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SSRN 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nannig China Guangxi Key Laboratory of Multimedia Communications and Network Technology China China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China College of Systems Engineering National University of Defense Technology Changsha China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGA also uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a high-quality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data tra

关键词： Profitability

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

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IEEE Transactions on Mobile Computing 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing China College of Computing and Data Science Singapore University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral Gables33146 United States University of Miami Department of Industrial and Systems Engineering Coral Gables33146 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing Advanced Information Network Laboratory Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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Water Quality Assessment of Reservoirs in Sri Lanka

Water Quality Assessment of Reservoirs in Sri Lanka

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Advancements in Computing (ICAC), International Conference on

作者： Kavindi Mudunkotuwa Chamath Senadeera Beuni Gunawardana Onara Pathiraja Dinuka Wijendra Harinda Fernando Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems and Network Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

This study presents a comprehensive assessment of water quality in Sri Lankan reservoirs using available data sources. The study focuses on the identification of microorganisms through image analysis, watercolor analysis in satellite-based images detection of water quality, and the inference of water quality index using relevant physical parameters. By analyzing images, the study quantifies microorganisms, providing insights into microbial diversity and potential risks. Image processing techniques enable the identification of contaminants based on watercolor analysis. Utilizing satellite data, it examines variations in water quality parameters, providing a comprehensive assessment of overall water quality. The derived water quality index, using relevant physical parameters, quantitatively assesses and compares water quality across different reservoirs. The findings contribute to understanding water quality in Sri Lankan reservoirs, supporting informed decision-making and sustainable water resource management.

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A novel identity resolution system design based on Dual-Chord algorithm for industrial Internet of Things

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Science China(Information Sciences) 2021年第8期64卷 21-34页

作者： Renchao XIE Zhiyuan WANG Fei Richard YU Tao HUANG Yunjie LIU School of Information and Communication Engineering Beijing University of Posts and Telecommunications Department of Future Network Purple Mountain Laboratories Department of Systems and Computer Engineering Carleton University

The industrial Internet of Things(IIoT) has attracted enormous attention from academics and industries, which is a significant component of the future transformation of industrial systems. The analysis,understanding, and application of all kinds of fragmented data in IIoT is one of the critical technologies that need to be studied in the future. The identity resolution technology is the most promising technology to achieve this goal at present. Although many identity resolution systems based on domain name system(DNS) have been proposed, there are technical problems with a single root node causing system performance bottlenecks and slow resolution rates. In this paper, we focus on a widely used distributed structured peer-to-peer(P2 P) object name system(ONS) and redesign a novel identity resolution system based on a Dual-Chord algorithm for IIoT. The support vector machine(SVM) algorithm is first introduced in the system designed in this paper to implement the clustering of the P2 P network. And then, the Dual-Chord algorithm is proposed, which can get better performance than the classical Chord algorithm. This system resolves the mismatch between logical and physical addresses and improves the resolution rate of the ***, through PeerSim network simulation software, this paper evaluates the simulation performance of the improved identity resolution system based on the Dual-Chord algorithm. Simulation results prove that the system proposed in this paper is a more efficient identity resolution system.

关键词： industrial Internet of Things identity resolution distributed Hash table small world model

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Customizing and Deploying Data Science Roadmapping: A Case Study of an Oil and Gas Company

Customizing and Deploying Data Science Roadmapping: A Case S...

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2022 IEEE International Conference on Technology Management, Operations and Decisions, ICTMOD 2022

作者： Kayabay, Kerem Gokalp, Mert Onuralp Gokalp, Ebru Alagoz, Serhat Murat Eren, P. Erhan Kocyigit, Altan Data Informatics Middle East Technical University Network Technologies Department TÜBITAK ULAKBIM Ankara Turkey Data Analytics Center TÜPRAŞ Ankara Turkey Hacettepe University Computer Engineering Department Ankara Turkey Information Systems Middle East Technical University Ankara Turkey

ISBN: (纸本)9781665470278

Many organizations still face challenges leveraging data science in production and need strategic planning for organization-wide data science efforts and assets. Data Science Roadmapping (DSR) customizes the widely used Technology Roadmapping to facilitate such planning in organizations, aligning market, business, data, technology, and organization perspectives. This paper employs the action research method to customize and apply DSR in an oil and gas company. The approach mostly met the expectations regarding objectives and outcomes, the first step toward generalizability. The case study provides researchers and practitioners insights into roadmapping customization, remote and agile roadmap development process, and strategic planning for technology adoption in a large organization. Lastly, we identify the future research directions using the recent roadmapping literature. © 2022 IEEE.

关键词： Big data

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Development of a VS ultrasound diagnostic system with image evaluation functions 11

Development of a VS ultrasound diagnostic system with image ...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Zhou, Jiayi Koizumi, Norihiro Nishiyama, Yu Tsumura, Ryosuke Tsukihara, Hiroyuki Matsumoto, Naoki The University of Electro-Communications Department of Mechanical and Intelligent Systems Tokyo Japan The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan Health and Medical Research Institute National Institute of Advanced Industrial Science and Technology Tokyo Japan The University of Tokyo Graduate School of Engineering Tokyo Japan Nihon University Department of Gastroenterology and Hepatology Tokyo Japan

ISBN: (纸本)9781665492324

An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross-section of the target organ, which is constantly shifting due to patient respiratory motions. Therefore, we developed an ultrasound diagnostic robot that works in cooperation with a visual servo system based on deep learning that will help alleviate the burdens imposed on physicians. Two different image processing methods (BiSeNet V2) were used to detect the target kidney location, as well as to evaluate the appropriateness of the obtained ultrasound images (ResNet 50) in developed system. © 2022 IEEE.

关键词： Visual servoing

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A Genetic Algorithm to Optimize Cargo Stability in Container Transportation 11

A Genetic Algorithm to Optimize Cargo Stability in Container...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Hsieh, Chi-Jung Wu, Kuang-Sung Wu, Tin-Yu Tey, Fu Jie Chen, Yi-Kai Ho, Jye Yi National Ilan University Department of Computer Science and Information Engineering Ilan County Taiwan National Ilan University Master Program of E-Learning for Multimedia and Network Communications Yilan Taiwan National Pingtung University of Science and Technology Department of Management Information Systems Pingtung County Taiwan National Taiwan University of Science and Technology Department of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9781665492324

In the era of the global village, frequent cross-border trade in goods has made container transportation a significant part in delivery of cargo. However, rollover accidents of container trucks often occur because of tall truck height, improper cargo stowage or crooked roads. Therefore, how to find the optimal center of gravity (CG) of the packed cargo remains an issue that the transportation industry should not neglect. To optimize cargo stability and reduce the incidence of rollovers, this paper proposes a genetic algorithm that scores the cargo stacking process to find the best CG of the cargo based on the CG of the vehicle. According to the deviation angle and the point closest to the best CG, we can Figure out the optimal cargo stacking method. © 2022 IEEE.

关键词： Genetic algorithms

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Deep Reinforcement Learning based Joint Spectrum Allocation and Configuration Design for STAR-RIS-Assisted V2X Communications

arXiv

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arXiv 2023年

作者： Aung, Pyae Sone Nguyen, Loc X. Tun, Yan Kyaw Han, Zhu Hong, Choong Seon The Department of Computer Science and Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of Division of Network and Systems Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Brinellvägen 8 Stockholm114 28 Sweden The Electrical and Computer Engineering Department University of Houston HoustonTX77004 United States

Vehicle-to-Everything (V2X) communications play a crucial role in ensuring safe and efficient modern transportation systems. However, challenges arise in scenarios with buildings, leading to signal obstruction and coverage limitations. To alleviate these challenges, reconfigurable intelligent surface (RIS) is regarded as an effective solution for communication performance by tuning passive signal reflection. RIS has acquired prominence in 6G networks due to its improved spectral efficiency, simple deployment, and cost-effectiveness. Nevertheless, conventional RIS solutions have coverage limitations. Therefore, researchers have started focusing on the promising concept of simultaneously transmitting and reflecting RIS (STAR-RIS), which provides 360°coverage while utilizing the advantages of RIS technology. In this paper, a STAR-RIS-assisted V2X communication system is investigated. An optimization problem is formulated to maximize the achievable data rate for vehicle-to-infrastructure (V2I) users while satisfying the latency and reliability requirements of vehicle-to-vehicle (V2V) pairs by jointly optimizing the spectrum allocation, amplitudes, and phase shifts of STAR-RIS elements, digital beamforming vectors for V2I links, and transmit power for V2V pairs. Since it is challenging to solve in polynomial time, we decompose our problem into two sub-problems. For the first sub-problem, we model the control variables as a Markov Decision Process (MDP) and propose a combined double deep Q-network (DDQN) with an attention mechanism so that the model can potentially focus on relevant inputs. For the latter, a standard optimization-based approach is implemented to provide a real-time solution, reducing computational costs. Extensive numerical analysis is developed to demonstrate the superiority of our proposed algorithm compared to benchmark schemes. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Detecting Characteristic Points for the Analysis of Bioimpedance Signal Through a Synergy of Fuzzy Rule-based models and Granular Neural networks

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IEEE Transactions on Fuzzy systems 2024年

作者： Wang, Dan Richter, Monika Zhu, Xiubin Pedrycz, Witold Gacek, Adam Sobotnicki, Aleksander Li, Zhiwu Xi'an University of Science and Technology College of Computer Science and Technology Xi'an710054 China Center for Biomedical Engineering Lukasiewicz Research Network-Krakow Institute of Technology Zabrze41-800 Poland Xidian University School of Mechano-Electronic Engineering Xi'an710071 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey Macau University of Science and Technology Institute of Systems Engineering Taipa China

In this study, we propose a novel methodology for determining accurate positions of characteristic points encountered in the analysis of bioimpedance signals. The proposed approach fully utilizes two fundamental modeling pursuits based on fuzzy rule-based models and neural networks. We take advantages of the unique capabilities of fuzzy rule-based models to characterize the nonlinear relationship between the acquired bioimpedance signals and its temporal coordinate. The fuzzy modeling approach is used to approximate the process that generates the bioimpedance signals through a collection of rules (if-then statements). In the sequel, the parameters of the models are used as the inputs of neural network models to determine the position of characteristic points. We further augment the numeric neural network to its granular counterpart to accommodate the uncertainty in the available experimental evidence through allocating a certain level of information granularity across the parameter space. The resulting granular outputs (intervals) become reflective of the quality and a level of confidence associated with the prediction results. The quality of the prediction results is quantified in terms of the coverage and specificity criteria. The performance index is also enhanced to deal with the situation when the positions provided by experts are also information granules (intervals). The performance of the proposed approach is justified through a collection of experiments carried out on the collected real-world bioimpedance signals. Experimental results show that the proposed approach achieved higher accuracy in determining the position of characteristic points in comparison with other existing methods. © 1993-2012 IEEE.

关键词： Fuzzy models

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ImConv_RNN: Improved Convolutional Recurrent Model Based Channel Estimation for 6G networks

ImConv_RNN: Improved Convolutional Recurrent Model Based Cha...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Raghavendra Kulkarni Venkata Satya Suresh kumar Kondeti Binu Sudhakaran Pillai Surendran R Department of Network Technology Principal Engineer T-Mobile USA Inc Bellevue W A USA Department of Professional Services Axyom. Core Senior Systems Engineer North Andover MA USA Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

This research introduces a novel deep learning for channel estimation for the 6G networks. The channel estimation for the 6G networks is employed using the proposed Improved Convolutional Recurrent (ImConv-RNN) model. In this, the loss function optimization is employed using the proposed ChSO algorithm. The chaotic mapping based on Chebyshev is included in the skill optimization assist the algorithm to enhance the convergence rate. The proposed ImConv-RNN model is evaluated based on RMSE, R2, MAE and MAPE acquired the values of 0.0136, 0.9584, 0.0114, and 5.8969 respectively.

关键词： 6G mobile communication Deep learning Analytical models Sentiment analysis Channel estimation Estimation Chebyshev approximation Feature extraction Optimization Convergence

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