2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)最新文献_第2页

Attention-based Stacked Supervised Poisson Autoencoders for Defects Prediction in Casting-rolling process 基于注意力的堆叠监督泊松自编码器在铸轧缺陷预测中的应用

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858528

Xinmin Zhang, Zhihuan Song, Zhiqiang Ge

{"title":"Attention-based Stacked Supervised Poisson Autoencoders for Defects Prediction in Casting-rolling process","authors":"Xinmin Zhang, Zhihuan Song, Zhiqiang Ge","doi":"10.1109/DDCLS55054.2022.9858528","DOIUrl":"https://doi.org/10.1109/DDCLS55054.2022.9858528","url":null,"abstract":"In this paper, we propose an attention-based stacked supervised Poisson autoencoders (ASSPAE) modeling framework to predict the occurrence of defects in the casting-rolling process. ASSPAE is a novel deep learning model, which is designed based on the stacked supervised Poisson autoencoders and attention-based multi-feature fusion strategy. Stacked supervised Poisson autoencoders are a novel deep neural networks model, which is designed by integrating the Poisson regression network layer into the deep autoencoders framework. Compared with the conventional autoencoder-based models, the proposed stacked supervised Poisson autoencoders not only can capture the quality-relevant deep features of the data, but also is suitable for predicting the count-type quality variable. Furthermore, in order to efficiently leverage information present in different layers of the network, an attention-based multi-feature fusion strategy is integrated into the stacked supervised Poisson autoencoders modeling framework. The effectiveness of the proposed ASSPAE method is evaluated by its application to the cast-rolling process of a steelmaking plant. The application results show that the prediction accuracy of the proposed ASSPAE modeling method was improved by 32%, 31%, 24%, and 13% in Root Mean Square Error (RMSE) compared with the partial least square regression, support vector regression, Poisson regression, and stacked autoencoders-Poisson regression, respectively.","PeriodicalId":412672,"journal":{"name":"2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116338377","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

Proportional-Integral Interval Observer for Linear Continuous-Time Systems 线性连续时间系统的比例-积分区间观测器

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858408

Tu Zhang, Xingzheng Wu, Liwei Li, M. Shen

引用次数: 0

On the Structure Modeling and Characteristics of Knowledge Ecosystem of Smart Library Based on Super-network 基于超网络的智能图书馆知识生态系统结构建模与特征研究

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858569

Qiongqiong Wei

引用次数: 0

An Automatic Reflective Clothing Detection Algorithm Based on YOLOv5 for Work Type Recognition 基于YOLOv5的工种识别反射服装自动检测算法

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858364

Xinyi He, Ping Ma, Yiyang Chen, Y. Liu

引用次数: 2

Hysteresis Feedforward Compensation of Reluctance Actuator: A Neural Network Approach Using Stochastic Configuration Network 磁阻执行器的磁滞前馈补偿:一种基于随机组态网络的神经网络方法

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858345

Yang Liu, Q. Miao, Lili

{"title":"Hysteresis Feedforward Compensation of Reluctance Actuator: A Neural Network Approach Using Stochastic Configuration Network","authors":"Yang Liu, Q. Miao, Lili","doi":"10.1109/DDCLS55054.2022.9858345","DOIUrl":"https://doi.org/10.1109/DDCLS55054.2022.9858345","url":null,"abstract":"Since the current voice coil motor cannot break through the physical limit, the next-generation ultra-precision motion stage cannot meet the needs of high speed, high acceleration and high precision. To solve this problem, a new type of actuator must be developed and reluctance actuator has been proposed as one of the most potential candidates. However, the strong nonlinearity of the reluctance motor limits its application in ultra-precision. For example, the hysteresis effect, the relationship between force and current, and force and displacement, and so on. In this paper, a combined control strategy composed of feedforward compensation and PID is introduced to the reluctance actuator system, and the BP neural network and stochastic configuration network (SCN) are applied to the construction of the hysteresis inverse model. According to the simulation results, it can be concluded that the accuracy of the inverse model constructed by the SCN method is higher than that of the inverse model constructed by the BP neural network. It is concluded that the Stochastic Configuration Network method is effective for the hysteresis nonlinearity compensation of the reluctance actuators, and promising in precision stage control.","PeriodicalId":412672,"journal":{"name":"2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131367320","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

Spatio-temporal Attention Transformer Model for Future Trajectory Forecast 未来轨迹预测的时空注意力转换模型

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858377

Zhiqiang Geng, Te Zhang, Yongming Han

引用次数: 0

Model-Free Adaptive Sliding Mode Control for Discrete-Time Nonlinear Systems With Sensor Fault and Prescribed Performance 具有传感器故障和规定性能的离散非线性系统的无模型自适应滑模控制

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858576

Liying Hao, Sen Yang, Dong Liu

引用次数: 0

Containment Control Via Iterative Learning of Singular Multi-agent Systems with Multiple Leaders 基于迭代学习的多领导奇异多智能体系统遏制控制

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858565

Sizhe Yang, Li-Yu Daisy Liu, Senping Tian, Xiangyang Li

引用次数: 2

Data-driven Control of Event-triggered Linear Systems 事件触发线性系统的数据驱动控制

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858423

Xin Wang, Jian Sun, G. Wang

引用次数: 0

A New IBE Scheme Based on Conjugate Search Problem 一种新的基于共轭搜索问题的IBE方案

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858380

Zhaoyang Deng, Bo Mi, Darong Huang, Lingyi Hao

引用次数: 0