Research On Gaussian Splatting Modeling Of Power Equipment In 3d Scenes,
2026
School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Research On Gaussian Splatting Modeling Of Power Equipment In 3d Scenes, Haiying Li, Haonan Xu, Junfang Hao
Journal of System Simulation
Abstract: To address the issues of missing camera poses in captured images and poor reconstruction quality in 3D modeling of power equipment, a 3D Gaussian splatting 3D modeling method for power equipment based on video sequences was proposed. Theffmpeg was adopted to extract video frames at a reduced rate, and the Scharr operator was employed to quantify the sharpness of video frames to screen high-quality images for forming an input dataset, ensuring the completeness of equipment poses and the quality of modeling data. Through multi-view feature point extraction and matching, combined with an incremental structure-from-motion algorithm to optimize and …
A Precise Damage Assessment Method For Lethal Blast Warheads Against Quadruped Robots,
2026
State Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Beijing 100081, China
A Precise Damage Assessment Method For Lethal Blast Warheads Against Quadruped Robots, Xueqian Wang, Jianbing Men, Xin Zhou, Shuyou Wang, Mei Li
Journal of System Simulation
Abstract: To accurately evaluate the damage efficiency of a lethal blast warhead on quadruped robots, a typical quadruped robot replication model and vulnerability damage tree were constructed through Autodesk Inventor. The power field calculation model of a lethal blast warhead was introduced. Based on the high-precision collision detection and graphic rendering technology of UE, this paper carried out the intersection detection of destructive elements and targets and realistic scene visualization. A visualization system for damage assessment of quadruped robots by a lethal blast warhead was developed, featuring capabilities such as parametric modeling of the lethal blast warhead, power field evolution …
Robot Path Planning By Reinforcement Learning Based On Sac3q-Hdm,
2026
School of Artificial Intelligence, Anhui University of Science and Technology, Hefei 231131, China; State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Anhui University of Science and Technology, Huainan 232001, China
Robot Path Planning By Reinforcement Learning Based On Sac3q-Hdm, Dequan Li, Wan Xiong
Journal of System Simulation
Abstract: To address the issues of overestimated and underestimated biases, low sample utilization rate, and the inability to balance exploration and exploitation in reinforcement learning for path planning, an improved SAC method was proposed. The size balance of entropy was explored and utilized through adaptive temperature coefficient adjustment; on the basis of the SAC framework, a triple Critic architecture was introduced to dynamically weight and fuse the minimum and average values through Qvalue uncertainty, balancing overestimated and underestimated biases. A mixed dynamic sampling experience replay buffer was designed; experience data was partitioned based on reward thresholds; sampling ratios were dynamically …
Nerf Optimization Method And Simulation Research Based On Pre-Training And Differentiable Fuzzy Modeling,
2026
Civil Aviation College, Zhengzhou University of Aeronautics, Zhengzhou 450015, China
Nerf Optimization Method And Simulation Research Based On Pre-Training And Differentiable Fuzzy Modeling, Yunjng Zhang, Minghui Yang, Hao Wang
Journal of System Simulation
Abstract: To address the challenges of significant geometric modeling errors, severe detail loss, and low training efficiency in neural radiance field(NeRF) reconstruction under defocused blurred input scenarios, this paper proposes two optimization strategies. One strategy is introducing Triplane features generated by the pre-trained LRM as prior knowledge, and combining a lightweight decoder and directional LoRA module to replace large MLP, thereby reducing parameters and shortening convergence time. The second strategy is integrating a differentiable blurring model into the volumetric rendering step. By jointly optimizing the radiation field and spatially variable blurring kernels, reconstruction accuracy under defocused blurred scenarios is enhanced …
Virtual Train Operation Platform Based On Digital Twin,
2026
CRRC Qingdao Sifang Rolling Stock Research Institute Co., Ltd., Qingdao 266000, China
Virtual Train Operation Platform Based On Digital Twin, Ziying Wang, Congjun Sun, Guihu Li, Tianhao Zhang
Journal of System Simulation
Abstract: In response to the limitations of traditional train operation simulation modeling, such as simplification, lack of adaptive adjustment capability for parameters, and proneness to error accumulation, a virtual train operation platform based on digital twin technology was proposed. A train model under specific railway lines was constructed. By combining with the intelligent operation and maintenance platform of the railway line, real-time train operation data was obtained and preprocessed. The adaptive chaos optimization algorithm was used to optimize the key parameters of train operation simulation online and establish a digital twin model of the railway line. This model adopted a …
Large-Scale Scene Registration Technology Based On 3d Gaussian Splatting Fusing Gps Prior Information,
2026
School of Computer and Artificial Intelligence, Henan University of Urban Construction, Pingdingshan 467036, China
Large-Scale Scene Registration Technology Based On 3d Gaussian Splatting Fusing Gps Prior Information, Fei Wan, Yong Yin
Journal of System Simulation
Abstract: To address the challenges of low computational efficiency, slow convergence, and limited accuracy in large-scale 3D scene registration, a 3D Gaussian splatting (3DGS) registration method integrating GPS prior information was proposed. Spatial position priors provided by GPS were utilized to establish initial alignment through coordinate system transformations, narrowing the registration search space. Dense point cloud models were efficiently reconstructed by combining 3DGS technology. Highprecision alignment was achieved through a two-stage optimization of GPS coarse registration and fine registration. Experiments demonstrate that the GPS-assisted method reduces translation errors by 25%~50% and increases success rates to 98% in vegetation-covered and …
Task Planning Method For Cross-Domain Cooperative Combat Operations Of Unmanned Systems Under Complex Constraints,
2026
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Task Planning Method For Cross-Domain Cooperative Combat Operations Of Unmanned Systems Under Complex Constraints, Haojie Fang, Ziyang Zhen, Huajun Gong, Xu Xie, Wei Luo
Journal of System Simulation
Abstract: In pre-combat task planning for cross-domain cooperative combat operations, to solve the problems of diverse and complex constraints and difficulties in solving planning models caused by performance differences of unmanned systems and increased requirements for cooperative combat operations, a multi-strategy enhanced grey wolf optimization (MSEGWO) algorithm was proposed. By considering various complex constraints such as performance of each type of unmanned systems, munition usage, task timing, task time window, and flight path, a task planning mathematical model with minimizing the comprehensive cost as the objective was established. Improvement strategies such as nonlinear adjustment of convergence factor, alternative solution space …
Research On Uav Path Planning Method Based On Collision Free Trajectory,
2026
Central China Branch of State Grid Corporation, Wuhan 430077, China
Research On Uav Path Planning Method Based On Collision Free Trajectory, Jun Xie, Qi Zhang, Yanyun Peng, Haonan Shi, Dongyang Li, Xi Liu
Journal of System Simulation
Abstract: In view of the problems of poor quality, long time consumption, and low efficiency of the autonomous path planning method for unmanned aerial vehicles, a path planning method for unmanned aerial vehicles based on a collision-free trajectory was proposed. Under the premise of uncertainty, the time-related virtual points and collision threshold were set; the obstacle was modeled as a rectangle; the interest points around the rectangle were defined. The uncertainty optimization model between the unmanned aerial vehicles and the obstacle was established, so as to obtain the allowable edge of the collision-free trajectory of the unmanned aerial vehicles. The …
Construction Approach Of Llm-Empowered Tactical Wargame Decision-Making Agents,
2026
Army Arms University of PLA, Beijing 100072, China; PLA 32302 Troops
Construction Approach Of Llm-Empowered Tactical Wargame Decision-Making Agents, Dayong Liu, Zhiming Dong, Qisheng Guo, Ang Gao, Xuehuan Qiu
Journal of System Simulation
Abstract: Decision-making agents are critical enablers for implementing human-machine, machinemachine, and hybrid human-machine adversarial interaction in tactical wargaming, where the intelligence level of the agent is crucial. To address the limitations of traditional decision agents such as insufficient adaptability, simplistic strategies, and high construction costs, a fusion decision framework driven by the large and small models was proposed. It specifically investigated the fusion approach of large language models with conventional decision-making agent construction approaches, including behavior trees, finite state machines, heuristic search, and deep reinforcement learning. New ideas and technical pathways are provided for the construction of tactical wargame …
Research On Visual Place Recognition Algorithms For Complex Urban Environments,
2026
School of Mechatronic Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
Research On Visual Place Recognition Algorithms For Complex Urban Environments, Peijin Liu, Minxin Zhang, Lin He, Yige Sun, Tingqi Su
Journal of System Simulation
Abstract: Dynamic factors such as traffic flow and crowd density in complex urban environments reduce the accuracy of visual place recognition (VPR) algorithms. To solve these problems, a semantic-guided visual place recognition (SG-VPR) algorithm was proposed. A semantic-guided feature suppression module was designed. A semantic-guided module and feature suppression layer were constructed to reduce the dynamic object interference and more accurately extract the key static features. An adaptive triplet margin loss function (ATML) was proposed by improving the traditional triplet margin loss. The margins were adaptively adjusted according to the sample distribution, solving the problem of suboptimal solution convergence …
Cylindformer: Image-To-Point Cloud Registration With Cylindrical Transformer,
2026
Singapore Management University
Cylindformer: Image-To-Point Cloud Registration With Cylindrical Transformer, Jingtao Wang, Hao Tang, Yanpeng Sun, Shengfeng He, Zechao Li
Research Collection School Of Computing and Information Systems
Accurate correspondence extraction between distinctive pixel-wise and point-wise features is critical for image-to-point cloud (I2P) registration. Recent efforts leveraging Transformers for I2P feature representation have demonstrated potential, primarily by first capturing intra-modality global contextual dependencies via self-attention, and then learning cross-modality correlations via cross-attention. The strength of vanilla Transformers lies in modeling cross-modality global feature correlations. However, such mechanisms often struggle with the structural disparity between dense image pixels and sparse 3D points, hindering the establishment of fine-grained correspondences. Moreover, global attention may introduce ambiguity, as interactions with many inconsistent regions of intra-modality may degrade feature distinctiveness. To address these …
Improving Public Transport Through Machine Learning Influence Flow Analysis (Mifa): Southern England Bus Case Study,
2026
Singapore Management University
Improving Public Transport Through Machine Learning Influence Flow Analysis (Mifa): Southern England Bus Case Study, Benjamin Lee, Wolfgang Garn, Masoud Fakhimi, Nick F. Ryman-Tubb
Research Collection School Of Accountancy
Public transport (PT) is crucial for enhancing the quality of life and enabling sustainable urban development. As part of the UK Transport Investment Strategy, increasing PT usage is critical to achieving efficient and sustainable mobility. This paper introduces Machine Learning Influence Flow Analysis (MIFA), a novel framework for identifying the key influencers of PT usage. Using survey data from bus passengers in Southern England, we evaluate machine learning models. Subsequently, MIFA uncovers that easy payments, e-ticketing, and mobile applications can substantially improve the PT service. MIFA’s implementation demonstrates that strength and importance lead to specific insights into how service characteristics …
Opencil: Benchmarking Out-Of-Distribution Detection In Class Incremental Learning,
2026
Beijing University of Aeronautics and Astronautics (Beihang University)
Opencil: Benchmarking Out-Of-Distribution Detection In Class Incremental Learning, Wenjun Miao, Guansong Pang, Trong-Tung Nguyen, Ruohuan Fang, Jin Zheng, Xiao Bai
Research Collection School Of Computing and Information Systems
Class incremental learning (CIL) aims to learn a model that can not only incrementally accommodate new classes, but also maintain the learned knowledge of old classes. Out-of-distribution (OOD) detection in CIL is to retain this incremental learning ability, while being able to reject unknown samples that are drawn from different distributions of the learned classes. This capability is crucial to the safety of deploying CIL models in open worlds. However, despite remarkable advancements in the respective CIL and OOD detection, there lacks a systematic and large-scale benchmark to assess the capability of advanced CIL models in detecting OOD samples. To …
Comprehensively Evaluating The Perception Systems Of Autonomous Vehicles Against Hazards,
2026
Singapore Management University
Comprehensively Evaluating The Perception Systems Of Autonomous Vehicles Against Hazards, Xiaodong Zhang, Jie Bao, Jianlei Chi, Jun Sun, Zijiang Yang
Research Collection School Of Computing and Information Systems
Perception systems are vital for the safety of autonomous driving. In complex autonomous driving scenarios, autonomous vehicles must overcome various natural hazards, such as heavy rain or raindrops on the camera lens. Therefore, it is essential to conduct comprehensive testing of the perception systems in autonomous vehicles against these hazards, as demanded by the regulatory agencies of many countries for human drivers. Since there are many hazard scenarios, each of which has multiple configurable parameters, the challenges are (1) how do we systematically and adequately test an autonomous vehicle against these hazard scenarios, with measurable outcome; and (2) how do …
Plm-Effector: Unleashing The Potential Of Protein Language Models For Bacterial Secreted Protein Prediction,
2026
Singapore Management University
Plm-Effector: Unleashing The Potential Of Protein Language Models For Bacterial Secreted Protein Prediction, Dandan Zheng, Lihong Chen, Guansong Pang, Jian Yang
Research Collection School Of Computing and Information Systems
Bacterial secreted proteins, particularly effectors delivered by specialized secretion systems, are key mediators of virulence and host-pathogen interactions. However, accurate computational identification remains challenging, as many existing methods rely heavily on sequence similarity or handcrafted features, and often focus on a single secretion system. Recent studies have reported that some bacterial effectors may be associated with more than one secretion system, highlighting the complexity of secretion system annotation and motivating the development of system-aware computational prediction approaches. Here, we present PLM-Effector, a hybrid deep learning framework that integrates modern protein language models (PLMs) with multiple neural architectures via a two-layer …
Research On System And Application Framework Of Tactical Wargaming Simulation Driven By Ai4s,
2026
Army Arms University of PLA, Beijing 100072, China; PLA 32302 Troops
Research On System And Application Framework Of Tactical Wargaming Simulation Driven By Ai4s, Dayong Liu, Qisheng Guo, Zhiming Dong, Xuehuan Qiu, Zhuoli Liu
Journal of System Simulation
Abstract: Tactical wargaming simulation, as a crucial tool for combat analysis, simulation training, and equipment demonstration and test, has become a significant means for generating combat effectiveness. Integrating AI into simulation not only enhances simulation efficiency but also diminishes reliance on humans. To assist professionals engaged in tactical wargaming simulation in mastering AI application methods, fostering a systematic mindset, and understanding evolving trends, this paper provided a concise overview of the principles behind AI for science (AI4S). Subsequently, it conducted an analysis of AI4S's application effectiveness in tactical wargaming simulation, established an AI4S-driven wargaming simulation system, and elucidated its composition, …
Social Cognition Simulation With Large Language Model-Driven Agents,
2026
College of Politics, National Defense University, Shanghai 200433, China
Social Cognition Simulation With Large Language Model-Driven Agents, Mingxin Zhang, Jinxuan Wu, Rui Zhu, Yunlong Wang, Wenjuan Meng, Zhe Liu, Xu Li, Xiaolei Chen, Yuxuan Liang, Yi Zheng, Xiangyang Xue
Journal of System Simulation
Abstract: With the continuous evolution of the capabilities of generative LLMs, their application in social cognition simulation is demonstrating paradigm-shifting potential. Traditional social simulation methods predominantly rely on static rules and simplified behavioral models, making it difficult to capture the dynamic evolution and cultural complexity of human social behavior. LLM-driven agents, equipped with contextual understanding and natural language generation capabilities, are emerging as novel tools for modeling social cognitive mechanisms, enabling the simulation of complex sociopsychological processes such as identity construction, value judgment, and intentional reasoning. This paper briefly introduced the technical foundations of LLMs and highlighted their suitability for …
Integrated Development Environment For Digital Test Applications Based On Cloud-Edge-End Architecture,
2026
Beijing Institute of Tracking and Telecommunications Technology, Beijing 100094, China
Integrated Development Environment For Digital Test Applications Based On Cloud-Edge-End Architecture, Wenguang Yu, Qun Li, Hongjie Dang, Hao Chen, Ping Yang
Journal of System Simulation
Abstract: Digital test applications need to be constructed using the unified digital test development tool. After analyzing the features of digital test applications such as large-sample autonomous run, high computational efficiency requirement, and diverse task scenarios, this paper proposes the integrated development environment (IDE) for digital test applications based on cloud-edge-end architecture. The layered expandable architecture, the hybrid integration framework of multi-source heterogeneous models, and the cloud-edge-end collaborative deployment architecture are designed for the IDE of digital test applications. The IDE supports the rapid development, integration, and execution of digital test models and enables development of digital test applications on …
Research On Chain-Of-Thought Technology For Situational Awareness Based On Modular Reasoning,
2026
National Key Laboratory of Modeling and Simulation for Complex Systems, Beijing Simulation Center, Beijing 100854, China
Research On Chain-Of-Thought Technology For Situational Awareness Based On Modular Reasoning, Hongyuan Ji, Duzheng Qing
Journal of System Simulation
Abstract: To address issues such as insufficient intelligence of situational understanding in traditional simulation systems, a situational visual question answering dataset was constructed, and a modular reasoning framework was proposed. The SACoT was built, which, under a zero-shot setting, employed expert prompts to guide the model in task decomposition and multimodal information fusion, generating reasoning chains to enhance semantic cognition and interpretability and offering a scalable solution with low computation cost. Experimental results indicate that SACoT improves task allocation, enables models to focus on query-relevant image details, mitigates the fragmentation of chain-of-thought induced by multi-step reasoning, and reduces long-form …
Simulation Of Robotic Arm Ball-Catching Strategy Based On Curriculum Rl Of Transformer,
2026
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Simulation Of Robotic Arm Ball-Catching Strategy Based On Curriculum Rl Of Transformer, Ziyao Zhang, Yunfeng Ji
Journal of System Simulation
Abstract: Method integrating the PPO algorithm with Transformer network architecture is proposed, and curriculum learning strategy is introduced to solve the difficult training convergence and low efficiency of traditional RL methods in complex and dynamic high-degree-of-freedom tasks such as robotic arm ball catching. The Transformer is employed to effectively capture the complex high-dimensional dependency between the robotic arm's state space, ball trajectory, and environmental physical parameters. Curriculum learning progressively increases catching difficulty by designing training tasks from simple to complex objectives. The experimental results show this method increases the ball-catching success rate by over 60% compared to the traditional …
