2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)最新文献_第3页

Command-Filter-Based Finite-Time Control for Human-in-the-Loop UAVs With Dead-Zone Inputs 具有死区输入的人在回路无人机的命令滤波有限时间控制

2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS) Pub Date : 2021-12-10 DOI: 10.1109/ICCSS53909.2021.9721999

Guohuai Lin, Zhijian Cheng, Hongru Ren, Hongyi Li, Renquan Lu

引用次数: 1

Observer-based feedback control for linear parabolic PDEs with quantized input 量化输入线性抛物型偏微分方程的观测器反馈控制

2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS) Pub Date : 2021-12-10 DOI: 10.1109/ICCSS53909.2021.9721970

Xuena Zhao, Jun-yuan Gu, Zhijie Liu, Wei He

引用次数: 0

Attitude Control of Quadrotor UAVs Using Adaptive Terminal Sliding Mode Control 基于自适应终端滑模控制的四旋翼无人机姿态控制

2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS) Pub Date : 2021-12-10 DOI: 10.1109/ICCSS53909.2021.9721963

Haiming Du, Jian Sun, Gang Wang

引用次数: 1

A Reference-Vector-Based Strength Pareto Evolutionary Algorithm 2 一种基于参考向量的强度Pareto进化算法

2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS) Pub Date : 2021-12-10 DOI: 10.1109/ICCSS53909.2021.9721982

Lu Zhang, Qinchao Meng

引用次数: 0

Finite-time synchronization of delayed chaotic neural networks based on event-triggered intermittent control 基于事件触发间歇控制的延迟混沌神经网络有限时间同步

2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS) Pub Date : 2021-12-10 DOI: 10.1109/ICCSS53909.2021.9722021

Zeyu Ruan, Junhao Hu, Jun Mei

引用次数: 0

Chebyshev Polynomial Broad Learning System Chebyshev多项式广义学习系统

2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS) Pub Date : 2021-12-10 DOI: 10.1109/ICCSS53909.2021.9721962

Shuang Feng, Bingshu Wang, C. L. Philip Chen

{"title":"Chebyshev Polynomial Broad Learning System","authors":"Shuang Feng, Bingshu Wang, C. L. Philip Chen","doi":"10.1109/ICCSS53909.2021.9721962","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721962","url":null,"abstract":"The broad learning system (BLS) has been attracting more and more attention due to its excellent property in the field of machine learning. A great deal of variants and hybrid structures of BLS have also been designed and developed for better performance in some specialized tasks. In this paper, the Chebyshev polynomials are introduced into the BLS to take advantage of their powerful approximation capability, where the feature windows are replaced by a set of Chebyshev polynomials. This new variant, named Chebyshev polynomial BLS (CPBLS), has a light structure with a reduction in computational complexity since the sparse autoencoder is removed. Instead, the dimension of each input sample is expended by n + 1 Chebyshev polynomials, mapping the original feature into a new feature space with higher dimension, which helps to classify the patterns in training. The proposed CPBLS is evaluated by some popular datasets from UCI and KEEL repositories, and it outperforms some representative neural networks and neuro-fuzzy models in terms of classification accuracy. The CPBLS also show some advantages over the recent developed compact fuzzy BLS (CFBLS) which indicates its great potential in future research and real-world applications.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122757051","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

Veracity: A Fake News Detection Architecture for MANET Messaging 真实性:用于MANET消息传递的假新闻检测体系结构

2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS) Pub Date : 2021-12-10 DOI: 10.1109/ICCSS53909.2021.9721975

A. Ramkissoon, W. Goodridge

{"title":"Veracity: A Fake News Detection Architecture for MANET Messaging","authors":"A. Ramkissoon, W. Goodridge","doi":"10.1109/ICCSS53909.2021.9721975","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721975","url":null,"abstract":"Mobile Ad Hoc Network Messaging has become an integral part of today’s social communication landscape. They are used in a variety of applications. One major problem that these networks face is the spread of fake news. This problem can have serious deleterious effects on our social data driven society. Detecting fake news has proven to be challenging even for modern day algorithms. This research presents, Veracity, a unique computational social system to accomplish the task of Fake News Detection in MANET Messaging. The Veracity architecture attempts to model social behaviour and human reactions to news spread over a MANET. Veracity introduces five new algorithms namely, VerifyNews, CompareText, PredictCred, CredScore and EyeTruth for the capture, computation and analysis of the credibility and content data features. The Veracity architecture works in a fully distributed and infrastructureless environment. This study validates Veracity using a generated dataset with features relating to the credibility of news publishers and the content of the message to predict fake news. These features are analysed using a machine learning prediction model. The results of these experiments are analysed using four evaluation methodologies. The analysis reveals positive performance with the use of the fake news detection architecture.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129913386","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

[Copyright notice] (版权)

2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS) Pub Date : 2021-12-10 DOI: 10.1109/iccss53909.2021.9722035

引用次数: 0

End-to-End Supervised Zero-Shot Learning with Meta-Learning Strategy 基于元学习策略的端到端监督零学习

2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS) Pub Date : 2021-12-10 DOI: 10.1109/ICCSS53909.2021.9721968

Xiaofeng Xu, Xianglin Bao, Ruiheng Zhang, Xingyu Lu

引用次数: 1

Channel Prediction for Real-Time Wireless Communication with MmWave SC-FDE in IIoT Systems 工业物联网系统中毫米波SC-FDE实时无线通信信道预测

2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS) Pub Date : 2021-12-10 DOI: 10.1109/ICCSS53909.2021.9721993

Changwei Lv, Ming Liu, Junwei Duan

{"title":"Channel Prediction for Real-Time Wireless Communication with MmWave SC-FDE in IIoT Systems","authors":"Changwei Lv, Ming Liu, Junwei Duan","doi":"10.1109/ICCSS53909.2021.9721993","DOIUrl":"https://doi.org/10.1109/ICCSS53909.2021.9721993","url":null,"abstract":"With the application of wireless sensor-actuator networks in the Industrial Internet of Things (IIoT), it is crucially important to ensure the real-timing of data transmission. The millimeter wave (mmWave) communicating at the extremely high frequency band is a promising solution for the rapidly expanding data throughput in IIoT, due to the wide usable frequency band. In extremely high frequency band, the channel coherent time will be obviously reduced and becomes shorter than the frame duration. In this case, the channel state information (CSI) acquisition based on channel estimation will provide outdated information for coherent signal detection. Therefore, forecasting the channel variation for real-time data transmission is necessary. In this paper, we investigate the channel prediction methods in both the frequency and time domains for mmWave single-carrier frequency-domain-equalization (SC-FDE) systems. In the frequency domain, the channel prediction is conducted on each subcarrier, while the time domain predictor on each channel tap. As a number of the channel taps in the time domain are mainly composed of estimation noise, we separate these channel taps composed of estimation noise from the significant taps before building the prediction model. In this paper, the autoregressive (AR) model is employed to perform the channel prediction in the both domains. The simulation results show that the time domain predictor increases the prediction accuracy while reducing the computation complexity.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114935523","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