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63rd IEEE Conference on Decision and control, CDC 2024

作者： Aravind, Ashwin Toghani, Mohammad Taha Uribe, Cesar A. Iit Bombay Centre for Systems and Control MH Mumbai India Rice University Department of Electrical and Computer Engineering HoustonTX United States

ISBN: (纸本)9798350316339

We study the problem of policy estimation for the Linear Quadratic Regulator (LQR) in discrete-time linear timeinvariant uncertain dynamical systems. We propose a Moreau Envelope-based surrogate LQR cost, built from a finite set of realizations of the uncertain system, to define a meta-policy efficiently adjustable to new realizations. Moreover, we design an algorithm to find an approximate first-order stationary point of the meta-LQR cost function. Numerical results show that the proposed approach outperforms naive averaging of controllers on new realizations of the linear system. We also provide empirical evidence that our method has better sample complexity than Model-Agnostic Meta-Learning (MAML) approaches. © 2024 IEEE.

关键词： Cost functions

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Energy-Efficient and Latency-Aware Task Offloading for Industrial Cloud-Edge Systems with Heterogeneous CPUs and GPUs

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IEEE Internet of Things Journal 2025年第13期12卷 25757-25772页

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Buyya, Rajkumar Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Southern Methodist University Department of Computer Science DallasTX75206 United States Lab School of Computing and Information Systems MelbourneVIC3010 Australia

The unprecedented prosperity of the Industrial Internet of Things has significantly driven the transition from traditional manufacturing to intelligent one. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multi-task processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an Improved Two-stage Multi-Objective Evolutionary Algorithm (IT-MOEA). IT-MOEA employs a novel multi-objective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multi-objective algorithms by 41.0% in terms of delay and energy consumption. © 2025 IEEE.

关键词： Mobile edge computing

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Worst-Case Integrity Attacks and Resilient State Estimation With Partially Secured Measurements

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IEEE Transactions on control of Network Systems 2025年第2期12卷 1769-1779页

作者： Zhou, Jing Shang, Jun Chen, Tongwen University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China

This paper examines the problem of optimal deception attacks against state estimation with partially secured measurements, where smart sensors transmit innovation sequences to the remote end for information fusion. Due to resource limitations or defensive countermeasures, the adversary can only modify data packets transmitted through unreliable channels. Meanwhile, the attack should be synthesized with sophistication to deceive an anomaly detector. To investigate the vulnerabilities of such estimation systems without feedback links and enhance security performance, the optimal attack policy is derived by formulating and explicitly solving a convex optimization problem, with the goal of maximizing the sum of estimation errors. Subsequently, a novel attack detection and resilient state estimation algorithm is proposed to ensure an acceptable level of estimation accuracy. The theoretical performance metrics, including false alarm rates for the proposed detector, are provided. Finally, the effectiveness of the results is confirmed through numerical examples. © 2014 IEEE.

关键词： Optimal detection

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Quasi-Z-Source Cascaded H-Bridge Multilevel Inverters for PV Applications with Fault

Quasi-Z-Source Cascaded H-Bridge Multilevel Inverters for PV...

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2023 IEEE International Conference on Environment and Electrical engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023

作者： Carrasco-González, David Horrillo-Quintero, Pablo García-Triviño, Pablo Sarrias-Mena, Raúl García-Vázquez, Carlos Andrés Fernández-Ramirez, Luis M. University of Cadiz SURET Research Group Department of Electrical Engineering Algeciras Spain University of Cadiz SURET Research Group Department of Engineering in Automation Electronics and Computer Architecture and Networks Algeciras Spain

ISBN: (纸本)9798350347432

Nowadays, the use of quasi-Z-source cascaded H-bridge multilevel inverters (qZS-CHBMLI) in photovoltaic (PV) has gained their attention. They permit the single-stage power transformation of a PV generator adjusting the DC voltage independently in each module to operate each PV generator at maximum power. This paper develops a new control strategy that allows the system to continue operating after a fault in a module, by increasing the DC voltage in the operating modules. The control strategy is employed to a grid-connected qZS-CHBMLI, which consists of three cascaded qZSI powered by independent 4.8 kW PV cells. Under several operating circumstances, the proposed control system is evaluated. The results demonstrate how the control strategy applied to the system successfully reacts to the failure of a module. © 2023 IEEE.

关键词： control systems

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Intelligent Accident Recognition and Notification System using Hybrid Deep Learning and IoT 4

Intelligent Accident Recognition and Notification System usi...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Sathish Kumar, P. Singh Rawat, Ramesh Shrimali, Manish Parikh, Swapnil Patil, Harshal Maranan, Ramya United Institute of Technology Department of Robotics & Automation Tamil Nadu Coimbatore641020 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Department of Computer Science and IT Rajasthan Udaipur313 003 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat 391760 India Symbiosis Institute of Technology Symbiosis International Deemed University Computer Science and Engineering Maharashtra Pune412 115 India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331515720

Ensuring the safety of individuals should be a top priority. Everyone wants to return home safely. Many riders continue to disregard safety constraints, increasing the chance of accidents that result in death. It is critical to incorporate the latest technology into road monitoring systems to make cities smarter and to ensure rider safety. For this research, Deep Learning (DL) and the Internet of Things (IoT) were chosen for effective road surveillance and alert systems. The primary goal of the study is to detect and inform riders about accidents that occur along their path and to provide details about the location of the detected accident. Additionally, the system provides the vehicle's speed. To identify automobile accidents in the footage, a hybrid DL model that includes Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-Bi-LSTM) is suggested. For the model's training, both accident and non-Accident data were collected. The model is evaluated using three metrics: Accuracy, loss, and execution time. The next step is to upload the recommended model to the ThingSpeak cloud. A Graphical User Interface (GUI) is being created in the ThingSpeak cloud. The GUI displays the vehicle's speed, accident images, and the precise location of the accident scene using the Raspberry Pi, speed sensor, Pi-camera, and Global Positioning System (GPS) module. An LED is also integrated into the GUI to alert the user. A real-world test is conducted on the designed system. The proposed solution has many advantages and will ensure driver safety. © 2024 IEEE.

关键词： Graphical user interfaces

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Customized Touch UI Development for E-Cockpit in Automobile 3

Customized Touch UI Development for E-Cockpit in Automobile

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3rd International Conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Sivaraj, D. Rathika, P.D. Jayasree, D. Dwarkesh Psg College of Technology Department of Electronics and Communication Engineering Coimbatore India Psg College of Technology Department of Robotics and Automation Engineering Coimbatore India Psg Polytechnic College Department of Electronics and Communication Engineering Coimbatore India Psg College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370867

In recent times, the automotive industry has made significant steps towards digitization by focusing on electronic cockpits (E-Cockpits) to enhance the driving experience, comfort and safety. The incorporation of Touch-Based User Interface (UI) technologies has played a crucial role in revolutionizing Driver-Vehicle interaction [10]. The project of E-Cockpit represents a concept that consolidates various internal car systems onto one unified digital platform, enabling seamless control and access to essential infotainment, safety features, and connectivity services [14]. For the realization of the complete potential of an E-Cockpit, it is important to develop an innovative and User-Friendly touch UI as this directly influences both user experience and safety concerns. © 2023 IEEE.

关键词： User interfaces

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Differential Morphing Attack Detection via Triplet-Based Metric Learning and Artifact Extraction 23

Differential Morphing Attack Detection via Triplet-Based Met...

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23rd International Conference of the Biometrics Special Interest Group, BIOSIG 2024

作者： Liu, Chengcheng Ferrara, Matteo Franco, Annalisa Borghi, Guido Zhong, Dexing Xi'an Jiaotong University School of Automation Science and Engineering Xi'an710049 China University of Bologna Department of Computer Science and Engineering Cesena47521 Italy University of Modena and Reggio Emilia Department of Education and Humanities Reggio Emilia42121 Italy

ISBN: (纸本)9798350373714

Face morphing attack has been demonstrated to pose significant security risks to face recognition systems. Therefore, recently, developing reliable Morphing Attack Detection (MAD) techniques has become an important research priority. This paper considers the failure of regular face recognition systems in handling morphed faces from the perspective of identity feature space, arguing that the morphed image creates a 'pathway' between the two subjects involved in the morphing generation process. This 'pathway' causes automatic face recognition systems to misidentify the morphed face as either contributing subjects. Based on this insight and considering the characteristics of the Differential Morphing Attack Detection (D-MAD) scenario (i.e., both the potential morphing attack image and the trusted live face image are accessible), we propose an end-to-end D-MAD solution based on metric learning with triplet feature separation and artifact analysis. The proposed approach aims to break the 'pathway' created by the morphed image in the identity feature space and to incorporate latent artifact features of the potential morphed image for D-MAD. Comparative results on different public benchmarks indicate that the proposed solution demonstrates satisfactory performance against state-of-the-art algorithms. © 2024 IEEE.

关键词： Contrastive Learning

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Al-Biruni Based Optimization of Rainfall Forecasting in Ethiopia

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computer Systems Science & engineering 2023年第6期45卷 2885-2899页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Fadwa Alrowais Mostafa Abotaleb Abdelhameed Ibrahim Doaa Sami Khafaga Department of Communications and Elecronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science F aculty of Computer and Infomation SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Progamming South Ual State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Rainfall plays a significant role in managing the water level in the *** unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the *** individuals,especially those in the agricultural sector,rely on rain *** rainfall is challenging because of the changing nature of the *** area of Jimma in southwest Oromia,Ethiopia is the subject of this research,which aims to develop a rainfall forecasting *** estimate Jimma's daily rainfall,we propose a novel approach based on optimizing the parameters of long short-term memory(LSTM)using Al-Biruni earth radius(BER)optimization algorithm for boosting the fore-casting accuracy.N ash-Sutcliffe model eficiency(NSE),mean square error(MSE),root MSE(RMSE),mean absolute error(MAE),and R2 were all used in the conducted experiments to assess the proposed approach,with final scores of(0.61),(430.81),(19.12),and(11.09),***,we compared the proposed model to current machine-learning regression models;such as non-optimized LSTM,bidirectional LSTM(BiLSTM),gated recurrent unit(GRU),and convolutional LSTM(ConvLSTM).It was found that the proposed approach achieved the lowest RMSE of(19.12).In addition,the experimental results show that the proposed model has R-with a value outperforming the other models,which confirms the superiority of the proposed *** the other hand,a statistical analysis is performed to measure the significance and stability of the proposed approach and the recorded results proved the expected perfomance.

关键词： Rainfall prediction long short-term memory Al-Biruni earth radius algorithm meta-heuristic optimization

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Skeleton-based Action Recognition Using Two-stream Graph Convolutional Network with Pose Refinement 41

Skeleton-based Action Recognition Using Two-stream Graph Con...

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第41届中国控制会议

作者： Biao Zheng Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development of science and technology,graph convolutional network has made great progress in improving the accuracy of action ***,there still exists some deficiencies in current ***,the human skeleton point coordinates entering into the network are barely refined,which may cause large ***,the second-order information(the length and direction of bones),which can reflect action characteristics discriminatively,is rarely *** solve the above issues,a two stream graph convolutional network with pose refinement for skeleton based action recognition is ***,we use an adaptive block to to help improve the *** test our method on Kinetics dataset and the experiment show it can get better results than some recent methods,which plays a positive role in future research.

关键词： Graph convolutional network Pose refinement Skeleton based action recognition Adaptive block

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Line-Start Permanent Magnet Machines for Low-Power Applications

Line-Start Permanent Magnet Machines for Low-Power Applicati...

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2024 International Conference on Electrical Machines, ICEM 2024

作者： Barta, Jan Toman, Marek Losak, Petr Aarniovuori, Lassi Vitek, Ondrej Brno University of Technology Department of Power Electrical and Electronic Engineering Brno Czech Republic Institute of Automation and Computer Science Brno University of Technology Brno Czech Republic Electrical Engineering LUT University Lahti Finland

ISBN: (纸本)9798350370607

The improvement of efficiency is one of the main areas of research in electrical machines today. Three-phase induction machines are widely used in industry, motivating researchers to explore methods to increase their efficiency or replace them with more efficient alternatives. A line-start synchronous machine is a promising machine design for achieving high-efficiency classes even for low-power applications. The main contribution of this paper is to show the achievable performance of the LSPMSM by simply replacing the rotor of an induction machine. In this work, two different geometries suitable for low-power machines are optimized and studied in terms of electromagnetic parameters. Finally, the results of the start-up simulations for both machine designs are presented. The presented method shows that by replacing the induction machine rotor with a line-start permanent magnet rotor, it is possible to significantly enhance machine efficiency, even for low-power applications. © 2024 IEEE.

关键词： Permanent magnets

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