Design And Verification Of Display And Control System Based On Mbse And Vaps For Civil Helicopter,
2025
China Helicopter Research and Development Institute, Jingdezhen 333000, China
Design And Verification Of Display And Control System Based On Mbse And Vaps For Civil Helicopter, Xi Cao, Bo Liu, Bingzhi Su, Tao Nie
Journal of System Simulation
Abstract: Aiming at the challenges of difficulties in tracing requirements, detecting interaction design defects, and achieving early system design verification, this paper proposes a design and verification for the display and control system (DCS) of civil helicopters based on model-based systems engineering (MBSE) and VAPS. The method begins with capturing stakeholder requirements to form system requirements, followed by the allocation of these requirements to system use cases. Black-box activity diagrams and sequence diagrams are constructed to conduct "requirement-function analysis" from the top down, describing the functional flow of the DCS. A running black-box statechart diagram is further established to verify …
Cae Simulation Optimization Method Based On Dynamic Coupling Model,
2025
Institute of Software, Chinese Academy of Sciences, Beijng 100190, China
Cae Simulation Optimization Method Based On Dynamic Coupling Model, Xue Chen, Jianwen Cao
Journal of System Simulation
Abstract: In order to solve the optimization problem of designing complex equipment under multi-factor coupling scene, a CAE simulation optimization method based on dynamic coupling model and multibranch parallel inference strategy is proposed. The dynamic hierarchical DEVS model is used to construct the automatic coupling model from pre-processing, numerical solution and post-processing phases of CAE software adaptively. Aiming at the key parameters of CAE model, multi-branch instance models with multi-factor constraints are constructed based on greedy algorithm. The multi-task parallel inference strategy is used to compute the multi-branch simulation results efficiently. The scheme optimization is realized based on the evaluation …
Vibration Simulation And Multivariate Statistical Analysis Method Of Composite Structures,
2025
Science and Technology on Space Physics Laboratory, Beijing 100076, China
Vibration Simulation And Multivariate Statistical Analysis Method Of Composite Structures, Bo Guo, Ming Tie, Wenhui Fan
Journal of System Simulation
Abstract: To investigate the natural frequency characteristics of composite laminates under parametric uncertainties and the different degree of influence of these parameters on the natural frequency under different boundary conditions and different vibration orders, a two-dimensional anisotropic medium-thick plate material model and a three-dimensional anisotropic cylindrical thin-shell material vibration model are established. Aiming at the uncertainty of structural parameters of these composite materials, the composite material vibration simulation and multivariate statistical analysis software are developed to simulate the structural vibration of composite materials. A multivariate statistical analysis method for natural frequency uncertainty of composite materials is presented. Through principal component …
An Intelligent Ambulance Regulation Model Based On Online Reinforcement Learning Algorithm,
2025
Joint Operations College, National Defence University, Beijing 100000, China; Joint Logistics College, National Defence University, Beijing 100858, China
An Intelligent Ambulance Regulation Model Based On Online Reinforcement Learning Algorithm, Lei Zhang, Xuechao Zhang, Chao Wang, Xianglei Bo
Journal of System Simulation
Abstract: In emergency scenarios where ambulances are used to evacuate casualties, it is necessary to fully coordinate the rescue capability of the ambulance with the real-time status of the casualties in the scenario to achieve the best rescue results. Such problems are generally non-deterministic polynomial problems, and the traditional deterministic scheduling algorithms are less effective. This paper aimed at the modeling research of the real-time regulation of ambulances in emergency scenarios, an online reinforcement learning DNQ algorithm frameworks based on the data enhancement method is proposed and applied to the solution of the ambulances control model. To solve the problems …
Research On Pedestrian Avoidance Strategy For Agv Based On Deep Reinforcement Learning,
2025
School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110000, China; Joint International Research Laboratory of Modern Construction Engineering Equipment and Technology, Shenyang Jianzhu University, Shenyang 110000, China
Research On Pedestrian Avoidance Strategy For Agv Based On Deep Reinforcement Learning, He Wang, Jianing Xu, Guangyu Yan
Journal of System Simulation
Abstract: To ensure the safety and comfort of pedestrians during Automated Guided Vehicle (AGV) obstacle avoidance in smart factory environments, a deep reinforcement learning-based end-to-end obstacle avoidance method is proposed. The YOLOv8 module is introduced to extract pedestrian pose information, and a visual-based state space is designed. A reinforcement learning mechanism is formulated based on personal space theory, penalizing AGV behaviors such as entering pedestrian comfort space and collisions. A virtual simulation system is constructed, utilizing PPO algorithm along with LSTM network layer for obstacle avoidance strategy training and simulation experiments. Simulation results indicate that this obstacle avoidance strategy, under …
City Regional Traffic Flow Prediction Based On Spatiotemporal Multi-View Attention Residual Network,
2025
School of Information Technology and Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
City Regional Traffic Flow Prediction Based On Spatiotemporal Multi-View Attention Residual Network, Jing Chen, Guowei Yang, Zhaochong Zhang, Wei Wang
Journal of System Simulation
Abstract: However, efficiently and comprehensively capturing the complex spatiotemporal correlations within urban traffic flow presents a key challenge. Existing research methods struggle to fully capture these spatiotemporal dependencies. To address these issues, we propose a novel end-to-end deep learning framework called the spatiotemporal multi-view attention residual network (ST-MVAR) for predicting traffic flow in urban areas. we integrate the proximity, periodicity, trend, and external factors of traffic flow as inputs to the network. This network employs skip connections to form a multi-layer nested residual network structure. Additionally, we design a Multi-View Extension module to capture spatial dependencies of traffic flow at …
Economic Optimal Scheduling Of Microgrid Considering Elastic Recovery,
2025
College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
Economic Optimal Scheduling Of Microgrid Considering Elastic Recovery, Jianghong Chen, Kanghao Shi, Jiahui Hu, Xiaohan Zhao
Journal of System Simulation
Abstract: To enhance the ability of microgrids (MGs) to withstand extreme disaster events, this paper proposes a multi-objective scheduling model considering resilience restoration and economic performance, based on the traditional concepts of power system resilience and reliability. Resilience is specifically quantified. The model integrates energy storage into the objective function and includes reliability indicators as constraints, building on traditional microgrid economic dispatch. The optimization problem is solved using an improved white shark optimizer (WSO) and multi-objective fuzzy programming, where different weights are assigned to each objective function, and the optimal weights are determined through case studies. A microgrid scheduling scheme …
Three-Way Decision Clustering Algorithm Fusion Of Mutant Fireflies Algorithm,
2025
College of Information Engineering, Nanchang Institute of Engineering, Nanchang 330000, China
Three-Way Decision Clustering Algorithm Fusion Of Mutant Fireflies Algorithm, Zhaobin Li, Jun Ye, Haoyan Zhou, Yixin Wang, Yuzhen Han
Journal of System Simulation
Abstract: To address problems such as the premature phenomenon in the three-way clustering algorithm caused by the random selection of initial cluster centers and the need for repeated experiments to determine the value of q in the q-nearest neighbor concept, a three-way clustering algorithm optimized by a variant of the firefly algorithm is proposed. The firefly algorithm is employed to solve the problem of sensitivity to initial cluster centers. The target function value is taken as the brightness intensity of firefly to search the clustering center point, and the optimal solution is taken as the clustering center of the algorithm …
Tohf: A Feature Extractor For Resource-Constrained Indoor Vslam,
2025
School of Cyberspce Security, Hainan University, Haikou 570228, China
Tohf: A Feature Extractor For Resource-Constrained Indoor Vslam, Ruoqing Li, Yaochi Zhao, Zhuhua Hu, Wenlu Qi, Guangfeng Liu
Journal of System Simulation
Abstract: To address the issues of sensitivity to texture and lighting variations, excessive local dependence caused by feature point redundancy, and storage overhead under hardware resource constraints in existing VSLAM feature extractors in indoor environments, We propose the Texture- Oriented and Homogenized FAST Feature Extractor (TOHF), which integrates HVS (Human Visual System) for enhanced texture analysis. TOHF employs a two-stage thresholding strategy and dynamically adjusts feature point distribution, balancing computational efficiency and storage needs. We conducted experimental verification based on the ORB-SLAM3 framework on dataset from resource-limited device and the EuRoc dataset, focusing on matching rate, reprojection error, absolute trajectory …
Electric Vehicle Dispatching Strategy And Incentive Evaluation Based On Virtual Energy Storage,
2025
State Grid Anhui Electric Power Co. , Ltd. Hefei Power Supply Company, Hefei 230022, China
Electric Vehicle Dispatching Strategy And Incentive Evaluation Based On Virtual Energy Storage, Shuo Chen, Hao Hu, Huimin Fang, Haiwei Wang, Xiaolong Chen, Chengcheng Mei, JiaʹNan Zhu, Qian Ai
Journal of System Simulation
Abstract: To address the multifaceted challenges arising from the widespread integration of electric vehicles into the power grid, harnessing the dispatchability features of electric vehicles becomes imperative. This paper based on a virtual energy storage aggregation model, optimizes the charging scheduling of electric vehicles and assesses their charging incentives through a composite weighting methodology. It establishes a framework for the participation of flexible loads in distribution network scheduling, formulates a second-order cone relaxation optimal power flow model, and develops dispatch strategies. By quantifying the contribution of electric vehicles concerning their flexibility and system stability, and simulating user charging preferences using …
Visual Slam Algorithm Based On Feature Point Selection In Dynamic Scenes,
2025
Department of Computer Science, North China Electric Power University, Baoding 071003, China
Visual Slam Algorithm Based On Feature Point Selection In Dynamic Scenes, Limei Jiang, Xinwei Chen
Journal of System Simulation
Abstract: To address low positioning accuracy and robustness in traditional visual SLAM algorithms under dynamic conditions, this paper proposes an improved dynamic SLAM algorithm based on feature point selection. Built upon the ORB-SLAM3 framework, it incorporates dynamic region partitioning and feature point filtering. The dynamic region partitioning module utilizes an enhanced RT-DETR object detection algorithm to detect dynamic objects in the images and divides the dynamic regions based on the detection boxes. The feature point selection module utilizes epipolar constraints and optical flow methods to filter out feature points on moving objects, retaining stationary dynamic objects and background points within …
Reinforcement Learning Modeling Of Missile Penetration Decision Based On Combat Simulation,
2025
College of College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China
Reinforcement Learning Modeling Of Missile Penetration Decision Based On Combat Simulation, Bin Zhang, Yonglin Lei, Qun Li, Yuan Gao, Yong Chen, Jiajun Zhu, Chenlong Bao
Journal of System Simulation
Abstract: Penetration capability is a primary measure of missile systems. In response to the shortcomings of traditional knowledge-based decision-making methods that are difficult to adaptively evolve, an intelligent penetration decision-making based on combat simulation and DRL is proposed. A missile intelligent decision-making training environment is constructed based on the WESS system. Taking missile maneuver penetration decision-making as an example, a maneuver penetration decisionmaking network model is designed and trained based on the SAC-discrete algorithm and the test of intelligence is conducted. Experimental results show that the intelligent decision model derived from machine learning has a better combat outcome than traditional …
Research On Transformer Fault Diagnosis Method Based On Digital Twin,
2025
School of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi 830046, China
Research On Transformer Fault Diagnosis Method Based On Digital Twin, Lun Jiang, Dajiang Wang, Wenlei Sun, Shenghui Bao, Han Liu, Saike Chang
Journal of System Simulation
Abstract: Aiming at the inability of existing intelligent algorithms for transformer fault diagnosis to quickly and efficiently identify transformer faults, resulting in fault misdetection and untimely detection, this paper proposes a transformer fault diagnosis method using the improved sparrow optimization algorithm to optimize the two-layer fault diagnostic model of XGBoost combined with the digital twin technology. The method adopts advanced sensors to collect oil and gas data and temperature data of the transformer, uses 5G module to transmit the real-time data to the digital twin system. The system monitors the temperature data in real-time by setting the equipment alarm threshold; …
Research On Air Target Threat Assessment Technology Based On Deep Learning,
2025
School of Electronic Engineering, Xidian University, Xi'an 710071, China
Research On Air Target Threat Assessment Technology Based On Deep Learning, Dawei Jiang, Yangyang Dong, Lidong Zhang, Xiao Lu, Chunxi Dong
Journal of System Simulation
Abstract: In order to realize the effective assessment of air combat targets, a deep learning-based air target threat assessment method is proposed. According to threat characteristics of the air target, the threat attributes of air target faced by electronic countermeasure operation are analyzed from the two perspectives of platform layer and equipment layer, the air target threat assessment index system is constructed, and the air target threat assessment index data set is established. Based on convolutional neural network, a residual structure is introduced to optimize the network, a threat assessment model is established, and the threat ranking of air targets …
Multi-Strategy Hybrid Mountain Gazelle Optimizer For Robot Path Planning,
2025
School of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
Multi-Strategy Hybrid Mountain Gazelle Optimizer For Robot Path Planning, Xu Jin, Yuanbin Mo
Journal of System Simulation
Abstract: Aiming at the problems of local optimum and premature convergence in the design of optimization path of robot navigation system, a multi-strategy hybrid MGO(HMGO) improved algorithm based on the mountain gazelle optimizer(MGO) is proposed. The algorithm uses the quasi-reverse learning strategy to optimize the population initialization ensuring its diversity, introduces the dynamic adaptive density factor to adjust the parameters of the optimization mechanism, and integrates arithmetic optimization and sine-cosine strategies for random perturbations. Through ablation experiments, 13 benchmark test functions, and simulation experiments on the solution of two-dimensional and threedimensional space robot path planning problems, the results demonstrate that …
Dynamic Spectral And Systems-Theoretic Approaches For Inference And Control Of Heterogeneous Complex Networks,
2025
Washington University – McKelvey School of Engineering
Dynamic Spectral And Systems-Theoretic Approaches For Inference And Control Of Heterogeneous Complex Networks, Bharat Kumar Singhal
McKelvey School of Engineering Graduate Student Theses & Dissertations
Networks of nonlinear systems are commonly employed to describe a diverse range of phenomena across physics, engineering, neuroscience, and biology. The undesirable behaviors of such systems, in the form of neurological disorders, power grid failures, or ecological collapses, have spurred significant interest in understanding their dynamic structures and developing effective control strategies. These systems are typically large-scale, consist of heterogeneous units, and are partially observable with limited measurement data, presenting theoretical and computational challenges for control design and connectivity inference. This thesis addresses these challenges by developing novel algorithms for pattern formation in populations of stable limit-cycle oscillators and connectivity …
Analysis Of Systematic Trade-Offs Between Military And Healthcare Expenditure Alongside Gdp Growth Of Select Asian And Western Exporting Economies In The 21st Century,
2025
Binghamton University, SUNY
Analysis Of Systematic Trade-Offs Between Military And Healthcare Expenditure Alongside Gdp Growth Of Select Asian And Western Exporting Economies In The 21st Century, Rahul Balamurugan, Carlos Gershenson, Preethi Nanjundan, Hiroki Sayama
Northeast Journal of Complex Systems (NEJCS)
This study explores the complexity in the trade-offs between military expenditure, healthcare expenditure, and GDP growth across select Asian nations and major weapon-exporting countries, examining how nations allocate finite resources between national security and human well-being over the past two decades. Using a systems science approach, the research integrates Granger causality testing to analyze temporal and directional relationships among GDP growth, military expenditure, and healthcare expenditure, uncovering their dynamic interdependencies. The methodology includes trend and slope analysis, Granger causality testing, outlier detection, and clustering to identify heterogeneity in resource allocation strategies. Developed, weapon-exporting nations exhibit complementary trends, with strong causality …
Impact Of Node Failures On Productivity In Multilayer Supply Chain Networks: An Influence Network Analysis In The Indian Electronics Sector,
2025
Binghamton University
Impact Of Node Failures On Productivity In Multilayer Supply Chain Networks: An Influence Network Analysis In The Indian Electronics Sector, Surendra Orupalli, Hiroki Sayama
Northeast Journal of Complex Systems (NEJCS)
Supply chain networks are essential for the delivery of goods and information, but disruptions such as natural disasters or trade embargoes can severely impact them. Resilience of entire networks under different types of disruptions when nodes or edges fail has been extensively studied. However, the extent to which the failure of a particular company affects another company of interest within a network has not been widely explored. To address this, we created a multilayer physical supply chain network of companies in an electronics supply chain concentrated in India. Through systematic node removal simulations, we examined how the productivity of one …
Comparative Evaluation Of Linear Regression, Cross Validation And Regularization Approaches In Multivariate Data Analysis,
2025
University of Maine
Comparative Evaluation Of Linear Regression, Cross Validation And Regularization Approaches In Multivariate Data Analysis, Ransford Owusu, Felix Yeboah, Francis Effah Boateng
Data Science and Data Mining
This study evaluates linear regression and its enhanced variants incorporating cross-validation and regularization techniques for high-dimensional, multivariate datasets. We address challenges such as multicollinearity and overfitting. Methods including Ridge, LASSO, and Elastic Net are compared against ordinary least squares regression. Empirical analysis using an automobile dataset for fuel efficiency prediction shows that while OLS regression captures basic relationships, its limitations are mitigated through regularization and cross-validation, resulting in improved model interpretability. The findings provide a comprehensive framework for predictive modeling in complex data environments and offer insights into statistical methodology and practical applications in the automobile industry.
Improved Bidirectional A* Quadratic Path Planning Algorithm For Mobile Robots,
2025
School of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
Improved Bidirectional A* Quadratic Path Planning Algorithm For Mobile Robots, Jiongyi Li, Qiang Li, Xinwen Zhang, Myo Htet Zin, Yongbin Cai
Journal of System Simulation
Abstract: Aiming at the problems of the traditional A* algorithm, such as the unhoped intersection between the planned path and the obstacles, the planned path has many inflection points and the search time is long, an improved bidirectional A* quadratic path planning algorithm for the indoor environments is proposed. Through the expansion of the map, the intersection between the planned path and the obstacle is solved. By new heuristic functions and bidirectional expansion methods, the search speed and accuracy of the bidirectional A* algorithm are improved. Turning cost function and adaptive weight are introduced to reduce the number of turning …
