z. Deng

引用次数: 3

Study on the output controllability of BUCK-BOOST Converter BUCK-BOOST变换器输出可控性研究

2017 IEEE International Conference on Mechatronics and Automation (ICMA) Pub Date : 2017-08-01 DOI: 10.1109/ICMA.2017.8015797

Xiaojing Li, Z. Zhang

引用次数: 9

Distributed event triggered control with state dependent threshold for leader following consensus of general linear multi agent systems 一般线性多智能体系统中领导者服从一致性的状态依赖阈值分布式事件触发控制

2017 IEEE International Conference on Mechatronics and Automation (ICMA) Pub Date : 2017-08-01 DOI: 10.1109/ICMA.2017.8015986

Nacera Bekhadda, Qing-he Wu, Changkun Du

引用次数: 2

Frequency analysis and PID controller design for a pump-controlled electrical hydraulic system 泵控电液系统的频率分析与PID控制器设计

2017 IEEE International Conference on Mechatronics and Automation (ICMA) Pub Date : 2017-08-01 DOI: 10.1109/ICMA.2017.8015979

L. Liang, Zhiwen Le, Jianfeng Li

引用次数: 2

Design of Wireless Power Transmission System based on magnetic coupling resonant for the capsule endoscopy 基于磁耦合谐振的胶囊内窥镜无线电力传输系统设计

2017 IEEE International Conference on Mechatronics and Automation (ICMA) Pub Date : 2017-08-01 DOI: 10.1109/ICMA.2017.8015782

Shuxiang Guo, Peng Zhang, Jian Guo, Lili Wang, Gang Sun

引用次数: 6

A hierarchical graph matching based key point correspondence method for large distance rover localization 基于层次图匹配的大距离漫游车定位关键点对应方法

2017 IEEE International Conference on Mechatronics and Automation (ICMA) Pub Date : 2017-08-01 DOI: 10.1109/ICMA.2017.8015973

Yinlin Li, Yuren Zhang, Chuankai Liu, Xu Yang, Hong Qiao

{"title":"A hierarchical graph matching based key point correspondence method for large distance rover localization","authors":"Yinlin Li, Yuren Zhang, Chuankai Liu, Xu Yang, Hong Qiao","doi":"10.1109/ICMA.2017.8015973","DOIUrl":"https://doi.org/10.1109/ICMA.2017.8015973","url":null,"abstract":"Key point correspondence plays a critical role in large distance lunar rover localization and navigation, which can decrease accumulative errors and facilitate the rover to approach probing target accurately. Lunar surface images often contain similar patterns, noisy points and obvious illumination changes, especially along with large distance movement, overlapping regions of image pairs have large deformations and sorely different scales. Traditional appearance based matching methods fail in such conditions as local appearance features become less distinctive. In this paper, we proposed a novel hierarchical graph matching strategy to fully utilize structure property of key points. For vertexes in bigger scale, their neighborhood graphs in smaller scale are regarded as vertex labels and the differences of neighborhood graphs are regarded as differences of vertexes. Based on the new strategy, both global and local constraint are considered such that the dependency on local appearance is alleviated. Furthermore, an active key point detection method is proposed to serve the graph matching method for better performance. Finally, several experiments are conducted on lunar surface images collected by Chang'E-3 rover, which demonstrates the robustness and effectiveness of the proposed method.","PeriodicalId":124642,"journal":{"name":"2017 IEEE International Conference on Mechatronics and Automation (ICMA)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116172967","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

Image deformation using modified Moving Least Squares with outlines 图像变形使用改进的移动最小二乘与轮廓

2017 IEEE International Conference on Mechatronics and Automation (ICMA) Pub Date : 2017-08-01 DOI: 10.1109/ICMA.2017.8015961

Chong Yu, Xi Chen, Qiwei Xie, Guoqing Li, Lei Yin, Hua Han

引用次数: 4

A landslide intelligent detection method based on CNN and RSG_R 基于CNN和RSG_R的滑坡智能检测方法

2017 IEEE International Conference on Mechatronics and Automation (ICMA) Pub Date : 2017-08-01 DOI: 10.1109/ICMA.2017.8015785

Hong Yu, Yi Ma, Longfei Wang, Yongsai Zhai, Xiaoqian Wang

引用次数: 55

Classification of four categories of EEG signals based on relevance vector machine 基于相关向量机的四类脑电信号分类

2017 IEEE International Conference on Mechatronics and Automation (ICMA) Pub Date : 2017-08-01 DOI: 10.1109/ICMA.2017.8015957

Enzeng Dong, Guangxu Zhu, Chao Chen

{"title":"Classification of four categories of EEG signals based on relevance vector machine","authors":"Enzeng Dong, Guangxu Zhu, Chao Chen","doi":"10.1109/ICMA.2017.8015957","DOIUrl":"https://doi.org/10.1109/ICMA.2017.8015957","url":null,"abstract":"EEG classification is an important signal acquisition equipment of brain-interface research and application. In this paper, an improved relevance vector machine (RVM) is proposed to classify four-class motor imagery EEG signals. The original EEG signals are processed by 3–24Hz band-pass filter. Thereafter, EEG feature vectors are extracted from the band-pass filtered EEG signals with one versus one common spatial patterns (OVO-CSP). Then, the improved RVM algorithm with kernel function, which combines Gaussian kernel function and Cauchy kernel function, is applied to classify the EEG signals. A public dataset (BCI Competition IV-II-a) is employed to verify the proposed improved kernel function method. Five-fold cross-validation was used to ensure that the classification of the experiment is more credible. The classification results of the combined kernel function are compared with results of the Gaussian kernel function and the Cauchy kernel function. The experimental results show that the highest classification accuracy of the proposed kernel function and the single kernel function in the first data set are 64.40% and 60.60%, respectively, and in the second data sets are 67.58% and 63.33%. The average classification accuracy of the proposed kernel function is 4%–4.19% higher than that of the single kernel function. These results show that the proposed method is advantageous in the classification of four-class motor imagery.","PeriodicalId":124642,"journal":{"name":"2017 IEEE International Conference on Mechatronics and Automation (ICMA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116797484","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: 7