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Asia communications and Photonics Conference and Exhibition (ACP)

作者： Huaxin Gu Shuaiqi Liu Feihong Yu Deyu Xu Xingwei Chen Liyang Shao Yu Wu Haifeng Zhang College of Innovation and Entrepreneurship Southern University of Science and Technology Shenzhen China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China School of Information and Communication Engineering University of Electronic Science and Technology Chengdu China Advanced Measurement and Control and Equipment R&D Center Research Institute of Tsinghua University Chengdu China

In this paper, fast independent vector analysis (FastIVA) based on convolutional aliasing model is proposed to separate the aliased signals collected by distributed acoustic sensing (DAS) system, and the time-frequency entropy is used to judge the separation performance. The results show that the time-frequency entropy interval of the separated signals and the source signals correspond to each other, which means that the aliased signals are separated. This method is used to improve the accuracy of DAS system signal detection and recognition in complex real environment, and reduce the number of false alarm events.

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Multi-strategy Improvement of the Sparrow Search Algorithm for Optimizing the UKF Method

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Science technology and engineering 2025年第1期25卷 227-237页

作者： Liu, Jian-Juan Li, Zhi-Wei Ji, Miao-Xin Wu, Hao-Ran Xu, Qiang-Wei School of Electrical Engineering Henan University of Technology Zhengzhou450001 China Institute of Electromechanical Equipment and Measurement & Control Technology Henan University of Technology Zhengzhou450001 China

A method for optimizing the control parameters of the sample point distribution state within the framework of the unscented transform (UT) for the unscented Kalman filter (UKF) was introduced. The issue of abnormal filtering performance arising from the state of sample point distributions was addressed by this method. A multi-strategy improved sparrow search algorithm(ISSA) was employed to finely tune the control parameters. The goal is to enhance the distribution of Sigma points, thereby improving the effectiveness of nonlinear approximations and ultimately enhancing the accuracy of filtering estimations. To address the shortcomings of traditional sparrow search algorithms, several refinements were implemented. Initially, a Cubic chaotic mapping was applied to diversify the initial population. Furthermore, during the exploration phase, a nonlinear adaptive convergence factor was introduced to balance the algorithm's capacity for global exploration and local exploitation. Additionally, a wavelet mutation strategy was integrated into the follower phase to prevent blind adherence to specific paths and mitigate the risk of becoming trapped in local optima. Lastly, an adaptive t-distribution perturbation capability was introduced to strengthen the algorithm's ability to perform wide-ranging global searches. The efficacy of the proposed ISSA was demonstrated through simulation experiments conducted on various test functions. The results consistently show that ISSA outperforms other methods in terms of convergence and solution accuracy. Furthermore, the benefits of ISSA are extended to the optimization of parameters within the UKF algorithm. Experimental outcomes indicate that the ISSA-UKF algorithm reduces the root mean square error (RMSE) of position by 52. 2% and the RMSE of velocity by 21. 9%, thus affirming the viability and effectiveness of the proposed enhancements. © 2025 Beijing Kexue Jishu yu Gongcheng Zazhishe. All rights reserved.

关键词： Approximation algorithms

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Physics-Based Deep Learning Framework for Terahertz Thickness measurement of Thermal Barrier Coatings

SSRN

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SSRN 2023年

作者： Sun, Fengshan Cao, Binghua Fan, Mengbao Liu, Lin School of Mechatronic Engineering China University of Mining and Technology Xuzhou221116 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Beijing Aerospace Institute for Metrology and Measurement Technology Beijing100076 China

A challenge in Terahertz (THz) thickness measurement of thermal barrier coatings (TBCs) is the change of refractive index due to the uneven microstructure of topcoat, which may degrade the accuracy of the time-of-flight approach. Here, we present a novel physics-based deep learning framework, a method which evolves the entire THz signals into optimal sparse features to decrease the errors of thickness measurement from the uneven microstructure and distorted THz echoes. Firstly, the basics of first three peaks with the feature of the topcoat refractive index are proved by THz transmission rules in TBCs, but there is a non-injective surjective function between the peaks and refractive index. To improve the accuracy of THz thickness measurements, a novel layer, termed as weight constraint layer is developed for assigning the weights for first three peaks with the constraint condition based on THz physics, yielding the optimal physics-based sparse features for thickness measurements. Then, an analytical model is built to generate the simulated THz traces, acting as the training dataset to minimize the expenditure of the specimen preparations. Finally, the experimental results demonstrate that the physics-based deep learning framework can enable the accurate TBCs thickness measurement. © 2023, The Authors. All rights reserved.

关键词： Thickness measurement

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Domain Generalization for Cross-Receiver Radio Frequency Fingerprint Identification

arXiv

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arXiv 2024年

作者： Zhang, Ying Li, Qiang Liu, Hongli Yang, Liu Yang, Jian School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Beijing Institute of Technology Beijing100081 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100083 China

Radio Frequency Fingerprint Identification (RFFI) technology uniquely identifies emitters by analyzing unique distortions in the transmitted signal caused by non-ideal hardware. Recently, RFFI based on deep learning methods has gained popularity and is seen as a promising way to address the device authentication problem for Internet of Things (IoT) systems. However, in cross-receiver scenarios, where the RFFI model is trained over RF signals from some receivers but deployed at a new receiver, the alteration of receiver's characteristics would lead to data distribution shift and cause significant performance degradation at the new receiver. To address this problem, we first perform a theoretical analysis of the cross-receiver generalization error bound and propose a sufficient condition, named Separable Condition (SC), to minimize the classification error probability on the new receiver. Guided by the SC, a Receiver-Independent Emitter Identification (RIEI) model is devised to decouple the received signals into emitter-related features and receiver-related features and only the emitter-related features are used for identification. Furthermore, by leveraging federated learning, we also develop a FedRIEI model to eliminate the need for centralized collection of raw data from multiple receivers. Experiments on two real-world datasets demonstrate the superiority of our proposed methods over some baseline methods. © 2024, CC BY-NC-ND.

关键词： Federated learning

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Unsupervised Deep Homography Estimation based on Transformer

Unsupervised Deep Homography Estimation based on Transformer

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Tianjian Jiang Qiu Fang Qing Zhu Yaonan Wang Zhen Zhou Lin Chen Jiaming Zhou Yuefan Luo Chengzhong Wu National Engineering Research Center for Robot Visual Perception and Control Technology Hunan University Changsha Hunan China Jiangxi Communication Terminal Industry Technology Research Institute.

Homography estimation is a crucial problem in computer vision, which aims to provide an optimal transformation matrix for aligning images captured from different viewpoints. Current methods extract shallow features from image pairs and introduce learnable mask modules to improve homography estimation performance. However, they struggle to capture long-term dependencies between features and comprehend the global structures of image features. A deep unsupervised homography learning framework is proposed in this paper, consisting of a weight-sharing feature extraction network and a homography estimation network based on the Transformer model. The former extracts the local features of images, while the latter learns the correlation between them and understands the global features of images, enabling the algorithm to better estimate the homography of unaligned images. Experimental results demonstrate that the proposed method outperforms the advanced methods for estimating homography matrices in the CA-Unsupervised dataset.

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INTELLIGENT TRACKING AND NAVIGATING MOVING OBJECTS IN A SMART ENVIRONMENT USING IOT NETWORK 1

INTELLIGENT TRACKING AND NAVIGATING MOVING OBJECTS IN A SMAR...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Parvathi, R. Kumar, T.Ch. Anil Nandhakumar, A. Arshad, Haqqani Singh, Ajith B. Department of Information Technology School of Computing Science Hindustan Institute of Technology and Science Padur Chennai India Department of Mechanical Engineering Vignan's Foundation for Science Technology and Research Vadlamudi Dt. Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Dhaanish Ahmed Institute of Technology Chennai India Department of Mechanical Engineering Yanbu Industrial Collge Yanbu Western Region Saudi Arabia Department- Instrumentation and Control Engineering Sri Krishna College of Technology Coimbatore India

ISBN: (纸本)9781607685395

Internet of things (IoT) constitutes the combinations of sensors, controllers, actuators, connectivity. Tracking and navigating moving objects are crucial tasks for security as well as a secure and smart environment. By using the sensory data, location of objects can be identified then that sensory data will be transferred to controllers for the further processes. One pivot role of the system is to identify the trajectory and location of the object using the speed, velocity, acceleration, maps etc and this can be done by machine learning programs. Analyzing service required, prediction of the trajectory and real-world knowledge of maps or locations, To avoid the loop-carry dependency, service request parameters are dynamically changed to locate relevant virtual objects. In order to track moving items, this study provides an architecture based on IoT self-aware environment technology that facilitates the tracking and allocation of relevant virtual objects. © The Electrochemical Society

关键词： IoT loop-carry dependency machine learning object identification self-aware environment sensory data

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Optimal Scheduling of Multi-Energy Combined Generation System Considering Source Load Uncertainty

SSRN

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SSRN 2023年

作者： Chunhui, Liang Chao, Ding Xiaoyang, Zuo Jinfa, Li Qing, Guo School of Electrical and Information Engineering Changchun Institute of Technology Changchun130025 China National local joint Engineering Research Center for Measurement and Control and Safe Operation Technology of Intelligent Distribution Network Changchun130103 China Jilin Institute of Chemical Technology Jilin132022 China

In order to solve the problem of wind and light abandonment in new energy power generation system, a scheduling strategy of wind, light and fire combined power generation system including concentrating solar power (CSP) station is proposed in this paper. The day-ahead scheduling model of the combined power generation system is established with the maximum net income and the maximum load tracking ability as the optimization objectives. The fixed scene method was used to simulate the source load uncertainty. The Latin Hypercube sampling method and synchronous back substitution method were used to generate and reduce the scene, and different typical scenes were selected according to the scene probability. The combined spiral motion control strategy is added to the grasshopper optimization algorithm (GOA) to solve the scheduling model, which enhances the optimization performance of the algorithm. The effectiveness and correctness of the proposed model and the improved algorithm are verified by simulation in IEEE-30 node system. The numerical results show that the optimal scheduling strategy of multi-energy system proposed in this paper has significant functions in calming load fluctuation and improving system absorption ability. © 2023, The Authors. All rights reserved.

关键词： Optimization

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Calibration of Piezoresistive Pressure Sensors in a Wide Temperature Range

Calibration of Piezoresistive Pressure Sensors in a Wide Tem...

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International Conference on Actual Problems of Electron Devices engineering, (APEDE)

作者： Mikhail S. Svetlov Alexandr A. Nikiforov Peter A. L’vov Ekaterina V. Filina Osama K. Jburi Mohammed H. Kareem Laboratory of Control and Automation Problems in Mechanical Engineering Institute of Precision Mechanics and Control of RAS Saratov Russian Federation Institute of Institute of Electronic Engineering and Instrumentation Yuri Gagarin State Technical University of Saratov Saratov Russian Federation Electronic Design Laboratory Digital Measurement Technologies Ltd Saratov Russian Federation Institute of Applied Information Technologies and Communication Yuri Gagarin State Technical University of Saratov Saratov Russian Federation

ISBN: (数字)9798350393316

ISBN: (纸本)9798350393323

Piezoresistive pressure sensors (PPS) are widely used in various microelectronic devices applied in modern technologies. The performance of these sensors are highly dependent on the ambient temperature. Unfortunately, known methods of PPS calibration do not allow measuring pressure with the required accuracy in the wide temperature range typical, for instance, in aviation from −60 to 140 °C. In this paper, a conventional approach is used, when the dependence of the output voltage on pressure is represented using a polynomial of a relatively low order, and the dependences of the coefficients of this polynomial on temperature are specified by the cubic splines. The paper describes in detail the identification method of the polynomial model under consideration and obtains expressions for correcting the readings of the pressure gauge during pressure measurements in wide temperature ranges, when the temperature dependence model is specified using cubic splines. The results of experimental studies of specific pressure sensors are presented.

关键词： Temperature sensors Temperature measurement Pressure sensors Temperature dependence Temperature distribution Polynomials Pressure measurement Calibration Splines (mathematics) Piezoresistance

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Capacity Configuration Optimization of Wind-Solar Combined Power Generation System Based on Improved Grasshopper Algorithm

SSRN

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SSRN 2023年

In this paper, a wind-solar combined power generation system is proposed in order to solve the absorption problem of new energy power generation. Based on the existing installed capacity of local wind power, a concentrating solar power (CSP) station and its energy storage system are configured, and a two-layer capacity optimization allocation method considering the incentive user response is proposed. Firstly, a two-layer capacity optimization model considering incentive user response is established. Secondly, grasshopper optimization algorithm based on embedded spiral motion control strategy was used to solve the upper configuration model, and the lower scheduling model was solved by Cplex solver. Finally, three typical scenarios are set up for simulation, and the wind power, CSP and energy storage configuration capacity are respectively given in different scenarios. The simulation results show that the addition of a CSP station can effectively improve the absorption capacity of local wind power generation system and reduce the amount of wind abandonment. The capacity optimization allocation method proposed in this paper can effectively alleviate the load peak demand, improve the optimization allocation model of wind-solar combined power generation system, make the configuration results more reasonable, and improve the economy of the system. © 2023, The Authors. All rights reserved.

关键词： Wind power

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IR Net: information restriction and information recovery for accurate binary neural networks

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Neural Computing and Applications 2023年第19期35卷 14449-14464页

作者： Xue, Ping Lu, Yang Chang, Jingfei Wei, Xing Wei, Zhen School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Mine IOT and Security Monitoring Technology Key Laboratory Hefei China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei University of Technology Hefei China Intelligent Manufacturing Institute of Hefei University of Technology Hefei China

Weight and activation binarization can efficiently compress deep neural networks and accelerate model inference, but they cause severe accuracy degradation. Existing optimization methods for binary neural networks (BNNs) focus on fitting full-precision networks to reduce quantization errors and suffer from a tradeoff between accuracy and efficiency. In contrast, considering information loss and the mismatch between model capacity and input information quantity caused by network binarization, we propose Information Restriction and Information Recovery Network (IR $$^2$$ Net) to stimulate the potential of BNNs and achieve improved network accuracy by restricting the input information and recovering feature information. The proposed approach includes (1) information restriction, which evaluates the feature information extracted from the input by a BNN, discards some of the information it cannot focus on, and reduces the amount of the input information to match the model capacity; and (2) information recovery: due to the information loss incurred during forward propagation, the extracted feature information of the network is not sufficient for supporting accurate classification. Shallow feature maps with richer information are selected, and these feature maps are fused with the final feature maps to recover the extracted feature information and further enhance the model capacity to match the amount of input information. In addition, the computational cost is reduced by streamlining the information recovery method to strike a better tradeoff between accuracy and efficiency. Experimental results demonstrate that our approach still achieves comparable accuracy even with a $$\sim$$ 10x floating-point operations (FLOPs) reduction for ResNet-18. The models and code are available at https://***/pingxue-hfut/IR2Net .

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