Engineering Commons^™

Path Planning Based On Improved A* And Dynamic Window Approach, Peng Ji, Xinyuan Zhang, Shuaixuan Gao, Shuorang Wei

Journal of System Simulation

Abstract: In response to the low efficiency, redundant turning points, and collision issues of the traditional A* algorithm, a smart vehicle path planning algorithm that integrates an improved A* algorithm with a dynamic window approach has been proposed. The algorithm has enhanced the search point selection method, optimized the evaluation function, selected key turning points based on the slope values between turning points, and removed redundant turning points. Between every two optimized key turning points, a dynamic window approach that balances speed and safety is used for local obstacle avoidance. Experiments show that compared to the traditional A* algorithm, this …

The Synchronous Grasping Method Of Virtual-Real Assembly Robot Based On Digital Twin, Jian Xu, Gaofeng Liu, Yijian Zhao, Zili Zheng, Huanying Yan

Journal of System Simulation

Abstract: A method based on digital twin for assembly robot virtual-real synchronization and grasping is proposed to address the issues of poor intelligent grasping accuracy and difficult data processing in assembly tasks for industrial robots. Based on the digital twin, a digital twin assembly robot virtual-real synchronization and grasping architecture is designed. The OPC UA information model is built by classifying multi-source heterogeneous data, and the OPC UA communication protocol is used as a bridge for data communication of the assembly robot, achieving virtual-real synchronization. The convolutional neural network is further trained using the virtual robot to improve the grasping …

Research On Autonomous Decision-Making In Air-Combat Based On Improved Proximal Policy Optimization, Dianwei Qian, Hongmin Qi, Zhen Liu, Zhiming Zho, Jianqiang Yi

Journal of System Simulation

Abstract: To address the problems of high information redundancy and slow convergence speed of traditional reinforcement learning in air-combat autonomous decision-making applications, a proximal policy optimization air-combat autonomous decision-making method, based on dual observation and composite reward is proposed. A dual observation space, which contains interaction information as the main information and individual feature information as a supplement, was designed to reduce the influence of redundant battlefield information on the training efficiency of the decision model. A composite reward function combining result reward and process reward was designed to improve convergence speed. The generalized advantage estimator was applied in the …

Research On System-Of-Systems Confrontation Simulation Method Based On Operation Loops, Shan Zhong, Yesheng Zhu, Menglu Zhou

Journal of System Simulation

Abstract: In the field of modeling and analyzing capabilities for operation system-of-systems (SoS), traditional structured capability assessment models lack the analysis of the interaction between both rivals and armies in different roles. The system model based on operation loop theory can be combined with the relationship between sensor, decision-making, influence, and target nodes for system capability calculation, but the existing model is usually only suitable for static analysis and cannot be used for dynamic simulation of SoS confrontation. In order to solve the problems above, a SoS confrontation simulation method based on operation loops is proposed. It abstracts both rivals’ …

An Intelligent Adversaries Behavior Simulation Technology Based On Improved Behavior Trees, Fang Zhou, Bo Fan, Xiaoyi Liu, Yishan Ding, Ningxin Zhang, Yachao Shao, Xiaoyu Zhai

Journal of System Simulation

Abstract: Intelligent algorithm/intelligent platform/intelligent system intelligence capability testing and evaluation need to solve high-level intelligent opponent simulation problems, an intelligent opponent behavior simulation technology based on improved behavior tree is proposed. Four types of behavior tress nodes are designed, including behavior control, combat tasks, behavior actions, and execution condition node. Five atomic behavior actions and parameters are established, including maneuver, reconnaissance and early warning, command and decision-making, firepower strike, and electronic interference node. Five atomic condition nodes are provided, including target selection, weapon launch, and incoming weapon judgment node. The intelligent adversarial behavior simulation system is designed, including a behavior …

Study On Invulnerability Of Urban Rail Network Considering Sum Of The Neighbors Degree, Shuqing Li, Yixiao Song, Guojian Zhong

Journal of System Simulation

Abstract: In order to solve the problem of network cascade paralysis caused by urban rail station or line failure, considering the influence of the first-order neighborhood of network nodes, the load distribution impedance coefficient is proposed based on the nonlinear capacity load model, and a nonlinear capacity load optimization model considering the sum of the neighbors degree is constructed. By optimizing load structure, the alternative probability of nodes during load redistribution is adjusted to reduce the number of node failures in the cascading process, thereby the rail network invulnerability is improved. Taking Chongqing rail network as an example, the rail …

Multi-Step Information Aided Q-Learning Path Planning Algorithm, Yuelong Wang, Songyan Wang, Tao Chao

Journal of System Simulation

Abstract: To improve the path planning capability of mobile robots in a static environment and solve the problem of slow convergence of the traditional Q-learning algorithm in path planning, this paper proposes a multi-step information-aided Q-learning improvement algorithm. Using the multi-step information of greedy action in ε -greedy strategy and length of the historical optimal path to update the eligibility traces, which makes the effective eligibility traces work continuously in the iteration of the algorithm and solves the loop traps that may fall into with the preserved multi-step information; using the local multiflower pollination algorithm to initialize the Q-value table …

Carbon Footprint Analysis And Low-Carbon Optimization Method Simulation Study Of Power Transformer Based On Digital Twin Technology, Dongxue Li, Yan Liu, Boyao Shen, Yongteng Jing, Qiang Ma, Ran Liu

Journal of System Simulation

Abstract: Power transformers are the main energy-consuming equipment for substations. According to the goal of “carbon peak, carbon neutralization” in China, it is of great significance to accurately calculate the carbon footprint of transformers and seek low-carbon optimization methods. A method for constructing a digital twin model of power transformer magnetic characteristics is proposed. Based on the three-dimensional electromagnetic time-harmonic field finite element analysis method, a threedimensional model of SZ11-31.5MVA/66kV power transformer is established. The transformer loss map is obtained under fluctuating load condition, and the transformer digital twin model is constructed. The carbon footprint of the transformer is analyzed, …

Uav Online Track Planning Based On Dmoea-Aptc Algorithm, Erchao Li, Shenghui Zhang

Journal of System Simulation

Abstract: In order to solve the dynamic multi-objective optimization problem with time correlation, this paper introduces the concept of time correlation feature and establishes the model of UAV timecorrelation dynamic multi-objective optimization problem moedl on the basis of UAV online track planning problem, and proposes a dynamic multi-objective double-layer optimization algorithm using adaptive predictive response mechanism and time-correlation optimization mechanism (DMOEA-APTC). The intensity of environmental change was judged according to the correlation of environmental change and different response mechanisms were used to quickly adapt to environmental change. In the optimization process, the least square method was used to learn the …

Visual Robot Obstacle Avoidance Planning And Simulation Using Mapped Point Clouds, Hanlin Huo, Xiangjun Zou, Yan Chen, Xinzhao Zhou, Mingyou Chen, Chengen Li, Yaoqiang Pan, Yunchao Tang

Journal of System Simulation

Abstract: In response to the large and complex data volume and high redundancy of visual point cloud obstacle recognition in complex unstructured orchard environments, which severely impacts the real-time performance and efficiency of harvesting operations, a point cloud compression algorithm is proposed based on point cloud segmentation to enhance the efficiency of point cloud obstacle recognition and environmental adaptability. An Informed RRT* based approach is used combined with an inverse projection algorithm, mapping-based informed RRT*(M-Informed RRT*) to solve the harvesting path problem. By constructing a highly real-time and robust integrated robot system for sampling, perception, and obstacle avoidance, efficient obstacle …

An Improved Path Planning Algorithm For Mobile Robots, Haijie Sun, Hongjun San, Le Xiao, Dexin Yao, Jiupeng Chen, Xiaoyuan Yang

Journal of System Simulation

Abstract: To solve the problems of invalid sampling and non-optimal paths of the RRT, the quasi-stream avoidance algorithm is proposed. The RRT algorithm is introduced to specify the sampling interval to limit the sampling points and enhance the goal-oriented nature of sampling. The quasi-stream avoidance algorithm incorporating the A* algorithm (QSA*) is used to quickly bypass the obstacle when it is encountered. A path optimization algorithm is used to smooth the searched path. The simulation results show that compared with the RRT algorithm, the computation time of the RRT-QSA* algorithm is reduced by 96.83%~99.88%, the number of search nodes is …

Text-To-Model Transformation: Natural Language-Based Model Generation Framework, Aditya Akundi, Joshua Ontiveros, Sergio Luna

Mechanical Engineering Faculty Publications

System modeling language (SysML) diagrams generated manually by system modelers can sometimes be prone to errors, which are time-consuming and introduce subjectivity. Natural language processing (NLP) techniques and tools to create SysML diagrams can aid in improving software and systems design processes. Though NLP effectively extracts and analyzes raw text data, such as text-based requirement documents, to assist in design specification, natural language, inherent complexity, and variability pose challenges in accurately interpreting the data. In this paper, we explore the integration of NLP with SysML to automate the generation of system models from input textual requirements. We propose a model …

Method Of Selecting The Optimal Variant Of Mine Closure With The Possibility Of Capturing Methane From Underground Excavations, Marian Turek, Małgorzata Magdziarczyk

Journal of Sustainable Mining

The article presents a method of selecting the optimal option for the closure of a coal mine, from which it is planned to capture the methane remaining in the goaf and deposit it for many years for its economic use. Using the example of several options for the closure of one of the mines, for which the evaluation criteria have been defined, and the weights of their relevance have been determined, it is described how to create a special algorithm for the selection of the optimal option.

Academic Presentation 'Navigating Transdisciplinary Communication: A Graduate Student’S Perspective On Collaborative Research', Sirimuvva Pathikonda, James Lipuma, Cristo Leon

STEM for Success Resources

This presentation was given at the 28th World Multi-Conference on Systemics, Cybernetics, and Informatics (WMSCI 2024) on September 13th, 2024.

Neuro-Symbolic Ai For Deep Analysis Of Social Media Big Data, Vedant Khandelwal, Manas Gaur, Ugur Kursuncu, Valerie Shalin, Amit P. Sheth

Faculty Publications

This tutorial introduces a neuro-symbolic AI framework to analyze big data from social media platforms. Integrating human-curated knowledge through symbolic AI with the pattern recognition capabilities of neural networks enhances the adaptability and efficiency of traditional neural network approaches. Knowledge-guided zero-shot learning techniques enable swift adaption to new linguistic contexts and emerging events [6]. Participants will explore how to design, develop, and utilize these models in specific domains, such as public health surveillance, that require dynamic adaptation to new terminologies. This session The tutorial aims to equip attendees with practical skills and a deep understanding of how to apply neuro-symbolic …

Multiple-Point Metamaterial-Inspired Microwave Sensors For Early-Stage Brain Tumor Diagnosis, Nantakan Wongkasem, Gabriel Cabrera

Electrical and Computer Engineering Faculty Publications

Simple, instantaneous, contactless, multiple-point metamaterial-inspired microwave sensors, composed of multi-band, low-profile metamaterial-inspired antennas, were developed to detect and identify meningioma tumors, the most common primary brain tumors. Based on a typical meningioma tumor size of 5-20 mm, a higher operating frequency, where the wavelength is similar or smaller than the tumor target, is crucial. The sensors, designed for the microwave Ku band range (12-18 GHz), where the electromagnetic property values of tumors are available, were implemented in this study. A seven-layered head phantom, including the meningioma tumors, was defined using actual electromagnetic parametric values in the frequency range of interest …

A Random Forest Classifier Model For Predicting The Impact Of Viral Infections On Adults With Chronic Conditions, Fungai Jacqueline Kiwa, Martin Muduva

African Conference on Information Systems and Technology

This study investigates the impact of viral infections on adults with chronic illnesses, focusing on the development of a Random Forest classifier model. The research aims to predict outcomes among individuals with conditions like diabetes, cancer, and tuberculosis, analyzing severity, age groups, and travel patterns. The study aims to assist healthcare professionals in resource allocation and patient prioritization based on disease severity. It reviews literature on viral infection risks for chronic illness patients and explores machine learning applications in infectious disease management. Methodologically, the study adopts a structured approach similar to the Cross-Industry Standard Process for Data Mining (CRISP -DM) …

Evaluating Ai Language Models For Patient Queries On Total Knee Replacement (Tkr), Brianna Guillen, Anesu Karen Murambadoro, Victoria Elizondo, Matthew Hnatow, Michael Sander

Research Colloquium

Introduction: Within the past few years, large language models (LLMs) (ChatGPT, LLaMa 3, Microsoft Copilot) have increasingly become a resource that patients engage with to learn about health care procedures, including total knee replacement (TKR). Previous studies have analyzed the efficacy of large language models in providing accurate and relevant responses to questions about various procedures. Our study aims to evaluate the clarity, validity, and understandability of LLMs to patient questions about total knee replacement and assess the consistency of these models and their effectiveness in providing accurate, valid, and guideline-adherent information to patients.

Methods: We selected 30 frequently asked …

A Bert-Based Model For Classifying Customers In The Financial Sector: A Case Of Zb Bank, Fungai Jacqueline Kiwa, Martin Muduva

African Conference on Information Systems and Technology

This study explores the implementation of a BERT-based model in ZB Bank, Zimbabwe, to improve complaint management and enhance customer satisfaction. Traditional manual handling of client complaints leads to slow responses and unresolved issues. The study aims to develop an accurate complaint classification model using advanced Natural Language Processing (NLP) and machine learning techniques, specifically the BERT model, and assess its real-world performance. The methodology involved collecting a dataset of customer complaints from ZB Bank, pre-processing the text data, and applying the BERT model within the Team Data Science Process (TDSP). The dataset was split into training (80%) and testing …

Anomalous Transaction Detection In Bank Credit Card Data Using Machine Learning, Lerdinia Varaidzo Mapepa, Jerremiah Musariwa, Lucia Makwasha, Samuel Mugijima

African Conference on Information Systems and Technology

Illegal money changers pose a number of risks to the financial system, including but not limited to money laundering, fraud, and other under-the-carpet dealings intended to frustrate regulatory efforts for financial integrity. The efficiency and accuracy of anti-money laundering (AML) measures using machine learning (ML) models in the detection of suspicious patterns in bank card transactions are investigated in this paper. The key focus will be to develop an efficient machine learning framework that should be proficient in underlining main transactions dealing with illegal money changers and other similar fraudulent activities. The features indicative of illicit behaviour are determined by …

Ai Bioelectricity Management System, Fungai Jacqueline Kiwa, Tawanda Bundukutu, Thoko Matnell Mawoyo, Batsiranai Linda Chiduku, Martin Muduva, Belinda Ndlovu

African Conference on Information Systems and Technology

This document emphasizes on the generation of electricity from trees and its usability in all the sectors of Zimbabwe. The research focused on positively changing the lives of citizens through the provision of uninterrupted and reliable bioelectricity. The literature review was completely and accurately performed through finding out the current news associated with the use of trees in producing electricity and the use of AI to manage the flow. The Scrum’s development model was adopted and followed during the research project to address issues like transparency, early mitigation of risks and constant feedback. The Scrum-model is one of the best …

Reconciling Modern Engineering Education With The Everyday Of Rural Schools And Youths, Malle R. Schilling, Jacob R. Grohs

Journal of Pre-College Engineering Education Research (J-PEER)

We highlight the impacts of inequitable distributions of resources across geographies, structural challenges faced in partnerships with rural schools, and the importance of asset-based arguments to recognize and engage with systemic challenges. Precollege engineering education often focuses on engaging students and teachers hands-on with novel technologies and experiences that frequently distract from systemic inequities, particularly pertaining to place. Given national efforts in rural STEM education, there is a need to recognize important contextual factors influencing precollege engineering education. Through a lens of working with rural Appalachian schools, we hope to challenge practices and assumptions in precollege engineering education.

Application Of Process Mining Algorithms To Surgical Training: A Study Of Healthcare Settings, Fazla Rabbi

Student Theses and Dissertations

Healthcare professionals must be experts at carrying out complex surgical procedures to fulfill their responsibilities. The aim of the medical treatments is fewer complications, shorter hospital stays, and a better patient experience. They often report on problems with surgical processes, skipped procedures, and lengthy transition times. The event log data allows process mining methods to deliver professionals with understandable findings using Petri Net. This study identifies the parallels and discrepancies between the pre-and post-stages and their respective frequency on each typical Central Venous Catheter (CVC) installation activity, where pre-stage they were given a briefing, and in post-stage they had hands-on …

Air Content Variation In Concrete At Different Stages Of Its Lifecycle, Mohammad Sarfaraz Uddin

Student Theses and Dissertations

In cold climates, concrete structures are susceptible to deterioration from the freezing and thawing of water within the concrete. The arrangement of air voids is crucial for the concrete to endure the freeze-thaw cycles. This research investigates changes in the air void system within concrete using a super air meter and the properties of both fresh and hardened concrete at various stages, including mixing and after pumping. Laboratory mixes used different dosages of air-entraining admixes and included Rice Husk Ash (RHA) as a supplementary material. Adding RHA to the mix resulted in a decrease in air content of about 2.2%, …

Prediction Of Moisture Resistance Of Aggregate Binder System Through Physicochemical And Mechanical Assessment, Khaja Sameer Sadat

Student Theses and Dissertations

Moisture sensitivity has been a significant factor in pavement deterioration for several decades. The primary objective of the proposed research is to quantify the interface-level adhesion forces between bitumen samples and various types of aggregates. Aggregates and binders from varying sources with an anti-stripping agent were utilized for routine tests, micro-level tests, and elemental analysis. Pneumatic Adhesion Tensile Testing Instrument (PATTI) was discovered to be a more straightforward, simple, and repeatable adhesion testing method. The PATTI test revealed that sandstone exhibited higher adhesion values due to its greater porosity and absorption potential than limestone. The Texas Boiling test removed human …

Fine-Tuning Cesium Lead Chloride Perovskite Field-Effect Transistors For Sensing Applications: Bridging Numerical Modeling And Experimental Validation, Gehad Ali, Reem Mahmoud, Mohamed Wafeek, Motaz Yousef, Sameh O. Abdellatif

Electrical Engineering

No abstract provided.

Application Of Haines And Terbrugge Chart For Suitable Slope Angles – A Case Study Of Artisanal And Small-Scale Mining, Carol Mgiba, Steven Rupprecht

Journal of Sustainable Mining

Significant hazards in Artisanal and small-scale mining (ASM) are rock failure and slope collapse caused by overly steep pit walls, poor mine design and water pressure. There is a lack of expertise and capital in ASM. Providing a simple and cost-effective slope stability analysis and designing systems that can mitigate the risk of slope collapse is essential. This article aims to assess whether stability charts and estimations can be used to establish suitable slope angles to mitigate the overwhelming cases of slope collapse in ASM. The Bieniawski’s Rock Mass Rating system and Heins and Terbrugge stability chart were used to …

Review Of Hardware Implementation For The Two-Wheeled Self-Balancing Robot, Ghaidaa Hadi Salih Elias

Al-Bahir

The working principle of a self-balancing robot is similar to that of an inverted pendulum, with the mobile robot's controller playing a crucial part in both self-balancing and stabilization. It is the kind that constantly modifies itself to keep balance when it rides on two wheels. This review will center on the basic construction of the suggested robot, summarize the recent studies relative to control methods and the building of the two-wheeled self-balancing robot, and discuss the outcomes of control experiments conducted on hardware systems. It will assist researchers in building and designing two-wheeled mobile robots in the future.

Mechanical Behavior Of Nixcr Alloys With Ti3sic2 Nanoparticle Inclusions Using Molecular Dynamics, Brendan Martin Crutchfield

Student Theses and Dissertations

Many materials used for solid lubrication are expensive and difficult to replace after abrasive wear, thus new compounds have to be considered and explored for their feasibility in solid lubrication. Nickel-chromium alloys are a potential replacement due to its high chemical and thermal stability, and Ti3SiC2 nanoparticle inclusions are considered to improve the wear rate of these materials. In this study, molecular dynamics (MD) simulations are utilized to assess the mechanical behavior of NixCr (x = 1, 2, 3, 4) alloys with and without Ti3SiC2 nanoparticle inclusions of radii 13, 20, and 27 Å. MD uniaxial tension tests are conducted …

Advanced Transistor-Based Dynamic Equivalent Circuit Modeling Of Mesostructured-Based Solar Cells, Eman Farouk Sawires, Sameh O. Abdellatif

Electrical Engineering

This study introduces a pioneering transistor-based equivalent circuit model explicitly tailored for mesostructured-based solar cells, primarily focusing on dye-sensitized solar cells (DSSCs) and perovskite solar cells (PSCs). By incorporating the experimental data spanning various inorganic, organic, and hybrid solar cell technologies across different optical injection levels, the model aims to provide a comprehensive understanding of the electrical behavior of these advanced photovoltaic devices. In addition to the circuit schematic, a Verilog-A script was created to elucidate the behavior of the cells, facilitating the utilization of such a block by the research community in developing interfacing circuits and implementing dc–dc converters …