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Sensitivity and Distance Based Performance Analysis for Batteryless Tags with Transmit Beamforming and Ambient Backscattering

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China communications 2022年第2期19卷 109-117页

作者： Minzheng Jia Chaochao Yao Wei Liu Ruyi Ye Tutun Juhana Bo Ai Department of Information Engineering Beijing Polytechnic CollegeBeijing 100042China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China School of Communication and Information Engineering Xi’an University of Posts and TelecommunicationsXi’an 710121China Chinese National Institute of Standalization Beijing 100088China School of Electrical Engineering and Informatics Institut Teknologi BandungIndonesia State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing 100044China

Ambient backscatter is a new green technology for Internet of Things(IoT)that utilizes surrounding wireless signals to enable batteryless devices to communicate with other *** battery-free devices first harvest energy from ambient wireless signals and then backscatter the signals for ***,sensitivity and distance are two important parameters for system ***,most existing studies on ambient backscatter communication systems do not consider the impact of the sensitivity of the energy-harvesting nodes and the distances between these *** this paper,we first provide a literature review for ambient communication technology and then take sensitivity and distance as two key parameters and investigate the sensitivity and distance based performance for ambient backscatter communication ***,we establish the mathematical model based on distances between transceivers and backscattering nodes,extract a parameter that can differentiate the direct path and the backscattering path,evaluate the effects of transmit beamforming,design an energy detector for the reader,and analyze the outage probability of energy harvesting at the tag and the bit error rate(BER)at the *** are then provided to corroborate the proposed studies.

关键词： ambient backscatter battery-free tag Internet of Things(IoT) outage probability performance analysis sensitivity transmission distance

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A Wind Power Scenario Simulation Method Considering Trend and Randomness 16th

A Wind Power Scenario Simulation Method Considering Trend an...

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16th Annual Conference of China Electrotechnical Society, ACCES 2021

作者： Ji, Xiu Li, Cong Xie, Beimin Wang, Yan Wang, Qisu National Local Joint Engineering Research Center for Smart Distribution Grid Measurement and Control with Safety Operation Technology Changchun130000 China School of Electrical and Information Engineering Changchun Institute of Technology Changchun130000 China Maintenance Branch State Grid Jilin Electric Power Co. Ltd Changchun130000 China

ISBN: (纸本)9789811918698

In the future, large-scale integration of new energy into the grid is an inevitable trend. In order to effectively analyze the trend and random characteristics of wind power output, a wind power scenario simulation method is proposed. It is divided into scene generation and scene reduction. In the scenario generation part, historical data is used to construct an improved wind power forecast box model, and a non-parametric kernel smooth distribution is used as a fitting function, and then a multivariate standard normal distribution matrix and inverse transform sampling method are used to generate a set of wind power output scenarios that conform to autocorrelation. In the scene reduction part, a BR scene reduction model is constructed to obtain wind power curves representing several original scene characteristics. Taking the wind power output data of the German TenneT Electric Power Company as an example, the probability calibration BS indicator is used as the quantitative evaluation standard for the generated scene, and the case analysis is carried out. Finally, it shows that the scenario simulation method is effective and reasonable, and it is also of great significance to the planning and operation of the wind power system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Calibration

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Infrared Traffic Object Detection Network for Edge Device Deployment

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Guangxue Xuebao/Acta Optica Sinica 2025年第9期45卷

作者： Han, Yulan Chen, Deao Wu, Tong Liu, Xianlu Lan, Chaofeng Heilongjiang Province Key Laboratory of Pattern Recognition and Information Perception School of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Heilongjiang Harbin150080 China

Objective The rapid advancement of intelligent transportation systems has increased the demand for precise and swift traffic object detection capabilities, particularly under challenging low-light conditions and complex backgrounds. Infrared (IR) imaging technology has emerged as a critical tool for such scenarios due to its ability to capture heat signatures, thus enabling reliable detection even in darkness or through obscurants. However, traditional IR detection methods are often hindered by high computational complexity, large parameter sizes, and dependency on high-performance computing resources, which makes them unsuitable for deployment on resource-limited mobile devices. We introduce the Edge-DETR network, specifically designed for the efficient and accurate detection of traffic objects in IR imagery on edge devices. Methods The Edge-DETR network is an innovative detection framework that builds upon the RT-DETR model with several enhancements. It incorporates a context anchor attention multistage efficient layer aggregation network (CAA-MELAN) to adaptively extract multi-scale features and understand global dependencies, thereby addressing the challenges posed by varying object sizes and environmental dynamics. Additionally, a global information supplement module (GISM) is employed for effective feature downsampling, which ensures the preservation of essential spatial information while reducing computational load. The cross-level feature fusion module (CFFM) facilitates interaction among features at different scales, enhancing the network’s capability to integrate high-level semantic information with low-level spatial details. The HiLo attention mechanism is introduced to optimize intra-scale feature interaction, with a focus on target contours while minimizing parameter and computational requirements. To address the detection of objects with complex shapes and sizes, a Shape-IoU loss function is utilized, which accounts for the shape and size of bounding b

关键词： Thermography (imaging)

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Physics-Informed LSTM Network-Based Nonlinear Model Predictive control

Physics-Informed LSTM Network-Based Nonlinear Model Predicti...

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Data Driven control and Learning Systems (DDCLS)

作者： Yujing Chen Qilin Qui Hong Zhang Yanwei Wang Ying Zheng The Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Mechanical Electrical Engineering Wuhan Institute of Technology Wuhan China

To address the problems of poor physical interpretability and huge sample size requirement when using neural networks to fit nonlinear control system models for state prediction, this paper proposes a model predictive control algorithm based on a physics-informed long short-term memory(LSTM) network. Firstly, the neural network incorporating physical information is extended to model the ordinary differential equations with variable initial states and external control quantities, which makes the network adaptable to the control task and makes the training model physically interpretable. Secondly, a network structure with a mixture of fully connected layers and LSTM layers is built by using the good learning ability of LSTM for time-series data, and the loss function is designed according to the system characteristics and prediction requirements. The trained neural network model is then used as an internal prediction model to construct a nonlinear model predictive control algorithm. Finally, taking the continuous stirring reactor system as an example, the method is verified to be able to fit the system model highly and reduce the time to reach the steady state with a small number of samples.

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Network Intrusion Detection Based on Graph Neural Network and Ensemble Learning

Network Intrusion Detection Based on Graph Neural Network an...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Lina Zhang Yang Wang Minyue Wu Yanwei Wang Ying Zheng The Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Mechanical & Electrical Engineering Wuhan Institute of Technology Wuhan China

Network technology has become an inseparable part of daily life, which is easily attacked by some criminals. Therefore, ensuring the security of network becomes an urgent problem. In the past few years, various deep learning techniques have been put forward for identifying network intrusions. However, the majority of these approaches overlook the non-Euclidean nature of relationships within network intrusion data. In this paper, graph neural network (GNN) is used to learn the relationship between data. The sample balancing module is to address the significant disparity between the number of intrusive samples and normal samples. The results obtained from the experiments indicate that the suggested approach can enhance the effectiveness of the intrusion detection system.

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Health Condition Monitoring of Satellite Momentum Wheel Bearing Based on Canonical Variable Analysis and Sliding Interval Variance

Health Condition Monitoring of Satellite Momentum Wheel Bear...

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Industrial Cyber-Physical Systems (ICPS)

作者： Sirui Du Shumei Zhang Yang Zhao Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University Tianjin China Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China

With the rapid development of space technology, the demand for satellite reliability is getting higher and higher. Momentum wheel is a key component of satellite attitude control system, and its reliability is an important factor affecting the life of satellite. The condition monitoring of momentum wheel bearing (MWB) is of great significance to ensure the long life and high reliable operation of the satellite. In this paper, a new monitoring method based on multivariate statistics and canonical variable analysis (CVA) is proposed, and a new health degree function is defined from both dynamic and static aspects. First, the time-frequency domain analysis technique is used to extract the features of MWB in time domain, frequency domain and time-frequency domain, and the multi-domain high-dimensional health condition feature set is constructed. Then, in order to reduce the complexity of the problem, feature reduction is realized based on CVA. On the basis of considering steady-state error and sliding interval variance (SIV), the health degree (HD) characterizing the performance condition of MWB is defined. Finally, the experimental results based on the bearing test-bed show that the proposed method is feasible and effective, and the data-driven health condition monitoring of satellite momentum wheel bearing is realized.

关键词： Condition monitoring Time-frequency analysis Analytical models Satellites Wheels Feature extraction Complexity theory

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Computationally efficient predictive control based on ANN state-space models

arXiv

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arXiv 2023年

作者： Hoekstra, Jan H. Cseppento, Bence Beintema, Gerben I. Schoukens, Maarten Kollár, Zsolt Tóth, Roland Dept. of Measurement and Information Systems The Budapest University of Technology and Economics Hungary The Dept. of Electrical Engineering The Eindhoven University of Technology Netherlands Institute for Computer Science and Control Budapest Hungary

Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for identification of nonlinear state-space models. However, if the resulting models are used directly for nonlinear model predictive control (NMPC), the resulting nonlinear optimization problem is often overly complex due the size of the network, requires the use of high-order observers to track the states of the ANN model, and the overall control scheme exploits little of the structural properties or available autograd tools for these models. In this paper, we propose an efficient approach to auto-convert ANN state-space models to linear parameter-varying (LPV) form and solve predictive control problems by successive solutions of linear model predictive problems, corresponding to quadratic programs (QPs). Furthermore, we show how existing ANN identification methods, such as the SUBNET method that uses a state encoder, can provide efficient implementation of MPCs. The performance of the proposed approach is demonstrated via a simulation study on an unbalanced disc system. © 2023, CC BY-SA.

关键词： Neural networks

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Fractional Time-Frequency Scattering Convolution Network 3rd

Fractional Time-Frequency Scattering Convolution Network

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3rd EAI International Conference on Artificial Intelligence for communications and Networks, AICON 2021

作者： Zheng, Jiabin Shi, Jun Chen, Gong Chen, Weiping Geng, Zhenya MEMS Center Harbin Institute of Technology Harbin150001 China Communication Research Center Harbin Institute of Technology Harbin150001 China Department of Control Science and Engineering Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9783030901950

The wavelet scattering convolution network (SCN) have recently developed as a kind of effective feature extractor, which has achieved a great performance in signal and image processing applications. Unfortunately, as feature extractor, SCN is not appropriate to mimic the visual system of mammals in image classification tasks, so that STFT-based time-frequency scattering convolution network (TFSCN) is proposed. However, TFSCN is limited by a major drawback: it is only available for stationary signals’analysis but not for non-stationary ones, since STFT can viewed as linear translation-invariant filters in the FT domain intrinsically. The aim of this paper is to overcome this weakness using the short-time fractional fourier transform (STFRFT) which is a bank of linear translation-variant bandpass filters and thus may be used for non-stationary signal analysis. First, We present the fractional time-frequency scattering transform based upon the STFRFT. Then a generalization of TFSCN’s structure dubbed FRTFSCN is illustrated. The significant performance of FRTFSCN are shown via experiment simulations. © 2021, ICST institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Convolution

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Deterministic Message Flow Scheduling in Industrial Internet: Message Middleware Use Case

Deterministic Message Flow Scheduling in Industrial Internet...

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International Conference on communication technology (ICCT)

作者： Xin Wang Chonghan Zhou Jing Jiang Key Laboratory of Information and Communication Systems Ministry of Information Industry and Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University (BISTU) Beijing China State Grid Economic and Technological Research Institute co. LTD Beijing China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

This article mainly focuses on message flow scheduling for industrial message middleware in the integration of deterministic network with industrial internet, which is to find the optimal matching between telecom operator and message requests from industrial users. However, current deterministic network is limited to two or three layers, and the whole transmission reliability and latency involving industrial networking and service layers cannot be guaranteed, resulting in uncontrollable message scheduling reliability, deterministic latency, low channel resource utilization etc. To this end, an integer linear programming (ILP) model is formulated for message flow scheduling issue, with multi-objectives optimization of resource utilization and profit under customized message sending latencies, and deterministic reliability. A heuristic algorithm is also proposed. Furthermore, we present a Deep Reinforcement Learning based Message Flow Scheduling algorithm (DRL-MFS), which not only solves complex message flow scheduling problems, but also achieves the self-optimization automatically. Proposed message flow scheduling approaches are simulated based on data from real cloud messaging service platforms. Simulations results prove that proposed DRL-MFS is superior to heuristic, and is close to ILP model in multi-objective optimization under three urgency levels of message sending. The reliability of three methods is proven to be deterministic.

关键词： Job shop scheduling Scheduling algorithms Simulation Heuristic algorithms Deep reinforcement learning Telecommunications Telecommunication network reliability Resource management Middleware Optimization

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Secrecy Performance Analysis of RIS Assisted Ambient Backscatter communication Networks

arXiv

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arXiv 2024年

作者： Pei, Yingjie Yue, Xinwei Huang, Chongwen Lu, Zhiping The Key Laboratory of Information and Communication Systems Ministry of Information Industry The Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China The State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China The Key Laboratory of Information and Communication Systems Ministry of Information Industry China The Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Hangzhou310027 China The School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Beijing100191 China

Reconfigurable intelligent surface (RIS) and ambient backscatter communication (AmBC) have been envisioned as two promising technologies due to their high transmission reliability as well as energy-efficiency. This paper investigates the secrecy performance of RIS assisted AmBC networks. New closed-form and asymptotic expressions of secrecy outage probability for RIS-AmBC networks are derived by taking into account both imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC) cases. On top of these, the secrecy diversity order of legitimate user is obtained in high signal-to-noise ratio region, which equals zero and is proportional to the number of RIS elements for ipSIC and pSIC, respectively. The secrecy throughput and energy efficiency are further surveyed to evaluate the secure effectiveness of RIS-AmBC networks. Numerical results are provided to verify the accuracy of theoretical analyses and manifest that: i) The secrecy outage behavior of RIS-AmBC networks exceeds that of conventional AmBC networks;ii) Due to the mutual interference between direct and backscattering links, the number of RIS elements has an optimal value to minimise the secrecy system outage probability;and iii) Secrecy throughput and energy efficiency are strongly influenced by the reflecting coefficient and eavesdropper’s wiretapping ability. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy efficiency

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