Computer Sciences Commons^™

Scheduling Method For Virtual Power Plants Based On Analysis And Forecasting Of Heterogeneous Load Characteristics, Runzhao Zhang, Yanbo Chen, Tao Huang, Haoxin Tian, Tuben Qiang, Zhi Zhang

Journal of System Simulation

Abstract: To improve the electricity supply-demand situation by rationally utilizing demand response resources, a two-layer optimal scheduling model for virtual power plants (VPPs) based on the analysis and forecasting of heterogeneous load characteristics was proposed. With the differences in response characteristics of multi-type loads considered, a demand response model for multi-type loads was constructed by using a customer baseline load (CBL) curve forecasting method that integrated dynamic scenario generation and K-means++ clustering. A two-layer optimal scheduling model for VPPs that incorporated load aggregators and demand response was established. In this model, the upper layer conducted optimal scheduling targeting maximizing the …

Vibration Control Of Offshore Wind Turbine Towers Based On Eddy Current Nonlinear Energy Sink, Xiangxing Yu, Yandong Zhao, Baolin Zhang

Journal of System Simulation

Abstract: To address the issue of tower vibrations induced by wind loads, which can damage the structure of wind turbines, a vibration control method for monopile offshore wind turbine towers based on an eddy current-nonlinear energy sink (EC-NES) was proposed. The dynamic model of monopile offshore wind turbines based on EC-NES was constructed according to the Euler-Lagrange equation, and based on the output response of FAST software, the unknown parameters of the model and the wind loads were identified in terms of parameters. The optimal parameters of EC-NES stiffness and damping were obtained using PSO. The eddy current damper …

Dual-Channel Supply Chain Network Equilibrium Model Under Retailers’ Risk Aversion, Hongchun Wang, Caifeng Lin, Xinyi He, Haiyue Yin

Journal of System Simulation

Abstract: To study the network equilibrium problem of dual-channel supply chains under the background of retailers' risk aversion, a dual-channel supply chain network equilibrium model including multiple competitive suppliers, manufacturers, retailers, and demand markets was established. The Mean-CVaR method was employed to quantify retailers' risk aversion characteristics, and variational inequalities were utilized to characterize the equilibrium conditions of decision-makers at each tier of the supply chain. The projection contraction algorithm was applied to solve the model and conduct numerical analysis, thereby revealing the impact of retailers' risk aversion behavior on equilibrium outcomes. The simulation results indicate that a higher …

Spatiotemporal Graph Convolution-Based Demand Forecasting And Simulation Analysis For Automotive Parts Supply Chain, Xiaobin Li, Bing Hu, Chao Yin, Bo Li, Jun Ma

Journal of System Simulation

Abstract: To address complex automotive after-sales parts supply network operations with insufficient demand forecasting accuracy, slow response, and low service efficiency, this study proposed a spatiotemporal graph convolution-based method for automotive parts supply chain demand forecasting. Sales network data of the automotive parts sales network was constructed as a heterogeneous graph, integrating node features like parts sales volume and value to build multi-dimensional node dependencies. A node update mechanism of the graph convolutional neural network was designed, combined with long short-term memory neural networks to capture temporal features, using spatiotemporal attention to integrate temporal and spatial features into updated nodes …

A Method Of Heuristic Human-Llm Collaborative Source Search, Yi Chen, Sihang Qiu, Zhengqiu Zhu, Yatai Ji, Yong Zhao, Rusheng Ju

Journal of System Simulation

Abstract: Traditional source search algorithms are prone to local optimization, and source search methods combining crowdsourcing and human-AI collaboration suffer from low cost-efficiency due to human intervention. In this study, we proposed a lightweight human-AI collaboration framework that utilized multi-modal large language models (MLLMs) to achieve visual-language conversion, combined chain-of-thought (CoT) reasoning to optimize decision-making, and constructed a heuristic strategy that incorporated probability distribution filtering and a balance between exploitation and exploration. The effectiveness of the framework was verified by experiments. The human-AI alignment heuristic strategy with large language model adaptation design provides a new idea to reduce manual …

Research On Cooperative Interference Allocation Of Jamming Resources Based On Improved Genetic Algorithm, Zhixia Xu, Rui Wang, Nan Sun, Bing He, Xiaowei Shen, Xiaofei Zhu

Journal of System Simulation

Abstract: To address the cooperative interference allocation of jamming tasks, a cooperative interference allocation method of jamming resources was proposed based on the improved genetic algorithm. In search and tracking modes of the target radar, a threat level assessment was conducted by the technique for order preference by similarity to an ideal solution (TOPSIS) based on the entropy weight method. The factors affecting the jamming effectiveness of jammers were analyzed. A cooperative interference evaluation model of jamming effectiveness was established, and the allocation model of jamming resources was built with the total interference effectiveness of multiple jammers as the …

Ankimedbench: Evaluating Hierarchical Medical Knowledge In Language Model Embeddings, Neel Patel

UNLV Theses, Dissertations, Professional Papers, and Capstones

Despite achieving over 90% accuracy on medical benchmarks, recent studies show physicians cannot effectively leverage language models to improve clinical reasoning. Current benchmarks test isolated factual recall, but clinical practice requires hierarchical navigation through diagnostic categories—starting broad and narrowing systematically from chest pain to cardiovascular pathology to myocardial infarction to specific STEMI types. Existing evaluations cannot measure whether models preserve this taxonomic structure essential for clinical reasoning.

We introduce AnkiMedBench, built from 16,512 medical flashcards used by students preparing for licensing exams. Cards are organized across six hierarchy levels spanning 16 broad medical specialties to 672 specific diseases and conditions. …

A Predictive Model For Multi- Week Respiratory Risk From Red Tide On Florida’S Gulf Coast., Elmer S. Ochaeta

Computer Science and Engineering Faculty Publications

Florida’s Gulf Coast red tide (Karenia brevis) can put toxins into the air, making people cough, irritating the throat, and worsening asthma or other breathing problems especially when winds blow from the ocean toward the beach. Right now, most public updates don’t really help with the question people actually ask when planning a weekend or vacation: “Will going to or close to the beach be risky in the next few weeks?”.

In this project, I build a weekly early warning system that estimates respiratory risk for specific beaches and predicts that risk 2 to 4 weeks ahead. The study covers …

Developing Accessible Narrative-Based Stem Learning Software For K-6 Braille Display Users, Dylan Ravel, Daniel Tsivkovski, Brandon Foley, Maryam Etezad, Franceli Cibrian, Ariel Han, Rajeev Joshi

Student Scholar Symposium Abstracts and Posters

This research develops a free, accessible web application that enables K-6 students who are blind or visually impaired (BVI) to learn STEM concepts using refreshable braille displays. Currently, most online learning tools are not designed for BVI students, creating a significant educational barrier.

The application interfaces with commercial braille displays and uses narrative-based learning to make STEM content approachable and engaging. By presenting material as interactive stories, students can connect with concepts while developing braille reading skills. The curriculum design prioritizes accessibility through the Accessible Rich Internet Applications (ARIA) standards and screen reader support.

The goal is to provide BVI …

Viability Of Widely Used Encryption Schemes In Drone Transmission, Emanuel Yasir Nelson

Cybersecurity Undergraduate Research Showcase

This paper presents throughout research on the security issues related to drone transmission. These topics were addressed and explained, in particular the aspects relating to cybersecurity, for utmost clarity. These include threats and vulnerabilities, drone transmission the impact of encryption on latency, and the details of the encryption methods AES-128, AES-256, and ChaCha20 that were used in the experiment described in the paper. Each encryption method performance was measured and outputted by the Python code developed and used in the experiment. Afterwards, the performance of each method was analyzed in relation to their decryption time, encryption time, end to end …

Confidential, Attestable, And Efficient Inter-Cvm Communication With Arm Cca, Sina Abdollahi, Amir Al Sadi, Marios Kogias, Hamed Haddadi, David Kotz

Other Faculty Materials

Confidential Virtual Machines (CVMs) are increasingly adopted to protect sensitive workloads from privileged adversaries such as the hypervisor. While they provide strong isolation guarantees, existing CVM architectures lack first-class mechanisms for inter-CVM data sharing due to their disjoint memory model, making inter-CVM data exchange a performance bottleneck in compartmentalized or collaborative multi-CVM systems. Under this model, a CVM's accessible memory is either shared with the hypervisor or protected from both the hypervisor and all other CVMs. This design simplifies reasoning about memory ownership; however, it fundamentally precludes plaintext data sharing between CVMs because all inter-CVM communication must pass through hypervisor-accessible …

Adaptive Deep Learning In Physical Layer Applications, Ali Owfi

All Dissertations

Traditionally, signal processing models in communication systems have been designed based on solid foundations in statistics and information theory, often assuming linearity and optimizing for simplified models. However, real-world communication systems exhibit numerous imperfections and non-linearities that traditional linear models struggle to capture accurately. Deep Learning (DL)-based approaches, unconstrained by rigid mathematical models, have shown promise in optimizing system performance by accommodating specific hardware configurations and dynamic channel conditions as an alternative to the traditional methods. Despite all the recent research efforts on DL-based methods for physical layer applications, DL models have still not been widely applied to physical layer …

Privacy Preserving-Based Artificial Intelligence For Precision Agriculture, Partha P. Sengupta

Dissertations

The research work finds a solution to precision agriculture of cotton cultivation using artificial intelligence (AI) models. Two sets of model performance based on the application are selected namely a low resource and a high resource setting. This is because using drone surveys to capture images identifying the classes of stressed and unstressed cotton plantation requires limited model architecture and CPU based computation. Thus, traditional AI models were selected for low resource settings. Again, for high computation intensive models like transfer learning-convolution neural network (CNN) based architectures were grouped into high resource settings. There was another issue of class imbalance …

Machine Learning For Anomaly Detection In Neural Network Security And Srf Cavities, Hal Ferguson

Electrical & Computer Engineering Theses & Dissertations

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications.

First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves …

Bridging The Gap Between Network Science And Network Systems To Identify And Mitigate Cyber Risk: Identify And Mitigate Backdoor Attacks On Graph Neural Networks And On Complex Systems, Sabah Ettahri

Electrical & Computer Engineering Projects for D. Eng. Degree

This doctoral project aims to bridge the gap between graph theory and network science to identify and mitigate cyber risk, represented as a CY-Triangular Network that connects different networks. The CY-Triangular Framework is a cybersecurity system that integrates graph theory and network science through an interoperable learning approach. The objective of this project is to bridge the gap between two domains: network science and network systems. Accordingly, it examines one representative network from each field, focuses on a complex system network, and explores Graph Neural Networks (GNNs). The connection between these domains lies in graph theory. This research demonstrates that …

Curvilinear Image Segmentation Using Multiscale Variational U-Net, Rebekah Fortes

LSU New Orleans Theses and Dissertations

Segmentation of curvilinear structures such as water contours, cracks in cement, and vascular networks in biomedical imaging, poses unique challenges due to extreme class imbalance, irregular morphology, low contrast against complex backgrounds, and the need to preserve global connectivity while detecting fine-scale details. We propose a Multiscale Variational U-Net (MSVU-Net) architecture designed specifically to address these challenges. The model integrates multiscale convolutional filters to capture both global context and local detail, while embedding a variational model in the bottleneck layer to enhance structural representation. To mitigate class imbalance and improve fidelity, the network optimizes a hybrid loss function that combines …

Universal Systems Simulation Via Constraint Hypergraphs With Applications To Digital Twins, John Morris

All Dissertations

The characterization of systems encompasses a variety of modeling frameworks designed to capture specific behaviors and components of various system domains. Whatever the framework, the core elements of a system representation are the information of the system and a description of how that information is related. The relations in deterministic systems are functions, which, when composed to form executable processes, can be used to simulate system data. A declarative modeling framework is one that encodes mechanisms for preparing these simulations within the model structure, allowing an external agent to form the execution processes required for a given context. To date, …

Enhancing Ad/Adrd Management Through Ihelpcare: A Compliant And Culturally Sensitive Ai-Driven Digital Healthcare Platform, Trisha Bhowmick

Master's Theses

The digital healthcare field is expanding fast, and now it requires platforms that use advanced technology and maintain robust data security and compliance practices. In the present paper, we present the main structure, key methods, and compliance strategies of the digital healthcare system iHelpCare, which, while fully meeting the HIPAA/GDPR requirements, provides health services more accessible, efficient, and inclusive. The proposed platform is powered by AI for personalized care solutions, with the main emphasis on preventive health management and providing tools for people with disabilities.

iHelpCare achieves real-time patient monitoring while securing medical data management and easy communication between patients, …

Making Deep Neural Networks Trustworthy: Intelligibility And Safety Through Symbolic Methods, Eleanor Catherine Quint

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

The rapid adoption of deep learning has come at the cost of properties long valued in artificial intelligence: intelligibility and safety. This dissertation develops methods that restore these properties by coupling neural networks with symbolic structure.

First, for supervised classification, I propose a differentiable decision tree integrated with a supervised variational autoencoder. The resulting model maintains competitive accuracy and generative performance while exposing clear macro-features in its latent space, improving interpretability.

Second, for reinforcement learning, I extend constrained Markov decision processes by specifying constraints in formal languages. This formal language constrained MDP enables the use of automata for state augmentation, …

The Future Is Now: Empowering Society Through Ai Literacy, Jason S. Wrench, Sanae Elmoudden

Milne Open Textbooks

Artificial Intelligence (AI) is no longer a futuristic concept—it is the reality of the present. From the algorithms shaping our social media feeds to the generative tools transforming our workplaces, AI has permeated every aspect of modern life. The Future is Now moves beyond the hype to provide a comprehensive roadmap for understanding, navigating, and shaping this technological revolution.

Demystifying the Machine

This textbook serves as a user-friendly guide to the “black box” of AI. It breaks down complex technical concepts—from machine learning and neural networks to large language models—making them accessible to students across all disciplines. By establishing a …

Securing Connected And Autonomous Vehicles, Owana Marzia Moushi

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

A vehicular network is susceptible to various security flaws and attacks. Cryptographic techniques are used in vehicular networks but these alone cannot provide proper security to the network. Identifying various types of attacks is necessary to secure vehicular communication networks. In this dissertation, we focused on detecting various insider attacks in vehicular networks to enhance the security of the network.

Our first contribution in this dissertation is the detection of both binary and multi-class data replay and data replay Sybil attacks in vehicular networks. A publicly available dataset, VeReMi-Extension is used to detect these attacks. This dataset has been reformulated …

Design Principles For Customer‑Engaging Digital Service Systems: An Action Research Study, Keng Siau, Xiaofeng Chen, Xin Tan

Research Collection School Of Computing and Information Systems

Digital services represent a business approach employed by organizations to operate in the digital environment. However, systematic development guidelines for developing quality digital service systems are lacking in the literature. The authors identified four general challenges for developing and implementing customer-engaging digital service systems (CEDSS). By employing the method of canonical action research in a digital service system project, they derived 10 design principles for developing high-quality CEDSS. They empirically evaluated the design principles in the development project and through follow-up focus group sessions. The design principles provide applicable and actionable guidelines for the development of CEDSS.

Qualitative Stereo Vision Using Distributed Extended Waltz Filtering, Ben Mathew

Theses and Dissertations

Stereo vision is a fundamental problem in computer vision, aimed at reconstructing three-dimensional scene structure from two or more two-dimensional images. Traditional stereo algorithms rely on quantitative disparity estimation, often constrained by calibration precision, lighting variations, and surface texture. In contrast, our proposed Qualitative Stereo Vision seeks to understand depth relationships and spatial configurations from multiple planar views through symbolic reasoning and constraint satisfaction, offering a more flexible and cognitively plausible approach to scene interpretation.

This dissertation presents a novel framework called Distributed Extended Waltz Filtering, designed to provide qualitative stereo vision, particularly in the presence of occlusions—a persistent challenge …

Human Identification And Action Recognition Using Small Data And Deep Domain Adaptation, Alexander M. Glandon

Electrical & Computer Engineering Theses & Dissertations

Human identification and human action recognition problems are two important research areas for real-world security and surveillance applications. In both human identification and action recognition, it is necessary to operate by collecting small datasets in the field, possibly in a short time window of observation. This dissertation studies and develops computational modeling and high-performance machine learning (ML) and deep learning (DL) models for human identification and human action recognition using small amounts of data. These methods and computational models may be useful for different security and surveillance applications.

This dissertation on human recognition develops a ML computational model to estimate …

Performance Analysis Of Ris-Empowered Ofdm-Im Communications Under Weibull Fading And Joint Tx/Rx I/Q Imbalance, Büşra Ceni̇kli̇oğlu, İbrahi̇m Develi̇, Ayşe Eli̇f Canbi̇len

Turkish Journal of Electrical Engineering and Computer Sciences

A modernist technique, reconfigurable intelligent surface (RIS) provides outstanding signal reflection and amplification, making it highly valuable for upcoming communication systems. Besides, a major contributor is index modulation (IM), attaining superior spectral and energy efficiency, and achieving hardware sufficiency. The primary and novel contribution of this work is the derivation of a highly accurate, closed-form approximate expression for the average bit error rate (ABER) of an orthogonal frequency division multiplexing (OFDM)-IM system operating in the complex and challenging environment characterized by joint transmitter/receiver (Tx/Rx) in-phase and quadrature phase imbalance (IQI) and Weibull fading. This essential analytical achievement is facilitated by …

A Feature-Free Deep Learning Approach For Midair Hand Gesture Recognition From Surface Electromyogram (Semg) Data, Yasir Altaf, Abdul Wahid, Mudasir Manzoor Kirmani

Turkish Journal of Electrical Engineering and Computer Sciences

Midair hand gesture recognition plays a crucial role in applications such as sign language recognition and human-computer interaction, particularly for supporting individuals with partial or complete hearing loss. However, recognizing gestures in midair remains challenging due to the rapid and complex nature of hand movements. To address this, noninvasive techniques like surface electromyography (sEMG)—which captures muscle activity through sensors placed on the skin—have gained attention. sEMG provides rich time-series data that reflect both spatial and temporal muscle dynamics. In this study, we propose a deep learning architecture that combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to classify …

Integrated Log Spectrogram Convolutional Neural Network (Ils-Cnn) For Robust Spoken Digit Recognition, Awais Ahmed

Turkish Journal of Electrical Engineering and Computer Sciences

Spoken digit recognition (SDR), a type of supervised automatic speech recognition, is essential for various human-machine interaction applications, including banking operations, dialing systems, price extraction, and airline reservation systems. However, designing an effective SDR system presents several challenges, such as developing labeled audio data, selecting appropriate feature extraction methods, and creating high-performance models. To overcome these challenges, a novel approach for robust spoken digit recognition using an integrated log spectrogram convolutional neural network (ILS-CNN) has been proposed. The proposed work presents an efficient SDR method by taking advantage of a log spectrogram layer directly within the neural network to enhance …

Railway Track Condition Monitoring Based On Sensor Data And Artificial Neural Networks, Ivan Kots, Alina Isaeva, Mark Denisenko, Alexander Sinyukin, Andrey Kovalev

Turkish Journal of Electrical Engineering and Computer Sciences

Monitoring the condition of engineering objects is one of the urgent tasks of industry, construction, and transport infrastructure. This article describes a system for condition monitoring and diagnostics of rail tracks in real time. Compared with other similar studies, the proposed system has the advantages of compactness, usability, scalability and versatility of application. The proposed monitoring system is based on an Nvidia Jetson Nano embedded computing board and also includes inertial sensor modules, a microphone, a geolocation module, communication modules, an SSD storage device, and a battery. The prototype of the diagnostic module is a portable device that can be …

Modeling And Simulation Of Dynamic Energy Management Systems For Smart Buildings, Onur Özel, Ali̇ Rifat Boynueğri̇, Hayri̇ Yi̇ği̇t, Burak Tekgün

Turkish Journal of Electrical Engineering and Computer Sciences

This study presents a dynamic energy management system tailored for smart residential buildings, integrating thermal and electrical models to achieve both natural gas and electricity bill cost reduction. By harnessing wind and solar energy sources, the system aims to meet the diverse energy needs of modern homes. Through load shifting and thermal storage strategies, known as power-to-heat (P2H) approaches, the system ensures efficient renewable energy utilization while maintaining resident comfort. Validation of the proposed system was conducted using real-world data from the Yıldız Technical University Smart Home Laboratory, demonstrating its practical applicability and effectiveness. Results indicate significant reductions in both …

Grey Wolf Optimization Of Pi Controller For Power Management In Wind Farms: A Novel Approach, Anis Feddaoui, Lotfi Farah, Abdelouahab Benretem, Mohammed Abdeldjalil Djehaf

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes a novel power management strategy for wind farms using a grey wolf optimization (GWO)-based PI controller. The method aims to enhance active and reactive power control in systems employing dou bly fed induction generators. Three control strategies are evaluated—namely, a classical frequency-domain PI controller, an Artificial Neural Network (ANN)-based controller, and the proposed GWO-based PI controller—the last of which represents the main contribution. The classical PI and ANN controllers are included strictly for comparative bench marking. MATLAB simulations demonstrate that the GWO-beased PI controller offers superior dynamic performance, particularly in settling time and overshoot reduction. A power …