2020 2nd International Conference on Industrial Artificial Intelligence (IAI)最新文献_第3页

The adaptive fuzzy tracking control for double inverted pendulums in the presence of unknown control directions 存在未知控制方向的双倒立摆自适应模糊跟踪控制

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262238

Ning Li, Yaguang Li, W. Xiang

引用次数: 0

A method of Fault Diagnosis of non-Gaussian Property and Performance Correlation Based on Independent Component Analysis 基于独立分量分析的非高斯特性和性能相关性故障诊断方法

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262197

Yu-tao Song, Sheng Yang, Chao Cheng

引用次数: 2

Model-Free Control of Time-Delay Systems via Policy Gradient Based Adaptive Learning Algorithm 基于策略梯度自适应学习算法的时滞系统无模型控制

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262213

Yongwei Zhang, Shunchao Zhang, Bo Zhao, Derong Liu

引用次数: 0

Probabilistic Consensus of Multi-agent System under Joint Control of SMC and Minimum Entropy Compensation SMC和最小熵补偿联合控制下多智能体系统的概率一致性

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262182

Xuerou Zhang, Jing Wang, Jinglin Zhou, Y. Chen, Cunwu Han

引用次数: 0

Multi-sliced Sampling-based Deep Forest Regression Algorithm for High-dimension Data 基于多切片采样的高维数据深度森林回归算法

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262209

Heng Xia, Jian Tang, J. Qiao, Wen Yu

{"title":"Multi-sliced Sampling-based Deep Forest Regression Algorithm for High-dimension Data","authors":"Heng Xia, Jian Tang, J. Qiao, Wen Yu","doi":"10.1109/IAI50351.2020.9262209","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262209","url":null,"abstract":"In the online soft measurement of difficult-to-measure parameters of complex industrial processes. With the rapid development of investigation, deep learning such as deep forest regression (DFR) have been applied. However, for high-dimension datasets, these methods usually can't implement better effects and high time cost. Therefore, in this paper, a multi-sliced sampling-based DFR (Mss-DFR) model is proposed to solve the above problems in high-dimension datasets. The improved model is different from the original model in three important aspects. Firstly, considering the diversity and time cost of sub-forest, the raw feature vector is segmented into three parts through multi slicing strategy. Further, based on the mutual information feature selection model, the optimized feature set is obtained that according to the principle of minimum redundancy and maximum correlation, and then combined with the layer regression vector. Finally, consider variance can projection effect the difference of each sub-forest in DFR, so that it added to the layer regression vector. Experimental results show that Mss-DFR performs significantly, and even outperforms neural networks and achieves state-of-the-art results in some cases.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116924416","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

Research on the mechanism and network model of China's public cultural service in the Internet Era 互联网时代中国公共文化服务机制与网络模式研究

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262175

Haiqin Xie, Weiwei Zhai, Xiaowei Zhang

引用次数: 1

Real-time vehicle detection under complex road conditions 复杂路况下的实时车辆检测

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262216

Zeying Tian, Yinbin Jin, Hui Cao, Feng Wang, Chao Chen, Xudong He

{"title":"Real-time vehicle detection under complex road conditions","authors":"Zeying Tian, Yinbin Jin, Hui Cao, Feng Wang, Chao Chen, Xudong He","doi":"10.1109/IAI50351.2020.9262216","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262216","url":null,"abstract":"Vehicle detection is an important component in unmanned driving systems. This paper presents a real-time vehicle detection method under complex road conditions to solve the problem of real-time detection of road vehicles in automatic driving. Firstly using yolo network to build a deeper depth neural network, which is used to identify and score the vehicles in the image. Then, using the dynamic threshold method to remove some false candidate boxes, and using the Gauss attenuation function to filter the overlapping candidate boxes. The loss function normalized by the change rate is used to train the neural network, and the batch normalization layer is used to correct the input data of the network to avoid data deviation during the training process. Finally, a full convolution layer is added at the end of the network layer to transform the two-dimensional data into one-dimensional data and output the final recognition results. It is verified that this method improves the efficiency and accuracy of real-time vehicle detection, it can effectively detect vehicles on roads with complex backgrounds, and satisfy the real-time requirements of road vehicle detection.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114375048","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

Critical Node Detection for Sequential Attacks in Complex Networks via Multi-objective Optimization 基于多目标优化的复杂网络序列攻击关键节点检测

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262204

Lei Zhang, Qing Liu, Jiajun Xia, Haipeng Yang, Xing-yi Zhang

{"title":"Critical Node Detection for Sequential Attacks in Complex Networks via Multi-objective Optimization","authors":"Lei Zhang, Qing Liu, Jiajun Xia, Haipeng Yang, Xing-yi Zhang","doi":"10.1109/IAI50351.2020.9262204","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262204","url":null,"abstract":"The critical node detection for sequential attacks based on cascading failure model is an important way for analyzing network vulnerability, which has attracted the attention of many researchers in the field of complex network recently. However, most of the existing cascading critical node detection algorithms focus on designing effective attack strategies leading to the maximal damage to the network (i.e. attack effect), while ignoring the cost of attacks. To this end, we transform the cascading critical node detection for sequential attacks as a bi-objective optimization problem (named BCVNDSeq), where the attack cost and the attack effect are simultaneously optimized. In order to solve the transformed problem, we propose a multiobjective cascading critical node detection algorithm (named MO-BCVNDSeq), which can provide decision makers with a holistic view for analyzing the network vulnerability. In MO-BCVNDSeq, a local search strategy based on sequential matrix is proposed to accelerate the population convergence and an individual repairing strategy is also suggested to further improve the search efficiency. Finally, the experimental results on 6 real-world complex networks demonstrate the effectiveness of the proposed algorithm compared with several representative baselines.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127074862","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

Provenance-based Hierarchical Encryption for Fine-grained Access Control in Cloud Computing 云计算中基于溯源的分层加密细粒度访问控制

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262177

Xinyu Fan, Faen Zhang, Eminjan Turamat, Chao Tong, Jiahong Wu, Kai Wang

引用次数: 1

Research on Modeling and Parameter Identification of Circulating Cooling Water System 循环冷却水系统建模与参数辨识研究

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262164

Ni Da-peng, Tian Xiang-yan, Jia Ming-xing

引用次数: 1