Uav Swarm Obstacle Avoidance Based On Visual Filed And Adaptive Radius,
2024
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Uav Swarm Obstacle Avoidance Based On Visual Filed And Adaptive Radius, Gaohang Ai, Chuntao Li
Journal of System Simulation
Abstract: Aiming at the obstacle avoidance problem of large-scale UAV swarm tracking flight route, a swarm obstacle avoidance algorithm based on distributed model predictive control combined with visual field and adaptive obstacle avoidance radius is proposed. In the process of swarm flight, the UAV obtains the reference route information of the current moment according to its own position, and obtains the predicted trajectory of its neighbors through local information interaction. When encountering obstacles, the adaptive obstacle avoidance radius and field of view topology method are combined to effectively solve the problem that the internal safety distance cannot be maintained and …
A Novel Research Pattern For The Simulation Of Complex Systems Sigd,
2024
The Institute of Intelligent Computing, University of Electronic Science and Technology of China, Chengdu 611731, China
A Novel Research Pattern For The Simulation Of Complex Systems Sigd, Bin Chen, Runkang Guo, Zhengqiu Zhu, Yong Zhao, Yatai Ji, Aiguo Chen, Guangquan Cheng
Journal of System Simulation
Abstract: The complexity of the system is mainly reflected in the numerous components and extremely complex interactions. Combined with the current trend of artificial intelligence development, this paper analyzes and considers the changes in thinking mode brought by simulation discipline research, and forms an understanding of the connotation and research scope of simulation intelligence. A new pattern for complex system simulation research is proposed: "simulation intelligence based generating decisions (SIGD)". In the SIGD pattern, the similar principles, modeling methods, and decision-guiding modes in simulation disciplines are different from those in traditional simulation. Under the guidance of this concept, a connection-oriented …
Simulation Of Cascade Failure In Urban Rail Transit Hypernetworks Based On Hypergraph Theory,
2024
School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China
Simulation Of Cascade Failure In Urban Rail Transit Hypernetworks Based On Hypergraph Theory, Zijin Han, Mingjun Qian, Xixian Wang, Kaiyue Zhang
Journal of System Simulation
Abstract: In order to enhance the resilience of urban rail transit networks to ensure stable operations and passenger safety in the face of emergencies, hypergraph theory is introduced to construct a hypergraph based urban rail transit hypernetwork model, and a nonlinear load-capacity cascading failure model based on passenger flow weighting is established. In response to the passenger evacuation process at actual transportation network stations, a load redistribution mechanism is proposed, taking into consideration both the network level and the importance of passenger flow. To address scenarios where stations in actual traffic networks can still accommodate loads during shutdowns, a node …
Research On Digital Twin Simulation Method Of Industrial Robot Integrated With Reinforcement Learning,
2024
School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032, China
Research On Digital Twin Simulation Method Of Industrial Robot Integrated With Reinforcement Learning, Tianyue Miao, Lu Wang, Jiaxiao He, Nenggang Xie
Journal of System Simulation
Abstract: In response to the lack of comprehensive functionality and limited application scenarios in the current field of industrial robot digital twin systems, which results in low versatility, a method for constructing a digital twin system for industrial robots with high versatility is proposed. A four-dimensional system architecture for the digital twin is designed, and the components and functions of the four-dimensional system are analyzed, based on the system level planning of the four-dimensional system, the concept of integrating reinforcement learning into the virtual replacement of real concept is defined. By constructing a multi-attribute virtual model and using TCP communication …
Optimal Operation Scheduling Of Integrated Energy System Considering Energy Priority,
2024
China Electric Power Research Institute Co., Ltd, Beijing 100192, China
Optimal Operation Scheduling Of Integrated Energy System Considering Energy Priority, Dongli Jia, Keyan Liu, Zhaoying Ren, Zezhou Wang, Dongsheng Tang
Journal of System Simulation
Abstract: Integrated with the actual situation of power grid and the growth of new energy, a multiobjective model for optimal scheduling of the integrated energy system(IES) is established based on the analysis of the energy-flow relationship of the IES and taking into account the priority of energy utilization and the load demand response in terms of the mismatch between the distributed energy sources and the loads, the net benefit of the unit cost of the IES, and the load response degree. Combined with the equipment and the environmental benefits system, a priority constraint for energy utilization has been established for …
Research On Scheduling Strategies Simulation For Building Air-Conditioning Systems Based On Transfer Imitation Learning,
2024
School of Environmental Science and Engineering, Tianjin University, Tianjin 300354, China
Research On Scheduling Strategies Simulation For Building Air-Conditioning Systems Based On Transfer Imitation Learning, Qiaochu Wang, Yan Ding, Chuanzhi Liang, Haozheng Zhang, Chen Huang
Journal of System Simulation
Abstract: To solve the problem of unstable performance and inefficient training process of low-quality data conditions at the initial stage of online deployment of air conditioner scheduling, we propose a migration-imitation learning-based air conditioning scheduling strategy simulation method. Reinforcement learning methods are used to generate building operation strategies. A standard building simulation model serves as the source domain, upon which migration learning is applied. An imitation learning loss function is incorporated into the intelligent loss function to enhance algorithm performance. The results indicate that, compared with the non-use of migration learning, the proposed method can improve the operational efficiency by …
Behavioral Modeling Of Manned-Unmanned Cooperative Air Combat Based On Improved Abc Algorithm,
2024
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Behavioral Modeling Of Manned-Unmanned Cooperative Air Combat Based On Improved Abc Algorithm, Peng Wang, Haoyu Liu, Ni Li, Zexi Yu, Shangjie Jia
Journal of System Simulation
Abstract: To solve the problem of difficulty in establishing collaborative behavior models and weak adversarial capabilities in typical MAV/UAV air combat scenarios, a mixed decision based MAV/UAV behavior modeling framework is proposed. Using collaborative rule sets, rule subsets, tactical action sets, and other tools, a hierarchical decision collaborative behavior model supporting five types of collaborative tactics, including grinding tactics and unilateral flanking tactics, is constructed in this framework. a behavior model parameter optimization method based on an improved artificial bee colony (ABC) algorithm is proposed. By using the Mason rotation method to initialize the population, a better initial honey source …
Harmonic Impedance Modeling And Oscillation Analysis Of Modular Multilevel Converter,
2024
College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Harmonic Impedance Modeling And Oscillation Analysis Of Modular Multilevel Converter, Yuhong Wang, Wensheng Chen, Shilin Gao, Jianquan Liao, Yangfan Cheng
Journal of System Simulation
Abstract: To facilitate rapid analysis of the oscillation stability mechanism in modular multilevel converter-based high voltage direct current (MMC-HVDC) systems and streamline the simulation process for determining MMC impedance characteristics, a simplified mathematical simulation model for MMC closed-loop impedance is developed using the harmonic state space method. This model considers various control strategies and includes both AC-side and DC-side impedance models. By applying a Nyquist criterion-based impedance analysis method, the stability mechanisms on the AC and DC sides of the MMC are examined. In addition, a data-driven oscillation stability analysis method is also proposed, leveraging a global sensitivity algorithm based …
Path Planning Of Desert Robot Based On Deep Reinforcement Learning,
2024
Energy and Transportation Engineering College, Inner Mongolia Agricultural University, Hohhot 010018, China
Path Planning Of Desert Robot Based On Deep Reinforcement Learning, Ming Li, Wangzhong Ye, Jiehua Yan
Journal of System Simulation
Abstract: Due to the complexity and variability of the desert environment, the key to the high-efficient of mobile robot is how to avoid obstacles and plan its path. To solve the problems of poor search efficiency and slow convergence of deep reinforcement learning algorithm in complex environment, an improved deep reinforcement learning path planning algorithm is proposed. The exploration factor is improved and dynamically adjusted according to the convergence degree of the algorithm, so that the exploration factor dynamically decreases with the increase of the understanding degree of the agent to the environment, thus speeding up the convergence speed of …
Calculation And Statistical Analysis Of Wins Above Replacement,
2024
University of Mary Washington
Calculation And Statistical Analysis Of Wins Above Replacement, Joshua Taylor
Departmental Honors & Graduate Capstone Projects
The Wins Above Replacement (WAR) statistic in Major League Baseball is a prominent metric used to estimate player value by quantifying all aspects of play in terms of wins added to a baseball team. We will use R to calculate WAR for all players from 1871 to 2012 and use data from those years to construct multivariate predictive models to attempt to estimate WAR for players from 2013 to 2024. We find strong correlations between predicted and actual WAR values for most models, with the exception of the polynomial predictive model for non-qualified pitchers.
Unpacking Bias, Accountability, And Ethical Practices In Ai,
2024
Chapman University
Unpacking Bias, Accountability, And Ethical Practices In Ai, Manya Chandra, Micol Hebron
Student Scholar Symposium Abstracts and Posters
This study is based on understanding how text-to-image generative AI platforms perpetuate biases such as racism and sexism and decoding how this bias is programmed within large language models and datasets. In this study, the results of generative AI are analyzed through the lens of affect and affect theory, as they are applied to investigate the machine learning and computer theory behind generative AI algorithms. The purpose of the study is to explain why generative AI is biased and whether this bias is generated due to current trends or to deficits and biases within the database that it draws information …
Visualization Of Paleocurrents On A Web Application Using Gplates,
2024
Southern Adventist University
Visualization Of Paleocurrents On A Web Application Using Gplates, Anjan Sapkota
MS in Computer Science Theses
Paleocurrents are flow directions derived from features of sedimentary rocks that reveal the direction of the current of wind or water that deposited the sediment. In 2015, Brand et al. created a global database of paleocurrents, which contains over 1,000,000 measurements worldwide: North America, South America, Australia, Great Britain, parts of Western Europe, China, Africa are fairly well represented; Antarctica, Eastern Europe, and Asia are modestly represented and Russia is poorly represented. The contribution of this thesis is a web application that uses the GPlates’ Application Programming Interface (API) to visualize global paleocurrents through time in an interactive way based …
Gotcha ! This Model Uses My Code ! Evaluating Membership Leakage Risks In Code Models,
2024
Singapore Management University
Gotcha ! This Model Uses My Code ! Evaluating Membership Leakage Risks In Code Models, Zhou Yang, Zhipeng Zhao, Chenyu Wang, Jieke Shi, Dongsum Kim, Donggyun Han, David Lo
Research Collection School Of Computing and Information Systems
Leveraging large-scale datasets from open-source projects and advances in large language models, recent progress has led to sophisticated code models for key software engineering tasks, such as program repair and code completion. These models are trained on data from various sources, including public open-source projects like GitHub and private, confidential code from companies, raising significant privacy concerns. This paper investigates a crucial but unexplored question: What is the risk of membership information leakage in code models? Membership leakage refers to the vulnerability where an attacker can infer whether a specific data point was part of the training dataset. We present …
Reinforcement Learning Based Online Request Scheduling Framework For Workload-Adaptive Edge Deep Learning Inference,
2024
Chinese Academy of Sciences
Reinforcement Learning Based Online Request Scheduling Framework For Workload-Adaptive Edge Deep Learning Inference, Xinrui Tan, Hongjia Li, Xiaofei Xie, Lu Guo, Nirwan Ansari, Xueqing Huang, Liming Wang, Zhen Xu, Yang Liu
Research Collection School Of Computing and Information Systems
The recent advances of deep learning in various mobile and Internet-of-Things applications, coupled with the emergence of edge computing, have led to a strong trend of performing deep learning inference on the edge servers located physically close to the end devices. This trend presents the challenge of how to meet the quality-of-service requirements of inference tasks at the resource-constrained network edge, especially under variable or even bursty inference workloads. Solutions to this challenge have not yet been reported in the related literature. In the present paper, we tackle this challenge by means of workload-adaptive inference request scheduling: in different workload …
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 …
