2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)最新文献_第3页

Temporal Flexibility of Spatial and Frequency Embedded Network Predicts Individual Learning Ability Variation in Neurofeedback Training 空间和频率嵌入网络的时间灵活性预测神经反馈训练中个体学习能力的变化

2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2021-06-18 DOI: 10.1109/CIVEMSA52099.2021.9493584

S. Liu, C. Wong, Peng Xu, Yong Hu, Feng Wan

{"title":"Temporal Flexibility of Spatial and Frequency Embedded Network Predicts Individual Learning Ability Variation in Neurofeedback Training","authors":"S. Liu, C. Wong, Peng Xu, Yong Hu, Feng Wan","doi":"10.1109/CIVEMSA52099.2021.9493584","DOIUrl":"https://doi.org/10.1109/CIVEMSA52099.2021.9493584","url":null,"abstract":"Neurofeedback (NF) learning ability measured by cognitive and behavioral performance improvement after NF training shows significant individual differences. Thus, predicting an individual’s future performance using before-training brain dynamics data is of great interest. Here, we introduce a novel network, which embeds spatial and frequency connectivity pat-terns to characterize the functional separation and integration ability of the brain in steady state visual evoked potentials (SSVEPs). We tested whether the flexible rewiring of this brain network can be used to predict future individual alpha band (IAB) variation, which is related to the learning ability in NF training. A total of 28 subjects underwent a two-day IAB down-regulating neurofeedback training to assess their learning ability via IAB changes. We found an as-yet-unknown significant negative correlation between the temporal flexibility of the brain network and the NF learning ability. Thus, the temporal flexibility of the brain network can serve as a predictor for the learning ability in NF training. This study will help researchers to better understand the mechanism of SSVEP and predict individual training effectiveness.","PeriodicalId":248405,"journal":{"name":"2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133645999","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

Simulation study and PID Tune of Automated Guided Vehicles (AGV) 自动导引车(AGV)仿真研究及PID整定

2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2021-06-18 DOI: 10.1109/CIVEMSA52099.2021.9493679

A. Moshayedi, Jinsong Li, Liefa Liao

{"title":"Simulation study and PID Tune of Automated Guided Vehicles (AGV)","authors":"A. Moshayedi, Jinsong Li, Liefa Liao","doi":"10.1109/CIVEMSA52099.2021.9493679","DOIUrl":"https://doi.org/10.1109/CIVEMSA52099.2021.9493679","url":null,"abstract":"Automated Guided Vehicles (AGV) is a famous robot group which is used in various applications with different shapes. These robot applications start with industries carrying and end with a hospital helping the elderly or carrying medicines. Even recently, AGV usage in libraries has been reported. AGVs mainly consist of fixed terms, and in different applications, they used some addon designed to increase the robot's ability. One of the main research contents of AGV is the control part of the system. Also, the strategies and methods among scholars are different. Some simulation work about AGV control and design has been reported, but most of them missed the complete simulation concept, making the work replication hard. This paper, after a detailed description of AGV SIMULATION description, the PID Tune process with the help of Ziegler Nichols and the empirical method described. Then the Simulation experiments are carried out on Four different paths of the circle, eclipse, spiral, and 8-shape to observe and test the Tuned PID parameters. Finally, after success tracking, the value of the PID controller shows the least and maximum of the following error signal of each applied path belongs to the Circle path and the Spiral path.","PeriodicalId":248405,"journal":{"name":"2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124378565","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: 15

Group Decision Making as Consistency Measure for Learning To Rank 群体决策作为学习排名的一致性度量

2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2021-06-18 DOI: 10.1109/CIVEMSA52099.2021.9493582

G. Fenza, Mariacristina Gallo, V. Loia, F. Nota, F. Orciuoli, E. Herrera-Viedma

引用次数: 1

A proposal of a new technique for meteorological data quality evaluation 一种气象资料质量评价新技术的提出

2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2021-06-18 DOI: 10.1109/CIVEMSA52099.2021.9493673

M. Popolizio, A. Amato, V. D. Lecce

引用次数: 0

Vessel Identification using Convolutional Neural Network-based Hardware Accelerators 基于卷积神经网络硬件加速器的血管识别

2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) Pub Date : 2021-06-18 DOI: 10.1109/CIVEMSA52099.2021.9493580

Alexandre Boyer, Rami Abiemona, M. Bolic, E. Petriu

引用次数: 1