2022 4th International Conference on Industrial Artificial Intelligence (IAI)最新文献_第4页

Doing-business Environment Assessment of Prefecture-level Cities in China based on Input-output: Logical Structure, Difference Comparison and Benchmark Analysis 基于投入产出的中国地级市营商环境评价:逻辑结构、差异比较与基准分析

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976861

Juan Liu, Jia-fu Tang

{"title":"Doing-business Environment Assessment of Prefecture-level Cities in China based on Input-output: Logical Structure, Difference Comparison and Benchmark Analysis","authors":"Juan Liu, Jia-fu Tang","doi":"10.1109/IAI55780.2022.9976861","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976861","url":null,"abstract":"This paper analyzes the doing-business environment (DBE) at prefecture-level city in China. Based on the input-process-output (IPO) thought of system theory, this study uses Porter Diamond Model and International Institute for Management Development (IMD) regional competitiveness model to construct the China City Doing-business Environment Index (CCDBEI) from five aspects: Factor Supply Index (FSI), Environmental Attraction Index (EAI), Demand Pull Index (DPI), Industrial Security Index (ISI) and Output Influence Index (OII). The weight of single index was calculated by entropy method, and the DBE quality of 289 prefecture-level cities in China was evaluated by correlation coefficient method on the basis of considering the logical relationship of secondary indexes. The evaluation results show that the overall quality of DBE in China is gradually improving, but the regional differences are significant, and the best region was always East China, the worst were in the Northwest and Northeast. Location analysis shows that the quality of DBE is closely related to regional development strategy. Furthermore, the advantages of DBE in different regions are also different. In addition, it is found that DBE quality and balance do not develop synchronously. The study provides guidance for further optimization of DBE in cities.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127293733","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

Privacy Preserving Task Allocation with Multi-objectives in Edge Computing Enhanced Mobile Crowdsensing 基于边缘计算的多目标隐私保护任务分配

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976771

Longxin Yu, Haofei Meng, Wenwu Yu

引用次数: 0

Cooling load prediction of air-conditioning based on VMD-TCN using PE-SG algorithm 基于PE-SG算法的VMD-TCN空调冷负荷预测

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976778

Ning He, Lijun Zhang, Liqiang Liu, Danlei Chu, Mengrui Zhang, Cheng Qian

{"title":"Cooling load prediction of air-conditioning based on VMD-TCN using PE-SG algorithm","authors":"Ning He, Lijun Zhang, Liqiang Liu, Danlei Chu, Mengrui Zhang, Cheng Qian","doi":"10.1109/IAI55780.2022.9976778","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976778","url":null,"abstract":"Accurate prediction of air-conditioning cooling load is not only beneficial to control energy consumption and improve energy efficiency, but also provides a theoretical basis and data support for energy conservation and emission reduction. Aiming at the problems that large deviation of original data and low prediction accuracy in air-conditioning cooling load prediction. An air-conditioning cooling load prediction model combined with time convolutional network (TCN) combined with permutation entropy (PE), savitzky-golay (SG) and variational mode decomposition (VMD) is proposed in this paper. Firstly, Pearson correlation coefficient is used to analyze historical data. Secondly, the complex multi-component cooling load signal is decomposed into multiple single-component amplitudes and frequency modulation (AFM) signals by VMD. The PE is used to quantitatively determine the noise content of each component after VMD decomposition, the high noise component is directly removed, the low noise component is smoothly processed by the SG smoothing method, then, the signal is reconstructed. Finally, the TCN model of air-conditioning cooling load prediction is established. The experimental results show that the prediction accuracy of the hybrid model is significantly improved compared with the conventional models.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"5 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128784584","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

Cooling load prediction based on correlative temporal graph convolutional network 基于相关时间图卷积网络的冷负荷预测

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976497

Zhengrun Zhao, Zhi-wen Chen, Qiao Deng, Peng-Fei Tang, Tao Peng

{"title":"Cooling load prediction based on correlative temporal graph convolutional network","authors":"Zhengrun Zhao, Zhi-wen Chen, Qiao Deng, Peng-Fei Tang, Tao Peng","doi":"10.1109/IAI55780.2022.9976497","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976497","url":null,"abstract":"The efficient operation of the cooling source system depends on a reasonable control strategy, and accurate cooling load prediction provides important guidance for optimal control. As there are numerous variables that affect the prediction of cooling loads, many cooling load prediction methods try to exploit the variables in the temporal domain. However, the correlations between the variables are not reasonably utilized by many methods. To exploit the implicit information of the data and obtain an accurate cooling load prediction, the correlative temporal graph convolutional network (CTGCN) is used to predict the cooling load, which can extracted the correlation information and the temporal information. Notably, the correlations between the key variables that affect the cooling load prediction are used for the correlation graph construction, which provides guidance for correlation information extraction. Some traditional prediction methods are compared to prove the effectiveness of the proposed method in the field of cooling load prediction. The results show that the proposed model has great practical value in cooling load prediction.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130703835","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

Chattering-free sliding mode tracking control for robotic manipulator with actuator dynamics 具有作动器动力学的机械臂无抖振滑模跟踪控制

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976862

Yijun Guo, Wu Zhou

引用次数: 0

Event-triggered tracking consensus for multi-agent systems with time-varying delays 时变延迟多智能体系统的事件触发跟踪一致性

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976840

Xue Yu, Gang Wang, Jinhai Liu, Zhen Wang

引用次数: 0

Observer-based Adaptive Tracking Control for One-Link Manipulator with Full State Constraints 基于观测器的全状态约束单连杆机械臂自适应跟踪控制

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976570

Jin-Zi Yang, Jin‐Xi Zhang

引用次数: 0

Sparse Causality Analysis Approach with Time-varying Parameters for Root Cause Localization of Nonstationary Process 非平稳过程根源定位的时变参数稀疏因果分析方法

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976691

Pengyu Song, Chunhui Zhao, Biao Huang, Jinliang Ding

{"title":"Sparse Causality Analysis Approach with Time-varying Parameters for Root Cause Localization of Nonstationary Process","authors":"Pengyu Song, Chunhui Zhao, Biao Huang, Jinliang Ding","doi":"10.1109/IAI55780.2022.9976691","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976691","url":null,"abstract":"Root cause diagnosis (RCD) is an important technique for maintaining the safe operation of industrial processes. Traditional RCD methods usually require stationarity assumptions. However, the process inevitably shows nonstationarity due to factors such as switching of operating conditions. Although there have been some previous studies trying to overcome the challenge of nonstationarity, these methods fail to guarantee the significance of the extracted causalities and lead to redundant relationships. To address the above issues, a sparse causal analysis model with time-varying parameters is extracted in this study. First, we propose an end-to-end information fusion and prediction task to characterize predictive relationships between variables and avoid repeated modeling. Second, we design time-varying parameters for the information fusion mechanism to cope with nonstationarity and automatically identify significant causality through sparse parameter updates. We design an update strategy that constrains the gradient information to guarantee sparsity. Finally, a causal metric is constructed for the time-varying predictive relationship to comprehensively obtain the overall causal relationship, which further guarantees causal significance. The validity of the proposed method is illustrated through a real industrial example collected from a thermal power plant.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123271580","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

Event-Driven Robust Guaranteed Cost Control via an Improved Adaptive Critic Learning Strategy 基于改进自适应批评学习策略的事件驱动鲁棒保证成本控制

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976561

Zihang Zhou, Ding Wang, Xin Xu

引用次数: 0

Hybrid Classification Method for Image-based Anomaly Detection in Manufacturing Processes 基于图像的制造过程异常检测混合分类方法

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976593

Yee Tat Ng, Xiang Li, Ji-Yan Wu, Van Tung Tran, Wenju Lu

引用次数: 0