Physical Sciences and Mathematics Commons^™

Sensorless Control Of Pmsm Based On An Improved Super-Twisting Sliding-Mode Observer, Shiyu Chen, Xinmin Chen, Xionglong Hu, Heng Wang, Yepeng Han, Jiajie Chen

Journal of System Simulation

Abstract: To address the chattering in back electromotive force estimation and the gain mismatch across a wide speed range when using a conventional super-twisting sliding-mode observer in the sensorless control system of a permanent-magnet synchronous motor, this paper proposed an improved adaptive-gain super-twisting sliding-mode observer. A linear correction term was introduced into the super-twisting algorithm and integrated with a gain adaptation law based on speed feedback, enabling the system to achieve finite-time convergence and high-precision back electromotive force estimation over a wide speed range. A variable-gain adaptive complex-coefficient filter was constructed to effectively suppress the harmonic components in the observed …

Line Spectrum Enhancement Technology Based On Second-Order Vector Hydrophone, Zhengkai Wang, Yirong Yu, Qing Hu

Journal of System Simulation

Abstract: In view of the problem of limited detection of line spectrum signals in low signal-to-noise ratio underwater environments, a spatial-temporal cooperative line spectrum enhancement technology based on a two-dimensional second-order vector hydrophone was proposed. A receiving signal model of the two-dimensional second-order vector hydrophone was established to clarify the spatial characteristics of its output signals. For spatial signal processing, the signals from each channel of the vector hydrophone were fused to suppress noise, and a channel combination method with high spatial directivity gain was proposed to achieve spatial processing gain. For temporal signal processing, an adaptive line spectrum enhancer …

Dodaf-Opm-Sd Cross-Layer Automated Mapping Method Based On A Unified Semantic Bridge, Lei Cheng, Gang Xiao, Binbin Wang, Siming Peng, Haozhe Liang, Xiangwu Gong

Journal of System Simulation

Abstract: To address the problems of the inability of DoDAF views to directly drive simulations and the insufficient cross-layer semantic alignment and consistency verification, a DoDAF-OPM-SD cross-layer semantic automated/semi-automated mapping and verification method was proposed in this paper. Targeting tactical/operational-level problems dominated by "conservation+feedback+time delay", the proposed method reduced manual mapping under expert adjudication based on a "minimal executable view set". The object-process methodology served as a semantic bridge to map architectural elements into the stock-flow-feedback structures of system dynamics; semantic embedding disambiguation, integer programming harmonization, and K-nearest neighbor parameter completion were integrated; end-to-end traceability was connected through traceability identifiers, …

Three-Dimensional Gaussian Reconstruction Of Large-Scale Scenes Under Multi-View Geometry Constraints, Haohao Cui, Yanqiang Di, Qing Liu, Xianguo Meng

Journal of System Simulation

Abstract: To enhance the geometry reconstruction quality of the GS algorithm in large-scale scene reconstruction, an optimization method constrained by multi-view geometry reconstruction results was proposed. 2D Gaussian planes were used as geometric primitives to overcome depth anisotropy, and dense depth maps generated by DUSt3R and aligned by sparse point clouds were introduced as constraints. By designing a multi-stage optimization strategy that decouples geometry and rendering, the gradient conflict problem in multi-objective training was solved. Experiments on the MatrixCity dataset indicate that the method surpasses comparison methods in related indicators of geometry reconstruction quality and rendering quality in large-scale scenes. …

Design And Implementation Of Hdrt Real-Time Simulation System, Huiji Zheng, Guangsen Wang, Qing Liu, Kang Wang, Zhiwei Wang, Zhenyu Zhang, Shuo Wang, Zhu Liu

Journal of System Simulation

Abstract: In view of the real-time simulation requirements of large-scale complex systems such as power electronics, a hidden dragon real-time(HDRT) simulation system was developed. The strict time constraints of simulation tasks were guaranteed based on a resource-dedicated real-time scheme, supporting fixed-step and multi-rate simulations from the second level to the hundred-nanosecond level. A hybrid CPU-field programmable gate array(FPGA) architecture was adopted to accelerate computation. The system could utilize multiple simulators for parallel simulation and realized microsecond-level real-time data interaction between simulators through a dedicated PCIe switch. A single simulator could be expanded through I/O interface equipment, supporting a maximum input …

Numerical Simulation Of Water Tank Solidification In A Firefighting Aircraft Under High-Altitude Cold-Soak Conditions, Guanmian Liu, Zhihang Cheng, Hejun Qin, Kangzhi Yang, Qing Wen, Kun Gao

Journal of System Simulation

Abstract: A systematic numerical simulation study was conducted to address the issue of internal water tank solidification in firefighting aircraft under high-altitude low-temperature conditions. Based on computational fluid dynamics methods, a solidification-melting model considering fluid-structure interaction heat transfer and phase change processes was adopted. Through reasonable simplification of the complex geometric model, a quasi-three-dimensional computational model suitable for engineering analysis was developed. The influence laws of key parameters, including high-altitude cold-soak temperature, ground initial water temperature, and cold-soak time, on the freezing characteristics of the water tank were investigated. Combining with the parameter influence laws, a safety criterion using the …

Optimization Of Node Deployment For Three-Dimensional Heterogeneous Wsn In Elongated Structural Space, Jiguang Yang, Jiuyuan Huo, Fang Cao, Cong Mu

Journal of System Simulation

Abstract: To achieve effective coverage of key monitoring points in an elongated structural space, a heterogeneous wireless sensor network(HWSN) deployment optimization method combining the virtual force algorithm(VFA) and multi-strategy improved whale optimization algorithm(MSIWOA), namely HVF-MSIWOA, was proposed. A dynamic adaptive weight mechanism and a t-distribution perturbation operator with heterogeneous degrees of freedom were designed, enabling the whale optimization algorithm(WOA) to balance global exploration and local exploitation and jump out of local optima; combining the topological characteristics of the elongated space and node density distribution, an adaptive virtual force distance threshold between heterogeneous nodes was constructed; the mapping relationship between network …

Combat Effectiveness Evaluation Of Anti-Ship Missiles For Intelligent Autonomous Recognition, Long Zhang, Xuanming Feng, Zhen Lei, Bo Yang, Ying Wang

Journal of System Simulation

Abstract: To address the core issues of poor adaptability of static fusion strategies in existing recognition models, as well as the simplistic evaluation system and its disconnection from dynamic confrontation requirements, a practical four-dimensional evaluation system encompassing "recognition accuracy, antijamming stability, decision timeliness, and modal complementarity" was constructed, and an operational effectiveness composite index (OECI) capable of dynamically adapting to tactical scenarios was proposed. A multimodal dynamic attention fusion network (MDA-Net) for anti-ship missiles in complex confrontation environments was designed. Through heterogeneous feature decoupling, dynamic weighting of cross-modal attention, and a hierarchical gating decision mechanism, the autonomous evaluation and adaptive …

Infrared Image Generation Method Based On Improved Cyclegan, Qiqi Jin, Xiang Zhang, Li Gao, Lin Zhang, Junliang Yan, Peiyao Li

Journal of System Simulation

Abstract: To address the problems in current infrared image generation such as insufficient contrast between target and scene, excessively large discrepancies from real scenes, indistinct thermal source features, and great difficulty in constructing measured infrared image datasets, an improved CycleGAN-based infrared image generation method was proposed. By optimizing the network structure of the generator and adding a non-local module and a CBAM convolutional attention mechanism into the generator, the extraction capability of CycleGAN for infrared features was enhanced, enabling the network to capture the subtle features of targets more accurately; a perceptual loss function was introduced to improve the detail …

Non-Uniform Mixing Of Quantum Walks On The Symmetric Group, Avah Banerjee

Computer Science Faculty Research & Creative Works

It is well-known that classical random walks on regular graphs converge to the uniform distribution. Quantum walks, in their various forms, are quantization's of their corresponding classical random walk processes. Gerhardt and Watrous (2003) demonstrated that continuous-time quantum walks do not converge to the uniform distribution on certain Cayley graphs of the Symmetric group, which by definition are all regular. In this paper, we demonstrate that discrete-time quantum walks, in the sense of quantized Markov chains as introduced by Szegedy (2004), also do not converge to the uniform distribution. We analyze the spectra of the Szegedy walk operators using the …

Why Technical Readiness Is Not Enough: Institutional Barriers To Full Harvest Strategy Adoption In The International Pacific Halibut Commission, Evelyn Roozee, Owen Temby, Gordon M. Hickey

School of Earth, Environmental, & Marine Sciences Faculty Publications

Harvest strategies are increasingly promoted as a mechanism to reduce political discretion and align fisheries management with pre-agreed scientific objectives. While substantial progress has been made in tuna regional fisheries management organizations (RFMOs), non-tuna RFMOs have lagged behind. Existing research has focused primarily on technical modeling challenges, with comparatively limited attention to the governance dynamics shaping adoption. Using the International Pacific Halibut Commission (IPHC) as an instrumental case study, we examine the progress and delays in the development of a harvest strategy policy (HSP) through semi-structured interviews and participant observation. We find that the IPHC successfully addressed commonly cited barriers, …

Wevit: Weight-Entangled Vision Transformers With Class-Specific Attention For Weakly Supervised Semantic Segmentation, Narges Saeedizadeh, Seyed Mohammad Jafar Jalali, Burhan Khan, Shady Mohamed

Research outputs 2022 to 2026

Weakly Supervised Semantic Segmentation (WSSS) is a challenging task in computer vision, as it relies on limited supervision to generate precise object localization maps, often using Class Activation Maps (CAMs). Traditional methods struggle with balancing localization accuracy and scalability due to their reliance on fixed network architectures and handcrafted strategies. Neural Architecture Search (NAS), despite its proven success in optimizing network designs across tasks, has not yet been explored in WSSS due to the need for efficient weight sharing. To address these limitations, we propose WEViT, a novel framework that integrates NAS with transformers to optimize network architectures and generate …

Sem-Pdpl: Semantic Exposure Graphs For Privacy-Law-Informed Risk Assessment Of Public Social-Media Data, Heba Ismail

All Works

Public social-media content often contains self-disclosed personal attributes that appear low-risk in isolation but become privacy-relevant when linked across posts, platform accounts, or user-level traces. Existing research has advanced privacy-sensitive content detection, de-anonymization analysis, social-media research ethics, and privacy-compliance workflows; however, limited work operationalizes how personal-data disclosures combine structurally and how these structures can be translated into auditable governance actions. This paper proposes SEM-PDPL, a computational, privacy-law-informed risk-assessment framework for modeling public social-media exposure as semantic exposure graphs and mapping graph patterns to controls aligned with the United Arab Emirates Personal Data Protection Law (PDPL) and compatible with GDPR principles. …

Modeling The Psychological And Technical Factors Influencing The Use Of Artificial Intelligence Tools Among Non-Native Arabic Learners: A Comparative Study In Egypt, Saudi Arabia, And Jordan., Mohammad Odeh, Alaa Al Din Musa, Ahmed Ragab Ali Ghalish, Montaser Adel Sayed Ahmed

All Works

This study aimed to develop a predictive longitudinal model of the psychological and technical factors influencing the use of artificial intelligence tools among non-native Arabic learners (international students) in three Arab countries: Egypt, the Kingdom of Saudi Arabia, and Jordan. The study adopted an extended Technology Acceptance Model (TAM) incorporating two psychological variables: trust in artificial intelligence and artificial intelligence anxiety. A quantitative longitudinal design with two time waves (T1 and T2) over a full academic semester was employed using Hierarchical Multiple Regression Analysis and PROCESS Macro for mediation. The sample consisted of 812 international students from public universities in …

Aqqd: Annotated Quranic Qira’At Dataset, Linda Smail, Mohammed Lataifeh, Md Sohazur Islam Sozib, Arthur Diniz De Souza

All Works

AQQD (Annotated Quranic Qira'at Dataset) is an open audio dataset of Quranic recitations annotated across canonical Qira'at styles. The dataset is designed to support research in machine learning, speech and audio processing, computational linguistics, and Quranic studies. The current release contains 24,183 WAV audio files from 309 reciters and covers 70 selected Quranic Surahs segmented into representative verses and phonetic variation points. Of these, 23,111 recordings were collected from publicly available sources, including official reciter websites, the Midad repository, MP3Quran, and verified YouTube channels, while an additional controlled subset of 1,072 recordings was obtained from a single reciter recorded as …

Efficient And Universal Watermarking For Llm-Generated Code Detection, Boquan Li, Zirui Fu, Mengdi Zhang, Peixin Zhang, Jun Sun, Xingmei Wang

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have significantly enhanced the usability of AI-generated code, providing effective assistance to programmers. This advancement also raises ethical and legal concerns, such as academic dishonesty and the generation of malicious code. For accountability, it is imperative to detect whether a piece of code is AI-generated. Watermarking is broadly considered a promising solution and has been successfully applied to identify LLM-generated text. However, existing efforts on code are far from ideal, suffering from limited universality and excessive time and memory consumption. In this work, we propose a plugand- play watermarking approach for AI-generated code detection, named ACW …

Spring Ai 2026 Workshop And Speaker Series Report, Gregory Blike, Hannah (Nyingi) Brown, Dora (Dawn) Nguyen, Rami Huu Nguyen, Ajanee Igharo, Frayni Calderon, Moumita Saha, Chengjie Zheng

Paul English Applied Artificial Intelligence (AI) Institute Publications

The AI Workshop and Speaker Series was organized by the Student Advisory Council of the Paul English Applied Artificial Intelligence Institute at the University of Massachusetts Boston with the guidance of Distinguished Professor of Computer Science Wei Ding to create a practical and student-centered AI learning space. The Spring 2026 series included workshops on LinkedIn Optimization, Data Mining, GitHub, Prompt Engineering, Machine Learning of Structured Data, and Applied LLMs with Responsible AI Use in Research, while the broader speaker series introduced students to AI career development, generative AI and LLMs for cybersecurity, transportation security, political sciences, and AI in biomedicine. …

Effects Of Bidisperse Gold Nanoparticles On Near-Infrared Surface-Enhanced Raman Spectroscopy (Sers) In Quick-Freezing-Induced Gold Nanoparticle Aggregates (Qfiaas), Elissa M. Dojka

Forensic Science Master's Projects

Gold nanoparticle (AuNP) aggregates formed via rapid freezing in liquid nitrogen exhibit strong near-infrared (NIR) plasmonic coupling. These quick-freezing-induced AuNP aggregates (QFIAAs) form moderately sized, colloidally stable structures that remain suspended for over three months, supporting a variety of analytical applications. Mechanistically, QFIAA formation arises from the confinement of nanoparticles and ions within advancing ice grain boundaries during freeze-concentration. This mechanical crowding promotes extreme physical proximity while simultaneously elevating local ionic strength, which screens electrostatic barriers to drive the assembly of dense, tightly coupled plasmonic networks.

This study investigates the plasmonic and surface-enhanced Raman scattering (SERS) behavior of bidisperse QFIAA …

The Role Of Non-Symmetric Weights In Hermite–Hadamard Inequalities For Coordinated Ga-Convex And Ga-Quasi-Convex Functions, Muhammad Amer Latif, Ayesha Shabbir

Publications and Research

This paper establishes new Fejér and Hermite–Hadamard-type inequalities for functions of two variables whose mixed second-order partial derivatives satisfy coordinated GA-convexity or coordinated GA-quasi-convexity on a rectangle in the positive quadrant. Our main results are formulated for non-negative continuous weight functions that are not necessarily symmetric with respect to the geometric means of the interval endpoints, thereby extending the classical framework to genuinely asymmetric weights. However, to obtain explicit and sharp integral bounds in certain cases, we also employ a technical lemma that assumes a special symmetric setting where the weight function is symmetric on each coordinate with respect to …

Alaska Earthquake Center Quarterly Review October-December 2025, Heather Mcfarland, Beth Grassi, Austin Holland, Nate Murphy, Elisabeth Nadin, Carolyn Parcheta, Michael West

Alaska Earthquake Center Reports

This series of technical quarterly reports from the Alaska Earthquake Center (AEC) includes detailed summaries and updates on Alaska seismicity, the AEC seismic network and stations, fieldwork, our online presence, public outreach, and lists publications and presentations by AEC staff. Multiple AEC staff members contributed to this report.

Ultraviolet Imaging Of Sr 12 C With Hst/Wfc3: Accretion And Variability Of A Giant Planet At The End Stages Of Growth, Claire O. Finley, Brendan P. Bowler, Ya-Lin Wu, Adam L. Kraus, Yifan Zhou, Yuhiko Aoyama, William Best, Ian Czekala, Catherine C. Espaillat, Katherine B. Follette, Gregory J. Herczeg, Raquel A. Martinez, Connor E. Robinson, Quang H. Tran, K. Ward-Duong

Astronomy: Faculty Publications

Many details of the gas accretion phase during giant planet formation remain untested. We present new 0.2–0.7 μm UV-through-red optical imaging of the young, wide-orbit planetary-mass companion SR 12 c from the Wide Field Camera 3 (WFC3) instrument on board the Hubble Space Telescope. SR 12 c exhibits strong accretion-related continuum excess blueward of∼5000 Å and clear signs of the Balmer jump at 3646 Å. We derive a total accretion luminosity of1.65 ±0.19× 10⁻⁵L_⊙and a mass accretion rate of 8 ±2 × 10⁻¹² M_⊙yr⁻¹. Based on its mass and age,SR12 c …

A Simulation Assessment Of The 'Law Of One Price', Caleb Wilkins

Computational and Data Sciences (MS) Theses

The ‘law of one price’ is an appealing notion regarding pricing of tradeable commodities that are priced in different currencies. It states that the prices of the same good in different markets should be equal after adjustment for exchange rates and that equality should persist through exchange rate fluctuations.

My research simulates the market conditions that should precipitate the ‘law of one price.’ Data was obtained from the simulated trade between algorithmic artificial intelligence agents that operated under induced boundedly rational market behaviors. Trade took place in two initially separate markets, a high-price market with a higher equilibrium price and …

Lessons Learned From The Adrenalin Load Disaggregation Challenge, András Balázs Tolnai, Zheng Ma, Igor Sartori, Clayton Miller, Stephen White, Matt Amos, Gustaf Bengtsson, Akram Hameed, Nørregaard Bo Jørgensen

Research Collection College of Integrative Studies

Crowdsourced data science competitions have emerged as a powerful mechanism for advancing research in energy informatics, offering scalable pathways for developing machine learning solutions that enhance energy efficiency and smart building operations. The ADRENALIN Load Disaggregation Challenge addressed a central problem in energy analytics—non-intrusive load monitoring (NILM) of heating and cooling loads in commercial buildings—while emphasizing the importance of model generalization across different buildings. This paper presents a comprehensive reflection on the lessons learned from organizing and executing the ADRENALIN competition, including technical insights, organizational challenges, and recommendations for future energy data challenges. In addition to the ADRENALIN case, a …

Ai-Ready Libraries Require Ai-Ready Librarians: Building Organisational Capability For Digital Transformation, Salihin Mohammed Ali

Research Collection Library

Academic libraries worldwide are rapidly experimenting with artificial intelligence (AI) to enhance research, learning, discovery, operations, and user engagement. However, many institutions continue to approach AI adoption primarily through isolated pilots, individual experimentation, or technology-centric initiatives. While these efforts generate innovation, they often struggle to scale sustainably without corresponding organisational capability development. This presentation argues that AI-ready libraries require AI-ready librarians and proposes an organisational capability approach for sustainable AI transformation in academic libraries. Drawing from the development of a library-wide AI strategy plans at Singapore Management University, the presentation explores how AI capability-building can be operationalised across diverse functional …

Prediction Of Pasture Condition In The Kimberley Rangelands Of Western Australia Using Simple Classification Tree Models, Ben Nestor, Kath Ryan, Charles Martin, Philip Thomas, Chris Hetherington, Matthew Fletcher, Robert Sudmeyer, Karyn Reeves

Natural Resources Research Articles

Pasture condition assessments assist pastoralists, regulators, and policy makers in making informed decisions around livestock production and the preservation of natural resources in the Kimberley rangelands of Western Australia (WA). Reliable qualitative assessments of pasture condition require assessors with extensive expertise, which means frequent and reproducible assessments can be difficult to achieve. To develop a quantitative approach that can complement existing qualitative assessment approaches in the Kimberley, we investigated the use of simple classification tree models to predict pasture condition using assessment data from the Western Australian Rangeland Monitoring System (WARMS). Quantitative traits were derived from WARMS observation data for …

Viability Assessment Of Bovine Embryos: A Public Dataset And Deep Learning Baselines, Erfan Khayyati

All Graduate Theses and Dissertations, Fall 2023 to Present

Improving the success rates of cattle breeding is essential for sustainable agriculture, global food security, and high-quality livestock production. Currently, determining whether a lab-grown bovine embryo is healthy enough for a successful pregnancy requires highly trained experts to manually evaluate days of continuous time-lapse video footage. This process is not only incredibly time-consuming but also highly subjective; human reviewers often suffer from visual fatigue when tracking subtle, microscopic cellular changes over a seven-day period, leading to significant disagreement among even top experts on an embryo’s true potential. Furthermore, assessing bovine embryos is notoriously difficult due to their dark, lipid-dense cellular …

Integrating Hydrology And Human Water Footprints: A Case Study Of The Great Salt Lake Basin, Rachel Lynne Seeley

All Graduate Theses and Dissertations, Fall 2023 to Present

Global demand for water is outrunning supply, and how we account for water is part of the problem. Many water management frameworks are based solely on how much physical water moves through rivers, aquifers, and other water bodies, missing the water that is embedded in food and other products a region produces and exports. This study developed a new, generalizable water accounting framework that incorporates this “hidden water,” known as virtual water, into water management and applied the framework in the Great Salt Lake Basin.

The Great Salt Lake is shrinking, and an often-overlooked driver of the lake’s decline is …

Mining Time Series Shapelets And Association Rules For Solar Flare Prediction, Drew Watson

All Graduate Theses and Dissertations, Fall 2023 to Present

Solar flares are the largest explosions in the solar system; they are caused by changes in the Sun’s magnetic field. Strong solar flares can disrupt power systems, damage satellites, and interfere with radio communication, so improving flare prediction is important. This thesis develops a way to predict severe solar flares while also helping researchers understand why those predictions are made. The approach looks for short patterns in solar magnetic field data that are linked to future flare activity. It then studies how these patterns appear together and in what order they happen over time. By doing this, the research not …

Spatial Prediction Under Uncertainty: Methodological And Computational Advances In Bayesian Maximum Entropy, Kinspride K. Duah

All Graduate Theses and Dissertations, Fall 2023 to Present

Environmental decisions such as infrastructure design, water management, and snow load estimation depend on spatial data that are often incomplete or uncertain. In many cases, measurements are not exact values but ranges, reflecting limitations in data collection methods. Traditional mapping techniques typically simplify these uncertain measurements, which can lead to less accurate predictions. This dissertation introduces improved statistical tools for making spatial predictions when data are uncertain or partially known. By utilizing a framework called Bayesian Maximum Entropy (BME), this research demonstrates how exact measurements and range-based data can be combined in a mathematically consistent way. The work demonstrates that …

Learning Latent Structure In High-Dimensional Data Via Geometry And Graphs, Haozhe Chen

All Graduate Theses and Dissertations, Fall 2023 to Present

Modern datasets often contain many measured variables for each observation, such as gene-expression levels, brain activity signals, or features in tabular data. These data are also often noisy, meaning that useful patterns are mixed with measurement error or irrelevant variation. Although such datasets can appear complex, they are frequently represented by simpler hidden structures, such as trajectories, clusters, or relationships between observations. This dissertation develops methods for uncovering these hidden structures by learning geometric and graph-based representations directly from data. The first part introduces Functional Information Geometry, which represents local patterns in high-dimensional data using functional features and constructs a …