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10th International Conference on Learning Representations, ICLR 2022

作者： Hong, Junyuan Wang, Haotao Wang, Zhangyang Zhou, Jiayu Department of Computer Science and Engineering Michigan State University United States Department of Electrical and Computer Engineering University of Texas Austin United States

Federated learning (FL) provides a distributed learning framework for multiple participants to collaborate learning without sharing raw data. In many practical FL scenarios, participants have heterogeneous resources due to disparities in hardware and inference dynamics that require quickly loading models of different sizes and levels of robustness. The heterogeneity and dynamics together impose significant challenges to existing FL approaches and thus greatly limit FL's applicability. In this paper, we propose a novel Split-Mix FL strategy for heterogeneous participants that, once training is done, provides in-situ customization of model sizes and robustness. Specifically, we achieve customization by learning a set of base sub-networks of different sizes and robustness levels, which are later aggregated on-demand according to inference requirements. This split-mix strategy achieves customization with high efficiency in communication, storage, and inference. Extensive experiments demonstrate that our method provides better in-situ customization than the existing heterogeneous-architecture FL methods. Codes and pre-trained models are available: https://***/illidanlab/SplitMix. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

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Design Drivers of an Attitude Control System for Small CubeSats Using Magnetorquers 73

Design Drivers of an Attitude Control System for Small CubeS...

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73rd International Astronautical Congress, IAC 2022

作者： Neagu, Catalin-Daniel Chams, Fadel Brune, Eric Friis-Liby, Linn Soria, Carmen Fuentes Graff, Erik Gustavsson, Jimmy Hestad, Theresia Golemis, Aris Grethen-Bußmann, Antonia Laufer, Rene Lindkvist, Edvin Manamon, Sorcha Mac Maneepong, Chayapol Mayer, Kyle Peroy, Cristobal Nieto Sanchez, Gabriel Cadenas, Carlos Sarille Sonna, Mrunmayee Witt, Gabrielle Widenfelt, Axel Luleå University of Technology Department of Computer Science Electrical and Space Engineering Sweden

Attitude control in the case of 1U CubeSats is a crucial design choice in any mission of such scale. This leads to extensive studies being conducted before deciding on the architecture of this subsystem. In the case of Luleå University Technology's first student-led CubeSat project, Atmospheric Polar Transmission Alignment Satellite (APTAS), the driving design choices of the Attitude Determination and Control System (ADCS) were predominately impacted by its onboard payload requirements. For successful operations of the camera, the ADCS must ensure a stability of less than 0.58 deg/s, while the pointing accuracy for both camera and EISCAT antenna system shall be less than 10 deg. In this regard, following a study of suitable hardware designs that would fulfill those requirements, the satellite shall be equipped with magnetometers, coarse sun sensors (CSS) and a gyroscope. It is widely-known that ADCS-related tasks are notoriously computationally heavy. This presents another limitation of 1U satellites: available computational load. Preliminary studies and simulation have shown that an usual algorithm in the form of a Kalman Filter (or similar applications) would interfere with payload operations due to subsequent sensor-fusion iterations and the heavy computational load resulting from it. For this reason, the TRIAD attitude determination method was deemed a fitting alternative, due to its computational efficiency and lower number of operations. All data-processing tasks must also be conducted through the one on-board computer, due to the volume constraints of 1U. In terms of control, the conducted simulations show that the B-dot algorithm for detumbling motion and the quaternion feedback control for nadir pointing are capable of meeting the requirements of the mission. For optimal payload operations, four operation modes have been defined, each to be functional at different stages of the satellite's mission. Each mode utilizes a distinct arrangement of ADCS component

关键词： Cameras

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Adaptive Traffic Signal Control System Using Object Detection Based Approach

Adaptive Traffic Signal Control System Using Object Detectio...

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International Conference on Robotics,electrical and Signal Processing Techniques (ICREST)

作者： Irfan Khan Mehedi Hasan Shamsul Islam Suman Reza Tuhin Reza Erteza Tawsif Efaz Department of Computer Science & Engineering Faridpur Engineering College Faridpur Bangladesh Division of Electrical & Computer Engineering Louisiana State University Baton Rouge Louisiana United States

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

In Bangladesh, most four-legged intersections use a static-timed or manually-controlled traffic signaling approach, which creates irrefutable congestion and requires human involvement. This paper proposes an adaptive traffic control system utilizing the YOLO object detection framework within CNNs to address this issue. The system captures and frames images using cameras facing every road at a roundabout to detect and count vehicles in real-time for providing lights based on the traffic conditions, prioritizing emergency vehicles such as ambulances, firetrucks, and police-car while also maintaining queues. It sets the signal duration considering road condition. Using a dataset of 10,462 images across 12 classifications of automobiles and a transfer learning approach in YOLOv8, the model achieved a mAP50 score of 0.90. This automated method reasonably enhances traffic flow management by dynamically adjusting signal lights without human intervention.

关键词： YOLO Adaptation models Adaptive systems Roads Transfer learning Traffic control Cameras Real-time systems Vehicle dynamics Software development management

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Predictive Maintenance for Industrial Equipment in Big Data: An Implementation with Random Forest

Predictive Maintenance for Industrial Equipment in Big Data:...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Nainwal, Ankita Hussein, Layth Dixit, Krishna Kant Michaelson, Jacob Sehgal, Archana Gupta, Priyanka Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Electrical Engineering Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This research explores the realm of predictive maintenance for industrial equipment, employing and comparing four prominent machine learning algorithms: 'Random Forest, Support Vector Machines (SVM)', Neural Networks, and Gradient Boosting. The study intends to offer information on their effectiveness regarding the ability to accurately predict failures of equipment and estimate maintenance time. Results indicate that Random Forest and Neural Networks emerge as top performers, exhibiting high precision (Random Forest: The only difference is in terms of precision (Random Forest: 0.65, Neural Networks: 0.72), recall (Random Forest: 0.8, Neural Networks was the best as opposed to other measures where Random Forrest performed well and Neurenl network did not over fit.). These algorithms also demonstrate superior accuracy in estimating maintenance times, as reflected in the Mean Absolute Error (Random Forest: 5.12, Neural Networks: 5.45). The comparative study can detect subtle strengths and weaknesses that are useful to practitioners in terms of algorithm selection according to specific industry needs. This study supports the continuous development of PMP, highlighting that choices in algorithms are essential to increase efficiency levels at the operation level, minimize downtime, and further encourage sustainable manufacturing. © 2024 IEEE.

关键词： Support vector machines

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A Hybrid-Logic Scheme for High-Performance and Low-Power Decoders in 7nm Process 17

A Hybrid-Logic Scheme for High-Performance and Low-Power Dec...

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17th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2024

作者： Xia, Donghao Zhang, Yuejun Xu, Mengfan Wen, Liang Guo, Yiting Ningbo University Faculty of Electrical Engineering and Computer Science Zhejiang315211 China China Coast Guard Academy Department of Electronic Technology Ningbo315211 China

ISBN: (纸本)9798350361834

Advancing memory circuitry has heightened the performance criteria for memory in diverse chip applications. Decoders, serving as peripheral circuits to memory devices, have be a prominent area of academic interest. The new NAND/NOR gate circuits are proposed using pass-transistor logic (PTL) and transmission gate logic (TGL). The two hybrid-logic design schemes of high-performance and low-power (HPLP) and HPLP inverting (HPLPI) utilizing PTL and TGL gate in combination with static CMOS logic for decoders are proposed, while achieving fewer transistors, faster speed, lower power dissipation as compared to conventional circuits. Experiments are conducted in TSMC 7nm FinFET process for hybrid-logic decoders. The simulation results indicate that, in comparison to conventional decoders, the proposed hybrid-logic 3-8 decoder demonstrates improvements, with the HPLP design reducing delay by over 19% and power by over 71%, and the HPLPI design reducing delay by over 17% and power by over 48%. © 2024 IEEE.

关键词： NAND circuits

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Deadlock-free Supervisor Design for Robotic Manufacturing Cells With Uncontrollable and Unobservable Events

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IEEE/CAA Journal of Automatica Sinica 2021年第3期8卷 597-605页

作者： Bo Huang MengChu Zhou Cong Wang Abdullah Abusorrah Yusuf Al-Turki School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia

In this paper,a deadlock prevention policy for robotic manufacturing cells with uncontrollable and unobservable events is proposed based on a Petri net ***,a Petri net for the deadlock control of such systems is *** admissible markings and first-met inadmissible markings(FIMs)are ***,place invariants are designed via an integer linear program(ILP)to survive all admissible markings and prohibit all FIMs,keeping the underlying system from reaching deadlocks,livelocks,bad markings,and the markings that may evolve into them by firing uncontrollable *** also ensures that the obtained deadlock-free supervisor does not observe any unobservable *** addition,the supervisor is guaranteed to be admissible and structurally minimal in terms of both control places and added *** condition under which the supervisor is maximally permissive in behavior is ***,experimental results with the proposed method and existing ones are given to show its effectiveness.

关键词： Deadlock prevention Petri nets robotic manufacturing cells structure-minimized supervisor supervisory control uncontrollability unobservability

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IDSDeep-CCD: intelligent decision support system based on deep learning for concrete cracks detection

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Multimedia Tools and Applications 2025年第12期84卷 10013-10026页

作者： Abualigah, Sayel M. Al-Naimi, Ahmad F. Sachdeva, Gagan AlAmri, Omran Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid Jordan Gagan Sachdeva Dubai United Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Computer Science Department Al al-Bayt University Mafraq25113 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Applied Science Research Center Applied Science Private University Amman11931 Jordan College of Engineering Yuan Ze University Taoyuan32003 Taiwan

In an era where Intelligent Decision Support Systems (IDSS) are integral to managing the vast data from Internet of Everything (IoE) systems, this study introduces IDSDeep-CCD, a novel IDSS approach for detecting concrete cracks, a critical issue in civil infrastructure maintenance. Traditional visual inspection methods for crack detection are time-consuming and error-prone. To address this, IDSDeep-CCD employs deep learning, utilizing an open-source dataset of concrete crack images for enhanced accuracy. The system's two-stage approach, featuring feature extraction via pre-trained models and a fully connected network for classification, significantly outperforms existing methods. Our results demonstrate a remarkable 99.9% accuracy rate in crack detection without data augmentation, marking a notable advancement in automatic detection technology. This breakthrough offers practical benefits for the industry by enabling more precise and efficient identification of concrete cracks, paving the way for timely and accurate remedial actions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Generating synthetic wind speed scenarios using artificial neural networks for probabilistic analysis of hybrid energy systems

Generating synthetic wind speed scenarios using artificial n...

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作者： Chen, Jun Zhao, Junhui Department of Electrical and Computer Engineering Oakland University RochesterMI48309 United States Department of Electrical Computer Engineering and Computer Science University of New Haven West HavenCT06516 United States

Hybrid energy systems (HESs) have been proposed to include and co-optimise multiple energy inputs and multiple energy outputs to enable increasing penetration of clean energy such as wind power. To optimise the system design, extensive datasets of renewable resources for the given location are required, whose availability may be limited. To address this limitation, this paper proposes an innovative methodology to generate synthetic wind speed data. Specifically, artificial neural networks are adopted to characterise historical wind speed data and to generate synthetic scenarios. In addition, Fourier transformation is used to capture the characteristics of the low frequency components in historical data, allowing the synthetic scenarios to preserve seasonal trends. The proposed methodology enables the possibility of Monte Carlo simulation of HES for probabilistic analysis using large volumes of heterogeneous scenarios. Case study of probabilistic analysis is then performed on a particular HES configuration, which includes nuclear power plant, wind farm, battery storage, electric vehicle charging station, and desalination plant. Wind power availability and requirements on component ramping rate are then investigated. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Wind power

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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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Learning-Based Joint Service Caching and Load Balancing for MEC Blockchain Networks

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China Communications 2023年第1期20卷 125-139页

作者： Wenqian Zhang Wenya Fan Guanglin Zhang Shiwen Mao College of Information Science and Technology Donghua UniversityShanghai 201620China Engineering Research Center of Digitized Textile and Apparel Technology Ministry of EducationShanghai 201620China Department of Electrical and Computer Engineering Auburn UniversityAuburnAL 36849-5201USA

Integrating the blockchain technology into mobile-edge computing(MEC)networks with multiple cooperative MEC servers(MECS)providing a promising solution to improving resource utilization,and helping establish a secure reward mechanism that can facilitate load balancing among *** addition,intelligent management of service caching and load balancing can improve the network utility in MEC blockchain networks with multiple types of *** this paper,we investigate a learningbased joint service caching and load balancing policy for optimizing the communication and computation resources allocation,so as to improve the resource utilization of MEC blockchain *** formulate the problem as a challenging long-term network revenue maximization Markov decision process(MDP)*** address the highly dynamic and high dimension of system states,we design a joint service caching and load balancing algorithm based on the double-dueling Deep Q network(DQN)*** simulation results validate the feasibility and superior performance of our proposed algorithm over several baseline schemes.

关键词： cooperative mobile-edge computing blockchain workload offloading service caching load balancing deep reinforcement learning(DRL)

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