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International Conference on control systems and Computer Science (CSCS)

作者： Balaban Bèatrix-May Sacală Ioan Ştefan Petrescu-Niţă Alina-Claudia Niţă Răzvan Department of System Engineering Faculty of Automatic Control and Computers Politehnica University of Bucharest Romania

Considering the constant growth and evolution of technology, being able to secure data is a challenge. This is why researchers find this a top research topic. Cyber-Physical systems are complex, large-scale systems that can be found in domains like healthcare, transportation, industrial controls systems or smart grids. Having many components that interact with each other, we can observe here the challenge in maintaining a secure environment for the data as data tampering or data extraction can occur, for example, in the actual transportation of data from one device to another. The main focus of this paper is to give an answer if blockchain technology provides a solution in building a secure network. We give a short review of the related work regarding blockchain integration in large-scale systems like Cyber-Physical systems. It was also briefly analyzed the layered structure of a blockchain and discussed some solutions for each layer. We then discussed about consensus algorithms for validation and authentication process. In general, consensus algorithms are implemented to assure the validation process of the nodes in a network. In this paper we tried to analyze and propose a model that offers an extra layer of security by adding the authentication step. In this way, the nodes that are validated are also considered trusted nodes in the network.

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Pango FPGA Implementation of a Real-Time Display system for Multi-Channel Video Processing

Pango FPGA Implementation of a Real-Time Display System for ...

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International Conference on Sensing, Diagnostics, Prognostics, and control (SDPC)

作者： Cao Huang Lifu Du Yu Peng Gang Xiang Yang Yu Jingyi Chen School of Electronics Information Engineering Habin Institute of Technology Harbin China Beijing Aerospace Automatic Control Institute Beijing China Department of System Engineering Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9798350388855

ISBN: (纸本)9798350388862

A Pango FPGA-based solution for merging and processing multiple independent video streams and reconstructing a real-time display system is presented. The system handles each video stream individually, then produces a combined video output in HDMI format. To ensure high efficiency and low latency, a parallel processing scheme and a hardware-oriented architecture are employed. The Pango PG50K FPGA board acts as the central hardware for managing inputs, reconstructing video, and producing outputs. Rapid-access DDR3-SDRAM ensures efficient data caching and signal integrity. This system is ideal for applications requiring rapid response, such as traffic monitoring and industrial automation, and supports quick algorithm iteration and optimization, enhancing performance and adaptability.

关键词： Visualization Surveillance Display systems Streaming media Real-time systems Sensors Field programmable gate arrays Streams Image reconstruction Signal integrity

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A Strategy for Aerial Vehicle Trajectory Tracking using Robust Model Predictive control 23

A Strategy for Aerial Vehicle Trajectory Tracking using Robu...

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23rd International Conference on control systems and Computer Science Technologies, CSCS 2021

作者： Radulescu, Iulia Stefanoiu, Dan University Politehnica of Bucharest Department of Automatic Control and System Engineering Bucharest Romania

ISBN: (纸本)9781665439398

The objective of this paper is trajectory tracking for an aerial vehicle in presence of wind disturbances. The approach first involves linearizing the non-linear model of the aerial vehicle around operating points chosen along the trajectory to be followed. Then, a proposed modified version of robust min-max model predictive control is employed by using only one linearized model along the prediction horizon. The trajectory is generated using the concept of differential flatness. The simulation results of the proposed approach show that the aerial vehicle effectively manages to follow the trajectory under wind disturbances. © 2021 IEEE.

关键词： Trajectories

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A Framework of LSTM Neural Network Model in Multi-Time Scale Real-Time Prediction of Ship Motions in Head Waves

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船舶力学 2024年第12期28卷 1803-1819页

作者： CHEN Zhan-yang ZHAN Zheng-yong CHANG Shao-ping XU Shao-feng LIU Xing-yun Department of Ocean Engineering Harbin Institute of Technology at WeihaiWeihai 264209China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian 116024China Flight Control System Department AVIC Xi'an Flight Automatic Control Research InstituteXi'an 710065China

Ship motions induced by waves have a significant impact on the efficiency and safety of offshore ***-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive ***,the obvious memory effect of ship motion time series brings certain difficulty to rapid and accurate ***,a real-time framework based on the Long-Short Term Memory(LSTM)neural network model is proposed to predict ship motions in regular and irregular head waves.A 15000 TEU container ship model is employed to illustrate the proposed *** numerical implementation and the real-time ship motion prediction in irregular head waves corresponding to the different time scales are carried out based on the container ship *** related experimental data were employed to verify the numerical simulation *** results show that the proposed method is more robust than the classical extreme short-term prediction method based on potential flow theory in the prediction of nonlinear ship motions.

关键词： deep learning LSTM ship motion real-time prediction irregular waves

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Evaluation of Theoretical and Empirical Log-Normal Models for Wi-Fi Fingerprint Mapping in Indoor Positioning

Evaluation of Theoretical and Empirical Log-Normal Models fo...

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IEEE International Conference on Artificial Intelligence in engineering and Technology (IICAIET)

作者： Mohamed Aizad Bin Mohamed Ghazali Aroland Kiring Lyudmila Mihaylova Hoe Tung Yew Seng Kheau Chung Yan Yan Farm Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Department of Automatic Control and System Engineering University of Sheffield Sheffield UK

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

This paper evaluates both the theoretical and empirical log-normal models for Wi-Fi fingerprint mapping in indoor positioning systems. Wi-Fi fingerprinting, leveraging the ubiquitous IEEE 802.11 protocol, offers a practical solution for indoor localization where Global Positioning system (GPS) is ineffective. It involves generating empirical data by systematically measuring Wi-Fi Received Signal Strength (RSS) within a complex indoor environment and formulating a log-normal path loss model to predict signal behavior. A comparative analysis was conducted between the theoretical model, calibrated with empirical RSS data, and the actual RSS measurements. The results showed that while the theoretical model captured overall signal propagation trends, the empirical data revealed variations due to multipath effects and environmental obstacles. It demonstrated the practical use of Wi-Fi fingerprint mapping, theoretical modeling, and comparative analysis. The findings underscore the importance of empirical validation in refining theoretical models and optimizing deployment strategies for accurate and reliable indoor positioning. Future research should focus on mitigating multipath effects and enhancing the theoretical model accuracy through optimized access point (AP) placement and advanced signal processing techniques.

关键词： Analytical models Accuracy Fingerprint recognition Signal processing Reliability theory Market research Data models Mathematical models Wireless fidelity Global Positioning system

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On the Gaussian Filtering for Nonlinear Dynamic systems Using Variational Inference 27

On the Gaussian Filtering for Nonlinear Dynamic Systems Usin...

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27th International Conference on Information Fusion, FUSION 2024

作者： Liu, Yi Li, Xi Yang, Le Mihaylova, Lyudmila Li, Ji University of Canterbury Department of Electrical and Computer Engineering Christchurch New Zealand National University of Defense Technology State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems Changsha China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Locaris Electronic Technology Co. Ltd Zhengzhou China

ISBN: (纸本)9781737749769

This paper introduces a new variational Gaussian filtering approach for estimating the state of a nonlinear dynamic system. We first assume that the predictive distribution of the state is Gaussian and derive an iterative method for updating the state posterior in the natural parameter space through KullbackLeibler divergence minimization. The obtained update rule is the same as that of the conjugate-computation variational inference technique in Bayesian learning. The derivation here is simpler and more insightful. We then impose a Wishart prior on the inverse of the state prediction covariance to take into account the impact of approximating the state predictive distribution using a Gaussian density on the state posterior estimation. The prediction covariance is identified jointly with the state using variational inference and the established state posterior update rule to achieve the desired Gaussian filtering. Simulation study examines the performance of the proposed filtering framework in target tracking based on bearing and range measurements. © 2024 ISIF.

关键词： Wiener filtering

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Embedded Implementation of a Neural Network emulating Nonlinear MPC in a process control application*

Embedded Implementation of a Neural Network emulating Nonlin...

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European control Conference (ECC)

作者： Sebastian Leonow Raphael Dyrska Martin Mönnigmann Department of Mechanical Engineering Automatic Control and System Theory Ruhr-Universität Bochum Bochum Germany

We present the design, training, and implementation of a nonlinear autoregressive neural network for the control of a multi-input, multi-output hydraulic plant. The network mimics the optimal control signals of a nonlinear model predictive controller and is implemented on a low-level microcontroller. While trained with simulation data only, experiments on the real plant show that not only the setpoint tracking, but to some degree also the constraint satisfaction and unmeasured disturbance rejection are adapted by the neural network. In contrast to the optimization-based predictive controller, the neural network easily runs on an ESP32 microcontroller and Micropython with guaranteed evaluation time and still achieves similar control performance as the predictive controller.

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Intermittent fault diagnosis based on EMD decomposition and convolutional neural network

Intermittent fault diagnosis based on EMD decomposition and ...

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Electrical, Automation and Computer engineering (ICEACE), International Conference on

作者： Yu Peng Yuhang Liu Ruishi Lin Gang Xiang Shenhang Wang Zhiming Yang Department of System Engineering Beijing Aerospace Automatic Control Institute Beijing China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

The paper introduces an intermittent fault diagnosis method for analog circuits using EMD and CNN. The proposed method obtains the circuit output characteristics under different intermittent fault type using SPICE simulation. Then, EMD is used to extract the features and finally, the CNN model for fault detection and localization is built. The effectiveness of the method is validated through experiments in a three-phase SVPWM circuit.

关键词： Fault diagnosis Space vector pulse width modulation Training Neural networks Analog circuits Feature extraction SPICE Software Convolutional neural networks Testing

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Adaptive Anomaly Detection in Industrial systems: An EVT-DTS Approach with LSTM Autoencoders

Adaptive Anomaly Detection in Industrial Systems: An EVT-DTS...

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IEEE Instrumentation and Measurement Technology Conference

作者： Bing Yu Jiakai Xu Gang Xiang RuiShi Lin LiGuo Zhao Yang Yu School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Department of System Engineering Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

The reliability of modern industrial systems is rigid; therefore, it is mandatory to monitor the system status and detect anomalies accurately. A long short-term memory (LSTM) network can be used to predict the trend of single-dimensional system test data and implement anomaly detection based on the prediction result. However, the structure of the modern system is complex, and strong dependencies may exist between different variables. The LSTM-based detection method cannot capture this dependency, and some anomalies can be ignored. Therefore, an anomaly detection framework based on the LSTM autoencoder is proposed in this paper. The auto encoder is applied to find the hidden dependency among variables by minimizing the reconstruction error of normal data, while the LSTM is used to capture the temporal dependencies in the time series. Moreover, a new dynamic error threshold selection strategy based on extreme value theory (EVT-DTS) is presented, which can avoid estimating the error distribution beforehand. The EVT-DTS method can dynamically adjust the error threshold according to the current input data error so that the overall optimal detection result can be obtained. Finally, we implement experiment on two industrial applications using the proposed method, which demonstrate its effectiveness in finding complex anomaly states in the system.

关键词： Adaptive systems Time series analysis Market research Reliability Long short term memory Anomaly detection Monitoring

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Real-time Image Identification for Vehicle Positioning system

Real-time Image Identification for Vehicle Positioning Syste...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Chun-Yu Liu Ching-Huei Huang Rong-Terng Juang Department of Automatic Control Engineering Feng Chia University Taichung City Taiwan Institute of Aerospace and System Engineering Taipei Tech Taipei City Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

An innovative vehicle real-time positioning method was developed to maximize the use of existing visible light camera systems on vehicles, especially in areas without Global Positioning system (GPS) signals or blind spots. This approach eliminates the need for additional positioning devices and leverages the potential of existing vehicle driver assistance systems. As vehicles are equipped with cameras for monitoring the surrounding environment, we optimized the use of these existing resources for an efficient and accurate innovation in vehicle positioning. Firstly, we used cameras in four directions (the front, rear, left, and right) of the vehicle to capture images. By integrating images in four directions and using a transformation algorithm, a complete top-down view was formed with geographic information to enhance positioning accuracy. The integrated top-down view image is useful for obtaining information on surrounding environments and identifying the vehicle's real-time location in parking lots. The developed method serves as a foundation for future applications in automated driving planning and positioning.

关键词： Location awareness Technological innovation Accuracy Wheels Cameras Real-time systems Planning

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