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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Liu, Haifei Zhao, Qin Hao, Jie Tao, Laifa Lu, Chen Beihang University Institute of Reliability Engineering Beijing China Science & Technology on Reliability & Environmental Engineering Laboratory Beijing China Beihang University School of Reliability and Systems Engineering Beijing China AVIC xi'An Flight Automatic Control Research Institute Xi'an China

ISBN: (纸本)9798350337754

With the rapid development of multi-electric and all-electric aircraft, the role of power supply systems in aircraft is becoming increasingly prominent. Traditional fault diagnosis methods have problems such as a single means of sensory modelling and unbalanced fault data. The rapid development of digital twin technology provides an opportunity to overcome these difficulties. However, how to achieve adaptive updating and how to improve the data-and-model-fusion capabilities are also urgent challenges to be solved. To address the lack of existing research, this paper combines time-frequency domain analysis, physical information neural network based on differential-algebraic equation (DAE-PINN) and transient stability analysis of the power supply system based on a digital twin model with dimensions including fault-behavior-twin and (FBT) fault-state-awareness-twin (FSAT). Ultimately, this paper achieves effective digital twin modelling, fault eigenfeature extraction and high accuracy fault diagnosis, reaching fault data balance and complete state characterization and adaptive update of the diagnostic model. © 2023 IEEE.

关键词： Failure analysis

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Generalized Likelyhood Ratio based Detection on Cyber-Attacks 37

Generalized Likelyhood Ratio based Detection on Cyber-Attack...

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37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Wu, Hao Zhang, Deyu Dept. of Software Henan University of Engineering Zhengzhou China Institute for Automatic Control and Complex Systems University of Duisburg-Essen Duisburg Germany

ISBN: (纸本)9781665465366

In this paper, a generalized likelihood ratio based detection algorithm with a statistical method for threshold setting is proposed to detect cyber-attacks on a unified remote control architecture. Based on an encoder and a decoder embedded on plant side and remote controller side, respectively, not only additive interrupted signal (only effecting output or input signal), but also multiplicative attack signal could be detected easily. Finally, three numerical examples are provided to validate our proposed scheme. © 2022 IEEE.

关键词： Remote control

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Analysis of fast prototyping of microcontroller-based ML software for acoustic signal classification 26

Analysis of fast prototyping of microcontroller-based ML sof...

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26th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Marciniak, Filip Marciniak, Wojciech Marciniak, Tomasz Poznan University of Technology Faculty of Computing and Telecommunications Poznań60-965 Poland Poznan University of Technology DECYBEL Science Club Poznań60-965 Poland Poznan University of Technology Faculty of Automatic Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Division of Electronic Systems and Signal Processing Poznań60-965 Poland

ISBN: (纸本)9798350304985

The paper analyzes the preparation of software for acoustic signal classification with machine learning techniques for microcontrollers. The design process was tested for three types of devices: Nordic Thingy:53, *** and Arduino Nano 33 BLE Sense Lite. The classifier training process was carried out using the Edge Impulse platform. Experimental studies were carried out for the process of classifying sound signals generated by the vacuum cleaner motor. The results of the training and the model test were presented for different configurations. © 2023 Division of Signal Processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： Microcontrollers

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Recent Developments in Self-Assembling Multi-Robot systems

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Current Robotics Reports 2023年第4期4卷 101-116页

作者： Bray, Edward Groß, Roderich Department of Automatic Control & Systems Engineering The University of Sheffield Sheffield UK School of Mechanical and Mechatronic Engineering University of Technology Sydney Sydney Australia

This review studies recent developments towards the physical design and control of self-assembling multi-robot systems. A wide range of novel robotic systems have been developed lately, for potential applications in terrestrial, aquatic, and aerospace environments. They increasingly make use of connectors which enable modules to join with each other at arbitrary points instead of discrete locations. Although the majority of contemporary algorithms are shape-driven, an increased focus on task-driven algorithms is observed. Self-assembling multi-robot systems allow the same set of robots to adopt specific morphologies for different tasks. The requirements for robots to be able to connect to each other, locomote, and communicate have led to a wide range of physical designs realising different trade-offs. While algorithms are validated extensively in simulation, only a small portion are yet tested on real robotic platforms. Future research should investigate the real-world application of these systems, possibly aided by the introduction of standardised and open hardware.

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Uncertainty-Informed Model Selection Method for Nonlinear System Identification and Interpretable Machine Learning 32

Uncertainty-Informed Model Selection Method for Nonlinear Sy...

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32nd Mediterranean Conference on control and Automation, MED 2024

作者： Gu, Yuanlin Wei, Hua-Liang University of Stirling Division of Computer Science and Maths StirlingFK9 4LA United Kingdom University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom

ISBN: (纸本)9798350395440

Modeling uncertainty has been an active and important topic in the fields of data-driven modeling and machine learning. Uncertainty ubiquitously exists in any data modeling process, making it challenging to identify the optimal models among many potential candidates. This article proposes an uncertainty-informed method to address the model selection problem. The performance of the proposed method is evaluated on a dataset generated from a complex system model. The experimental results demonstrate the effectiveness of the proposed method and its superiority over conventional approaches. This method has minimal requirements for the length of training data and model types, making it applicable for various modeling frameworks. © 2024 IEEE.

关键词： Machine learning

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Fail-Safe Decision Architecture for Positioning Failures on Automated Vehicles 9

Fail-Safe Decision Architecture for Positioning Failures on ...

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9th IEEE Smart World Congress, SWC 2023

作者： Rodriguez-Arozamena, Mario Aranguren-Mendieta, Inigo Perez, Joshue Zubizarreta, Asier Tecnalia Basque Research and Technology Alliance Bilbao Derio Spain Basque Research and Technology Alliance Industry and Mobility Unit Tecnalia Derio Spain Dept. of Automatic Control and Systems Engineering Bilbao Spain

ISBN: (纸本)9798350319804

Precise localization is essential for the operation of Connected and Automated Vehicles (CAVs) in urban scenarios. Camera and LiDAR-based solutions are currently used in some of the CAVs around the world, but they entail an expensive performance in terms of computational time and economic cost. Due to this, Global Navigation Satellite System (GNSS) based solutions remains the preferred solution for positioning Automated Vehicles in different Operational Design Domains (ODDs). Although several improvements in system reliability have been made recently, in case of positioning failures most of the architectures rely on system redundancy or automation disengagements to achieve minimal risk conditions. This paper presents a fail-safe decision architecture for mitigating positioning failures of automated vehicles in urban scenarios. The proposed architecture utilizes a combination of sensor fusion localization and decision-making modules to ensure the safe and efficient operation of the vehicle in the event of a positioning failure. The proposed approach is evaluated through simulation in a representative urban scenario and is shown to effectively handle positioning failures, improving the localization accuracy provided by each information source. © 2023 IEEE.

关键词： automated driving fail-safe operation fallback strategy positioning failure safe state

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Experimental Validation of the Capture Chamber Model in Mutriku MOWC Wave Power Plant

Experimental Validation of the Capture Chamber Model in Mutr...

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OCEANS

作者： Aitor J. Garrido Jon Lekube Amparo Villasante Izaskun Garrido Automatic Control Group - ACG Inst. of Research and Development of Processes - IIDP Automatic Control and Systems Engineering Dept. Faculty of Engineering of Bilbao University of the Basque Country (UPV/EHU) Bilbao Spain Automatic Control Group - ACG BiMEP – Biscay Marine Energy Armintza Bizkaia Spain Automatic Control Group - ACG Faculty of Engineering of Bilbao University of the Basque Country (UPV/EHU). Bilbao Spain

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

Ocean wave energy has de potential to satisfy 15% of EU energy demand, cutting 136 MT/MWh off the CO2 emissions by 2050, as stated by the EU Energy Road Map. Analogously, the Spanish Renewable Energies Plan specifically highlighted the Spanish marine energy potential with special emphasis in wave energy. In this context, Oscillating Water Column (OWC) converters are maybe nowadays the most promising wave energy converters, with the potential capability of sea energy harnessing from diverse on-shore and floating structures. This paper presents an analytic modeling of the wave capture chamber for a fixed on-shore OWC wave power plant. The model is particularized and parameterized for the case of the Mutriku MOWC wave power plant, located in the Spanish Basque Country coast, and then validated using both measured real wave entry data and experimental generated output power from the plant

关键词： Renewable energy sources Power measurement Wave power Roads Sea measurements Predictive models Particle measurements

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A Heuristically Accelerated Reinforcement Learning-Based Neurosurgical Path Planner

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Cyborg and Bionic systems 2023年第1期4卷 298-308页

作者： Guanglin Ji Qian Gao Tianwei Zhang Lin Cao Zhenglong Sun School of Science and Engineering The Chinese University of Hong KongShenzhenChina Shenzhen Institute of Artificial Intelligence and Robotics for Society ShenzhenChina Department of Automatic Control and Systems Engineering The University of SheffieldUK

The steerable needle becomes appealing in the neurosurgery intervention procedure because of its flexibility to bypass critical regions inside the brain;with proper path planning,it can also minimize the potential damage by setting constraints and optimizing the insertion ***,reinforcement learning(RL)-based path planning algorithm has shown promising results in neurosurgery,but because of the trial and error mechanism,it can be computationally expensive and insecure with low training *** this paper,we propose a heuristically accelerated deep Q network(DQN)algorithm to safely preoperatively plan a needle insertion path in a neurosurgical ***,a fuzzy inference system is integrated into the framework as a balance of the heuristic policy and the RL *** are conducted to test the proposed method in comparison to the traditional greedy heuristic searching algorithm and DQN *** showed promising results of our algorithm in saving over 50 training episodes,calculating path lengths of 0.35 after normalization,which is 0.61 and 0.39 for DQN and traditional greedy heuristic searching algorithm,***,the maximum curvature during planning is reduced to 0.046 from 0.139 mm−1 using the proposed algorithm compared to DQN.

关键词： needle surgical insertion

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Aspects of Using Rotating Modes of Hydraulic Units in Pump Complexes 5

Aspects of Using Rotating Modes of Hydraulic Units in Pump C...

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5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Korenkova, Tetyana Kovalchuk, Viktoriya Qawaqzeh, Mohamed Zaidan Kremenchuk Mykhailo Ostrohradskyi National University Department of Systems of Automatic Control and Electric Drive Kremenchuk39600 Ukraine Al-Balqa' Applied University Department of Electrical and Electronics Engineering Al-Salt66110 Jordan

ISBN: (纸本)9798350359787

It is shown that pumping complexes of industrial and communal water supply are complex energy-intensive objects with low controllability in both normal and emergency modes of operation. It is proposed to use reversible modes of operation of turbomechanisms to increase the controllability of pump complex equipment. The structure of the system of active regulation of pump complex parameters with various options for including the adjustable hydroturbine unit has been developed. It is shown that active devices can perform both regulatory and protective functions. A device for controlling and protecting the pumping unit against water hammer based on an active water flow energy quencher is proposed. The algorithm of operation of the software control device of the electrohydraulic equipment protection system against excess pressure in the hydraulic network has been developed. © 2023 IEEE.

关键词： controllability

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The Analysis of biological signals using Machine Learning Techniques 23

The Analysis of biological signals using Machine Learning Te...

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

作者： Ciocirlan, Marina Udrea, Andreea Lupu, Ciprian University Politehnica of Bucharest Department of Automatic Control and Systems Engineering Bucharest Romania

ISBN: (纸本)9781665439398

This paper aims at developing new techniques that study vital functions of the heart by analysing biological signals. ECG prediction is important for many current medical applications. Currently, there are many machine learning (ML) and deep learning (DL) solutions which are used for prediction of ECG data estimated in a period of time. We analyze the anomalies of the cardiac coherence model with an LSTM architecture with layers specifically designed to address the problem of long term dependency. Our method significantly outperforms the state of the art on the PhysioNet dataset. © 2021 IEEE.

关键词： Forecasting

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