2021 3rd International Conference on Industrial Artificial Intelligence (IAI)最新文献_第5页

A Formation Control Approach Considering Rollover Avoidance for Two-wheeled Mobile Agents 考虑侧翻避免的两轮移动agent编队控制方法

2021 3rd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2021-11-08 DOI: 10.1109/IAI53119.2021.9619325

Zhao Yinxiang, H. Yuqing

引用次数: 0

Optimization of SOC fractional PID control parameters for solid oxide battery based on improved firefly algorithm 基于改进萤火虫算法的固体氧化物电池荷电状态分数PID控制参数优化

2021 3rd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2021-11-08 DOI: 10.1109/IAI53119.2021.9619450

Tao Zhang, Honglin Li, Xu-Yen Tu, Huizhen Pang, Yu Huang

引用次数: 1

Solving specified-time distributed optimization problem with local inequality constraint based on penalty method 基于惩罚法求解局部不等式约束下的指定时间分布式优化问题

2021 3rd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2021-11-08 DOI: 10.1109/IAI53119.2021.9619230

Yu-quan Zhang, Chengxin Xian, Yu Zhao

引用次数: 0

A Self Optimizing Control Framework and A Benchmark for Smart Process Control 智能过程控制的自优化控制框架与基准

2021 3rd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2021-11-08 DOI: 10.1109/IAI53119.2021.9619356

J. Viola, Y. Chen

引用次数: 2

Error Analysis of Kalman Filter Applied to Phased Array Antenna Alignment 卡尔曼滤波用于相控阵天线对准的误差分析

2021 3rd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2021-11-08 DOI: 10.1109/IAI53119.2021.9619235

Yutong Liu, Ning Chen, Chuang Yang, Haocong Ji

{"title":"Error Analysis of Kalman Filter Applied to Phased Array Antenna Alignment","authors":"Yutong Liu, Ning Chen, Chuang Yang, Haocong Ji","doi":"10.1109/IAI53119.2021.9619235","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619235","url":null,"abstract":"With growing traffic needs, it has become an inevitable trend to apply information, communication, and control to the field of transportation. In real-time communication process, the coordination between satellites and logistics trucks requires precise position information for phased array antenna alignment. However, in mountain areas and forests with weak GPS signals, the information provided by GPS often has coordinate deviations caused by environmental and measurement noise. Therefore, it is difficult to provide accurate location information for phased array antenna alignment. Considering the above problems, this paper firstly compares the mean square error of the Kalman filter algorithm under the constant acceleration(CA) motion model and the Singer motion model, and analyze their respective adaptation environments. Then a Kalman filter is applied to a phased-array antenna alignment. This method mainly uses the latitude and longitude coordinate information to predict trajectory, and analyzes the off-axis angle error and the phase error in the antenna alignment. The results show that the coordinate error fluctuation amplitude of this algorithm is low, and the converge time is short. After being applied to the antenna alignment, it effectively reduces the off-axis angle error and the phase error. It is indicated that the application of Kalman filter algorithm can control these two kinds of errors within a range, which has little impact on the selection of the antenna array.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115820953","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

Quadrotor Mapless Navigation in Static and Dynamic Environments based on Deep Reinforcement Learning 基于深度强化学习的静态和动态环境四旋翼无地图导航

2021 3rd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2021-11-08 DOI: 10.1109/IAI53119.2021.9619200

Tsung-Hsi Tsai, Qing Li

引用次数: 0

IAI 2021 Index IAI 2021指数

2021 3rd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2021-11-08 DOI: 10.1109/IAI53119.2021.9619321

引用次数: 0

A Robust Surrogate-assisted Evolutionary Algorithm based on Maximum Correntropy Criterion⋆ 基于最大相关熵准则的稳健代理辅助进化算法

2021 3rd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2021-11-08 DOI: 10.1109/IAI53119.2021.9619406

Shenyu Su, Daofu Guo, An Chen, Jiaqi Yun, Yichuan Wang, Zhigang Ren

{"title":"A Robust Surrogate-assisted Evolutionary Algorithm based on Maximum Correntropy Criterion⋆","authors":"Shenyu Su, Daofu Guo, An Chen, Jiaqi Yun, Yichuan Wang, Zhigang Ren","doi":"10.1109/IAI53119.2021.9619406","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619406","url":null,"abstract":"By remarkably reducing real fitness evaluations, surrogate-assisted evolutionary algorithms (SAEAs) have been successfully applied to expensive optimization problems. However, existing SAEAs generally ignore the widespread simulation evaluation noise when constructing surrogate models, which severely limits their robustness and applications. To alleviate this issue, this study proposes a robust SAEA based on maximum correntropy criterion (MCC). MCC can robustly measure the similarity between two random variables by weakening the negative influence of outlier data. With it as the loss function, the trained surrogate model thus could have a good tolerance of the simulation evaluation noise. Taking the radial basis function (RBF) as the basic surrogate model and the differential evolution (DE) algorithm as the optimizer, this study then develops a specific SAEA named MCC-RBF-DE. Comprehensive experimental results on various benchmark functions with evaluation noise show that the introduction of MCC can effectively suppress the influence of noise. Moreover, MCC-RBF-DE shows stronger robustness compared to traditional SAEAs.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128900719","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-Triggered Control of Multi-Player Zero-Sum Games via Adaptive Dynamic Programming 基于自适应动态规划的多玩家零和博弈事件触发控制

2021 3rd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2021-11-08 DOI: 10.1109/IAI53119.2021.9619415

Yongwei Zhang, Shunchao Zhang, Bo Zhao, Qiuye Wu, Derong Liu

引用次数: 0

Research on Forecasting Method for Effluent Ammonia Nitrogen Concentration Based on GRA-TCN 基于GRA-TCN的出水氨氮浓度预测方法研究

2021 3rd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2021-11-08 DOI: 10.1109/IAI53119.2021.9619251

Li Kang, Yang Cui-li, Qiao Jun-fei

{"title":"Research on Forecasting Method for Effluent Ammonia Nitrogen Concentration Based on GRA-TCN","authors":"Li Kang, Yang Cui-li, Qiao Jun-fei","doi":"10.1109/IAI53119.2021.9619251","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619251","url":null,"abstract":"Aiming at the characteristics of high coupling degree, strong nonlinearity, and serious time delay in the measurement of ammonia nitrogen concentration in wastewater treatment process (WWTP), a prediction model of ammonia nitrogen concentration based on gray relational analysis (GRA) and time convolution network (TCN) was proposed. Firstly, based on the relevant water quality parameters collected in WWTP, the grey correlation analysis method was used to find out other characteristic variables highly related to the ammonia nitrogen concentration. Then, a new group of multivariate time series data was constructed by using the sliding window method. Finally, based on the advantages of the time convolution network in processing time series data, such as simple, flexible, and easy to parallel, the constructed time series data were modeled to predict the concentration of effluent ammonia-nitrogen. To verify the validity of the model, the predicted results were compared with the other four models. The experimental results show that the ammonia-nitrogen concentration prediction model based on GRA and TCN has good prediction performance, which is helpful to realize the accurate prediction of effluent ammonia-nitrogen concentration. At the same time, it can also provide timely and effective guidance for the control and optimization of the wastewater treatment process.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"232 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116203449","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: 3