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Operations Research, Systems Engineering and Industrial Engineering Commons™
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Articles 841 - 870 of 5232
Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Simulation And Optimization Of Permanent Magnet Linear Machine Based On Deep Neural Network, Yan Shiliang, Yinling Wang, Dandan Lu, Xiaoqin Pan
Simulation And Optimization Of Permanent Magnet Linear Machine Based On Deep Neural Network, Yan Shiliang, Yinling Wang, Dandan Lu, Xiaoqin Pan
Journal of System Simulation
Abstract: The finite element model (FEM) of permanent magnet linear synchronous machines (PMLSMs) takes a long computing time and cannot directly display the relationship between structural parameters and output thrust, thus failing to guide the structural parameter optimization of the machine. An improved simulation model of PMLSMs based on the subdomain analytical method and deep neural network (DNN) algorithm is proposed. The magnetic flux density, no-load counter electromotive force (EMF), and other data are obtained according to Maxwell's equations. The nonlinear relationship between the structural parameters of the machine and output thrust is fitted by the DNN algorithm. Based on …
Formation Strategy Of Hybrid Obstacle Avoidance Algorithm For Multiple Mobile Robots, Fulin Liu, Qingxin Li
Formation Strategy Of Hybrid Obstacle Avoidance Algorithm For Multiple Mobile Robots, Fulin Liu, Qingxin Li
Journal of System Simulation
Abstract: For the obstacle avoidance problem of multiple mobile robots in the unknown static obstacle environment, this paper proposed a formation strategy of a hybrid obstacle avoidance algorithm for multiple mobile robots, ensuring that multiple mobile robots do not collide during operation, can maintain the formation to the maximum extent in the unknown static obstacle environment for effective obstacle avoidance, and can reach the designated target point in a short time. Based on the leaderfollower method and artificial potential field (APF) method, the formation strategy divided the robots in the system into the leader robot and the follower robot. According …
Research On Hybrid Experimental Scheme Design For Combat Simulation, Fei Liu, Peng Lai, Yingbo Lu, Min Wang, Zhifeng Lu
Research On Hybrid Experimental Scheme Design For Combat Simulation, Fei Liu, Peng Lai, Yingbo Lu, Min Wang, Zhifeng Lu
Journal of System Simulation
Abstract: Combat simulation experimental design refers to sampling the values of experimental factors based on baseline combat scenarios using various experimental design methods and then generating a set of experimental schemes for the sequential simulation. The complexity of combat simulation, such as numerous experimental factors and distinct factor types, including continuous and discrete numeric types, poses several challenges and requires efficient hybrid experimental design methods. To address these issues, this paper conducts a study on the hybrid experimental scheme design for combat simulation. This paper gives a brief classification and review of experimental design methods and presents three hybrid experimental …
Intelligent Optimization Of Coal Terminal Unloading Scheduling Based On Improved D3qn Algorithm, Baoxin Qin, Yuxiao Zhang, Sirui Wu, Weichong Cao, Zhan Li
Intelligent Optimization Of Coal Terminal Unloading Scheduling Based On Improved D3qn Algorithm, Baoxin Qin, Yuxiao Zhang, Sirui Wu, Weichong Cao, Zhan Li
Journal of System Simulation
Abstract: Intelligent decision scheduling can improve the operation efficiency of large ports, which is one of the important research directions for the implementation of artificial intelligence technology in the smart port scenario. This article studies the intelligent unloading scheduling tasks of coal terminals and abstracts them as a Markov sequence decision problem. A deep reinforcement learning model for this problem is established, and an improved D3QN algorithm is proposed to realize intelligent optimization of unloading scheduling decisions by considering the characteristics of high action space dimension and sparse feasible action in the model. The simulation results show that for the …
Path Planning For Improvement Of A* Algorithm And Artificial Potential Field Method, Xiang Yu, Chen Jiang, Sirui Duan, Qianrui Deng
Path Planning For Improvement Of A* Algorithm And Artificial Potential Field Method, Xiang Yu, Chen Jiang, Sirui Duan, Qianrui Deng
Journal of System Simulation
Abstract: A* algorithm has the problem of too many polyline paths and search nodes, while the artificial potential field (APF) method has the problems of local optimality and unattainability. These problems are investigated in this paper. A new hybrid heuristic function is proposed based on the Euclidean distance and projection distance, based on which the A* algorithm process is improved accordingly. The search nodes of the A* algorithm are reduced, and the search efficiency is improved. The optimal node generated by the new A* algorithm is used as the local target point of the APF algorithm to assist in getting …
Effectiveness Evaluation Of Heterogeneous Uav Swarms Based On A Hybrid Model, Yuanjie Lu, Shanshan Long, Hang Zhao, Guoxu Feng, Xiaojia Zhao
Effectiveness Evaluation Of Heterogeneous Uav Swarms Based On A Hybrid Model, Yuanjie Lu, Shanshan Long, Hang Zhao, Guoxu Feng, Xiaojia Zhao
Journal of System Simulation
Abstract: This paper presents a hybrid model based on availability dependability capability (ADC) system performance evaluation and back propagation (BP) neural network prediction to realize a rapid performance evaluation of UAV swarms and cope with the diversity of UAV swarm configuration and state and the complexity of performance calculation. By analyzing the components of swarm performance, a capability index system including the general platform capability, system-level capability, and task execution capability of UAVs is established. By using the ADC method, a swarm combat performance sample set is generated, and the BP neural network is used to construct a comprehensive combat …
Construction Of Surrogate Model Driven By Model And Data, Jing An, Guangya Si, Miaoting Zeng
Construction Of Surrogate Model Driven By Model And Data, Jing An, Guangya Si, Miaoting Zeng
Journal of System Simulation
Abstract: By taking the three-dimensional projection action in a certain combat style as the research object, a surrogate model construction method driven by model and data is proposed to support the operational action research, so as to solve the problem that the calculation factors are too much during simulated deduction; the calculation resource cost is too large, and the calculation accuracy of the general analytical model is insufficient. Firstly, an analytical model group of three-dimensional projections with coefficients to be optimized is constructed based on military theory, including weapons and equipment, forces, etc. In addition, the composition and parameter setting …
On Intrinsic Dimensionality Of Data Sets And Neural Networks, Ori Chachmo
On Intrinsic Dimensionality Of Data Sets And Neural Networks, Ori Chachmo
Theses and Dissertations
The concept of Intrinsic Dimensionality (ID) is of special interest in the field of Neural Networks (NNs) since it promotes both (a) a deeper understanding of the underlying mechanisms, and (b) embraces parsimonious modeling (that is, building the right-sized model for the task) with associated benefits to processing speed and storage requirements. This thesis explores the concept of ID via two separate, but related, questions. First, we study the potential of NN ID prediction by exploiting easily obtained quantities measured on the data. We then explore NN ID as an independent concept by comparing the results of different methods for …
Reinforcement Learning For Team Based Air Combat Maneuvering Decisions With Directed Energy Weaponry, Joshua D. Combs
Reinforcement Learning For Team Based Air Combat Maneuvering Decisions With Directed Energy Weaponry, Joshua D. Combs
Theses and Dissertations
Leveraging the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) we investigate the use of reinforcement learning (RL) techniques for imbuing AUCAV agents with high-quality behaviors for the within-visual-range air combat maneuvering problem (ACMP). We formulate the 2v2 WVR ACMP as a Markov decision process wherein friendly AUCAVs are equipped with DEW capabilities and operate with 6 degrees of freedom. We utilize the Double Deep Q-Network RL algorithm, which centrally trains two friendly AUCAVs and employ a phased learning approach, initially exposing the AUCAVs to a dense reward environment for early training, followed by a sparse reward environment to encourage …
Artificial Intelligence And Perception: An Empirical Study, Anthony J. Neil
Artificial Intelligence And Perception: An Empirical Study, Anthony J. Neil
Theses and Dissertations
This thesis investigates the impact of adjusting artificial intelligence explainability levels’ outputs on user perception. The overarching study extends within the Explainable Artificial Intelligence (XAI) domain. It examines a spectrum of variables, including performance, cognizance, familiarity, transparency, system bias, and the overall impact of AI, to understand their collective and individual effects that enable effective professional use in an organization. The study aims to illuminate the relationship between the degree of explainability provided by large language models such as ChatGPT, Bard, and Bing AI and the performance of these models when tasked with XAI adjustments.
A Model-Based And System-Theoretic Approach To Design Advanced Autonomy For Air Dominance Missions: A Loyal Wingman Case Study, Elizabeth S. Pennington
A Model-Based And System-Theoretic Approach To Design Advanced Autonomy For Air Dominance Missions: A Loyal Wingman Case Study, Elizabeth S. Pennington
Theses and Dissertations
In contested air environments, safe coordination between decision-makers is paramount. Although the Department of Defense (DoD) prioritizes the development of Artificially Intelligent (AI) wingmen for air combat, a lack of methodology exists to design safe, holistic coordination between human and autonomous wingmen in the same environment. This thesis delivers a framework using Systems Theoretic Process Analysis Extended for Coordination (STPA-Coord) to analyze and design holistic coordination for the Loyal Wingman concept in an Air Dominance mission. STPA-Coord is a safety and hazard analysis process that uses Systems Theory to analyze and design coordination between decisionmakers in a system-of-systems architecture. Using …
Performance Of Humans And Agents In A Systems Modeling Language V2 Task: A User-Centered Evaluation Approach, Allen W. Dukes
Performance Of Humans And Agents In A Systems Modeling Language V2 Task: A User-Centered Evaluation Approach, Allen W. Dukes
Theses and Dissertations
The Department of Defense is adopting Digital Engineering practices for its workforce. Simultaneously, the larger Systems Engineering community strives to modernize and define those Digital Engineering practices. These efforts to move from traditionally document-based approaches to pure digital implementations will require enhanced capabilities to manage and digitally track the lifecycle of a program or product. However, this growth must address tool design through an iterative process focusing on usability for many user types. Many tools and technologies exist but often lack an assessment of usability when engineers design tools for other engineers. Including usability when developing solutions for Digital Engineering …
Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel Chang, Nan Hu, Peng Liang, Morgan Swink
Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel Chang, Nan Hu, Peng Liang, Morgan Swink
Research Collection School Of Computing and Information Systems
Integrating the real options perspective and resource dependence theory, this study examines how firms adjust their innovation investments to trade policy effect uncertainty (TPEU), a less studied type of firm specific, perceived environmental uncertainty in which managers have difficulty predicting how potential policy changes will affect business operations. To develop a text-based, context-dependent, time-varying measure of firm-level perceived TPEU, we apply Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art deep learning approach. We apply BERT to analyze the texts of mandatory Management Discussion and Analysis (MD&A) sections of annual reports for a sample of 22,669 firm-year observations from 3,181 unique …
U.S. Army Cadet Command Branch Prediction Model, Daniel M. Krizan
U.S. Army Cadet Command Branch Prediction Model, Daniel M. Krizan
Theses and Dissertations
The current system for providing US Army ROTC cadets their branches leaves significant uncertainty until the final pronouncement of branch assigned. This uncertainty can be alleviated by providing a prediction model for cadets to input personal data and desired branch to identify likelihood of receiving the request. This thesis produces a machine learning model capable of producing branch prediction for cadets.
Automated Image Registration For Titanium Aircraft Components Via Resolution-Robust Parallel Neural Networks, Paige T. Luebbering
Automated Image Registration For Titanium Aircraft Components Via Resolution-Robust Parallel Neural Networks, Paige T. Luebbering
Theses and Dissertations
Titanium alloys are vital to the structural integrity of military and commercial aircraft, comprising numerous critical components. These components are composed of microtexture regions (MTRs) that, at a specific size and orientation, can lead to aircraft failure. Existing MTR testing methods, such as Electron Backscatter Diffraction, often fall short in effectively detecting these MTRs without causing damage to the component. Addressing this gap, this thesis develops a Parallel Convolutional Neural Network (CNN) model tailored for multi-resolution image registration of Polarized Light Microscopy (PLM) images to enhance MTR identification in a non-invasive manner. The findings reveal a significant enhancement in the …
An Analysis Of China’S Perceived Geographic Locations Of Interest By Use Of Value Informed Facility Location Models, Layton C. Hedge
An Analysis Of China’S Perceived Geographic Locations Of Interest By Use Of Value Informed Facility Location Models, Layton C. Hedge
Theses and Dissertations
This research examines China and derives insights specific to it and the First Island Chain and the Second Island Chain. In doing so, this research demonstrates a methodology to examine other competitors and their geostrategic interests. In the first phase of analysis, it develops a value hierarchy to depict objectives within subregions of the area of interest and considers four alternative weightings of the value hierarchy. In the second phase of analysis, it applies four location-covering models to assess how the competitor would emplace a range of limited resources to deter and/or control points of interest. Results indicate that land-based …
Simulating Human-Autonomous Aircraft Teams In An Anti-Access Area Denial (A2ad) Environment, Michael Kaminski
Simulating Human-Autonomous Aircraft Teams In An Anti-Access Area Denial (A2ad) Environment, Michael Kaminski
Theses and Dissertations
The role of autonomy has evolved recently, demanding tighter integration between human and autonomous systems, particularly in highly contested A2AD environments. Near-peer adversaries have modernized their integrated air defense systems (IADS), diminishing the current advantages of the United States Air Force. To regain air dominance, efforts like the Collaborative Combat Aircraft (CCA) program are underway, aiming to deploy unmanned autonomous alongside manned next-generation fighter aircraft. This research assesses various operational concepts, focusing on autonomous tactics post-manned fighter loss, strike timing of independent teams, and weapon configuration observability. Using the Advanced Framework for Simulation, Integration and Modeling (AFSIM), an agent-based model …
A Reinforcement Learning Approach To The 2v2 Beyond Visual Range Air Combat Maneuvering Problem, Jacob J. Pike
A Reinforcement Learning Approach To The 2v2 Beyond Visual Range Air Combat Maneuvering Problem, Jacob J. Pike
Theses and Dissertations
This research examines a 2v2 air combat maneuvering problem (ACMP) in a Beyond Visual Range (BVR) environment. A discrete-time, infinite-horizon Markov Decision Process (MDP) model represents the BVR-ACMP, seeking to determine high-quality policies for a pair of autonomous aircraft to execute tactical maneuvers and firing decisions. The Advanced Framework for Simulation, Integration, and Modeling (AFSIM) characterizes the complex six-degree of freedom (6-DOF) aircraft operations, encompassing kinematics, sensors, and weapons. Given the high dimensionality and continuous nature of the state and decision variables, a deep reinforcement learning (RL) solution approach is adopted wherein the value function is approximated via a Neural …
A Random Forest-Based Q-Learning Algorithm: Toward Interpretable Artificial Intelligence, Victor R. Rae
A Random Forest-Based Q-Learning Algorithm: Toward Interpretable Artificial Intelligence, Victor R. Rae
Theses and Dissertations
A growing demand exists for interpretable artificial intelligence models, leading to extensive research efforts to enhance the explainability and transparency of policies generated by reinforcement learning (RL) methods. This research develops random forest-based RL algorithms as a logical progression in this academic pursuit. The algorithms are evaluated using three standard benchmark environments from OpenAI gym — CartPole, MountainCar, and LunarLander — and compared to implementations of the Deep Q-learning Network (DQN) and Double DQN (DDQN) algorithms for various metrics, including performance, robustness, efficiency, and interpretability. The random forest-based algorithms exhibit superior performance to both neural network-based algorithms in two out …
Federated Analysis Of Wearables Data For United States Air Force Mental And Physical Readiness, Sung Yong O
Federated Analysis Of Wearables Data For United States Air Force Mental And Physical Readiness, Sung Yong O
Theses and Dissertations
This thesis investigates the use of machine learning and deep learning models within a federated learning framework to predict physical and mental readiness in military personnel, using wearable technology data. The collaboration with the 711th Human Performance Wing’s STRONG Lab highlights the importance of readiness as emphasized by the National Defense and Security Strategies. The study evaluates various predictive models, incorporating federated learning to ensure data privacy and security in healthcare systems. By analyzing a comprehensive dataset, the research aims to contribute to military readiness enhancement through technological advancements, supporting health and wellness initiatives to bolster the effectiveness of military …
T-Pickseer: Visual Analysis Of Taxi Pick-Up Point Selection Behavior, Shuxian Gu, Yemo Dai, Zezheng Feng, Yong Wang, Haipeng Zeng
T-Pickseer: Visual Analysis Of Taxi Pick-Up Point Selection Behavior, Shuxian Gu, Yemo Dai, Zezheng Feng, Yong Wang, Haipeng Zeng
Research Collection School Of Computing and Information Systems
Taxi drivers often take much time to navigate the streets to look for passengers, which leads to high vacancy rates and wasted resources. Empty taxi cruising remains a big concern for taxi companies. Analyzing the pick-up point selection behavior can solve this problem effectively, providing suggestions for taxi management and dispatch. Many studies have been devoted to analyzing and recommending hotspot regions of pick-up points, which can make it easier for drivers to pick-up passengers. However, the selection of pick-up points is complex and affected by multiple factors, such as convenience and traffic management. Most existing approaches cannot produce satisfactory …
Environmental Analysis Of Codfish Production And Consumption In Portugal, Ayla Frost, Joana Almeida, Joana Dionisio, Radu Godina, Cátia Magro
Environmental Analysis Of Codfish Production And Consumption In Portugal, Ayla Frost, Joana Almeida, Joana Dionisio, Radu Godina, Cátia Magro
Journal of Critical Global Issues
Codfish, a typical dish consumed frequently in Portugal, is fished in the cold Northern Atlantic waters far from the Portuguese coast. Thus, regional tensions and the environmental impacts of Portuguese codfish importation and production are significant current issues. The present study uses a life cycle assessment methodology to evaluate the array of environmental impacts of the production of 1 kg of codfish, following a cradle to gate approach. The life cycle inventory was grouped into catch and preprocessing, transit, and curing stages. The data collection of products and processes in each stage was based on scientific peer reviewed documents, technical …
Identification Of Faults In Highways Using Approximation Methods And Algorithms, Khudayberdiyev Khakkulmirzayevich Mirzaakbar, Anvar Asatilloyevich Ravshanov
Identification Of Faults In Highways Using Approximation Methods And Algorithms, Khudayberdiyev Khakkulmirzayevich Mirzaakbar, Anvar Asatilloyevich Ravshanov
Chemical Technology, Control and Management
Many fast Fourier transforms are used to identify defective parts of uneven surfaces on roads and send information to relevant organizations on the road, using the " RAVON YO‘LLAR" application installed on a mobile device during car movement. We determine the uneven parts of the road. Smooth and well-maintained roads reduce the risk of vehicle collisions, skidding and other road-related incidents. Timely measures contribute to overall safety, comfort and economic efficiency.
A Two-Stage Based Life Cycle And Principal Component Analysis For Decision Support Of Potential Municipal Solid Wastes Management Scenarios, Case Study Dar Es Salaam, Tanzania, Edwin Richard Ndibalema Dr
A Two-Stage Based Life Cycle And Principal Component Analysis For Decision Support Of Potential Municipal Solid Wastes Management Scenarios, Case Study Dar Es Salaam, Tanzania, Edwin Richard Ndibalema Dr
Tanzania Journal of Engineering and Technology (TJET)
In most of the urban areas of Tanzania, poor and inadequate solid waste management is a growing environmental and public health concern. Despite an array of solid waste management options, choosing an appropriate one remains a challenge. This study applied a novel two-stage-based approach to assess the environmental impacts and sustainability performance of the 27 scenarios for municipal solid wastes (MSW) management of Dar es Salaam City, Tanzania. In the first stage, the study utilized a life cycle analysis (LCA) to quantify the environmental impacts of MSW scenarios. In the second stage, the study used the principal component analysis (PCA) …
Efficiency Optimization Method For Data Sampling In Power Grid Topology Scheduling Simulation, Yingying Zhao, Pusen Dong, Tianchen Zhu, Fan Li, Yun Su, Zhenying Tai, Qingyun Sun, Hang Fan
Efficiency Optimization Method For Data Sampling In Power Grid Topology Scheduling Simulation, Yingying Zhao, Pusen Dong, Tianchen Zhu, Fan Li, Yun Su, Zhenying Tai, Qingyun Sun, Hang Fan
Journal of System Simulation
Abstract: To address the large simulation computational workload and low simulation speed caused by the scale and complexity of the new power system, a simulation acceleration method for topology scheduling based on the distributed and quantization mechanisms is proposed. The parallelization of topology scheduling models is used to increase the scale of data simulation sampling in unit time. The introduced quantization operators accelerate the computation speed of the topology scheduling model operators, reduces the time cost of the every single simulation. Case studies confirm the effectiveness of the topology simulation acceleration, in which the available transfer capacity of the simulated …
Runoff Intelligent Prediction Method Based On Broad-Deep Fusion Time-Frequency Analysis, Ying Han, Lehao Wang, Shumei Wang, Xiang Zhang, Xingxing Luo
Runoff Intelligent Prediction Method Based On Broad-Deep Fusion Time-Frequency Analysis, Ying Han, Lehao Wang, Shumei Wang, Xiang Zhang, Xingxing Luo
Journal of System Simulation
Abstract: Broad learning system(BLS) is introduced to tackle the existed disadvantage that LSTM-based runoff prediction model is easy to fall into local optimization. To reduce the influence of noise on the prediction results, the variational mode decomposition (VMD) is adopted to transform the onedimensional time-domain runoff signal to the two-dimensional time-frequency plane. The runoff prediction model based on VMD-LSTM-BLS is proposed. The simulation results demonstrate that the prediction accuracy of the new model is more significantly improved compared with the baseline model and the existing LSTM-based runoff prediction model.
Simulation Platform Of Agv System Scheduling Algorithms In Uncertain Environment, Zhihao Shi, Haihui Shen
Simulation Platform Of Agv System Scheduling Algorithms In Uncertain Environment, Zhihao Shi, Haihui Shen
Journal of System Simulation
Abstract: As a complete automated guided vehicle(AGV) system scheduling algorithm, it must include conflict-handling strategy, together with common dispatching strategy and routing algorithm. However, due to the uncertainty, characteristics of such scheduling algorithm are difficult to be analyzed theoretically, and the relevant study is lacking. An AGV system simulation platform based on the discreteevent simulation technique is designed and developed, which can flexibly set the scheduling problem, choose the dispatching strategy, routing algorithm, and conflict-handling strategy for the scheduling algorithm, to run the simulation. The platform has a visual interface, from which the running status of AGVs and the performance …
Research On Motion Planning Of Hexapod Robot Based On Drl And Free Gait, Xinpeng Wang, Huiqiao Fu, Guizhou Deng, Kaiqiang Tang, Chunlin Chen, Canghao Liu
Research On Motion Planning Of Hexapod Robot Based On Drl And Free Gait, Xinpeng Wang, Huiqiao Fu, Guizhou Deng, Kaiqiang Tang, Chunlin Chen, Canghao Liu
Journal of System Simulation
Abstract: To improve the passability and the motion performance of the hexapod robot in the unstructured environment, a multi-contact motion planning algorithm based on DRL and free gait planner is proposed. Firstly, the free gait planner obtains the reachable footholds under the target state and outputs the optimal gait sequence. The center of mass motion policy of the hexapod robot in the randomly generated plum blossom pile environment is obtained by using deep reinforcement learning training. To ensure the reachability between adjacent states of the robot in motion, the state transition feasibility model is used to judge the state transition …
Robust Predictive Control Of Nonplanar Fully-Actuated Uavs, Yun Ma, Yuan Wang, Meng Li, Peng Wang, Yanling Tang
Robust Predictive Control Of Nonplanar Fully-Actuated Uavs, Yun Ma, Yuan Wang, Meng Li, Peng Wang, Yanling Tang
Journal of System Simulation
Abstract: Targeting the problem that nonplanar fully-actuated unmanned aerial vehicles (UAVs) are susceptible to external winds and unmodeled dynamics, the predictive control system with good robustness is designed. A nonlinear motion model with six degrees of freedom is established through the Newton-Euler approach. A linear extended state observer is designed to estimate the state variables by transforming the system affected by matched and unmatched disturbances into an equivalent system only affected by the matched disturbances. A predictive controller is designed for the equivalent system to reduce the output oscillation and input surging and a disturbance compensator is also designed to …
Fault Detection Based On Sliding Window And Multiblock Convolutional Autoencoders, Jianpeng Mou, Weili Xiong
Fault Detection Based On Sliding Window And Multiblock Convolutional Autoencoders, Jianpeng Mou, Weili Xiong
Journal of System Simulation
Abstract: In order to further improve the fault detection performance and fully mine the timing and hidden feature information, a fault detection method based on convolutional auto encoder is proposed. On the basis of modeling the original information set, the modeling of cumulative information and rate of change information is added to enhance the mining of implicit information; The three reconstructed information sets are sampled by sliding windows, and time series feature extraction and modeling are performed based on convolutional auto encoders. Bayesian fusion of the decision results of the convolutional auto encoder is performed to obtain the statistics, and …