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Data collection of wireless sensor network based on trajectory optimization of laser-charged UAV

High-Confidence Computing 2024年第2期4卷 116-128页

作者： Chuanwen Luo Jian Zhang Jin Qian Yi Hong Zhibo Chen Yunan Hou Xiujuan Zhang Yuqing Zhu 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 College of Information Engineering Taizhou UniversityTaizhou 225300China School of Computer Science Qufu Normal UniversityRizhao 276826China Research Center of Applied Mathematics and Machine Intelligence Zhejiang LabHangzhou 311121China Department of Computer Science California State UniversityCA 90032USA

Unmanned Aerial Vehicle(UAV)can be used as wireless aerial mobile base station for collecting data from sensors in UAV-based Wireless Sensor Networks(WSNs),which is crucial for providing seamless services and improving the performance in the next generation wireless ***,since the UAV are powered by batteries with limited energy capacity,the UAV cannot complete data collection tasks of all sensors without energy replenishment when a large number of sensors are deployed over large monitoring *** overcome this problem,we study the Real-time Data Collection with Lasercharging UAV(RDCL)problem,where the UAV is utilized to collect data from a specified WSN and is recharged using Laser Beam Directors(LBDs).This problem aims to collect all sensory data from the WSN and transport it to the base station by optimizing the flight trajectory of UAV such that realtime data performance is ensured It has been proven that the RDCL problem is *** address this,we initially focus on studying two sub-problems,the Trajectory Optimization of UAV for Data Collection(TODC)problem and the Charging Trajectory Optimization of UAV(CTO)problem,whose objectives are to find the optimal flight plans of UAV in the data collection areas and charging areas,*** we propose an approximation algorithm to solve each of them with the constant ***,we present an approximation algorithm that utilizes the solutions obtained from TODC and CTO problems to address the RDCL ***,the proposed algorithm is verified by extensive simulations.

关键词： Wireless sensor networks Laser-charged UAV Trajectory optimization Data collection

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Research on Supply Planning and Transshipment Scheme Based on Genetic Algorithm 10

Research on Supply Planning and Transshipment Scheme Based o...

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10th International Conference on Information Systems and Computing Technology, ISCTech 2022

作者： Zhong, Zhenkun Ren, Junyu Li, Heng Peng, Hui School of Software Engineering Xinjiang University Xinjiang830008 China School of Mathematics and Applied Mathematics Xinjiang University Xinjiang830017 China School of Computer Science and Engineering Central South University Changsha410087 China

ISBN: (纸本)9798350332933

Aiming at the optimal cost planning problem in the process of raw material ordering and transshipment, this pa-per studies and develops a supply chain planning manage-ment model based on genetic algorithm by constructing ob-jective function and constraints. On the basis of analyzing the raw material demand of production enterprises, combined with the supplier's supply capacity and the transshipment loss rate of the transshipment carrier, the mathematical model of the optimal ordering scheme and the transshipment scheme is constructed, and the optimal supply planning model that can meet the actual supply chain planning needs is proposed, and the planning model is improved by using genetic algorithms, which greatly improves the operation speed and has good applicability. © 2022 IEEE.

关键词： Genetic algorithms

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Neural Estimation of Entropic Optimal Transport

Neural Estimation of Entropic Optimal Transport

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IEEE International Symposium on Information Theory

作者： Tao Wang Ziv Goldfeld Applied Mathematics and Computational Science University of Pennsylvania School of Electrical and Computer Engineering Cornell University

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

Optimal transport (OT) serves as a natural frame-work for comparing probability measures, with applications in statistics, machine learning, and applied mathematics. Alas, statistical estimation and exact computation of the OT distances suffer from the curse of dimensionality. To circumvent these issues, entropic regularization has emerged as a remedy that enables parametric estimation rates via plug-in and efficient computation using Sinkhorn iterations. Motivated by further scaling up entropic OT (EOT) to data dimensions and sample sizes that appear in modern machine learning applications, we propose a novel neural estimation approach. Our estimator parametrizes a semi-dual representation of the EOT distance by a neural network, approximates expectations by sample means, and optimizes the resulting empirical objective over parameter space. We establish non-asymptotic error bounds on the EOT neural estimator of the cost and optimal plan. Our bounds characterize the effective error in terms of neural network size and the number of samples, revealing optimal scaling laws that guarantee parametric convergence. The bounds hold for compactly supported distributions, and imply that the proposed estimator is minimax-rate optimal over that class. Numerical experiments validating our theory are also provided.

关键词： Costs Scalability Sociology Estimation Artificial neural networks Machine learning Probability

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Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

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computer Modeling in engineering & Sciences 2024年第4期139卷 801-816页

作者： D.Dorathy Prema Kavitha L.Francis Raj Sandeep Kautish Abdulaziz S.Almazyad Karam M.Sallam Ali Wagdy Mohamed Department of Mathematics Voorhees CollegeVelloreTamil NaduIndia Chandigarh Group of Colleges JhanjeriMohaliPunjabIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Faculty of Science and Technology School of IT and SystemsUniversity of CanberraCanberraAustralia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

The intuitive fuzzy set has found important application in decision-making and machine *** enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images *** image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye *** degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal *** proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between *** methodology was used to clarify the input images and make them adequate for the process of glaucoma *** objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were *** the peak regions were identified,the recurrence relationships among those peaks were then *** partitioning was done due to varying degrees of similar and dissimilar concentrations in the *** and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and *** distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

关键词： Convolutional Neural Network(CNN) glaucomatous eyes fuzzy difference equation intuitive fuzzy sets image segmentation retinal images

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Feature Selection for Text Classification Using Genetic Algorithm 1

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1st international conference on Machine Intelligence and computer Science Applications, ICMICSA 2022

作者： Belkarkor, Salma Hafidi, Imad Nachaoui, Mourad Laboratory of Process Engineering Computer Science and Mathematics at the National School of Applied Sciences Sultan Moulay Slimane University Khouribga Morocco Laboratory of Mathematics and Applications Team of Mathematics and Interactions at the Faculty of Science and Technology Sultan Moulay Slimane University Beni-Mellal Morocco

ISBN: (数字)9783031293139

ISBN: (纸本)9783031288456

Today’s large amount of text data makes feature classification a difficult text processing challenge. High dimensionality is the primary challenge in text processing, and feature selection is a common method for reducing dimensions. The most crucial factors in text classification are a strong text representation and a very accurate classifier. As a result, choosing the right features is essential for using machine learning algorithms effectively. Different optimization techniques, including the Genetic Algorithm (GA), have been effectively used for dimensionality reduction in the field of text classification. To evaluate the performance of GA for Feature Selection (FS), we compared the GA for FS with other filtering methods to prove the efficiency of the GA for FS, for that, we used the NB classifier and three benchmark document collections: SMS, BBC, and 20Newsgroups. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature Selection

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Collaborative Task Offloading with Digital Twin in Multi-Vehicle and Multi-Edge Environments

Collaborative Task Offloading with Digital Twin in Multi-Veh...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Gu, Anqi Wu, Huaming Wang, Yixiao Li, Ruidong Tang, Chaogang Tianjin University Center for Applied Mathematics Tianjin300072 China Kanazawa University Institute of Science and Engineering Kanazawa920-1192 Japan China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China

ISBN: (纸本)9798350351255

In recent years, the effective utilization of edge servers to assist vehicles in handling compute-intensive and latency-sensitive tasks has emerged as a pivotal concern in Vehicular Edge Computing (VEC). In this paper, we adopt a cooperative approach that leverages the collective capabilities of multiple edge servers. This strategy is designed to effectively manage tasks and alleviate the computational burden imposed on these servers. Specifically, Graph Neural Network (GNN) is applied to extract and classify features such as the geographical locations and communication statuses of multiple edge servers, enabling the selection of the most suitable servers for collaborative task execution. We have utilized solar energy for local computing, effectively achieving environmental protection and reducing the local energy burden on vehicles. Moreover, a novel edge attraction formula is defined to refine the rationality of clustering. In addition, Deep Reinforcement Learning (DRL) is employed to make real-time offloading decisions. To ensure experimental accuracy while mitigating costs, we establish a corresponding digital twin environment to acquire experimental data. By conducting a comparative analysis against three other baseline methods, we effectively reduce task completion time and thus meet the stringent demands of time-sensitive tasks. © 2024 IEEE.

关键词： Deep reinforcement learning

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Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism

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Mathematical Biosciences and engineering 2025年第1期22卷 73-105页

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand

Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis require enhancement in the creation of dependable detection approaches. Despite advances in machine learning and deep learning, capturing the intricate spatial and temporal patterns in EEG data remains challenging. This study introduced a novel deep learning framework combining a convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and convolutional block attention module (CBAM). The CNN extracts spatial features, the BiGRU captures long-term temporal dependencies, and the CBAM emphasizes critical spatial and temporal regions, creating a hybrid architecture optimized for EEG pattern recognition. Evaluation of a public EEG dataset revealed superior performance compared to existing methods. The model achieved 99.00% accuracy in binary classification, 96.20% in three-class tasks, 92.00% in four-class scenarios, and 89.00% in five-class classification. High sensitivity (89.00–99.00%) and specificity (89.63–99.00%) across all tasks highlighted the model’s robust ability to identify diverse EEG patterns. This approach supports healthcare professionals in diagnosing epileptic seizures accurately and promptly, improving patient outcomes and quality of life. © 2025 American Institute of Mathematical Sciences. All rights reserved.

关键词： Electroencephalography

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Secure Watermarking Scheme for Color DICOM Images in Telemedicine Applications

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computers, Materials & Continua 2022年第2期70卷 2525-2542页

作者： Kamred Udham Singh Hatem Salem Abu-Hamatta Abhishek Kumar Achintya Singhal Mamoon Rashid A.K.Bashir Department of Computer Science and Information Engineering National Cheng Kung UniversityTainanTaiwan Department of Applied Sciences Aqaba University CollegeAl Balqa Applied UniversityAqabaJordan School of Computer Science&IT JAIN(to be Deemed University)BangaloreIndia Department of Computer Science Banaras Hindu UniversityVaranasiIndia Department of Computer Engineering Faculty of Science and TechnologyVishwakarma UniversityPune411048India Department of Computing and Mathematics ManchesterMetropolitan UniversityUK

Teleradiology plays a vital role in the medical field,which permits transmitting medical and imaging data over a communication *** ensures data reliability and provides convenient communication for clinical interpretation and diagnostic *** transmission of this medical data over a network raises the problems of legal,ethical issues,privacy,and copyright *** copyright protection of medical images is a significant issue in the medical *** schemes are used to address these issues.A gray-level or binary image is used as a watermark frequently in color image watermarking *** this paper,the authors propose a novel non-blind medical image watermarking scheme based on 2-D LiftingWavelet Transform(LWT),Multiresolution Singular Value Decomposition(MSVD),and LU factorization to improve the robustness and authenticity of medical *** this scheme,multiple color watermarks are embedded into the colored DICOM(Digital Imaging and Communications inMedicine)images obtained from Color Doppler images(DICOM format),and the average results achieved by our proposed scheme is 46.84 db for Peak Signal-to-Noise Ratio(PSNR),37.46 db for Signal-to-Noise Ratio(SNR),0.99 for Quality of Image and 0.998 for Normalized Correlation for various image processing *** results make our watermarking technique an ideal candidate for medical image watermarking.

关键词： Digital imaging medicine multiresolution medical image watermarking

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Representation with Minimized Max-Error in Optimal Piecewise Linear Approximation of Time Series Data 25th

Representation with Minimized Max-Error in Optimal Piecewi...

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25th International Conference on Web Information Systems engineering, WISE 2024

作者： Zhao, Huanyu Li, Tongliang Wen, Shiting Shu, Zhenyu Deng, Ke Yang, Jian Pang, Chaoyi School of Computer Science and Technology Donghua University Shanghai China Institute of Applied Mathematics Hebei Academy of Sciences Shijiazhuang China Hebei Authentication Technology Engineering Research Center Shijiazhuang China Institute of Biology Hebei Academy of Sciences Shijiazhuang China School of Computer and Data Engineering NingboTech University Ningbo China RMIT University Melbourne Australia Macquarie University Sydney Australia

ISBN: (纸本)9789819605781

In the past two decades, Piecewise Linear Approximation under maximum error (max-error) bound (PLA∞) has been intensively studied for effective qualified representation and analysis of time series data. It divides a time series into fragments and then represents each fragment with a straight line to approximate the data points of that time slot. In this paper, to elevate the representation quality in the optimal PLA∞ results, we present a linear-time algorithm FindMin to construct the unique line representative of minimized maximum error (min-max error) for each fragment. Through extended experimental tests, we demonstrate that the proposed algorithm is very efficient in execution and achieves better performances than the state-of-the-art solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Time series analysis

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Exact Output Synchronization of Discrete-Time Multi-Agent Systems in the Presence of Disturbances and Measurement Noise with Known Frequencies via Scale-Free Protocol Design 42

Exact Output Synchronization of Discrete-Time Multi-Agent Sy...

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42nd Chinese Control Conference, CCC 2023

作者： Liu, Zhenwei Zhang, Meirong Saberi, Ali Stoorvogel, Anton A. College of Information Science and Engineering Northeastern University Shenyang110819 China School of Engineering and Applied Science Gonzaga University SpokaneWA United States School of Electrical Engineering and Computer Science Washington State University PullmanWA99164 United States Mathematics and Computer Science Department of Electrical Engineering University of Twente Enschede Netherlands

ISBN: (纸本)9789887581543

This paper aims to achieve scalable exact output synchronization for discrete-time homogeneous multi-agent systems in the presence of disturbances and measurement noise with known frequencies. The key contribution of this paper is on the distributed protocol that only uses the information of agent models, rather than the communication network information and the agent number, so as to achieve the scalable exact synchronization under disturbances and measurement noise. The validity of the protocol is verified by numerical simulations with arbitrarily chosen number of agents. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Multi agent systems

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