Engineering Commons^™

Construction Method Of Digital Twin System For High-Low Temperature Test Chamber, Qinghua Chen, Zuoyou Liang, Weijuan Guan, Jiadong Ji, Ping Liu

Journal of System Simulation

Abstract: In view of the construction requirements of the digital twin system of the high-low temperature test chamber, the EMQX server with MQTT as the communication protocol is used for data transmission. Driven by real-time data, real-time dynamic interactive mapping between the physical entity and the virtual model is realized. The neural network model and genetic algorithm are used to evaluate and predict the running state of the equipment and provide the system adjustment strategy, so as to realize the whole climate, life and working condition of the staff to understand the running state of the equipment, and effectively ensure …

Cooperative Guidance For Multigroup Flight Vehicles Against Multiple Targets With Separated Impact Time, Guofei Li, Shituo Li, Yilun Huangfu, Yueyang Hua, Yunjie Wu, Zongyu Zuo

Journal of System Simulation

Abstract: To cope with cooperative guidance against multiple targets, a distributed cooperative guidance for multigroup flight vehicles to strike multiple targets with separated impact time is proposed. The collaborative variables for multigroup flight vehicles with separated impact time are given, and the guidance law in the line of sight (LOS) is proposed. The guidance laws on the normal and lateral directions of the LOS are proposed to make the LOS deflection angle rate and LOS the inclination angle rate converge rapidly, which ensures that each vehicle is able to strike the target. The finite-time convergence of the proposed guidance laws …

Automatic Multi-Objective Optimization Based On Dynamic Storage Location Allocation Strategy, Juan Chen, Wang Zheng, Qianqian Liu, Bin Lu

Journal of System Simulation

Abstract: Based on the dynamic storage allocation strategy, the two-stage optimization model is constructed with the whole warehouse as the main optimization body, in order to meet the safety and rationality of the storage allocation goals, and to meet the dispatching goals of the shortest operation time and the lowest energy consumption of each stacke. The upper and lower levels of the model are typical multi-objective optimization problems, and the ideal solution of the upper level model will be the initial condition of the lower level model. The multi-objective genetic algorithm is used to solve the ideal solution of the …

Soft Sensor Modeling Based On Adaptive Sparse Broad Learning System⋅, Kangping Du, Lin Sui, Weili Xiong

Journal of System Simulation

Abstract: To address the challenges posed by nonlinearity and the coupling of multiple features in complex industrial processes, resulting in increased model complexity and decreased performance, a soft sensor modeling method based on adaptive sparse broad learning system is proposed. Building upon the lateral enhancement transmission of features, the trace least absolute shrinkage and selection operator (LASSO) is further used to optimize the feature weights of the network, adaptively adjusting the penalty intensity based on the correlation between different variables to enhance the feature extraction capabilities of the model. The Dropout mechanism is introduced in the enhanced part, and the …

Enhanced Artificial Gorilla Algorithm For Mobile Robot Path Planning, Chen Ye, Peng Shao, Shaoping Zhang, Wenting Li, Tengming Zhou

Journal of System Simulation

Abstract: To address the issues of susceptibility to local optima and slow convergence in mobile robot path planning within complex terrain scenarios, an enhanced artificial gorilla troops optimizer with integration of quadratic interpolation and elite individual genetic strategies (QGGTO) is proposed. The algorithm integrates quadratic interpolation and elite individual genetic strategies to promote information exchange among candidate solutions, thereby accelerating convergence, while maintaining population diversity to avoid local optima. For complex terrains containing both regular and irregular obstacles, a cost function that comprehensively considers walking distance, safety, and turning angles is constructed to uniformly evaluate the path planning performance of …

Multi-Model Based Iterative Method For System-Of-Systems Architecture Design, Xuemeng Zhao, Tianzhu Ren, Zhemei Fang

Journal of System Simulation

Abstract: In order to solve the problems of difficulties in expressing dynamic characteristics and lack of decision analysis support in developing models of the department of defense architecture framework (DoDAF), an integrated iterative method for combat SoS architecture design is proposed. The DoDAF architecture model integrates and expresses combat-related information from multiple perspectives; the ExtendSim executable model simulates the emergence behavior and dynamic characteristics of combat SoS architecture in multiple scenarios; and the decision model quantitatively analyzes and selects architecture schemes by multi-objective decision rules. Ultimately, a SoS architecture integrated iterative design method of "view-simulate-decide-iterate" is formed. The design process …

A Drl⁃Based Approach For Distributed Equipment Nodes Selection, Ziyi Wang, Kai Zhang, Dianwei Qian, Yuzhen Liu

Journal of System Simulation

Abstract: Aiming at the problem of insufficient solution speed and poor generalization of traditional algorithms in large-scale scenarios, this paper intelligently solves the large-scale distributed equipment system preference problem based on deep reinforcement learning. According to the characteristics of distributed equipment system combat, using the complex network to its graph form modeling, and based on the attention mechanism to the equipment between the connecting edge relationship for the characterization, in order to build a distributed equipment system digital simulation environment. Simulation results show that compared with the genetic evolutionary algorithm, the obtained model has obvious advantages in terms of solution …

Research On Improving Design Efficiency Of Coaxial Magnetic Gear Based On Linear Model, Shuguang Zhao, Ce Chen, Xiaochang Xie, Fuping Li, Jin Han

Journal of System Simulation

Abstract: To address the issues of large model computation load and cumbersome magnetization direction setting during the simulation design of coaxial magnetic field modulation type magnetic gears, a simplified design method is proposed, which uses a linear model to replace the original conventional circular ring model. Based on the periodicity of the structure and magnetic field of each part of the magnetic gear, the modeling work is simplified and the computational load of the simulation analysis is reduced. The results show that compared with the circular ring structure, the number of magnetization coordinate system settings for the linear structure is …

Emt Vision, Logan Calder, Grant Johnson, John Alvarado, Jack Landers

Computer Science and Engineering Senior Theses

Augmented Reality (AR) has demonstrated considerable promise for future mobile technologies, offering the ability to overlay crucial information within a user’s vision while they can still maintain awareness of the surrounding environment. Similarly, Artificial Intelligence (AI) is an increasingly influential technology with significant potential to revolutionize the medical field. Its ability to rapidly learn and adapt to specific tasks makes it particularly promising for supporting paramedics during emergency calls. AI can efficiently analyze real-time data and present it in a concise, actionable format, enhancing decision making in critical situations.

Given the potential of these technologies, we have developed a smart …

Artificial Intelligence In Surveillance And Privacy, Elizabeth D. Brasher

NEXUS: The Liberty Journal of Interdisciplinary Studies

This paper attempts to provide insight into the new and developing world of artificial intelligence and its integration into surveillance technologies. These technologies being implemented by the government, retail companies, healthcare organizations, and more, all raise ethical questions and implications addressed in this article; other topics, such as the integration of Christian ethics and responsibilities, are also explored.

Empowering Tanzanian Education: Personalized And Accessible Test Preparation, Brian Wiebe, Shiv Jhalani

Computer Science and Engineering Senior Theses

Only 20% of students performed well enough on their secondary exams to continue their A-level studies. This challenge exists due to a lack of resources, classroom overcrowding, and absenteeism of the instructors. The project focuses on improving the pass rate for these national exams; we have built an intelligent quiz generating platform that helps Tanzanian students prepare more effectively for exams by meeting user needs including active recall, answer explanations, increasing difficulty, and filtered studying. Students can select the subject, form level, topic, and the difficulty level, and the platform provides different types of questions, including true/false, multiple choice, and …

Bridging Virtual Robots And Physical Tasks Via Augmented Reality, Xiangfei Kong

USF Tampa Graduate Theses and Dissertations

Entry to human-robot interaction research, e.g., conducting empirical experiments, faces a significant economic barrier due to the high cost of physical robots, ranging from thousands to tens of thousands. This cost issue also severely limits the field’s ability to replicate user studies and reproduce the results to verify their reliability, thus offering more confidence to incorporate these findings. Although virtual reality (VR) user studies present a potential solution, it is unclear whether we can confidently transfer the findings to physical robots and physical environments because VR isolates both the physical robot and the physical world where robots operate. To address …

Cross-Cultural Inspiration Coach, Romeo Nickel, Veronica Flores, Rahul Rani, Andrew Yang

Computer Science and Engineering Senior Theses

Personal inspiration and motivation are fundamental drivers of well-being and growth, yet current digital wellness solutions predominantly reflect Western perspectives, failing to address the diverse cultural contexts through which inspiration manifests globally. This thesis presents the development of an Inspirational Coach platform that leverages artificial intelligence to deliver culturally-adaptive personal development content. The system employs a fine-tuned Llama 3.1 8B model using Low-Rank Adaptation (LoRA) techniques to generate personalized motivational content that incorporates users’ cultural backgrounds, personal themes, and individual preferences.

The platform integrates four core features within a comprehensive React-based web application: guided journaling with mood tracking, goal setting …

Teaching Computer Science Through An Educational Game, Aidan Walker, Matthew Leonard, Grant Goldman

Computer Science and Engineering Senior Theses

In our ever-evolving technological landscape, it is becoming more and more important for students of all ages to have at least a basic concept of coding fundamentals. It’s not just the basic knowledge of how to code; knowing important coding concepts is just as important. To help introduce these concepts to young students, we developed an educational game in the Roblox platform aimed at teaching foundational programming concepts—specifically recursion—to young learners through interactive and engaging gameplay. Leveraging Roblox’s accessibility and popularity among younger audiences, the game introduces players to recursive thinking in a visual and intuitive manner. Players control a …

Self Driving Robot Car, Eric Hicks, Ruby Huynh

Computer Science and Engineering Senior Theses

In the real world, vehicular crashes are the result of human error and inadequate reaction time and are often deadly. Due to the issue of accidents being frequent occurrences, using machine learning to drive vehicles has quickly become a relevant topic as it could potentially become a way to minimize fatalities and damages. Thus, the project aims to approach self-automation with the focus on maximizing the vehicle’s performance and the machine’s ability to make the best decisions within that time.

Today’s self-driving cars use radar or cameras as well as digital signal processing algorithms to sense the environment while using …

Tcras: Traffic Control Risk Analysis System, Owen Matejka

Computer Science and Engineering Senior Theses

Traffic intersections represent critical points of conflict in urban transportation networks, with over 40,000 traffic-related fatalities occurring annually in the United States alone. Traditional intersection monitoring systems, based on timer controls and inductive loop detectors, lack the sophisticated detection capabilities needed to address modern traffic safety challenges. This thesis presents the Traffic Control Risk Analysis System (TCRAS), a low-cost, computer vision-based solution that democratizes access to advanced intersection monitoring capabilities. TCRAS leverages edge AI processing through Hailo neural network accelerators combined with open-source computer vision algorithms to provide comprehensive intersection analysis. The system performs real-time multi-class object detection and tracking …

A Universal Lstm Stock Price Predictor Utilizing News Sentiment Analysis And Technical Indicators, Kelly Zhou, Zhirong Wang

Computer Science and Engineering Senior Theses

The stock market is influenced by a complex interplay of factors, including historical price trends, technical indicators, news sentiment, and macroeconomic conditions. Traditional stock prediction models typically focus on a single stock, limiting their ability to capture broader market relationships. Investors require a model that can accurately forecast price movements across multiple stocks to optimize trading decisions.

We propose a universal stock prediction model that leverages relationships across all S&P 500 stocks. Unlike traditional single-stock models, our approach utilizes a multi-stock LSTM architecture trained on a combination of historical stock prices, technical indicators, and news sentiment. The model is designed …

Preserving Yucatán’S Agricultural Heritage: A Mobile App For Agricultural Sustainability, Fernando Rojas, Jason Serrano, Kavya Sharma

Computer Science and Engineering Senior Theses

We developed a mobile application that preserves Yucatán’s agricultural heritage and supports local farmers, while addressing the urgent need for the technology-driven integration of Mayan agricultural knowledge, specifically the Milpa system, into an accessible mobile platform. Our objective was to create an intuitive, culturally appropriate, and low-technology resource to support farmers in making informed, sustainable agricultural decisions, regardless of their internet connection. We saw this as critical for enhancing agricultural practices in Yucatán and beyond. We accomplished this by creating a trilingual (Spanish, English, and Yucatec Maya) mobile app with features such as agrarian cycle information, mapping and location services, …

Bilingual Buddy, Adian Alvarado, Farhaan Pishori, Luis Villalta, Andrea Yao, Ekam Singh

Computer Science and Engineering Senior Theses

Bilingual students often face challenges in mathematics not due to a lack of ability, but because of linguistic barriers that hinder their understanding of math-specific terminology and consequently their problem-solving language ability. This project addresses that issue through the development of a mobile application designed to support English language acquisition in the context of mathematics for Spanish-speaking students. Rather than teaching mathematical concepts directly, the app focuses on vocabulary, syntax, and contextual comprehension through scaffolded lessons, gamified elements, and an integrated AI chatbot.

Built using Flutter for cross-platform compatibility, the app prioritizes accessibility, simplicity, and engagement for young users. Ethical …

New Attack Surfaces Against Emerging Cloud And Web Based Infrastructures And Defenses, Junjie Xiong

USF Tampa Graduate Theses and Dissertations

Emerging network security threats, ranging from cloud-based infrastructure attacks to web-based content subversion, pose significant challenges to modern computing environments. In this dissertation, we explore two novel attack vectors that disrupt both cloud-based infrastructures and web-based content systems.

In this dissertation, we first introduce the Warmonger attack, a novel attack vector that can cause denial-of-service between a serverless computing platform and an external content server. The Warmonger attack exploits the fact that a serverless computing platform shares the same set of egress IPs among all serverless functions, which belong to different users, to access an external content server. As a …

How’S It Growing? Tools For Observing Snow And Sea Ice In A Changing Arctic Ocean, Ian Alexander Raphael

Dartmouth College Ph.D Dissertations

September Arctic sea ice extent has diminished by roughly 50% in the 45 years since satellite observations began. The Arctic Ocean may experience ice-free summers within the next decade, with implications for habitat, resource extraction, geopolitics, and local and global climate change. To predict how Arctic sea ice will change in the future, we need to understand its behavior in the present. In situ sea ice mass balance measurements (snow accumulation, ice growth, snow and ice surface melt, and bottom melt) are essential for studying the processes driving rapid changes in the ice pack, and for validating remote sensing measurements …

Cutting-Edge Methods For Analyzing Student Behavior In Educational Settings: A Review, Shatha Talib Rashid, Hasanen S. Abdullah

Journal of Soft Computing and Computer Applications

The ability to predict students' performance in educational settings like schools and universities is crucial. A key objective of this effort is to increase academic outcomes and prevent dropout rates, among other benefits. Automating student activities, encouraged by information collected from any technology-based learning tool, has an important role in the process here. Those big quantities of information ought to be completely studied theoretically and processed for gaining worthy insights concerning a student's background as well as interacting with scientific missions, facilitating the development of advanced ways and algorithms to predict students' performance. The current study reviews several contemporary mechanisms …

Blockchain-Based Physical Election Votes Digitally Secure Transfer, Mohanad A. Mohammed, Hala B. Abdul Wahab

Journal of Soft Computing and Computer Applications

Responsibility for maintaining election transparency over time and ensuring democratic values intact is held by the Iraqi Independent High Electoral Commission (IHEC). However, transferring physical election votes from election centers is a critical duty, where many challenges appear regarding accountability and security measures. This study proposes a system that utilizes blockchain technology to solve any challenges or difficulties and ensure an effective and improved election process by providing its highest trustworthiness and legitimacy and ensuring a decentralized security process. This system offers unique blockchain characteristics such as immutability, decentralization, and transparency, providing an extra level of security to the data …

Modern Face Recgognition Systems: A Review Of Methods And Empirical Findings, Zahraa Naji Razoqi, Raheem Ogla, Abdul Monem S. Rahma

Journal of Soft Computing and Computer Applications

The face recognition system is a biometric technique that replaces traditional passwords and personal identification. This research is dedicated to presenting a study of some facial recognition systems. Since it is unlikely to replicate and is more stable over time, the domain of facial feature extraction has proven to be more effective in attaining exact facial recognition, which is important, especially in intelligent security surveillance systems. Face recognition systems encounter several challenges, primarily related to pose variations, illumination conditions, and occlusions such as hair, glasses, and so on. To address these challenges, enhance performance, and boost the accuracy and speed …

Arson Event Detection Using Yolov9, Ali Abbas Abbod, Matheel E. Abdulmunimb, Ismail A. Mageed

Journal of Soft Computing and Computer Applications

Detecting event anomalies is crucial for surveillance systems, as it enables the identification of occurrences in videos, both temporally and spatially. It can identify deviations from patterns without requiring human oversight by learning from past information to distinguish normal behavior and pinpoint irregularities. Early detection of arson fires is critical to mitigating damage, public safety, property, and the environment, as well as saving lives and aiding in law enforcement investigations. The objective of this study is to evaluate a system for detecting events using the You Only Look Once version 9 (YOLOv9) model in surveillance videos with a focus on …

Enhancing Cybersecurity Based On Blockchain Technology: A Systematic Review, Sarah Mohammed Shareef, Rehab Flaih Hassan

Journal of Soft Computing and Computer Applications

Cybersecurity is a crucial component of the security system that guards against unauthorized access to digital transactions. Blockchain is a decentralized ledger used to securely exchange digital currencies and conduct trades and transactions. Blockchain technology has led to significant changes in electronic transactions. The enormous potential is being exploited in many areas such as financial services, real estate, supply chain, and the Internet of Things. Despite being a security system, it has suffered from security threats to sensitive data. Phishing and 51% attacks can circumvent blockchain security, highlighting the need for thorough user education and awareness. Additionally, blockchains based on …

Predicting Earthquake Location Using Convolutional Neural Network-Attention Mechanism Approach, Mohammed A. Jaleel Shaneen, Suhad M. Kadhem

Journal of Soft Computing and Computer Applications

In seismically active areas, earthquake prediction is essential for minimizing potential damages and preserving lives. However, precise forecasts are complicated to achieve because of seismic events’ complex and unpredictable nature. The current study presents an advanced prediction approach to address such issues, combining Convolutional Neural Networks (CNNs) and Attention Mechanism (AM). The primary goal is to improve the accuracy of the earthquake predictions and the generalizability across various mainland Chinese regions. AM layer emphasizes significant features for improving the prediction performance, whereas CNNs are utilized to extract spatial features of seismic data. The efficiency and effectiveness of the proposed approach …

A Machine Learning-Driven Framework For Real Time Detection And Prevention Of Replica Node Attacks In Wireless Sensor Networks, Maram Pavani, Tanguturi Sharani, Amutha Jeevakumari S A

Northeast Journal of Complex Systems (NEJCS)

Mobile devices and wireless sensor networks (WSNs) are increasingly vulnerable to security threats such as unauthorized access and replica node attacks. Mobile devices face risks from replication and anomalous behavior, while attackers compromise WSNs by cloning legitimate nodes, thus threatening network integrity. Traditional security mechanisms often fall short in detecting such sophisticated threats, especially in resource-constrained environments. This research proposes a dual-component security system. A Machine Learning-Based Intrusion Detection System (IDS) for WSNs leverages Graph Neural Networks (GNNs) to detect replica nodes through structural network analysis and applies Federated Learning to preserve data privacy. The Sequential Probability Ratio Test (SPRT) …

Gbotuner: Autotuning Of Openmp Parallel Codes With Bayesian Optimization And Code Representation Transfer Learning, Kimsong Lor

Computer Science and Engineering Master's Theses

Empirical autotuning methods such as Bayesian optimization (BO) are a powerful approach that allows us to optimize tuning parameters of parallel codes as black-boxes. However, BO is an expensive approach because it relies on empirical samples from true evaluations for varying parameter configurations. In this thesis, we present GBOTuner, an autotuning framework for optimizing the performance of OpenMP parallel codes, where OpenMP is a widely used API that enables shared-memory parallelism in C, C++, and Fortran using simple compiler directives. GBOTuner improves sample efficiency of BO by combining code representation learning from a Graph Neural Network (GNN) into a BO …

Development Of An Embedded Iot Board For Real-Time Floor Estimation Of Autonomous Robots, Carter J. Sorensen

Electronic Theses and Dissertations

As service robots become more prevalent in multi-story environments such as hospitals, hotels, and laboratories, accurate floor-level detection is critical to ensuring operational reliability. Consider a robot tasked with delivering medical samples in a multi-story laboratory. Without accurate feedback, a robot exiting on the wrong floor could introduce delays, disrupt workflows, or compromise sample integrity. Internet of Things (IoT) technologies offer a way to address these risks by providing real-time error detection and corrective capability. However, current IoT-based floor estimation systems often require invasive modifications to building infrastructure—particularly elevator control panels. These approaches introduce challenges related to cost, liability, backward …