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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Akash Guna, R.T. Benitez, Raul Sikha, O.K. Amrita School of Engineering Department of Computer Science and Engineering Coimbatore India Universitat Politècnica de Catalunya BarcelonaTech Departament of Automatic Control Spain

ISBN: (纸本)9798350302493

Existing explainability approaches for convolutional neural networks (CNNs) are mainly applied after training (post-hoc) which is generally unreliable. Ante-hoc explainers trained simultaneously with the CNN are more reliable. However, current ante-hoc explanation methods mainly generate inexplicit concept-based explanations tailored to specific tasks. To address these limitations, we propose a task-agnostic ante-hoc framework that can generate interpretation maps to visually explain any CNN. Our framework simultaneously trains the CNN and a weighting network - an explanation generation module. The generated maps are self-explanatory, eliminating the need for manual identification of concepts. We demonstrate that our method can interpret classification, facial landmark detection, and image captioning tasks. We show that our framework is explicit, faithful, and stable through experiments. To the best of our knowledge, this is the first ante-hoc CNN explanation strategy that produces visual explanations generic to CNNs for different tasks. © 2023 IEEE.

关键词： computer vision

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On Sparse Nonlinear System Identification Using Orthogonal Matching Pursuit, Orthogonal Least Squares and LASSO

On Sparse Nonlinear System Identification Using Orthogonal M...

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Mediterranean Conference on control and Automation (MED)

作者： Hua-Liang Wei The Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

System identification, as a rich and vital discipline, provides a practical and general methodology and tool for quantitatively modelling the input-output relationships of dynamical systems. Sparse nonlinear system identification (SNSI), especially parametric sparse nonlinear system identification (PSNSI), is an important and vital field of research with a wide range of applications. This work is concerned with PSNSI and particular attention is paid to the assessment of three well-known mainstream sparse learning methods, namely, orthogonal least squares (OLS), orthogonal matching pursuit (OMP) and least absolute shrinkage and selection operator (LASSO). The performances of these methods are tested and evaluated through three case studies relating to PSNSI problems. The research results and findings of this work provide practical useful information and guidance for researchers to better choose or adapt methods when solving PSNSI problems.

关键词： Learning systems Automation Matching pursuit algorithms System identification Nonlinear systems Dynamical systems

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Stochastic Optimization Problem Solved Using SVM Based Prediction For Photovoltaic Plants 27

Stochastic Optimization Problem Solved Using SVM Based Predi...

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27th International Conference on System Theory, control and Computing, ICSTCC 2023

作者： Colbu, Ștefania-Cristiana Băncilă, Daniel-Marian Popescu, Dumitru Olteanu, Severus-Constantin Petrescu-Niță, Alina Department of Automatic Control and Systems Engineering University POLITEHNICA of Bucharest Bucharest Romania Department of Mathematical Methods and Models University POLITEHNICA of Bucharest Bucharest Romania

ISBN: (纸本)9798350337983

In the current paper, a strategy for handling a stochastic optimization problem based on metaheuristic techniques is presented. This optimization problem is defined based on the impact of environmental factors such as temperature and irradiance on photovoltaic panels. The objective aims to solve the defined problem by finding an SVM model of a photovoltaic panel. To enhance the efficiency of power generation, this model will be utilized for the purpose of predicting the voltage linked to the Maximum Power Point of the panel. © 2023 IEEE.

关键词： Support vector machines

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Designing a Robust control System Based on Sliding Mode control for Two-axis Gimbal systems

Designing a Robust Control System Based on Sliding Mode Cont...

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2024 Conference of Young Researchers in Electrical and Electronic engineering, ElCon 2024

作者： Phuong, Nguyen Viet Tuyen, Nguyen Trong Khanh, Nguyen Duy Hung, Nguyen Ngoc Academy of Military Science and Technology Department of Aerospace Control Systems Hanoi Viet Nam Le Quy Don Technical University Department of Biomedical Engineering Hanoi Viet Nam Saint Petersburg State Electrotechnical University Department of Automatic Control Systems Hanoi Viet Nam Le Quy Don Technical University Department of Aerospace Control Systems Hanoi Viet Nam

ISBN: (纸本)9798350360646

Two-axis gimbals are used in stabilizing and controlling the aiming of optical systems. The main task is to isolate the angular motion of the payload and the disturbances affecting the optical axis of the camera while maintaining the camera's orientation locked onto the target. Currently, two commonly used gimbal structures are the Yaw-Pitch type and the Roll-Swing type. In this paper, we delve into an in-depth study to develop the full mathematical models for both types, thereby analyzing and evaluating the influence of the cross-channel noise moment effects caused by gimbal imbalance on the stability and aiming control quality under complex angular motion conditions of the payload. Simulation results conducted using MATLAB/SIMULINK with the two-axis gimbal model demonstrate that employing a robust control system significantly reduces the impact of disturbances and enhances the quality of control and stability of the transmission system. © 2024 IEEE.

关键词： adaptive inertially stabilized platform self-driving transmission sliding mode control two-axis gimbal

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Novel scheme for a PCM-based cold energy storage system. Design, modelling, and simulation

arXiv

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arXiv 2024年

作者： Bejarano, Guillermo Suffo, José J. Vargas, Manuel Ortega, Manuel G. Department of Systems Engineering and Automatic Control University of Seville Spain

This paper studies the design and dynamic modelling of a novel thermal energy storage (TES) system combined with a refrigeration system based on phase change materials (PCM). Cold-energy production supported by TES systems is a very appealing field of research, since it allows flexible cold-energy management, combining demand fulfilment with cost reduction strategies. The paper proposes and compares two different simulation models for a cold-energy storage system based on PCM. First, a continuous model is developed, the application of which is limited to decoupled charging/discharging operations. Given such conditions, it is a relatively precise model, useful for the tuning of the TES parameters. The second proposed model is a discrete one, which, despite implementing a discrete approximation of the system behaviour, allows to study more general conditions, such as series of partial charging/discharging operations. Simulation results of both models are compared regarding decoupled charging/discharging operations, and the ability of the discrete model to represent more realistic partial operations is analysed. © 2024, CC BY-NC-ND.

关键词： Thermal energy

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Pneumonia Detection from X-ray Images Using Convolutional Neural Networks

Pneumonia Detection from X-ray Images Using Convolutional Ne...

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International Conference on control systems and computer Science (CSCS)

作者： Viorel Dediu Andreea Udrea Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science Univesity Politehnica of Bucharest Bucharest Romania

Pneumonia is one of the top causes of death in Romania and early detection of this disease improves the recovery chances and shortens the length of hospitalization. In this work, we develop a solution for automatic pneumonia detection based on convolutional neural networks. Four network models are investigated. They are trained on 4.163 images from a public dataset and tested on 530 images. The best results are obtained by one of the proposed models conducting to a sensitivity of 98.72%, an accuracy of 89.81%, and ROC 93.46%. Thus, this research proposes a lightweight screening tool that can help triaging the patients with pneumonia.

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Second-Order Discrete-Analog Filters on Switched Capacitors with Two Electronic Keys

Second-Order Discrete-Analog Filters on Switched Capacitors ...

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2024 IEEE Ural-Siberian Conference on Biomedical engineering, Radioelectronics and Information Technology, USBEREIT 2024

作者： Denisenko, Darya Prokopenko, Nikolay Butyrlagin, Nikolay Ivanov, Yuri Don State Technical University Department Of Scientific Research Russia Southern Federal University Department Of Automatic Control Systems Rostov-on-Don Russia Don State Technical University Department Of Information Systems And Radio Engineering Rostov-on-Don Russia

ISBN: (纸本)9798350362893

Two new modifications of second-order low-frequency discrete-analog filters (DAFs) based on switched capacitors with two electronic keys have been developed and investigated. The first modification contains a resistive signal adder based on a buffer amplifier, in the second modification the signal adder is implemented on a differential difference operational amplifier. The basic formulas for the transmission coefficient at zero frequency and pole frequency, and also a frequency and attenuation of the pole are given. The transmission coefficient of new circuits in the passband does not depend on the capacitances of the frequency-setting capacitors. computer modeling was performed in the Micro-Cap environment, which confirms the coincidence of the calculated main parameters of the DAF and their time characteristics. The developed DAFs were studied at various input signal frequencies, which differ by several orders of magnitude, including the low, medium and high frequency ranges. It is shown that the graph of the DAF output signal has a 'step' character, which corresponds to the physical processes of signal conversion in filters of the class under consideration. The low parametric sensitivity of presented DAF circuits is achieved by the fact that the pole attenuation and transmission coefficient of such DAFs cease to depend on the parameters of the tuning elements. © 2024 IEEE.

关键词： Poles

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SightAid: empowering the visually impaired in the Kingdom of Saudi Arabia (KSA) with deep learning-based intelligent wearable vision system

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Neural Computing and Applications 2024年第19期36卷 11075-11095页

作者： Talaat, Fatma M. Farsi, Mohammed Badawy, Mahmoud Elhosseini, Mostafa Machine Learning Department Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & amp Engineering New Mansoura University Gamasa35712 Egypt College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah46537 Saudi Arabia

In the Kingdom of Saudi Arabia, visual impairment poses significant challenges for approximately 17.5% of school-aged children, mainly due to refractive errors. These challenges extend to everyday navigation, environmental interaction, and overall life quality. Motivated by the desire to empower visually impaired individuals, who face navigational limitations, difficulties in object recognition, and inadequate assistance from traditional technologies, we propose SightAid. This innovative wearable vision system utilizes a deep learning-based framework, addressing the gaps left by current assistive solutions. Traditional methods, such as canes and GPS devices, often fail to meet the nuanced and dynamic needs of the visually impaired, especially in accurately identifying objects, understanding complex environments, and providing essential real-time feedback for independent navigation. SightAid comprises a seven-phase framework involving data collection, preprocessing, and training of a sophisticated deep neural network with multiple convolutional and fully connected layers. This system is integrated into smart glasses with augmented reality displays, enabling real-time object detection and recognition. Interaction with users is facilitated through audio or haptic feedback, informing them about the location and type of objects detected. A continuous learning mechanism, incorporating user feedback and new data, ensures the system's ongoing refinement and adaptability. For performance assessment, we utilized the MNIST dataset, and an Indoor Objects Detection dataset tailored for the visually impaired, featuring images of everyday objects crucial for safe indoor navigation. SightAid demonstrates remarkable performance with accuracy up to 0.9874, recall values between 0.98 and 0.99, F1-scores ranging from 0.98 to 0.99, and AUC-ROC values reaching as high as 0.9999. These metrics significantly surpass those of traditional methods, highlighting SightAid's potential to substan

关键词： Object recognition

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Data-Driven control of Distributed Event-Triggered Network systems

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IEEE/CAA Journal of automatica Sinica 2023年第2期10卷 351-364页

作者： Xin Wang Jian Sun Gang Wang Frank Allgower Jie Chen the National Key Laboratory of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081 the Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China IEEE the Institute for Systems Theory and Automatic Control University of StuttgartStuttgart 70550Germany the Department of Control Science and Engineering Tongji UniversityShanghai 201804 the National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081China

The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems(*** systems).To this end,we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling,under which a model-based stability criterion for the closed-loop network system is derived,by leveraging a discrete-time looped-functional *** the model-based criterion with a data-driven system representation recently developed in the literature,a purely data-driven stability criterion expressed in the form of linear matrix inequalities(LMIs)is ***,the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input ***,numerical results corroborate the efficacy of the proposed distributed data-driven event-triggered network system(ETS)in cutting off data transmissions and the co-design procedure.

关键词： Data-driven control distributed event-triggered network system(ETS) linear matrix inequalitie(LMI) looped-functional stability

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Real-Time Intrusion Detection Based on Decision Fusion in Industrial control systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2024年 2卷 143-153页

作者： Xue, Yawen Pan, Jie Geng, Yangyang Yang, Zeyu Liu, Mengxiang Deng, Ruilong Information Engineering University Zhengzhou450001 China Singapore University of Technology and Design ITrust 487372 Singapore The University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS10 2TN United Kingdom Zhejiang University State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou313000 China

Industrial control systems (ICSs) are becoming increasingly interconnected as the rapid convergence of information technology (IT) and operation technology (OT) networks, and meanwhile massive attack surfaces have been exposed. However, traditional intrusion detection systems (IDSs) are difficult to be directly deployed in ICSs due to the hard real-time requirement and rare patching chance. Besides, the design of effective and practical IDSs is hampered by the lack of benchmarking ICS cybersecurity datasets. To bridge the gaps, this paper makes the first attempt by open-sourcing the developed ICS cybersecurity datasets and proposing a decision fusion based real-time IDS. Firstly, we design a customized cybersecurity dataset in a full-hardware and high-fidelity platform, including 7 types of cyber threats tailored for ICSs. The collected dataset includes network traffic, sensor readings, actuator status, and system parameters, providing the state-of-the-art benchmark dataset for ICSs consisting of cross-layer characteristics. Furthermore, we design an online decision fusion-based IDS by strategically integrating 4 widely-used machine learning models. The proposed IDS is deployed on a real-time running ethanol distillation, surpassing the performance of single detection models in terms of precision and F1-score, which substantially enhances intrusion detection accuracy and cybersecurity of ICS. © 2023 IEEE.

关键词： Intrusion detection

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