2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)最新文献_第9页

Research on real-time export container allocation considering loading efficiency 考虑装货效率的出口集装箱实时调度研究

2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004156

Jianlin Qian, Weimin Wu, Yangfei Zhu

引用次数: 0

A DWT-Utilized Classifier for UPJO Diagnosis Using Ultrasound Images 基于dwt的超声图像UPJO诊断分类器

2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004150

Yu Guan, Pengceng Wen, Jianqiang Li, Zhilong Ma

引用次数: 0

Trajectory Tracking Control for Autonomous Parking Based on Adaptive Reduced-horizon Model Predictive Control 基于自适应缩减地平线模型预测控制的自动泊车轨迹跟踪控制

2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004145

Minghan Cai, Weimin Wu, Xiaoling Zhou

引用次数: 0

Scheduling single-arm cluster tools with emergency order insertion 调度带有紧急订单插入的单臂集群工具

2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004059

JinCheng Li, Chunrong Pan, Zhengchao Liu, YiMing Lai

引用次数: 0

Boosting Gas Classification with Attention-based Mechanism 基于关注机制的气体分类方法研究

2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004151

Jiwei Qin, Xiang Li, Meiqi Pan, Gaofei Liu, Zhiyuan Ma

引用次数: 0

An On-Chain Smart Contract Protocol for Tangle 一种用于缠结的链上智能合约协议

2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004163

Guangcheng Li, Qianchuan Zhao, MengChu Zhou, Hong Liang

{"title":"An On-Chain Smart Contract Protocol for Tangle","authors":"Guangcheng Li, Qianchuan Zhao, MengChu Zhou, Hong Liang","doi":"10.1109/ICNSC55942.2022.10004163","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004163","url":null,"abstract":"Chain-structured blockchains (e.g., Bitcoin and Ethereum) are often criticized for resource waste, low scalability, and high transaction fees. Tangle has been proposed to overcome these drawbacks by adopting a directed acyclic graph structure, new consensus mechanisms, etc. Particularly, Tangle defines a transaction-processing rule, which requires that new incoming transactions should approve several existing transactions before being attached to Tangle, to exclude miners and transaction fees. However, this rule makes it difficult to support smart contract (SC) in Tangle, an essential component of numerous decentralized applications, because the execution and verification of SC usually require transaction fees as incentive awards. In this work, we propose an “on-Tangle” SC protocol called equivalent-exchange-based smart contract (EESC), which runs on the Tangle core, to address this challenge. EESC extends the transaction-processing rule to SC and hence maintains Tangle's advantages of no fees and no mining. In EESC, a user should verify other users' SCs before submitting its SC. The workload of verifying these existing SCs is greater than that of verifying the newly submitted one. Extensive simulations verify that EESC is fast and efficient and can well achieve our goal.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132268656","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

Multi-Objective fuzzy flexible jobshop of dynamic scheduling optimization based on hybrid-nondominanted sorting algorithm 基于混合非主导排序算法的多目标模糊柔性作业车间动态调度优化

2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004125

Wei Shen, Weimin Wu, Haoyi Niu

引用次数: 0

Domain Adaptation Soft Sensing with Parameter Transferring 基于参数传递的域自适应软测量

2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004072

Xudong Shi, Jing Xu, Hanqiu Bao, Qi Kang

{"title":"Domain Adaptation Soft Sensing with Parameter Transferring","authors":"Xudong Shi, Jing Xu, Hanqiu Bao, Qi Kang","doi":"10.1109/ICNSC55942.2022.10004072","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004072","url":null,"abstract":"Soft sensor modeling often encounters a distribution discrepancy problem, when working conditions or environmental factors change. Such problem leads to an insufficient number of training data samples for an accuracy regression model construction. In addition, a soft sensor constructed for a specific mode is unlikely to obtain reliable prediction results for other modes. This paper presents a new transfer learning-based soft sensor model to handle the domain adaptation issue with a transferring parameter, which is suitable for multi-mode processes with limited target training samples. The difference between the source and target domains is considered as a parameterized maximum mean discrepancy regularization term in the objective function, based on which a trade-off between minimizing the two domains' difference and maximizing the prediction performance on the target domain's testing samples can be realized. Furthermore, an alternating optimization algorithm is formulated to optimize the transferring parameter along with the output weights. The proposed method is expected to fully leverages the limited target samples and the related source ones simultaneously to construct an adaptive target soft sensor. Comparative studies with several popular soft sensing approaches are conducted to demonstrate the effectiveness and advantages of our approach.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122896743","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 Novel Machine Learning Method for Delayed Labels 一种新的延迟标签机器学习方法

2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004167

Haoran Gao, Zhijun Ding

{"title":"A Novel Machine Learning Method for Delayed Labels","authors":"Haoran Gao, Zhijun Ding","doi":"10.1109/ICNSC55942.2022.10004167","DOIUrl":"https://doi.org/10.1109/ICNSC55942.2022.10004167","url":null,"abstract":"Most research on machine learning relies on the availability of ground truth labels immediately after prediction. However, in many cases, the ground truth labels become available with a non-negligible delay. Considering that there is a large amount of unlabeled data in delayed labels, supervised model cannot utilize unlabeled data. Therefore, most of the research on delayed labels begins to train semi-supervised models in delayed labels. However, most research on delayed labels ignores that the labels of unlabeled data will arrive after several periods in delayed labels. Neither supervised nor semi-supervised models can solve the problem in delayed labels effectively. Besides, there remains a problem of concept drift due to the long period of data. In this paper, we propose an incremental learning model that can adapt to delayed labels. First, we should detect whether the concept drift takes place. Then we use knowledge distillation to update supervised and semi-supervised models while retaining the corresponding knowledge of past labeled data. Finally, we combine the supervised and semi-supervised models to make predictions. Finally, we apply our algorithms to synthetic and real credit scoring datasets. The experiment results indicate our algorithms have superiority in delayed labels.","PeriodicalId":230499,"journal":{"name":"2022 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125974912","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

A modified YOLOv5 for object detection in UAV-captured scenarios 一种改进的YOLOv5用于无人机捕获场景中的目标检测

2022 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2022-12-15 DOI: 10.1109/ICNSC55942.2022.10004160

Jiale Yang, Han Yang, Fei Wang, Xiong-Zi Chen

引用次数: 1