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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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Energy Demand Management in an Industrial Manufacturing Plant using MPC and Neurofuzzy Models

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IFAC-PapersOnLine 2023年第2期56卷 8738-8743页

作者： Javier Gómez William D. Chicaiza Juan M. Escaño Carlos Bordons Dept. System Engineering and Automatic Control Universidad de Sevilla Spain ENGREEN - Laboratory of Engineering for Energy and Environmental Sustainability Universidad de Sevilla Spain

An MPC controller is proposed to maximise the use of renewable energy in a manufacturing process. The strategy has been applied in a manufacturing system which has several machines, renewable generation resources, a combined heat and power (CHP) generator for power production, and a battery bank for energy storage. The work aims to maximise the use of renewable energy sources in this process, also taking into account the price of the electricity market, to reduce the cost. The use of neurofuzzy models for the prediction of the energy produced by renewable generators allows a dynamic prediction, using input values obtained from typical forecasting variables (wind speed, global irradiance, etc.).

关键词： Manufacturing processes Model Predictive control Neurofuzzy systems Energy management systems Genetic algorithms Intelligent control

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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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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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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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The Aerodynamics and Hover control for the X-wing VTOL MAV

The Aerodynamics and Hover Control for the X-wing VTOL MAV

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International Conference on Autonomous Unmanned systems, ICAUS 2021

作者： Cao, Yu Tang, Wei Luan, TianJiao Yao, Na Beijing Aerospace Automatic Control Institute Beijing China Beijing Institute of Astronautical System Engineering Beijing China China Academy of Launch Vehicle Technology Beijing China

ISBN: (纸本)9789811694912

X-wing tail-sitter Vertical Take-off and landing (VTOL) Micro Air Vehicle (MAV) is a tailless flying wing vehicle with a X-type wing configuration. It inherits the capability of helicopter VTOL and fixed wing high-efficiency cruising. Based on the X-wing VTOL MAV, the design method is studied in detail. According to the special characteristics of X-wing VTOL MAV, the design method focuses on two aspects, including airframe aerodynamics and yawing control under wind disturbance. The aerodynamics is estimated by vertex lattice method, numerical simulation and experiments. The method to improve the yaw control ability under wind disturbance is analyzed. After the iteration, the X-wing VTOL MAV is obtained and verified by flight testing. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Aircraft landing

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Cyber-Physical systems - A New Approach for Keyless Entry systems 23

Cyber-Physical Systems - A New Approach for Keyless Entry Sy...

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

作者： Balaban, Beatrix-May Sacala, Ioan Stefan Petrescu-Nita, Alina Claudia Pop, Florin University Politehnica of Bucharest Department of System Engineering Faculty of Automatic Control and Computers Bucharest Romania

ISBN: (纸本)9781665439398

Nowadays, intelligent vehicles are becoming more and more important in the transportation field. What makes such a vehicle vulnerable is the number of interconnected components that individually have a strong security but can cause weakness points when they are connected. The challenge is to design and build a powerful system that uses both cyber-physical components. The proposed model is a system with integrated modules for fingerprint and face recognition to certify that a driver has the right to access the vehicle. The aim is to develop a security system for intelligent vehicles that can be also a secure keyless entry system using the power of biometrics. When a driver approaches the car, a sensor will activate the recognition modules. Only after the total completion of the recognition process the driver is authenticated and has the right to use the vehicle. We simulated real-time face detection and eye tracking using Matlab 2020a. Face detection is performed using Viola-Jones algorithms and the tracking is made with bbox-es. Besides normal conditions, other tests were made to challenge the algorithm, for example: angle rotations, the use of glasses, face mask, glasses and face mask together. © 2021 IEEE.

关键词： Cyber Physical system

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0.4 Research on common mode voltage cancellation strategy for four-module motor driven by parallel balanced inverter

0.4 Research on common mode voltage cancellation strategy fo...

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Energy, Power and Grid (ICEPG), International Conference on

作者： Fengjiao Lee Yongqing Meng Jinpeng Kan Wenjie Chen Yixin Shi Yanwei Du College of Electrical Engineering Xi’an Jiaotong University Xi’an China Electronic System Engineering AVIC Xi’an Flight Automatic Control Research Institute Xi’an China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

In response to the issues of common mode voltage and the resultant common mode currents and leakage currents in PWM motor drive systems, this paper proposes a topology using a parallel balanced inverter to drive a four-module motor. Traditional three-phase inverters, with an odd number of bridge arms, inherently struggle to eliminate common mode voltage. By employing a balanced inverter, the proposed method extends the degrees of freedom, achieving internal group common mode voltage cancellation. Furthermore, replacing the conventional three-phase motor with a four-module motor and altering the stator winding connections, combined with a carrier phaseshifting modulation strategy, allows the system to cancel common mode voltages using a parallel balanced inverter. To verify the efficacy of the proposed solution, a simulation of a parallel balanced inverter driving a four-module motor system was constructed and compared with a traditional three-phase inverter system. The simulation results demonstrate that the new topology effectively suppresses system-conducted interference, thereby improving the system’s electromagnetic interference performance.

关键词： Motor drives Phase modulation Simulation Electromagnetic interference Stator windings Voltage Motors Inverters Topology Leakage currents

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