Article
Computer Science, Artificial Intelligence
Jaume Jordan, Pasqual Marti, Javier Palanca, Vicente Julian, Vicente Botti
Summary: With the increasing sales of electric vehicles, there is a significant lack of infrastructure to support charging, especially in interurban environments. To minimize the economic costs of installing sufficient charging points, this paper proposes using an evolutionary approach to calculate the most suitable locations for electric charging stations in interurban environments.
Article
Computer Science, Information Systems
Ashwani Kumar, Ravinder Kumar, Ashutosh Aggarwal
Summary: This paper presents a solution to the problem of charging convenience for electric vehicles (EVs). By analyzing factors such as the availability and convenience of charging stations, an improved distributed system is proposed to plan energy efficient charging routes. The system also introduces an agile charging slot reservation approach to minimize energy consumption, waiting time, and charging expenditure.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Metallurgy & Metallurgical Engineering
Zheng Xue-qin, Yao Yi-ping
Summary: This study addresses the challenges of large-scale electric vehicles connected to microgrids, proposing a solution through implementing an orderly charging and discharging mode with V2G. Mathematical models and algorithms were used to validate the effectiveness of the proposed mode, showing significant improvements in energy efficiency and system investment reduction compared to disorderly charging and discharging modes.
JOURNAL OF CENTRAL SOUTH UNIVERSITY
(2021)
Article
Engineering, Civil
Tianyang Zhang, Xi Chen, Bin Wu, Mehmet Dedeoglu, Junshan Zhang, Ljiljana Trajkovic
Summary: This paper focuses on the interactions between electric vehicle fleets and charging stations/battery swapping stations, developing a stochastic model and deriving revenue boundaries through simulations. The findings are valuable for future studies in public transportation.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yongqiang He, Yanjun Zhang, Tian Fan, Xingjuan Cai, Yubin Xu
Summary: This paper proposes a many-objective joint site selection model for battery swapping stations and battery centralized charging stations. The model considers construction cost, coverage rate, investment income, and satisfaction as objective functions, aiming to address the issue of not simultaneously considering the needs of enterprises and users in existing site selection models. A Grid-based evolutionary algorithm with a segmented integer coding strategy is utilized to solve the optimization problem. Experimental results demonstrate the reasonableness and effectiveness of the proposed model.
APPLIED INTELLIGENCE
(2023)
Article
Green & Sustainable Science & Technology
Pannee Suanpang, Pitchaya Jamjuntr, Phuripoj Kaewyong, Chawalin Niamsorn, Kittisak Jermsittiparsert
Summary: This paper proposes an intelligent public-accessible charging station framework based on Spatio-Temporal Multi-Agent Reinforcement Learning (STMARL), considering long-term spatio-temporal parameters. The framework aims to reduce the overall charging wait time, average charging price, and charging failure rate for electric vehicles (EVs).
Article
Computer Science, Artificial Intelligence
Zhenzhong Wang, Kai Ye, Min Jiang, Junfeng Yao, Neal N. Xiong, Gary G. Yen
Summary: This study proposes a framework to reuse knee points in a new environment to address the Dynamic Vehicle Routing Problem based on Hybrid Charging Strategy. Reusing knee points helps generate a better initial population and brings convenience to decision makers.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Green & Sustainable Science & Technology
Ines Mehouachi, Mariem Trojette, Khaled Grayaa
Summary: This study proposes an optimal electric vehicle charging station infrastructure based on a Photovoltaic system in a heavily congested and polluted area in Tunis. The proposed multi-objective neural network algorithm facilitates the establishment of the charging stations in optimal locations. The results show that the proposed solution can meet user demands and achieve significant reductions in carbon emissions and costs.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Environmental Studies
Tai-Yu Ma, Simin Xie
Summary: A new online vehicle-charging assignment model is proposed to reduce charging delays in electrified shared mobility services, showing promising results in minimizing charging operation time with an efficiency optimization approach.
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
(2021)
Article
Thermodynamics
Deok Hwan Jeon, Jae Yong Cho, Jeong Pil Jhun, Jung Hwan Ahn, Sinwoo Jeong, Se Yeong Jeong, Anuruddh Kumar, Chul Hee Ryu, Wonseop Hwang, Hansun Park, Cheulho Chang, Hyoungjin Lee, Tae Hyun Sung
Summary: This study presents a novel concept to enhance the energy generation performance of piezoelectric energy harvesters used as charging stations on roads, overcoming the limitations of low electrical output and durability under extremely low road displacement conditions. The proposed lever-type piezoelectric energy harvester achieved significantly higher electrical performance compared to previous studies, with 467% higher output power than vibration type road energy harvesters. The results demonstrate that the energy generated by the proposed harvester can potentially be used as a power source for electric vehicles on smart roads.
Article
Computer Science, Artificial Intelligence
Alessandro Niccolai, Leonardo Bettini, Riccardo Zich
Summary: The study introduces a novel evolutionary-based approach for solving the deployment problem of charging stations, showing high convergence rate and quality of solutions. The proposed method is compared with a greedy optimization in a case study of Milan and proves to be effective and flexible in managing different quality-of-service performance parameters with various Evolutionary Algorithms (EAs).
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Energy & Fuels
Qifu Cheng, Lei Chen, Qiuye Sun, Rui Wang, Dazhong Ma, Dehao Qin
Summary: This paper introduces a DC FCS architecture based on a PV system without ESS to reduce costs and improve GIC utilization. The proposed smart charging algorithm (SCA) can coordinate EV charging needs while maximizing power output of the PV system and utilization of GICs.
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS
(2021)
Review
Energy & Fuels
Muhammad Shahid Mastoi, Shenxian Zhuang, Hafiz Mudassir Munir, Malik Haris, Mannan Hassan, Muhammad Usman, Syed Sabir Hussain Bukhari, Jong-Suk Ro
Summary: This paper discusses key factors in planning electric vehicle charging infrastructure, provides information and technological developments for improving the design and implementation of charging station infrastructure.
Article
Computer Science, Information Systems
Kuntao Li, Weizhong Wang, Hai-Lin Liu
Summary: This paper proposes a 6G shared base stations optimization model to enhance the utilization of infrastructure resources and reduce operator costs in future 6G network construction. The model is a bi-level multi-objective optimization problem, where tower companies handle the base station construction at the upper level, and operators share the tower company's base station resources at the lower level. Additionally, two strategies are proposed to efficiently solve the optimization problem, namely using surrogate models to fit lower-level Pareto fronts and migrating modified populations to accelerate lower-level optimization. The proposed method achieves superior or comparable results with less computation overhead compared to existing works, as confirmed through benchmark problems and generated test instances.
INFORMATION SCIENCES
(2023)
Article
Engineering, Civil
Mahsa Ghavami, Mohammad Haeri, Hamed Kebriaei
Summary: In this paper, the problem of managing traffic and Charging Stations (CSs) crowdedness in the context of Electric Vehicles (EVs) is addressed. The authors propose a non-cooperative game model and a linear pricing policy to optimize the efficiency of EVs' decision strategies. The model considers a hierarchical game between a Smart City Coordinator (SCC) and EVs, where the SCC designs optimal price functions for CSs and Traffic Coordinator (TC) to maximize social profits. The proposed method enables simultaneous and global-optimum management of traffic and CSs' crowdedness, and a decentralized algorithm is introduced to preserve the privacy of EVs.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Chemistry, Multidisciplinary
Jaume Jordan, Javier Palanca, Elena del Val, Vicente Julian, Vicente Botti
APPLIED SCIENCES-BASEL
(2018)
Article
Computer Science, Artificial Intelligence
Jaume Jordan, Stella Heras, Soledad Valero, Vicente Julian
COMPUTATIONAL INTELLIGENCE
(2015)
Article
Computer Science, Information Systems
Stella Heras, Jaume Jordan, Vicente Botti, Vicente Julian
INFORMATION SCIENCES
(2013)
Article
Computer Science, Artificial Intelligence
Stella Heras, Jaume Jordan, Vicente Botti, Vicente Julian
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2013)
Article
Computer Science, Artificial Intelligence
Juan Fdez-Olivares, Eva Onaindia, Luis Castillo, Jaume Jordan, Juan Cozar
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2019)
Article
Chemistry, Multidisciplinary
Jaume Jordan, Javier Bajo, Vicent Botti, Vicente Julian
APPLIED SCIENCES-BASEL
(2019)
Article
Computer Science, Information Systems
Jaume Jordan, Soledad Valero, Carlos Turro, Vicent Botti
Summary: New challenges in education are met with new ways of education, such as massive online open courses and flipped classrooms in higher education. The use of high quality learning objects, particularly learning videos, is crucial for the success of these new educational forms. A hybrid learning recommender system combining content-based techniques and collaborative filtering has been successfully applied to the Universitat Politecnica de Valencia's central video repository.
Article
Computer Science, Information Systems
Pasqual Marti, Jaume Jordan, Fernando De la Prieta, Holger Billhardt, Vicente Julian
Summary: This paper explores demand-responsive shared transportation as a system that aims to reduce pollution and serve users' displacement needs. Unlike previous works, it proposes a distributed proposal that allows vehicles to retain their private information, and describes a partially dynamic system where vehicles make service decisions based on reported benefits.
Article
Computer Science, Artificial Intelligence
Jaume Jordan, Javier Palanca, Pasqual Marti, Vicente Julian
Summary: The increasing adoption of electric vehicles in urban environments requires the establishment of sufficient charging stations. This study utilizes a genetic algorithm to analyze open data sources and propose optimal locations for these stations. Through experiments, the benefits of the proposed solution are demonstrated.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Management
Jaume Jordan, Alejandro Torreno, Mathijs de Weerdt, Eva Onaindia
Summary: FENOCOP is a game-theoretic approach designed to solve non-cooperative planning problems among self-interested agents, aiming to achieve conflict-free plan combinations through coordination. It involves a two-level game approach with a General Game selecting Nash equilibrium and a Scheduling Game introducing delays in plans to generate executable outcomes. Two algorithms are developed for the Scheduling Game to return Pareto optimal and fair equilibriums.
GROUP DECISION AND NEGOTIATION
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Jaume Jordan, Javier Palanca, Elena del Val, Vicente Julian, Vicente Botti
INTERNATIONAL JOINT CONFERENCE SOCO'18-CISIS'18- ICEUTE'18
(2019)
Article
Computer Science, Artificial Intelligence
Jaume Jordan, Alejandro Torreno, Mathijs de Weerdt, Eva Onaindia
APPLIED INTELLIGENCE
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Angelo Costa, Stella Heras, Javier Palanca, Jaume Jordan, Paulo Novais, Vicente Julian
MULTI-AGENT SYSTEMS AND AGREEMENT TECHNOLOGIES, EUMAS 2016
(2017)
Proceedings Paper
Computer Science, Cybernetics
Angelo Costa, Stella Heras, Javier Palanca, Jaume Jordan, Paulo Novais, Vicente Julian
PERSUASIVE TECHNOLOGY: DEVELOPMENT AND IMPLEMENTATION OF PERSONALIZED TECHNOLOGIES TO CHANGE ATTITUDES AND BEHAVIORS, PERSUASIVE 2017
(2017)
Editorial Material
Computer Science, Theory & Methods
Kiho Lim, Christian Esposito, Tian Wang, Chang Choi
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Jesus Carretero, Dagmar Krefting
Summary: Computational methods play a crucial role in bioinformatics and biomedicine, especially in managing large-scale data and simulating complex models. This special issue focuses on security and performance aspects in infrastructure, optimization for popular applications, and the integration of machine learning and data processing platforms to improve the efficiency and accuracy of bioinformatics.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Renhao Lu, Weizhe Zhang, Qiong Li, Hui He, Xiaoxiong Zhong, Hongwei Yang, Desheng Wang, Zenglin Xu, Mamoun Alazab
Summary: Federated Learning allows collaborative training of AI models with local data, and our proposed FedAAM scheme improves convergence speed and training efficiency through an adaptive weight allocation strategy and asynchronous global update rules.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Qiangqiang Jiang, Xu Xin, Libo Yao, Bo Chen
Summary: This paper proposes a multi-objective energy-efficient task scheduling technique (METSM) for edge heterogeneous multiprocessor systems. A mathematical model is established for the task scheduling problem, and a problem-specific algorithm (IMO) is designed for optimizing task scheduling and resource allocation. Experimental results show that the proposed algorithm can achieve optimal Pareto fronts and significantly save time and power consumption.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Editorial Material
Computer Science, Theory & Methods
Weimin Li, Lu Liu, Kevin I. K. Wang, Qun Jin
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Mohammed Riyadh Abdmeziem, Amina Ahmed Nacer, Nawfel Moundji Deroues
Summary: Internet of Things (IoT) devices have become ubiquitous and brought the need for group communications. However, security in group communications is challenging due to the asynchronous nature of IoT devices. This paper introduces an innovative approach using blockchain technology and smart contracts to ensure secure and scalable group communications.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Robert Sajina, Nikola Tankovic, Ivo Ipsic
Summary: This paper presents and evaluates a novel approach that utilizes an encoder-only transformer model to enable collaboration between agents learning two distinct NLP tasks. The evaluation results demonstrate that collaboration among agents, even when working towards separate objectives, can result in mutual benefits.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Hebert Cabane, Kleinner Farias
Summary: Event-driven architecture has been widely adopted in the software industry for its benefits in software modularity and performance. However, there is a lack of empirical evidence to support its impact on performance. This study compares the performance of an event-driven application with a monolithic application and finds that the monolithic architecture consumes fewer computational resources and has better response times.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Haroon Wahab, Irfan Mehmood, Hassan Ugail, Javier Del Ser, Khan Muhammad
Summary: Wireless capsule endoscopy (WCE) is a revolutionary diagnostic method for small bowel pathology. However, the manual analysis of WCE videos is cumbersome and the privacy concerns of WCE data hinder the adoption of AI-based diagnoses. This study proposes a federated learning framework for collaborative learning from multiple data centers, demonstrating improved anomaly classification performance while preserving data privacy.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Maruf Monem, Md Tamjid Hossain, Md. Golam Rabiul Alam, Md. Shirajum Munir, Md. Mahbubur Rahman, Salman A. AlQahtani, Samah Almutlaq, Mohammad Mehedi Hassan
Summary: Bitcoin, the largest cryptocurrency, faces challenges in broader adaption due to long verification times and high transaction fees. To tackle these issues, researchers propose a learning framework that uses machine learning to predict the ideal block size in each block generation cycle. This model significantly improves the block size, transaction fees, and transaction approval rate of Bitcoin, addressing the long wait time and broader adaption problem.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Rafael Duque, Crescencio Bravo, Santos Bringas, Daniel Postigo
Summary: This paper introduces the importance of user interfaces for digital twins and presents a technique called ADD for modeling requirements of Human-DT interaction. A study is conducted to assess the feasibility and utility of ADD in designing user interfaces, using the virtualization of a natural space as a case study.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Xiulin Li, Li Pan, Wei Song, Shijun Liu, Xiangxu Meng
Summary: This article proposes a novel multiclass multi-pool analytical model for optimizing the quality of composite service applications deployed in the cloud. By considering embarrassingly parallel services and using differentiated parallel processing mechanisms, the model provides accurate prediction results and significantly reduces job response time.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Seongwan Park, Woojin Jeong, Yunyoung Lee, Bumho Son, Huisu Jang, Jaewook Lee
Summary: In this paper, a novel MEV detection model called ArbiNet is proposed, which offers a low-cost and accurate solution for MEV detection without requiring knowledge of smart contract code or ABIs.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Sacheendra Talluri, Nikolas Herbst, Cristina Abad, Tiziano De Matteis, Alexandru Iosup
Summary: Serverless computing is increasingly used in data-processing applications. This paper presents ExDe, a framework for systematically exploring the design space of scheduling architectures and mechanisms, to help system designers tackle complexity.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Article
Computer Science, Theory & Methods
Chao Wang, Hui Xia, Shuo Xu, Hao Chi, Rui Zhang, Chunqiang Hu
Summary: This paper introduces a Federated Learning framework called FedBnR to address the issue of potential data heterogeneity in distributed entities. By breaking up the original task into multiple subtasks and reconstructing the representation using feature extractors, the framework improves the learning performance on heterogeneous datasets.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
