2018 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第9页

The Impact of an Adversary in a Language Model 语言模型中对手的影响

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628894

Zhengzhong Liang, G. Ditzler

引用次数: 0

A Literature Review on Machine Learning Based Medical Information Retrieval Systems 基于机器学习的医学信息检索系统研究综述

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628846

Akhil Gudivada, Nasseh Tabrizi

引用次数: 11

Decoding of EEG Signals Using Deep Long Short-Term Memory Network in Face Recognition Task 基于深度长短期记忆网络的脑电信号解码研究

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628757

Lidia Ghosh, Sayantani Ghosh, A. Konar, P. Rakshit, A. Nagar

{"title":"Decoding of EEG Signals Using Deep Long Short-Term Memory Network in Face Recognition Task","authors":"Lidia Ghosh, Sayantani Ghosh, A. Konar, P. Rakshit, A. Nagar","doi":"10.1109/SSCI.2018.8628757","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628757","url":null,"abstract":"The paper proposes a novel approach to classify the human memory response involved in the face recognition task by the utilization of event related potentials. Electroencephalographic signals are acquired when a subject engages himself/herself in familiar or unfamiliar face recognition tasks. The signals are analyzed through source Iocalization using eLORETA and artifact removal by ICA from a set of channels corresponding to those selected sources, with an ultimate aim to classify the EEG responses of familiar and unfamiliar faces. The EEG responses of the two different classes (familiar and unfamiliar face recognition)are distinguished by analyzing the Event Related Potential signals that reveal the existence of large N250 and P600 signals during familiar face recognition.The paper introduces a novel LSTM classifier network which is designed to classify the ERP signals to fulfill the prime objective of this work. The first layer of the novel LSTM network evaluates the spatial and local temporal correlations between the obtained samples of local EEG time-windows. The second layer of this network models the temporal correlations between the time-windows. An attention mechanism has been introduced in each layer of the proposed model to compute the contribution of each EEG time-window in face recognition task. Performance analysis reveals that the proposed LSTM classifier with attention mechanism outperforms the efficiency of the conventional LSTM and other classifiers with a significantly large margin. Moreover, source Iocalization using eLORETA shows the involvement of inferior temporal and frontal lobes during familiar face recognition and pre-frontal lobe during unfamiliar face recognition. Thus, the present research outcome can be used in criminal investigation, where meticulous differentiation of familiar and unfamiliar face detection by criminals can be performed from their acquired brain responses.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121351865","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

Industrial Device Monitoring and Control System based on oneM2M for Edge Computing 基于oneM2M边缘计算的工业设备监控系统

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628736

Changyong Um, Jaehyeong Lee, Jongpil Jeong

{"title":"Industrial Device Monitoring and Control System based on oneM2M for Edge Computing","authors":"Changyong Um, Jaehyeong Lee, Jongpil Jeong","doi":"10.1109/SSCI.2018.8628736","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628736","url":null,"abstract":"Traditional manufacturing systems consist of devices with limited functionality and are constructed in a vertical structure. This is not suitable for smart factory environment where Internet of Things (IoT) is actively utilized and connected to external environment. Cyber Physical System (CPS), a key element of the Smart Factory, means that cyber and physical systems are tightly connected and intelligent. To build a CPS, IoT needs to be handled effectively, and stability and connectivity must be ensured. Therefore, the system in the Smart Factory is preferably built according to the IoT standard. In this paper, we propose an industrial device monitoring and control system based on oneM2M, and discuss that this system can be applied in a smart factory environment. The proposed system is based on Mobius, developed by Korea Electronics Technology Institute (KETI) as an open source IoT platform, and its components are open source hardware which is high performance with low cost. The Smart Factory system can be constructed in various forms using Mobius. In this paper, a model for its structure and utilization is presented.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126464032","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

Analysis of Packet drops and Channel Crowding in Vehicle Platooning using V2X communication 基于V2X通信的车辆队列中丢包和信道拥挤分析

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628872

Nagacharan Teja Tangirala, Anuj Abraham, Apratim Choudhury, Pranjal Vyas, Rongkai Zhang, J. Dauwels

{"title":"Analysis of Packet drops and Channel Crowding in Vehicle Platooning using V2X communication","authors":"Nagacharan Teja Tangirala, Anuj Abraham, Apratim Choudhury, Pranjal Vyas, Rongkai Zhang, J. Dauwels","doi":"10.1109/SSCI.2018.8628872","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628872","url":null,"abstract":"With the increase in road fatalities and energy consumption, there is a need to improve road traffic in terms of safety and fuel efficiency. Vehicle platooning is one of the areas in road transportation that can be improved to reduce road freight operational costs. In this paper, an MPC (Model Predictive Control) algorithm is formulated based on the combination of Constant Distance (CD) and Headway Time (HT) topology. The simulations are carried out for platooning of Heavy Duty Vehicles (HDVs) using an integrated simulation platform, which combines VISSIM, MATLAB and Network Simulator (NS3). Deliberate communication failures are introduced through NS3 to study the platoon behavior. Further, a solution is proposed to avoid the channel crowding issue. Simulations of the platoon controller indicate that the vehicles follow a desired speed and maintain a desired intervehicular distance. It is also found that the platoon controller avoids collisions due to consecutive packet drops. Finally, an improvement in Packet Delivery Ratio (PDR) is observed with the proposed solution to avoid channel crowding issue.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127990402","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

Seeking Multiple Solutions of Combinatorial optimization Problems: A Proof of Principle Study 寻求组合优化问题的多重解:原理证明研究

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628856

Ting Huang, Yue-jiao Gong, Jun Zhang

引用次数: 8

CI-based Analytics for Photovoltaic Power Predictions and Tie-line Bias Control in Smart Grid 基于ci的智能电网光伏功率预测与配线偏置控制分析

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628722

Iroshani Jayawardene, R. Kulkarni, G. Venayagamoorthy

{"title":"CI-based Analytics for Photovoltaic Power Predictions and Tie-line Bias Control in Smart Grid","authors":"Iroshani Jayawardene, R. Kulkarni, G. Venayagamoorthy","doi":"10.1109/SSCI.2018.8628722","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628722","url":null,"abstract":"The smart grid enables two-way flows of electricity and information by introducing vast quantities of data. Advanced metering technologies, such as weather sensors, phasor measurement units and smart meters generate variety of data at very high velocities. Proper analysis of this data allows intelligently monitored and controlled power systems having self-healing, fault-tolerant and secured functioning features. Increasing the integration of renewable energy sources, such as photovoltaic (PV) power has a significant impact on the power system operation and control. The variability and uncertainty of renewable energy poses the challenges of power and frequency fluctuations. Predictive analytics in power generation can provide a smarter grid with better control. Computational intelligence (CI) paradigms based on adaptive learning play a pivotal role in predictive analytics in smart grid. A comparison of CI approaches for predicting PV power and tie-line bias control is presented in this paper. Typical results indicate that PV power predictions improve tie-line bias control performance and better utilization of available PV power.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129082236","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 Game Theoretic Based Biologically-Inspired Distributed Intelligent Flocking Control for Multi-UAV Systems with Network Imperfections 具有网络不完善的多无人机系统基于博弈论的生物启发分布式智能群集控制

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628814

Mohammad Jafari, Hao Xu

引用次数: 2

Investigating the Democracy Behavior of Swarm Robots in the Case of a Best-of-n Selection 群机器人在最优化选择情况下的民主行为研究

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628646

Yann Pochon, Rolf Dornberger, Vivienne Jia Zhong, S. Korkut

引用次数: 2

An Empirical Study on Unsupervised Pre-training Approaches in Regression Problems 回归问题中无监督预训练方法的实证研究

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628674

P. Saikia, R. Baruah

引用次数: 1