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arXiv 2024年

作者： Najarro, Lismer Andres Caceres Song, Iickho Salman, Muhammad Kim, Kiseon Department of Computer Science of Chosun University Gwangju61452 Korea Republic of School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of College of Electrical & Mechanical Engineering National University of Sciences and Technology Islamabad Pakistan School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of

This paper presents a novel approach to deal with the cooperative localization problem in wireless sensor networks based on received signal strength measurements. In cooperative scenarios, the cost function of the localization problem becomes increasingly nonlinear and nonconvex due to the heightened interaction between sensor nodes, making the estimation of the positions of the target nodes more challenging. Although most of existing cooperative localization algorithms assure acceptable localization accuracy, their computational complexity increases dramatically, which may restrict their applicability. To reduce the computational complexity and provide competitive localization accuracy at the same time, we propose a localization algorithm based on the differential evolution with multiple populations, opposite-based learning, redirection, and anchoring. In this work, the cooperative localization cost function is split into several simpler cost functions, each of which accounts only for one individual target node. Then, each cost function is solved by a dedicated population of the proposed algorithm. In addition, an enhanced version of the proposed algorithm which incorporates the population midpoint scheme for further improvement in the localization accuracy is devised. Simulation results demonstrate that the proposed algorithms provide comparative localization accuracy with much lower computational complexity compared with the state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Cost functions

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Robust Model Predictive Control for Onboard Interleaved Multilevel Boost DC-DC Converter in DC Shipboard Microgrids

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IEEE Transactions on Transportation Electrification 2025年

作者： Li, Zhan Chen, Junyu Li, Kerui Xu, Zhao Liu, Yonglu Wang, Minghao The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Hong Kong The City University of Hong Kong Department of Electrical Engineering Hong Kong Hong Kong Shenzhen Research Institute Department of Electrical and Electronic Engineering China The Hong Kong Polytechnic University Research Institute of Smart Energy Hong Kong Hong Kong Central South University School of Automation Changsha China The Department of Electrical and Computer Engineering China University ofMacau Advanced Research Institute in Hengqin Zhuhai China

DC shipboard microgrids (SMGs) are a low-carbon and feasible solution to host various distributed energy resources (DERs) and shipboard loads. However, the intermittency of DERs and constant power load (CPL) characteristic of the shipboard loads will compromise the stability of DC SMGs. To accommodate DERs and address the stability issue in space-stringent DC SMGs simultaneously, a compound large-signal stabilization scheme is proposed in this paper to operate the interleaved multilevel boost DC-DC converter (IMBDC) in DC SMGs. It combines the high-order nonlinear disturbance observer (NDO) technique and the offset-free model predictive control (MPC) algorithm. Specifically, the uncertain variations of CPLs and circuit parameters are modeled as disturbances. High-order NDOs are used to estimate these disturbances accurately within a finite time. Depending on the estimation and system model, the MPC will regulate the system states against large-signal disturbances. Simulation results demonstrate that the proposed algorithm outperforms the traditional linear and nonlinear control schemes with better stability and dynamic performances. Both the real-time simulation and hardware experiment results are provided to further validate the applicability and effectiveness of the proposed control strategy. © 2015 IEEE.

关键词： Predictive control systems

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Low-overhead distribution strategy for simulation and optimization of large-area metasurfaces

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npj Computational Materials 2022年第1期8卷 735-740页

作者： Jinhie Skarda Rahul Trivedi Logan Su Diego Ahmad-Stein Hyounghan Kwon Seunghoon Han Shanhui Fan Jelena Vučković E.L.Ginzton Laboratory Stanford UniversityStanfordCA 94305USA Max-Planck-Institut fur Quantenoptik Hans-Kopfermann-Str.185748 GarchingGermany Department of electrical and computer engineering University of WashingtonSeattle 98195USA Samsung Advanced Institute of Technology Samsung ElectronicsSuwon-siGyeonggi-do 443-803Republic of Korea

Fast and accurate electromagnetic simulation of large-area metasurfaces remains a major obstacle in automating their *** this paper,we propose a metasurface simulation distribution strategy which achieves a linear reduction in the simulation time with the number of compute *** this distribution strategy with a GPU-based implementation of the Transition-matrix method,we perform accurate simulations and adjoint sensitivity analysis of large-area *** demonstrate ability to perform a distributed simulation of large-area metasurfaces(over 600λ×600λ),while accurately accounting for scatterer-scatterer interactions significantly beyond the locally periodic approximation.

关键词： distribution surfaces scatter

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Analysis of Reinforcement Learning Schemes for Trajectory Optimization of an Aerial Radio Unit

Analysis of Reinforcement Learning Schemes for Trajectory Op...

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IEEE International Conference on Communications (ICC)

作者： Hossein Mohammadi Vuk Marojevic Bodong Shang Department of Electrical and Computer Engineering Mississippi State University Mississippi State MS USA Eastern Institute for Advanced Study Ningbo China

This paper introduces the deployment of unmanned aerial vehicles (UAVs) as lightweight wireless access points that leverage the fixed infrastructure in the context of the emerging open radio access network (O-RAN). More precisely, we introduce the aerial radio unit (A-RU) that dynamically serves an underserved area and connects to the distributed unit (ODU) via a wireless fronthaul between the UAV and the closest fixed network infrastructure tower. In this paper we employ artificial intelligence (AI) for determining the UAV trajectory for serving User Equipment (UEs) while maintaining the fronthaul connectivity to the O-DU at the same time in a multiple-input multiple-output (MIMO) fading channel. We first formulate the trajectory time and throughput rate; however, owing to the nonconvexity of the problem of maximizing the network throughput based on UAV location, we put our effort to achieve these goals by RL approach. Three different approaches have been presented. We first divide the area into a grid and let the UAV explore the environment by flying from point A to point B using both the offline Q-learning and the online SARSA algorithm and the pathloss as the reward. With the intention of maximizing the average payoff, the trajectory in the first scenario is described as a Markov decision process (MDP). According to simulations, MDP produces better results in a smaller area and in less time. In contrast, SARSA performs better in larger environments at the expense of a longer flight duration.

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COVID19 detection in chest x-ray using vision-transformer with different patch dimensions 2

COVID19 detection in chest x-ray using vision-transformer wi...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kadry, Seifedine Abualigah, Laith González Crespo, Rubén Verdú, Elena Damasevicius, Robertas Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

computerized disease detection systems (CDDs) have proven effective for automatic screening in recent years. Among the standard procedures in hospitals for faster and more accurate diagnosis is medical imaging-based disease screening. We aim to develop a CDD that detects COVID-19 using chest X-rays pre-trained vision transformers (PVTs). This scheme includes the following steps: (1) collecting images and resizing them, (2) implementing PVT for feature extraction, and (3) binary classifying the results and validating the proposed schemes. To prove the merit of the developed scheme, 4800 images (2400 normal and 2400 COVID-19) are analyzed. MLP classifiers verify the PVT performance using patch sizes of 6, 12, and 24. A patch size 24 results in 97.5% accuracy for the proposed CDD system. When patch sizes are increased to 12, accuracy increases to over 98%. For this specific task, smaller patch sizes are more effective. These high-accuracy results demonstrate the effectiveness of the developed scheme for detecting COVID-19 in chest X-rays. © 2024 Elsevier B.V.. All rights reserved.

关键词： Diagnosis

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Generation and manipulation of skyrmions and other topological spin structures with rare metals

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Rare Metals 2022年第7期41卷 2200-2216页

作者： Chu Ye Lin-Lin Li Yun Shu Qian-Rui Li Jing Xia Zhi-Peng Hou Yan Zhou Xiao-Xi Liu Yun-You Yang Guo-Ping Zhao College of Physics and Electronic Engineering Sichuan Normal UniversityChengdu 610068China Center for Magnetism and Spintronics Sichuan Normal UniversityChengdu 610068China Guangdong Provincial Key Laboratory of Optical Information Materials and Technology Guangzhou 510006China Institute for Advanced Materials South China Normal UniversitySouth China Academy of Advanced OptoeletronicsGuangzhou 510006China School of Science and Engineering The Chinese University of Hong KongShenzhen 518172China Department of Electrical and Computer Engineering Shinshu UniversityNagano 380-8553Japan

Skyrmions are nano-scale quasi-particles with topological protection,which have potential applications in next-generation spintronics-based information *** papers have been published to review various aspects of skyrmions,including physics,materials and ***,no review paper has focused on rare metals which play important roles in nucleating and manipulating skyrmions and other topological *** this paper,various roles of rare metals have been classified and summarized,which can tune Curie temperature(TC),Dzyaloshinskii-Moriya interaction(DMI),magnetocrystalline anisotropy,Ruderman-Kittel-Kasuya-Yosida(RKKY)interaction and four-spin interaction so as to trigger the generation of skyrmions and other topological spin *** materials covered include typical B20 crystals,various layered systems with interfacial DMI,frustrated materials,antiferromagnets,ferrimagnets,twodimensional(2D)materials,*** addition,the rare-earth(RE)permanent magnets can provide an energy barrier and enrich the dynamic behaviors of skyrmions,which has also been reviewed.

关键词： Magnetic skyrmions Rare metals Generation and manipulation

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WSe2 Field-effect Transistor with Electron-beam-induced W-shaped IV Characteristic and its Application to a Ternary NAND Gate

WSe2 Field-effect Transistor with Electron-beam-induced W-sh...

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IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Maksim Andreev Juncheol Kang Taeran Lee Jin-Hong Park Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea SKKU Advanced Institute of Nanotechnology (SAINT) Sungkyunkwan University Suwon Korea

We report WSe 2 homojunction-based field-effect transistors (FETs) with W-shaped transfer IV characteristics, enabled by area-selective tailoring of the WSe 2 channel work function with the electron-beam treatment. We demonstrate that FETs with such IV curves are suitable for the implementation of the two-input ternary NAND logic gate, one of the essential building blocks required for the realization of a functionally complete set of logic gates in ternary logic.

关键词： Electron beams Field effect transistors Metals Logic gates Conductivity Multivalued logic Manufacturing

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CNN segmentation of skin melanoma in pre-processed dermoscopy images 2

CNN segmentation of skin melanoma in pre-processed dermoscop...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kadry, Seifedine Verdú, Elena Damasevicius, Robertas Abualigah, Laith Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

Using medical data to improve diagnosis accuracy has recently become common practice in hospitals. A modern computing environment has enabled real-time diagnosis of medical data using Convolutional Neural Networks (CNNs). To extract and evaluate skin melanoma recorded with digital dermatoscopy images (DDI), we developed a CNN segmentation framework. In this proposal, four phases are proposed: (i) DDI collection and resizing, (ii) DDI enhancement using pre-processing techniques, (iii) CNN segmentation for lesion extraction, (v) Comparing the extracted sections to the ground truth images, and (v) Verifying whether the framework is valid. Using DDI pre-processed with (i) Traditional procedures, (ii) Otsu's thresholding, (iii) Kapur's thresholding, and (iv) Fuzzy-Tsallis thresholding, this proposal examines the different CNN segmentation schemes presented in the literature. For mining skin lesions, the Moth-Flame Algorithm (MFA) combined with tri-level thresholding achieves an optimal threshold for the DDI. With Fuzzy-Tsallis thresholding images, the VGG-UNet performs better than the alternatives. This framework helps to achieve better values of Jaccard (88.47±2.13%), Dice (93.08±1.17%), and Accuracy (98.64±0.71%) on the chosen DDI database. © 2024 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

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Automatic liver-vessel examination from CT slice using Kapur's thresholding and watershed algorithm 2

Automatic liver-vessel examination from CT slice using Kapur...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

computerized medical image examination (CMIE) plays a significant role in modern hospitals to achieve the necessary tasks, like segmentation and classification. By segmenting an image, we can extract a particular section for examination. A two-dimensional computed tomography (CT) slice was used for liver-vessel examination (LiVE). A simple automatic technique for supporting LiVE is being developed in this research. A CT slice is collected, a 3D to 2D conversion is done, (ii) Kapur's tri-level thresholding and Hummingbird-Optimizer is used to enhance the CT slice, (iii) the watershed algorithm (WA) is used to extract the vessel, and (iv) the WA is compared and verified against the segmentation methods chosen. WA provides better segmentation results than other methods because it is an automatic approach. Using the chosen image database, the proposed technique achieves an overall segmentation accuracy of >97%. Other segmentation problems can be used in the future to verify the merit of this scheme. © 2024 Elsevier B.V.. All rights reserved.

关键词： Medical imaging

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LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning

arXiv

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arXiv 2023年

作者： Abaskohi, Amirhossein Rothe, Sascha Yaghoobzadeh, Yadollah School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Google DeepMind Zürich Switzerland Tehran Institute for Advanced Studies Khatam University Iran

In recent years, there has been significant progress in developing pre-trained language models for NLP. However, these models often struggle when fine-tuned on small datasets. To address this issue, researchers have proposed various adaptation approaches. Prompt-based tuning is arguably the most common way, especially for larger models. Previous research shows that adding contrastive learning to prompt-based fine-tuning is effective as it helps the model generate embeddings that are more distinguishable between classes, and it can also be more sample-efficient as the model learns from positive and negative examples simultaneously. One of the most important components of contrastive learning is data augmentation, but unlike computer vision, effective data augmentation for NLP is still challenging. This paper proposes LM-CPPF, Contrastive Paraphrasing-guided Prompt-based Fine-tuning of Language Models, which leverages prompt-based few-shot paraphrasing using generative language models, especially large language models such as GPT-3 and OPT-175B, for data augmentation. Our experiments on multiple text classification benchmarks show that this augmentation method outperforms other methods, such as easy data augmentation, back translation, and multiple templates.1 © 2023, CC BY.

关键词： Natural language processing systems

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