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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Mahnsuk Yoon Jihun Seo Kyucheol Cho Future Mobile Communication Research Center GERI(Gumi Electronics & Information Technology Research Institute) Gumi Korea Center for Connected & Automated Driving Research Department of The Fourth Industrial Revolution & Transport Korea Transport Institute Sejong Korea School of Computer Secience and Engineering Software Education Center Kyungpook National University Daegu Korea

5G SA communication technology is being recognized as a crucial technology shaping the future of wireless communication. Compared to previous generations of networks, 5G offers ultra-high-speed, low latency, and massive connectivity, presenting both enterprises and individual users with opportunities for new business models and an enhanced quality of life. One of the most significant changes between the conventional mobile communication environment and 5G is the activation of various application cases in B2B services based on the local 5G network. The local 5G network, tailored to specific sites, requires effective management of high-volume data generated in dense areas and necessitates minimizing manual intervention by administrators. The ZSM (Zero-touch Service Management) technology is gaining attention to meet these requirements. ZSM focuses on automated operations for managing large systems like commercial mobile communication networks. It is anticipated that ZSM will also be applied to the local 5G network for efficient management and operation of tailored services. This paper proposes a network management technique that minimizes administrator intervention in the local 5G network environment. The proposed network management technique collects statistical logs from local 5G network base stations and core systems, supports real-time network status checks as well as traffic predictions using machine learning. Additionally, it offers automated operation techniques for the local 5G network by incorporating functions such as disconnecting network access for devices that are factors of malfunction within the specialized network.

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Consensus-Based Algorithms for Controlling Swarms of Unmanned Aerial Vehicles 19th

Consensus-Based Algorithms for Controlling Swarms of Unmann...

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19th International Conference on Ad-Hoc Networks and Wireless, ADHOC-NOW 2020

作者： Carli, Raffaele Cavone, Graziana Epicoco, Nicola Di Ferdinando, Mario Scarabaggio, Paolo Dotoli, Mariagrazia Department of Electrical and Information Engineering Polytechnic of Bari Bari Italy Center of Excellence DEWS Department of Information Engineering Computer Science and Mathematics University of L’Aquila L’Aquila Italy

ISBN: (纸本)9783030617455

Multiple Unmanned Aerial Vehicles (multi-UAVs) applications are recently growing in several fields, ranging from military and rescue missions, remote sensing, and environmental surveillance, to meteorology, logistics, and farming. Overcoming the limitations on battery lifespan and on-board processor capabilities, the coordinated use of multi-UAVs is indeed more suitable than employing a single UAV in certain tasks. Hence, the research on swarm of UAVs is receiving increasing attention, including multidisciplinary aspects, such as coordination, aggregation, network communication, path planning, information sensing, and data fusion. The focus of this paper is on defining novel control strategies for the deployment of multi-UAV systems in a distributed time-varying set-up, where UAVs rely on local communication and computation. In particular, modeling the dynamics of each UAV by a discrete-time integrator, we analyze the main swarm intelligence strategies, namely flight formation, swarm tracking, and social foraging. First, we define a distributed control strategy for steering the agents of the swarm towards a collection point. Then, we cope with the formation control, defining a procedure to arrange agents in a family of geometric formations, where the distance between each pair of UAVs is predefined. Subsequently, we focus on swarm tracking, defining a distributed mechanism based on the so-called leader-following consensus to move the entire swarm in accordance with a predefined trajectory. Moreover, we define a social foraging strategy that allows agents to avoid obstacles, by imposing on-line a time-varying formation pattern. Finally, through numerical simulations we show the effectiveness of the proposed algorithms. © 2020, Springer Nature Switzerland AG.

关键词： Unmanned aerial vehicles (UAV)

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Process of Magnetization in Strong Magnetic Fields of Amorphous Fe-Based Alloys

Process of Magnetization in Strong Magnetic Fields of Amorph...

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作者： Stambula, S. Nabialeka, M. Paszta, P. Nabialek, M.M. Pietrusiewicz, P. Somidin, F. Jez, B. Department of Physics Faculty of Production Engineering and Materials Technology Czestochowa University of Technology al. Armii Krajowej 19 Częstochowa42-200 Poland Department of Technology and Automation Faculty of Mechanical Engineering and Computer Science Czestochowa University of Technology al. Armii Krajowej 19c Częstochowa42-200 Poland Center of Excellence Geopolymer and Green Technology Universiti Malaysia Perlis Taman Muhibbah Perlis Arau02600 Malaysia

Amorphous alloys exhibit a similar course of the magnetization process as crystalline materials. This means that there is also a domain structure in their volume, the changes of which describe the magnetization process well. These defects are a kind of inhomogeneity of the material structure and become an area where there are disturbances in the alignment of spins in relation to the intensity of the external magnetic field. These disturbances are the reason for the change in the course of the M-H curve. Using the theory developed by H. Kronmüller, it is possible to study the process of magnetization of amorphous ferromagnetic alloys in strong magnetic fields. The work analyzes the change in the process of magnetization of amorphous iron alloys along with the change in their chemical composition. © 2025 Polska Akademia Nauk. All rights reserved.

关键词： Amorphous alloys

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Estimation of continuous environments by robot swarms: Correlated networks and decision-making

Estimation of continuous environments by robot swarms: Corre...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Mohsen Raoufi Pawel Romanczuk Heiko Hamann Science of Intelligence Research Cluster of Excellence Berlin Germany Department of Biology Institute for Theoretical Biology Humboldt Universität zu Berlin Berlin Germany Department of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany Bernstein Center for Computational Neuro-science Berlin Germany Department of Computer and Information Science University of Konstanz Konstanz Germany

Collective decision-making is an essential capability of large-scale multi-robot systems to establish autonomy on the swarm level. A large portion of literature on collective decision-making in swarm robotics focuses on discrete decisions selecting from a limited number of options. Here we assign a decentralized robot system with the task of exploring an unbounded environment, finding consensus on the mean of a measurable environmental feature, and aggregating at areas where that value is measured (e.g., a contour line). A unique quality of this task is a causal loop between the robots' dynamic network topology and their decision-making. For example, the network's mean node degree influences time to convergence while the currently agreed-on mean value influences the swarm's aggregation location, hence, also the network structure as well as the precision error. We propose a control algorithm and study it in real-world robot swarm experiments in different environments. We show that our approach is effective and achieves higher precision than a control experiment. We anticipate applications, for example, in containing pollution with surface vehicles.

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Resource Optimization for LEO Constellation Networks: A Multi-satellite Cooperative Coverage Design

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IEEE Transactions on Communications 2025年

作者： Ma, Xu Zhang, Haijun Song, Wei Lu, Yang Wu, Yuan Leung, Victor C.M University of Science and Technology Beijing Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Beijing100083 China China Electric Power Research Institute Co. Ltd Beijing102209 China University of Macau State Key Laboratory of Internet of Things for Smart City Macau China University of Macau Department of Computer and Information Science Macau China Shenzhen University Department of Computer Science and Software Engineering Shenzhen518061 China

With its low latency, high throughput, good deployment flexibility, and cost-effectiveness, the low earth orbit (LEO) constellation is regarded as a promising technology for seamless coverage. Unlike geosynchronous orbiting (GSO) satellites, the highly dynamic evolution of LEO constellation topology poses a significant challenge to the traditional scheme of allocating wireless resources. This paper proposes a multisatellite cooperative coverage resource allocation for the LEO constellation. A multi-objective optimization problem focusing on throughput and coverage time is proposed for the overall service quality of the LEO constellation. To solve the complex coupling between multi-domain resources, the optimization problem is decomposed into three key subproblems, which are the beam placement problem, the joint beam association with power allocation problem, and the beam hopping time slot allocation problem. For each subproblem, an adaptive algorithm is proposed that effectively exploits the payload of LEO satellites to ensure the coverage performance of the constellation network. Simulation results demonstrate that the proposed coverage scheme enables fast convergence, enhances network throughput utility, and guarantees the stability and fairness of the communication services. © 1972-2012 IEEE.

关键词： Multiobjective optimization

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Two-Stage Inverse Design of Superchiral Dielectric Metasurfaces

Two-Stage Inverse Design of Superchiral Dielectric Metasurfa...

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International Review of Progress in Applied Computational Electromagnetics (ACES)

作者： Omer Yesilyurt Vahagn Mkhitaryan Jer-Shing Huang Ilya Shadrivov Alexander V. Kildishev Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette IN USA Department of Energy (DOE) The Quantum Science Center (QSC) a National Quantum Information Science Research Center of the U.S. Oak Ridge TN USA Leibniz Institute of Photonic Technology Jena Germany ARC Centre of Excellence for Transformative Meta-Optical Systems (TMOS) Research School of Physics the Australian National University Canberra Australia

ISBN: (数字)9781733509671

ISBN: (纸本)9798350362978

Chirality is a fundamental property of asymmetry and is crucial in distinguishing molecular enantiomers, with profound implications across chemistry, biology, and materials science. Circular dichroism (CD) spectroscopy, which measures the differential absorption of left-handed (LCP) and right-handed (RCP) circularly polarized light, serves as a conventional method for identifying and separating chiral enantiomers. Nevertheless, due to the inherently weak chiral-optical interactions, the CD signals of numerous chiral molecules are minimal, typically spanning 10– 3 to 10– 6 within the ultraviolet spectrum. Superchiral metasurfaces, producing fields with enhanced optical chirality that surpasses the chirality of circularly polarized light, emerge as a solution to this challenge. Such metasurfaces, with enhanced interaction between light and chiral molecules, offer a significant increase in CD signal, underscoring the necessity for their advanced development for more accurate and sensitive analysis. This study presents a novel, two-stage inverse design methodology for superchiral metasurfaces with iterative optimization of the topology of a dielectric metasurface unit cell to enhance optical chiral density in the near field. In the first stage, a neural network-based optimizer works in tandem with a rigorous coupled wave analysis (RCWA) solver. In the second stage, adjoint gradients of the optical chirality density are derived from the first principles and calculated with a Finite Difference Time Domain (FDTD) scheme. Our results offer a new platform for high-sensitivity CD spectroscopy applications, allowing for fast, constrained optimization of the unit cells.

关键词： Spectroscopy Optical polarization Biomedical optical imaging Metasurfaces Optical network units Optical coupling Optical sensors

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Collaborative Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud Network

arXiv

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arXiv 2023年

作者： Shen, Shihao Han, Yiwen Wang, Xiaofei Wang, Shiqiang Leung, Victor C.M. The College of Intelligence and Computing Tianjin University Tianjin300350 China IBM T. J. Watson Research Center Yorktown HeightsNY10598 United States The College of Computer Science and Software Engineering Shenzhen University Shenzhen518052 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Kubernetes (k8s) has the potential to coordinate distributed edge resources and centralized cloud resources, but currently lacks a specialized scheduling framework for edge-cloud networks. Besides, the hierarchical distribution of heterogeneous resources makes the modeling and scheduling of k8soriented edge-cloud network particularly challenging. In this paper, we introduce KaiS, a learning-based scheduling framework for such edge-cloud network to improve the long-term throughput rate of request processing. First, we design a coordinated multiagent actor-critic algorithm to cater to decentralized request dispatch and dynamic dispatch spaces within the edge cluster. Second, for diverse system scales and structures, we use graph neural networks to embed system state information, and combine the embedding results with multiple policy networks to reduce the orchestration dimensionality by stepwise scheduling. Finally, we adopt a two-time-scale scheduling mechanism to harmonize request dispatch and service orchestration, and present the implementation design of deploying the above algorithms compatible with native k8s components. Experiments using real workload traces show that KaiS can successfully learn appropriate scheduling policies, irrespective of request arrival patterns and system scales. Moreover, KaiS can enhance the average system throughput rate by 15.9% while reducing scheduling cost by 38.4% compared to baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Mitigating machining damage: Advanced modelling of pumice loading effects in Kevlar fibre-pumice sandwich polymer composites at the AWJM zone

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Journal of Materials Research and Technology 2025年 37卷 1019-1028页

作者： Venkatesh Chenrayan A. Saravanakumar Kiran Shahapurkar S. Vaishnavi S. Selvam Chandru Manivannan Vineet Tirth Ali Algahtani Jana Petrů Muhammad Nasir Bashir Manzoore Elahi M. Soudagar Krishna Prakash Arunachalam H.M. Vidhya Centre of Excellence-Advanced Materials Synthesis (CoE-AMS) Department of Mechanical Engineering Alliance School of Applied Engineering Alliance University Bengaluru 562106 India AU-Sophisticated Testing and Instrumentation Centre (AU-STIC) Alliance University Bengaluru 562106 India Department of Computer Science Engineering PSG Institute of Technology and Applied Research Coimbatore India Department of Mechanical Engineering Coimbatore Institute of Technology Coimbatore India Department of Mechanical Engineering Dhirajlal Gandhi College of Technology Salem 63840: India Mechanical Engineering Department College of Engineering King Khalid University Abha 61421 Asir Kingdom of Saudi Arabia Centre for Engineering and Technology Innovations King Khalid University Abha 61421 Asir Kingdom of Saudi Arabia Research Center for Advanced Materials Science (RCAMS) King Khalid University Guraiger PO Box 9004 Abha 61413 Asir Kingdom of Saudi Arabia Department of Machining Assembly and Engineering Metrology Faculty of Mechanical Engi-neering VŠB Technical University of Ostrava Ostrava Czech Republic Department of Mechanical Engineering University of Malaya 50603 Kuala Lumpur Malaysia Multi-Scale Fluid Dynamics Lab Department of Mechanical Engineering Yonsei University Seoul 120-749 Republic of Korea Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India Centre for Promotion of Research Graphic Era (Deemed to be University) Clement town Dehradun India Departamento de Ciencias de la Construcción Facultad de Ciencias de la Construcción Ordenamiento Territorial Universidad Tecnológica Metropolitana Santiago Chile Centre of Molecular Medicine and Diagnostics (COMManD) Saveetha Dental College and Hospitals Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai 600 077 India

The development of varieties of fibre-reinforced polymer composite (FRP) has become a part of day-to-day life and is the core agenda of the manufacturing industries in new product development. Despite the wider applications, the machining of FRPs is encountering a greater challenge in overcoming the machining damage. It is the right proposal to deplete the prominent machining damages like kerf taper, delamination and surface roughness by introducing novel hybrid statistical and soft computing models. The novel Kevlar fiber woven mat-pumice particles reinforced sandwich is fabricated using open moulding and machined with Abrasive Water Jet Machining by following L9 orthogonal guidelines. The three different pumice compositions, standoff distance, abrasive flow rate and hydraulic pressure were considered the prime variables to influence the damages. The hybrid analysis results declare that trial No.3 is an optimal parameter, which comprises 100 (MPa) of hydraulic pressure, 2 mm of standoff distance, 300 (gm/min) of abrasive flow rate and 10 % of pumice particles to scale down the damages. A noticeable 1.69 %, 25.12 % and 40.71 % decrement in machining damages, along with the finding of a higher contribution of pumice content in minimizing the damages, would benefit the FRP industries to march towards the implementation of lean manufacturing and new product development (NPD).

关键词： Kevlar fibre Pumice Delamination Kerf taper Surface roughness Grey relational analysis (GRA) Machine learning (ML)

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Classification of Pancreatic Cystic Lesions Using ResNet Deep Learning Network in Confocal Laser Endomicroscopy Videos

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Procedia computer Science 2024年 234卷 357-363页

作者： Clara Lavita Angelina Chien-Ming Pan Tsung-Chun Lee Ming-Lun Han Pradermchai Kongkam Hsiu-Po Wang Chuan-Yu Chang Hsuan-Ting Chang Department of Electrical Engineering National Yunlin University of Science and Technology Yunlin Taiwan Department of Internal Medicine Taipei Medical University Shuang Ho Hospital New Taipei City Taiwan Department of Integrated Diagnostics and Therapeutics National Taiwan University Hospital Taipei Taiwan Gastrointestinal Endoscopy Excellence Center and Division of Gastroenterology Chulalongkorn University and King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok Thailand Department of Internal Medicine National Taiwan University Hospital and College of Medicine National Taiwan University Taipei Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan

Accurate classification of pancreatic cystic lesions is crucial to differentiate mucinous lesions of malignant potential. We utilized the ResNet-50 and ResNet-101 network to develop a model for classification of the pancreatic cystic lesions. A total of 50 videos, 13,425 images, from five types of pancreatic cystic lesions and utilize the image rotation and contrast reversal scheme for the training. We adopt a contrast limited adaptive histogram equalization method onto the test video. Our method can automatically classify the feature type and record the prediction results frame by frame. The method has been evaluated on 18 test videos and achieves an accuracy 94% overall.

关键词： Contrast Limited Adaptive Histogram Equalization Contrast Reversal Deep Learning Pancreatic Cystic Lesions ResNet-50 ResNet-101

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A Contrastive Learning-based PPC-UNet for Colorectal Histopathology Whole Slide Image Segmentation

A Contrastive Learning-based PPC-UNet for Colorectal Histopa...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Wang, Yuxuan Li, Xuechen Liu, Jingxin Shen, Linlin Sun, Kunming Wang, Suying AI Research Centre for Medical Image Analysis and Diagnosis Marshall Laboratory of Biomedical Engineering Shenzhen University School of Computer Science and Software Engineering Shenzhen China Xi'An Jiaotong-Liverpool University Histo Pathology Diagnostic Center Shanghai School of AI and Advanced Computing Suzhou China Histo Pathology Diagnostic Center Shanghai China Ningbo Diagnostic Pathology Center Department of Pathology Ningbo China

ISBN: (纸本)9781665401265

Colorectal cancer (CRC) is the third most common cancer and is usually diagnosed using colonoscopy and biopsy. Diagnosis of pathological biopsy requires professional knowledge and technology. computer-aided gland and lesion segmentation systems have been proposed to help pathologists in diagnosis of CRC. However, to the best of our knowledge, there has not been a literature work trying to segment different levels of intraepithelial neoplasia in CRC pathological image. To reduce such a research gap, in this paper, we firstly collect a colorectal cancer biopsy histopathology whole slide image (WSI) dataset, named Histo-CRC Biopsy dataset, for algorithm evaluation. We further propose a PPC-UNet network to segment high level, low level intraepithelial neoplasia and normal tissues. The proposed PPC-UNet consists of two modules i.e., a UNet-based network for segmentation, and a pixel-to-propagation consistency (PPC) contrastive learning-based network for UNet encoder pre-training. As the important feature can be learned from the unannotated data during pre-training, our approach can consistently improve the Dice of UNet by around 2% when different ratios of the training data are labeled. © 2021 IEEE.

关键词： Image segmentation

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