2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)最新文献_第9页

Characterization of Kinesthetic Motor Imagery paradigm for wrist and forearm using an algorithm based on the Hurst Exponent and Variogram 基于赫斯特指数和变异函数的手腕和前臂动觉运动意象范式表征

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9282888

A. Mosqueda-Herrera, D. Martinez-Peon, L. Gomez-Sanchez, M. I. Ramirez-Sosa, S. Delfin-Prieto, F. Benavides-Bravo

引用次数: 0

Multi-Agent Technology for Industrial Applications: Barriers and Trends 工业应用中的多智能体技术:障碍与趋势

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283071

V. Marík, V. Gorodetsky, P. Skobelev

引用次数: 7

A Hybrid Approach Based on SVM and Bernoulli Mixture Model for Binary Vectors Classification 基于支持向量机和伯努利混合模型的二值向量分类方法

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283349

Fahdah Alalyan, Nuha Zamzami, N. Bouguila

引用次数: 2

Unsupervised Stacked Autoencoders for Anomaly Detection on Smart Cyber-physical Grids 用于智能网络物理网格异常检测的无监督堆叠自编码器

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283064

Abdulrahman Al-Abassi, Jacob Sakhnini, H. Karimipour

{"title":"Unsupervised Stacked Autoencoders for Anomaly Detection on Smart Cyber-physical Grids","authors":"Abdulrahman Al-Abassi, Jacob Sakhnini, H. Karimipour","doi":"10.1109/SMC42975.2020.9283064","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9283064","url":null,"abstract":"Smart Cyber Physical Grids are the new wave of power system technology that integrates networks of sensors with power stations for more efficient power generation and distribution. While utilizing communication networks is accompanied with tremendous advantages, it also increases the vulnerability of power systems to cyber attacks. Many methods for security and attack detection have been proposed in literature; however, most papers do not consider the imbalance of data in real power systems. In this paper, we propose a deep learning based method, referred to as Ensemble Stacked AutoEncoder (ESAE), aimed at tackling the problem of data imbalance. This method achieves superior performance on imbalanced data by developing a deep representation learning model to construct new balanced representations. The detection accuracy and model performance is improved by utilizing an ensemble architecture based on Stacked Autoencoders and Random Forest classifiers to detect attacks from the new representations. The proposed method is tested on all degrees of data imbalance using test cases of IEEE 14-bus, 30-bus, and 57-bus systems. Comparisons are made to several classifiers to demonstrate the effectiveness of the proposed algorithm","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"94 1","pages":"3123-3129"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87670731","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

Multi-objective Discrete Grey Wolf Optimizer for Solving Stochastic Multi-objective Disassembly Sequencing and Line Balancing Problem 求解随机多目标拆解排序与线路平衡问题的多目标离散灰狼优化算法

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283184

Zhiwei Zhang, Xiwang Guo, Mengchu Zhou, Shixin Liu, Liang Qi

引用次数: 10

Multi-resolution Collaborative Representation for Face Recognition 人脸识别的多分辨率协同表示

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283275

Yanting Li, Junwei Jin, Huaiguang Wu, Lijun Sun, C. L. P. Chen

引用次数: 1

An Ant Colony Optimization Approach to Connection-Aware Virtual Machine Placement for Scientific Workflows 科学工作流中连接感知虚拟机布局的蚁群优化方法

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283379

Li-Tao Tan, Wei-neng Chen, Xiao-Min Hu

{"title":"An Ant Colony Optimization Approach to Connection-Aware Virtual Machine Placement for Scientific Workflows","authors":"Li-Tao Tan, Wei-neng Chen, Xiao-Min Hu","doi":"10.1109/SMC42975.2020.9283379","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9283379","url":null,"abstract":"The virtual machine (VM) placement problem with the objective to save energy consumption and improve machine utility has been studied extensively in Cloud computing. However, the connection information among VMs during the execution of scientific workflows is seldom considered in existing studies. Therefore, this paper intends to build a novel connection-aware model for VM placement in scientific workflows. Different from existing studies, as the connection information of VMs is considered following the topology of workflows, not only the CPU capacity and memory capacity but also the transmission bandwidth among machines should be considered. An energy- aware, traffic-aware, connection-aware ant colony optimization (ETCACO) approach is developed. The proposed ETCACO combines Ant Colony Optimization (ACO) with a scheduler, namely greedy placeman. Experiments are performed to compare the proposed model with the traditional approach. It is discovered that by taking the connection information into consideration, the proposed approach can reduce energy consumption by 7%.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"61 1","pages":"3515-3522"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87526179","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

Cross-impact Balances: A Method for Bridging Social Systems and Cybernetics 交叉影响平衡:连接社会系统和控制论的方法

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283480

V. Schweizer, A. Lazurko

引用次数: 2

Using a swarm to detect hard-to-kill mutants 用蜂群来探测难以杀死的变种人

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9282883

Alfredo Ibias, M. Núñez

引用次数: 1

Deep Multi-Task Learning for SSVEP Detection and Visual Response Mapping SSVEP检测与视觉反应映射的深度多任务学习

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Pub Date : 2020-10-11 DOI: 10.1109/SMC42975.2020.9283310

Hong Jing Khok, Victor Teck Chang Koh, Cuntai Guan

{"title":"Deep Multi-Task Learning for SSVEP Detection and Visual Response Mapping","authors":"Hong Jing Khok, Victor Teck Chang Koh, Cuntai Guan","doi":"10.1109/SMC42975.2020.9283310","DOIUrl":"https://doi.org/10.1109/SMC42975.2020.9283310","url":null,"abstract":"Glaucoma is an eye disease that occurs without the onset of symptoms at initial, and late diagnosis results in irreversible degeneration of retinal ganglion cells. Standard automated perimetry is the gold standard for assessing glaucoma; however, the examination is subjective, where responses can fluctuate each time the test is performed, significantly confounding the test’s interpretation. In this study, we present our approach that aims to provide a rapid point-of-care diagnostics for glaucoma patients by eliminating the cognitive aspect in existing visual field assessment. Unlike existing methods that mostly report the foveal target detection’s accuracy, we employed a multi-task learning architecture that efficiently captures signals simultaneously from the fovea and the neighboring targets in the peripheral vision, generating a visual response map. Furthermore, we designed a multi-task learning module that learns multiple tasks in parallel efficiently. We evaluated our model classification on a 40-classes dataset, with yields 92% and 95% in accuracy and F1 score respectively. Our model is able to perform on a calibration-free user-independent scenario, which is desirable for clinical diagnostics. Our proposed approach could be a stepping stone for an objective assessment of glaucoma patients’ visual field.","PeriodicalId":6718,"journal":{"name":"2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","volume":"91 1","pages":"1280-1285"},"PeriodicalIF":0.0,"publicationDate":"2020-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86192295","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