2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA)最新文献_第3页

Revisiting the State-of-the-art Deployment and Coverage Preserving Performance for WSN 重新审视WSN最先进的部署和覆盖保持性能

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2019-06-19 DOI: 10.1109/ICIEA.2019.8833832

Zhixiao Wang, Abdul Hadi

{"title":"Revisiting the State-of-the-art Deployment and Coverage Preserving Performance for WSN","authors":"Zhixiao Wang, Abdul Hadi","doi":"10.1109/ICIEA.2019.8833832","DOIUrl":"https://doi.org/10.1109/ICIEA.2019.8833832","url":null,"abstract":"The deployment and coverage are considered as the fundamental performance indicators in wireless sensor networks (WSNs). Finding an optimal sensors deployment strategy that is low cost and provides high-quality coverage and connectivity is a difficult problem. Quality of coverage (QoC) and quality of service (QoS) in WSN are the direct result of the sensors deployment strategy employed in the region of interest. Sensors deployment strategy in WSN aligns with the goals of the WSN application. The aim of this study is to review the optimization techniques for sensors deployment and network coverage and connectivity used in the past few years. An optimized sensor deployment strategy is the one where the network delivers the desired sensing goal while keeping the network connected. Both problem areas are interconnected and can greatly affect the performance of the wireless sensor network. The cost and success of a wireless sensor network greatly depend on the sensing objective of the network. A WSN can be heavily deployed with sensors has greater performance but the operating cost is also higher. While achieving the sensing goal with a minimal number of sensors is a low-cost operation, it cannot be fault tolerant and can also be prone to delays in communication. Therefore, most wireless sensor networks discussed here are a tradeoff between the two objectives.","PeriodicalId":311302,"journal":{"name":"2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127682021","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

Recognition of Composite Motions based on sEMG via Deep Learning 基于表面肌电信号的深度学习复合运动识别

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2019-06-19 DOI: 10.1109/ICIEA.2019.8834270

Shuhao Qi, Xingming Wu, Jianhua Wang, Jianbin Zhang

{"title":"Recognition of Composite Motions based on sEMG via Deep Learning","authors":"Shuhao Qi, Xingming Wu, Jianhua Wang, Jianbin Zhang","doi":"10.1109/ICIEA.2019.8834270","DOIUrl":"https://doi.org/10.1109/ICIEA.2019.8834270","url":null,"abstract":"Surface electromyography(sEMG) is a reliable physiological electrical signal, which represents real-time human motion intents. And the EMG-based motion recognition has the characteristics of convenient operation, non-invasion and noninterference, which has broad application prospects. This paper focuses on composite motion(including multiple actions) recognition, such as sign language motions and handwritten motions. We proposed a novel method for composite motion recognition by using deep learning. To begin with, we defined a novel data structure called sEMG image and established convolution Neural Network designed for sEMG images. In order to reduce the demand of training data, we proposed to pre-train the network by MNIST data set based on the thought of transfer learning. To verify the methods that we proposed, we acquired and preprocessed the surface EMG signals of composite motions, including handwritten number motions and sign language motions. From the results, it can be concluded that deep learning methods perform better than traditional methods, including support vector machine(SVM) and Dynamic Time Warping(DTW). Especially in different sizes handwritten number recognition experiments, the deep learning methods is still very excellent, while accuracies of traditional methods are greatly reduced. In addition, we discovered that transfer learning can help ConvNet to quickly converge and reduce the demand for data.","PeriodicalId":311302,"journal":{"name":"2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128548625","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

A variable inertance and variable damping vibration control system with electric circuit 一种带电路的变惯性变阻尼振动控制系统

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2019-06-19 DOI: 10.1109/ICIEA.2019.8834310

D. Ning, Zhijuan Jia, H. Du, Weihua Li, Nong Zhang

引用次数: 2

Single Image Dehazing Using Non-local Total Generalized Variation 基于非局部全广义变分的单幅图像去雾

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2019-06-19 DOI: 10.1109/ICIEA.2019.8833710

Renjie He, Xiucai Huang

引用次数: 1

A Torque Ripple Reduction Method of Small Inductance Brushless DC Motor Based on Three-level DC Converter 基于三电平直流变换器的小电感无刷直流电动机转矩脉动抑制方法

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2019-06-19 DOI: 10.1109/ICIEA.2019.8834385

Yue Liu, Jianhui Hu, Shili Dong

引用次数: 0

Electrochamical Model-based SOC Estimations by Using Different Algorithms for Lithium-ion Batteries 基于电化学模型的锂离子电池荷电状态估计

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2019-06-19 DOI: 10.1109/ICIEA.2019.8834045

Chao Lyu, Lulu Zhang, Junfu Li, Yanben Zhao, W. Luo, Lixin Wang

引用次数: 0

A Simplified Output Voltage Ripple Calculation Method of Intrinsically Safe Buck Converter with LC Low Pass Filter LC低通滤波器本质安全降压变换器输出电压纹波的简化计算方法

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2019-06-19 DOI: 10.1109/ICIEA.2019.8833828

Yan Li, Shu-lin Liu, Hai-zhou Gao

{"title":"A Simplified Output Voltage Ripple Calculation Method of Intrinsically Safe Buck Converter with LC Low Pass Filter","authors":"Yan Li, Shu-lin Liu, Hai-zhou Gao","doi":"10.1109/ICIEA.2019.8833828","DOIUrl":"https://doi.org/10.1109/ICIEA.2019.8833828","url":null,"abstract":"Intrinsically safe Buck converter is an efficient DC-DC step-down switching circuit applied in flammable and explosive environment. The output voltage ripple (OVR) is an important electrical performance of the converter. It affected by switching frequency, filter inductance, capacitance, duty cycle, load resistance and other parameters. It is not easy to accurately calculate. The OVR calculation of the intrinsically safe Buck converter with LC low pass filter is more complicated compared with that of the traditional Buck converter. In this paper, an ideal circuit topology which is completely equivalent to the intrinsically safe Buck converter with LC low pass filter in continuous conduction mode (CCM) is constructed. By analyzing the frequency response of the equivalent circuit, the steady-state output characteristics of the circuit is obtained. Based on the voltage harmonic analysis, the OVR calculation method of the converter is obtained. Further ripple theoretical calculation and simulation analysis show that the fundamental ripple ratio can be equivalent to the total ripple ratio of the circuit completely and accurately in numerical computation. The final simulation results show that this method can effectively calculate the OVR of the intrinsically safe Buck converter with LC low pass filter.","PeriodicalId":311302,"journal":{"name":"2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127447761","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: 3

The Analysis of Dead time's Influence on the Operating Characteristics of LLC Resonant Converter 死区时间对LLC谐振变换器工作特性的影响分析

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2019-06-19 DOI: 10.1109/ICIEA.2019.8834022

Tian Gao, Ze Cheng, Qi Wang, Yue Li, KeJing Zeng, Yan Yang

引用次数: 4

Distribution Network Supplier Selection with Fuzzy AHP and TOPSIS 基于模糊层次分析法和TOPSIS的配电网供应商选择

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2019-06-19 DOI: 10.1109/ICIEA.2019.8834014

Zhiwei Li, Liping Sun, Lanfei He, Xuejun Tang, X. Zou, Minghua Chen

引用次数: 3

A review of methods for preparing superhydrophobic surfaces on paper 纸上超疏水表面制备方法综述

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2019-06-01 DOI: 10.1109/ICIEA.2019.8833692

Shangjie Jiang, Shisheng Zhou, Bin Du, Rubai Luo

引用次数: 1