2020 2nd International Conference on Industrial Artificial Intelligence (IAI)最新文献

Local Neighborhood Reliability Weighted Support Vector Machine 局部邻域可靠性加权支持向量机

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262215

Yunlong Gao, Yisong Zhang, Baihua Chen, Yuhui Xiong

引用次数: 0

Operator-Based Robust Nonlinear Control for Calorimetric Power Loss Measurement System Using Peltier Device 基于算子的Peltier热损测量系统鲁棒非线性控制

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262207

K. Mitsugi, M. Deng

引用次数: 2

Virtual Commissioning and Machine Learning of a Reconfigurable Assembly System 可重构装配系统的虚拟调试与机器学习

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262158

Liandong Zhang, Z. Cai, Lim Joo Ghee

{"title":"Virtual Commissioning and Machine Learning of a Reconfigurable Assembly System","authors":"Liandong Zhang, Z. Cai, Lim Joo Ghee","doi":"10.1109/IAI50351.2020.9262158","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262158","url":null,"abstract":"The digital twin application in manufacturing is mainly based on the virtual simulation model of a digital twin to build a solid model, which is applied to the product processing and assembly to achieve precise production control. This paper presents a virtual commissioning digital twin model for the modularized automatic assembly system running in our lab. First, the Siemens NX MCD software tool is used to develop the virtual commissioning digital twin model for the system. Then the different working scenarios are simulated and implemented in the virtual physical simulation environment. The data from the proposed virtual commissioning digital twin model is collected and trained with 6 different machine learning algorithm such as Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Gaussian Naive Bayes (NB) and Support Vector Machines (SVM). The advantage of our newly developed virtual commissioning model is that it is able to simulate different working conditions without risk and cost-free. It is also convenient to mimic the worsening working status and failed operation scenarios which need long time to collect for the real system. We use the collected data as input for the machine learning to implement the system monitoring and predicting. The machine learning results for 6 learning algorithms are presented and it shows the possibilities and advantages of our proposed virtual commissioning digital twin model.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"47 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":"122673443","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: 2

Pre-processing for UAV Based Wildfire Detection: A Loss U-net Enhanced GAN for Image Restoration 基于无人机野火检测的预处理:用于图像恢复的损失U-net增强GAN

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262172

Linhan Qiao, Youmin Zhang, Y. Qu

引用次数: 3

Real-time Wind Estimation with a Quadrotor using BP Neural Network 基于BP神经网络的四旋翼实时风估计

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262193

Kaixin Wu, Ji-gong Li, Jing Yang, Fanfu Zeng, Jia Liu

引用次数: 4

Research on the development of intelligent chemical manufacturing industry in Shandong Province based on big data analysis 基于大数据分析的山东省智能化工制造业发展研究

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262184

Yuan Jiyang, Z. Yanbin, Gao Jian

引用次数: 0

Hierarchical model predictive control of greenhouse climate to reduce energy cost 降低能源成本的温室气候分层模型预测控制

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262227

Dong Lin, Lijun Zhang, X. Xia

{"title":"Hierarchical model predictive control of greenhouse climate to reduce energy cost","authors":"Dong Lin, Lijun Zhang, X. Xia","doi":"10.1109/IAI50351.2020.9262227","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262227","url":null,"abstract":"This paper proposes a hierarchical control strategy for greenhouse climate control. The proposed hierarchical control consists of two layers (an upper layer and a lower layer). The upper layer is to generate set points by solving an optimization problem. The objective is to minimize the energy cost under the time-of-use (TOU) tariff while keeping greenhouse climate (temperature, relative humidity and carbon dioxide concentration) within the required range. The lower layer is to track the trajectories obtained by the upper layer. A model predictive controller is designed to address system disturbances and the results are compared with that of an open loop controller. A performance index, relative average deviation (RAD), is introduced to compare the tracking performance of the open loop control and proposed closed-loop model predictive control. Simulation results show that the proposed strategy can reduce 7.86% energy cost compared with the strategy that aims to minimize energy consumption. Moreover, the proposed model predictive control can track reference trajectories better than open loop control under system disturbances.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"2000 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":"128275209","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

Wind Turbine Condition Monitoring Based on Variable Importance of Random Forest 基于随机森林变重要度的风电机组状态监测

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262220

Kai Shi, Chenni Wu, Yuechen Wang, Hai Yu, Zhiliang Zhu

{"title":"Wind Turbine Condition Monitoring Based on Variable Importance of Random Forest","authors":"Kai Shi, Chenni Wu, Yuechen Wang, Hai Yu, Zhiliang Zhu","doi":"10.1109/IAI50351.2020.9262220","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262220","url":null,"abstract":"SCADA data lacks sensory data such as vibration and strain measurement for traditional wind turbine condition monitoring; it is updates in low frequency, one piece of data per 10 minutes in the main, which is also low for failure prediction. Thus it is a tough work to monitoring wind turbines' working condition based on SCADA data. To this end, this paper proposes a wind turbine condition monitoring method based on variable importance of random forest by utilizing the SCADA data. First, to minimize the misjudgment caused by individual outliers, we divide the SCADA time series into segments in unit of time period T. Second, we use decrease accuracy method to calculate the variable importance of random forest, as the feature vector of each segment, which characterizes a turbine's condition. Third, we compare a specific turbine's variable importance with the standard feature of healthy turbines to obtain the proximity of them. Fourth, the monitoring baseline is determined according to 3σ, and the deterioration function is applied to construct the failure probability model. To show the effectiveness, we apply the proposed method to four real cases from wind farms in China.","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":"123189283","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: 2

Data Asset Management and Analytics in China High Speed Railways: Challenges and Perspectives 中国高速铁路数据资产管理与分析:挑战与展望

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262186

J. Sun, Z. Yuan, Q. Zhang, X. Dai, D. Cui

{"title":"Data Asset Management and Analytics in China High Speed Railways: Challenges and Perspectives","authors":"J. Sun, Z. Yuan, Q. Zhang, X. Dai, D. Cui","doi":"10.1109/IAI50351.2020.9262186","DOIUrl":"https://doi.org/10.1109/IAI50351.2020.9262186","url":null,"abstract":"With the fast expansion of China's high-speed railway network and the rapid development of information and communication technologies (ICTs) in railways, more and more operation data of the China high-speed railway have been collected and will continue increasing forever. As a valuable asset, the big data of China high-speed railway need effective and dedicated management, which not only helps the railway operators to transform its daily operation to provide better services to passengers, but also has the potential to create new value to the whole industry chain and the stake-holders. The rise of artificial intelligence (AI) and big data technologies open up a new era allowing new application of railway data and the realization of data asset appreciation. This article discusses how AI-driven data management technology may tap and play the value of data assets to convert the value asset of data assets. Since dispatching and transportation are the core business of railways, the data asset management in the new generation of train dispatching system and railway passenger transportation system are investigated from the perspective of the acquisition, analysis and application of China's high-speed railway operation data. Future trends in data analysis technologies and evaluation of data assets are also discussed.","PeriodicalId":137183,"journal":{"name":"2020 2nd International Conference on Industrial Artificial Intelligence (IAI)","volume":"23 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":"114818057","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

Nonlinear vibration control of a flat plate structure using multiple piezoelectric devices 基于多压电器件的平板结构非线性振动控制

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2020-10-23 DOI: 10.1109/IAI50351.2020.9262157

Kazuya Tonomura, M. Deng

引用次数: 0