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Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem

IEEE/CAA Journal of Automatica Sinica 2021年第6期8卷 1199-1209页

作者： Ziyan Zhao Shixin Liu MengChu Zhou Abdullah Abusorrah State Key Laboratory of Synthetical Automation for Process Industries and the College of Information Science and Engineering Northeastern UniversityShenyang 110819China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21481Saudi Arabia Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia

Group scheduling problems have attracted much attention owing to their many practical *** work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time,release time,and due *** is originated from an important industrial process,i.e.,wire rod and bar rolling process in steel production *** objective functions,i.e.,the number of late jobs and total setup time,are minimized.A mixed integer linear program is established to describe the *** obtain its Pareto solutions,we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods,i.e.,an insertion-based local search and an iterated greedy *** computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its *** high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.

关键词： Insertion-based local search iterated greedy algorithm machine learning memetic algorithm nondominated sorting genetic algorithm II(NSGA-II) production scheduling

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Practical aberration correction using deep transfer learning with limited experimental data

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Optics Express 2025年第6期33卷 14431-14444页

作者： Kok, Yong En Bentley, Alexander Parkes, Andrew J. Somekh, Michael G. Wright, Amanda J. Pound, Michael P. University of Nottingham School of Computer Science NottinghamNG8 1BB United Kingdom Optics and Photonics Group Department of Electrical and Electronic Engineering University of Nottingham NottinghamNG7 2RD United Kingdom Research Center for Humanoid Sensing Zhejiang Laboratory Hangzhou3111100 China

Adaptive optics is a technique for correcting aberrations and improving image quality. When adaptive optics was first used in microscopy, it was common to rely on iterative approaches to determine the aberrations present. It is advantageous to avoid iteration, and therefore there has been a shift to deep learning for aberration prediction. However, issues remain regarding the practicalities of machine learning for adaptive optics, an important one being the requirement for a large training dataset. Here, we explore transfer learning to overcome this need for data by pre-training a network on a large simulated dataset and fine-tuning it with reduced experimental data for application in an experimental setting. We demonstrate that the pre-trained network can make noticeable improvements with fine-tuning on just 24 experimental samples. To further enhance practicality, we significantly extend the range of aberrations present, predicting up to 25 Zernike modes with each coefficient ranging from −1 to 1, and perform a thorough analysis of the type and magnitude of phase-diversity required in the input data for a successful network. Our approach demonstrates substantial aberration reduction on experimental data for 10 Zernike modes, with an average 73% decrease in RMS wavefront error from 1.81 to 0.48 rad when correction is applied. This method achieves complete experimental image capture and aberration inference at rates comparable to the image acquisition time of a typical laser scanning microscope. Additionally, we consider the benefits of further improvements via an iterative step. As such, this work addresses some of the key practical hurdles that remain in the use of deep learning for aberration prediction and correction. © 2025 Optica Publishing Group (formerly OSA). All rights reserved.

关键词： Adaptive optics

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On Partial Optimal Transport: Revising the Infeasibility of Sinkhorn and Efficient Gradient Methods 38

On Partial Optimal Transport: Revising the Infeasibility of ...

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Nguyen, Anh Duc Nguyen, Tuan Dung Nguyen, Quang Minh Nguyen, Hoang H. Nguyen, Lam M. Toh, Kim-Chuan Department of Mathematics National University of Singapore Singapore Department of Computer and Information Science University of Pennsylvania United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States School of Industrial and Systems Engineering Georgia Institute of Technology United States IBM Research Thomas J. Watson Research Center United States Institute of Operations Research and Analytics National University of Singapore Singapore

ISBN: (纸本)1577358872

This paper studies the Partial Optimal Transport (POT) problem between two unbalanced measures with at most n supports and its applications in various AI tasks such as color transfer or domain adaptation. There is hence a need for fast approximations of POT with increasingly large problem sizes in arising applications. We first theoretically and experimentally investigate the infeasibility of the state-of-the-art Sinkhorn algorithm for POT, which consequently degrades its qualitative performance in real world applications like point-cloud registration. To this end, we propose a novel rounding algorithm for POT, and then provide a feasible Sinkhorn procedure with a revised computation complexity of Oe(n2/Ε4). Our rounding algorithm also permits the development of two first-order methods to approximate the POT problem. The first algorithm, Adaptive Primal-Dual Accelerated Gradient Descent (APDAGD), finds an Ε-approximate solution to the POT problem in Oe(n2.5/Ε). The second method, Dual Extrapolation, achieves the computation complexity of Oe(n2/Ε), thereby being the best in the literature. We further demonstrate the flexibility of POT compared to standard OT as well as the practicality of our algorithms on real applications where two marginal distributions are unbalanced. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Gradient methods

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Novel materials and methods for fabricating memristors for use in RF applications 18

Novel materials and methods for fabricating memristors for u...

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18th IEEE Nanotechnology Materials and Devices Conference, NMDC 2023

作者： Tsipas, Evangelos Stavroulakis, Emmanouil Chatzipaschalis, Ioannis K. Rallis, Konstantinos Vasileiadis, Nikolaos Dimitrakis, Panagiotis Kostopoulos, Athanasios Konstantinidis, George Sirakoulis, Georgios Ch. Democritus University of Thrace Department of Electrical and Computer Engineering Xanthi Greece Universitat Politécnica de Catalunya Department of Electronic Engineering Barcelona Spain National Center for Scientific Research 'Demokritos' Athens Greece Greece

ISBN: (纸本)9798350335460

Due to the technological limitations, nanotechnology-enabled novel RF switches have emerged leading to research efforts in the telecommunications sector focusing on the gradual replacement of conventional switches with switches harnessing the unique properties of nanoelectronic devices such as memristors. Such novel switches have recently been fabricated and characterized, providing optimal behavior under high-frequency stimuli. In this study, we focus on the investigation and fabrication aspects of the numerous novel memristive switches based on 2D materials with prospects of enhancing the performance of memristor-based RF switches. The measurements presented demonstrate the efficacy of the fabricated devices for use in RF switches across diverse applications. More specifically, we highlight their suitability for RF applications, emphasizing their appropriateness for the intended functionality. The performance of the investigated switches is further evaluated with various key figures of merit. Through this presentation, we aim to contribute to the ongoing discussion on the application of memristor-based switches in RF circuits and systems, highlighting the potential advantages and addressing the need for more efficient and reliable alternatives. Additionally, we discuss various aspects of memristors and the implications of their wider adoption in the telecom sector. © 2023 IEEE.

关键词： Memristors

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CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification

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computers, Materials & Continua 2023年第9期76卷 2779-2793页

作者： Mehwish Zafar JaveriaAmin Muhammad Sharif Muhammad Almas Anjum Seifedine Kadry Jungeun Kim Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan Department of Computer Science University ofWahWah Cantt47040Pakistan National University of Technology(NUTECH) Islamabad44000Pakistan Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Worldwide cotton is the most profitable cash *** year the production of this crop suffers because of several *** an early stage,computerized methods are used for disease detection that may reduce the loss in the production of *** several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex *** to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease *** in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×*** that,the extracted features are serially concatenated having a feature vector lengthN×*** prominent features are selected usingEmperor PenguinOptimizer(EPO)*** method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle *** EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,*** classification is performed using 5,7,and 10 folds *** Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.

关键词： Deep learning cotton disease detection features selection classification efficientnet-b0 inception-v3 quadratic discriminant analysis subspace KNN

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Analysis of the EMF Parameters of the Stator Winding in the Run-Down Mode for Diagnosing Faults in an Induction Motor

Analysis of the EMF Parameters of the Stator Winding in the ...

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2023 IEEE International Conference on Smart Information Systems and Technologies, SIST 2023

作者： Yussupova, Assel Potapenko, Alexandra Neftissov, Alexandr Urazalimova, Dariya Serbin, Oleh Toraighyrov University Department of Electrical Engineering and Automation Pavlodar Kazakhstan Toraighyrov University Faculty of Computer Science Pavlodar Kazakhstan Astana It University Research and Innovation Center 'Industry 4.0' Astana Kazakhstan Chief Executive Officer Ceo & Executive Director Yaroslav Mudryi Ukraine

ISBN: (纸本)9798350335040

In industry, as well as in power plants and substations of developed countries, induction motor (IM) are the most common electrical machine. They consume a large amount of the generated electricity. At the same time, they account for a sufficient number of failures of all electrical equipment. In this case, the proportion of failures due to a break in a squirrel-cage rotor, according to various authors, can reach about 5-10%. However, experimental studies at a number of plants and power plants in the Pavlodar region have shown that the damage factor of the squirrel-cage IM rotor is much higher. As a rule, the breakage of the squirrel-cage rotor rods does not lead to an immediate failure of the IM, but is accompanied by a deterioration in its electromechanical characteristics. Basically, the harm from the operation of such IM is expressed in increased energy consumption. Moreover, a year of its operation leads to an excessive consumption of electricity, comparable to the cost of the IM itself. The article shows that today there are a large number of proposals for a method for diagnosing damage to the short-circuited rotor winding of an induction motor (IM) during operation. However, their sensitivity is limited by the need for detuning from fluctuations in the parameters of the electrical network, vibrations of the IM and the uneven moment of resistance of its load. Most of these effects can be eliminated if IM diagnostics are carried out based on the analysis of the EMF parameters in the stator winding in the rundown mode. The authors have analyzed the parameters of the EMF of the stator winding at all stages of the rundown mode and substantiated the use of the presented method for diagnosing faults in an induction motor. © 2023 IEEE.

关键词： Stators

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Machine learning assisted snort and zeek in detecting DDoS attacks in software-defined networking

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1627-1643页

作者： AbdulRaheem, Muyideen Oladipo, Idowu Dauda Imoize, Agbotiname Lucky Awotunde, Joseph Bamidele Lee, Cheng-Chi Balogun, Ghaniyyat Bolanle Adeoti, Joshua Oluwatobi Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin 240003 Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum 44801 Germany Research and Development Center for Physical Education Health and Information Technology Department of Library and Information Science Fu Jen Catholic University New Taipei City 24205 Taiwan Department of Computer Science and Information Engineering Asia University Taichung City 41354 Taiwan

A new network architecture called the Software-Defined Network (SDN) gives next-generation networks a more flexible and efficiently controlled network architecture. Using the programmable central controller design, network supervisors may easily supervise and manage the entire infrastructure. However, due to its centralized structure, SDN has been a target of various attack vectors. The most successful attack method against the SDN among these has been Distributed Denial of Service (DDoS). Therefore, this study proposes a snort and Zeek enabled with machine learning (ML) based model to classify the benign traffic from DDoS attack traffic. This study main contribution is the discovery of new features for DDoS attack detection, which made it difficult to distinguish authorized traffic from attack traffic when spread across so many points of origin. Using the ML-based enabled RYU controller with SNORT and ZEEK created fewer false positives and a smaller variety of true positives per attack than the existing methods. The processing time of ML-based enabled with SNORT and ZEEK on the real-time testbed is better contrasted to the existing methods. Using the open resource technologies offered a far better understanding of cyber safety and its benefits from the readily available programs to construct a solid network keeping an eye on the traffic. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Cybersecurity Distributed denial of service Intrusion detection systems Snort Software-defined networks Virtual machine Zeek

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HDCGD-CBAM:Satellite Interference Recognition Algorithm Based on Improved CLDNN and CBAM

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China Communications 2024年第12期21卷 257-274页

作者： Duan Ruifeng Chen Ziyu Meng Wei Wang Xu Yang Guoting Cheng Peng Li Yonghui School of Information Science and Technology Beijing Forestry UniversityBeijing 100083China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing 100083China Jining Economic and Social Development Research Center Jining 272000China School of Electrical and Information Engineering the University of SydneySydneyNSW 2006Australia Department of Computer Science and Information Technology La Trobe UniversityMelbourneVIC 3086Australia

Satellite communication systems are facing serious electromagnetic interference,and interference signal recognition is a crucial foundation for targeted *** this paper,we propose a novel interference recognition algorithm called HDCGD-CBAM,which adopts the time-frequency images(TFIs)of signals to effectively extract the temporal and spectral *** the proposed method,we improve the Convolutional Long Short-Term Memory Deep Neural Network(CLDNN)in two ***,the simpler Gate Recurrent Unit(GRU)is used instead of the Long Short-Term Memory(LSTM),reducing model parameters while maintaining the recognition ***,we replace convolutional layers with hybrid dilated convolution(HDC)to expand the receptive field of feature maps,which captures the correlation of time-frequency data on a larger spatial ***,Convolutional Block Attention Module(CBAM)is introduced before and after the HDC layers to strengthen the extraction of critical features and improve the recognition *** experiment results show that the HDCGD-CBAM model significantly outper-forms existing methods in terms of recognition accuracy and *** Jamming-to-Signal Ratio(JSR)varies from-30dB to 10dB,it achieves an average accuracy of 78.7%and outperforms the CLDNN by 7.29%while reducing the Floating Point Operations(FLOPs)by 79.8%to ***,the proposed model has fewer parameters with 301k compared to several state-of-the-art methods.

关键词： attention mechanism CLDNN HDC interference recognition satellite communication

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Design and Implementation of a Novel Double-Layered Wavy Mattress for the Prevention and Recovery of Pressure Ulcers: A Feasibility Study

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Journal of Biomedical Physics and engineering 2023年第5期13卷 489-494页

作者： Ghaderi, Mehrdad Chehre, Hossein Fathpour, Helia Department of Electrical and Computer Technical and Engineering Faculty Mohaghegh Ardabili University Ardabil Iran Department of Biomedi-cal Medical Physics and Engineering School of Medicine Shiraz University of Medical Sciences Shiraz Iran Department of Biotech-nology Reproductive Biomedicine Research Center Royan Institute for Biotechnology ACECR Isfahan Iran

Pressure ulcers are a significant concern in patient care, particularly for those with limited mobility and extended hospital stays. Wavy mattresses are shown an effective tool for preventing or promoting the healing of these wounds by preventing pressure localization in different body regions. The current study aimed to present a novel dou-ble-layered wavy mattress design for the prevention and recovery of pressure ulcers, addressing some of the limitations of existing mattresses. The novel mattress includes double-layered cells, with the upper and lower layers, filled with water and air, respec-tively. The temperature of water in the cells can be manually adjusted to meet patients’ needs, prevent skin sweating, regulate body temperature, and promote blood flow in areas susceptible to pressure ulcers. Patients who used this novel mattress during their hospitalization experienced a significantly shorter recovery period for bedsores compared to those who used other mattresses, showing that the novel wavy mattress is an effective tool for preventing and recovering from pressure ulcers in long-term hospitalized patients with limited mobility. © 2023, Shiraz University of Medical Sciences. All rights reserved.

关键词： Hospitalization Pressure Ulcer Sweating Temperature Wavy Mattress

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MIM and MIIM-Based Optical Rectennas for Infrared Energy Harvesting at 10.6 μm 22

MIM and MIIM-Based Optical Rectennas for Infrared Energy Har...

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22nd IEEE Mediterranean Microwave Symposium, MMS 2023

作者： Yahyaoui, Ali Elsharabasy, Ahmad Yousaf, Jawad Rmili, Hatem Aguili, Taoufik Tunis Tunisia McMaster University Electrical and Computer Engineering Department HamiltonONL8S 4K1 Canada Abu Dhabi University Department of Electrical Computer and Biomedical Engineering United Arab Emirates King Abdulaziz University Faculty of Engineering Electrical and Computer Engineering Department Jeddah Saudi Arabia King Abdulaziz University K. A. Care Energy Research and Innovation Center Jeddah21589 Saudi Arabia

ISBN: (纸本)9798350308471

This work presents a comparative study between metal-insulator-metal (MIM) and metal-insulator-insulator-metal (MIIM) based optical log spiral rectennas for infrared IR energy harvesting at 10.6 μm. We have considered the spiral antenna terminals as the electrodes of the rectifying diode and we have integrated the insulators between the two arms (electrodes) to enhance the harvested energy with the proposed nano-antennas. The study presents a comparison between the performance (E-field, I/V, responsivity, and resistivity) of the two proposed rectennas. © 2023 IEEE.

关键词： Metal insulator boundaries

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