2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)最新文献_第5页

A Two-Sided Stable Matching Method in Ridesharing 拼车中的双边稳定匹配方法

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016360

Jingwei Lv, Jiannan Hao, Shuzhen Yao, Huobin Tan

引用次数: 0

Design of Variance-Constrained H∞ State Estimation Algorithm for Delayed Memristive Neural Networks with Attacks: An Adaptive Event-Triggered Approach 具有攻击的延迟记忆神经网络的方差约束H∞状态估计算法设计:一种自适应事件触发方法

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016333

Yan Gao, Jun Hu, Huijun Yu, Chaoqing Jia

引用次数: 0

Adaptive Threshold for Unknown Traffic Identification 未知流量识别自适应阈值

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016415

Pengcheng Wang, Jianfeng Guan, Zhuang Han

引用次数: 0

Interval-value replacement-based neighborhood rough set model in incomplete fuzzy information systems 不完全模糊信息系统中基于区间值替换的邻域粗糙集模型

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016402

Xiong Meng, Jilin Yang, T. Liu, Dié Wu

{"title":"Interval-value replacement-based neighborhood rough set model in incomplete fuzzy information systems","authors":"Xiong Meng, Jilin Yang, T. Liu, Dié Wu","doi":"10.1109/ccis57298.2022.10016402","DOIUrl":"https://doi.org/10.1109/ccis57298.2022.10016402","url":null,"abstract":"With the development of big data, incomplete fuzzy information systems (IFISs) exist in many applications. The processing of incomplete information (missing values) is an essential issue in the study of IFIS. Existing studies either increase the uncertainty of missing values, e.g., the neighborhood tolerance relation, or discard the uncertainty of missing values completely, e.g., the imputation approaches based on attribute relevancy. They may lead to unreasonable classification results. In this paper, we propose an interval-value replacement-based neighborhood rough set model (IVR-NRSM) from the perspective of preserving uncertainty to some extent rather than two extremes. According to two semantics of missing values, we first replace lost values in IFIS with interval values. Then the IFIS can be transformed into a replaced IFIS with only one semantic (i.e., the do not care). In the replaced IFIS, we define a distance function for numerical data and interval-value data. Furthermore, we construct the improved neighborhood tolerance relation and the corresponding neighborhood tolerance classes in the replaced IFIS. Finally, we design two experiments on 4 UCI data sets by introducing three performance metrics. Experimental results illustrate that the proposed IVR-NRSM has higher classification performance than the two representative models.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116168191","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

Design of Event-Based Resilient Distributed Filtering Algorithm for Time-Varying Stochastic Systems with Correlated Noises over Sensor Networks 传感器网络上具有相关噪声时变随机系统的基于事件的弹性分布式滤波算法设计

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016344

Zehao Li, Jun Hu, Jiaxing Li, Peixia Gao, Ruijie Dong

引用次数: 0

Short-Term Power Load Forecasting Based on IWOA-Attention-BiLSTM 基于IWOA-Attention-BiLSTM的短期电力负荷预测

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016322

Zhanpeng Liu, Xiuquan Wang, Jiwei Xing, M. Ren, Xinying Xu

{"title":"Short-Term Power Load Forecasting Based on IWOA-Attention-BiLSTM","authors":"Zhanpeng Liu, Xiuquan Wang, Jiwei Xing, M. Ren, Xinying Xu","doi":"10.1109/ccis57298.2022.10016322","DOIUrl":"https://doi.org/10.1109/ccis57298.2022.10016322","url":null,"abstract":"Accurate power load forecasting can significantly improve the economic benefits of power systems. To improve the prediction accuracy, aiming at the complexity and volatility of power load, a forecasting model based on improved whale optimization algorithm (IWOA) optimized the bidirectional long short-term memory (BiLSTM) combined with attention mechanism (IWOA-Attention- BiLSTM) is proposed. The model comprehensively considers the influence of meteorological factors and date types, learns the bidirectional series features of power load data by BiLSTM, calculates the weights of the hidden layer state by the attention mechanism, and finds the hyperparameters of Attention-BiLSTM by IWOA, such as the learning rate, iteration times and batch size. The results show that compared with BP, LSTM and Seq2Seq, IWOA-Attention-BiLSTM has the highest prediction accuracy, and its MAPE, RMSE, MAE and R2 are 1.44 %, 128.83MW, 97.83MW and 0.9931 respectively, which are the best among all the prediction models. It is proved that IWOA-Attention- BiLSTM can effectively improve the prediction accuracy of short-term power load.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132756010","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

Supervised Multi-view Feature Selection Via Maximum Margin Criterion Joint Distributed Optimization 基于最大余量准则联合分布优化的监督多视图特征选择

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016341

Qiang Lin

引用次数: 0

Multi-Task Multi-Attention Graph Neural Network for Mobile Crowd Sensing Data Reconstruction and Prediction 移动人群感知数据重构与预测的多任务多关注图神经网络

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016351

Jianjun Tong, Yunhao Xing, Zijian Cao, Dong Zhao

{"title":"Multi-Task Multi-Attention Graph Neural Network for Mobile Crowd Sensing Data Reconstruction and Prediction","authors":"Jianjun Tong, Yunhao Xing, Zijian Cao, Dong Zhao","doi":"10.1109/CCIS57298.2022.10016351","DOIUrl":"https://doi.org/10.1109/CCIS57298.2022.10016351","url":null,"abstract":"In recent years, the number of static or mobile sensing devices in the city has increased rapidly, which offers a new sensing paradigm, Mobile crowd sensing (MCS). However, MCS data confronts the common sparseness issue due to the limitation of human mobility and sensing costs, which is difficult to meet the data quality requirements of urban sensing applications represented by smart traffic. It is important to not only reconstruct the largely and randomly missing data but also predict the future data to enable rich applications, which is still a non-trivial task due to two major challenges: 1) error propagation, and 2) complex spatio-temporal correlations. To this end, we propose a Multi-Task Multi-Attention Graph Neural Network (MTMAG) model: on the one hand, it alleviates the error propagation using a dynamic multi-task learning framework and a transform attention block; on the other hand, it models the complex spatio-temporal correlations over a graph structure using a multi-attention module. Extensive experiments on two real-world datasets demonstrate the advantages of MTMAG over multiple state-of-the-art baselines for both data reconstruction and prediction.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129103458","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

Dynamic Extend Domain Name Server Client Subnet IP Address Aggregation Solution Based On Dynamic Network Information 基于动态网络信息的动态扩展域名服务器客户端子网IP地址聚合方案

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016311

Jinxia Hai, Qi Chen, Yirong Zhuang, Peilin Xue, Zhifan Yin, Ge Chen

引用次数: 0

Learnable Gated Graph Convolutional Residual Network for Traffic Prediction 交通预测的可学习门控图卷积残差网络

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016373

Yong Zhang, X. Wei, Xinyu Zhang, Feng Lin, Yongli Hu, Baocai Yin

{"title":"Learnable Gated Graph Convolutional Residual Network for Traffic Prediction","authors":"Yong Zhang, X. Wei, Xinyu Zhang, Feng Lin, Yongli Hu, Baocai Yin","doi":"10.1109/CCIS57298.2022.10016373","DOIUrl":"https://doi.org/10.1109/CCIS57298.2022.10016373","url":null,"abstract":"In Intelligent Transportation Systems (ITS), traffic data prediction is a crucial component. Accurate traffic state prediction depends on appropriate modeling of complex spatio-temporal correlations of traffic data. The traffic data contains nonlinear and intricate correlations, which poses a huge challenge for accurate prediction. To completely capture spatio-temporal correlations, a traffic data prediction model based on a learnable gated graph convolution residual network is proposed. This model uses multi-receptive field dilated causal convolution (MRDCC) and learnable graph convolution to capture the spatio-temporal correlations respectively. Furthermore, the proposed model also designs a gating mechanism between different graph convolutional layers to alleviate the over-smoothing problem which is caused by multi-layer graph convolution stacking. To further capture temporal trends across different periods, a multi-branch residual network strategy is also introduced in this paper. The experimental results on multiple traffic datasets demonstrate that the predictive performance of our proposed model exceeds existing models.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122548579","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