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Impedance Spectroscopy (IWIS), International Workshop on

作者： Nour Ammar Cherif Ouni Ahmed Yahia Kallel Hanen Nouri Bilel Ben Atitallah Nabil Derbel Olfa Kanoun Measurement and Sensor Technology Faculty of Electrical Engineering and Information Technology Chemnitz University of Technology Chemnitz Germany Control and Energy Management Laboratory National Engineering School of Sfax University of Sfax Sfax Tunisia Laboratory of Signals Systems Artificial Intelligence and Networks Digital Research Centre of Sfax National School of Electronics and Telecommunications of Sfax Technopole of Sfax Ons City Tunisia

Cole-Cole impedance model is widely used in electrical bioimpedance measurements to characterize biological tissues. Accurate estimation of the Cole-Cole impedance parameters is crucial in bioimpedance measurements to obtain reliable results. Many studies have proposed approaches to estimate the Cole-Cole impedance parameters with high accuracy and minimal error. However, there is still a need to investigate the minimum number and positions of frequencies required to accurately estimate the Cole-Cole impedance parameters and, thus, efficiently reconstruct the impedance spectrum. This paper proposes a novel approach to reconstruct Cole-Cole bioimpedance parameters using a minimum number of frequencies. The study utilizes an IV method-based system implemented on an STM32 platform to perform impedance measurements. The results obtained from this system are compared with those obtained from a commercial impedance analyzer, Agilent 4294A, in the frequency range of 1 kHz to 1 MHz. After applying nonlinear least squares fitting, the results of two and three impedance measurement frequencies showed that selecting certain positions significantly affects the accuracy. Using three frequencies, including the extreme points and an additional point in the high-frequency range, gave better results with 0.56%. The results described here may be useful in other implementations of impedance spectroscopy.

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Single-View Multi-Sensor Based Tomographic Thermometry of Combustion Flame

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SSRN 2024年

作者： Guan, Jinge Huang, Feng Wei, Naying Zheng, Yongqiu Shi, Jinhao Cheng, Bo School of Information and Communication Engineering North University of China Taiyuan030051 China College of Mechanical Engineering and Automation Fuzhou University Fuzhou350108 China State Key Laboratory of Dynamic Measurement Technology North University of China Taiyuan030051 China Inner Mongolia Test Institute of Aerospace Dynamic Machine Hohhot010076 China

Three-dimensional temperature measurement of flame plays an important role in the field of combustion diagnosis. Traditional radiation thermometry method based on multi-view is applicable to exposed combustion environments, but faces the problem of limited optical testable space in closed ones. In order to solve this problem, single-view multi-sensor structure with ten imaging channels, which is integrated by optical, mechanical and electrical modules, is designed to analyze the spatiotemporal evolution characteristics of combustion temperature by means of tomography. In theory, the mapping relationship between divided combustion slices and image plane of the thermometer is established, and light radiation originating from different combustion slices is independently calculated based on Fourier optics. The temperature of combustion slice is further obtained utilizing Planck law. In experiment, firstly, multiple imaging sensors are operated with common optical axis and synchronous trigging. Then, both photoelectric conversion relationship and point spread function of radiation tomography thermometer are calibrated by checkerboard and blackbody furnace, respectively. Finally, the temperature of three-dimensional transient combustion flame is measured by the thermometer. The results show that the temperature distribution of dynamic combustion flame can be effectively revealed, which validates the practicality of the designed radiation tomography thermometer. © 2024, The Authors. All rights reserved.

关键词： Tomography

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Imdet: Injecting More Supervision to Centernet-Like Object Detection

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SSRN 2023年

作者： Jia, Shukun Song, Chen Cao, Yichao Lu, Xiaobo School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Nanjing Enbo Technology Co. Ltd. Nanjing China

CenterNet-like object detectors are known for the concise post-processing method. But there are two bottlenecks that limit its further research and development. The first one is they merely encode one positive sample for each object in the classification task, failing to make full use of supervision information. And simply encoding more positive samples is not as effective as expectation. The other one lies in that they perform predictions on large-resolution feature maps, ignoring to utilize the insights of multi-level-feature object detection. The two shortcomings make their accuracy lag behind the detectors with one-to-many label assignment strategy and feature pyramids networks (FPN). To solve the two issues, we first find three rules based on Gaussian kernel to set proper target values for multiple positive samples, which are achieved uniformly and cost-free. Second, we devise a balanced and sufficient supervision network to ease the optimization of backbones. Both proposed methods inject more supervision signal to CenterNet-like detectors, and improve CenterNet-like detectors to a competitive performance. The effectiveness of each method is fully verified on large-scale datasets with different data distributions, i.e., the MS-COCO and PASCAL VOC dataset. © 2023, The Authors. All rights reserved.

关键词： Object detection

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Environmentally Friendly Optimization of Coupled Power-Transportation Network considering Mixed Traffic Flow

Environmentally Friendly Optimization of Coupled Power-Trans...

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Electronics and Electrical engineering technology (EEET), International Conference on

作者： Zhiao Cao Yinghua Han Qiang Zhao Shuyi Zhao Wenzhuo Zhao School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Accounting and Finance Taylor’s University Lakeside Campus Subang Jaya Malaysia Sydney Smart Technology College Northeastern University Qinhuangdao China

ISBN: (数字)9798350395594

ISBN: (纸本)9798350395600

The explosive growth of electric vehicles makes the deep coupling between the power distribution network and the urban transportation network, in which the mixed flow consisting of electric vehicles and conventional gasoline vehicles exposes the coupled network to operational problems of congestion and carbon emission overruns. Considering this, this paper constructs an environmentally friendly oriented optimization model for coupled power and transportation networks. Specifically, the mixed flow in a coupled network is considered, the charging price is optimized to guide the charging behavior of electric vehicle users, and the carbon emission cost is adopted to schedule the routes of gasoline vehicles. To solve the optimization model of the coupled network, an accelerated Gauss-Seidel algorithm is designed to obtain the equilibrium state of the entire system, the optimization problems of both networks are solved in an iterative manner and eventually converge to the equilibrium state. A coupled power and transportation network consisting of a modified IEEE-33 bus distribution network and a 20 link transportation network is conducted as a case study, and the impact of charging prices and carbon emissions on the operational strategies of coupled networks is analyzed. The effectiveness of the proposed environmentally friendly oriented optimization model in promoting the cost reduction and flow equilibrium of the coupled network is verified.

关键词： Costs Uncertainty Optimization models Heuristic algorithms Transportation Carbon dioxide Electric vehicles

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Optimal Minimum Energy Path Planning of Mobile Robot Based on Improved WOA-GA with Hybrid Strategy for Community-Centric Application

Optimal Minimum Energy Path Planning of Mobile Robot Based o...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Du, Jiang Da-Lin, Zhou Gong-Fa, Li Bao-Li, Lu Jun-Tong, Yun Ying, Liu Wuhan University of Science and Technology Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan430081 China School of Computing University of Portsmouth PortsmouthPO1 3HE United Kingdom China Three Gorges University Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance Yichang443002 China Wuhan University of Science and Technology Key Laboratory of Mechanical Transmission and Manufacturing Engineering of Hubei Province Wuhan430081 China Precision Manufacturing Research Institute Wuhan University of Science and Technology Wuhan430081 China Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China

ISBN: (纸本)9798350303780

The path planning of mobile robots is investigated to minimize energy loss in intelligent manufacturing. To achieve optimal energy consumption planning, this paper proposes a hybrid strategy that enhances the whale optimization algorithm (WOA) and the genetic algorithm (GA). The WOA-GA algorithm combines their respective advantages and effectively mitigates premature convergence exhibited by the standard traditional algorithm. To enhance the global and local search capabilities of the standard WOA, we employ adaptive weighting and a Cauchy mutation strategy. Additionally, we introduce an adaptive crossover mutation probability and an elite strategy to mitigate the detrimental effects of the genetic algorithm's evolutionary operation in the late iterations, thereby improving its performance. This paper compares the performance of the standard GA, the standard WOA-GA algorithm, and the enhanced WOA-GA algorithm. The experimental results demonstrate that the enhanced WOA-GA algorithm can achieve optimal energy-efficient path planning using the hybrid strategy. The average energy consumption of the enhanced WOA-GA algorithm can be reduced by 30%, while the maximum energy consumption can be reduced by 47%. The results confirm the effectiveness and feasibility of the proposed method in enhancing the level of green manufacturing. © 2023 IEEE.

关键词： Genetic algorithms

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Playback Method for Dynamic Star Map Simulation by Fusing Cosmic Background Radiation Information

SSRN

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SSRN 2024年

作者： Zhao, Bin Zhang, Yu Yang, Dongpeng Yang, Songzhou Zhang, Jian Sun, Gaofei Yao, Meng Ren, Taiyang Junjie, Yang Meng, XiangKai Zhang, Guoyu School of Optoelectronic Engineering Changchun University of Science and Technology Changchun130022 China State Key Laboratory of High Power Semiconductor Laser of Changchun University of Science and Technology Jilin Changchun130022 China Institute of Aerospace Ground Simulation Test and Testing Technology Changchun University of Science and Technology Changchun130022 China Optical Measurement and Control Instrumentation Jilin Province Engineering Research Center Changchun130022 China Key Laboratory of Opto-electronic Measurement and Optical Information Transmission Technology Ministry of Education Department of Instrumentation Science and Technology Changchun University of Science and Technology Changchun130022 China School of Life Science and Technology Changchun University of Science and Technology Changchun130022 China

In this study, a playback method for dynamic star map simulation fusing cosmic background radiation information was proposed to improve the playback speed of star maps. The image distortion effect before and after the fusion of star maps and cosmic background radiation was evaluated using peak signal-to-noise ratios. The performance of the proposed and the global catalog traversal generation algorithms without cosmic background radiation simulation capability, as well as the indicators, were experimentally compared. The results showed that the average refresh rates of the view cone filtering star map fast-generation algorithm and the fusion playback algorithm of the star map and cosmic background radiation were 74.2 and 73.7 Hz, respectively. Compared with the entire catalog traversal generation algorithm, the instantaneous stability of the single-frame star map was improved by 3.045 and 3.305 times, respectively, and the consumed time consistency was improved by 4.646 and 5.068 times, respectively. © 2024, The Authors. All rights reserved.

关键词： Image fusion

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Cuboid-Net: A Multi-Branch Convolutional Neural Network for Joint Space-Time Video Super Resolution

SSRN

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SSRN 2022年

作者： Fu, Congrui Yuan, Hui Xu, Hongji Zhang, Hao Shen, Liquan School of Information Science and Engineering Shandong University Shandong Qingdao266237 China School of Control Science and Engineering Shandong University Shandong Jinan250061 China School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China School of Communication and Information Engineering Shanghai University Shanghai China

We propose a novel neural network for space-time super resolution (ST-SR) of videos, in which the spatial resolution and the temporal resolution (frame rate) of a video are simultaneously increased. The proposed network contains four modules, i.e., a multi-branch-based hybrid feature extraction (MBFE) module, a multi-branch-based reconstruction (MBR) module, a first stage quality enhancement (QE) module, and a second stage cross frame quality enhancement (CFQE) module for interpolated frames only. The MBFE and MBR modules are used to extract hybrid features for spatial and temporal reconstruction by taking the input video as a cuboid, while the following QE and CEQE modules are used to further improve the reconstruction quality frame by frame. Experimental results demonstrate the effectiveness of the proposed method. © 2022, The Authors. All rights reserved.

关键词： Optical resolving power

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Research on Robot Assembly Line Balancing Considering Energy Consumption

Research on Robot Assembly Line Balancing Considering Energy...

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作者： Haotian, Feng Hongjun, Wang School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing100192 China Intelligent Perception and Control of High-End Equipment Beijing International Science and Technology Cooperation Base Beijing100192 China Key Laboratory of Modern Measurement and Control Technology of Ministry of Education Beijing Information Science and Technology University Beijing100192 China

In order to improve the efficiency of robot assembly line and optimize the energy consumption, an optimization scheme of multi position task allocation for the same robot is designed. Based on a robot assembly line, a mathematical model of robot assembly line balance is established, which takes smoothness index, assembly energy consumption and input cost as optimization objectives. On this basis, an improved multi-objective hybrid genetic algorithm is proposed. In this algorithm, the penalty function method is introduced to adjust the convergence of the solution, and an adaptive weighting mechanism based on bat algorithm is added to expand the global search ability. Finally, based on the actual situation of a robot assembly line, Flexsim was used to conduct simulation experiments on the optimization results to verify the feasibility and effectiveness of the optimization scheme. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Key Technologies of IoT Intelligent Sensing Terminal for Smart Energy

Key Technologies of IoT Intelligent Sensing Terminal for Sma...

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2022 IEEE International Conference on High Voltage engineering and Applications, ICHVE 2022

作者： Liu, Siyang Mao, Yuxing Long, Yunfeng Pan, Jianyu Jiang, Tianyan Cao, Min State Key Laboratory of Power Equipment & System Security and New Technology School of Electrical Engineering Chongqing University Chongqing China Yunnan Key Laboratory of Green Energy Electric Power Measurement Digitalization Control and Protection Electric Power Research Instute Yunnan Power Grid Co. Ltd. Yunnan Kunming China

ISBN: (数字)9781665407502

ISBN: (纸本)9781665407502

To build a smart energy system, we must first deploy a fully aware terminal layer to connect all energy things through perception. This paper proposes key technologies of IoT intelligent sensing terminal for smart energy. Firstly, smart energy Internet of things function is given to analyze the architecture for smart energy Internet of things. Analysis of Internet of Things terminal ecosystem is located. Finally, the key technologies including resource unified modeling, hardware and software system of Internet of things terminal are proposed. Customized design of intelligent sensing terminal is given to manufacture intelligent sensing terminal for different application scenarios of smart energy. © 2022 IEEE.

关键词： Internet of things

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Blind identification of MIMO Space-Time Block Codes Based on Convolutional Neural Network 21

Blind identification of MIMO Space-Time Block Codes Based on...

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Proceedings of the 7th International Conference on communication and Information Processing

作者： Conglin Pan Si Chen Huijie Zhu Wei Wu Jiachuan Qian Lijun Wang Science and Technology on Communication Information Security Control Laboratory No.36 Research Institute of CETC China and School of Electronic and Optical Engineering Nanjing University of Science and Technology China Science and Technology on Communication Information Security Control Laboratory No.36 Research Institute of CETC China

ISBN: (纸本)9781450385190

Aiming at the blind identification of space-time block codes (STBC) in multiple input multiple output (MIMO) system, this paper proposes a new convolutional neural network (CNN-N) to realize the blind identification. Compared to traditional algorithms using statistical features of received signal, CNN-N can reduce the computation with a higher correct identification rate. Consist of multiple layers with special functions, CNN-N has good generalization ability in complex MIMO channels. The network in this paper can recognize 6 STBC codes include spatial multiplexing signal (SM) and some OSTBC codes. The simulation result shows that this new convolutional neural network can finish STBC identification with a high correct rate even in low SNR by consuming moderate amounts of time.

关键词： Generalization Ability Convolutional Neural Network Blind Identification STBC MIMO

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