Computer Engineering Commons^™

Adaptive Recognition Method Of Capability Boundary Parameters For Unmanned Autonomous Systems, Jinwen Li, Peng Wang, Youmei Pan, Xinyao Hui

Journal of System Simulation

Abstract: To effectively cope with the dimension curse in simulation testing and reduce the number of simulations times needed in the traditional full-space parameter traversal, it is necessary to obtain specific simulation data to accurately reflect the modeling characteristics of the test data to obtain the informative and representative samples of the original data with a smaller number of simulations. A digital simulation test model for adaptive recognition ;/of capability boundary parameters for UAS is proposed. The model is initially constructed with a good point set with a multi-weight structure; In combination with an adaptive kernel function boundary point recognition, …

Operational Effectiveness Analysis Method Based On Spherical Fibonacci Lattice, Weiran Guo, Jiahao Zhou, Xiang Huang, Xin Zhao

Journal of System Simulation

Abstract: An operational effectiveness modeling and computing method based on spherical Fibonacci lattice is proposed to address the low simulation accuracy and computational efficiency of large-scale sampling for traditional latitude and longitude grids. The formal description of operational effectiveness in command information systems and the generation method of spherical Fibonacci grid points are provided. A command information systems capability analysis framework is constructed, which focuses on ensuring the continuous mission support and uses the responsibility area of combat tasks as a reference. Through a top-down system design approach, a layered and decoupled combat effectiveness computing framework is proposed which studies …

Ground Robot Relocation Method Based On Uav Point Cloud Map, Hongzhi Huang, Kai Yan, Changfeng Liu, Jianwen Wang, Bin Luo

Journal of System Simulation

Abstract: In response to the challenge of relocalization in air-ground collaborative systems without the support of Global Navigation Satellite System (GNSS), and the associated issues of insufficient accuracy, a coarse-to-fine relocalization algorithm based on a three-dimensional point cloud map is proposed. The algorithm eliminates the influence of invalid point clouds from the sky and ground through index filtering, performs coarse localization by extracting global features from the point cloud and applying truncated least squares estimation, and then employs voxel-based iterative closest point (ICP) for precise optimization to obtain the more accurate localization results. A ground robot localization and autonomous navigation …

Research On Digital Twin Technology Of Coal Mine Tunneling Machine System, Xingwang Cai, Yaoqiang Pei, Jihua Yang, Ruifeng Pang

Journal of System Simulation

Abstract: Aiming at the problems in coal mine tunneling operations, including complex geological structures, harsh on-site conditions, low automation of excavation equipment, and frequent safety incidents, a digital twin solution is proposed. PLC as hardware core, MQTT protocol, and EMQX as foundation, through the integration of IoT, internet, digital twins, and virtual reality technologies, the mapping rules for parameter matrices and a fault cause analysis table are established, which realize the functions such as data collection, processing during excavation, data mapping between equipment and twin models, data storage, process simulation, remote control, intelligent fault detection, and early warning, and significantly …

Research On Application Of Intelligent Indication Signs In Improving Efficiency Of Crowd Evacuation, Heng Niu, Yanbin Han, Liang Li, Weilin Chen, Sijie Niu, Qingtao Hou

Journal of System Simulation

Abstract: Aiming at the low emergency evacuation efficiency of dense crowds in confined spaces, a crowd evacuation guidance model based on intelligent signs is constructed in this paper. A balanced congestion strategy to optimize the signs layout and a social network search-based sign guidance weights optimization method are proposed to improve evacuation efficiency. Simulation experiments show that the optimized intelligent sign layout can effectively alleviate the local congestion during evacuation and reduce the time consumption of pedestrians caused by congestion. The optimized intelligent sign guidance weights can further balance the utilization rate of exits, which is conducive to making full …

Garage Agv Path Planning And Simulation Based On Improved Dwa, Zongfang Ma, Linxuan Zhang, Lin Song, Jia Wang

Journal of System Simulation

Abstract: Aiming at the path planning and real-time obstacle avoidance of AGV in complex path environment of intelligent garage, an improved hybrid algorithm combining ant colony algorithm and dynamic window method is proposed. In the global planning, the adaptive adjustment of pheromone volatilization coefficient and the fusion of angle parameters are introduced to establish the garage direction pheromone matrix to increase the guidance ability of target points, expand the direction selectivity of ants. In the local planning, the improved DWA of the obstacle distance evaluation subfunction based on elliptic equation is designed. By extracting the global path node of the …

Adaptive Tracking Control For Omnidirectional Vehicle Based On Characteristic Modeling, Lei Cheng, Haoyou Wang, Xinjie Chen

Journal of System Simulation

Abstract: Aiming at the stable and high-precision tracking control of omnidirectional mobile vehicles affected by their own characteristics and external disturbances, an adaptive tracking control approach for omnidirectional vehicles based on characteristic modeling is designed. The characteristic model is established by integrating the model properties into the characteristic parameters, and the characteristic parameters are estimated online by using the projected gradient method. A full coefficient adaptive control law based on the characteristic model is designed, and the stability of the proposed control method is analyzed by using Lyapunov theory. The effectiveness and rationality of the proposed adaptive control scheme are …

Modeling Method Of Blue Army Warship Formation Air Defense Command And Control For System-Of-Systems Simulation, Shikang Chen, Zhimin Wang, Biao Liang, Kunren Gu, Keman Song, Yuan Gao

Journal of System Simulation

Abstract: The command and control of warship formation air defense is a key link for the blue army's sea to air combat tasks and the modeling of air defense for warship formation is an important component of simulation modeling of blue army's maritime combat system. Based on the background of system-of-systems simulation, the air defense command and control modeling for blue army is designed. Through a modular modeling method, functional module including asset management and plan, unified situation generation, command decision are designed. According to blue army's command and control logic, these modules are integrated and the air defense command …

Search Technology For Aircraft Debris Integrating Data Augmentation And Deep Learning Algorithm, Zhe Yang, Yinghan Cui, Lingxi Guo, Jiaxin Li, Xusheng Wu

Journal of System Simulation

Abstract: The reliable recovery of aircraft debris is of great significance for the complete acquisition of flight test data and the subsequent research and development of models. To ensure the safety of flight tests,the landing area of aircraft experiments is generally an unmanned area,and the actual landing point of the aircraft often deviates from the theoretical landing point. The characteristics of the debris target are complex and the dispersion area is large, making it difficult to search for aircraft debris solely by manpower. Aiming at the difficult problem of aircraft debris recovery in the landing area, through on UAV platforms …

Two-Level Optimal Dispatch Of Power System Based On Load-Storage Carbon Flow Model, Yang Yu, Yuxing Xia, Wentao Lu, Mai Liu, Shixu Gao, Dongyang Chen

Journal of System Simulation

Abstract: In order to reduce the output of high energy consuming units on the power generation side, increase the absorption capacity of wind power, and consider the flexible resource allocation such as load and energy storage, a two-level economic low-carbon optimal scheduling method for power systems based on a carbon storage and discharge model is proposed. Based on the carbon emission flow theory of power system, a model for load and energy storage equipment is established; A demand response model based on the electricity carbon coupling price is established on the load side, and in view of the limitation on …

Research On Flexible Operational Optimization Of Cchp System Based On Intelligent Fusion Algorithm, Zhe Bao, Xiaofang Zhang, Wei Li, Ye Xu, Xu Wang

Journal of System Simulation

Abstract: To further improve the accuracy of gas turbine simulation models, based on the construction of a gas turbine mechanism simulation model and BP simulation model, through model substitution technology and BP neural network algorithm three intelligent fusion simulation models for gas turbines, and two intelligent fusion simulation models for parallel gas turbines are constructed respectively as well as the combination of, by comparing the simulated results of the above models with the actual operating data, the simulation model with the best performance was selected. Using the intelligent fusion simulation model of the gas turbine as the output constraint, a …

Chaotic-Encode Quantum Pso Algorithm For Flexible Job-Shop Scheduling Problem, Yuanxing Xu, Mengjian Zhang, Deguang Wang

Journal of System Simulation

Abstract: To solve the flexible job-shop scheduling problem (FJSP), a chaotic-encode quantum PSO (CQPSO) algorithm is proposed. Aiming at the premature convergence of particles to local optimum in standard QPSO, the methods for computing the adaptive contraction-expansion coefficient and mean best position using fitness values of associated particles are proposed to improve the global search ability of QPSO. Through chaotic boundary variation strategy, the probability of a large number of particles gathering at the boundary is reduced and the population diversity is increased to enhance the ability of searching the optimal solution. According to the iterative property of QPSO, a …

Multi-Strategy Partheno-Genetic Algorithm Based On Dynamic Reduction Mechanism For Solving Cvrp Problem, Jiajun Chen, Dailun Tan

Journal of System Simulation

Abstract: Aiming at the problems of premature, slow convergence and low accuracy of traditional genetic algorithm in solving capacitated vehicle routing problem,a multi-strategy partheno-genetic algorithm based on dynamic reduction mechanism is proposed. The algorithm divides the optimization space based on similar individuals, and uses simulated annealing criterion to eliminate or update the lowest category subspace, which constitutes the reduction and movement mechanism of the optimization space. Based on parthenogenetic algorithm,a variety of genetic evolution strategies including intra-group, inter-group, global search, disturbance and jump strategy are designed Based on the three penalty factors of individual development, population evolution and overall convergence, …

Unmanned Vehicle Path Planning And Tracking Control Based On Improved Artificial Potential Field Method, Minghao Guo, Peng Ji, Haiwei Huang

Journal of System Simulation

Abstract: A path planning algorithm based on improved artificial potential field method and a tracking control strategy based on model predictive controller are proposed for the unmanned vehicle avoiding dynamic obstacles in the complex scene of lane changing and overtaking. The theory of safety ellipse and the concept of prediction distance are introduced to adjust the influence region of potential field. By adding velocity potential field to change potential field function, the problem of vehicle avoiding dynamic obstacles is solved. Based on the linear three-degree-of-freedom vehicle dynamics model, a model prediction controller including potential field environment is established. The effectiveness …

Path Following Control And Simulation Analysis Of Multi-Articulated Vehicles, Yu Zhao, Caijin Yang, Tanming Wang, Jing Xu, Shuai Zhou

Journal of System Simulation

Abstract: The structure of multi-articulated vehicle body limits the flexibility of the vehicle and causes the deviation of the rear vehicle. Taking the ideal articulation angle as the control target, a feedforward plus feedback path following control method is proposed, which realizes the precise path following of rear vehicle bodies by minimizing the deviation between the ideal articulation angle and the actual articulation angle. According to the geometric position relationship between the vehicle and the desired path, the traditional calculation method of the ideal articulation angle is improved from two perspectives of application range and error accumulation. Based on the …

Improved Foggy Pedestrian And Vehicle Detection Algorithm Based On Yolov5, Tong Su, Ying Wang, Qiyang Deng, Zhaobin Li

Journal of System Simulation

Abstract: Due to the poor environment perception of car in bad weather, the detection ability on dynamic targets is significantly reduced, and thus the problems such as low accuracy and poor robustness of the deep learning-based target detection network will occur when detecting pedestrians and vehicles in foggy days. A YOLOv5-SGE foggy detection network is proposed on the basis of the combination of image dehazing DehazeNet and the improved YOLOv5. The adaptive calculation of anchor frame is realized by canceling the initial anchor frame of YOLOv5, and the anchor frame suitable for the current dataset is generated. A three-dimensional weighted …

A Method For Battlefield Situation Information Ontology Construction Based On Top-Down And Bottom-Up Integration, Cong Zhou, Sihang Zhou, Jian Huang, Dong Wang

Journal of System Simulation

Abstract: The construction of the unified expression model of battlefield situational information is challenging due to the complexity of data sources and the significant differences in data structures and expression methods. Ontologies, as semantic conceptual models, are often used to describe concepts, relationships, and attributes within knowledge domains. An ontology construction method for the battlefield situational information domain based on a top-down and bottom-top integration is proposed. The top-down method is used to construct the upper ontology, in which a conceptual hierarchy model with a clear top-down structure is designed to establish the hierarchical relationships and semantic associations. A bottom-up …

Peer-To-Peer Energy-Carbon Management Method Of Multiple Integrated Energy Systems Considering Multi-Agent Interaction Strategy, Yudong Wang, Junjie Hu

Journal of System Simulation

Abstract: To explore a new energy management model of P2P transaction of electricity, heat and carbon among IES with the participation of ESP, a P2P energy-carbon management method of IES considering multi-agent interaction strategy is proposed. A two-layer energy management framework with the multiagent participation of involving ESP and IES is established. A two-layer electricity-heat-carbon energy management model is constructed in which the upper model is constructed based on reinforcement learning framework to optimize the energy management strategy between ESP and IES cooperative alliance and the lower model is based on Nash negotiation game theory to optimize the cooperative operation …

Towards An Iot-Enabled Digital Earth For Sdgs: The Data Quality Challenge, Msb Syed, Paula Kelly, Paul Stacey, Damon Berry

Articles

Digital Earth (DE), a technology offering real-time visualisation of Earth's processes, has shown promising results in aiding decision-making for a sustainable world, raising awareness about individual impacts on our planet, and supporting the United Nations Sustainable Development Goals (UN SDGs) agenda. However, both DE and SDGs face a common obstacle: Data Quality (DQ). This review investigates the challenge of DQ in the context of DE for SDGs and explores how IoT can address this challenge and extend the reach of DE to support SDGs. Furthermore, the study discusses three core aspects; first, the potential of IoT as a data source …

Mouasla: Integrating Iot And Ai For An Intelligent Trans-Portation Payment System, Hany El-Ghaish Dr., Haitham Darweesh

Journal of Engineering Research

Smart payment systems have emerged as vital components of global public transportation, offering passengers a more efficient and convenient fare payment method. The Mouasla system addresses traditional payment limitations through IoT devices and AI-backed backend services. Features of Mouasla It employs RFID smart card and IoT features from the device to ensure all components such as a card reader function, driver functions, charging units function, and payment are combined with this system alongside a mobile application for quick access backend services. Each passenger dataset is analyzed by an AI-powered backend service to provide insight that can be used to improve …

A Parallel Methodology For Early Fake News Detection Based On Hybrid Features On Social Media, Asmaa Mohemed Elsaieed Dr

Journal of Engineering Research

The increased use of social media platforms has made it easier to publish and distribute news items, but it has also opened up new opportunities for distributing fake news. Fake news is information that has been written with the goal of misleading or deceiving readers. As a result, there is a need for efficient false news identification tools where the information can be gathered from the text of posts or from publicly available social data (such as user information or feedback on articles or the social network). The detection of fake news in its early stages is a major challenge. …

Early Autism Detection Using Machine Learning Techniques: A Review, Shaimaa Fouad Sharabash, Hany Ali Elghaish

Journal of Engineering Research

Abstract- This article provides a comprehensive literature review on technology-based interventions for Autism Spectrum Disorder (ASD). It emphasizes the challenges in early detection and treatment of ASD, highlighting the spectrum nature of the disorder. The review discusses traditional diagnostic strategies such as behavioural observations, developmental screening and medical testing and goes on to explore advanced machine learning and deep learning models, including SVM, k-nearest neighbours, decision tree and LSTM, for predicting ASD characteristics in toddlers and children. Additionally, recent techniques employing more than ten strategies for ASD detection are summarized and various datasets used in early detection are described. The …

Digital Assessments For Children And Adolescents With Adhd: A Scoping Review, Franceli L. Cibrian, Elissa M. Monteiro, Kimberley D. Lakes

Engineering Faculty Articles and Research

Introduction: In spite of rapid advances in evidence-based treatments for attention deficit hyperactivity disorder (ADHD), community access to rigorous gold-standard diagnostic assessments has lagged far behind due to barriers such as the costs and limited availability of comprehensive diagnostic evaluations. Digital assessment of attention and behavior has the potential to lead to scalable approaches that could be used to screen large numbers of children and/or increase access to high-quality, scalable diagnostic evaluations, especially if designed using user-centered participatory and ability-based frameworks. Current research on assessment has begun to take a user-centered approach by actively involving participants to ensure the development …

M3t-Lm: A Multi-Modal Multi-Task Learning Model For Jointly Predicting Patient Length Of Stay And Mortality, Junde Chen, Qing Li, Feng Liu, Yuxin Wen

Engineering Faculty Articles and Research

Ensuring accurate predictions of inpatient length of stay (LoS) and mortality rates is essential for enhancing hospital service efficiency, particularly in light of the constraints posed by limited healthcare resources. Integrative analysis of heterogeneous clinic record data from different sources can hold great promise for improving the prognosis and diagnosis level of LoS and mortality. Currently, most existing studies solely focus on single data modality or tend to single-task learning, i.e., training LoS and mortality tasks separately. This limits the utilization of available multi-modal data and prevents the sharing of feature representations that could capture correlations between different tasks, ultimately …

2024 Gateway Magazine, College Of Computing, Michigan Technological University

College of Computing Annual Magazines

Table of Contents

• 50 Years of Computer Science at Michigan Tech
• Data Science for a Changing Planet
• Healthcare Transformed
• Mechatronics Matters
• Powered by Michigan Tech Talent
• Esports: Bringing Everything Great about Sports to More People
• The Michigander Scholars Program: Electrifying Careers in Michigan
• College of Computing News

Development Of Brain Tumor Detection And Feature Extraction Through Deep Learning Approach, Sivapathi A

Theses and Dissertations

As the body's central control system, the human brain is susceptible to a wide variety of disorders, including tumors characterized by abnormal cell growth. It is imperative to detect these tumors as early as possible to plan effective treatment and improve patient outcomes. By using contemporary medical imaging methods, this research seeks to improve the accuracy and efficiency of brain tumor detection through the careful preprocessing and analysis of images, particularly Magnetic Resonance Imaging (MRI) [1]. To provide context for the subsequent research efforts, the challenges inherent in brain tumor detection are discussed comprehensively, including segmentation accuracy, small lesion detection, …

Pig Butchering In Cybersecurity: A Modern Social Engineering Threat, Sharon L. Burton, Pamela D. Moore

Publications

Pig butchering is an escalating cybersecurity threat that exploits social engineering to build trust and execute financial fraud. The relevance of this research problem lies in the growing incidence and sophistication of these scams, which have severe financial and psychological impacts on victims. The main purpose of this research is to uncover the methods used in pig butchering scams and their impact on individuals and businesses. The research focuses on digital platforms such as social media, dating apps, and professional networking sites, chosen for their wide user bases and the ease of establishing personal connections. The study period encompasses recent …

Instructional Systems Design: The Diffusion And Adoption Of Technology: (Volume 2), Cassandra Celaya (Author), Pamela J. Downing (Author), Jessica Shifflett (Author), Debbie Gdula (Author), Tracie Barr (Author), Miguel Ramlatchan (Author & Editor)

University Administration Bookshelf

Instructional designers, instructional systems designers, and other educational technologists are, by their nature, innovators. These professionals apply and extend the applied science of learning, systems, communication, and instructional design theory to help students learn. Technology in some capacity is used to make the connections between subject matter experts, teachers, instructors, and their learners. It is common for instructional designers to seek new tools, techniques, and innovations for the improvement of learning, access, quality, and student satisfaction. However, the adoption and diffusion of new educational technology and innovation is a complex process that depends on many variables. Understanding these processes and …

H2d: Hierarchical Heterogeneous Graph Learning Framework For Drug-Drug Interaction Prediction, Ran Zhang, Xuezhi Wang, Sheng Wang, Kunpeng Liu, Yuanchun Zhou, Pengfei Wang

Computer Science Faculty Publications and Presentations

Accurately predicting Drug-Drug Interactions (DDIs) is critical to designing effective drug combination therapies. Recently, Artificial Intelligence (AI)-powered DDI prediction approaches have emerged as a new paradigm. However, most existing methods oversimplify the complex hierarchical structure within molecules and overlook the multi-source heterogeneous information external to molecules, limiting their modeling and predictive capabilities. To address this, we propose a Hierarchical Heterogeneous graph learning framework for DDI prediction, namely H2D. H2D employs an internal-toexternal, local-to-global hierarchical perspective, exploiting intramolecular multi-granularity structures and inter-molecular biomedical interactions to mutually enhance across hierarchical levels. Extensive experimental results demonstrate H2D’s effectiveness on three …

2024 (Fall) Ensi Informer Magazine, Morehead State University. Engineering Sciences Department

ENSI Informer Magazine Archive

The ENSI Informer Magazine published in the fall of 2024.