Engineering Commons^™

Optimization Of Multi-Target Interception Scheme Based On Performance Simulation Modeling, Hanwen Liu, Zhimin Zhuo, Xue Yang

Journal of System Simulation

Abstract: The air attack scenarios faced by air defense weapons and equipment show the trend of saturation, diversification and intelligence. It is very important to establish multi-target interception efficiency model and optimize interception scheme according to simulation. The current intercepting efficiency index mainly considers the whole operation process, and can not guide the optimization of the intercepting scheme of specific intercepting rounds. The generation of interception schemes mainly relies on experience and simple mathematical model, which is difficult to cope with the increasingly complex and changeable battlefield environment. Therefore, an interception scheme advantage index that comprehensively considers interception probability and …

Design And Prediction Of Deep Fuzzy Neural Network, Chengbiao Wei, Taoyan Zhao, Jiangtao Cao, Ping Li

Journal of System Simulation

Abstract: A deep fuzzy neural network (DFNN) is proposed to solve the problem that the deep neural network has poor interpretability and the correction of the model is not targeted when dealing with the big data regression prediction problem. The proposed deep fuzzy neural network adopts an adaptive fuzzy Cmeans (AFCM) clustering algorithm in structural learning. The structure of the model, namely the number of rules and the antecedent parameters of the rules, is determined by calculating the introduced validity function. The identification of consequent parameters uses an improved grey wolf optimization (IGWO) algorithm. By replacing the linear decreasing strategy …

Kill Chain Efficiency Evaluation Model Based On Gray Dematel-Anp, Zejing Zhao, Junliang Shang, Yanpei Qin

Journal of System Simulation

Abstract: In modern conflict scenarios, the kill chain is integral to the comprehensive understanding, orchestration, and execution of military operations. Accurately appraising the efficiency of the kill chain is imperative for gaining insights into battle dynamics and strategically distributing military assets. However, traditional assessments of kill chain efficacy have been hampered by fragmented and isolated indicators that frequently overlook the interplay and influence among various segments of the kill chain. To address these limitations, based on the characteristics of each phase of the kill chain and the OODA loop theory, a new set of performance evaluation indices has been proposed. …

Optimization Method For Multi Agricultural Machinery Collaborative Operation Based On Genetic Algorithm And A* Algorithm, Yiran Yu, Huicheng Lai, Guxue Gao, Guo Zhang, Wangyinan Peng, Longfei Yang, Junhao Huang

Journal of System Simulation

Abstract: To address the uneven task distribution among multiple agricultural machines (referred to as farm machinery) and the high time cost due to numerous turning points at intersections, this paper proposes a task planning method that combines a pre-heat multi grouped genetic algorithm (PHMGA) with the turn A* algorithm (tA*). PHMGA allocates tasks to each piece of farm machinery based on the known environment, ensuring balanced workload through a cost objective function that considers travel, operation, and turning distances. It also designs various operators and strategies to search for nearoptimal solutions. The tA* algorithm is used to select paths …

Research On The Truth, Function And Common Principles Of Simulation, Haohua Xu, Bin Xiao, Yunhao Cui

Journal of System Simulation

Abstract: Simulation applications are becoming increasingly widespread and have a greater impact, while the theoretical foundation of simulation is relatively weak. This article provides a new definition of simulation by analyzing the common activities of simulation, which can include both virtual and real simulation forms; referring to Popper's three worlds theory, this paper discusses the objective authenticity of simulation from a philosophical perspective; From a methodological perspective, this paper elaborates on the methodological characteristics of simulation as an indirect cognitive object, revealing its significance in integrating human-machine intelligence and promoting knowledge evolution. It also discusses the common principles of simulation, …

A Model Combining Self-Attention And Weight Sharing For Human Activity Recognition, Lun Ma, Yue Yang, Daihe Wang, Guisheng Liao, Xing Li

Journal of System Simulation

Abstract: With the prevalence of wearable devices, human activity recognition based on wearable sensor data has garnered significant attention. The central issue in this field is how to extract effective behavioral information from raw sensor data to form corresponding feature vectors. Currently, convolutional neural networks and recurrent neural networks have been widely utilized for feature extraction from multisensory data. However, these networks struggle to globally capture the crucial temporal features inherent of human activity over time. To address this, a multi-CNN-BiLSTM-self attention (Multi-CBSA) model based on self-attention and weight sharing has been proposed, taking into consideration the logical correlations among …

Wingtip Docking Control Of Composite Aircraft Based On Adrc Theory, Chunlei Xie, Hongxia Hu, Weibo Han

Journal of System Simulation

Abstract: The process of wingtip docking in composite aircraft is challenged by significant unsteady vortex aerodynamic disturbances arising from the close-range coupling of wingtips, thereby posing considerable constraints on docking precision and flight safety. This study endeavors to address the intricate task of airborne wingtip docking control amidst wingtip vortex disturbances through a comprehensive investigation of airborne wingtip docking control technology, grounded in the tenets of active disturbance rejection control (ADRC) theory. Initially, a mathematical model encapsulating the dynamics of three-channel attitude/displacement during the docking operation, incorporating both the wingtip docking mechanism and the wingtip vortex model, is established. …

Solving The Vehicle Routing Problem Based On Deep Reinforcement Learning, Ming Jiang, Tao He

Journal of System Simulation

Abstract: The capacitated vehicle routing problem (CVRP) is a well-known combinatorial optimization challenge recognized as NP-hard due to its significant complexity. Building upon existing research, this paper introduces a novel end-to-end deep reinforcement learning approach based on a multi-pointer Transformer to tackle the CVRP. The proposed algorithm employs an invertible residual network in the encoder to encode input features, effectively reducing memory consumption. In the decoder, a multipointer network determines the probability distribution of solutions. To further enhance the performance of CVRP solutions, the algorithm leverages the symmetry in combinatorial optimization by implementing multi-trajectory parallel processing during both training …

Lane Detection In Dark Light Based On Instance Association, Yanji Jiang, Yingyang Zhang, Hao Dong, Xiaoguang Zhang, Meihui Wang

Journal of System Simulation

Abstract: In current research on lane detection, existing algorithms can efficiently detect lane lines under good lighting conditions. However, lane detection in low light still faces the challenge of a high false negative rate. A detection algorithm called Instance Association Net(IANet) is proposed to address this issue by utilizing the structural relationships between lane lines, which is helpful for low light conditions. The algorithm first generates unique masks for different lane lines using features at the starting points of the lane lines and a global feature map, achieving instance-level feature separation of the lane lines. It employs an instance-level attention …

Anylogic-Based Platform-Enterprise Collaborative Scheduling Simulation System For Cloud Manufacturing, Linxuan Wang, Yongkui Liu, Lin Zhang, Tingyu Lin, Lihui Wang

Journal of System Simulation

Abstract: Aiming at the lack of research on collaborative scheduling between a cloud manufacturing platform and associated enterprises, as well as the lack of simulation systems to simulate scheduling strategy combinations and to visualize dynamic scheduling processes, a simulation system that supports visualization of cloud manufacturing platform-enterprise collaborative dynamic scheduling processes is designed and developed. System requirements are analyzed in detail, and then a scalable platform-enterprise collaborative scheduling model and system functional architecture based on hierarchical multi-agents is proposed. Combined with a case of supply chain of industrial robots, considering random selection, time optimal strategy in the cloud manufacturing …

Digital Twin Modeling Method For Bulk Cargo Stacks Based On 2d Lidar, Houjun Lu, Yifei Zhu, Yanping Rong, Wanghui Zhang

Journal of System Simulation

Abstract: Due to the characteristics of large equipment, harsh working environment and time-varying shape of the material pile in bulk cargo terminal, there are some disadvantages such as low data accuracy and poor stability when building the storage yard model, which affects the unmanned and intelligent operation control. In this paper, we use two-dimensional laser radar combined with equipment mechanism motion to scan material pile point cloud data, present a digital twin modeling method for bulk storage yard, which includes static scene construction of storage yard and real-time modeling of material pile. Prefabricated models are used for the static scenes …

Digital Imaging Simulation Of Complex Scene Of Space-Based Space Small Target, Pengfei Li, Wei Xu, Yongjie Piao, Yinghong Fang, Dunpan Shi

Journal of System Simulation

Abstract: In response to the universal demand for space target detection technology research in space image data sources, this study focuses on the problems of insufficient training data for intelligent algorithms and the use of single data for traditional algorithms, with the goal of generating dynamic digital sequence images of small space targets in complex scenes. A visible light digital imaging simulation system based on a space observation platform is designed. A small target imaging model is proposed, which is based on two-dimensional shape feature point description and imaging analysis model to carry out digital modeling and imaging simulation of …

Control Strategy For Uav Cluster Formation Rendezvous Based On Lde-Maddpg Algorithm, Wei Xiao, Jiabo Gao, Xueliang Ke

Journal of System Simulation

Abstract: To solve the problem of difficulty in UAV cluster formation rendezvous based on MADDPG algorithm, an autonomous collaborative control strategy based on LDE-MADDPG algorithm is proposed. To address the issues of weak generalization, poor scalability, and slow cluster training process of MADDPG algorithm, LDE-MADDPG algorithm was proposed by designing a state feature learning network and a decoupled Critical network. By integrating LDE-MADDPG algorithm with strategy generation elements such as the decoupled reward function, cluster state space, and UAV action space, a control strategy for UAV cluster formation endezvous that can adapt to diverse formations and varying quantities has been …

Robot Path Planning Based On Improved A-Ddqn Algorithm, Peilong Ni, Pengjun Mao, Ning Wang, Mengjie Yang

Journal of System Simulation

Abstract: An improved A-DDQN algorithm is proposed to address the challenges of reward sparsity and the inability to distinguish sample importance in traditional DQN algorithms during robot path planning. Building on the original DQN, an enhancement is made by incorporating the Double-DQN approach, which updates the predictive Q-value network based on actions selected by the Q network, rather than directly using the predicted Q-values for action selection, thereby mitigating overestimation issues. Secondly, the concept of artificial potential field (APF) is introduced to design specific rewards for each step of the robot's movement, guiding the robot and addressing the problem of …

Research On Real-Time Cgf Maneuvering State Generation Method Based On Random Finite Set, Xiaoyan Zhang, Ge Li, Peng Wang

Journal of System Simulation

Abstract: With the rapid development of sensor networks and other technologies, the acquisition of measurement data in the real physical space has become easier. How to utilize the measurement data from the real battlefield space to improve the accuracy and credibility of CGF simulation is the key issue to realize the CGF simulation combining virtual and real. The method is studied of using real measurement data to generate CGF model maneuvering state data in real time, in order to realize the virtual-real synchronization and real-time mapping between the real battlefield and CGF simulation system, and to provide environmental inputs for …

Benefit Distribution Optimization Model And Simulation For Multi-Mode Operation Of Industrial Software Platforms, Rongyu Guo, Xiaobin Li, Pei Jiang, Chuanjiang Li, Shanhui Liu, Jun Ma

Journal of System Simulation

Abstract: Industrial software service platforms, characterized by low-cost investment, customized services, and rapid application deployment, have been widely adopted in small and medium-sized industrial clusters. The benefit distribution mechanism under multi-mode operation is crucial to the sustainable development of such platforms. To address the current challenges of single-operation models and the difficulty in adapting to diverse service scenarios, this study focuses on two core stakeholders that users and software developers to analyze the core service components and cooperation mechanisms of industrial software service platforms in a multi-mode operational environment. By integrating the function point method, a multi-mode user demand quantification …

Second-Order Cone Optimization Modeling And Simulation For Three-Phase Unbalanced Active Distribution Networks, Yiran Zhao, Yong Xue, Haoxin Tian, Ruixin Zhang, Zhi Zhang, Yanbo Chen

Journal of System Simulation

Abstract: Guided by the carbon peaking and carbon neutrality goals, and propelled by the development of new type power systems, the significance of distribution networks as key energy infrastructure has been increasingly underscored. Amidst the burgeoning rise of distributed photovoltaics, electric vehicles, and novel energy storage technologies, distribution networks are transitioning from passive entities to active systems capable of bidirectional interaction, heralding the advent of active distribution networks with a critical mission. This research tackles the optimal power flow issue in three-phase unbalanced active distribution networks, incorporating inter-phase coupling relationships. By employing dimensionality lifting and rank relaxation, along with the …

Station Layout Optimization Method And Simulation For Non-Cooperative Target In Angle Of Arrival Positioning, Yida Ning, Jiongqi Wang, Juhui Wei, Zhenzu Bai, Zhangming He

Journal of System Simulation

Abstract: In the context of angle of arrival (AOA) positioning system for non-cooperative target tracking and positioning, accurately determining true location of the target poses a significant challenge. Conventional station deployment indicators like geometric dilution of precision (GDOP) fail to provide effective guidance for optimization station layout. To address the issue, this study introduces a novel indicator for station optimization and evaluation based on factors that influence positioning accuracy within an angle measurement system. These factors encompass angular differencing, baseline intersection angles, and the observer-target line distance. Moreover, this indicator encompasses the challenges associated with data conformity in "air to …

Research On Strong Real-Time Synchronisation Algorithm For Lvc Co-Simulation, Junhui Li, Songtao Sun, Fei Liu

Journal of System Simulation

Abstract: Live, virtual, and constructive(LVC) joint simulation has become a hot research topic of current military simulation; however, existing time management strategies usually fail to meet the needs of strict real-time performance of LVC. A LVC joint simulation synchronization algorithm is proposed that starts with a window sliding-based median smoothing strategy and real time drift rate-based clock compensation strategy for effective node synchronization. A novel hybrid timing strategy is introduced combining long and short cycles implemented in software, which balances precision and efficiency. A simulation catch-up strategy is proposed to address software delays, which combined with the highprecision timing strategy, …

Algorithm Simulation Of Multi-Targets Track Correlation Based On Spectral Feature, Zhenping Ding, Huidong Guo

Journal of System Simulation

Abstract: In order to solve the problems of multi-targets track correlation in dense scenes, a method of track sequential real-time processing for multi-source track correlation system modeling is proposed. By calculating the absolute and relative position of the spectral features, the unified correlation matrix can be defined based on fuzzy decision theory, and the multi-target track correlation can be realized. Numerical simulations have shown the effectiveness of the track correlation algorithm on the basis of spectral features. Especially, the accuracy of correlation is much larger than that of the nearest-neighbor distance algorithm under the condition of dense target environment …

Robotic System With Tactile-Enabled Leaf Tracking For High-Resolution Hyperspectral Imaging Device For Autonomous Corn Leaf Phenotyping In Controlled Environments, Xuan Li, Ziling Chen, Raghava Uppuluri, Pokuang Zhou, Tianzhang Zhao, Darrell Zachary Good, Yu She, Jian Jin

School of Industrial Engineering Faculty Publications

Hyperspectral imaging of individual corn leaves provides valuable data for analyzing nutrient content and diagnosing diseases. However, existing leaf-level imaging techniques face challenges such as low spatial resolution and labor-intensive processes. To address these limitations, this study developed a robotic system integrated with a high-resolution line-scanning hyperspectral imaging device to autonomously scan a corn leaf. The hyperspectral imaging device used a vision-based tactile sensor for active leaf tracking throughout the scanning process, ensuring high image quality. Additionally, the device incorporated an in-hand leaf manipulation mechanism that ensured the leaf was properly positioned on the tactile sensing area at the start …

A Feasibility Study Into The Usability And Application Of An Unmanned Aerial Vehicle For Aircraft Inspection And Quality Assurance Inspections, Reece P. Bhave

Journal of Aviation Technology and Engineering

The global aviation industry is often characterized as one of the safest modes of transportation in the modern world. With an abundance of quality assurance inspections and checks to determine operations safety, modern-day commercial aircraft that are utilized for passenger and cargo flights are held to a higher safety standard defined by regulatory bodies, such as the Federal Aviation Administration in the United States of America and the European Union Aviation Safety Agency in the European Union. While these quality standards are maintained via a series of inspections, checks, and preventative maintenance procedures, they are limited to only visual or …

Transparent Eeg Analysis: Leveraging Autoencoders, Bi-Lstms, And Shap For Improved Neurodegenerative Diseases Detection, Badr Mouazen, Ahmed Bendaouia, Omaima Bellakhdar, Khaoula Laghdaf, Aya Ennair, El Hassan Abdelwahed, Giovanni De Marco

Manufacturing & Industrial Engineering Faculty Publications

Highlights

• Novel hybrid architecture: Combined autoencoders with bidirectional LSTM networks for enhanced EEG signal classification, achieving 98% accuracy in distinguishing AD, FTD, and healthy controls.

• Explainable AI integration: Implemented SHAP (SHapley Additive exPlanations) framework to enhance model transparency and identify entropy as the most influential feature for neurodegenerative disease detection.

• Optimal temporal segmentation: Demonstrated that 5-s EEG windows with 50% overlap provide the best balance between classification accuracy and computational efficiency.

• Comprehensive feature extraction: Utilized Power Spectral Density (PSD) analysis across standard frequency bands (Delta, Theta, Alpha, Beta, Gamma) following autoencoder-based dimensionality reduction.

• Superior performance validation: Outperformed traditional machine learning …

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Detecting Electrical Submersible Pump (Esp) Failures And Estimating Run Life Using Artificial Neural Networks, Mostafa Ahmed Sobhy

Theses and Dissertations

Electric Submersible Pumps (ESPs) are one of the important artificial lift methods for sustaining production in mature and high-water-cut wells; but may suffer frequent failures due to mechanical, electrical, hydraulic, chemical, and operational failures. These failures can yield substantial deferred production and intervention costs. Plenty of ESP installations are fitted with downhole sensors. Yet, it is observed that the current industry practice underutilizes the wealth of available sensor and operational data and lacks standardized, explainable failure-type identification and classification.

In this thesis, a comprehensive Machine Learning (ML) and Deep Learning (DL) framework was introduced for ESPs that simultaneously estimates remaining …

Optimizing Beer Fermentation Through Intelligent Control, Azizbek Nodirbekovich Yusupbekov, Mirjalol Yusupov

Chemical Technology, Control and Management

This paper presents an intelligent control approach for optimizing the beer fermentation process using fuzzy logic and adaptive neuro-fuzzy inference systems. By incorporating multivariable inputs—temperature error and pH deviation—the proposed system effectively handles the nonlinear dynamics and biological variability inherent in fermentation. Simulation results demonstrate improved control accuracy, responsiveness, and robustness compared to conventional methods, making the approach suitable for integration in modern brewery automation systems.

Application Of Neural Networks For Intelligent Processing Of Sensor Signals In The Control Of Technological Process Parameters, N.R. Yusupbekov, Yu.Sh. Avazov, G.Kh. Rashidov

Chemical Technology, Control and Management

This scientific article investigates the problem of analyzing technological process parameters in the fields of chemistry, energy, and metallurgy based on sensor data and applying intelligent signal processing methods. The main objective is to evaluate the effectiveness of artificial intelligence and deep learning models for intelligent analysis, forecasting, and anomaly detection of data obtained from sensors. Time-series data collected from industrial sensors were analyzed using LSTM (Long Short-Term Memory) and Autoencoder neural networks, as well as the Kalman filter. At the first stage of the study, sensor signals were denoised and their true state was estimated using the Kalman filter. …

Increasing The Robustness Of A Control System For A Complex Dynamic Plant By Correcting Nonlinearity In The Warping Process, Tukhtamurod Khayitmurodovich Avezov, Zokhid Ergashboyevich Iskandarov

Chemical Technology, Control and Management

The paper discusses the challenges of enhancing the robustness of a control system for a complex dynamic plant by addressing nonlinearity in the warping process. Devices that ensure the stability of the control system against parameter non-stationarity on the warping machine are referred to as state controllers. The operating principle of these devices relies on providing artificial nonlinearity to the rear connection circuit of the control system's actuator. However, this nonlinearity is implemented using components that consider the parameters of low control quality. Therefore, it is necessary to continuously adjust the nonlinearity parameters to, on one hand, reduce the load …

Evaluation Of Deep Learning Techniques In Road Sign Recognition, Latafat Abbas Gardashova, Haji Fakhraddin Hajiyev

Chemical Technology, Control and Management

Deep learning has transformed the computer vision field and greatly improved the performance and efficiency of road sign recognition systems. This research compares different deep learning methods, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models, in terms of their ability to effectively detect and classify road signs under various conditions. The study compares performance measures such as accuracy, processing speed, and robustness to environmental conditions like low lighting, occlusion, and adverse weather. The results show that CNN-based methods, especially those with transfer learning and ensemble techniques, have better performance in real-time scenarios. Problems like computational …

Modern Methods Of Self-Monitoring, Diagnostics, And Fault Tolerance In Flow Measurement Systems, Elbek Ortiqov

Chemical Technology, Control and Management

This article investigates various methods and tools for self-monitoring and fault tolerance in flow measurement transducers used in industrial processes. The study focuses on key techniques such as the use of redundancy, generation of reference values, analysis of measurement signals, and control of disturbance variables. These methods allow transducers to detect potential faults, ensure reliable operation, and maintain measurement accuracy even under adverse conditions. The article highlights how self-monitoring contributes to improving system safety, increasing reliability, and reducing downtime. It also discusses the integration of intelligent monitoring systems that support predictive maintenance and real-time diagnostics. Fault-tolerant sensors with self-monitoring capabilities …

Finding The Shortest And Optimal Path With Metaheuristic And Madm Methods, Narmin Ibrahim Hasanli

Chemical Technology, Control and Management

The article had described a method for finding the shortest and most optimal path among cities. The data had been taken from the TSPLIB library, which had provided standard examples for the Traveling Salesman Problem. This approach had integrated the advantages of the meta-heuristic technique and the Multi Attribute Decision Making method to solve the problem effectively. In the first stage, the population based meta-heuristic method ACO (Ant Colony Optimization) had found optimal solutions in large search spaces. The use of pheromone trails, heuristic information and an iterative search process had given the opportunity to find the best or near-best …