Weighted Local Complexity Invariance For Time Series Classification,
2022
1.Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China;
Weighted Local Complexity Invariance For Time Series Classification, Yitong Li, Xiaotao Liu, Jing Liu, Kai Wu
Journal of System Simulation
Abstract: Aiming at the misclassification of existing algorithms for long or unevenly distributed time series, the local complexity information is extracted and weighted local complexity-invariant distance (WLCID) is proposed, which includes the local complexity representation model and the weighted global complexity integration model. Sliding window is used to split up time series, and combined with the complexity-invariant distance, the local complexity information can be extracted. As to the class representation model, the integration weights are quantified with the normalized cumulative between-class distance, with the perspective that the subsequence contributes more greatly with larger between-class distance. Compared with other …
A K-Modes Clustering Method Based On Maximal Information Coefficient Data Preprocessing,
2022
1.Hangzhou Dianzi University, Hangzhou 310018, China;
A K-Modes Clustering Method Based On Maximal Information Coefficient Data Preprocessing, Mingmei Li, Chenglin Wen, Shaolin Hu
Journal of System Simulation
Abstract: The existing k-modes clustering method ignores the weak correlation of variable attributes, which often results in poor clustering performance in practical applications. A new k-modes clustering method that includes the weak correlation of attributes is proposed. Maximum information coefficient (MIC) is introduced to measure the correlation of variable attributes in the data set. The obtained MIC value is merged with the original distance to establish a new measurement method containing weak attribute correlation information to enhance the completeness of related information of variable attributes, and a more refined k-modes clustering method is established. Three different data sets are used …
Research On Satellite Navigation Confrontation Deduction Model For Wargame System,
2022
1.College of Joint Operations, National Defense University, Beijing 100091, China;2.Graduate School, National Defense University, Beijing 100091, China;
Research On Satellite Navigation Confrontation Deduction Model For Wargame System, Runqing Yang, Xi Wu
Journal of System Simulation
Abstract: Following the increasing reliance on space-based time-space metric of information warfare, satellite navigation confrontation becomes the major combat operation of competing for superiority in space-based position, navigation, and timing. Aiming at building a satellite navigation operation deduction model, on the basis of analyzing the relevant research, the navigation information environment computing model and the navigation deduction operation simulation model are designed, and the way to realize the visualization of navigation information environment of the wargame system and a substitution of high-resolution simulation computing model low-resolution probability effect model are explored, which support the pre-war planning and wartime evaluation …
Wind Power Primary Frequency Regulation Simulation Based On Wind Speed Prediction Model,
2022
School of Economics and Management, North China Electric Power University, Beijing 102206, China;
Wind Power Primary Frequency Regulation Simulation Based On Wind Speed Prediction Model, Zhenyu Zhao, Xu Ma, Geriletu Bao
Journal of System Simulation
Abstract: To furtherly improve the accuracy of wind speed prediction, considering the coupled comprehensive features of data internal structure external influencing factors, and the characteristics of wind turbines power, a BP-ARIMA combined prediction model is constructed and the wind speed of wind farms is simulated. Compared with the actual data, the high prediction accuracy of the model is verified. Bases on the curve of typical power characteristic of wind turbine, the optimal configuration of energy storage device capacity and the stability of wind turbine are considered, and the MATLAB-SIMULINK platform is applied to simulate the primary frequency regulation of …
Multi-Sensory Fusion Method For Power Transformer Virtual Assembly,
2022
North China Electric Power University, Baoding 071000, China;
Multi-Sensory Fusion Method For Power Transformer Virtual Assembly, Xuqiang Shao, Haowei Zhang, Xiaohua Feng
Journal of System Simulation
Abstract: Virtual assembly technology is to truly restore the equipments and physical scenarios. In the real world, people can interact with the physical world through visual, auditory, tactile and other sense organ. Aiming at the existing virtual assembly system being limited the single sense human-computer interaction mode, so a multi-sense fusion information interaction method is proposed to improve the sense of immersion and operability. An improved AABB Octree bounding box collision detection algorithm of large size diffidence component to be assembled is proposed, which can greatly reduce the amount of calculation and improve the calculation accuracy. The experimental result verifies …
Reinforcement-Learning-Based Adaptive Tracking Control For A Space Continuum Robot Based On Reinforcement Learning,
2022
1.Dalian University of Technology, Dalian 116024, China;
Reinforcement-Learning-Based Adaptive Tracking Control For A Space Continuum Robot Based On Reinforcement Learning, Da Jiang, Zhiqin Cai, Zhongzhen Liu, Haijun Peng, Zhigang Wu
Journal of System Simulation
Abstract: Aiming at the tracking control for three-arm space continuum robot in space active debris removal manipulation, an adaptive sliding mode control algorithm based on deep reinforcement learning is proposed. Through BP network, a data-driven dynamic model is developed as the predictive model to guide the reinforcement learning to adjust the sliding mode controller's parameters online, and finally realize a real-time tracking control. Simulation results show that the proposed data-driven predictive model can accurately predict the robot's dynamic characteristics with the relative error within ±1% to random trajectories. Compared with the fixed-parameter sliding mode controller, the proposed adaptive controller …
Simulation Research On Aerodynamic Characteristics Of A Miniature Munition,
2022
Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science & Technology University, Beijing 100192, China;
Simulation Research On Aerodynamic Characteristics Of A Miniature Munition, Haisen Wang, Junfang Fan
Journal of System Simulation
Abstract: Aiming at the existing constraints of layout design of miniature munition, a new layout optimization scheme is proposed. Based on the normal layout structure, a three-dimensional aerodynamic model of munition is established, and the aerodynamic hydrodynamics simulation of miniature munition is carried out, and the influence of wing size on static stability of munition is studied. To verify the feasibility of the design scheme, the aerodynamic simulation calculation is carried out, and the lift, drag and pitching moment parameters of miniature munition are obtained through simulated aerodynamic calculation under different flight conditions, and the lift-drag ratio …
Fatigue Detection Method Based On Facial Features And Head Posture,
2022
1.School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China;2.Key Laboratory of Advanced Control and Optimization of Jiangxi Province, Nanchang 330013, China;
Fatigue Detection Method Based On Facial Features And Head Posture, Rongxiu Lu, Bihao Zhang, Zhenlong Mo
Journal of System Simulation
Abstract: Aiming at the of the single fatigue characteristics, low robustness and inability to customize fatigue thresholds for different drivers of fatigue detection methods, a method based on facial features and head posture is proposed. In face detection and face key point positioning HOG feature operator and regression tree algorithm are used. In head posture estimation, head posture Euler angle is estimated by combining the face key points with the coordinate system transformation. In fatigue feature extraction, a deep residual neural network model is established to extract the eye fatigue features, which the eye, mouth aspect ratio and head posture …
Task Allocation Method For Multi-Uav Cooperative Reconnaissance In Complex Environment,
2022
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;
Task Allocation Method For Multi-Uav Cooperative Reconnaissance In Complex Environment, Fuzhen Zhang, Yaoqin Zhu
Journal of System Simulation
Abstract: The existing cooperative planning methods of multiple UAVs often carry out path planning and task allocation separatly, which causes the cooperative scheme not being the best in a complex environment. The cost matrix of multi-UAV cooperative reconnaissance on heterogeneous targets is established. Aiming at the various obstacle constraints and the characteristics of UAV motion and track in complex environment, an improved PSO-AFSA is used to solve the single UAV track planning model. Hungarian algorithm is used to complete the cooperative allocation of reconnaissance tasks of UAVs. The simulation results show that the algorithm can make the flying range of …
Simulation Of Emergency Medical Materials Collaborative Distribution Considering Supplier Clustering,
2022
1.China Research Center for Emergency Management, Wuhan University of Technology, Wuhan 430070, China;2.School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan 430070, China;
Simulation Of Emergency Medical Materials Collaborative Distribution Considering Supplier Clustering, Zhe Wang, Hongyuan Shao, Zihao Cong, Wenwen Ma
Journal of System Simulation
Abstract: Aiming at the insufficient local government medical material reserves and low distribution efficiency to public health emergencies, consider supplier clustering a two-stage emergency medical supplies public-private collaborative location and alloation model is designed. In for disaster preparedness, fuzzy clustering algorithm to realize supplier clustering, and a cooperation mechanism with the government is establish realize the joint storage of emergency medical supplies; In the early stage of rescue, relying on the government and various suppliers' material storage and transportation capabilities, a location allocation simulation model to minimize the weighted sum of total logistics cost and demand unsatisfied rate is …
Research On High-Performance Emulation Technology Of Starlink Constellation Based On Cloud Platform,
2022
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China;
Research On High-Performance Emulation Technology Of Starlink Constellation Based On Cloud Platform, Yuan Liu, Xinyi Xue, Xiaofeng Wang
Journal of System Simulation
Abstract: Network emulation on Starlink constellation is an important verification and evaluation tool for the design and construction of low earth orbit constellations in the future. Aiming at the characteristics of large-scale and complex structure of the Starlink network, a high-performance satellite network emulation system is designed. Based on the distributed network emulation architecture of cloud platform, through the development of STK Engine underlying interface, satellite model library storage optimization and asynchronous message transmission technology, the rapid deployment of Starlink constellation is carried out, and has good scalability. The experimental results show that the proposed method can carry out …
Analysis And Research On End-To-End Optical Image Quality Of Large Aperture Off-Axis Space Telescope,
2022
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China;
Analysis And Research On End-To-End Optical Image Quality Of Large Aperture Off-Axis Space Telescope, Zhang Ban, Xiaobo Li, Xun Yang, Yuxi Jiang
Journal of System Simulation
Abstract: In order to evaluate the image quality of space on orbit telescope under the comprehensive constraints, the chain calculation analysis method is adopted. The main error factors of the optical image quality degradation are divided into six static errors and two dynamic errors. After calculation, the optical system wavefront aberration under the static loading error is reduced to 0.057λ on average. Under the influence of the static and dynamic errors, the 80% energy concentration of point spread function increases gradually. The method can be used to analyze and evaluate the influence of image stabilization control and precise temperature …
Towards Qos-Based Embedded Machine Learning,
2022
Chapman University
Towards Qos-Based Embedded Machine Learning, Tom Springer, Erik Linstead, Peiyi Zhao, Chelsea Parlett-Pelleriti
Engineering Faculty Articles and Research
Due to various breakthroughs and advancements in machine learning and computer architectures, machine learning models are beginning to proliferate through embedded platforms. Some of these machine learning models cover a range of applications including computer vision, speech recognition, healthcare efficiency, industrial IoT, robotics and many more. However, there is a critical limitation in implementing ML algorithms efficiently on embedded platforms: the computational and memory expense of many machine learning models can make them unsuitable in resource-constrained environments. Therefore, to efficiently implement these memory-intensive and computationally expensive algorithms in an embedded computing environment, innovative resource management techniques are required at the …
Class Is Invariant To Context And Vice Versa: On Learning Invariance For Out-Of-Distribution Generalization,
2022
Nanyang Technological University
Class Is Invariant To Context And Vice Versa: On Learning Invariance For Out-Of-Distribution Generalization, Jiaxin Qi, Kaihua Tang, Qianru Sun, Xian-Sheng Hua, Hanwang Zhang
Research Collection School Of Computing and Information Systems
Out-Of-Distribution generalization (OOD) is all about learning invariance against environmental changes. If the context in every class is evenly distributed, OOD would be trivial because the context can be easily removed due to an underlying principle: class is invariant to context. However, collecting such a balanced dataset is impractical. Learning on imbalanced data makes the model bias to context and thus hurts OOD. Therefore, the key to OOD is context balance.We argue that the widely adopted assumption in prior work—the context bias can be directly annotated or estimated from biased class prediction—renders the context incomplete or even incorrect. In contrast, …
Machine Learning To Predict Warhead Fragmentation In-Flight Behavior From Static Data,
2022
Embry-Riddle Aeronautical University
Machine Learning To Predict Warhead Fragmentation In-Flight Behavior From Static Data, Katharine Larsen
Doctoral Dissertations and Master's Theses
Accurate characterization of fragment fly-out properties from high-speed warhead detonations is essential for estimation of collateral damage and lethality for a given weapon. Real warhead dynamic detonation tests are rare, costly, and often unrealizable with current technology, leaving fragmentation experiments limited to static arena tests and numerical simulations. Stereoscopic imaging techniques can now provide static arena tests with time-dependent tracks of individual fragments, each with characteristics such as fragment IDs and their respective position vector. Simulation methods can account for the dynamic case but can exclude relevant dynamics experienced in real-life warhead detonations. This research leverages machine learning methodologies to …
Equivariance And Invariance Inductive Bias For Learning From Insufficient Data,
2022
Nanyang Technological University
Equivariance And Invariance Inductive Bias For Learning From Insufficient Data, Tan Wang, Qianru Sun, Sugiri Pranata, Karlekar Jayashree, Hanwang Zhang
Research Collection School Of Computing and Information Systems
We are interested in learning robust models from insufficient data, without the need for any externally pre-trained model checkpoints. First, compared to sufficient data, we show why insufficient data renders the model more easily biased to the limited training environments that are usually different from testing. For example, if all the training "swan" samples are "white", the model may wrongly use the "white" environment to represent the intrinsic class "swan". Then, we justify that equivariance inductive bias can retain the class feature while invariance inductive bias can remove the environmental feature, leaving only the class feature that generalizes to any …
Pruning The Communication Bandwidth Between Reinforcement Learning Agents Through Causal Inference: An Innovative Approach To Designing A Smart Grid Power System,
2022
Dalian University of Technology
Pruning The Communication Bandwidth Between Reinforcement Learning Agents Through Causal Inference: An Innovative Approach To Designing A Smart Grid Power System, Xianjie Zhang, Yu Liu, Wenjun Li, Chen Gong
Research Collection School Of Computing and Information Systems
Electricity demands are increasing significantly and the traditional power grid system is facing huge challenges. As the desired next-generation power grid system, smart grid can provide secure and reliable power generation, and consumption, and can also realize the system’s coordinated and intelligent power distribution. Coordinating grid power distribution usually requires mutual communication between power distributors to accomplish coordination. However, the power network is complex, the network nodes are far apart, and the communication bandwidth is often expensive. Therefore, how to reduce the communication bandwidth in the cooperative power distribution process task is crucially important. One way to tackle this problem …
Explanation Guided Contrastive Learning For Sequential Recommendation,
2022
Singapore Management University
Explanation Guided Contrastive Learning For Sequential Recommendation, Lei Wang, Ee-Peng Lim, Zhiwei Liu, Tianxiang Zhao
Research Collection Lee Kong Chian School Of Business
Recently, contrastive learning has been applied to the sequential recommendation task to address data sparsity caused by users with few item interactions and items with few user adoptions. Nevertheless, the existing contrastive learning-based methods fail to ensure that the positive (or negative) sequence obtained by some random augmentation (or sequence sampling) on a given anchor user sequence remains to be semantically similar (or different). When the positive and negative sequences turn out to be false positive and false negative respectively, it may lead to degraded recommendation performance. In this work, we address the above problem by proposing Explanation Guided Augmentations …
Dynamic Return Relationships In The Market For Cryptocurrency: A Var Approach,
2022
James Madison University
Dynamic Return Relationships In The Market For Cryptocurrency: A Var Approach, Julian Gouffray
James Madison Undergraduate Research Journal (JMURJ)
This paper examines how the Bitcoin-altcoin return relationship has evolved in periods between 2015 and 2020. To understand this relation, we observe data on the cryptocurrency Bitcoin and prominent altcoins Ethereum, Litecoin, Ripple, Stellar, and Monero, which collectively represent over 90% of the market throughout the observed period. We employ a vector autoregressive model (VAR) to produce forecast error variance decompositions, orthogonal impulse response functions, and Granger-causality tests. We find evidence that Bitcoin return variation has increasingly explained altcoin returns and that market inefficiency increased between 2017 and 2020, as shown by increased Granger causality between Bitcoin and altcoins. These …
Parallel Simulation System Of Equipment Precision Maintenance Based On Cloud-Edge-End Architecture,
2022
Shijiazhuang Campus, Army Engineering University, Shijiazhuang 050003, China;
Parallel Simulation System Of Equipment Precision Maintenance Based On Cloud-Edge-End Architecture, Yanqiang Di, Ting Li, Shaochong Feng, Qiongyao Liu, Jianhong Lü, Zhijia Chen, Yang Zhang, Pengfei Cao
Journal of System Simulation
Abstract: Aiming at the demands of equipment precision maintenance, based on the previous research results of equipment parallel simulation, the algorithm of equipment remaining useful life (RUL)prediction is optimized and a parallel simulation system for equipment precision maintenance with cloud-edge-end architecture is designed. At the equipment end, the system collects equipment status data and preprocesses it with edge devices. At the cloud end, based on simulation model, in parallel with the equipment entity, the system dynamically predicts the RUL of equipment. The prediction results are applied to the formulation and deduction of equipment maintenance plans to support the equipment …
