2022 4th International Conference on Industrial Artificial Intelligence (IAI)最新文献_第9页

Joint Scheduling of Material Pickup and Delivery Towards Intelligent Material Yard 面向智能物料堆场的物料取送联合调度

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976699

Fan Wu, Lei Hao, Hongfeng Wang

{"title":"Joint Scheduling of Material Pickup and Delivery Towards Intelligent Material Yard","authors":"Fan Wu, Lei Hao, Hongfeng Wang","doi":"10.1109/IAI55780.2022.9976699","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976699","url":null,"abstract":"Compared to traditional material yards with simple supply requirements and centralized material storage, intelligent material yards can significantly reduce storage space, improve material pickup efficiency, and reduce additional costs due to material mutual contamination. However, the current material delivery process is still dominated by a manual decision-making model, which is difficult to adapt to the complex and changing supply requirements. To this end, an integrated scheduling problem of material pickup and delivery considering multi-factory order requirements is proposed in this paper, which originates from a real-world scenario of Binxin intelligent material yard. By introducing the concept of spatio-temporal network flow, a discrete time-based integer linear programming model is established and then the CPLEX solver is used to solve the model. Compared with the traditional continuous-time based model, the established model shows significant advantages in terms of both solution quality and solution time, which can greatly improve the overall efficiency of the Binxin intelligent material yard.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"10 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":"128297240","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

Quantized Prescribed Performance Control for Second-Order Nonlinear Systems 二阶非线性系统的量化规定性能控制

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976589

Junguo Song, Jin‐Xi Zhang

引用次数: 0

A Semi-Supervised Learning-based Dynamic Prediction Method for Semi-molten Condition of Fused Magnesium Furnace 基于半监督学习的熔镁炉半熔状态动态预测方法

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976704

Yichen Zhong, Zhe Zhang, Gaochang Wu

{"title":"A Semi-Supervised Learning-based Dynamic Prediction Method for Semi-molten Condition of Fused Magnesium Furnace","authors":"Yichen Zhong, Zhe Zhang, Gaochang Wu","doi":"10.1109/IAI55780.2022.9976704","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976704","url":null,"abstract":"Fused magnesium furnace (FMF) is an important equipment for producing magnesium oxide, which is prone to occurring the semi-molten abnormal condition during the production. If the abnormal condition is not predicted in time, the furnace shell will be burned through, endangering the personal safety of the staff on site. Therefore, it is necessary to predict the semi-molten abnormal condition in time and accurately. Existing machine learning-based methods adopt static models for recognizing and predicting anomaly. However, the model accuracy will degrade as data features shifting over time and melting processes. To address the above problems, this paper proposes a dynamic prediction method for semi-molten abnormal condition of multiple FMFs based on semi-supervised learning. We introduce a consistent regularization strategy and dynamically update the model weights by learning multiple FMF smelting process video data with a sparse set of condition labels. The algorithm is able to dynamically adapt to the shifted data features for accurate anomaly prediction. The proposed algorithm can predict the semi-molten abnormal condition in real time and accurately under the condition of only 1% label, enabling the safe and reliable operation of FMF.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"22 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":"122522059","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

Data arising from hyperchaotic financial systems. Control through Koopman operators and EDMD 来自超混沌金融系统的数据。通过Koopman操作器和EDMD进行控制

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976809

J. Leventides, E. Melas, C. Poulios, A. Vardulakis

{"title":"Data arising from hyperchaotic financial systems. Control through Koopman operators and EDMD","authors":"J. Leventides, E. Melas, C. Poulios, A. Vardulakis","doi":"10.1109/IAI55780.2022.9976809","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976809","url":null,"abstract":"We present a method for linearizing control and stabilization of chaotic systems in finance. This method considers the deviation of some trajectory of the system from an ideal or desirable orbit. Using Koopman operators and EDMD, we model this deviation as a linear dynamical system. The linear system is necessarily defined in some augmented state space whose dimension is bigger than the dimension of the original state space. The linear system can then be used for control and stabilization properties. Namely, one may apply feedback control to drive the deviation to zero, which means that the trajectory is close to the desired one. This approach can also be applied to more than one trajectories. However, in order to maintain good approximation properties, the more trajectories we consider the larger the dimensions of the linear system will become and at some stage the method will not be computationally effective. For this reason, we do not take into consideration the whole set of trajectories, but we start with a smaller set of orbits. This is a realistic scenario, since in economic studies the macroeconomic variables (such as the gross domestic product) are not arbitrary numbers but depend on the data of the economy.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"26 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":"124163673","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

Short-term Power Load Forecasting Based on Grey Relational Analysis and Support Vector Machine 基于灰色关联分析和支持向量机的短期电力负荷预测

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976828

Wei Sun, Xinfu Pang, Wei Liu, Yibao Wang, Changfeng Luan

{"title":"Short-term Power Load Forecasting Based on Grey Relational Analysis and Support Vector Machine","authors":"Wei Sun, Xinfu Pang, Wei Liu, Yibao Wang, Changfeng Luan","doi":"10.1109/IAI55780.2022.9976828","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976828","url":null,"abstract":"Short-term power load forecasting is an important guarantee to ensure the smooth and efficient operation of power systems, and an important basis for building new digital and intelligent power systems. Given that short-term power system load is affected by various factors (e.g., climate, time), power system load has strong randomness and volatility while being periodic. Hence, the traditional power load forecasting method is no longer applicable. To improve the accuracy of short-term power load forecasting, this paper proposes a support vector machine (SVM) short-term power load forecasting method based on grey relational analysis and K-means clustering. First, similar days in historical days are extracted by using the grey relational analysis method to form a rough set of similar days. Second, the rough set of similar days is classified by K-means clustering, and the final set of similar days is obtained. Third, SVM is trained to determine the final predicted daily load. Lastly, the proposed method is verified by the actual electricity consumption data of a city in China, and the results show the effectiveness of this method.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"72 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":"126351024","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 Hybrid Intelligent Method for Rolling Bearing Fault Diagnosis Integrated with Expert Knowledge and Deep Learning 结合专家知识和深度学习的滚动轴承故障诊断混合智能方法

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976758

Shupeng Yu, Xiang Li, Bin Yang, Y. Lei

{"title":"A Hybrid Intelligent Method for Rolling Bearing Fault Diagnosis Integrated with Expert Knowledge and Deep Learning","authors":"Shupeng Yu, Xiang Li, Bin Yang, Y. Lei","doi":"10.1109/IAI55780.2022.9976758","DOIUrl":"https://doi.org/10.1109/IAI55780.2022.9976758","url":null,"abstract":"The rolling bearing is essential for the rotating machinery and can be easily damaged in the real working conditions. It is very important to monitor the health status of rolling bearings. Aiming at this problem, fault diagnosis based on deep learning at present is popular, which automatically extracts features from raw data. However, the accuracy of fault diagnosis based on deep learning is dependent mostly on the quantity of data. In the real industries, a large amount of data may not be available, which largely deteriorates the performance of deep learning. To solve this problem, it is promising to exploit the features extracted with the expert knowledge for relaxing the limitations of deep learning. In this paper, a new hybrid intelligent method for rolling fault diagnosis is proposed, which is integrated with deep convolutional neural network and the expert knowledge. The features extracted with expert knowledge are used to improve the feature learning effect and efficiency of deep learning. The experiments on the Case Western Reserve University (CWRU) bearing data validate the effectiveness of the proposed hybrid rolling bearing fault diagnosis method.","PeriodicalId":138951,"journal":{"name":"2022 4th International Conference on Industrial Artificial Intelligence (IAI)","volume":"279 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":"132833959","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

Research on prediction method of fusion forming coefficient at the bottom of ultra-narrow gap weld bead 超窄间隙焊头底部熔合成形系数预测方法研究

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976604

Qian Ma, A. Zhang, Jing Ma, Yongqiang Ma, Yajun Zhang, Tingting Liang

引用次数: 0

Implementing a modified Smith predictor using chemical reaction networks and its application to protein translation 利用化学反应网络实现改进的Smith预测器及其在蛋白质翻译中的应用

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976643

Yijun Xiao, Hui Lv, Xing’an Wang

引用次数: 1

EDMD methods for analysis and prediction of bilinear compartmental models 双线性室室模型的EDMD分析和预测方法

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976837

J. Leventides, E. Melas, C. Poulios

引用次数: 0

Quality defect analysis of injection molding based on gradient enhanced Kriging model 基于梯度增强Kriging模型的注射成型质量缺陷分析

2022 4th International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2022-08-24 DOI: 10.1109/IAI55780.2022.9976740

Zhuocheng Wang, Cuimei Bo, Zheng Sun, Jun Li, F. Gao

引用次数: 0