2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)最新文献_第6页

Minimizing Completion Time of Processing and Assembly Operations Using Simulated Annealing 利用模拟退火最小化加工和装配操作的完成时间

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702253

Shuangquan Hu, Jufeng Wang

引用次数: 0

Algorithms on a variable-size rectangular interface 变大小矩形界面上的算法

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702195

I. Kacem, Ilyes Kadri, B. Martin, Isabelle Pecci

{"title":"Algorithms on a variable-size rectangular interface","authors":"I. Kacem, Ilyes Kadri, B. Martin, Isabelle Pecci","doi":"10.1109/ICNSC52481.2021.9702195","DOIUrl":"https://doi.org/10.1109/ICNSC52481.2021.9702195","url":null,"abstract":"The purpose of this paper is to elaborate approximate algorithms capable of generating two-dimensional interfaces that contain a given set of services adapted to the user’s context. The proposed methods give good solutions that minimize the used surface, by taking in consideration the placement and the positioning of the tiles. The obtained interface from packing all the tiles into a variable-size bin should be adapted and very simple to use. In order to increase the usability of these solutions (configurations) we develop a mathematical modeling incorporating different Human-Computer Interaction constraints for an exact resolution. Such a placement is performed on the basis of the importance and the category of the proposed service in the tiles. Moreover, we suggest two approximate approaches: a heuristic based on shelf strategy and a memetic algorithm. The exact resolution of the proposed multi-objective mathematical model gives an optimal solution only for small and medium instances. For the other instances, it is difficult to reach the optimal value in a short running time, which shows the practical interest of the proposed approaches.","PeriodicalId":129062,"journal":{"name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"284 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":"125872922","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

Research on Coupled Task Allocation and Scheduling of Multi-type Robots 多类型机器人耦合任务分配与调度研究

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702203

Xingkai Wang, Zichao Xing, Weimin Wu, Xinyu Chen

引用次数: 0

Maintenance Operation Plan Generation Method Based on E-CARGO Model 基于E-CARGO模型的维修作业计划生成方法

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702259

Haiqiang Mei, Wanwen Zheng, Ke Hao, Tao Zhang, Yuxiang Sun, Xianzhong Zhou

引用次数: 0

Multi-Sensor Based IoT Indoor Localization 基于多传感器的IoT室内定位

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702235

D. Tan, C. Seow, Kai Wen

{"title":"Multi-Sensor Based IoT Indoor Localization","authors":"D. Tan, C. Seow, Kai Wen","doi":"10.1109/ICNSC52481.2021.9702235","DOIUrl":"https://doi.org/10.1109/ICNSC52481.2021.9702235","url":null,"abstract":"A typical indoor localization system relies on the availability of infrastructure such as Wi-Fi Access Points, blue-tooth beacons or antenna arrays. This increases the overall system cost and it may not be feasible for deployment in real environments such as shopping malls. A practical indoor localization system should be one that can function with mini-mum existing infrastructure. The proposed system in this paper leverages on the embedded sensors in off-the-shelf Internet of Things (IoT) devices such as smartphone in conjunction with Quick Response (QR) codes which are widely deployed under the authorities requirement due to COVID-19 pandemic. Our proposed stationary inertial measurement unit (IMU) feature is implemented through a first order finite impulse response (FIR) filter that works along with the QR codes. It has successfully reduced the drift errors suffered by IMU. The performance was evaluated in the testing environment at an university campus. From the evaluation results, the proposed method outperformed the conventional method (IMU only) and hybrid model (IMU + QR code) by 94.9% and 57.7% respectively, making the proposed method a promising technique that can be readily applied to other indoor environments.","PeriodicalId":129062,"journal":{"name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"8 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":"132097360","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

Research on UAV Multi-Object Tracking Based on Deep Learning 基于深度学习的无人机多目标跟踪研究

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702158

Xian-xian Luo, Ruili Zhao, Xiangyan Gao

{"title":"Research on UAV Multi-Object Tracking Based on Deep Learning","authors":"Xian-xian Luo, Ruili Zhao, Xiangyan Gao","doi":"10.1109/ICNSC52481.2021.9702158","DOIUrl":"https://doi.org/10.1109/ICNSC52481.2021.9702158","url":null,"abstract":"UAV is widely used in civil or military fields due to its advantages of flexibility, compact and lightness, and it can replace human beings to explore unknown regions or perform various dangerous tasks, such as terrain survey, border patrol, intelligent transportation, power grid detection and disaster detection. Multi-object tracking is an important field in computer vision that has been studied for a long time. In recent years, with the rapid development of UAV technology, object tracking based on UAV has become a research hotspot. In this paper, the application of deep learning in UAV object tracking is studied based on the improved tracking-by-detection multi-object tracking neural network. The processed public data set is used to train the backbone network based on CSPDarknet53 as the detector while the dataset of cars is used to train a pretraining apparent feature vector based on deep learning. Besides, the Kalman filter is used to extract object motion information and update the prediction. Finally, tracking results are obtained by Hungarian matching algorithm. Several groups of experiments on the UAV123 data set show that the trained multi-object tracking algorithm on UAV platform can track the object stably under rapid object movement, fast turning of object motion, multi-object motion, object scale changes and other conditions.","PeriodicalId":129062,"journal":{"name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"1 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":"125372090","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: 2

Distributed Adaptive Consensus via Event-triggered Sampling: An Edge-based Method 基于事件触发采样的分布式自适应共识:一种基于边缘的方法

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702132

Dongdong Yue, S. Baldi, Wenying Xu, Jinde Cao

引用次数: 0

Health-aware Fast-charging Control of Lithium-Ion Battery Based on Reinforcement Learning 基于强化学习的锂离子电池健康感知快速充电控制

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702172

Yikun Yang, Jingwen Wei, Chunlin Chen

{"title":"Health-aware Fast-charging Control of Lithium-Ion Battery Based on Reinforcement Learning","authors":"Yikun Yang, Jingwen Wei, Chunlin Chen","doi":"10.1109/ICNSC52481.2021.9702172","DOIUrl":"https://doi.org/10.1109/ICNSC52481.2021.9702172","url":null,"abstract":"Lithium-ion battery stands out from many kinds of energy storage devices due to its promising characteristics and has become the energy supply unit of Electric Vehicles (EVs). However, the charging speed of lithium-ion batteries limits the sustainable range of EVs, resulting in \"range anxiety\" for drivers and necessitating a faster charging method. To fulfill the safety and economic requirements of battery operations, the temperature and degradation of batteries must be taken into account during fast charging control. Consequently, a reinforcement-learning-based health-aware fast charging control scheme is proposed in this paper. First, a coupling model that considers aging and thermal dynamics simultaneously is established to capture battery behaviors. Then, the health-aware fast-charging problem is formulated as a comprise between charging time and battery degradation. After that, a reinforcement learning control scheme is proposed to find the optimal fast charging solution, where the influence of battery parameter drift on the charging strategy is considered. Finally, simulations are performed to validate the effectiveness and superiority of the proposed method. Compared with the commonly used constant current and constant voltage charging strategy, the proposed charging strategy can improve safety and prolong battery lifetime by sacrificing some charging speed.","PeriodicalId":129062,"journal":{"name":"2021 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"45 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":"126582146","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

Reducing Differences Between Real and Realistic Samples to Improve GANs 减少真实样本和真实样本之间的差异以改进gan

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702260

Shen Zhang, Huaxiong Li, Yaohui Li, Xianzhong Zhou, Chunlin Chen

引用次数: 0

Battery Charge Dispatching in Multi-robot Systems 多机器人系统中的电池充电调度

2021 IEEE International Conference on Networking, Sensing and Control (ICNSC) Pub Date : 2021-12-03 DOI: 10.1109/ICNSC52481.2021.9702226

Zichao Xing, Weimin Wu, Haoyi Niu, Ruifen Hu

引用次数: 1