2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)最新文献_第3页

Research on Intelligent Maneuvering Decision in Close Air Combat Based on Deep Q Network 基于深度Q网络的近距离空战智能机动决策研究

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10166948

Tingyu Zhang, Chen Zheng, Mingwei Sun, Yongshuai Wang, Zengqiang Chen

引用次数: 0

Model - free Adaptive Sliding Mode Predictive Control of Linear Ultrasonic Motor 线性超声电机的无模型自适应滑模预测控制

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10165912

Li Yifan, Cao Rongmin, Hou Zhongsheng, Zhou Hui Xing, Chang Debiao, Jia Jihui

引用次数: 0

Event-Based Object Detection using Graph Neural Networks 基于事件的图神经网络目标检测

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10166491

Daobo Sun, H. Ji

{"title":"Event-Based Object Detection using Graph Neural Networks","authors":"Daobo Sun, H. Ji","doi":"10.1109/DDCLS58216.2023.10166491","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166491","url":null,"abstract":"Event-based object detection is a challenging but promising task, as the nature of sparsity and asynchrony of events is incompatible with state-of-the-art object detection approaches. Conventional deep neural networks do not take advantage of the event camera's high event sampling rate, low power consumption and robustness of brightness changes. Recent works addresses the problem of redundant computations by using a graph representation to model the feature of event streams that the graph representation and graph neural networks for event streams can efficiently extract the meaningful information and reduce the computational complexity. Nevertheless, there is still room for improvement in terms of accuracy and computation efficiency. In this work, we propose a graph-based architecture and a new mechanism for updating the graph, which significantly increases the capacity of graph neural networks while maintaining highly efficient per-event processing. In object detection task, our model achieves higher accuracy and lower FLOPS per event compared to various synchronous/asynchronous methods. To our belief, the framework we proposed is effective and robust, as well as being a significant reduction in the amount of redundant computation.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123908405","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

Event-Triggered Adaptive Cooperative Control for Nonstrict-Feedback Nonlinear Multiagent Systems 非严格反馈非线性多智能体系统的事件触发自适应协同控制

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10166530

Liduo Nie, Xin Wang

引用次数: 0

Heterogeneous AGVs Scheduling in Hospital Using ALNS-based Metaheuristic Algorithm 基于alns的医院异构agv调度元启发式算法

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10167266

Xueming Song, Ke Zhu, Yuxing Zhao, Jianming Zhang

引用次数: 0

A Fault Diagnosis Method Based on Wavelet Denoising and 2DCNN under Background Noise 背景噪声下基于小波去噪和2DCNN的故障诊断方法

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10167183

Kexin Liu, Zhe Li, Wenbin He, Jia Peng, Xudong Wang, Yaonan Wang

引用次数: 0

Two-Dimensional Model Predictive Iterative Learning Control based on Just-in-Time Learning Method for Batch Processes 基于实时学习方法的批处理二维模型预测迭代学习控制

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10166437

Chuangkai Zheng, Liuming Zhou, Feng Li

引用次数: 0

Data Driven Strip Crown Prediction for a Hot Strip Rolling Mill Process 数据驱动的热轧带钢凸度预测

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10166447

Yanyan Zhang, Kai Zhang, Pengcheng Yang, Kai-xiang Peng

引用次数: 0

Novel virtual sample generation using Gibbs Sampling integrated with GRNN for handling small data in soft sensing 基于吉布斯采样与GRNN相结合的新型虚拟样本生成方法用于软测量中的小数据处理

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10166679

Qun Zhu, Qianchuan Zhao, Yuan Xu, Yanlin He

{"title":"Novel virtual sample generation using Gibbs Sampling integrated with GRNN for handling small data in soft sensing","authors":"Qun Zhu, Qianchuan Zhao, Yuan Xu, Yanlin He","doi":"10.1109/DDCLS58216.2023.10166679","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166679","url":null,"abstract":"In order to optimize complex industrial processes, an accurate model is essential. The mainstream approach for complex industrial modeling is data-driven soft sensors. However, the accuracy of the established models is often low due to an insufficient amount of effective data, so the method of generating virtual samples has been proposed to achieve data augmentation, but the previous virtual sample generation methods have ignored the correlation between samples. To solve this problem, an effective virtual sample generation method based on Gibbs Sampling algorithm (GS- VSG) is proposed in this paper. In the proposed method, virtual input samples are first generated using the prior knowledge of the original data through the Gibbs Sampling method. Next, a generalized regression neural network (GRNN) model is constructed from the raw data, which is used to predict the output values of the virtual samples. Finally, the input and output parts of the virtual samples are combined to create a virtual sample set, which completes the extension of the original data set. To demonstrate the feasibility of the proposed GS- VSG method, numerical example and real industrial process dataset are used for simulation experiments. The results show that GS- VSG generated samples can improve the model accuracy and is a good technique for virtual sample generation.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130002956","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

Hierarchical Label Text Classification Method with Deep-Level Label-Assisted Classification 基于深度标签辅助分类的分层标签文本分类方法

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10166293

Cao Yu-kun, Wei Zi-yue, Tang Yi-jia, Jin Cheng-kun

{"title":"Hierarchical Label Text Classification Method with Deep-Level Label-Assisted Classification","authors":"Cao Yu-kun, Wei Zi-yue, Tang Yi-jia, Jin Cheng-kun","doi":"10.1109/DDCLS58216.2023.10166293","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166293","url":null,"abstract":"Hierarchical label text classification is a challenging task in the field of natural language processing, where each document needs to be correctly classified into multiple labels with hierarchical structure. However, in the label set, due to the insufficient semantic information contained in the labels and the small number of documents classified under deep-level labels, the training of deep-level labels is insufficient, leading to a significant imbalance in label training. To address this, a hierarchical label text classification method with deep-level label-assisted classification (DLAC) is proposed. The method proposes a deep-level label-assisted classifier, which effectively utilizes text features and rich features of shallow label nodes corresponding to deep label nodes (i.e., shallow label's rich features) on the basis of enhanced label semantics to enhance the classification performance of deep labels. The comparison experiment results with eleven algorithms on three datasets show that the model can effectively improve the classification performance of deep-level labels and achieve good results.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133091871","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