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Angle-Based SLAM on 5G mmWave Systems: Design, Implementation, and measurement

IEEE Internet of Things Journal 2023年第20期10卷 17755-17771页

作者： Yang, Jie Wen, Chao-Kai Xu, Jing Que, Hang Wei, Haikun Jin, Shi Southeast University Sch. of Automat. and the Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China National Sun Yat-sen University Institute of Communications Engineering Kaohsiung804 Taiwan Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Nanjing210096 China

Simultaneous localization and mapping (SLAM) is a key technology that provides user equipment (UE) tracking and environment mapping services, enabling the deep integration of sensing and communication. The millimeter-wave (mmWave) communication, with its larger bandwidths and antenna arrays, inherently facilitates more accurate delay and angle measurements than sub-6 GHz communication, thereby providing opportunities for SLAM. However, none of the existing works have realized the SLAM function under the 5G new radio (NR) standard due to specification and hardware constraints. In this study, we investigate how 5G mmWave communication systems can achieve situational awareness without changing the transceiver architecture and 5G NR standard. We implement 28-GHz mmWave transceivers that deploy OFDM-based 5G NR waveform with 160-MHz channel bandwidth, and we realize beam management following the 5G NR. Furthermore, we develop an efficient successive cancellation-based angle extraction approach to obtain angles of arrival and departure from the reference signal received power measurements. On the basis of angle measurements, we propose an angle-only SLAM algorithm to track UE and map features in the radio environment. Thorough experiments and ray tracing-based computer simulations verify that the proposed angle-based SLAM can achieve submeter-level localization and mapping accuracy with a single base station and without the requirement of strict time synchronization. Our experiments also reveal many propagation properties critical to the success of SLAM in 5G mmWave communication systems. © 2014 IEEE.

关键词： 5G mobile communication systems

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EEG Based Deep Learning Model for Automated Classification of Frequent and Infrequent Readers

EEG Based Deep Learning Model for Automated Classification o...

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2024 International Conference on Brain Computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Veerasingam, Sridevi Boby, Kiran Paulose, Amal P Varsha, V. Raju, Dhole Shubham Anisha, S. Rahul, R.D. Joseph Ponniah, R. NIT Tiruchirappalli Department of Instrumentation and Control Engineering Tiruchirappalli India RIT Kottayam Department of Electronics and Communication Engineering Kottayam India SSN College of Engineering Department of Biomedical Engineering Chennai India PSG College of Arts and Science Department of English and Social Sciences Coimbatore India Indian Institute of Technology Department of Humanities and Social Sciences Dhanbad India NIT Tiruchirappalli Department of Humanities and Social Sciences Tiruchirappalli India

ISBN: (纸本)9798331540067

Reading comprehension is a complex cognitive process that involves semantic decoding, and schema integration influenced by reading practice. This study investigates the neural correlates of reading comprehension and explores how brain activation patterns differ between frequent and infrequent readers. Using Electroencephalography (EEG), the brain activation patterns were recorded from participants while performing the reading comprehension tasks. The EEG signal is recorded with a sampling rate of 1024 Hz to capture the dynamic changes in brain activation during reading tasks, revealing how frequent and infrequent readers differ in their cognitive processing and neural efficiency. The 21 channels of EEG signal acquired from 9 participants were pre-processed to obtain a 512 Hz band-limited signal. The pre-processed EEG signals were segmented before being fed into a twenty-four-layered convolutional neural network (CNN) model. The convolution stage extracts the temporal and spatial features, and the most significant features are extracted at the max-pooling stage. Frequent and infrequent readers are classified using the fully connected layer based on their EEG signals. The proposed model obtained a classification accuracy of 93.94%. © 2024 IEEE.

关键词： Convolutional neural networks

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Temperature Response Analysis of Intelligent OPPC Cable Core Based on Finite Element Simulation

Temperature Response Analysis of Intelligent OPPC Cable Core...

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International Conference on Optical communications and Networks (ICOCN)

作者： Wenping Xie Ming Nie Yongchun Liang Xiaoyu Luo Yu Han Hongwei Li Xiaohui Tang Electric Power Research Institute of Guangdong Power Grid Co. Ltd. Guangzhou China Harbin University of Science and Technology Harbin China Heilongjiang Provincial Key Laboratory of Quantum Control School of Measurement and Communication Engineering Harbin China

ISBN: (数字)9798350367652

ISBN: (纸本)9798350367669

The Optical Fiber Composite Overhead Conductor (OPPC) cable is a special type of power optical cable that combines overhead conductor and communication capabilities. It has advantages such as strong wind resistance, good corrosion resistance, high bandwidth transmission, low loss transmission, and high reliability. However, as the OPPC cable operates for a long time power transmission, its long-term working temperature has a significant impact on transmission performance and lifespan. Therefore, monitoring the temperature of the OPPC cable and internal fibers can improve the transmission capacity of the line, ensure good thermal stability, and provide important references for fault warning and maintenance. This study aims to explore the temperature characteristics of the OPPC cable through actual monitoring and analysis, providing guidance and decision-making basis for practical applications.

关键词： Temperature sensors Temperature measurement Resistance Optical fibers Temperature Optical fiber cables communication cables

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Adaptive control Strategy of Wind-Solar-Storage Parallel Virtual Synchronous Generator Parameters Based on Variable Universe Fuzzy control

Adaptive Control Strategy of Wind-Solar-Storage Parallel Vir...

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Power and Electrical technology Conference (APET), Asia

作者： Zhao Zijun Wang Hui Zhang Zhuanqin Yang Zhilong Zhang Yadong National Local Joint Engineering Research Center for Smart Distribution grid Measurement and control with Safety Operation Technology Changchun Institute of Technology Changchun China

Virtual Synchronous Generator technology improves the frequency dynamic performance of the system by introducing virtual inertia and damping coefficients into the power electronic inverter. In order to further suppress power oscillations and solve the problem that constant virtual parameters cannot take into account the maximum frequency overshoot and response speed, firstly, a small-signal model of active and reactive power rings of virtual synchronous machine is established to study the influence of virtual inertia on the frequency stability and power equalization effect of parallel connection of multiple machines. Secondly, based on the fuzzy control method in variable universe, the control strategy of coordinated adaptive adjustment of the virtual inertia of the parallel system according to the output angular frequency is proposed, and at the same time, the automatic adjustment of the virtual damping coefficient with the inertia is realized by combining with the appropriate damping ratio. Finally, the parallel parameter matching principle is designed so that the output power can be distributed according to the capacity ratio. Two different virtual synchronous machine control strategies are compared and analyzed by matlab/simulink to verify the correctness and feasibility of the proposed adaptive control strategy.

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Research on Resonance Suppression Method of Multi-Inverter Parallel System Based on Improved Fcs-Mpc

SSRN

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SSRN 2024年

作者： Chunhui, Liang Li, Jinfa Zuo, Xiaoyang Huang, Chenglong Liu, Renjie 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

A mathematical model of the multi-NPC three-level LCL inverter system is constructed with the goals of suppressing the resonance of the multi-inverter grid-connected system, improving the output power quality, and improving the pure output current waveform on the grid side of the system. Firstly, The system's resonance characteristics are studied by the equivalent NORTON model of multiple inverters in parallel. Secondly, because of the resonance existing in the multi-inverter parallel system, by integrating active damping and improving the Finite control Set Model Predictive control (FCS-MPC) method, realize the harmonic design in both the low- and high-frequency domains. It can successfully reduce the resonance phenomenon and raise the system's general stability. Meanwhile, It is possible to greatly lower the harmonic distortion rate and enhance the output current waveform. Finally, two parallel NPC three-point flat LCL filters inverters were built for simulation analysis, and the suggested analysis method's viability and accuracy were confirmed. © 2024, The Authors. All rights reserved.

关键词： Model predictive control

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Flame region segmentation of a coal-fired power plant boiler burner through YOLOv8-MAH model

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Fuel 2025年 398卷

作者： Pu, Sixu Li, Jian Han, Zhezhe Zhu, Xiaoyu Xu, Chuanlong National Engineering Research Center of Power Generation Control and Safety School of Energy and Environment Southeast University Nanjing China School of Communication and Artificial Intelligence Nanjing Institute of Technology Nanjing China

Effective segmentation of flame regions in coal-fired power plant boiler burners is critical for enhancing combustion monitoring and operational safety. Traditional methods often suffers from the interference of complicated furnace environments, such as furnace background, adjacent burners and fly ash. This study introduces an advanced flame segmentation approach utilizing the YOLOv8 framework, improved by a multi-level augment head (MAH) and single-object tracking techniques to integrate shallow and deep feature information. Moreover, a convolutional block attention module (CBAM) is embedded in the backbone network to refine feature extraction, while the reparameterization and cross-stage feature reuse are applied in the neck network to bolster multi-scale feature fusion. Experimental results demonstrate that the improved model achieves a mean intersection over union (mIoU) of 82.6—an 8.8 increase over the original YOLOv8—while maintaining high computational efficiency with an inference time of 4.43 ms. Compared with other semantic segmentation models, the proposed model achieves superior accuracy and inference speed. Its performance is validated on a 660 MW opposed-fired boiler, where it can mitigate external disturbances and effectively segment the flame regions under varying load conditions. This method provides a reliable foundation for image-based flame detection systems, enabling real-time and precise monitoring of combustion conditions. © 2025 Elsevier Ltd

关键词： Semantic Segmentation

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Classification algorithm-assisted multi-objective optimization in terahertz hollow core anti-resonant fiber design

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Optics Express 2025年第12期33卷 26651-26670页

作者： Lihong Zhai Jiyang Luo Hairuo Wang Jinxiao Pan Ping Chen Shanxi Key Laboratory of Intelligent Detection Technology and Equipment North University of China Taiyuan 030051 China School of Information and Communication Engineering North University of China Taiyuan 030051 China Department of Electronic Engineering Taiyuan Institute of Technology Taiyuan 030008 China chenping@*** North Automatic Control Technology Institute Taiyuan 030006 China zhailh@***

This study applies multi-objective optimization algorithms to the design of hollow anti-resonant fiber structures in the terahertz frequency band, focusing on four distinct models. It introduces a classification algorithm as a surrogate model to assist in multi-objective optimization, addressing challenges related to computational efficiency and data classification. The optimization targets key performance indicators, including low loss, high higher-order mode extinction ratio, high birefringence, and high polarization extinction ratio. Multiple multi-objective optimization algorithms are compared to evaluate their performance. The classification algorithm aids in parameter tuning and facilitates a comparative analysis of different optimization algorithms, enhancing the overall optimization process.

关键词： Effective refractive index Extinction ratios Fiber design Hollow core fibers Neural networks Refractive index

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Fast Informer-Based Time Series Detection

Fast Informer-Based Time Series Detection

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International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Weiguo Shen Tianhao Xia Dongwei Xu Qi Xuan Yun Lin Wei Wang Science and Technology on Communication Information Security Control Laboratory Jiaxing China Zhejiang University of Technology College of Information Engineering Institute of Cyberspace Security Binjiang Institute of Artificial Intelligence HangZhou China Zhejiang University of Technology Institute of Cyberspace Security Binjiang Institute of Artificial Intelligence HangZhou China College of Information and Communication Engineering Harbin Engineering University Binjiang Institute of Artificial Intelligence Harbin China

ISBN: (数字)9798350353129

ISBN: (纸本)9798350353136

Time series data are pivotal in modern technology and industrial applications,but they are frequently impacted by anomalies due to system failures,cyberattacks,or unexpected events. Therefore,swift and accurate anomaly detection in time series is crucial. This paper introduces ‘TransIDS’,an intrusion detection method that focuses on the efficient extraction and detection of time series features. TransIDS uses the Informer model to capture temporal features within data frames more effectively,improving detection performance and reducing runtime. We also fine-tune the Informer model and introduce an automatic anomaly threshold selection method,optimizing it for anomaly detection tasks. We validate TransIDS through experiments on various datasets and compare it with three existing methods. The results show that TransIDS outperforms the compared models in detection performance.

关键词： Runtime Accuracy Antenna theory Time series analysis Intrusion detection Predictive models Feature extraction Transformers Data models Anomaly detection

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Survey on Foundation Models for Prognostics and Health Management in Industrial Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 264-280页

作者： Liu, Ruonan Zhang, Quanhu Han, Te Yang, Boyuan Zhang, Weidong Yin, Shen Zhou, Donghua Shanghai Jiao Tong University Department of Automation Shanghai200240 China Tianjin University College of Intelligence and Computing Tianjin300350 China Beijing Institute of Technology Center for Energy and Environmental Policy Research Beijing100081 China Beijing Institute of Technology School of Management Beijing100081 China Beijing Laboratory for System Engineering of Carbon Neutrality Beijing100081 China Nanjing University Center for Advanced Control and Smart Operations Suzhou215163 China Hainan University School of Information and Communication Engineering Haikou570228 China Shanghai Jiaotong University Department of Automation Shanghai200240 China Norwegian University of Science and Technology Trondheim7491 Norway

Industrial Cyber-Physical Systems (ICPS) integrating disciplines such as computer science, communication technology, and engineering, have become a crucial component of modern manufacturing and industry. However, ICPS faces numerous challenges during long-term operation, including equipment faults, performance degradation, and security threats, etc. To achieve efficient maintenance and management, prognostics and health management (PHM) has been widely applied in the critical tasks of ICPS such as fault prediction, health monitoring, and maintenance decision-making. The emergence of large-scale foundation models (LFMs) like BERT and GPT marks a significant advancement in artificial intelligence (AI) technology, demonstrating substantial application potential in multiple fields. The accumulation of AI technology, rapid development of LFMs, and the abundance of industrial data and industrial process knowledge provide the foundational conditions for the construction and advancement of industrial LFMs. However, there is currently a lack of consensus on applying LFMs of PHM in ICPS, necessitating a systematic review and roadmap to clarify future development directions. To bridge this gap, this survey provides a comprehensive survey and understanding of the recent advances in LFMs of PHM in ICPS. It provides valuable references for decision makers and researchers in the industry, and helps to further improve the reliability, availability and safety of ICPS. © 2023 IEEE.

关键词： Cyber Physical System

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NavPathNet: Onboard Trajectory Prediction for Two-Wheelers using Navigation Maps

NavPathNet: Onboard Trajectory Prediction for Two-Wheelers u...

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IEEE Symposium on Intelligent Vehicle

作者： Fabian Immel Alessandro Moia Haohao Hu Intelligent Systems and Production Engineering FZI Research Center for Information Technology Karlsruhe Germany Robert Bosch GmbH Gerlingen-Schillerhoehe Germany Institute of Measurement and Control Systems Karlsruhe Institute of Technology (KIT) Karlsruhe Germany

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

This work proposes NavPathNet, a graph neural network based trajectory prediction system capable of accurately predicting the trajectory of a two-wheeler with onboard sensors for up to 6 seconds using the vehicle state and only navigation map information. Possible paths are generated from the local road network in a regular navigation map using Bézier curves and a multimodal prediction captures the different possibilities of the road network. A kinematic model integrated into the network allows to give guarantees on physical realism and on motion constraints, essential for safety-relevant *** proposed system was trained and evaluated on an in-house e-bike data set and was able to reach a top 1 final displacement error of 2 m for four seconds and 3.8 m for six seconds of prediction time, significantly outperforming other baselines. The improvement in prediction quality compared to purely physical models opens up new possibilities for driver assistance systems in connected vehicles.

关键词： Connected vehicles Navigation Intelligent vehicles Roads Kinematics Predictive models Graph neural networks

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