Robot Path Planning Optimization Based On Fusion Of Improved Ant Colony Algorithm And A* Algorithm,
2025
School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610000, China
Robot Path Planning Optimization Based On Fusion Of Improved Ant Colony Algorithm And A* Algorithm, Lanying Yang, Chao Li, Haifeng Zou, Jiangtao Wan, Renqiang Zhang, Hui Liu, Hong Lu
Journal of System Simulation
Abstract: To improve slow search efficiency and achieve real-time obstacle avoidance in traditional ant colony algorithms, an adaptive ant colony algorithm was proposed. A guidance direction mechanism was introduced to shorten the time of node selection. The A* algorithm's path-finding mechanism was introduced into the heuristic function to reduce the length and number of circles of the optimal path solution. The route planned by the traditional A* algorithm was used as the initial iteration data of the ant colony algorithm in global path planning, so as to solve the problem of slow initial convergence of the ant colony algorithm. The …
Optimization Of Service Caching And Computation Offloading In Digital Twin Cloud-Edge Networks,
2025
College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China; Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou 350002, China; Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University), Fuzhou 350116, China
Optimization Of Service Caching And Computation Offloading In Digital Twin Cloud-Edge Networks, Jiayu Zheng, Zhuxue Mai, Zheyi Chen
Journal of System Simulation
Abstract: In mobile edge computing (MEC), to satisfy diverse user demands by jointly optimizing service caching and computation offloading and address low-efficiency resource utilization caused by irrational resource allocation, this paper proposed a novel joint optimization of service caching and computation offloading with a convex-optimization-enabled deep reinforcement learning (JCO-CR) method. Additionally, a new model for digital twin cloud-edge networks (DTCEN) was constructed. The joint optimization of service caching and computation offloading was decoupled into two sub-problems, which were solved by an improved deep reinforcement learning method and convex optimization theory, respectively. Simulation experiments demonstrate that the proposed JCO-CR method …
Research On Cgf-Oriented Natural Language Interaction Framework,
2025
Hunan Institute of Advanced Technology, Changsha 410205, China
Research On Cgf-Oriented Natural Language Interaction Framework, Xinmeng Li, Kai Xu, Yue Hu, Hesong Huang, Quanjun Yin
Journal of System Simulation
Abstract: To address the mismatch between existing natural language interaction frameworks and training tasks in simulation-based military training, which limits smooth interaction between trainees and Computer Generated Forces (CGF), this paper proposes a Natural Language Interaction framework for Computer Generated Forces (NLI4CGF). The framework analyzes the logic and functional requirements of natural language interaction between trainees and CGF, and establishes an interaction architecture tailored for military simulation training scenarios. It supports semantic parsing and knowledge query tasks within a prototype system developed for infantry squad simulation training. Experimental results demonstrate that the proposed model performs effectively, meets the requirements of …
Self-Calibrating Passenger Flow Simulation And Spatial Optimization For Public Building Based On Gru-Sa,
2025
Polytechnic Institute, Zhejiang University, Hangzhou 310015, China; Shanghai Construction No. 4 (Group) Co. , Ltd. , Shanghai 201103, China
Self-Calibrating Passenger Flow Simulation And Spatial Optimization For Public Building Based On Gru-Sa, Jinglin Xu, Qianru Chen, Yang Peng, Fangqiang Yu
Journal of System Simulation
Abstract: Real-time and precise passenger flow simulation provides critical data support for the optimal allocation of resources in public building facilities and the rational design of spatial layouts. This study proposed a self-calibrating passenger flow simulation and spatial optimization method for public buildings based on the GRU-simulated annealing algorithm. A simulation model incorporating spatial structures and flow lines was constructed using Anylogic. A self-calibrating passenger flow simulation method for public buildings was designed based on the GRU-simulated annealing algorithm and applied to the outpatient department of a hospital in Shanghai for passenger flow simulation. The effectiveness of the method was …
Cooperative Guidance Method Considering Impact Time And Area Sealing,
2025
School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China
Cooperative Guidance Method Considering Impact Time And Area Sealing, Zheng Guo, Guofei Li, Hua Xiong, Yunjie Wu
Journal of System Simulation
Abstract: To address the problem of multi-vehicle cooperative strike against maneuvering targets, a cooperative guidance method considering impact time control and terminal area sealing was proposed. The distributed disturbance observer was utilized to estimate target maneuvers. Based on the consensus errors of the impact time, the cooperative guidance law in the line-of-sight direction was proposed to achieve simultaneous hits on targets at a specified time. By considering the motion states of targets, the instructions of the terminal area sealing were designed to construct the sliding mode surface and design the line-of-sight guidance law, so as to ensure the convergence of …
Modeling Synaptic Dysfunction As Neural Contagion: A Graph-Based Sedr Framework For Simulating Signal Spread,
2025
Osbourn Park High School
Modeling Synaptic Dysfunction As Neural Contagion: A Graph-Based Sedr Framework For Simulating Signal Spread, Michelle Marfo, Dr. Padmanabhan Seshaiyer, Alonso Ogueda-Oliva
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Modeling The Cancer Cell Growth Predictions Based On Classical Mathematical Models With Physics-Informed Neural Network,
2025
Jarvis Christian University
Modeling The Cancer Cell Growth Predictions Based On Classical Mathematical Models With Physics-Informed Neural Network, Widodo Samyono
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Quantification Of Parameters To Predict The Rupture Of Intracranial Saccular Aneurysms Using Physics Informed Neural Networks,
2025
George Mason University
Quantification Of Parameters To Predict The Rupture Of Intracranial Saccular Aneurysms Using Physics Informed Neural Networks, Alonso Gabriel Ogueda, Padmanabhan Seshaiyer
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Modeling The Probability Of N Clonal Rosettes In A Bromeliaceae Genetic Individual,
2025
Rhodes College
Modeling The Probability Of N Clonal Rosettes In A Bromeliaceae Genetic Individual, Erin N. Bodine, Layla K. Lammers
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Shifted Frequency Analysis Hybrid Simulation Algorithm Based On Multi-Rate Asynchronous Coordination,
2025
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Shifted Frequency Analysis Hybrid Simulation Algorithm Based On Multi-Rate Asynchronous Coordination, Yankan Song, Libin Wen, Ying Chen, Jinji Xi, Haoyuan Zhang, Li Xiong
Journal of System Simulation
Abstract: Large-scale AC/DC power systems exhibit complex dynamics across multiple time scales, and existing hybrid simulations suffer from interface delays and frequency losses during multi-rate coordination, compromising accuracy. To address this issue, a multi-rate asynchronous coordination method was proposed to construct hybrid simulations using shifted frequency analysis (SFA). Within the multi-area Thevenin equivalence (MATE) framework, the algorithm introduced an interpolation-based asynchronous coordination mechanism, effectively eliminating interface delays; by extending SFA theory and designing a universal interface model, it achieved lossless data exchange between partitions with different rates and model types. Case studies on an AC/DC test system demonstrate that …
Path Planning Of Improved Rrt Algorithm Based On Deep Reinforcement Learning,
2025
College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, China
Path Planning Of Improved Rrt Algorithm Based On Deep Reinforcement Learning, Xiuman Liang, Ziliang Liu, Zhendong Liu
Journal of System Simulation
Abstract: To address the low planning efficiency, poor safety, and limited practicability of the RRT algorithm in global path planning within complex three-dimensional environments, which fail to meet the requirements of planning the safe flight path of UAVs, an improved SAC-RRT algorithm was proposed, which fused SAC deep reinforcement learning algorithm and RRT algorithm. A target point bias strategy and a dynamic step size based on the SAC decision-making network were designed to reduce the blindness of RRT. A random point correction process was designed to optimize the position of random points based on actions from the decision network and …
Multisource Information Fusion Method For Human Gait Perception,
2025
School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
Multisource Information Fusion Method For Human Gait Perception, Guiliang Chen, Guowei Liu, Yongchao Li, Chao Cai, Zihao Li, Dong Yang
Journal of System Simulation
Abstract: In response to the insufficient gait perception capability during lower limb exoskeleton assistance, a human lower limb gait phase optimization classification model was proposed. A wireless transmission gait information collection system was designed for collecting the required gait phase feature information. Human joint angles were accurately calculated by fusing acceleration and angular velocity information using extended Kalman filtering. Additionally, kernel principal component analysis was applied to reduce dimensionality in conjunction with plantar pressure data. The LSSVM algorithm was employed to classify gait data, and the PSO algorithm was utilized to find the optimal classification parameters. Experimental results demonstrate that …
Distributed Dispatch Method For Distribution Network And Microgrid Considering Diverse Regulating Resources,
2025
State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Beijing 102206, China
Distributed Dispatch Method For Distribution Network And Microgrid Considering Diverse Regulating Resources, Yanbo Chen, Jiahao Yin, Tuben Qiang, Haoxin Tian, Yuxin Liang, Zhi Zhang
Journal of System Simulation
Abstract: Traditional centralized optimization-based dispatch methods for distribution networks struggle to balance the interests of multiple stakeholders while ensuring economic efficiency and operational reliability of the system. To address this issue, a distributed dispatch method for distribution network and microgrid considering diverse regulating resources was proposed. The operational models of the distribution network and microgrid were established by comprehensively incorporating active management elements and demand response mechanisms. A fuzzy chance-constrained method was employed to model the uncertainty in renewable energy output, thereby constructing a coordinated optimization dispatch model for distribution network and microgrid under renewable generation uncertainty. An improved goal …
Deep Learning Modeling Of Multi-Scale Characteristics Of Large-Scale Wind Turbine Gearbox,
2025
State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China; School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Deep Learning Modeling Of Multi-Scale Characteristics Of Large-Scale Wind Turbine Gearbox, Yang Hu, Zihao Li, Deyi Fu, Ziqiu Song, Fang Fang, Jizhen Liu
Journal of System Simulation
Abstract: To address challenges in characterizing high-frequency vibrations of wind turbine gearboxes, the long computation time of rigid-flexible coupled multi-body dynamics models, and the complexity of configuring gearbox models across multiple scenarios, this study proposed a deep learning modeling method for multi-scale operation using full-condition digital testing. The study proposed a cascaded extended simulation scheme based on stream data-driven OpenFAST and Adams and utilized dynamic mode decomposition technology to construct a multi-scale dataset for the flexible multi-body dynamics characteristics of the gearbox under all operating conditions of the wind turbine. Based on this dataset, a digital surrogate model covering multiple …
Optimal Scheduling Of Integrated Energy Systems Considering Source-Load Uncertainty And Linear Carbon Trading,
2025
College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Optimal Scheduling Of Integrated Energy Systems Considering Source-Load Uncertainty And Linear Carbon Trading, Huaping Zhong, Yubo Fan, Jijun Shui, Danhao Wang, Daogang Peng
Journal of System Simulation
Abstract: In order to overcome the impact of source-load uncertainty on the scheduling of integrated energy systems (IES) and reflect the flexibility of the carbon trading price with the change in trading volume, an optimal scheduling method for integrated energy systems considering source-load uncertainty and linear carbon trading was proposed. The equipment within the IES was modeled, and nonparametric kernel density estimation was used to obtain the probability density function for each time period, generating the set of scenes through Monte Carlo simulation and calculating the probability of each scene. For the time shift of wind and solar output peaks …
Optimization Dispatch Method For High-Proportion Renewable Energy Power Systems Based On Sc-Ppo,
2025
NR Electric Co. , Ltd. , Nanjing 211102, China
Optimization Dispatch Method For High-Proportion Renewable Energy Power Systems Based On Sc-Ppo, Zhongkai Xu, Chenyang Chu, Kai Xie, Ruizhuo Zhao, Wenjun Ke
Journal of System Simulation
Abstract: The high proportion of renewable energy integration brings significant challenges of randomness, multi-objective coupling, and security constraints to power systems. Traditional model-driven methods have limitations in modeling accuracy and adaptability. To address these issues, this paper proposed a safety-constrained PPO algorithm (SC-PPO). The method included three improvements. A temporal convolutional network was utilized to construct a dynamic state encoder that integrated historical operation, real-time monitoring, and prediction data to form a causal state representation. A hierarchical reward structure was designed, and an adaptive weighting mechanism based on constraint satisfaction degree was introduced to coordinate multi-objective optimization. Physical constraint projection …
Microsimulation Of Infectious Disease Transmission Considering Virus Release, Transmission, And Action,
2025
Business School, University of Shanghai for Science & Technology, Shanghai 200093, China; School of Intelligent Emergency Management, University of Shanghai for Science & Technology, Shanghai 200093, China
Microsimulation Of Infectious Disease Transmission Considering Virus Release, Transmission, And Action, Zhiming Fang, Shengdong Yuan, Ge Huang, Jingqian Yang, Zhongyi Huang
Journal of System Simulation
Abstract: Existing infection risk assessment methods mostly evaluate infection probability through mathematical models or simulation, but they lack analysis of the relationship between air circulation and individual infection probability. This study proposed a risk prediction model for infectious disease transmission based on indoor air circulation. At the microscopic scale, the space was discretized into grid points. By integrating CFD numerical simulation, the entire process of virus droplet release, transmission, and action was fully simulated. The simulation results show that in an obstacle-free room, the error between the total indoor viral load predicted by the model under windless and low wind …
Path Planning For Mobile Robots Based On Improved Rrt-Connect And Dwa Fusion,
2025
School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Path Planning For Mobile Robots Based On Improved Rrt-Connect And Dwa Fusion, Yi Luo, Jia Deng
Journal of System Simulation
Abstract: To improve the efficiency and quality of dynamic path planning for mobile robots in complex environments, this paper proposed a path planning algorithm that combined an improved RRT-connect with the DWA. Two expanding random trees were introduced for alternating expansion, and a dynamically restricted sampling area was set to reduce the randomness of the sampling process while ensuring the probability completeness of the algorithm. A target bias adaptive step size strategy was employed to enhance the target orientation of the random tree expansion process. A greedy strategy was adopted to prune redundant nodes in the path and smooth the …
Modeling And Simulation Of Traffic Signal Control Based On Mlp With Improved Gcn-Td3,
2025
School of Electrical Engineering, Xinjiang University, Urumqi 830017, China
Modeling And Simulation Of Traffic Signal Control Based On Mlp With Improved Gcn-Td3, Deqi Huang, Yating Tu, Zhenhua Zhang, Xin Guo
Journal of System Simulation
Abstract: To address the issues of uneven traffic flow at urban intersections, limited road capacity, and the poor coordination of existing traffic signal control algorithms, a traffic signal control algorithm based on graph convolutional reinforcement learning was proposed. By utilizing a multilayer perceptron, the dynamic features of vehicles and phase information at the controlled intersection and its neighboring intersections were extracted. A graph convolutional neural network was then employed to aggregate these vehicle dynamic features into potential features representing regional traffic. The control strategy was derived through multiple iterations of an improved twin delayed deep deterministic policy gradient (TD3) algorithm. …
Soft Sensor Modeling Based On Improved Transformer In Dual-Stream Framework,
2025
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
Soft Sensor Modeling Based On Improved Transformer In Dual-Stream Framework, Hao Gu, Jiayu Wang, Weili Xiong
Journal of System Simulation
Abstract: Industrial process information is highly nonlinear and dynamic, with long-term dependencies between data, making it difficult to adequately extract time-series features. To address this issue, an improved Transformer-based soft sensor model in a dual-stream framework was proposed. The data were segmented and expanded. The features were extracted in parallel using a dual-stream structure combining a convolutional neural network with a self-attention mechanism and the improved Transformer model. The dual-stream features were fused for soft sensor regression. Residual connections were further introduced to accelerate the convergence speed of the model, and an orthogonal random features-based improved multi-head attention mechanism was …
