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作者： Ashry, Nesma Attia, Radwa Nashaat, Heba Rizk, Rawya Computer and Control Department Faculty of Engineering Port-Said University Port-Said Egypt

The increasing amount of data processing through cloud systems leads to high energy costs and CO2 emissions challenges. Advanced monitoring tools are used to track resource utilization and identify possible optimization areas to mitigate CO2 emissions and reduce energy consumption. Thus, resource and power management in cloud environments is essential to ensure efficient performance and effective cost. Recently, container-based clouds become more widely used than hypervisor-based clouds. Scheduling the huge number of user-submitted tasks and optimizing both network and cloud resources are important aspects of cloud computing to decrease network overhead, reduce both Data Center (DC) energy consumption and Service-Level Agreement (SLA) violations and in turn, mitigate CO2 emissions. However, the state-of-the-art resource schedulers that consider power management and network transmissions are unfortunately not container-aware schedulers. The main objective of this paper is to introduce a solid analysis of the most significant techniques and tools in resource management to minimize power consumption and CO2 emissions with the advantage of being a container-aware scheduler;Applying the first stage of the proposed solution leads to mitigating the CO2 by an average of 2.12% against the Non-Clustered approach and 18.04% against the Clustered approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Containers

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Multi-Agent Imitation Learning Based Energy Management of a Microgrid With Hybrid Energy Storage and Real-Time Pricing

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IEEE Internet of Things Journal 2025年第12期12卷 19801-19817页

作者： Gao, Shuhua Xu, Yizhuo Zhang, Zhaoqian Wang, Zhengfang Zhou, Xiaoyu Wang, Jing Shandong University School of Control Science and Engineering Jinan250100 China The Hong Kong Polytechnic University Department of Electronic and Information Engineering Hong Kong Hong Kong National University of Singapore Department of Electrical and Computer Engineering Singapore117583 Singapore Second Construction Limited Company of China Construction Eighth Engineering Division Jinan Shandong China

Microgrids equipped with hybrid energy storage systems (ESSs) are increasingly critical for balancing the intermittency of renewable energy sources (RESs) and the fluctuations in demand. This paper introduces a novel Multi-Agent Imitation Learning (MAIL) framework for real-time energy management in microgrids, particularly under real-time pricing conditions. The approach leverages a problem decomposition strategy, which separates the energy management task into two phases: first, optimal actions for each ESS are estimated using individual agents, each employing a deep neural network (DNN) trained to emulate an ideal mixed-integer linear programming (MILP) solver;then, a one-step online optimization reacts to these decisions to optimize the remaining system components holistically. Rigorous proofs establish the equivalence of the decomposed subproblems to the original optimization problem, ensuring integrity and effectiveness. Comparative simulation studies with real-world data reveal that our MAIL framework offers significant cost advantages and superior training efficiency relative to both traditional single and emerging multi-agent reinforcement learning (RL) methods. Notably, the operational costs achieved through our approach are markedly lower than those of its counterparts, closely approximating the theoretical minimum. This underscores the method's proficiency and its potential as an effective solution for microgrid energy management. Moreover, the MAIL strategy's multi-agent design allows for straightforward scalability to more extensive systems involving multiple ESSs and shows promising potential for cooperative management across interconnected microgrids. © 2014 IEEE.

关键词： Costs

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A dual adaptive unscented Kalman filter algorithm for SINS-based integrated navigation system

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Journal of Systems engineering and Electronics 2024年第3期35卷 732-740页

作者： LYU Xu MENG Ziyang LI Chunyu CAI Zhenyu HUANG Yi LI Xiaoyong YU Xingkai Department of Precision Instrument Tsinghua UniversityBeijing 100084China College of Mechanical and Power Engineering Three Gorges UniversityYichang 443002China Unit 91001 of the PLA Beijing 100161China School of Control and Computer Engineering North China Electric Power UniversityBeijing 100096China

In this study, the problem of measuring noise pollution distribution by the intertial-based integrated navigation system is effectively suppressed. Based on nonlinear inertial navigation error modeling, a nested dual Kalman filter framework structure is developed. It consists of unscented Kalman filter (UKF)master filter and Kalman filter slave filter. This method uses nonlinear UKF for integrated navigation state estimation. At the same time, the exact noise measurement covariance is estimated by the Kalman filter dependency filter. The algorithm based on dual adaptive UKF (Dual-AUKF) has high accuracy and robustness, especially in the case of measurement information interference. Finally, vehicle-mounted and ship-mounted integrated navigation tests are conducted. Compared with traditional UKF and the Sage-Husa adaptive UKF (SH-AUKF), this method has comparable filtering accuracy and better filtering stability. The effectiveness of the proposed algorithm is verified.

关键词： Kalman filter dual-adaptive integrated navigation unscented Kalman filter(UKF) robust

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Semi-mind controlled robots based on Reinforcement Learning for Indoor Application 8

Semi-mind controlled robots based on Reinforcement Learning ...

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8th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： Naidji, Ilyes Tibermacine, Ahmed Guettala, Walid Tibermacine, Imad Eddine Department of Computer Science University Mohamed Khider of Biskra Algeria Department of Computer Control and Management Engineering Sapienza University of Rome Italy

This paper presents a solution to this challenge by introducing interactive feedback derived from brain signals to train robots using deep reinforcement learning, particularly in the context of indoor maze navigation. Our objective is to enhance the learning process in a human-robot interaction scenario by incorporating human emotion or attention feedback. To accomplish this, we empowered the robot to learn new tasks through a dynamic policy network based on human feedback, and we augmented this input with other sensor data, including LIDAR. Various experiments are conducted to compare the efficacy of manual feedback, brain signal feedback, and no brain signal feedback, employing diverse Reinforcement Learning models. Additionally, we explore different models for emotion classification, employing Graph Neural Network models and traditional deep learning models, and subsequently compare the outcomes. © 2023 Copyright for this paper by its authors.

关键词： Feedback

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Robust and fault-tolerant spacecraft attitude control based on an extended-observer design

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control Theory and Technology 2022年第3期20卷 323-337页

作者： Alessandro Giuseppi Francesco Delli Priscoli Antonio Pietrabissa Department of Computer Control and Management EngineeringLa SapienzaUniversity of RomeVia Ariosto 25Rome00185Italy

The aim of this work is to develop a robust control strategy able to drive the attitude of a spacecraft to a reference value,despite the presence of unknown but bounded uncertainties in the system parameters and external *** to the use of an extended observer design,the proposed control law is robust against all the uncertainties that affect the high-frequency gain matrix,which is shown to capture a broad spectrum of modelling issues,some of which are often neglected by traditional *** proposed controller then provides robustness against parametric uncertainties,as moment of inertia estimation,payload deformations,actuator faults and external disturbances,while maintaining its asymptotic properties.

关键词： Extended observer Spacecraft control Attitude stabilization

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A Dual-Stage Optimal controller Design for controlling AVR System 27

A Dual-Stage Optimal Controller Design for Controlling AVR S...

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27th International computer Science and engineering Conference, ICSEC 2023

作者： Abood, Layla H. Mohamed, Mohamed J. Abood, May H. University of Technology Department of Control and System Engineering Baghdad Iraq Al-Iraqia University Department of Computer Engineering Baghdad Iraq

ISBN: (纸本)9798350342109

In this study a cascade controller is proposed to maintain the Automatic Voltage Regulator (AVR) system, its comprises from two stage the first one is a conventional Proportional and Derivative (PD) controller and the second one is a Fractional Order PID (FOPID) controller is combined together for regulating the terminal voltage in AVR system, the gains of the proposed controller is tuned used a smart optimization algorithm called Squirrel Search algorithm (SSA) and adopt an Integral Time Absolute Error (ITAE) for monitoring for monitoring desired response to obtain a stable and robust response, the system response is tested using Matlab 2019 and the transient analysis is used to calculate overshoot, settling time, rise time, and peak time then compared these values selected classical controllers(PID,FOPID) to verify the robust response of the proposed controller. A robustness analysis verified the system response when there is an external disturbances, positive and negative disturbances signals are added, the controller try to face these signals, it takes 0.15 sec in negative disturbance and 0.1 sec in positive disturbance then in both states force the response to return to its desired response. © 2023 IEEE.

关键词： controllers

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Vision-based fingerspelling recognition for an assistive technology system

Vision-based fingerspelling recognition for an assistive tec...

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2023 International Conference on Industry Sciences and computer Science Innovation, iSCSi 2023

作者： Kapuscinski, Tomasz Majcher, Marcel Department of Computer and Control Engineering Rzeszow University of Technology W. Pola 2 Rzeszow35-959 Poland

In this paper, a vision-based method for fingerspelled acronyms recognition is proposed. First, the visual information carried by the image is reduced to the person's edges. The heterogeneous and changing background is removed by semantic segmentation of the person using a pre-trained R-CNN Mask. Then, the feature vector is generated by the encoder - part of the autoencoder, previously trained in an unsupervised manner. Finally, the sequence of feature vectors obtained in this way is fed into the input of the Bidirectional Long Short-Term Memory network used for classification. The method is tested on a demanding dataset, including the execution of acronyms fingerspelled by Deaf people who use sign language on a daily basis. Promising results suggest that the method can be incorporated into the Signed Communication System developed by the research team. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Deep neural networks

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Novel Framework of Segmentation 3D MRI of Brain Tumors

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computers, Materials & Continua 2023年第2期74卷 3489-3502页

作者： Ibrahim Mahmoud El-Henawy Mostafa Elbaz Zainab H.Ali Noha Sakr Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt Department of Computer Science Faculty of Computers and InformaticsKafrelsheikh UniversityKafrelsheikhEgypt Department of Embedded Network Sys.and Tec. Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikhEgypt Computers Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt

Medical image segmentation is a crucial process for computer-aided diagnosis and *** image segmentation refers to portioning the images into small,disjointed parts for simplifying the processes of analysis and *** and speckle noise are different types of noise in magnetic resonance imaging(MRI)that affect the accuracy of the segmentation process ***,image enhancement has a significant role in MRI *** paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free *** proposed techniques consider the values of Peak Signal to Noise Ratio(PSNR)and the level of noise as inputs to the attention-U-Net model for segmentation of the *** framework has been divided into three stages:removing speckle and Rician noise,the segmentation stage,and the feature extraction *** framework presents solutions for each problem at a different stage of the *** the first stage,the framework uses Vibrational Mode Decomposition(VMD)along with Block-matching and 3D filtering(Bm3D)algorithms to remove the ***,the most significant Rician noise-free images are passed to the three different methods:Deep Residual Network(DeRNet),Dilated Convolution Auto-encoder Denoising Network(Di-Conv-AE-Net),andDenoising Generative Adversarial Network(DGAN-Net)for removing the speckle *** Bm3D have achieved PSNR values for levels of noise(0,0.25,0.5,0.75)for reducing the Rician noise by(35.243,32.135,28.214,24.124)and(36.11,31.212,26.215,24.123)*** framework also achieved PSNR values for removing the speckle noise process for each level as follows:(34.146,30.313,28.125,24.001),(33.112,29.103,27.110,24.194),and(32.113,28.017,26.193,23.121)forDeRNet,Di-Conv-AE-Net,and DGAN-Net,*** experiments that have been conducted have proved the efficiency of the proposed framework a

关键词： MRI Rician noise speckle noise segmentation deep learning

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Frequency Domain Auto-tuning of Structured LPV controllers for High-Precision Motion control 63

Frequency Domain Auto-tuning of Structured LPV Controllers f...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Broens, Yorick Butler, Hans Toth, Roland Asml Veldhoven Netherlands Eindhoven University of Technology Department of Electrical Engineering Eindhoven Netherlands Hunren Institute for Computer Science and Control Systems and Control Laboratory Hungary

ISBN: (纸本)9798350316339

Motion systems are a vital part of many industrial processes. However, meeting the increasingly stringent demands of these systems, especially concerning precision and throughput, requires novel control design methods that can go beyond the capabilities of traditional solutions. Traditional control methods often struggle with the complexity and position-dependent effects inherent in modern motion systems, leading to compromises in performance and a laborious task of controller design. This paper addresses these challenges by introducing a novel structured feedback control auto-tuning approach for multiple-input multiple-output (MIMO) motion systems. By leveraging frequency response function (FRF) estimates and the linear-parameter-varying (LPV) control framework, the proposed approach automates the controller design, while providing local stability and performance guarantees. Key innovations include norm-based magnitude optimization of the sensitivity functions, an automated stability check through a novel extended factorized Nyquist criterion, a modular structured MIMO LPV controller parameterization, and a controller discretization approach which preserves the continuous-time (CT) controller parameterization. The proposed approach is validated through experiments using a state-of-the-art moving-magnet planar actuator prototype. © 2024 IEEE.

关键词： Continuous time systems

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Gesture Recognition for Human-Robot Interaction Through Virtual Characters 15th

Gesture Recognition for Human-Robot Interaction Through Vir...

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15th International Conference on Social Robotics, ICSR 2023

作者： Sabbella, Sandeep Reddy Kaszuba, Sara Leotta, Francesco Nardi, Daniele Department of Computer Control and Management Engineering DIAG Sapienza Universitá di Roma Via Ariosto 25 Rome Italy

ISBN: (纸本)9789819987177

Human-Robot Interaction (HRI) has grown increasingly important with the integration of various types of robots into industrial and daily life aspects. Non-verbal communication plays a vital role in HRI and heavily relies on gesture recognition. The ability of robot’s interpretation of human gestures is essential for enhancing the overall user experience. To achieve precision and effectiveness of gesture recognition in HRI, the incorporation of Virtual Reality (VR) technology offers promising avenues. In this article, we describe the development of a gesture recognition pipeline tailored to HRI in Precision Agriculture scenarios. Its key feature is the use of Virtual characters and Machine learning techniques to allow agricultural robots to recognize gestures performed by both digital agents and humans. We address challenges by presenting a set of gesture definitions, data generation, and system evaluation using varies combinations of real and virtual simulated data. Our results demonstrate how the adoption of virtual simulations can significantly increase the system’s accuracy and efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Human robot interaction

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