2018 International Conference on Control, Automation and Information Sciences (ICCAIS)最新文献_第5页

Research on Heave Compensation Control of Floating Crane Based on Permanent Magnet Synchronous Motor 基于永磁同步电机的浮式起重机升沉补偿控制研究

2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570420

Xiaogang Huang, Jiancheng Wang, Yonghua Xu, Yizhen Zhu, Tianhao Tang, Meilei Lv

引用次数: 2

Stochastic Geometry Based Hierarchical Power Allocation for the Uplink Ultra-Dense Networks 基于随机几何的上行超密集网络分层功率分配

2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570550

Mengting Liu, Yinglei Teng, N. An, Mei Song, Linkun Wang

{"title":"Stochastic Geometry Based Hierarchical Power Allocation for the Uplink Ultra-Dense Networks","authors":"Mengting Liu, Yinglei Teng, N. An, Mei Song, Linkun Wang","doi":"10.1109/ICCAIS.2018.8570550","DOIUrl":"https://doi.org/10.1109/ICCAIS.2018.8570550","url":null,"abstract":"Ultra-Dense Networks (UDNs) is widely considered as a key technology of fifth generation (5G) networks, where access points (APs) are densely deployed to meet the high capacity requirements in various environments. However, the large scale of UDNs inevitably increases the computational complexity and signaling overhead for resource allocation processes. In this paper, employing discrete power levels, we propose a stochastic geometry based hierarchical power allocation scheme to maximize the user sum rate in uplink UDNs. Using stochastic geometry theory, we first group all users in terms of their transmit powers, and then derive two key metrics including statistical information-based signal to interference and noise ratio (SSINR) and the lower bound of statistical information-based user rate (SR). Furthermore, an alternative genetic algorithm (AGA) is utilized to obtain the power allocation scheme. Simulation results show that the proposed power allocation algorithm is not only able to improve the user sum rate, but also can significantly reduce the signaling overhead and computation complexity.","PeriodicalId":223618,"journal":{"name":"2018 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123803514","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

A Resolvable Performance Analysis for the Group Targets 一种可分解的集团目标绩效分析

2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570649

Guilin Zhang, Yudong Chi, Shuiun Zhu

引用次数: 0

Cell Lineage Tracking Based on Labeled Random Finite Set Filtering 基于标记随机有限集滤波的细胞谱系跟踪

2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570327

B. Wei, Lin Zhou

引用次数: 1

Military Scenario Named Entity Recognition Method Based on Deep Neural Network 基于深度神经网络的军事场景命名实体识别方法

2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570698

Xuefeng Wang, Xiaoping Zhou, Dongsheng Li, Jianfeng Hou

引用次数: 0

A Novel Short Time H∞ Filtering for Discrete Linear Systems 一种新的离散线性系统短时间H∞滤波

2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570563

Xiaoliang Feng, Xianpeng Shi, Chenglin Wen, Yanfeng Wang

引用次数: 1

Electricity Market Clearing Price Forecast Based on Adaptive Kalman Filter 基于自适应卡尔曼滤波的电力市场出清价格预测

2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570534

Liang Ding, Quanbo Ge

引用次数: 6

Accelerated Multi-Sensor Control for Selective Multi-Object Tracking 选择性多目标跟踪的多传感器加速控制

2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570589

Sabita Panicker, A. Gostar, A. Bab-Hadiashar, R. Hoseinnezhad

{"title":"Accelerated Multi-Sensor Control for Selective Multi-Object Tracking","authors":"Sabita Panicker, A. Gostar, A. Bab-Hadiashar, R. Hoseinnezhad","doi":"10.1109/ICCAIS.2018.8570589","DOIUrl":"https://doi.org/10.1109/ICCAIS.2018.8570589","url":null,"abstract":"This paper focuses on selective multi-object tracking applications where objects with particular labels are of interest, and multiple sensors need to be controlled to achieve the optimum tracking performance for those objects. We formulate a novel solutions for a centralized multi-sensor multi-object system with labeled multi-Bernoulli filters in each sensor node. Our solution includes optimizing a closed-form objective function that can be directly calculated after prediction step in the central node of the sensor network. Importantly, our proposed cost function does not need to be computed after pseudo-update operations, hence a large amount of computation is saved. Simulation results indicate how the proposed methods can lead to significant improvements in terms of tracking accuracy of objects of interest, compared to using the generic (non-selective) sensor control methods. Numerical experiments involving a challenging multisensor target tracking application demonstrate that while our proposed method significantly outperforms the common (non-selective) sensor control methods, it performs similar to the state of art method for selective sensor control (selective-PEECS) in terms of the mean-square-error of tracking of the targets of interest. Despite similar performance in terms of tracking error, our method is significantly faster than the state of art (eight times faster in our experiments).","PeriodicalId":223618,"journal":{"name":"2018 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133113115","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

Resting-State Regional Homogeneity Analysis on Real-Time fMRI Emotion Self-Regulation Training 实时fMRI情绪自我调节训练静息状态区域同质性分析

2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570530

Qiang Yang, Yulong Qin, Linyuan Wang, Liang Sun, Bin Yan, Li Tong, Fan Zhang, Zhonglin Li

{"title":"Resting-State Regional Homogeneity Analysis on Real-Time fMRI Emotion Self-Regulation Training","authors":"Qiang Yang, Yulong Qin, Linyuan Wang, Liang Sun, Bin Yan, Li Tong, Fan Zhang, Zhonglin Li","doi":"10.1109/ICCAIS.2018.8570530","DOIUrl":"https://doi.org/10.1109/ICCAIS.2018.8570530","url":null,"abstract":"Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) is biofeedback of realtime fMRI signals for self-regulating brain function. Most studies focus on the task-state rtfMRI-NF for the moment. Furthermore, resting-state analysis brings another angle to explore the neural mechanism of emotion regulation and can be an important way to reflect brain function. Regional homogeneity (ReHo) analysis is a method to study the consistency of the spontaneous neuronal activity between the given voxel and its adjacent voxel in the resting-state. It can reveal the functional and anatomical closely linked neural circuits. In this paper, we designed a rtfMRI-NF experiment for human emotion self-regulation and analyzed brain function after rtfMRI-NF training by ReHo method. In the experiment, the subjects divided into two groups, the experiment and the control group. For the experiment group, through the multivariate voxel pattern analysis (MVPA) calculation of the real-time fMRI system, objects are provided with continuous tracking information display of their emotional state. In the control group, it was the same as that of the experiment group, except for that the feedback value was independent to emotion. We performed the method of ReHo analysis on the resting state data collected from the two groups after the self-regulation tasks, and experiment results showed that there were significantly different between the ReHo maps of the two groups in the temporal lobe, prefrontal cortex, parahippocampal gyrus, and fusiform gyrus. These brain regions were closely related to emotional processing, and the results are consistent with previous studies. Furthermore, ReHo can reflect the characteristic changes of spontaneous neural activity in the brain, it makes another way to help discover the mechanism of neurofeedback, and it can be used as a research direction to assist in the prediction and diagnosis of mental illness.","PeriodicalId":223618,"journal":{"name":"2018 International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125045276","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

A RANSAC-Based Track Initialization Algorithm for Multi-Sensor Tracking System 一种基于ransac的多传感器跟踪初始化算法

2018 International Conference on Control, Automation and Information Sciences (ICCAIS) Pub Date : 2018-10-01 DOI: 10.1109/ICCAIS.2018.8570444

Feng Yang, Weikang Tang, Yongting Wang, Shaodong Chen

引用次数: 0