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arXiv 2022年

作者： Wzorek, Piotr Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Kraków Poland Department of Digital Systems Silesian University of Technology Gliwice Poland

In recent years, event cameras (DVS – Dynamic Vision Sensors) have been used in vision systems as an alternative or supplement to traditional cameras. They are characterised by high dynamic range, high temporal resolution, low latency, and reliable performance in limited lighting conditions – parameters that are particularly important in the context of advanced driver assistance systems (ADAS) and self-driving cars. In this work, we test whether these rather novel sensors can be applied to the popular task of traffic sign detection. To this end, we analyse different representations of the event data: event frame, event frequency, and the exponentially decaying time surface, and apply video frame reconstruction using a deep neural network called FireNet. We use the deep convolutional neural network YOLOv4 as a detector. For particular representations, we obtain a detection accuracy in the range of 86.9-88.9% mAP@0.5. The use of a fusion of the considered representations allows us to obtain a detector with higher accuracy of 89.9% mAP@0.5. In comparison, the detector for the frames reconstructed with FireNet is characterised by an accuracy of 72.67% mAP@0.5. The results obtained illustrate the potential of event cameras in automotive applications, either as standalone sensors or in close cooperation with typical frame-based cameras. © 2022, CC BY.

关键词： Cameras

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EEG-GNet: GCN-Based EEG Signal Fatigue Detection Network for Edge Devices

EEG-GNet: GCN-Based EEG Signal Fatigue Detection Network for...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Fuquan Zhang Yu Wu Xinxin Xu Chuansheng Wang Minjia Ma Antoni Grau Jiayan Huang School of Computer and Big Data Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of New Engineering Industry Putian University Putian China Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain School of Robotics Xi’an Jiaotong-Liverpool University Suzhou China College of New Engineering Industry Putian University Fujian Ruituo Information Technology Limited Company Putian China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Fatigue driving is a common potential traffic safety hazard. To detect and analysis the driver Fatigue degree in real-time, more attention have been increasing paid to application research of EEG fatigue detection device. However, existing methods suffer from complex model structure or large-scale model parameters. In the other words, they requires high computational consumption and large memory footprint, which creates a contradiction between model and portable EEG machine with limited hardware resources. In this work, we take both time and frequency domains of EEG signal into account and design an lightweight and efficient driver fatigue detection network based on GCN (EEG-GNet), which is available edge-end device application. Experiments on 4th Raspberry Pi on a public baseline dataset show that our EEG-GNet outperforms the state-of-the-art methods and takes good tradeoff between detection accuracy and resource consumption. Our EEG-GNet provides a new potential opportunity for future applications in practical portable/wearable (P/W) devices.

关键词： Accuracy Computational modeling Image edge detection Frequency-domain analysis Fatigue Brain modeling Feature extraction Electroencephalography Real-time systems Time-domain analysis

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Adaptive Design of a Cyber-Physical System for Industrial Risk Management Decision Support

Adaptive Design of a Cyber-Physical System for Industrial Ri...

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International Conference on control, Automation, robotics and Vision (ICARCV)

作者： Andrzej M.J. Skulimowski Paweł Łydek Decision Science Laboratory AGH University of Science and Technology Kraków Poland Progress & Business Foundation International Centre for Decision Sciences and Forecasting Kraków Poland Decision Science Laboratory AGH University of Science and Technology Chair of Automatic Control and Robotics Kraków Poland

ISBN: (纸本)9781665476881

This article presents an integrated design of intelligent decision support systems (DSSs) for industrial risk management. The need for this class of systems has been necessitated by resilience building and threat response planning problems faced by complex industrial installations in energy and mining sectors. The proposed DSS software architecture is AI-based and applies causal and anticipatory networks, multi-criteria analysis, information fusion and knowledge engineering techniques. The use of AI-tools follows the AI-alignment paradigm, where AI evolution provides clues regarding the most suitable techniques to solve anticipated industrial safety problems in different time scales. We propose a general scheme of industrial risk management which includes threats, sensors, information flows, and decision-making models. This scheme is complemented by the risk optimization module, which selects the actions and actuators to implement them. Signals received from sensors are fused and confronted with threat management scenarios contained in the knowledge base. The recommendations concerning prevention, protection, and threat mitigation measures are generated by the DSS and conveyed to human decision makers for approval. Selected actions can also be autonomously initiated. Previous activity assessments result on an improved configuration of sensors and actuators, as well as more effective first responder actions. Threat and risk management modules of the DSS are linked by a sequential machine learning procedure, so that the results of prior decisions can be used to learn managerial preferences and parameters of risk mitigating scenarios.

关键词： Decision support systems Economics Decision making Cyber-physical systems Hazards Sensors Risk management

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An Innovative Smart Irrigation Using Embedded and Regression-Based Machine Learning Technologies for Improving Water Security and Sustainability

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IEEE Access 2025年 13卷 100731-100751页

作者： Abdennabi Morchid Abdennacer Elbasri Zahra Oughannou Hassan Qjidaa Rachid El Alami Badre Bossoufi Saleh Mobayen Paweł Skruch LIMAS Laboratory Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah (SMBA) University Fes Morocco Department of Data Science Mundiapolis University of Casablanca Campus Nouaceur Casablanca Morocco Innovation in Mathematics and Intelligent Systems (IMIS) Laboratory Ibn Zohr University Agadir Morocco Systems and Sustainable Environment Laboratory (SED) Faculty of Engineering Sciences (FSI) Private University of Fez (UPF) Fes Morocco Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliu Taiwan Energy Systems Research Center Khazar University Baku Azerbaijan Department of Automatic Control and Robotics AGH University of Science and Technology Kraków Poland

Global agriculture is facing major challenges such as food security, sustainable water management, and the preservation of natural resources. Water scarcity, exacerbated by climate change, requires adopting more efficient agricultural practices. Traditional irrigation systems, often imprecise, contribute to water wastage. The use of embedded systems and machine learning offers a solution for optimizing irrigation according to local conditions and actual crop needs while contributing to food security and environmental sustainability. This study proposes an innovative approach to irrigation management, integrating real-time data and predictive models to improve irrigation efficiency. This study proposes an irrigation system based on embedded systems, using sensors and algorithms to collect and analyze data in order to optimize water management. The system adjusts irrigation levels according to specific crop needs, thus contributing to more sustainable water management. Using ML algorithms like linear regression algorithms to model the relationships between environmental factors and crop water requirements, enabling accurate prediction of required irrigation levels based on data collected by sensors. The use of embedded systems such as the ESP32, combined with temperature, humidity, and water level sensors, has enabled the development of an autonomous and efficient system for collecting data in real-time and processing it for decision-making. The proposed model has an MAE of 0.8434, an RMSE of 0.8434, and a coefficient (R2 Score) of 0.4044, offering soil moisture prediction accuracy. Furthermore, the training time of our model is 0.00253 seconds, while the prediction time is 0.00117 seconds. These results show not only the performance of the proposed model in terms of accuracy but also its computational efficiency, outperforming some of the studies mentioned. The results of the study show a significant reduction in water consumption, with a marked improvement in water

关键词： Climate change Embedded systems Smart agriculture Irrigation Food security Optimization methods Machine learning Machine learning algorithms Sustainable development Water resources Water monitoring Environmental management

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Investigating the Power Budget of a 3U Nanosatellite Designed for Earth Observation

Investigating the Power Budget of a 3U Nanosatellite Designe...

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IEEE Metrology for Aerospace

作者： Amina Daghouri Youssef El Hachimi Abdelaali Ouhammam Mohammed Alae Chanoui Soumia Elhani Hassane Mahmoudi Energy Optimization Diagnosis and Control Team ENSAM University Center for Research in Space Technologies EMI Mohammed V University Rabat Morocco Power Electronics Smart Control Techniques Automatic and Robotics Team EMI University Center for Research in Space Technologies EMI Mohammed V University Rabat Morocco LASTIMI Laboratory EST University Center for Research in Space Technologies EMI Mohammed V University Rabat Morocco

The Electrical Power System (EPS) is one of the most critical subsystems of a nanosatellite. Thus, its design should be done carefully for a successful mission. To verify that the power needs of its subsystems does not exceed the power available, a power budget should be established. The purpose of this work is to evaluate the 3U nanosatellite's EPS by analyzing its power budget using Systems Tool Kit (STK) software. In this study, the orbital design, the sizing of the PV panels and the batteries used are presented. The simulation is done taking into account the worst case assumptions: the sun-synchronous Low Earth Orbit (LEO) orbit with the longest eclipse period, the worst case solar irradiance of 1316 W/m 2 . The results of the power budget established confirm the selection and the sizing of PV panels and the batteries.

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Terminal Sliding Modes-Based PLLs for Three-Phase Grid-Connected Inverters

Terminal Sliding Modes-Based PLLs for Three-Phase Grid-Conne...

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Annual Conference of Industrial Electronics Society

作者： Pooyan Alinaghi Hosseinabadi Mostefa Kermadi Hemanshu Pota Saad Mekhilef Ali Soltani Sharif Abadi The School of Engineering and Information Technology The University of New South Wales Canberra ACT Australia The Power Electronics and Renewable Energy Research Laboratory (PEARL) University of Malaya Kuala Lumpur Malaysia The School of Science Computing and Engineering Technologies Swinburne University of Technology Hawthorn VIC Australia Faculty of Mechatronics Institute of Automatic Control and Robotics Warsaw University of Technology Warsaw Poland

This paper proposes and experimentally implements new phase-locked loop algorithms using non-adaptive and adaptive terminal sliding modes for the three-phase grid-connected inverters. The proposed algorithms aim to extract the grid voltage phase and frequency and provide them for the inverter controller. The proposed sliding mode-based PLL algorithms provide faster, more robust, and more accurate detection of the grid voltage information compared to the conventional PI-PLL. This leads to better current controller performance and subsequently, improves the quality of the injected currents into the grid. The finite-time stability notion is incorporated with robust sliding modes to ensure a fast convergence rate. An adaptive law is proposed to determine a proper value for the switching gain which leads to a better overall performance of the PLL compared with a non-adaptive one. The Lyapunov stability theory is used to obtain the stability proofs of the closed-loop systems under both designed sliding modes-based PLLs. The proposed PLL algorithms are validated by simulation and experimental results for the three-phase grid-connected inverter under normal and various abnormal grid conditions.

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Environment-Driven Online LiDAR-Camera Extrinsic Calibration

arXiv

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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Evaluation of a cost-effective photoelectric sensor for daylight harvesting in smart public lighting systems

Measurement: Energy

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Measurement: Energy 2024年 3卷

作者： Aytaç Gören Fatima Khanfar Ahmed Rachid Laboratory of Innovative Technologies University of Picardie Jules Verne Amiens 80000 France Automatic Control and Robotics Laboratories Department of Mechanical Engineering Dokuz Eylul University 35320 Izmir Turkey

Global warming concerns, along with international agreements and regulations, reflect a broader effort to change the public's high energy demand in recent years. Smart public lighting systems are gaining popularity due to their energy-saving capabilities, reduction in carbon dioxide emissions, and improved public comfort. However, transitioning to smart public lighting requires careful planning and multiple stages. This is not only to accommodate public behavior, revise scenarios, and test citizen acceptance but also due to the necessary infrastructure investments. Smart public lighting incorporates new technologies, often with a breakeven point that takes several years to reach. To promote the widespread adoption of smart public lighting, it is essential to produce relatively expensive components in large quantities and explore cost-effective solutions. This research focuses on investigating a cost-effective photoelectric sensor for smart public purposes. The primary originality of this study lies in identifying a cost-effective photoelectric sensor that can replace technically equivalent but more expensive sensor solutions for indoor and outdoor lighting control purposes.

关键词： Smart public lighting Daylight harvesting Photoelectric sensor Light quality

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Development and Modeling of an Underwater Ship's Hull Inspection Station Equipped with Two Arms Manipulator 3

Development and Modeling of an Underwater Ship's Hull Inspec...

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3rd International Conference on Advanced Systems and Emergent Technologies, IC_ASET 2019

作者： Hamzaoui, Nidhal Zaoui, Chiheb Hachicha, Saber Dallagi, Habib Nejim, Samir Maalej, Aref Automatic Control and Marine Robotics Naval Academy Tunisia Laboratory of Electromechanical Systems University of Sfax Tunisia

ISBN: (纸本)9781728113173

Due to its different benefits, the underwater hull inspection has become a focus of interest of a big number of researchers and companies. In this article, we look to aid and simplify the ship's hull inspection task through a new robotic system using two arms manipulators. Therefore, we established the geometric and dynamic models of an underwater ship's hull inspection station components. In effect, the mentioned station has fourteen freedom degrees, which are eight rotations and six translations in both arms. Furthermore, we applied a PID controller in order to establish the dynamic simulation of the station using the SimMechanics software. This study demonstrates that such a station using two arms manipulators technology can make the ship's hull inspection process simple and more effective. © 2019 IEEE.

关键词： Manipulators

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Artificial Neural Network-Based Direct Power control to Enhance the Performance of a Pmsg-Wind Energy Conversion System Under Real Wind Speed and Parameter Uncertainties: An Experimental Validation

SSRN

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SSRN 2023年

作者： Majout, Btissam Bossoufi, Badre Karim, Mohammed Skruch, Pawel Mobayen, Saleh El Mourabit, Youness El Zair Laggoun, Zakaria Laboratory of Engineering Modelling and Systems Analysis SMBA University Fez Morocco Department of Automatic Control and Robotics AGH University of Science and Technology Kraków30-059 Poland Graduate School of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou640301 Taiwan National School of Applied Sciences Abdelmalek Essaadi University Tetouan Morocco Laboratory of Electrotechnique Constantine Mentouri Brothers Constantine 1 University Constantine Algeria

With the increasing emphasis on embedding advanced technology into system controls, the Direct Power control (DPC) approach has garnered significant attention, owing to its straightforward and highly adaptable algorithm. It has been adopted in many applications. However, the variable frequency, harmonic distortion of the currents, and power ripples caused by Hysteresis controllers and switching tables decrease its effectiveness and robustness, affecting the system’s performance. For this reason, this paper proposed a new DPC based on Artificial Neural Network (ANN) approaches. In this approach, the hysteresis comparator and the switching table are substituted with ANN controllers and then applied on both sides: machine-side converter (MSC) and grid-side converter (GSC) of a Permanent Magnet Synchronous Generator based Wind Energy Conversion System (PMSG-WECS). Moreover, to make the system more efficient in varying wind conditions, this study expands the utilization of the artificial neural networks (ANN) to encompass the maximum power point tracking (MPPT) control strategy. To demonstrate the effectiveness of the proposed approach on the system behaviors, a simulation test was carried out in the Matlab/Simulink environment, using a real wind profile of a Moroccan city (Essaouira). In comparison to the classical DPC control, the simulation results showed the superior performance of the proposed ANN-DPC control in terms of reference tracking, response time, overshoot, precision, and its capacity to reduce the rate of power ripples and total harmonic distortion (THD) in the injected currents. Furthermore, a robustness test was also included in this work to check the robustness of the proposed control against parameters variation. In conclusion, the feasibility and effectiveness of the ANN-DPC control approach were confirmed through experimental validation using the dSPACE DS1104 board. © 2023, The Authors. All rights reserved.

关键词： MATLAB

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