Digital Commons Network^™

Robot Trajectory Planning And Adjustment Method For Abnormal Pose Of Actuator, Lang Qin, Jiacheng Xie, Xiaojun Qiao, Xuewen Wang, Zhijie Xiao

Journal of System Simulation

To address the influence of the abnormal pose of the robot actuator on the robot trajectory, an adaptive planning and adjustment method of trajectory based on virtual-real fusion was proposed. AR technology was introduced to couple with the robot kinematics model, and the hardware dependence on multiple sensors was replaced by synchronous comparison of three-dimensional virtual and real poses; AR gestures and voice interaction were combined to simplify the operation process; based on the actual pose of the actuator, the trajectory of the robot terminal axis was dynamically adjusted. The experimental results show that this method breaks through the technical …

Hyperspectral Anomaly Detection Algorithm Based On Window Reconstruction And Collaborative Representation, Shuanghao Fan, Fang He, Jianwei Zhao, Haojie Hu, Fengchao Zhu, Xiangyang Li

Journal of System Simulation

Hyperspectral anomaly detection refers to identifying ground objects that deviate from normal background distributions and have low probability and small scales from scenes involving mixed multi- class ground objects, spectral feature overlaps, and noise interference. This technology has received extensive attention in recent years. Although collaborative representation-based anomaly detection algorithms demonstrate excellent performance in hyperspectral image anomaly detection, their time costs are too high to enable widespread application.To address this issue, this paper proposes a hyperspectral image anomaly detection algorithm based on window reconstruction and collaborative representation, which consists of two stages. Window reconstruction is performed on hyperspectral background …

Detection Method For Laboratory Ppe Compliance Wearing Based On Human Key Points, Lijun Peng, Tingqi Su, Peijin Liu, Lin He, Xiewu Zhou, Minxin Zhang

Journal of System Simulation

To address the problems of high missed detection rate and inaccurate judgment of wearing compliance when multi-scale and multi-category targets of laboratory personnel's safety protective equipment are detected in a complex laboratory environment, this paper proposes a laboratory personnel's standard personal protective equipment (PPE) wearing detection method (multi-scale multi- target joint key point detection method, MSMT-JKDM) that integrates multi-scale features and human keypoints. The multi-scale adaptive down sampling (MSA-Down) module and the cascaded group attention transformer (CGA Former) are introduced to enhance the feature representation ability of PPE (especially small targets such as goggles and gloves) in laboratory detection scenarios, …

Modeling Of Penicillin Fermentation Process Based On A Multi-Stage Lhs-Eprcc Method, Quan Li, Peng Su, Haiying Wan, Chengxi Zhang, Zhijian He, Yiyang Ni

Journal of System Simulation

This paper focused on the modeling of microbial fermentation processes under varying production environments and proposed a novel approach. Considering that the dynamic characteristics of microorganism s differ across growth stages, we introduced the concept of multi-stage sensitivity analysis, in which each stage was investigated separately. The fuzzy C-means (FCM) algorithm was employed to cluster process data under nominal conditions, thereby dividing the penicillin fermentation process into distinct growth stages. Based on this division, the Latin hypercube sampling with partial rank correlation coefficient (LHS-EPRCC) method was applied to conduct sensitivity analysis for each stage, identifying an importance parameter set (IPS) …

Annotation-Free 6-Dof Grasp Detection Method Integrating Physical And Geometric Priors, Min Shi, Shisheng Guo, Suqin Wang, Zhaoxin Li, Dengming Zhu

Journal of System Simulation

To improve the stability and cross-category generalization capability of grasp pose estimation in complex stacked scenes, an annotation-free 6-DoF grasp detection method integrating physical rules and geometric structure priors was proposed. In the offline stage, a template library of feasible grasp poses was constructed based on multi-physical constraints, without relying on manual grasp annotations. In the network design, the modeling of structural symmetry of objects and spatial overlap relationships was introduced; a geometric guidance mechanism with occlusion perception and exposure modeling capabilities was designed, and robust pose alignment of target objects was achieved by combining keypoint regression. A multi-type stacked …

Detection Method For 3d Lanes Based On Graph Relationship Optimization Integrating Point And Lane Features, Yanji Jiang, Xingyi Xiao, Hao Dong, Miao Yu, Jinshan Huang, Daqian Liu, Bowen Fei

Journal of System Simulation

Under complex road conditions, the thin and elongated structure and small proportion of lanes lead to blurred visual features and insufficient positioning accuracy, which in turn threatens the road safety of autonomous driving. To address these issues, a 3D lane detection method or graph-based point and lane optimization network (GPLNet), based on graph relationship optimization integrating point and lane features, was proposed. Preliminary feature extraction was completed by the backbone network. 3D spatial positional coding with geometric constraints was obtained through a joint query embedding generation module. A graph relationship optimization network was utilized to perform graph relationship calculation and …

Multi-Level Digital Model Of Dynamic Earned Value Management For Complex Engineering Projects And Its Applications, Wei Wang, Dong Liu, Xinhao Cui, Bo Li, Yiyong Xiao, Yi Ren

Journal of System Simulation

The economic management of existing engineering projects is usually based on organizational structure, which presents problems such as complex processes and difficulty in clarifying main responsibilities when applied to complex engineering projects. In response to this limitation, a multi-level digital model of dynamic earned value management is proposed for complex engineering projects, which extends the traditional cost performance indicators to engineering resource utility indicators, thereby decomposing the earned value of costs into segmented earned values of different engineering resources. This enables managers to dynamically supervise projects based on traditional "schedule-cost" performance indicators and carry out more refined cost control …

Hierarchical Motion Planning Of Mobile Robot Based On Dynamic Corridor Inflation And Convex Optimization, Dingkun Zhang, Haizhao Liang

Journal of System Simulation

Motion planning for robots with Ackermann chassis in dynamic complex environments faces nonholonomic constraints and kinematic-dynamic coupling challenges. However, traditional methods suffer from path redundancy, random fluctuations, and local optimality. A hierarchical motion planning method based on dynamic corridor inflation and convex optimization is proposed. Topologically sparse paths are generated by fusing the Ramer-Douglas-Peucker (RDP) path compression operator with the A* algorithm to reduce redundant path points' interference with backend optimization. Dynamic corridor inflation strategies are designed considering Ackermann steering characteristics, and safe corridors satisfying kinematic constraints are constructed via convex decomposition. Corridor constraints are then transformed into linear inequalities …

Power Flow Calculation Based On Block-Encoded Adiabatic Quantum Newton-Raphson Method, Shengchao Jiang, Yunqing Pei, Hongying Zhai, Guojian Wu, Fang Gao

Journal of System Simulation

To overcome the efficiency bottleneck of the traditional Newton-Raphson (NR)method in high- dimensional power flow calculations for modern power systems and the constraints of variational quantum algorithm frameworks, this paper proposed a power flow calculation framework integrating block encoding technology and adiabatic quantum computing principles. Based on block encoding technology, adiabatic quantum theory, and the NR method, a block-encoded adiabatic quantum power flow calculation framework (BQ-NR) was constructed. The NR correction equations were mapped to a quantum system, and the quantum state encoding of the correction equations was realized by constructing an extended Hermitian matrix and a projection operator; a …

The Impact Of Optimization Approximation Algorithms On The Performance Of The Bht-Qaoa, Ali Al-Bayaty, Marek Perkowski

Electrical and Computer Engineering Faculty Publications and Presentations

This article investigates the performance impact of five classical optimization approximation algorithms on our previously introduced quantum search algorithm, termed the Boolean–Hamiltonians Transform for Quantum Approximate Optimization Algorithm (BHT-QAOA), to effectively search for all best-approximated solutions for Boolean-based problems. These optimization approximation algorithms are BFGS, L-BFGS-B, SLSQP, COBYLA, and COBYQA. Their performance impact is evaluated and compared using two proposed performance metrics—(i) the final number of function evaluations (the lower numbers denote the best optimization approximation algorithms) and (ii) the final quality of qubit measurements (the higher values indicate all best-approximated solutions were found for a problem). Arbitrary classical Boolean …

Predictive Natural Language Metrics Of Alzheimer's Disease And Cognitive Decline Trend Analysis, Zerui Ma

Computer Science and Engineering Theses and Dissertations

Inspired by Dr. David Snowden's Nun Study, which linked early-life Propositional Idea Density (PID) to later-life Alzheimer's disease, this thesis investigates two questions: whether fine-tuned Transformer-based large language models (LLM) can detect cognitive decline from patient speech transcripts with meaningful feature attribution, and whether longitudinal PID trends are observable across large-scale internet and academic text corpora. We evaluate dementia prediction on the DementiaBank Pitt Corpus and conduct an exploratory longitudinal PID analysis across seven diverse datasets spanning up to 29 years and over 12.6 million documents. This work suggests that linguistic ability metrics, traditional PID metrics and novel LLM-based analysis, …

Machine Learning And Formal Methods In Quantum Chemistry: Theory And Application, Ishna Satyarth

Computer Science and Engineering Theses and Dissertations

In recent years, the progress in inter-disciplinary application of machine learning and artificial intelligence (ML/AI) have truly transformed various fields, from weather forecasting and drug development to medical diagnostics, energy, and sustainability. Computational chemistry uses computational tools to model, predict, analyze, and explain chemical phenomena, while the Quantum chemistry specifically uses techniques based on quantum mechanics (as opposed to classical mechanics or empirical models). Quantum chemistry or Computational chemistry has also observed a momentum in application of ML techniques over the past decade significantly accelerating results and providing valuable insights into vast datasets, often surpassing traditional methods.

This dissertation explores …

Alternative Positioning, Navigation, And Timing In Global Navigation Satellite System Denied Environments, Joshua R. Atencio

Electrical and Computer Engineering ETDs

Global Navigation Satellite Systems (GNSS) provide the majority of critical positioning, navigation, and timing (PNT) services for civilian, commercial, and military applications. However, GNSS is vulnerable to service denial from spoofing and jamming from adversaries and environmental obstruction. These vulnerabilities highlight the need for resilient Alternative PNT (APNT) methods. This dissertation investigates APNT frameworks operating in GNSS denied environments. We develop coalition formation and matching theoretic models that allow users APNT services from anchor nodes under resource constraints and in adversarial or emergency conditions. The proposed frameworks optimize positioning accuracy, network utility, and system stability while accounting for geometric dilution …

Quantum Control Protocols For Robust Quantum Computing, Leeseok Kim

Electrical and Computer Engineering ETDs

The fundamental goal of quantum computing is to precisely control quantum systems to perform meaningful tasks, including implementing high-fidelity quantum gates for reliable quantum computation and accurately simulating complex quantum many- body dynamics. In this dissertation, we develop improved quantum control protocols for three distinct objectives, quantum error suppression, quantum optimal control, and analog quantum algorithms, achieving performance beyond standard approaches. First, we introduce new dynamical decoupling protocols, including both determin- istic and randomized constructions, that can substantially outperform conventional deterministic sequences. We then extend the randomized approach to dynamically corrected gates. Second, we propose a randomized quantum optimal control …

Classification Of Hif Detection In Nev Profile Using Wavelet Transform And Convolution Neural Network, Abdul Hafiz Kassim, Mohd Abdul Talib Mat Yusoh, Aster Smith Valentinie Wilson Nottelmarc, Ahmad Farid Abidin, Sim Sy Yi, Daw Saleh Sasi Mohammed

Turkish Journal of Electrical Engineering and Computer Sciences

High impedance faults (HIFs) present a critical challenge in power systems due to their subtle signal characteristics, which often remain undetected by conventional protection methods. These faults typically do not produce significant phase disturbances, making reliable detection difficult. However, analysis of the neutral-to-earth voltage (NEV) profile under fault conditions provides a promising alternative for fault identification. Existing approaches for detecting and classifying HIFs using NEV signals remain limited and may result in inaccurate maintenance decisions. This paper proposes a fault classification framework for multiple fault types, including HIF, three-phase fault, three-phase fault to ground, double line, double line to ground, …

Complex-Valued Convolutional Neural Network With Time-Frequency Representation For Electrocardiogram-Based Arrhythmia Detection, Kajeeth Kumar Gurusamy, Muthurajkumar Sannasy

Turkish Journal of Electrical Engineering and Computer Sciences

This research proposes an end-to-end procedure for arrhythmia detection based on electrocardiogram (ECG) signals using complex-valued convolutional neural network (CVCNN) incorporated with time-frequency representation. The proposed model leverages complex numbers to capture amplitude and phase information that enhances the ability of the model for detecting time-frequency variation in cardiac signals. First, signal preprocessing techniques---including normalization, wavelet denoising, and R-peak detection---are applied. Subsequently, the model extracts complex features from raw ECG data by employing the Hilbert transform to derive the analytic signal and the short-time Fourier transform (STFT) to generate a time–frequency representation. The proposed CVCNN framework effectively learns spatial-temporal features …

Accurate Diagnosis Of Diseases By A Novel Ai Pipeline Based On Feature Extraction, Feature Ranking, And Feature Selection From Medical Images, Tuğba Nur Bozkurt, Mehmet Emi̇n Yüksel

Turkish Journal of Electrical Engineering and Computer Sciences

The rapid growth of the global population has led to a substantial increase in the number of patients, while the availability of healthcare professionals has not expanded at a comparable rate. This imbalance highlights the urgent need for efficient and reliable computer-aided decision support systems that can reduce clinical workload while maintaining high diagnostic accuracy. In this study, a novel and systematically integrated artificial intelligence-based pipeline is proposed for medical image classification, combining statistical significance-driven feature ranking with evolutionary feature selection in a unified framework. The proposed pipeline consists of four sequential stages: feature extraction, ranking, selection, and classification. Features …

Adapting Independent Large-Scale Pretrained Models For Human Action Recognition, Selen Pehli̇van

Turkish Journal of Electrical Engineering and Computer Sciences

Transferring knowledge from large-scale, independently pretrained image and text models to video understanding requires addressing several challenges, including maintaining generalization capabilities of models, integrating them into multimodal architectures, and fine-tuning with temporal dynamics. This study evaluates the effectiveness of parameter-efficient fine-tuning (PEFT) techniques in transferring pretrained knowledge from two independent models for video action recognition within a simple, streamlined multimodal fusion pipeline. Specifically, we adapt CLIP as the text branch and DINOv2 as the image branch, keeping both backbones frozen to preserve their pretrained robustness, while introducing lightweight, task-specific modules to adapt and fuse the branches with temporal dynamics. A …

Adaptive Backstepping Nonsingular Fast Terminal Sliding Mode Control For Robotic Manipulators Based On Disturbance Observer, Xin Zhang, Xu Wang

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents an adaptive backstepping nonsingular fast terminal sliding mode controller integrated with a nonlinear disturbance observer to achieve precise trajectory tracking of robotic manipulators subject to model uncertainties and unknown time-varying disturbances. A dead-zone–based adaptive gain mechanism is introduced to dynamically adjust the control gain according to the deviation of the sliding surface, thereby enhancing robustness and reducing chattering. The proposed reaching law ensures fast, nonsingular, and adaptive convergence, suppressing high-frequency oscillations without compromising stability and the nonlinear disturbance observer enables real-time estimation and compensation of modeling errors, friction, and external disturbances for superior rejection. The semiglobal uniform …

Chaotic Artificial Bee Colony-Optimized Stacking Ensemble For Robust Multifault Diagnosis Of Wind Turbines, Veilraj Revathi, Solaimalai Jeyadevi, Madasamy Sudalaimani

Turkish Journal of Electrical Engineering and Computer Sciences

The complex electromechanical structure of wind turbines, along with harsh operating conditions, poses significant challenges for precise and robust fault diagnosis. To address this challenge, an ensemble multifault diagnostic framework based on an adaptive chaotic artificial bee colony (C-ABC)-optimized support vector machine (SVM) and gradient boosting machine (GBM) is proposed. In the proposed framework, data redundancy and overfitting are reduced through a two-stage hybrid filter-transformer-based feature reduction approach using ReliefF, followed by Principal Component Analysis. The chaos function of the proposed C-ABC maintains an adaptive balance between the exploration and exploitation phases, thereby preventing premature convergence, which is a common …

A Descriptive Analysis Of Plant Leaf Disease Detection Using Machine Learning And Deep Learning Models: A Systematic Review, Arzoo Chamoli, Anuj Kumar

Turkish Journal of Electrical Engineering and Computer Sciences

Plant leaf disease detection (PLDD) is a growing active research area with burgeoning practical applications across various sectors such as agricultural monitoring, food security, and environmental conservation. Accurate segmentation and classification of plant leaf diseases remains a key challenge in the field of plant leaf disease prediction. The challenge demands automated methods for the plant disease identification because it needs to develop better crop management systems, which will boost agricultural production. In this article, we provide a systematic review of various machine learning (ML) and deep learning (DL) methods extensively used for PLDD. The review strategy follows a formal protocol, …

Multiobjective Optimization Framework For Renewable Energy Iot Networks Balancing Security, Energy Efficiency, And Communication Reliability, Muhammad Hjouj Btoush, Ashraf S. Mashaleh, Amjad Gawanmeh

Turkish Journal of Electrical Engineering and Computer Sciences

The deployment of Internet of things (IoT) networks powered by renewable energy sources presents unique challenges in balancing security requirements, energy efficiency, and communication reliability. This paper presents a comprehensive multiobjective optimization framework for secure renewable energy IoT nodes that addresses fundamental trade-offs between these competing objectives. We develop a mathematical model incorporating energy harvesting dynamics, security protocols, and communication performance metrics across various environmental scenarios. The proposed framework employs a modified NSGA-II algorithm to identify Pareto-optimal configurations for different deployment contexts. Through extensive simulation analysis, we demonstrate that hybrid energy sources (solar-wind combinations) with lightweight security protocols achieve optimal …

Predictive Current Control Approach For Grid-Integrated Multifunctional Converter Under Source And Load Disturbances, Ravi Kumar Majji, Tirumalasetty Chiranjeevi, Chilukoti Varaha Narasimha Raja, Nagulapati Kiran

Turkish Journal of Electrical Engineering and Computer Sciences

This paper discusses and presents a model predictive control (MPC)-based predictive current control technique for a solar photovoltaic (PV)-integrated grid system during dynamic operation. This control technique employs extension pq (EPQ) theory to estimate reference currents and utilizes an MPC framework for tracking reference currents. Various MATLAB/Simulink simulations were conducted for solar PV generation (source disturbances) and dynamic loading. The results of the OPAL-RT OP4510 real-time simulation are also presented. A multifunctional grid-integrated converter (MFGC) integrates solar active power into the utility grid while achieving unity power factor, reactive power compensation, current balancing, and harmonic suppression. EPQ optimizes mathematical calculations, …

Exploring Ai-Driven Scaffolding For Critical Questioning In Argument Evaluation, Ebenezer A. Belete

UNLV Theses, Dissertations, Professional Papers, and Capstones

The fast-paced changes caused by generative AI (GenAI) innovations call for exploring the potential benefits of GenAI in empowering 21st-century pedagogical strategies. Previous studies in the field of argumentation have shown how students can benefit from using critical questions. However, scaffolding argument evaluation through custom GenAI using critical questions has not been systematically investigated. This study involved two components: (1) designing and determining the usability of a GPT-powered conversational assistant (CQMAA Conversational Assistant) and (2) testing its impact on participants' efficacy for argument evaluation and their acceptance of GenAI as a learning tool through a pretest–posttest experiment. A convergent mixed-methods …

Visual Interpretability Of Multimodal Tissue Perfusion Classification Using Grad-Cam And Saliency Maps, Metehan Zorluoglu

UNLV Theses, Dissertations, Professional Papers, and Capstones

Accurate identification of the tissue perfusion phase from hand images can aid doctors in decision-making with non-invasive techniques. The present study proposes a multimodal deep learning model for classifying the tissue perfusion phase using infrared, thermal, and visible spectrum images of the human hand. The proposed model consists of various preprocessing techniques such as manipulation, homography alignments, and masking. The significant contribution of this thesis is the interpretability analysis of deep learning models, achieved through the analysis of saliency maps and the Gradient-weighted Class Activation Mapping (Grad-CAM) methods. The purpose of this method is to find out how the convolutional …

An Optimization Method For Near-Linear Phase Analog Frequency Sampling Filter Design, Leonardo Ledesma

UNLV Theses, Dissertations, Professional Papers, and Capstones

Analog frequency sampling filters (FSFs) realize a desired frequency response by interpolating a frequency response through a set of harmonically related frequency samples from the filter’s frequency response and are magnitude and phase coefficients used in the filters transfer function. FSFs can be designed to have exact linear phase which makes the FSF attractive for many applications. A FSF’s system transfer function (STF) shows that the filter can be implemented by a series connection of a comb filter and a parallel array of resonators. However, the FSF requires that the zeros created by the comb filter cancel the imaginary axis …

Native Wayland Compositing On Apple Ecosystems: Assessing The Feasibility Of “Wawona” Compositor, Alex Spaulding

2026 Symposium

The Wayland display protocol is the modern standard for Linux window management, emphasizing security, performance, and simplicity. Expanding this ecosystem to macOS, iOS, and Android introduces technical hurdles due to proprietary windowing systems and divergent hardware APIs. This research evaluates the feasibility of developing a native Wayland Compositor for Apple and Android, given the closed nature of these ecosystems.

“Wawona” bridges this gap by architecting a native Wayland Compositor capable of executing unmodified Linux applications. The methodology involves implementing the Wayland protocol stack into native abstractions leveraging Metal, Android’s graphics pipeline, and CoreAnimation.

Investigating Creative Possibility Using Automated Composition, Kian Drees

Undergraduate Honors Theses

As a constructive method, Johann Joseph Fux’s theory of counterpoint defines a space of musical possibility for contrapuntal composition. I developed a computational method for generating melodies to systematically investigate selected properties of this space. My program recursively generates a tree of all possible cantus firmus melodies of a specified length or all possible first species counterpoints on a given cantus firmus, starting with an empty root node and adding child nodes representing possible musical notes at each step until the specified length is reached. I then investigated several properties of the generated melodies, such as the approximate relationship between …

Meeting The Moment With Ai-Employer Informed Education, Brent Terwilliger, John Faraca

Publications

As artificial intelligence transforms aviation, aerospace, and autonomy-related sectors, higher education must adapt to meet evolving workforce demands. This session shares emerging findings from a nationwide study led by Embry-Riddle Aeronautical University, focused on employer perceptions of AI adoption, responsible use, and workforce preparedness in domains including uncrewed systems, space systems, robotics, and advanced air mobility. Based on a structured survey and follow-up interviews, the presentation explores how organizations are using AI tools, from generative platforms to enterprise systems, and defining effective and inappropriate use in operational contexts. Participants will gain insight into critical concerns (e.g., data privacy, compliance, security, …

Improving Human Visual Search: Enhancing Lung Cancer Nodule Detection In Medical Images, Christopher Khajira

Masters Theses

Human visual search involves the identification of relevant signals within information-rich environments, which is a fundamental problem in visual perception. While detection accuracy and response time are commonly used to evaluate performance in visual search, these measures do not reveal the underlying cognitive and computational structure that produces observable behavior. A key challenge lies in distinguishing between competing processing architectures, particularly in complex visual domains where different models can produce similar behavioral outcomes. This study addresses this challenge by developing a computational experimental framework for analyzing visual search behavior using System Factorial Technology (SFT). The experimental framework integrates naturalistic medical …