2019 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第4页

Accelerated Distributed Optimization over Directed Graphs with Row and Column-Stochastic Matrices 具有行和列随机矩阵的有向图的加速分布优化

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002658

Jinhui Hu, Yifan Zhu, Huaqing Li, Zheng Wang

引用次数: 0

Single-trial Classification of Fixation-related Potentials in Guided Visual Search Tasks using A Riemannian Network 利用黎曼网络对引导视觉搜索任务中注视相关电位的单次分类

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002946

Junjie Shen, Xiao Li, Hong Zeng, Aiguo Song

引用次数: 1

Heart Rate Measurement Using Air Pressure Sensor for Elderly Caring System 用气压传感器测量老年人护理系统的心率

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003039

Kouhei Yamamoto, Shuai Shao, N. Kubota

引用次数: 0

Scheduling method of maintenance support resource with task timing constraint 具有任务时间约束的维修保障资源调度方法

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003132

Chongchong Guan, Hui Lu

{"title":"Scheduling method of maintenance support resource with task timing constraint","authors":"Chongchong Guan, Hui Lu","doi":"10.1109/SSCI44817.2019.9003132","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9003132","url":null,"abstract":"Maintenance support resource scheduling (MSRS) problem has attracted increasing attention in modern battle. It aims to allocate resources from multi-supply points to multi-tasks with the shortest time. However, there exists various constraints which are difficult to satisfy at the same time, such as limited resource reserves, different resource requirements, complex route conditions and strict task timing. As a result, we first obtain the shortest routes and task sequence with route planning and topological sorting algorithms separately. Then, with these information, an integrated meta-heuristic algorithm (IMHA) is designed to solve all the constraints. Furthermore, two improved algorithms, CMHA and GMHA are generated with classical and greedy scheduling strategies respectively. Experiment results show the feasibility of IMHA in solving the MSRS problem with timing constraint. Besides, compared with the IMHA, the GMHA and CMHA can generate scheduling schemes with lower cost and time in the whole 24 instances. In addition, as the increase of proportion of timing tasks, the advantages of GMHA in cost and time are more evident.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"154 1","pages":"2698-2705"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79729343","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

Fixation Prediction based on Scene Contours 基于场景轮廓的注视预测

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002897

Tengfei Zhan, M. Ye, Wen-Wen Jiang, Yongjie Li, Kaifu Yang

引用次数: 0

A Video Salient Object Detection Model Guided by Spatio-Temporal Prior 基于时空先验的视频显著目标检测模型

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002971

Wen-Wen Jiang, Kai-Fu Yang, Yongjie Li

{"title":"A Video Salient Object Detection Model Guided by Spatio-Temporal Prior","authors":"Wen-Wen Jiang, Kai-Fu Yang, Yongjie Li","doi":"10.1109/SSCI44817.2019.9002971","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002971","url":null,"abstract":"Neurobiology researches suggest that the motion information attracts more attention of human visual system than other low-level features such as brightness, color and texture. Consequently, video saliency detection methods not only consider the spatial saliency caused by the underlying features of images, but also the motion information in temporal domain. In this study, we proposes a model of video salient object detection based on a two-pathway framework that the spatio-temporal contrast guides the search for salient targets. Firstly, along the non-selective pathway, which is computed with the intra-frame and inter-frame maps of the color contrast and motion contrast, combining with the previous saliency map, to represent the prior information of the possible target locations. In contrast, the low-level features such as brightness, color and motion features are extracted in the selective pathway to search target accurately. Finally, the Bayesian inference is used to further obtain the optimal results. Experimental results show that our algorithm improves the performance of salient object detection on video compared to the representative method of Contour Guided Visual Search.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"24 1","pages":"2555-2562"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88773281","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

Algorithm Portfolio for Parameter Tuned Evolutionary Algorithms 参数调整进化算法的算法组合

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003101

Hao Tong, Shuyi Zhang, Changwu Huang, X. Yao

{"title":"Algorithm Portfolio for Parameter Tuned Evolutionary Algorithms","authors":"Hao Tong, Shuyi Zhang, Changwu Huang, X. Yao","doi":"10.1109/SSCI44817.2019.9003101","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9003101","url":null,"abstract":"Evolutionary algorithms’ performance can be enhanced significantly by using suitable parameter configurations when solving optimization problems. Most existing parametertuning methods are inefficient, which tune algorithm’s parameters using whole benchmark function set and only obtain one parameter configuration. Moreover, the only obtained parameter configuration is likely to fail when solving different problems. In this paper, we propose a framework that applying portfolio for parameter-tuned algorithm (PPTA) to address these challenges. PPTA uses the parameter-tuned algorithm to tune algorithm’s parameters on one instance of each problem category, but not to all functions in the benchmark. As a result, it can obtain one parameter configuration for each problem category. Then, PPTA combines several instantiations of the same algorithms with different tuned parameters by portfolio method to decrease the risk of solving unknown problems. In order to analyse the performance of PPTA framework, we embed several test algorithms (i.e. GA, DE and PSO) into PPTA framework constructing algorithm instances. And the PPTA instances are compared with default test algorithms on BBOB2009 and CEC2005 benchmark functions. The experimental results has shown PPTA framework can significantly enhance the basic algorithm’s performance and reduce its optimization risk as well as the algorithm’s parametertuning time.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"7 1","pages":"1849-1856"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79555534","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

A Practical Model for Educators to Predict Student Performance in K-12 Education using Machine Learning 教育工作者使用机器学习预测K-12教育学生表现的实用模型

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003147

Julie L. Harvey, S. Kumar

引用次数: 19

Design of IGBT Parameter Automatic Test System Based on LabVIEW 基于LabVIEW的IGBT参数自动测试系统设计

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9003135

Xiumei Cai, Jingwei Bian, Yan Wang, Y. Ning

引用次数: 1

Supervised Learning with Small Training Set for Gesture Recognition by Spiking Neural Networks 基于脉冲神经网络的小训练集监督学习手势识别

2019 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2019-12-01 DOI: 10.1109/SSCI44817.2019.9002720

Natabara Máté Gyöngyössy, Márk Domonkos, J. Botzheim, P. Korondi

引用次数: 11