2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)最新文献_第4页

Experimental Study on Nonlinear Vibration Control for a Flat Plate with Multiple Piezoelectric Elements 多压电元件平板非线性振动控制实验研究

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702263

Kazuya Tonomura, Guang Jin, M. Deng

引用次数: 0

Data-driven Industrial Robot Arm Calibration: A Machine Learning Perspective 数据驱动的工业机械臂校准:机器学习视角

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702246

Zhibin Li, Shuai Li, Xin Luo

{"title":"Data-driven Industrial Robot Arm Calibration: A Machine Learning Perspective","authors":"Zhibin Li, Shuai Li, Xin Luo","doi":"10.1109/ICNSC52481.2021.9702246","DOIUrl":"https://doi.org/10.1109/ICNSC52481.2021.9702246","url":null,"abstract":"Robot arms have been widely used in industry. The absolute positioning error of robots without calibration can reach several millimeters, which cannot meet the application requirements of accurate operation. Therefore, it is almost a mandatory procedure for industrial robots to take on-site calibration before being used. Generally, most researchers on robot calibration have mechanical and instrumentation background as the collection of calibration data is tedious and it is usually difficult to access to industrial robots for researchers in other fields. This research explores the calibration problem from a machine learning perspective and provides the first open-access dataset called \"RobotCali\" in this area so that machine learning scientists can step into this field and verify their algorithms on this problem. In the meanwhile, a new calibration method based on the Levenberg-Marquardt (LM) algorithm and extended Kalman filter (EKF) algorithm is proposed, which can significantly improve the absolute positioning accuracy of the robot after calibration. Firstly, the error model of robot is established, and kinematic parameters are initially identified by LM algorithm. Then the EKF algorithm is used to further calibrate these parameters, which has been verified the effectiveness of the proposed method by experimental results. Lastly, the future research work is discussed.","PeriodicalId":129062,"journal":{"name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126934468","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: 4

Fuzzy C-Means Clustering With Neighbor Information Constraint Using Sparse Self-Representation 基于稀疏自表示的模糊c均值聚类

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702237

Cun Sun, Yan Song, Ming Li, Min Li

{"title":"Fuzzy C-Means Clustering With Neighbor Information Constraint Using Sparse Self-Representation","authors":"Cun Sun, Yan Song, Ming Li, Min Li","doi":"10.1109/ICNSC52481.2021.9702237","DOIUrl":"https://doi.org/10.1109/ICNSC52481.2021.9702237","url":null,"abstract":"Fuzzy c-means (FCM) clustering is a significant yet efficient unsupervised learning methods in many fields such as image segmentation, pattern recognition, etc. However, the traditional FCM algorithm cannot perform well at segmentation on images with vague boundaries especially in the presence of noises. To address this problem, by means of the sparse self-representation technique and the incorporation of the neighbor information, a novel FCM clustering method is put forward, which is called fuzzy c-means clustering with neighbor information constraint using sparse self-representation (SSRFCM_N). The main idea of the proposed SSRFCM_N is two fold: 1) besides the traditional cluster center regarding the global information of similarity, another center with respect to the local information is introduced into the objective by using the sparse self-representation technique; and 2) to consider the data distribution adequately, the neighbor information constraint is also incorporated into the objective, contributing to a better accuracy as well as the good robustness to the noise. Finally, experiments on different images show that SSRFCM_N is effective and more competitive than state-of-the-art clustering methods.","PeriodicalId":129062,"journal":{"name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131561704","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 Method to Quickly Determine Some Non-Reachable Markings in Cyclic Petri Nets Based on State Equation 一种基于状态方程的循环Petri网不可达标记快速确定方法

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702245

Yueh-chih Su, Liang Qi, Xiwang Guo, Kun Wang

引用次数: 1

A novel hierarchical charging algorithm for the AGV dispatching problem in a multi-robot system 针对多机器人系统中AGV调度问题，提出了一种新的分层计费算法

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702206

Haoyi Niu, Weimin Wu, Tao Zhang

引用次数: 0

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/icnsc52481.2021.9702193

引用次数: 0

Dual Modal Meta Metric Learning for Attribute-Image Person Re-identification 属性-图像人再识别的双模态元度量学习

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702261

Rongxian Xu, Fei Shen, Hanxiao Wu, Jianqing Zhu, Huanqiang Zeng

{"title":"Dual Modal Meta Metric Learning for Attribute-Image Person Re-identification","authors":"Rongxian Xu, Fei Shen, Hanxiao Wu, Jianqing Zhu, Huanqiang Zeng","doi":"10.1109/ICNSC52481.2021.9702261","DOIUrl":"https://doi.org/10.1109/ICNSC52481.2021.9702261","url":null,"abstract":"Attribute-image person re-identification (AIPR) aiming to retrieve persons from massive images via an attribute query is a meaningful but challenging cross-modal retrieval task. Because there is a huge modal difference between person images and attributes, e.g., on the image modal one subject usually contains of varying instances, but on the attribute modal, one subject only contains an explicit instance. Unlike most existing AIPR methods focusing on shrinking feature differences crossing modals, we propose a dual modal meta metric learning (DM3L) method for AIPR in this paper. Specifically, in each episode, we sample a subset as a new task and split the training data into a single-modal support set of person images and a dual modal query set consisting of both person images and attributes. Based on the single-modal support set and the dual modal query set, our DM3L learns not only attribute-image cross-modal metrics but also learns image-image intra-modal metrics. Therefore, our DM3L method encourages data on both attribute and image modalities are discriminate to improve AIPR. Experiments show that our DM3L outperforms state-of-the-art approaches on Market-1501 Attribute and PETA datasets.","PeriodicalId":129062,"journal":{"name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133348509","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: 4

Fault identification of DES using Petri net 基于Petri网的DES故障识别

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702163

Wenyan Hou, Jianhong Ye, Jiayao Wang

引用次数: 0

Blind Recognition of Channel Coding Based on CNN-BLSTM 基于CNN-BLSTM的信道编码盲识别

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702153

Shuying Zhang, Lina Zhou, Yiduo Tang, Lin Wang, Qiwang Chen

{"title":"Blind Recognition of Channel Coding Based on CNN-BLSTM","authors":"Shuying Zhang, Lina Zhou, Yiduo Tang, Lin Wang, Qiwang Chen","doi":"10.1109/ICNSC52481.2021.9702153","DOIUrl":"https://doi.org/10.1109/ICNSC52481.2021.9702153","url":null,"abstract":"In cognitive radio or military communication systems, the channel coding type recognition of the primary user signal is an important task to realize full awareness of the wireless communication environment. Previous methods to solve this problem usually have high computational complexity, which are not suitable for real-time applications and require rich experience and professional knowledge in manual feature extraction. In this paper, a blind channel coding recognition algorithm based on CNN-BLSTM is proposed. Firstly, this method uses convolutional neural network to extract the data features of coding sequence and also avoids the problem of low recognition accuracy caused by inputting the original codeword data with inconspicuous features directly into neural network. Then, the context dependence of features is obtained through bidirectional long short-term memory network. Finally, the classification task is accomplished by softmax function. The experiments use spatially coupled LDPC codes and 5G NR LDPC codes as candidate codes. The experimental results show that the algorithm achieves quite high recognition accuracy under good channel conditions.","PeriodicalId":129062,"journal":{"name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116359546","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

Two-stage no-wait hybrid flow shop scheduling with inter-stage flexibility: A mathematical model 具有阶段间柔性的两阶段无等待混合流水车间调度:一个数学模型

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702162

Mohamed Naceur Azaiez, A. Gharbi, I. Kacem, Yosra Makhlouf, Malek Masmoudi

引用次数: 0