Sustainable Mobility: Machine Learning-Driven Deployment Of Ev Charging Points In Dublin,
2024
Technological University Dublin
Sustainable Mobility: Machine Learning-Driven Deployment Of Ev Charging Points In Dublin, Ruairí De Fréin, Alexander Mutiso Mutua Mr
Articles
Electric vehicle (EV) drivers in urban areas face range anxiety due to the fear of running out of charge without timely access to charging points (CPs). The lack of sufficient numbers of CPs has hindered EV adoption and negatively impacted the progress of sustainable mobility. We propose a CP distribution algorithm that is machine learning-based and leverages population density, points of interest (POIs), and the most used roads as input parameters to determine the best locations for deploying CPs. The objects of the following research are as follows: (1) to allocate weights to the three parameters in a $6$ km …
A Novel Reinforcement Learning Method For Efficient Cross-Training Between Real And Simulated Robots,
2024
San Jose State University
A Novel Reinforcement Learning Method For Efficient Cross-Training Between Real And Simulated Robots, Zhonglin Liang
Master's Theses
Training reinforcement learning policy in a simulated environment can be a go-to choice for many research topics, as a simulated environment provides flexibility and costs less than building a physical environment for training. However, policy trained in a simulated environment often fails to transfer to the real environment for problems that have a more complex dynamics. As the solution, Sim2Real was proposed and it categorizes techniques that improve the performance of the transfer from simulation to reality. Sim2Real is an area of study that focuses on utilizing simulation data to train models that can apply to real world environment. It …
An Open Approach To Clinical Study Recruitment Automation,
2024
Nova Southeastern University
An Open Approach To Clinical Study Recruitment Automation, Andrew Bouras, Andrew Bouras, Dhruv Patel, Rahul Kataria
HCA-NSU MD Research Day
Objective: To present an innovative Patient Recruitment Automation System that leverages open standards and promotes open data in clinical research. Background: Patient recruitment for clinical studies faces challenges in efficiency, data security, and regulatory compliance. These issues can delay research timelines and impact healthcare advancements. Methods: We developed a system that integrates with Electronic Health Record (EHR) systems to identify potential participants using predefined criteria. The platform employs Azure-based infrastructure with advanced data protection techniques, ensuring HIPAA compliance. It features an automated screening process using adaptive questionnaires and a communication module for participant engagement. Results: The system demonstrates improved efficiency …
Collapse Of Pre-Covid-19 Differences In Performance In Online Vs. In-Person College Science Classes, And Continued Decline In Student Learning,
2024
University of Mississippi
Collapse Of Pre-Covid-19 Differences In Performance In Online Vs. In-Person College Science Classes, And Continued Decline In Student Learning, Gregg R. Davidson, Hong Xiao, Kristin Davidson
Faculty and Student Publications
Abstract: Studies comparing student outcomes for online vs. in-person classes have reported mixed results, though with a majority finding that lower-performing students, on average, fare worse in online classes, attributed to the lack of built-in structure provided by in-person instruction. The online/in-person outcome disparity was normative for non-major geology classes at the University of Mississippi prior to COVID-19, but the difference disappeared in the years after 2020. Previously distinct trendlines of GPA-based predictions of earned-grade for online and in-person classes merged. Of particular concern, outcomes for in-person classes declined to match pre-COVID-19 online expectations, with lower-GPA students disproportionally impacted. Objective …
Platform Path Optimization Method Based On Cumulative Detection Probability Of Sonar Search,
2024
PLA Naval Submarine Academy, Qingdao 266000, China
Platform Path Optimization Method Based On Cumulative Detection Probability Of Sonar Search, Xiang Wei, Xingxuan Liu, Dianzheng Fu, Tianji Yang, Jiaxuan Yang
Journal of System Simulation
Abstract: To address the lack of research on the optimal path of mobile search platform to search for moving targets, this paper proposes a path optimization method of mobile search platform based on cumulative search probability theory. Based on the cumulative detection probability (CDP), one of the important criteria of sensor performance evaluation, a single-peak CDP calculation formula is constructed by using a time series correlation model, namely the (λ, σ) process model. A set of target motion scenarios are constructed, and the trajectory probability of target scenarios and their CDP at different time are corrected by Bayesian posterior probability. …
Modeling For Decision Support Of Flight Ground Support Process,
2024
College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
Modeling For Decision Support Of Flight Ground Support Process, Zhiwei Xing, Ruiwen Yu, Biao Li, Zhaoxin Chen
Journal of System Simulation
Abstract: Aiming at the problems of insufficient decision-making ability and low operational efficiency of the flight ground support process, a decision support model of the flight ground support process based on the department of defense architecture framework (DoDAF) is proposed. Starting from the support operation, support resources, and the relationship between them, the quantitative description of the flight ground support process is performed. DoDAF and the model-based systems engineering (MBSE) modeling method are combined to establish a decision support model of the flight ground support process. The decision utility function is established to analyze the utility value of the comprehensive …
End-To-End Motion Planning Of Unmanned Vehicles Based On Multimodal Deep Reinforcement Learning,
2024
School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China; Xinjiang Key Laboratory of Signal Detection and Processing, Urumqi 830017, China
End-To-End Motion Planning Of Unmanned Vehicles Based On Multimodal Deep Reinforcement Learning, Kaiyuan Ding, Askar Hamdulla, Bin Zhu, Eksan Firkat, Zhengtang Ma
Journal of System Simulation
Abstract: Since the agent cannot sense the surrounding environment and cannot successfully avoid obstacles, reinforcement learning fails to be generalized to robot motion planning in difficult terrain. Therefore, a solution based on multimodal deep reinforcement learning, which learns to blend proprioceptive states with high-dimensional depth sensor inputs, is proposed for the motion planning of unmanned vehicles. To be specific, proprioceptive states offer contact measurement for immediate reaction, and the unmanned vehicle can learn and forecast environmental changes with its attached visual sensors, proactively navigating around obstacles and uneven terrains numerous time steps ahead. TransProAct (transformer-based proactive action), a unique end-to-end …
Flexible Job Shop Scheduling Method Based On Collaborative Agent Reinforcement Learning Algorithm,
2024
School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471000, China; Henan Collaborative Innovation Center for Advanced Manufacturing of Mechanical Equipment, Luoyang 471000, China
Flexible Job Shop Scheduling Method Based On Collaborative Agent Reinforcement Learning Algorithm, Jian Li, Huankun Li, Pengbo He, Huabei Wang, Liping Xu, Kui He
Journal of System Simulation
Abstract: To enhance the efficiency of flexible job shop scheduling, this paper develops a Markov decision process with specific constraints tailored to the scheduling problem. A cooperative agent reinforcement learning method is proposed to solve the problem of concurrent selection of workpieces and machines. During the construction of the Markov decision process, a disjunctive graph is introduced to represent the state characteristics. Two agents are introduced to select the workpieces and machines. The reward parameters governing the entire scheduling process are established by predicting variations in the minimum-maximum completion time across different time points. A GIN(graph isomorphic network) graph neural …
Dual-Resource Constrained Distributed Flexible Scheduling For Aerospace Structural Components,
2024
School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China; Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China; Jiangsu Engineering Research Center on Meteorological Energy Using and Control, Nanjing 210044, China
Dual-Resource Constrained Distributed Flexible Scheduling For Aerospace Structural Components, Yufang Wang, Dianqing Zhang, Xiaolin Hua, Binbin Yao, Fan Chen
Journal of System Simulation
Abstract: A dual-resource constrained distributed flexible job-shop scheduling model was established by taking into account the worker constraints of the finishing process and the requirements of distributed multi-factory collaboration in the production of aerospace structural components. A hybrid grey wolf optimization algorithm based on the critical factory was proposed to solve this problem. The model contained four subproblems: factory selection, operation sequencing, machine selection, and worker selection. In view of these four sub-problems, a four-layer coding and a new decoding method were designed to avoid the use conflict of machines and workers. In addition, a new mechanism for hunting and …
Automatic Detection Algorithm For Typical Defects Of Substation Based On Improved Yolov5,
2024
NR Electric Co. , Ltd. , Nanjing 211102, China
Automatic Detection Algorithm For Typical Defects Of Substation Based On Improved Yolov5, Zhongkai Xu, Yanling Liu, Xiaojuan Sheng, Chao Wang, Wenjun Ke
Journal of System Simulation
Abstract: In response to the challenges present in the context of defect recognition in substations, such as complex substation defects and sample imbalance, an improved YOLOv5 algorithm was proposed. The Transformer model was introduced into the YOLOv5 network structure, leveraging the self-attention mechanism to capture long-range dependencies among features. A focal loss-based optimization was employed to improve the loss function, as well as the detection accuracy and robustness of defects of small sample substations. To meet the requirements of substation defect recognition, a dedicated dataset was constructed. A clustering algorithm was applied to the real annotation boxes to generate more …
Object Detection Of Lightweight Transformer Based On Knowledge Distillation,
2024
College of Artificial Intelligence, Tianjin University of Science & Technology, Tianjin 300457, China; Hubei Key Laboratory of Optical Information and Pattern Recognition, Wuhan Institute of Technology, Wuhan 430205, China
Object Detection Of Lightweight Transformer Based On Knowledge Distillation, Gaihua Wang, Kehong Li, Qian Long, Jingxuan Yao, Bolun Zhu, Zhengshu Zhou, Xuran Pan
Journal of System Simulation
Abstract: In autonomous driving, the efficiency and accuracy of object detection are significant. Object detection based on Transformer structure has gradually become the mainstream method, eliminating the complex anchor generation and non-maximum suppression (NMS). It has problems of high computing cost and slow convergence. An object detection model of the based lightweight pooling transformer (LPT) is designed, which contains a pooling backbone network and dual pooling attention mechanism. A general knowledge distillation method is intended for the DETR (detection transformer) model, which transfers prediction results, query vector, and features extracted by the teacher as knowledge to the LPT model to …
A Hybrid Genetic Search Algorithm For Capacitated Electric Vehicle Routing Problem,
2024
School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China; Hunan Key Laboratory for Service Computing and Novel Software Technology, Xiangtan 411201, China
A Hybrid Genetic Search Algorithm For Capacitated Electric Vehicle Routing Problem, Dongyao Jin, Mi Liu, Yena Zhu, Yijiang Zhao
Journal of System Simulation
Abstract: The capacitated electric vehicle routing problem (CEVRP) is an NP-hard combinatorial optimization problem in logistics distribution, aiming to minimize the total delivery distance of electric vehicles while satisfying carrying capacity and battery charge constraints. A hybrid genetic search algorithm is proposed to solve CEVRP by decomposing it into two subproblems: capacitated vehicle routing problem (CVRP) and fixed-route vehicle charging problem (FRVCP). A coding scheme with a two-layer chromosome structure is designed to represent the decision variables of these two subproblems. A Split operation is employed to generate vehicle routes for solving CVRP, and five neighborhood search operators, including Relocate, …
Traffic Sign Recognition Model With Long-Tail Distribution Based On Yolox-Tiny,
2024
Hubei University, Wuhan 430000, China
Traffic Sign Recognition Model With Long-Tail Distribution Based On Yolox-Tiny, Yunpeng Wu, Yingxiong Fu, Lijun Shen, Feng Cui
Journal of System Simulation
Abstract: Accurate recognition of traffic signs plays an important role in the field of intelligent driving. Traffic sign training datasets with long-tail distribution increase the difficulty of traffic sign recognition. A traffic sign recognition model with long-tail distribution based on YOLOX-Tiny was proposed to improve the poor performance of the model trained on long-tail distribution datasets. A long-tail traffic sign dataset was created based on the TT100K_2021 (tsinghua-tencent 100K 2021) dataset. YOLOX-Tiny was chosen as the underlying model by considering picture numbers in datasets, sample distribution, and model size. Equalization loss v2 (EQL v2) was used as classification loss to …
Real-Time Lidar Slam Algorithm Based On Distribution Optimal Registration,
2024
Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China; School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
Real-Time Lidar Slam Algorithm Based On Distribution Optimal Registration, Weigang Li, Chuxiang Yu, Yongqiang Wang, Shaofeng Zou
Journal of System Simulation
Abstract: When scanning the surrounding environment, a lidar will generate some cluttered and sparse point cloud, which will cause excessive distribution fitting errors and correlation distances in the registration process, thus affecting the accuracy of the registration algorithm and the effect of simultaneous localization and mapping (SLAM). To address this problem, a real-time lidar SLAM algorithm based on distribution optimal registration is proposed. An eigenspectrum filter is designed, which takes the normalized minimum eigenvalue as the filtering object to filter out the points that do not match the set distribution in order to reduce the distribution fitting error. Secondly, a …
Research On Green Job Shop Scheduling Based On Herd Immunity Optimizer,
2024
School of Information Engineering, Ningxia University, Yinchuan 750021, China
Research On Green Job Shop Scheduling Based On Herd Immunity Optimizer, Xunde Ma, Li Bi, Junjie Wang
Journal of System Simulation
Abstract: In view of the green flexible job shop scheduling problem where machines have multiple speeds, a green flexible job shop scheduling model under multiple speeds was constructed to minimize the makespan and total energy consumption under different speeds. A discrete coronavirus herd immunity optimizer (DCHIO) was proposed for a solution. A discrete individual updating method was introduced for the relatively large solution space of the multi-speed problem, based on which a population updating mechanism with multi-scale joint search was proposed to search the solution space quickly and uniformly. A dynamic mutation operation was designed to enhance the population diversity …
Global-Local Fusion For Efficient 3d Object Detection,
2024
Department of Computer, North China Electric Power University, Baoding 071003, China; Key Laboratory of Energy and Electric Power Knowledge Calculation in Hebei Province, Baoding 071003, China
Global-Local Fusion For Efficient 3d Object Detection, Bin Lu, Minghan Wang, Yang Sun, Zhenyu Yang
Journal of System Simulation
Abstract: As the 3D object detection based on point clouds shows an incapacity of feature extraction and incongruity between classification and regression, this research introduces a novel ResCST architecture based on the SECOND network. It incorporates residual connections into the 3D sparse convolutional layer, with the advantages of capturing long-distance dependent relation by SwinTransformer and obtaining local features by convolutional neural network integrated, proposing the CNN-SwinTransformer hybrid model for enhanced feature extraction. It introduces the RCIoU method for the joint optimization of classification and regression tasks. The experimental results show that the model achieves a 3D detection accuracy of 91.21%, …
Optimization Of Crucial Targets For Air Defense Based On Combined Weighting-Topsis Model,
2024
College of Field Engineering, Army Engineering University of PLA, Nanjing 210000, China
Optimization Of Crucial Targets For Air Defense Based On Combined Weighting-Topsis Model, Peng Zhang, Ke Feng
Journal of System Simulation
Abstract: To optimize the selection of crucial defended targets in regional air defense operations and improve the selection accuracy, this paper constructs a factor model considering the importance of air defense targets from the perspectives of target value, defense urgency, target vulnerability, and target recovery. Under the optimization of the TOPSIS method through a combination of the ANP and entropy weight methods, tendentious opinions of commanders and the excessive reliance on objective data can be overcome to ensure the factor weighting is more reasonable and accurate; Super Decisions is used to calculate the weights of the ANP method, accelerating data …
Eecbs Multi-Robot Path Planning Based On Variable Suboptimal Factors Of Prioritizing Conflicts,
2024
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
Eecbs Multi-Robot Path Planning Based On Variable Suboptimal Factors Of Prioritizing Conflicts, Xingyu Yan, Niya Wang, Jianlin Mao, Zhigang He, Dayan Li
Journal of System Simulation
Abstract: In the process of multi-robot path planning (MRPP), the unavoidable key conflicts between the optimal paths have a significant impact on the efficiency of path solving. To address this issue, an MRPP method based on variable suboptimal factors of prioritizing conflicts (PC) was proposed. Path search was performed at the lower layer of the explicit estimation conflict-based search (EECBS) algorithm; in the upper layer of the EECBS algorithm framework, the PC was determined, and the suboptimal factor of the robot with key conflicts was adaptively increased; by analyzing the distribution of obstacles in the surrounding neighborhoods of key conflicts, …
Slam Dynamic Algorithm Based On Improved Feature Description,
2024
Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China; School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China
Slam Dynamic Algorithm Based On Improved Feature Description, Qiang Fu, Xianyun Teng, Yuanfa Ji, Fenghua Ren
Journal of System Simulation
Abstract: The original ORB descriptor algorithm has a low matching accuracy and long matching time, the positioning accuracy and robustness of the SLAM(simultaneous localization and mapping) system are severely disturbed by moving objects in dynamic scenes, and the ORB-SLAM3 system is incapable of constructing dense maps. To address the above problems, this paper proposes an improved ORB-SLAM3 based on the BEBLID descriptor and object detection. A lightweight YOLOv5s dynamic object detection network and dynamic feature removal module are fused with the tracking thread to improve the system's positioning accuracy. Replacing the original feature description algorithm, an improved local image descriptor …
Agv Scheduling Problem At Automated Terminals Based On Improved Dqn Algorithm,
2024
Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
Agv Scheduling Problem At Automated Terminals Based On Improved Dqn Algorithm, Chengji Liang, Shidong Zhang, Yu Wang, Bin Lu
Journal of System Simulation
Abstract: A future tasks considering deep Q-network (F-DQN) algorithm was proposed to output realtime scheduling results of automated guided vehicles (AGVs) at automated terminals. This algorithm combined the advantages of real-time scheduling and static scheduling, improving the system status by considering static future task information when making real-time decisions, so as to obtain a better scheduling solution. In this study, the actual layout and equipment conditions of the Yangshan phase IV automated terminal were considered, and a series of simulation experiments were conducted using the Plant Simulation software. The experimental results show that the F-DQN algorithm can effectively solve the …
