2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)最新文献

A Novel Auditory-tactile P300-based BCI Paradigm 一种新的基于听觉触觉p300的脑机接口范式

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071600

Jing Jiang, Boyang Zhang, E. Yin, Chunhui Wang, Baosong Deng

引用次数: 3

Cooperative Multi-agent for The End-Effector Position of Robotic Arm Based on Consensus and PID Controller 基于共识和PID控制器的机械臂末端执行器位置多智能体协作

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071621

Arif Nugroho, E. M. Yuniarno, M. Purnomo

{"title":"Cooperative Multi-agent for The End-Effector Position of Robotic Arm Based on Consensus and PID Controller","authors":"Arif Nugroho, E. M. Yuniarno, M. Purnomo","doi":"10.1109/CIVEMSA45640.2019.9071621","DOIUrl":"https://doi.org/10.1109/CIVEMSA45640.2019.9071621","url":null,"abstract":"In a multi-agent case, we consider not only an agent but also several agents. The problem of multi-agent is that each of the agents has a randomly different initial state. The existence of multi-agent in a system needs to be managed in order to be a structured system for making certain decision cooperatively. This paper presents the implementation of cooperative multi-agent for synchronizing the end-effector position of the robotic arm based on consensus and PID controller. Physically, the robotic arm has three servo motors that mean the robotic arm has three joints. Thus, to synchronize the end-effector position of the robotic arm among agents, all of the servo motors in all agents must be controlled in order to achieve the same point of view. In addition, this paper expounds how to derive the consensus equations and PID controller based on designed graph topology. The presence of two proposed methods is intended to compare whether or not the problem of multi-agent regarding randomly different initial state can be overcome by using consensus and PID controller. From the experimental results, the derived consensus equations and PID controller can be implemented to synchronize all of the joints possessed by the robotic arm. As a result, the end-effector of the robotic arm in all of the agents successfully pointed to the same position in three-dimensional space. It indicates that the derived consensus equations and PID controller can handle the different state of each joint in all of the agents.","PeriodicalId":293990,"journal":{"name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129496531","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

Path planning and trajectroy tracking of a mobile robot using bio-inspired optimization algorithms and PID control 基于仿生优化算法和PID控制的移动机器人路径规划和轨迹跟踪

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071596

A. Moshayedi, A. Abbasi, Liefa Liao, Shuai Li

{"title":"Path planning and trajectroy tracking of a mobile robot using bio-inspired optimization algorithms and PID control","authors":"A. Moshayedi, A. Abbasi, Liefa Liao, Shuai Li","doi":"10.1109/CIVEMSA45640.2019.9071596","DOIUrl":"https://doi.org/10.1109/CIVEMSA45640.2019.9071596","url":null,"abstract":"Path planning and trajectory tacking are the fundamental task in mobile robotic science, and they enable the robot to navigate autonomously. In this work, the path planning task is carried out using three bio-inspired optimization algorithms, including PSO, ABC and FA. The duty of the algorithms is to determine a collision-free path through fixed obstacles in the working environment. The maximum speed of the robot is applied to the optimization problem as a constraint. In order to evaluate the performance of the algorithms, four workspaces with different obstacle layout are simulated in MATLAB, and the quality of path planning task is analyzed statistically and numerically, considering four different criteria, including, convergency quality, convergency time, path length and success rate. In the next step, a control model is designed to track the path curve determined by the path planning algorithms. A PID-based control structure is simulated in MATLAB Simulink and the controller was able to track the pre-determined traj ectories with proper approximation. The controller is applied on a dynamic model of a two-wheeled mobile robot offered by [1]. In order to validate the control inputs it is necessary to apply them on a real platform. The experimental study is implemented on a two-wheeled mobile robot which is designed and built based on the authors' previous paper [2] in various enverioment and obstacles. The result shows control inputs were applied to the real robot and the robot was able to imitate the applied path curve, and find its way toward the target point without colliding obstacles in real and simulation task.","PeriodicalId":293990,"journal":{"name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132014033","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: 18

A comparing network for the classification of steady-state visual evoked potential responses based on convolutional neural network 基于卷积神经网络的稳态视觉诱发电位分类比较网络

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071633

Jiczhen Xing, Shuang Qiu, Chenyao Wu, Xuelin Ma, Jinpeng Li, Huiguang He

{"title":"A comparing network for the classification of steady-state visual evoked potential responses based on convolutional neural network","authors":"Jiczhen Xing, Shuang Qiu, Chenyao Wu, Xuelin Ma, Jinpeng Li, Huiguang He","doi":"10.1109/CIVEMSA45640.2019.9071633","DOIUrl":"https://doi.org/10.1109/CIVEMSA45640.2019.9071633","url":null,"abstract":"Brain-computer interfaces (BCIs) based on Steady-State Visual Evoked Potentials (SSVEPs) has been attracting much attention because of its high information transfer rate and little user training. However, most methods applied to decode SSVEPs are limited to CCA and some extended CCA-based methods. This study proposed a comparing network based on Convolutional Neural Network (CNN), which was used to learn the relationship between EEG signals and the templates corresponding to each stimulus frequency of SSVEPs. This novel method incorporated prior knowledge and a spatial filter (task related component analysis, TRCA) to enhance detection of SSVEPs. The effectiveness of the proposed method was validated by comparing it with the standard CCA and other state-of-the art methods for decoding SSVEPs (i.e., CNN and TRCA) on the actual SSVEP datasets collected from 17 subjects. The comparison results indicated that the CNN-based comparing network significantly could significantly improve the classification accuracy compared with the standard CCA, TRCA and CNN. Furthermore, the comparing network with TRCA achieved the best performance among three methods based on comparing network with the averaged accuracy of 84.57% (data length: 2s) and 70.21% (data length: 1s). The study validated the efficiency of the proposed CNN-based comparing methods in decoding SSVEPs. It suggests that the comparing network with TRCA is a promising methodology for target identification of SSVEPs and could further improve the performance of SSVEP-based BCI system.","PeriodicalId":293990,"journal":{"name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129263075","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

P1 as an objective auditory rehabilitation assessing indicator for cochlear implant children P1作为人工耳蜗患儿听觉康复的客观评价指标

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071598

G. Ni, Q. Zheng, Yidi Liu, Hai-hong Liu, Zihao Xu, Dong Ming

{"title":"P1 as an objective auditory rehabilitation assessing indicator for cochlear implant children","authors":"G. Ni, Q. Zheng, Yidi Liu, Hai-hong Liu, Zihao Xu, Dong Ming","doi":"10.1109/CIVEMSA45640.2019.9071598","DOIUrl":"https://doi.org/10.1109/CIVEMSA45640.2019.9071598","url":null,"abstract":"Cochlear implants (CIs) provide a tool for hearing reconstruction. How to effectively and reasonably evaluate the auditory rehabilitation level of CI children has always been a difficult problem, especially for those with prelingual deafness. Many studies have used electroencephalograph (EEG) technology for CI users, which have shown that EEG is suitable for clinical postoperative evaluation. This study aims to explore changes of latencies and amplitudes of P1 wave in CI children under auditory stimulation. A pure tone at 1000 Hz was used as the stimuli to induce cortical auditory evoked potentials (CAEP). The characteristics of P1 wave were compared between normal hearing children and CI children over different implantation period. Results show that CI children started to have improved auditory perception after implantation, moreover, their P1 wave amplitude becomes similar to that of normal hearing children after six months. One year after implantation, the characteristics of P1 wave of CI children become similar to those of normal hearing children. Therefore, it seems that P1 could be used as an objective auditory rehabilitation assessing indicator for CI children.","PeriodicalId":293990,"journal":{"name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122228872","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

Comparison of Contributions between Facial and Neck Muscles for Speech Recognition Using High-Density surface Electromyography 面部和颈部肌肉对高密度表面肌电图语音识别的贡献比较

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071636

Jiashuo Zhuang, Mingxing Zhu, Xiaochen Wang, Dan Wang, Zijian Yang, Xin Wang, Lin Qi, Shixiong Chen, Guanglin Li

{"title":"Comparison of Contributions between Facial and Neck Muscles for Speech Recognition Using High-Density surface Electromyography","authors":"Jiashuo Zhuang, Mingxing Zhu, Xiaochen Wang, Dan Wang, Zijian Yang, Xin Wang, Lin Qi, Shixiong Chen, Guanglin Li","doi":"10.1109/CIVEMSA45640.2019.9071636","DOIUrl":"https://doi.org/10.1109/CIVEMSA45640.2019.9071636","url":null,"abstract":"Speaking is an important way for human beings to communicate each other. Generally, voice signals are used for speech recognition, which is easily affected by environmental interference. Additionally, surface electromyography (sEMG) of articulatory muscles has been proposed in previous studies to enable speech recognition, which is insensitive to noisy environments. However, it remains unclear what are the contributions of facial and neck muscles for speech recognition, which would be vital for selecting locations of sEMG recording electrodes. In this study, the high-density (HD) sEMG technique was proposed to explore the major articulatory muscles contributed to speaking. The HD sEMG signals were acquired from four subjects by surface electrodes over the face and neck during speaking five Chinese daily phrases, from which four features (mean absolute value, waveform length, number of zero crossing, and slope sign change) were extracted. Then a linear-discriminant-analysis classifier was built by the sEMG features for speech recognition. The primary results showed that the sEMG and RMS waveforms illustrated obvious difference when speaking different Chinese phrases. And the classification accuracy using signals from the neck was higher than that from the facial muscles, whereas the accuracy was increased by using the whole facial and neck muscles. Our pilot results revealed that the facial and neck muscles were both contributed to the speech recognition while the neck muscles were more crucial than the facial muscles during speaking. This pilot study may suggest that the HD sEMG might pave a way to find the major muscles of speech recognition.","PeriodicalId":293990,"journal":{"name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131808770","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

A Computation Resource Friendly Convolutional Neural Network Engine For EEG-based Emotion Recognition 一种计算资源友好的基于脑电图的情感识别卷积神经网络引擎

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071594

Yi-Ju Zhan, M. Vai, S. Barma, S. Pun, Jia Wen Li, P. Mak

{"title":"A Computation Resource Friendly Convolutional Neural Network Engine For EEG-based Emotion Recognition","authors":"Yi-Ju Zhan, M. Vai, S. Barma, S. Pun, Jia Wen Li, P. Mak","doi":"10.1109/CIVEMSA45640.2019.9071594","DOIUrl":"https://doi.org/10.1109/CIVEMSA45640.2019.9071594","url":null,"abstract":"EEG-based Emotion recognition is a crucial link in Human-Computer Interaction (HCI) application. Nowadays, Convolutional Neural Network (CNN) and its related CNN-hybrid approaches have achieved the state-of-art accuracy in this field. However, most of these existing techniques employ large-scale neural networks which cause performance bottleneck in portable systems. Moreover, traditional convolution kernel confuses EEG multiple frequency bands information, which is critical for investigating emotion status. To improve these issues, firstly, we extract power spectral features from four frequency bands (θ,α,β,γ) and transform obtained features into cortex-like frames while preserving spatial information of electrodes position, so that the multi-channel, multi-frequency bands and time series EEG signals can be efficiently represented. Then, we design a shallow depthwise parallel CNN inspired by Mobilenet technique to learn spatial representation from labeled frames. Segment-level emotion recognition experiments are implemented to verify the proposed architecture with DEAP database. Our approach achieves the competitive accuracy of 84.07% and 82.95% on arousal and valence respectively. Besides, the experimental results prove the computation-effectiveness of the proposed method. Compared with the state-of-art approach, our approach saves 69.23% GPU memory and reduces 30% GPU peak utilization with only 6.5% accuracy drop. Therefore, our method shows extensive application prospects for EEG-based emotion recognition on resource-limited devices.","PeriodicalId":293990,"journal":{"name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115772482","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

Impact of Sink Node Placement on Partial Connectivity in Wireless Sensor Networks 无线传感器网络中汇聚节点放置对部分连通性的影响

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071631

Yun Wang

{"title":"Impact of Sink Node Placement on Partial Connectivity in Wireless Sensor Networks","authors":"Yun Wang","doi":"10.1109/CIVEMSA45640.2019.9071631","DOIUrl":"https://doi.org/10.1109/CIVEMSA45640.2019.9071631","url":null,"abstract":"Network connectivity as a fundamental issue in WSNs has been attracting researchers' attention for decades, primarily focusing on strategies to achieve or maintain full connectivity. However, in practice, sensor isolation is a state of normality, and full connectivity is not a requirement for many WSN applications to operate successfully. Due to this, this paper investigates the partial connectivity problem in a randomly deployed WSN, taking into consideration the particular function of the sink node and its skewed placement due to environmental and terrain factors in network deployment. We strive to identify the tradeoffs between partial connectivity and the required network parameters in contrast to full connectivity under various circumstances. Through mathematically modeling, theoretical analysis and simulation evaluations, we demonstrate the significant impact of the sink node and its placement on the network connectivity and that partial connectivity, as compared with full connectivity, is a more appropriate metric to assess the connectivity of random WSNs. For example, we show that the sensor connection rate drops from 98.8% to 72.5% when the sink node is relocated from the network center to the border and that 1.367 times more energy is needed to connect less than 4% of the remote sensors, under the studied network settings. The results help in defining appropriate performance metrics and in selecting critical network parameters for real-life WSN design and implementation.","PeriodicalId":293990,"journal":{"name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124734009","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

Research on Human Error Analysis in the Simulated Main Control Room of Nuclear Power Plant Based on EEG Brain Network 基于脑电图脑网络的核电站模拟主控室人为误差分析研究

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071568

Hao Feng, Ying Li, Dongying Zhang, Jipeng Li

{"title":"Research on Human Error Analysis in the Simulated Main Control Room of Nuclear Power Plant Based on EEG Brain Network","authors":"Hao Feng, Ying Li, Dongying Zhang, Jipeng Li","doi":"10.1109/CIVEMSA45640.2019.9071568","DOIUrl":"https://doi.org/10.1109/CIVEMSA45640.2019.9071568","url":null,"abstract":"With the digital development of the control system in the main control room of nuclear power plant (NPP), the reliability of the objective conditions is continuously improved, and the proportion of mistakes caused by the operators themselves increases, which poses a risk to the safe operation of the nuclear power plant. Therefore, it is especially important to analyze the reasons caused by human factors. In this paper, the digital operation interface of the main control room of the nuclear power plant is simulated, 15 subjects are selected to complete the monitoring and judgment process of the digital interface, and the EEG data are collected simultaneously. The cross-correlation analysis method is used to construct the brain network of the EEG data, and the network parameters are analyzed. The results show that the mental load of the subjects may be overloaded when they meet the case of system accidents, which has an impact on subsequent operations. When judging the parameter stimulus, the brain resources occupied are more, and the number of mistakes increases. These results can provide the references for the training of operators in NNP main control room, and are hopeful for the improvement of the digital interface of the NNP's main control room.","PeriodicalId":293990,"journal":{"name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132604552","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

Emotion Recognition with the Feature extracted from brain Networks 基于脑网络特征提取的情绪识别

2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2019-06-01 DOI: 10.1109/CIVEMSA45640.2019.9071616

Cunbo Li, Peiyang Li, Lin Jiang, Xuyang Zhu, Yajing Si, Ying Zeng, D. Yao, Peng Xu

引用次数: 1