2022 4th International Conference on Industrial Artificial Intelligence (IAI)最新文献_第6页

Pipeline Leak Detection Method based on DTWSVM 基于DTWSVM的管道泄漏检测方法

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976575

Xianming Lang, Yongqiang Zhu, Lin Zhang, Zefeng Cai

引用次数: 0

Dynamic multi-objective optimization algorithm based on weighted differential prediction model 基于加权差分预测模型的动态多目标优化算法

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976797

Yumeng Zhao, Xianpeng Wang, Zhiming Dong, Yao Wang, Hangyu Lou, Tenghui Hu, Kai Fu

{"title":"Dynamic multi-objective optimization algorithm based on weighted differential prediction model","authors":"Yumeng Zhao, Xianpeng Wang, Zhiming Dong, Yao Wang, Hangyu Lou, Tenghui Hu, Kai Fu","doi":"10.1109/IAI55780.2022.9976797","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976797","url":null,"abstract":"In this paper, a new algorithm for solving dynamic multi-objective optimization problems(DMOPs) is proposed. Most of the traditional dynamic multi-objective optimization algorithms will make predictions based on the overall average evolutionary direction of the population, which is hardly applicable to problems where the solution set and frontier do not vary with the environmental rules. In this paper, a dynamic multi-objective optimization algorithm based on weight difference prediction model is designed to solve such problems. The algorithm contains a weighted differential prediction strategy, and a differential model is built for each individual using the weights to predict the initial population after environmental changes. With this approach, each individual in the population can be made to respond quickly to environmental changes. We used three classical comparison algorithms to conduct experiments on a series of test problems. The experimental results show that the WD-MOEA/D algorithm can significantly improve the dynamic optimization performance and is effective in solving different types of dynamic problems.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"8 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":"114437859","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

A data-driven distributed control method for performance optimization of interconnected industrial processes 面向互联工业过程性能优化的数据驱动分布式控制方法

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976574

Hao Wang, Hao Luo, Yuchen Jiang, Xiaoyi Xu, Xiang Li

{"title":"A data-driven distributed control method for performance optimization of interconnected industrial processes","authors":"Hao Wang, Hao Luo, Yuchen Jiang, Xiaoyi Xu, Xiang Li","doi":"10.1109/IAI55780.2022.9976574","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976574","url":null,"abstract":"In this paper, distributed plug-and-play (PnP) optimization control method for interconnected industrial processes is studied. Due to the influence of information interaction between subsystems, the performance of each subsystems are affected by their neighbor subsystem. Decentralized control approaches face difficulty in achieving satisfactory performance where the impact of information interaction between subsystems. Centralized optimal control method can achieve good results, but it burden on communication and computing. Compared with centralized and decentralized controller, distributed approaches has less online computational load and more flexible implementation schemes in interconnected industrial processes. In this work, a residual-driven distributed PnP optimization control approach is further developed, in which local residuals and residuals from neighbor subsystems are used to drive PnP optimization controllers. The influence between adjacent subsystems is fully considered in this design method. Experimental results on a three tank benchmark system show the effectiveness of the proposed algorithm.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"2013 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":"123827030","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

Retinal Medical Image Classification Based on Deep Convolutional Neural Network AlexNet 基于深度卷积神经网络的视网膜医学图像分类

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976745

Zehan Tian, Jing Wang, Meng Zhou, Yanzhu Zhang, Mingyu Shi

引用次数: 0

Application of Variable Domain Hybrid FFOPID Controller and Dynamic Data Reconciliation Filtering Technology in Islanded Microgrid 变域混合FFOPID控制器与动态数据协调滤波技术在孤岛微电网中的应用

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976749

Dongyang Li, Zhengjiang Zhang, Xiangyu Li, Zhihui Hong, Shipei Huang

引用次数: 0

Variational Adversarial Active Learning Assisted Process Soft Sensor Method 变分对抗主动学习辅助过程软测量方法

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976817

Yun Dai, Ying Zhang, Y. Yao, Yi Liu

{"title":"Variational Adversarial Active Learning Assisted Process Soft Sensor Method","authors":"Yun Dai, Ying Zhang, Y. Yao, Yi Liu","doi":"10.1109/IAI55780.2022.9976817","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976817","url":null,"abstract":"Soft sensor methods have been widely applied in process industries to predict key quality variables that cannot be measured online. However, labeled samples to construct models are often limited because quality variables are difficult to be obtained. Additionally, due to the instrument of redundant sensors, the process data is high-dimensional with strong correlations. In this paper, an active learning soft sensor framework named variational adversarial active learning (VAAL) is developed to select informative unlabeled samples to enhance prediction performance. The sampling strategy of VAAL learns a latent space using a variational autoencoder (VAE) and an adversarial network trained in a way of minimax game. The VAE tries to trick the adversarial network into predicting that all samples are from the labeled pool, while the adversarial network learns how to discriminate between dissimilarities in the latent space. The Gaussian process regression model is adopted in VAAL as a base soft sensor. The prediction results of an industrial debutanizer column demonstrate the advantages of VAAL as compared to the existing active learning strategies.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"17 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":"125350465","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

Optimization Setting of Reagent Dosage in Rare Earth Extraction Process Based on JITL 基于JITL的稀土萃取工艺药剂用量优化设置

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976515

Wenhao Dai, Hui Yang, Rongxiu Lu, Jianyong Zhu, Pengzhang Chen

{"title":"Optimization Setting of Reagent Dosage in Rare Earth Extraction Process Based on JITL","authors":"Wenhao Dai, Hui Yang, Rongxiu Lu, Jianyong Zhu, Pengzhang Chen","doi":"10.1109/IAI55780.2022.9976515","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976515","url":null,"abstract":"The mechanism of the rare earth extraction process is complex, and the extraction efficiency is greatly affected by the environment. The optimal dose setting value determined by the mechanism model is not the optimal setting value for the actual extraction process. In order to make the rare earth extraction process always run in the most economical state, this paper proposes an optimal setting approach for the dosage of the rare earth extraction process based on just-in-time learning (JITL). Firstly, according to the mechanism model of the rare earth extraction process, an optimization model of the rare earth extraction process with the goal of maximizing economic benefits is established, and the theoretical optimal dose value is obtained; Then, a local model of reagent dosage increment and economic benefit increment is established utilizing the JITL, and the dosage increment that maximizes the economic benefit increment is then obtained; Finally, the increment is applied to the rare earth production process, and the approach is iterated continuously to maximize the economic benefit. The simulation result of the CePr/Nd extraction process demonstrates the effectiveness of the proposed method.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"35 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":"126376132","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

Optimal Control of Flotation Industrial Process Using Model-based Reinforcement Learning 基于模型强化学习的浮选工业过程最优控制

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976694

Runda Jia, Xuli Chen, Jun Zheng, Gang Yu

引用次数: 0

Recent Advance on State Estimation of Stochastic Hybrid Systems 随机混合系统状态估计研究进展

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976825

L. Wang

引用次数: 0

CNN-Informer-Based Remaining Useful Life Prediction for Electrical Devices 基于cnn信息的电气设备剩余使用寿命预测

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976668

Shufan Chen, N. Lu

{"title":"CNN-Informer-Based Remaining Useful Life Prediction for Electrical Devices","authors":"Shufan Chen, N. Lu","doi":"10.1109/IAI55780.2022.9976668","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976668","url":null,"abstract":"Accurate Remaining Useful Life (RUL) prediction plays an important role in the health management and predictive maintenance of electrical systems. Advanced AI technologies, such as Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory Network (LSTM), have been heavily involved into RUL prediction methods. However, the existing RUL prediction models, still do not fully consider the sequence information, or suffering the problem of long-term dependence. A RUL prediction model combining the advantages of CNN and Informer is proposed in this paper. In this model, CNN is used to reduce the dimension and denoise the original sensor data and transform it into a time series that is easy to be accepted by Informer. Then, Informer extracts the life-related sequence information contained in the time series based on the attention mechanism, and relies on the sparsity matrix to simplify the calculation of attention. Finally, the full connection layer maps the output of Informer into a lifetime vector. Comprehensive experiments have been conducted using two popular public datasets, and the comparison results show that the proposed method over-performs the existing data-driven-based methods.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"220 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":"130459234","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