Digital Commons Network^™

Multi-Model Safe Neuro-Optimal Output Tracking Control Of Autonomous Surface Vessels With Explainable Ai, Behzad Farzanegan, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a safety-aware deep reinforcement learning (DRL)-based trajectory tracking control of autonomous surface vessels (ASVs). A multilayer neural network (MNN) observer estimates the ASV's state and uncertain dynamics. By utilizing the estimate state vector from the observer, a safety-aware DRL-based optimal policy is formulated using control barrier function (CBF) and Karush-Kuhn-Tucker (KKT) conditions. An actor-critic MNN with singular value decomposition (SVD)-based update mitigates vanishing gradients. To enhance adaptability, an online safe lifelong learning (SLL) scheme counters catastrophic forgetting across varying ASV dynamics. The Shapley Additive Explanations (SHAP) method identifies key features influencing the control policy. Simulations on an …

Anti-Jamming Attack Mixed Strategy For Formation Tracking Control Via Game-Theoretical Reinforcement Learning, Lei Xue, Bei Ma, Yongbao Wu, Jian Liu, Chaoxu Mu, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Communication plays a role in multi-UAV to perform formation tracking missions. In complex environments, UAV communication is often subject to jamming attacks, affecting the formation process. Therefore, studying the formation tracking control problem in jamming attacks is of great significance. Typically, the actions of the UAV consist of two fundamental modules: mobility strategy and communication strategy. In this paper, we design an anti-jamming attack mixed strategy for formation tracking control of the multi-UAV system. In practical scenarios, multi-UAV systems not only require the accomplishment of formation maneuvers but also necessitate effective mitigation of jamming attacks caused by other UAVs. Therefore, …

Multi-Dimensional Iot-Based Energy Management Approach For Smart Homes: A Unified Model For Comfort And Energy Efficiency, Muhammad Ans, Teodoro Montanaro, Ilaria Sergi, Ahmad Alsharoa, Miriam Pezzuto, Luigi Patrono

Electrical and Computer Engineering Faculty Research & Creative Works

As smart home technologies evolve, achieving energy-efficient indoor climate management while maintaining comfort and air quality is a growing priority. This paper introduces a novel optimization framework for smart buildings that minimizes energy costs and dynamically manages indoor environmental conditions, specifically temperature, CO₂ concentration, and illuminance. Unlike conventional systems, our model incorporates dynamic constraints that respond to day-night comfort requirements and leverage real-time variations in electricity prices and environmental conditions. By optimally controlling the power levels of air conditioning, air purification, and lighting systems, the framework ensures indoor comfort while significantly reducing operational costs.A nonlinear optimization approach with dynamic …

Aperiodically Intermittent Dynamic Event-Triggered Control For Predefined-Time Synchronization Of Stochastic Complex Networks, Lei Xue, Haoyu Zhou, Yongbao Wu, Jian Liu, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, the problem of practical predefined-time synchronization in mean square (PTSMS) of stochastic complex networks (SCNs) is investigated through dynamic event-triggered control (E-TC). Different from the existing literature, this paper considers the dynamic E-TC in an a periodically intermittent control framework and employs the average control rate, which makes it easier to satisfy the conditions of the theorem. In comparison to existing finite-time and fixed-time synchronization, by introducing the time-varying function, it can be guaranteed that all states of SCNs achieve the practical PTSMS within a preset time without calculating the convergence time. Combined with stochastic analysis theory, …

Safe Optimal Control Of Quadrotor Formations Using Multilayer Neural Networks And Continual Learning, Ehsan Soleimani, Irfan Ahmad Ganie, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article presents an integral reinforcement learning-based optimal formation tracking scheme for multiple quadrotors unmanned aerial vehicles (QUAVs) experiencing nonlinear coupled dynamics and subject to constraints. We use multilayer neural networks (MNN) within an actor-critic framework where the MNN weights are tuned using singular value decomposition (SVD) of the activation function gradient to approximate optimal control policy via backstepping. Additionally, barrier Lyapunov functions (BLF) are introduced to ensure set invariance, thereby maintaining the quadrotors within a defined safety space due to constraints. A novel weight update law for each layer is derived using the HJB approximation error and control input …

Prescribed-Time Nash Equilibrium Seeking For Pursuit-Evasion Game Under Intermittent Control With Undirected/Directed Graph, Lei Xue, Jianfeng Ye, Yongbao Wu, Jian Liu, D. C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This paper studies the prescribed-time Nash equilibrium (PTNE) seeking problem of the pursuit-evasion game (PEG) with second-order dynamics under the intermittent control (IC) strategy. To achieve Nash equilibrium (NE) in a user-defined prescribed-time, a time-varying high-gain function is incorporated into the design. The core challenge lies in applying IC to NE seeking, which complicates the convergence analysis and control design. To address this sticking point, we construct an auxiliary function and propose a Lyapunov function considering second-order dynamics to solve the PTNE seeking problem of PEG. Building upon the results for undirected graphs, we further extend our findings to directed …

Personalized Persuasion In The Digital Age: A Data-Driven Approach To Effective Communication, Annye Braca

Doctoral

This thesis investigates the potential of Machine Learning (ML) to personalize persuasive marketing messages. It explores the identification of individuals receptive to specific persuasion techniques based on their psychometric profiles. By developing ML models that incorporate these profiles, the thesis aims to predict the impact of tailored messages and improve the effectiveness of marketing communication.

Highly Sensitive Diffractive Optical Structures For Detection Of Volatile Organic Compounds, Faolan Radford Mcgovern

Doctoral

From aerospace to agriculture, sensors play a fundamental role in many aspects of modern life. Sensors form an integral part of the complex systems and devices required for the continued functioning and development of services and industries across society. The use of sensors is paramount in areas affecting human health, one such area being the monitoring of indoor air quality, in particular the detection of volatile organic compounds (VOCs). Human contact with VOCs has been associated with many health complications, including skin and eye irritation, cardiovascular damage, and cancers. Optical, electrical, gravimetric, and chemical sensors have been developed for VOC …

Surface Modifications Of Titanium Alloys For Biomedical Applications, Justynne Fabian

Doctoral

Hip implant failure remains a critical concern in healthcare, primarily driven by poor bone integration and bacterial infection. These issues contribute to a rising number of revision surgeries, posing significant challenges that must be addressed, particularly in light of an ageing global population. Titanium surface anodisation emerges as a promising solution for biomedical applications, offering a cost-effective, controllable, and efficient process. The anodisation technique results in the formation of titanium dioxide nanotubes (TiO2 NTs) on the metal surface. These nanostructures offer several advantages, including enhanced mechanical properties, and potential for surface modification through the incorporation of antibacterial and osteoinductive properties. …

A Patent System For Temple Grandin, Andrew Chin

Faculty Publications

This article explores the intersection of neurodiversity and innovation through the lens of Temple Grandin’s contributions to livestock handling systems and her philosophical alignment with the U.S. patent system. Drawing on cognitive research by Simon Baron-Cohen, archaeological insights from Penny Spikins, and personal reflections, I argue that autistic cognitive styles—characterized by heightened “folk physics” aptitudes and visual thinking—have historically driven technological progress. Grandin’s patented inventions, motivated by sensory-based empathy for animals, exemplify how neurodivergent inventors seek to embed their conceptions of ethical utility into the nation’s technological infrastructure. The article critiques modern patent litigation trends, advocating instead for a system …

Retrenchment By Diversion: The New Law And Politics Of Parental Rights, Mary Ziegler, Maxine Eichner, Naomi Cahn

Faculty Publications

For the past century, the Supreme Court has repeatedly affirmed the importance of parents’ rights to direct their children’s upbringing and education. Yet suddenly the rhetoric of parental rights is being used to ground a broad range of claims on issues such as what can be taught in public schools, when minors can access gender-affirming care, or who will be punished for helping minors travel for abortion care. Why have parental-rights claims surged so visibly in contemporary law and politics? And are all the new arguments made under the banner of parental rights equally rooted in constitutional precedent?

This Article …

Classification Of Indonesian Sign Language (Sibi) Using Data Mining Algorithms K-Nearest Neighbor And Random Forest, Muhammad Zaki Wirawan, Achmad Afif, Anik Nur Handayani, Imanuel Hitipeuw, Osamu Fukuda

Knowledge Engineering and Data Science

This study aims to address the communication hallenges faced by the Indonesian deaf community by developing an automatic classification model for Sistem Bahasa Isyarat Indonesia (SIBI) using data mining techniques. The main objective is to identify a practical algorithm for recognizing SIBI hand gestures to enhance accessibility and inclusiveness in digital communication. A comprehensive dataset consisting of 32,850 gesture samples representing SIBI alphabet signs was collected and processed through feature extraction, data cleaning, and normalization using Z-Transform and Min-Max methods. Two classification algorithms, K-Nearest Neighbor (KNN) and Random Forest, were implemented and evaluated using metrics such as accuracy, precision, recall, …

A Comparative Study Of Machine Learning Models For Javanese Wuku Classification: Exploring Svm, Naïve Bayes, And Cnn For Cultural Texts, Danang Arbian Sulistyo, Aji Prasetya Wibawa, Didik Dwi Prasetya, Fadhli Almu'iini Ahda, Agung Bella Putra Utama

Knowledge Engineering and Data Science

This study rigorously evaluates machine learning models for classifying culturally significant Javanese Wuku texts from the “Keagamaan atau Spiritual” category, a domain challenged by unique linguistic nuances and limited digitized resources. We compared Support Vector Machine (SVM), Naïve Bayes, and Convolutional Neural Network (CNN) on texts from five pivotal Wuku types (Sinta, Galungan, Kuningan, Sungsang, Warigalit) sourced from sastra.org, aiming to identify the most effective computational approach. The dataset comprises N = 1419 documents (T = 751.290 tokens), with per-class document counts reported for all five Wuku types. Our evaluation uses accuracy, precision, recall, F1-score, and …

Analysis And Research On The Guiding Role Of Xi Jinping Thought On Socialism With Chinese Characteristics For A New Era In The Discipline Of Information Resources Management, Sanhong Deng, Yiqin Zhang, Hao Wang

Journal of Scientific Information Research

[Purpose/significance]This paper explores the guiding role of Xi Jinping Thought on Socialism with Chinese Characteristics for a New Era in the development of the Information Resource Management discipline with Chinese characteristics, providing significant insights for the innovative advancement of China's Information Resource Management discipline and strengthening the discourse power of Chinese social sciences. [Method/process]This paper systematically reviews the core elements of the development philosophy of the Information Resource Management discipline within Xi Jinping Thought on Socialism with Chinese Characteristics for a New Era from a holistic perspective,elucidates the logical system of the development of the discipline from the diverse perspectives …

Extraction Of Fine-Grained Research Methods In The Field Of Information Science, Jiayi Hao, Yuzhuo Wang, Chengzhi Zhang

Journal of Scientific Information Research

[Purpose/significance]Research methods in information science are one of the critical research directions in this field. Constructing a fine-grained research method corpus and extracting research method entities can help scholars quickly understand the research methods in this field, explore the evolution of methods and their future development trends, and lay the foundation for the service and application of the research method corpus in the subsequent digital wave. [Method/process]Firstly, based on academic articles published in the Journal of the China Society for Scientific and Technical Information from 2000 to 2023, this study randomly selected 50 articles and manually annotated the research methodology …

Method Entity And Relation Extraction Based On Automatically Generated Syntactic Templates: A Case Study Of Csdn Artificial Intelligence Blog, Kuiliang Li, Bolin Huang

Journal of Scientific Information Research

[Purpose/significance]There are many relationships between method entities and application scenarios, problems,organizations and other entities. Extracting these entity relationships helps to capture the development trend of technology and promote the improvement of innovation ability.[Method/process]This paper discusses a method for extracting method entities and relations based on automatically generated syntactic templates. By designing a new adaptive template, the method improves flexibility and adaptability, reducing dependence on large-scale labeled data. Using a small number of seed triples, the method iteratively generates syntactic templates and extracts method entities and relations for the CSDN artificial intelligence topic blog. It also improves the extraction quality using …

Identifying Research Paths Based On Main Path Analysis: A Case Study Of Knowledge Graphs, Ruibin Wei, Yidan Wang, Yan Xu

Journal of Scientific Information Research

[Purpose/significance]The main path analysis of citation networks can be used to identify important literature in specific fields and can achieve the extraction of mainstream research threads. This paper will use the main path analysis method to analyze the research path of knowledge graphs and sort out the context of their research development. [Method/process]This paper firstly obtains research papers in the field of knowledge graphs from the Web of Science platform, then uses the HistCite software to generate a direct citation network of the literature, and then imports the data into Pajek to generate multiple main paths of the dataset, and …

The Evolution Of Research Methods In The Digital Humanities Perspective: A Quantitative Analysis Based On Cnki Data And A Large Language Model, Guangyao Sun, Dongbo Wang

Journal of Scientific Information Research

[Purpose/significance]This paper aims to explore the evolution trend of research methods in the field of digital humanities with the help of large language model technology. [Method/process]This paper mainly focuses on the data of CNKI journal articles, selects the general Chinese large language model GLM-4, uses prompt engineering and chain of thought to extract and cluster the abstract data, of papers and analyzes its evolution trend through quantitative processing. [Result/conclusion]The study shows that GLM-4 can well identify and extract research methods from complex abstract data. Analyzing the evolution trend in chronological order, it is found that research methods such as "interview …

Research On Automated Generation And Evaluation Of Patent Claimsbased On Gpt-4, Junhua Li, Qian Yuan, Xiang Yan, Changhong Lv

Journal of Scientific Information Research

[Purpose/significance]This study aims to automatically generate claims using the GPT-4 model, in order to reduce the writing difficulty for inventor and improve the work efficiency and quality. [Method/process]The article constructs Prompts suitable for automatically generating patent claims and implements four prompting strategies: ZeroShot, Exact-Drafting, Stepwise-Claim, and Exact-Step Claim. By inputting patent specifications and technical disclosure documents into the GPT-4 model and using Prompts to guide its output, the automated generation of patent claims is achieved. The ROUGE and BERTScore evaluation metrics were used to assess the quality of the text, and the generated text was analyzed in comparison with the …

Research On Emerging Technology Topic Identification Based On Bertopic, Dakun Wang, Bolin Hua

Journal of Scientific Information Research

[Purpose/significance]Identifying and foreseeing emerging technologies, bring technological first-mover advantages to enterprises and governments, and grasp technological development trends in a timely manner. [Method/process]This study uses BERTopic's topic modeling method to obtain domain topic distribution, and merges paper and patent topics based on the cosine similarity of topic vectors to identify emerging topics. [Result/conclusion]Using the BERTopic topic modeling method combined with index evaluation can effectively identify emerging topics and emerging terms.Taking the field of new energy vehicles as an example to carry out empirical research, using two methods: divided verification period and data verification method, 12 of the 16 identified topics …

An Explainable Ai And Optimized Multi-Branch Convolutional Neural Network Model For Eye Anemia Diagnosis, Kamel K. Mohammed, Nadia Dahmani, Rania Ahmed, Ashraf Darwish, Aboul Ella Hassanien

All Works

This paper proposes a novel, non-invasive approach to diagnosing eye anemia using deep learning techniques. Traditional methods, reliant on invasive procedures like venipuncture, are costly and can cause patient discomfort. Our model leverages a multi-branch convolutional neural network (CNN) architecture, incorporating the Hippopotamus Optimization (HO) algorithm and multiclass support vector machines (SVMs) for enhanced accuracy. To address data imbalance, we employ the Synthetic Minority Oversampling Technique (SMOTE) and data augmentation. The model is trained and evaluated on a dataset of 211 eye images. The model achieves a remarkable 97.06% accuracy, with a Receiver Operating Characteristic (ROC) curve demonstrating an Area …

Digital Transformation Of Education: An Integrated Framework For Metaverse, Blockchain, And Ai-Driven Learning, Mousa Al-Kfairy, Omar Alfandi, Ravi S. Sharma, Saed Alrabaee

All Works

The integration of Metaverse, Blockchain, and Artificial Intelligence (AI) has the potential to revolutionize the educational landscape by providing immersive, secure, and personalized learning environments. This study proposes a conceptual framework that combines these technologies to address the key challenges faced by contemporary education systems, including accessibility, engagement, security, and personalization. The Metaverse serves as the immersive platform, offering virtual classrooms, interactive simulations, and gamified learning experiences. Blockchain provides the foundation for secure and transparent academic records, enabling tamper-proof credential verification and decentralized data management. AI enhances the educational experience by powering adaptive learning systems, predictive analytics, and intelligent tutoring …

Victoria Welby's Semiotics, Significs And Gender: Through A Modernist Lens, Zoe Hurley

All Works

While there is an established body of scholarship discussing Victoria Welby's contribution to semiotics, her philosophical ideas engage with many of the intellectual currents that would later define the modernist movement. Exploring Welby's extensive articles, letters, speeches, and monographs - through a genealogical framework and modernist lens - conveys Welby's semiotic portrait of language and gender as both building upon and extending the classical semiotic approaches of Peirce as well as demonstrating nascent modernist elements. This casts a new light on the importance of Welby's semiotic concepts of Significs and mother-sense. Yet, although Welby's works correspond with proto-modernist concepts, including …

Leveraging Sentiment Analysis Of Food Delivery Services Reviews Using Deep Learning And Word Embedding, Dheya Mustafa, Safaa M. Khabour, Mousa Al-Kfairy, Ahmed Shatnawi

All Works

Companies that deliver food (food delivery services, or FDS) try to use customer feedback to identify aspects where the customer experience could be improved. Consumer feedback on purchasing and receiving goods via online platforms is a crucial tool for learning about a company’s performance. Many English-language studies have been conducted on sentiment analysis (SA). Arabic is becoming one of the most extensively written languages on the World Wide Web, but because of its morphological and grammatical difficulty as well as the lack of openly accessible resources for Arabic SA, like as dictionaries and datasets, there has not been much research …

Deep Learning Approaches For Eeg-Based Biometrics: A Systematic Review, Ali E. Albaiati, Muhammad Firdaus Akbar, Murtadha D. Hssayeni, Ashraf Khalil, Mohd Nadhir Ab Wahab, Sundus Sulaiman Weli, Enas A. Raheema

All Works

Biometics such as fingerprint, face, and iris are vulnerable to spoof attacks. The unique characteristics of Electroencephalography (EEG) make it a promising biometric modality especially because of its resistance to spoofing attacks. Many deep learning methods have been proposed for EEG-based biometric systems. This systematic review examines these methods in terms of their feature extraction ability and authentication performance. We follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search IEEE Xplore, PubMed, Web of Science, ScienceDirect, and Springer databases. Initially, we identified 285 relevant articles published between 2018 and 2024. After removing duplicates and applying …

Association Of Socio-Demographic Factors, Perinatal Characteristics, And Hospital Maternity Practices With Breastfeeding Outcomes In The Uae, Zainab Taha, Farid El Ktaibi, Rafiq Hijazi

All Works

Breastfeeding (BF) rates remain suboptimal in the United Arab Emirates (UAE), despite global and national efforts. This study examined the association of socio-demographic factors, perinatal characteristics, and hospital maternity practices with breastfeeding outcomes in the UAE. In this cross-sectional study, 1,815 participating mothers with children below the age of 2 answered structured questions related to socioeconomics, hospital practices, and BF. Multivariate analysis showed that a non-Emirati nationality and vaginal birth were significantly associated with higher initiation rates (AOR = 6.19, 95% CI 1.96–19.54 and AOR = 2.65, 95% CI 1.35–5.21, respectively), timely initiation (AOR = 0.48, 95%CI 0.35–0.66, respectively), longer …

No Detected Signal Of The 2022 Epizootic From Multiannual Diadema Antillarum Settlement Data In The Florida Keys, Jason Albrecht

Theses, Dissertations and Culminating Projects

The sea urchin Diadema antillarum is a key coral reef grazer that remains functionally extinct on many Caribbean reefs following a mass mortality event in 1983–84. In 2022, another widespread mortality event, likely caused by a scuticociliate parasite, again decimated D. antillarum at reefs across the Caribbean. Understanding the population-level impacts of this recent epizootic will be important for coral reef conservation efforts. This study examines multi-year larval settlement data from the Florida Keys to determine if the 2022 mortality event reduced larval settlement. Larval settlement was assessed monthly at three long-term monitoring sites (Delta Shoal, Tennessee Reef, and Pickles …

Quantitative Preventive Approaches To Diabetes: Mathematical Modeling And Analysis, Rushi P. Bhatt

Theses, Dissertations and Culminating Projects

The rising prevalence of diabetes presents a pressing global health concern, necessitating effective control strategies. This study aims to construct a mathematical model to analyze the influence of diverse factors on blood sugar levels, with a focus on identifying optimal methods for maintaining healthy glucose levels. Employing ordinary differential equations (ODE), the model investigates variables including leptin resistance, fat mass, glucose, insulin resistance, beta cell mass, daily physical activity, and dietary intake. Utilizing parameter estimates from existing literature, the model’s framework is established, and simulation results elucidate the intricate interplay between lifestyle choices and blood glucose dynamics. Furthermore, the model …

Exploring Coupled Land Use -- Climate Change Impacts In The Northeast United States From 2020-2025 With Implications For Bombus Habitat Suitability, Archana Prasad

Theses, Dissertations and Culminating Projects

The combined impacts of land use and climate change pose significant challenges to bumblebee populations in the Northeast United States (NEUS), a region characterized by dense urbanization, diverse landscapes, and exposure to climate extremes. As urban and suburban areas expand, the loss of agricultural lands and open spaces alters habitat availability for bumblebees, which rely on grasslands, meadows, and forest edges for nesting and foraging. Simultaneously, the NEUS is experiencing an increase in extreme climate events, including prolonged heatwaves, heavy precipitation, and shifts in temperature patterns. Using CMIP6 climate model projections, we analyzed trends in nine climate indices. Our results …

Copyrighting Style, Christopher Buccafusco

Faculty Scholarship

Does copyright law protect an artist’s style? The federal courts that have considered the question are equally split. They all agree, however, that the answer to the question resides in copyright law’s idea/expression distinction. According to this doctrine, ideas, techniques, and methods cannot be copyrighted, but expressions of ideas can be. The question courts have faced, then, is whether artistic style is an idea or a matter of expression. The answer, perhaps unfortunately, is that style is both.

This is unfortunate because, this Article argues, copyright law’s idea/expression distinction is inadequate to the task of determining the copyrightability of style. …