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International Conference on Computational and Characterization Techniques in engineering & Sciences (CCTES)

作者： Vishwanadham Mandala Srinivas Naveen R Dolu Surabhi R. Senthamil Selvan Department of Analytics Data Engineering Lead Indiana University Indiana USA Department of Control System General Motors R & D Indiana USA Department of Electronics and Communication Engineering Annamacharya Institute of Technology and Sciences (Autonomous) Tirupati India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

In shared spectrum networks, proper signal type identification is essential for equitable coexistence and effective resource allocation. To increase system throughput and prevent accidents, particularly in situations when the environment is changing quickly, it may be utilised for scheduling gearbox chances. To train deep neural networks, or DNNs, to distinguish coexisting signal kinds without encoding them, researchers use the In-phase/Quadrature (I/Q) sample as input. Two neural networks, one convolutional and one recurrent, are trained using input segments taken from the received signal samples utilising categorical cross-entropy (CE) optimisation. Results for the 5-6 GHz unlicensed band, where LTE LAA, Wi-Fi, and 5G NR-U signals coexist, demonstrate that the suggested architecture is quite accurate. After that, use spectral analysis on the I/Q sequence to boost classification precision even further. More data in the frequency domain may be shown as a spectrogram by using a Short-time Fourier Transform (STFT). Increasing the size of the DNN's input accordingly. Researchers use USRP radios to carry out over-the-air (OTA) tests that validate the efficacy of the suggested detection system. In both software and hardware trials, the suggested method achieves precise categorization.

关键词： Accuracy Wireless networks Computer architecture Throughput Software Resource management Spectrogram Wireless fidelity Spectral analysis Signal detection

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UKF-Based Multi-sensor Data Processing Algorithm for SLAM

UKF-Based Multi-sensor Data Processing Algorithm for SLAM

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IEEE International Conference on control Science and Systems engineering (CCSSE)

作者： Kun Xu Boheng Zhang Jiangang Li Guizhong Wang Mingjian Sun School of Information Science and Engineering Harbin Institute of Technology Weihai Weihai China Department of Control Science and Engineering Harbin Institute of Technology Harbin China Woan Technology (Shenzhen) Co. Ltd. Shenzhen China Hebei Key Laboratory of Grid Advanced Measurement and Monitor Jinyanhagong Industrial Technology Research Institute Zhangjiakou China School of Information Science and Engineering Harbin Institute of Technology Harbin China

Simultaneous localization and mapping (SLAM) algorithm is widely used in unmanned and robotic applications. SLAM usually uses sensor data, which are from the LiDAR, the inertial measurement unit (IMU) and the wheeled odometry, to build maps of the surrounding environment. However, the motion distortion of LiDAR data and the error accumulation of wheeled odometry can affect the accuracy of the front-end odometry of the SLAM algorithm, which can lead to serious errors in the maps created. Therefore, in order to solve the problem of poor map construction results of SLAM algorithm due to various types of sensor errors. we propose the traceless Kalman filtering based (UKF-based) multi-sensor data processing algorithm. Firstly, the proposed algorithm uses the UKF to fuse IMU and wheeled odometry data to reduce the cumulative error of wheeled odometry. And then the algorithm uses IMU and corrected wheeled odometry data and combines with the motion states of the robot to solve the motion distortion problem of LiDAR data. Finally, we verified the effectiveness of the proposed algorithm for wheeled odometry data correction in Gazebo simulation environment and real environment experiments, and compared the mapping effectiveness of SLAM by using the original data and the data processed by our algorithm. The experimental results show that the map construction effect of SLAM algorithm is significantly improved when using the sensor data processed by the algorithm. Experimental validation on the robot shows that the proposed algorithm has universal applicability and good compatibility.

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Retired Lithium-ion Batteries Screening Based on Partial Discharge Curves and an Improved Crossformer

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IEEE Transactions on Transportation Electrification 2025年

作者： Lin, Mingqiang Xu, Zijun Zheng, Gengfeng Meng, Jinhao Wang, Wei Wu, Ji Fujian Key Laboratory of Special Intelligent Equipment Safety Measurement and Control Fuzhou350003 China Chinese Academy of Sciences Quanzhou Institute of Equipment Manufacturing Fujian Institute of Research on the Structure of Matter Fujian Jinjiang362200 China Fujian Special Equipment Inspection and Research Institute Fuzhou350008 China Fujian Key Laboratory of Special Intelligent Equipment Safety Measurement and Control China Xi’an Jiaotong University School of Electrical Engineering Xi’an710049 China Xi’an Jiaotong University School of Management Xi’an710049 China Hefei University of Technology School of Automotive and Transportation Engineering Anhui Hefei230009 China

With the increasing number of retired lithium-ion batteries from electric vehicles, effective screening and classification are necessary to enable their secondary utilization. The existing retired lithium-ion battery screening methods have limitations in accuracy and high energy consumption. This paper proposes a method based on local discharge curves and an improved Crossformer model to reduce energy consumption and improve screening accuracy. Considering the actual conditions of retired lithium-ion batteries, features are extracted from the discharging stage. Select the segments of the capacity sequence that exhibit the most significant variation with the voltage sequence and construct a partial capacity-voltage sequence. The standard deviation and Shannon entropy of the capacity sequence, as well as the variance, energy, and the maximum amplitude of the low-frequency part of the Fourier-transformed voltage sequence, are used as features. Then, the Crossformer utilizes a flexible embedding mechanism and a hierarchical structure for handling the dependencies of the feature. The Crossformer is improved for classification by adding a cross-attention layer at the final stage of the Crossformer decoder, which enhances its ability to capture the dependencies between features and improves the model’s performance in battery screening classification. Finally, the validation of 252 retired batteries shows that the method achieves a classification accuracy of 94.74%, outperforming other advanced models, including the original Crossformer, Transformer, ResNet, and MLP models. © 2015 IEEE.

关键词： Lithium-ion batteries

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Towards Safe Inter-train Distance Minimization via Reachability Analysis and Mixed H2/H∞control

Towards Safe Inter-train Distance Minimization via Reachabil...

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2022 Chinese Automation Congress, CAC 2022

作者： Yang, Min Lv, Jidong Liu, Hongjie Zhang, Miao Beijing Jiaotong University National Engineering Research Center of Rail Transportation Operation and Control System Beijing China China Academy of Railway Sciences Corporation Limited National Research Center of Railway Intelligence Transportation System Engineering Technology Signal and Communication Research Institute Beijing China

ISBN: (纸本)9781665465335

In high-speed railway, designing a reasonable controller of train control system is an important way to enhance the transportation capacity, while considering more dynamic behaviors of the preceding train, such as position, speed and acceleration etc, through V2V communication. In this paper we propose a novel method towards the safe inter-train distance minimization problem via reachability analysis and mixed H2/H∞ control. Firstly, by considering the disturbance behavior of the preceding train, a linear closed-loop system is modeled, in which the gains of a state feedback and a state feedback/feedforward, refer to controller A and controller B respectively, are designed using mixed H2/H∞ control theory. Then, the N-step forward reachable set for the two given controllers is presented to compute the maximum position error, which determines the minimum safe inter-train distance. Furthermore, backward reachability analysis algorithm is used to find the maximal initial state set, which guarantees that the state trajectory stay within the safe set in presence of disturbance. Finally, the proposed method is applied to a typical emergency braking scenario during two-train tracking. The experimental results show that the safety margin can be reduced by 54% and the maximal initial state set can be enlarged conspicuously when a feedforward term is used in the controller. © 2022 IEEE.

关键词： controllers

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Auto-Text Completion Framework to Improve Online Conversation

Auto-Text Completion Framework to Improve Online Conversatio...

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Congress on Advances in Materials Science and engineering, CAMSE 2020

作者： Rajput, Vishal Verma, Om Prakash Ansari, Irshad Ahmad Department of Electronics and Communication Engineering PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur India Department of Instrumentation and Control Engineering Dr. B. R. Ambedkar National Institute of Technology Jalandhar India

ISBN: (纸本)9789811609411

There are many abilities that make human intelligent. One test which often separates machines and human is how well a machine is able to understand the text and its meaning. The task of sentence completion is still one of the hardest problems in natural language processing. Making a machine understand what is written is a tremendous task. Traditionally, machines were good at performing mathematical calculations like addition, subtraction, etc. only;but with time, new techniques to map language to some mathematical functions emerged. In this work, we discuss why and how to use long short-term memory (LSTM) to create a basic auto-text completion model on the Hindi written in English script (Hinglish) dataset. This study aims at showing that LSTM works very well with Hinglish dataset as it does with English dataset. A Hinglish dataset is prepared and cleaned before processing on LSTM. The obtained result proves the usefulness of LSTM networks for auto-text completion. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory (LSTM) Natural language processing Sentence completion

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Integrated communication and Learned Recognizer with Customized RIS Phases and Sensing Durations

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IEEE Transactions on communications 2025年

作者： Huang, Yixuan Yang, Jie Wen, Chao-Kai Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education The Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China National Sun Yat-sen University Institute of Communications Engineering Kaohsiung80424 Taiwan Southeast University Frontiers Science Center for Mobile Information Communication and Security National Mobile Communications Research Laboratory Nanjing210096 China

Future wireless communication networks are expected to be smarter and more aware of their surroundings, enabling a wide range of context-aware applications. Reconfigurable intelligent surfaces (RISs) are set to play a critical role in supporting various sensing tasks, such as target recognition. However, current methods typically use RIS configurations optimized once and applied over fixed sensing durations, limiting their ability to adapt to different targets and reducing sensing accuracy. To overcome these limitations, this study proposes an advanced wireless communication system that multiplexes downlink signals for environmental sensing and introduces an intelligent recognizer powered by deep learning techniques. Specifically, we design a novel neural network based on the long short-term memory architecture and the physical channel model. This network iteratively captures and fuses information from previous measurements, adaptively customizing RIS phases to gather the most relevant information for the recognition task at subsequent moments. These configurations are dynamically adjusted according to scene, task, target, and quantization priors. Furthermore, the recognizer includes a decision-making module that dynamically allocates different sensing durations, determining whether to continue or terminate the sensing process based on the collected measurements. This approach maximizes resource utilization efficiency. Simulation results demonstrate that the proposed method significantly outperforms state-of-the-art techniques while minimizing the impact on communication performance, even when sensing and communication occur simultaneously. Part of the source code for this paper can be accessed at https://***/kiwi1944/CRISense. © 1972-2012 IEEE.

关键词： Long short-term memory

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Transformer based Online Continuous Multi-Target Tracking with State Regression

Transformer based Online Continuous Multi-Target Tracking wi...

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International Conference on control, Automation and Information Sciences (ICCAIS)

作者： Xinwei Wei Linxiu Chen Chenyu Zhang Yiru Lin Linao Zhang Xiaokai Liu Jixiang Jiang Wei Yi School of Information and Communication Engineering University of Electronic Science and Technology of China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Research Institute of Electronic Science and Technology University of Electronic Science and Technology of China

Multi-target Tracking (MTT) is the process of processing received measurements to maintain estimates of the current status of multiple targets, with important applications to autonomous driving, aerial reconnaissance, underwater operations, and others. In the model-based setting, Bayesian filtering can provide the theoretical optimal estimate in a single target scenario. However, in complex situations, uncertain factors such as changes in the number of targets will cause the amount of calculation to increase exponentially, resulting in a decline in tracking accuracy. To solve that problem, model-free methods based on deep-learning provide an attractive alternative, especially the state-of-the-art architecture Transformer based encoder-decoder prediction model, which outperforms the Bayesian filters in the single frame prediction tasks. However, when switching to continuous tracking, these algorithms need to be trained separately frame by frame to adapt to the new tasks. Still, there is no correlation between their predictions from different frames, which prevents them from fully utilizing all the measurements. In this paper, we propose an end-to-end Transformer based MTT method with state autoregression, which allows the model to have the capability of online continuous tracking and make total use of the entire trajectory. The results show that the proposed model is a great extension from single-frame prediction to online continuous tracking.

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Vehicle state parameter estimation based on improved adaptive observer

Vehicle state parameter estimation based on improved adaptiv...

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Conference on Vehicle control and Intelligence (CVCI)

作者： Lianbing Wang Xu Zhao Juncheng Wan Hongyan Guo Yiming Zhang State Key Laboratory of Automotive Simulation and Control and the College of Communication Engineering Jilin University Changchun China College of Communication Engineering Jilin University Changchun China Department of Aircraft Control and Information Engineering Jilin Institute of Chemical Technology Changchun China SAIC Motor R&D Innovation Headquarter Shanghai China

To accurately obtain the state parameters during vehicle driving, this paper designs an adaptive nonlinear observer for vehicle state parameters to mitigate the influence of measurement noise within the framework of extended Kalman filtering. Based on the vehicle nonlinear three degree of freedom, a dead zone observer was designed to accurately estimate the sideslip angle, yaw rate, and longitudinal velocity of the vehicle in answer to the problem of significant noise in lateral acceleration. According to the output variable error, determine the dead zone boundary of the observed state variables at each time based on observation errors, limiting the estimation error within the dead zone boundary and improving noise suppression performance. The validity of this method is validated through a variety of different simulation conditions and by comparison to the EKF algorithm. Results demonstrate that the improved algorithm significantly enhances the estimation precision compared to Extended Kalman Filter algorithm.

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Design and Implementation of Coal Mine Monitoring Networking System Based on Service-Oriented Architecture

Design and Implementation of Coal Mine Monitoring Networking...

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International Conference on Network and Information Systems for Computers (ICNISC)

作者： Yunqi Chen Jin Xu Measurement and Control Technology Research Institute China Coal Technology Engineering Group Chongqing Research Institute Chongqing China School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9781665453523

Coal is the most important basic energy supporting the development of the national economy, safe production and effective supervision and management are of great significance. Currently, the coal mine safety monitoring system can be monitored locally, but it cannot help the supervision department to realize the remote supervision and centralized supervision. Establishing a corresponding coal mine safety monitoring network system has become a urgent need of coal mine safety supervision. In the paper, we first study the service-oriented architecture(SOA), and describes the windows communication foundation(WCF) technology, the technology is a main way to achieve SOA architecture. Then combined with WCF technology, the function and logic structure of coal mine safety monitoring network system are designed. Finally, the network system of mine monitoring system based on SOA was realized in the background of a mine monitoring system networking project of a company.

关键词： Economics Companies Computer architecture Coal Safety Coal mining Service-oriented architecture

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Slow Feature Thermography for Non-Destructive Evaluation of Defects in Composite Materials

Slow Feature Thermography for Non-Destructive Evaluation of ...

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Data Driven control and Learning Systems (DDCLS)

作者： Kaixin Liu Xin Zhong Ping Chen Yun Dai Yi Liu The State Key Lab for Electronic Testing Technology North University of China China Department of Information and Communication Engineering North University of China Taiyuan China College of Mechanical Engineering Nantong Vocational University Nantong China Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In recent years, various methods for processing and analyzing thermographic data have been applied in non-destructive testing to improve the visibility of defects. Representatively, traditional linear dimensionality reduction methods (e.g. principal component analysis) have achieved good results in thermographic data analysis. In this work, a slow feature thermography (SFT) method for non-destructive evaluation of defects in composite materials. Thermographic data can be regarded as a continuous time series generated by an infrared imager at different moments. Slow feature analysis algorithm is subsequently used to extract slow features of the thermal image sequence. Slow features refer to features that are unchanged or slowly changing in time-series data, such as the background of a thermal image. By extracting the slow features of data, the inhomogeneous background, noise and defect features contained in thermal images can be effectively distinguished. Test results on carbon fiber reinforced polymer specimens demonstrate the feasibility and effectiveness of the SFT method.

关键词： Image recognition Composite materials Time series analysis Infrared imaging Feature extraction Thermal noise Thermal analysis

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