2022 12th International Conference on Information Science and Technology (ICIST)最新文献_第2页

Robotic Arm Trajectory Tracking Control Based on An RBF Neural Network Adaptive Control Algorithm 基于RBF神经网络自适应控制算法的机械臂轨迹跟踪控制

2022 12th International Conference on Information Science and Technology (ICIST) Pub Date : 2022-10-14 DOI: 10.1109/ICIST55546.2022.9926773

Baojian Qin, Wenhao Zhang, Shijian Dong, Shenquan Wang, Yu-lian Jiang

引用次数: 0

Distributed Observer Design For Multi-agent Systems With Semi-Markov Switching Topology And Incremental Quadratic Constraints 具有半马尔可夫交换拓扑和增量二次约束的多智能体系统的分布式观测器设计

2022 12th International Conference on Information Science and Technology (ICIST) Pub Date : 2022-10-14 DOI: 10.1109/ICIST55546.2022.9926765

Qingyuan Li, Tian Chen, Yueyuan Zhang, Jun Huang, Lei Yu

引用次数: 0

Masked Autoencoder for ECG Representation Learning 用于心电表征学习的掩码自编码器

2022 12th International Conference on Information Science and Technology (ICIST) Pub Date : 2022-10-14 DOI: 10.1109/ICIST55546.2022.9926900

Shunxiang Yang, Cheng Lian, Zhigang Zeng

引用次数: 1

Online Event-Triggered Adaptive Optimal Control of Nonlinear Large-Scale Systems 非线性大系统在线事件触发自适应最优控制

2022 12th International Conference on Information Science and Technology (ICIST) Pub Date : 2022-10-14 DOI: 10.1109/ICIST55546.2022.9926827

Hanguang Su, Xinyang Luan, Yiwen Zheng, Qianhui Xu, Jinzhu Yang

{"title":"Online Event-Triggered Adaptive Optimal Control of Nonlinear Large-Scale Systems","authors":"Hanguang Su, Xinyang Luan, Yiwen Zheng, Qianhui Xu, Jinzhu Yang","doi":"10.1109/ICIST55546.2022.9926827","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926827","url":null,"abstract":"In this work, we proposed a new online decentralized event-triggered control method which is applicable to some of large-scale systems with nonlinear inter-connection affected by unknown inside system dynamics. This work first designs a recognizer based on neural network to rebuild the uncertain internal dynamics in interconnected system. In the presence of an event triggering mechanism, we next study an approximate optimal control method by adopting the adaptive critic learning method. In this paper, the decentralized event trigger conditions are influenced by only partial state messages of the relevant subsystems, so are controllers. Thus this approach eliminates some problems arising from the process of transmitting status information between subsystems via wireless communication networks. By using Lyapunov's theorem, we show that the state and critical weight estimation errors of the developed closed-loop control system are uniformly ultimately bounded. At last, two cases confirm the validity and suitability of the approach which designed in this paper.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122600041","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

Marine Aquaculture Information Extraction from Optical Remote Sensing Images via MDOAU2-net 基于MDOAU2-net的光学遥感影像海洋养殖信息提取

2022 12th International Conference on Information Science and Technology (ICIST) Pub Date : 2022-10-14 DOI: 10.1109/ICIST55546.2022.9926847

Guanghu Kuang, Jichao Wang, Jianchao Fan, Jun Wang

引用次数: 0

A novel energy carbon emission codes based carbon efficiency evaluation method for enterprises 一种新的基于能源碳排放码的企业碳效率评价方法

2022 12th International Conference on Information Science and Technology (ICIST) Pub Date : 2022-10-14 DOI: 10.1109/ICIST55546.2022.9926851

Jianxu Xing, F. Lu, Liang Cen, Xiaoming Yin, Kang Pan, Hai-Fen Liu, Xiaofei Chen, Chao Li

{"title":"A novel energy carbon emission codes based carbon efficiency evaluation method for enterprises","authors":"Jianxu Xing, F. Lu, Liang Cen, Xiaoming Yin, Kang Pan, Hai-Fen Liu, Xiaofei Chen, Chao Li","doi":"10.1109/ICIST55546.2022.9926851","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926851","url":null,"abstract":"This article studies the energy carbon emission (ECE) evaluation problem for enterprises, and designs ECE-codes to conduct multi-factor grading evaluation of the ECE. The ECE evaluation results of enterprises need to be displayed to non-professionals such as consumers. Therefore, in addition to being able to characterize the ECE level, the ECE evaluation results of enterprise should be intuitive and easy to understand. For this purpose, the ECE-codes are designed, including the horizontal identification code (HIC), the efficiency identification code (EIC), and the neutralization identification code (NIC). Define carbon emission intensity (CEI) as the ECEs required to produce an unit of profit. The HIC is formed by dividing the CEI of enterprises into three levels, and is used to grade an enterprise's carbon emission level in the entire industrial sector. The EIC is formed through five-level evaluation of CEI in sector (CEIS), which is used to measure the CEI level of an enterprise in its sector. The CEIS is the ratio of CEI to the average value of the sector CEI. NIC is used to display an enterprise's carbon neutrality process, which is the ratio of carbon neutrality to total carbon emissions. To achieve effective grading of CEI and CEIS, a Gaussian mixture model (GMM) is established to describe the distribution of the CEI and CEIS, and the model parameters are identified by the expectation maximization (EM) algorithm. Then, the grading thresholds can be obtained according to the GMM probability density function with given percentage parameters. The effectiveness of the ECE-codes based grading evaluation method is verified by applying this method to the carbon emission evaluation of the registered enterprises in Huzhou, china.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114555451","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 Relation Network Based Approach for Few-Shot Point Cloud Classification 基于关系网络的少射点云分类方法

2022 12th International Conference on Information Science and Technology (ICIST) Pub Date : 2022-10-14 DOI: 10.1109/ICIST55546.2022.9926921

Yayun Wang, Shiwei Fu, Chun Liu

{"title":"A Relation Network Based Approach for Few-Shot Point Cloud Classification","authors":"Yayun Wang, Shiwei Fu, Chun Liu","doi":"10.1109/ICIST55546.2022.9926921","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926921","url":null,"abstract":"As a commonly used format of 3D data, point clouds preserve the original geometric information in 3D space without any discretization. In recent years, many deep learning methods have been proposed for recognizing and classifying 3D point cloud data. These methods often require a large number of labeled point clouds for training. However, it is obviously difficult to obtain enough labeled samples for all classes of point clouds in practice. To address this issue, this paper proposes a relation network based on point cloud classification method which can recognize the objects that the point cloud data represents with only few labeled samples. In order to better obtain the local neighborhood information, we use EdgeConv operator to extract the features of each point of the point clouds. And the class of a point cloud will be predicted by measuring the similarity between its feature and the prototypes of a few marked point clouds. Based on the dataset of ModelNet40, the experiments have shown that the proposed method can achieve 92.48% in accuracy and shows better performance compared with related works.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127201072","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

Design of Airline Baggage Automatic Handling System Based on Depth Camera 基于深度相机的航空行李自动处理系统设计

2022 12th International Conference on Information Science and Technology (ICIST) Pub Date : 2022-10-14 DOI: 10.1109/ICIST55546.2022.9926789

Pan Zhang, Yuhan Liu, Wei Zhang

引用次数: 0

Discrete Zhang Neural Dynamics Algorithms for Time-Varying Matrix Generalized Sinkhorn Scaling 时变矩阵广义Sinkhorn标度的离散张神经动力学算法

2022 12th International Conference on Information Science and Technology (ICIST) Pub Date : 2022-10-14 DOI: 10.1109/ICIST55546.2022.9926881

Ji Lu, Jianzhen Xiao, Canhui Chen, Mingzhi Mao, Yunong Zhang

引用次数: 0

Feature Selection via Normalized Dynamic Change of Selected Feature with Class 通过对所选特征与类的归一化动态变化进行特征选择

2022 12th International Conference on Information Science and Technology (ICIST) Pub Date : 2022-10-14 DOI: 10.1109/ICIST55546.2022.9926909

Yadi Wang, Xiangyu Wang, Xianyu Zuo, Hangjun Che, Baojun Qiao, Ying Du

引用次数: 0