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Joint Optimization of Wireless Charging Station Location and Operation Scheduling for Electric Buses under Uncertain Demand

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IEEE Access 2025年 13卷 91765-91776页

作者： Li, Jiacheng Noto, Masato Zhang, Yang Guo, Jia Kanagawa University Department of Applied Systems and Mathematics Yokohama Japan Kanagawa University Department of Computer Science Yokohama Japan Hubei Key Laboratory of Digital Finance Innovation Hubei University of Economics Wuhan China Hosei University Graduate School of Computer and Information Sciences Tokyo Japan Hubei University of Economics Japan School of Information Engineering Hubei Wuhan430205 China

In the context of electric bus route operations, the introduction of wireless charging facilities significantly impacts the operational scheduling of bus fleets. This study focuses on a single bus route and proposes a joint optimization model for determining departure frequencies and charging station locations under uncertain demand conditions. The model aims to minimize both user travel costs and transit operation costs by jointly optimizing departure schedules across different time periods, electric bus assignments, and wireless charging station placement. An adaptive genetic algorithm (AGA) is developed to solve the model efficiently. A case study based on a real-world bus route in China demonstrates that the proposed strategy not only improves system performance but also yields tangible benefits—reducing the required number of electric buses by 29.4%, shortening departure intervals by 12.5%, and lowering daily operational costs by 10.5%. © 2013 IEEE.

关键词： Genetic algorithms

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Improving the stockpiling sites for inbound, outbound containers by timing the place of stacking

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International Journal of information Technology (Singapore) 2023年第6期15卷 3071-3077页

作者： Saleh, Mohammed Ahmed Moqbel Hicham, Attariuas Ben Maâti, M.L. Taha, Hatem Admeur, Smail Computer Science and Systems Engineering Laboratory Faculty of Science University of Abdelmalek Essaadi Tetouan Morocco Research Operational and Statistic Applied (LAROSA) Faculty of Sciences Abdelmalek Essaâdi University Tetouan Morocco

Seaports face rising pressure in container stacking. Nations are endeavoring to get the most advantage from it, increase their portion of this area’s assets, and achieve their aspirations. Because of this increment, the ports experience the bad effects of numerous issues, including how to make the proper choice to store and extract different containers. In this paper, The method for stacking containers in a suitable location has been proposed in the maritime terminal in light of the speculation of time’s elements for choosing the ideal position for the container in the yard in light of the speculation of time’s elements for choosing the ideal position for the container in the yard. This method gives ideal capacity areas to organize containers by time to stay away from random scattering. The scattering of containers is not a solution to stacking because it causes retrieval issues. As well as to give more flexibility during work with the option of reorganizing containers that should be put away in the terminal to permit the arrangement of new stockpiling places that decrease time and working expenses. This work contributes to the decision about organizing the stacking of containers. We propose to apply artificial intelligence techniques to take an optimal decision for stacking the containers that have various departure dates in light of the fact that artificial intelligence algorithms read the data styles in the historical data and predict the optimal solution for new inputs. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Artificial intelligence Container terminal Container time Improve container stacking Real-time stacking

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Optimizing Resource Allocation Framework for Multi-Cloud Environment

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computers, Materials & Continua 2023年第5期75卷 4119-4136页

作者： Tahir Alyas Taher M.Ghazal Badria Sulaiman Alfurhood Ghassan F.Issa Osama Ali Thawabeh Qaiser Abbas Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebansaan Malaysia(UKM)43600BangiSelangorMalaysia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia Skyline University College University City Sharjah1797SharjahUAE Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351Saudi Arabia

Cloud computingmakes dynamic resource provisioning more *** a functioning service is crucial,and changes are made when particular criteria are *** research explores the decentralized multi-cloud environment for allocating resources and ensuring the Quality of Service(QoS),estimating the required resources,and modifying allotted resources depending on workload and parallelism due to *** allocation is a complex challenge due to the versatile service providers and resource *** engagement of different service and resource providers needs a cooperation strategy for a sustainable quality of *** objective of a coherent and rational resource allocation is to attain the quality of *** also includes identifying critical parameters to develop a resource allocation mechanism.A framework is proposed based on the specified parameters to formulate a resource allocation process in a decentralized multi-cloud *** three main parameters of the proposed framework are data accessibility,optimization,and *** an optimization technique,these three segments are further divided into subsets for resource allocation and long-term service *** CloudSim simulator has been used to validate the suggested *** experiments have been conducted to find the best configurations suited for enhancing collaboration and resource allocation to achieve sustained *** results support the suggested structure for a decentralized multi-cloud environment and the parameters that have been determined.

关键词： Multi-cloud query optimization cloud resources allocation modelling virtualization

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Leveraging Support Vector Machine for Sports Injury Classification 15

Leveraging Support Vector Machine for Sports Injury Classifi...

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15th International Conference on information Technology and Electrical Engineering, ICITEE 2023

作者： Surasak, Thattapon Praking, Phornnatee Kitchat, Kotcharat King Mongkut's University of Technology North Bangkok Faculty of Applied Science Dept. of Computer and Information Science Bangkok Thailand National Central University Dept. of Computer Science and Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798350304466

This study explores the utility of Support Vector Machines (SVM) for the classification of injuries in Athletes including tennis, table tennis, and badminton. The primary objective of this research was to develop an automated method to detect between normal and injury pose, providing a poten-tially powerful tool for early detection. In other words, this research is the pioneer study to represent one another solution for sports injury classification using the image data apart from the current approaches that might be focused on the sensor-based. Therefore, we collect a dataset from various social media across the three sports, labeled into two categories: 'Normal' and 'Injury'. The SVM model was trained on this dataset, and tuned to optimize its performance in distinguishing the two classes. Achieving an accuracy of 96.63%, the result demonstrates that the SVM model can effectively identify injury patterns in the considered sports. The implications of this study are sig-nificant, presenting the initial steps towards applying computer vision tasks in sports topics. By demonstrating the feasibility of an SVM-based approach to injury detection in these racket sports. © 2023 IEEE.

关键词： Sports

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Optimal Method Combining Blockchain and Multi-Agent System to Ensure Data Integrity and Deduplication in the Cloud Environment

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第10期18卷 90-105页

作者： El Ghazouani, Mohamed Ikidid, Abdelouafi Zaouiat, Charafeddine Ait Aziz, Layla Ichahane, Moulay Youssef Er-Rajy, Latifa ESIM Polydisciplinary Faculty of Sidi Bennour Chouaîb Doukkali University El Jadida Morocco Laboratory of System Analysis Information Processing and Industrial Management EST Salé Mohammed V University Salé Morocco Information Technology Laboratory National School of Applied Sciences Chouaîb Doukkali University El Jadida Morocco Computer Science Department Laboratory of Information Systems Engineering Cadi Ayyad University Marrakesh Morocco

Cloud computing (CC) refers to the transmission, storage, and processing of any type of information at a location that is not owned or controlled by the information owner. This information can be stored and accessed anytime, from anywhere, and using any device. However, several challenges threaten the deployment of cloud computing technology within organizations, including user privacy, data integrity, and managing the retention of the vast amount of data that will be stored and analyzed in this environment. In recent years, various data integrity checking systems and data storage mechanisms have been proposed to address these diverse challenges. Nevertheless, some protocols do not guarantee data confidentiality against auditors during the auditing process, while others do not provide batch auditing. Additionally, the majority of these protocols do not support the deduplication technique. Therefore, in this research work, the security features of blockchain technology and those of multi-agent systems (MAS) have prompted us to propose models that enable both data deduplication, saving storage space, and data integrity auditing, efficiently verifying the accuracy of data outsourced to CC environments. Implementation findings indicate that the suggested system is highly feasible and applicable in real-world scenarios. © 2024 by the authors of this article.

关键词： Multi agent systems

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Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

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computers, Materials & Continua 2023年第11期77卷 2545-2564页

作者： Medhat A.Tawfeek Nacim Yanes Leila Jamel Ghadah Aldehim Mahmood A.Mahmood Department of Computer Science College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia Department of Computer Science Faculty of Computers and InformationMenoufia UniversityP.O.Box 6121890MenoufiaEgypt Department of Software Engineering College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia RIADI Laboratory La Manouba UniversityP.O.Box2010ManoubaTunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems and Technology Faculty of Graduate Studies and ResearchCairo UniversityP.O.Box 3753450GizaEgypt

The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water *** study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research *** proposed model uses a one-dimensional convolutional neural network(CNN)deep learning model to control the growth of strategic crops,including cucumber,pepper,tomato,and *** proposed model uses the Internet of Things(IoT)to collect data on agricultural operations and then uses this data to control and monitor these operations in real *** helps to ensure that crops are getting the right amount of fertilizer,water,light,and temperature,which can lead to improved yields and a reduced risk of crop *** dataset is based on data collected from expert farmers,the photovoltaic construction process,agricultural engineers,and research *** experimental results showed that the precision,recall,F1-measures,and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%,98.2%,97.25%,and 97.56%,*** new smart greenhouse automation system was also evaluated on four crops with a high turnover *** system has been found to be highly effective in terms of crop productivity,temperature management and water conservation.

关键词： Greenhouse wireless sensor network deep learning Internet of Things strategic crops monitoring smart irrigation

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Warehouse Demand Variation Prediction and Optimization Based on Machine Learning Algorithms 24

Warehouse Demand Variation Prediction and Optimization Based...

引用

4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Lu, Yuting Jomhari, Nazean Liao, Shengshi Goyal, Shyam Bihari Jiangxi College of Applied Technology Jiangxi Ganzhou China Faculty of Information Technology City University Malaysia Kuala Lumpur Malaysia Faculty of Computer Science & Information Technology Universiti Malaya Kuala Lumpur Malaysia Chitkara University Rajpura India

ISBN: (纸本)9798400710865

With the intensification of market competition and the diversification of customer demands, efficient inventory management has become a key factor for the success of enterprises. Warehouse demand forecasting, as a core aspect of inventory management, is of great significance for enterprises to reasonably arrange production, optimize inventory levels, and reduce costs. This paper delves into the prediction and optimization methods of warehouse demand changes based on machine learning algorithms, with a particular focus on three powerful algorithms: Random Forest, XGBoost, and *** LightGBM model performed the best. The lowest MSE can provide a lower prediction error, and the highest R2 value, which is close to 0.786, can better fit the *** research results fully demonstrate that machine learning algorithms have significant application value and potential in the field of predicting changes in warehouse demand. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Propagation of EM Waves Through Biomaterial Based on Harvest Waste - Influence of Biomaterial Density, Moisture and Temperature 33rd

Propagation of EM Waves Through Biomaterial Based on Harvest...

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33rd International Conference on Organization and Technology of Maintenance, OTO 2024

作者： Mandrić, Vanja Rupčić, Slavko Pavković, Bogdan Bogdanović, Nataša Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia Department of Information and Communication Technologies Academy of Applied Technical and Preschool Studies Niš Serbia

ISBN: (纸本)9783031805967

This paper deals with the simulation calculation of the transmission (S21) parameter in a biomaterial calculated parametrically on the basis harvest waste (residues) as a function of the density, moisture and temperature of the material. Since this material is essentially wood, empirical and semi-empirical models are used to determine the electromagnetic parameters of the wood material as a function of the density, moisture and temperature of the material according to the available literature. The structural - geometric and environmental parameters of the material that are the subject of the research are: density, moisture and temperature of the material, and the electromagnetic parameters that "describe" them are: relative permittivity Εr, loss tangent tan δ and electrical conductivity σ. The simulation model is based on a system for measuring coupling parameters in free space, which consists of two antennas, between which a 600 × 600 mm sample of the biomaterial is placed, and a network analyzer. With increasing density and moisture significantly increases the relative dielectric constant, the loss tangent and the electrical conductivity of the measured biosamples. The temperature change in a wide temperature range (20 ℃–100 ℃) has no significant influence on the electromagnetic parameters. The measured results were used to verify the simulation model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electric conductivity

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NPDP Benchmark Suite for Loop Tiling Effectiveness Evaluation 1

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14th International Conference on Parallel Processing and applied Mathematics, PPAM 2022

作者： Palkowski, Marek Bielecki, Wlodzimierz West Pomeranian University of Technology in Szczecin Faculty of Computer Science and Information Systems Zolnierska 49 Szczecin71210 Poland

ISBN: (数字)9783031304453

ISBN: (纸本)9783031304446

The paper introduces ten non-serial polyadic dynamic programming (NPDP) kernels as a benchmark suite dedicated to effectiveness evaluation of tiled code generated by means of polyhedral optimization compilers. Most of the applications implement bioinformatics algorithms which are challenging and ongoing tasks for automatic loop nest tiling transformations. The paper describes mathematically examined kernels and uniformizes them in the form of loop nests presented in the C language. In an experimental study, we applied the two automatic source-to-source compilers, TRACO and PluTo, to generate cache-efficient codes and analysed their performance on three multi-core machines. We discuss the limitations of well-known tiling approaches and outline future tiling strategies for the introduced NPDP Benchmark suite. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Dynamic programming

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RAFA-Net: Region Attention Network for Food Items and Agricultural Stress Recognition

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 121-133页

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani333031 India University of Hradec Kralove Center for Basic and Applied Science Faculty of Informatics and Management Hradec Kralove50003 Czech Republic Skoda Auto University Mlada Boleslav29301 Czech Republic Universiti Teknologi Malaysia Malaysia Japan International Institute of Technology Kuala Lumpur81310 Malaysia Jadavpur University Department of Computer Science and Engineering Kolkata700032 India

Deep convolutional neural networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pairs aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channelwise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed region attention network for food items and agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracy of RAFA-Net is 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net's generalization capability. © 2023 IEEE.

关键词： Plant diseases

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