2015 IEEE International Conference on Mechatronics and Automation (ICMA)最新文献_第9页

The vector control research of brushless doubly-fed shaft generator for running independently 独立运行无刷双馈轴发电机矢量控制研究

2015 IEEE International Conference on Mechatronics and Automation (ICMA) Pub Date : 2015-09-03 DOI: 10.1109/ICMA.2015.7237587

Jingnan Zhang, Linlin Bai, Qiang Li

引用次数: 0

A nanorobot control algorithm using acoustic signals to identify cancer cells in Non-Newtonian blood fluid 一种利用声学信号识别非牛顿血液中癌细胞的纳米机器人控制算法

2015 IEEE International Conference on Mechatronics and Automation (ICMA) Pub Date : 2015-09-03 DOI: 10.1109/ICMA.2015.7237607

Qing-Ying Zhao, Min Li, Jun Luo, Lianhang Dou, Yang Li

引用次数: 2

Efficient body babbling for robot's drawing motion 高效的身体抖动，提高了机器人的牵引运动

2015 IEEE International Conference on Mechatronics and Automation (ICMA) Pub Date : 2015-09-03 DOI: 10.1109/ICMA.2015.7237650

Kanta Watanabe, Akio Numakura, S. Nishide, M. Gouko, Chyon Hae Kim

{"title":"Efficient body babbling for robot's drawing motion","authors":"Kanta Watanabe, Akio Numakura, S. Nishide, M. Gouko, Chyon Hae Kim","doi":"10.1109/ICMA.2015.7237650","DOIUrl":"https://doi.org/10.1109/ICMA.2015.7237650","url":null,"abstract":"This paper discusses the learning through body babbling, which is the initial stage of development and learning of human, from the view point of constructive approach for the recognition and behavior architectures of human. In previous researches, the body babbling and learning in the developmental process of drawing manipulation is modeled with two processes, a random joint angle generation process and an offline learning process with neural network. However, there is much gap between these models and the development of human. Human's developmental process is featured by the two processes, a judgment process for exploratory behaviors and an online incremental learning process. In this research, we propose a babbling-and-learning model that includes a judgment process for planned exploratory behaviors and an online incremental learning process. The online incremental learning process is modeled by Continuous System (Dynamics) Learning Tree (CSLT, DLT). CSLT realizes similar learning with neural network with an additional feature, online incremental learning. The proposed model introduces ε-greedy method for this judgment. After joint angles are executed, CSLT learns the obtained data by its online incremental learning process. As results of the validation of the proposed model, the proposed model gathered more number of effective data. The learning model decreased its prediction error faster than the previous model.","PeriodicalId":286366,"journal":{"name":"2015 IEEE International Conference on Mechatronics and Automation (ICMA)","volume":"54 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126767731","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: 5

An attitude estimation system for amphibious spherical robots 水陆两栖球形机器人姿态估计系统

2015 IEEE International Conference on Mechatronics and Automation (ICMA) Pub Date : 2015-09-03 DOI: 10.1109/ICMA.2015.7237806

Liwei Shi, Rui Xiao, Shuxiang Guo, Ping Guo, Shaowu Pan, Yanlin He

引用次数: 10

Adaptive control with unknown parameters estimation for motion tracking of piezo-driven micromanipulator 基于未知参数估计的压电驱动微机械臂运动跟踪自适应控制

2015 IEEE International Conference on Mechatronics and Automation (ICMA) Pub Date : 2015-09-03 DOI: 10.1109/ICMA.2015.7237668

Yulong Zhang, Qingsong Xu

引用次数: 1

Force model-based haptic master console design for teleoperated minimally invasive surgery application 基于力模型的远程微创手术触觉主控制台设计

2015 IEEE International Conference on Mechatronics and Automation (ICMA) Pub Date : 2015-09-03 DOI: 10.1109/ICMA.2015.7237579

Xuanchun Yin, Shuxiang Guo, Yu Wang

引用次数: 12

An adaptive square root cubature Kalman filter based SLAM algorithm for mobile robots 基于自适应平方根立方卡尔曼滤波的移动机器人SLAM算法

2015 IEEE International Conference on Mechatronics and Automation (ICMA) Pub Date : 2015-09-03 DOI: 10.1109/ICMA.2015.7237830

Jun Cai, X. Zhong

引用次数: 3

The dynamics research on the composite undercarriage of the Unmanned Aerial Vehicle 无人机复合材料底盘动力学研究

2015 IEEE International Conference on Mechatronics and Automation (ICMA) Pub Date : 2015-09-03 DOI: 10.1109/ICMA.2015.7237480

Yu-xiu Hou, Yong-liang Guan, Hong-guang Jia, Liu Bo, Wufu Ma

引用次数: 2

Preparation and characterization of molecularly imprinted mesoporous organosilica for biphenol Z recognition and separation 分子印迹介孔有机硅的制备与表征及其对双酚Z的识别与分离

2015 IEEE International Conference on Mechatronics and Automation (ICMA) Pub Date : 2015-09-03 DOI: 10.1109/ICMA.2015.7237617

Shu Yang, Y. Liu, Huamin Yi, Liquan Sun, Aiqin Luo

引用次数: 0

A two-layer semi-Markov model for recognizing the destination of a moving agent 一种用于移动智能体目的地识别的二层半马尔可夫模型

2015 IEEE International Conference on Mechatronics and Automation (ICMA) Pub Date : 2015-09-03 DOI: 10.1109/ICMA.2015.7237608

Shi-guang Yue, Kai Xu, Long Qin, Quanjun Yin

{"title":"A two-layer semi-Markov model for recognizing the destination of a moving agent","authors":"Shi-guang Yue, Kai Xu, Long Qin, Quanjun Yin","doi":"10.1109/ICMA.2015.7237608","DOIUrl":"https://doi.org/10.1109/ICMA.2015.7237608","url":null,"abstract":"Recognizing the destination of a moving agent is quite significant in many systems such as real time strategy games. Probabilistic graphical models are widely used to solve this problem, but existing models cannot recognize the changeable destination with noisy and partially missing observations in a grid based map. To solve this problem, a two-layer semi-Markov model (TLSMM) is proposed. In this model, two layers represent the transition of destinations and the grids where the agent is respectively; the duration of being in one grid is modeled by a discrete Coxian distribution. The particle filtering is also used to solve inference problem of TLSMM with noisy and partial data. In experiments, we simulate an agent's movements in an urban field and employ the agent's traces to evaluate the performance of TLSMM and PF. The results indicate that no matter the destination changes or not, our methods can effectively recognize it. In addition, through comparing the metrics like precision, recall, and F-measure of our model and a two-layer Markov model, we conclude that the explicit duration modeling improves the recognizing performance when the agent is not closed to the destination.","PeriodicalId":286366,"journal":{"name":"2015 IEEE International Conference on Mechatronics and Automation (ICMA)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130739324","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