2016 Artificial Intelligence and Robotics (IRANOPEN)最新文献

Development of myoelectric interface based on pattern recognition and regression based models 基于模式识别和回归模型的肌电界面开发

2016 Artificial Intelligence and Robotics (IRANOPEN) Pub Date : 2016-04-09 DOI: 10.1109/RIOS.2016.7529505

Armin Ehrampoosh, A. Yousefi-Koma, M. Ayati

{"title":"Development of myoelectric interface based on pattern recognition and regression based models","authors":"Armin Ehrampoosh, A. Yousefi-Koma, M. Ayati","doi":"10.1109/RIOS.2016.7529505","DOIUrl":"https://doi.org/10.1109/RIOS.2016.7529505","url":null,"abstract":"This paper proposes a combinatorial strategy for myoelectric control of robotic arm. Activation of main muscles responsible for 1 DOF of elbow joint is recorded. The goal was to create a mapping between muscles' Surface Electromyogram (sEMG) data and kinematics of the joints. The proposed strategy includes two main phases. In the first phase, Linear Discriminant Analysis (LDA) was utilized to classify several classes in user's arm motions. Due to fast training, simple implementation and robustness against long term effect of non-stationary characteristics of sEMG signals, LDA is a common classifier in myoelectric signal classification researches. In the second phase, two Time Delayed Artificial Neural Networks (TDANN) were trained to estimate proportional and continuous angle and velocity related to joint motion classes. Furthermore, two additional methods were used to enhance the prediction results accuracy. First, noise reduction of sEMG signals plays a key role in accurate joint kinematics prediction. Therefore, a new noise reduction approach is investigated based on classification results. Second, final predicted angles were achieved by data fusion of angles and angle difference rates, estimated by TDANN. Results show that, LDA classifies the motion classes with 95% accuracy and final estimated angular positions are significantly close to actual values. Therefore, proposed method is able to create a mapping between muscles' sEMG data and joint kinematics with acceptable error. Practical results confirm the performance of the proposed method.","PeriodicalId":416467,"journal":{"name":"2016 Artificial Intelligence and Robotics (IRANOPEN)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126921032","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

Detection of both positive and negative correlated rows in biclusters using Squared Transposed Virtual Error 利用平方转置虚误差检测双聚类中的正负相关行

2016 Artificial Intelligence and Robotics (IRANOPEN) Pub Date : 2016-04-09 DOI: 10.1109/RIOS.2016.7529515

S. Mahmoudi, M. Menhaj

引用次数: 0

A hybrid localization method for a soccer playing robot 足球机器人的混合定位方法

2016 Artificial Intelligence and Robotics (IRANOPEN) Pub Date : 2016-04-09 DOI: 10.1109/RIOS.2016.7529502

Meisam Teimouri, M. Salehi, M. Meybodi

引用次数: 7

Path following of an omni-directional four-wheeled mobile robot 四轮全向移动机器人的路径跟踪

2016 Artificial Intelligence and Robotics (IRANOPEN) Pub Date : 2016-04-09 DOI: 10.1109/RIOS.2016.7529487

M. Emam, A. Fakharian

{"title":"Path following of an omni-directional four-wheeled mobile robot","authors":"M. Emam, A. Fakharian","doi":"10.1109/RIOS.2016.7529487","DOIUrl":"https://doi.org/10.1109/RIOS.2016.7529487","url":null,"abstract":"Navigation of omni-directional four wheeled mobile robot usually is done by trajectory tracking controller. But, in this article two methods of path following have been presented to reach this purpose. Path following is another navigation approach that is solved some of trajectory tracking navigation systems difficulties like `turning back'. Path following navigators usually are designed by two different viewpoints, geometrical approaches or control approaches which both are utilized in this paper. The main contribution of this paper is to present tow simple methods with acceptable navigation performance. Based on the simulation results, it is proofed that, the presented methods are able to navigate the robot on desired paths even in hard situations like measurement delay and external disturbance. Also, advantages of each method is presented based on comparison of simulation results. Another contribution of this work is using dynamic model of the robot instead of kinematic model which is usually is used in path following articles. To reach this goal, at the first step, a Linear Quadratic Regulator (LQR) controller is designed as low level controller, then path following navigator is mounted on high level controller. Note that, weighting matrix of LQR controller (matrix Q) is calculated by Genetic Algorithm to achieve the best possible time performances.","PeriodicalId":416467,"journal":{"name":"2016 Artificial Intelligence and Robotics (IRANOPEN)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114459902","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: 6

H∞ output feedback controller design for flexible needles guidance 柔性针制导的H∞输出反馈控制器设计

2016 Artificial Intelligence and Robotics (IRANOPEN) Pub Date : 2016-04-09 DOI: 10.1109/RIOS.2016.7529508

Alireza Farhamfard, M. Menhaj, A. Fakharian

引用次数: 1

Time series forecasting using improved ARIMA 使用改进的ARIMA进行时间序列预测

2016 Artificial Intelligence and Robotics (IRANOPEN) Pub Date : 2016-04-09 DOI: 10.1109/RIOS.2016.7529496

Soheila Mehrmolaei, M. Keyvanpour

引用次数: 33

Improving the heart disease diagnosis by evolutionary algorithm of PSO and Feed Forward Neural Network 基于PSO和前馈神经网络的进化算法改进心脏病诊断

2016 Artificial Intelligence and Robotics (IRANOPEN) Pub Date : 2016-04-09 DOI: 10.1109/RIOS.2016.7529489

Majid Ghonji Feshki, Omid Sojoodi Shijani

{"title":"Improving the heart disease diagnosis by evolutionary algorithm of PSO and Feed Forward Neural Network","authors":"Majid Ghonji Feshki, Omid Sojoodi Shijani","doi":"10.1109/RIOS.2016.7529489","DOIUrl":"https://doi.org/10.1109/RIOS.2016.7529489","url":null,"abstract":"The considerable growing of cardiovascular disease and its effects and complications as well as the high costs on society makes medical community seek for solutions to prevention, early identification and effective treatment with lower costs. Thus, valuable knowledge can be established by using artificial intelligence and data mining; the discovered knowledge makes improve the quality of service. Until now, different researches have been carried out in order to predict heart disease based on data mining methods such as classification and clustering methods; however, what has been less noticed is the exact diagnosis of disease with the lowest cost and time. In this paper, by using feature ranking on effective factors of disease related to Cleveland clinic database and by using Particle Swarm Optimization as well as Neural Network Feed Forward Back-Propagation, 13 effective factors reduced to 8 optimized features in terms of cost and accuracy. The assessment of selected features of classified methods also showed that PSO method along with Neural Networks of Feed Forward Back-Propagation has the best accurate criteria of the rate of 91.94% on these features.","PeriodicalId":416467,"journal":{"name":"2016 Artificial Intelligence and Robotics (IRANOPEN)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114303965","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: 62

Text summarization using concept graph and BabelNet knowledge base 使用概念图和BabelNet知识库进行文本摘要

2016 Artificial Intelligence and Robotics (IRANOPEN) Pub Date : 2016-04-09 DOI: 10.1109/RIOS.2016.7529500

Haniyeh Rashidghalam, M. Taherkhani, F. Mahmoudi

引用次数: 7

Self-localization of humanoid robots using particle swarm optimization algorithm 基于粒子群优化算法的仿人机器人自定位

2016 Artificial Intelligence and Robotics (IRANOPEN) Pub Date : 2016-04-09 DOI: 10.1109/RIOS.2016.7529497

Hossein Vahid Dastjerdi, M. Menhaj, Saeideh Shataei

引用次数: 2

LQG-I control for attitude stabilization of V8 octorotor flying robot V8旋翼飞行机器人姿态稳定的LQG-I控制

2016 Artificial Intelligence and Robotics (IRANOPEN) Pub Date : 2016-04-09 DOI: 10.1109/RIOS.2016.7529506

Parichehr Shahidi Sadeghi, A. M. Shahri, M. Ardestani, S. Rezazadeh

引用次数: 1