Computer Sciences Commons^™

ปัญญาประดิษฐ์ในบทบาทนักวิเคราะห์ข้อมูล : กรอบแนวคิดสำหรับการวิเคราะห์เชิงข้อมูลแบบอัตโนมัติด้วยแบบจำลองภาษาขนาดใหญ่และตัวแทนปัญญาประดิษฐ์, วิชญาดา เล้าสุบินประเสริฐ

Chulalongkorn University Theses and Dissertations (Chula ETD)

การวิเคราะห์เชิงข้อมูลที่ดำเนินการโดยมนุษย์มีความท้าทาย เนื่องจากต้องใช้เวลา ทักษะเฉพาะทาง และทรัพยากรจำนวนมาก งานวิจัยนี้มีจุดมุ่งหมายเพื่อค้นหาวิธีในการใช้ปัญญาประดิษฐ์เชิงสร้างสรรค์เพื่อทำให้กระบวนการวิเคราะห์ข้อมูลเป็นแบบอัตโนมัติ โดยปฏิบัติตามวิธี 6 ขั้นตอน ได้แก่ ถาม เตรียม ประมวลผล วิเคราะห์ แบ่งปัน และดำเนินการ โดยไม่มีมนุษย์เข้ามาแทรกแซงตลอดกระบวนการวิเคราะห์ การดำเนินการเริ่มตั้งแต่ผู้ใช้ป้อนชุดข้อมูล วัตถุประสงค์ที่ต้องการ บริบทของข้อมูล และสมมติฐานที่มีอยู่ก่อน จากนั้นระบบจะสร้างคำสั่ง และดำเนินงานต่าง ๆ โดยอัตโนมัติผ่านตัวแทนปัญญาประดิษฐ์ที่ออกแบบเฉพาะทาง โดยตัวแทนเหล่านี้มีบทบาทในการวางแผนและกำหนดการดำเนินงาน โดยอาศัยแบบจำลองภาษาขนาดใหญ่ในการสร้างแนวคิดและใช้เหตุผลเพื่อกำหนดแนวทางการวางแผนและการดำเนินการ ผลการทดลองจาก 5 ชุดข้อมูลในสาขาที่แตกต่างกัน ได้แก่ การศึกษา สุขภาพ ธุรกิจ สิ่งแวดล้อม และเศรษฐกิจ แสดงให้เห็นว่า ผลการประเมินตามเกณฑ์คะแนนการวิเคราะห์เชิงข้อมูลเฉลี่ย 8.1 – 9.4 จากคะแนนเต็ม 10 โดยมีความสอดคล้องระหว่างผลการประเมินโดยแบบจำลองภาษาขนาดใหญ่และมนุษย์เฉลี่ย 0.94 – 0.97 มีเวลาในการดำเนินงานเฉลี่ย 1.8 - 6.6 นาที มีข้อผิดพลาดเฉลี่ย 0.2 - 1.8 ครั้ง และมีความสามารถในการทำงานต่าง ๆ เช่น ประมวลผลโค้ด คำนวณสถิติหรือสร้างแบบจำลองการเรียนรู้ของเครื่อง และแสดงผลภาพได้ งานวิจัยนี้ชี้ให้เห็นถึงศักยภาพของแบบจำลองภาษาขนาดใหญ่ในการทำหน้าที่เป็นนักวิเคราะห์ข้อมูลเสมือน และสามารถต่อยอดระบบวิเคราะห์เชิงข้อมูลแบบอัตโนมัติในสาขาต่าง ๆ ได้ในอนาคต

แบบจำลองเชิงรูปนัยของกระบวนการจัดการโครงการในมาตรฐาน Iso/Iec29110 โดยใช้คัลเลอร์เพทริเน็ต, วรันณ์ธร ศิริกระจาย

Chulalongkorn University Theses and Dissertations (Chula ETD)

วิทยานิพนธ์นี้นำเสนอแนวทางการตรวจสอบความถูกต้องเชิงรูปแบบ (Formal Verification) ร่วมกับกรอบการประเมินผลผ่านระบบเว็บแอปพลิเคชันสำหรับองค์กรขนาดเล็กประเมินตนเอง เพื่อสนับสนุนการปฏิบัติตามกระบวนการบริหารโครงการ (Project Management: PM) ของมาตรฐาน ISO/IEC 29110 สำหรับองค์กรขนาดเล็กมาก (Very Small Entities: VSEs) โดยใช้แบบจำลอง Colored Petri Nets (CPNs) ในการนิยามและจำลองกระบวนการหลักของ PM ได้แก่ การวางแผน การดำเนินโครงการ การควบคุม และการปิดโครงการ พร้อมทั้งตรวจสอบคุณสมบัติที่สำคัญของกระบวนการเหล่านี้อย่างเป็นระบบและระบบเว็บแอปพลิเคชันถูกพัฒนาขึ้นเพื่ออำนวยความสะดวกในการเก็บรวบรวมข้อมูล เรียกใช้การจำลอง และนำเสนอผลการประเมิน ข้อมูลที่ได้รับจากผู้ใช้จะถูกแปลงให้อยู่ในรูปของโทเคน เพื่อนำไปประเมินผ่านแบบจำลอง CPN และเชื่อมโยงผลลัพธ์กับระดับการปฏิบัติตามมาตรฐานในแต่ละกระบวนการของ ISO/IEC 29110 PM การทดลองใช้งานระบบยืนยันถึงความถูกต้อง ความสามารถในการตรวจสอบย้อนกลับ และประสิทธิภาพในการดำเนินงานของระบบที่นำเสนอ การผสานรวมระหว่างแบบจำลองเชิงรูปแบบกับระบบอัตโนมัติผ่านเว็บแอปพลิเคชันนี้ เป็นทางเลือกที่มีความยืดหยุ่นและเชื่อถือได้ โดยช่วยเสริมสร้างความเข้มงวดในการตรวจสอบ การประเมินกระบวนการอย่างเป็นระบบ และรองรับการนำไปใช้งานจริงผ่านส่วนติดต่อผู้ใช้ที่เข้าใจง่ายและเหมาะสมกับองค์กรพัฒนาซอฟต์แวร์ขนาดเล็กก่อนรับการตรวจสอบจากผู้ตรวจสอบ

Optimal Control Of Queueing Systems With Error-Prone Servers, Junqi Hu, Sigrún Andradóttir, Hayriye Ayhan

Information Technology & Decision Sciences Faculty Publications

Consider a Markovian tandem line with finite intermediate buffers and an equal number of stations and servers. Servers are flexible but noncollaborative, so that a job can be processed by at most one server at any time. When a job is being processed, it can be damaged and wasted depending on the proficiency of the server. We identify the dynamic server assignment policy that maximizes the long-run average throughput of the system with two stations and two servers. We find that the optimal policy is either a single or a double threshold policy on the number of jobs in the …

Temporal Machine Learning For Predicting Accidents And Violations In The Mining Industry, Nathan T. Kelley

Theses and Dissertations--Mining Engineering

This thesis examines the predictive capability of a temporal machine learning model for forecasting future accidents and violations at individual mines, based on historical data. Mine accidents were categorized by accident classification and violations were categorized by the Part Section. The primary datasets utilized were the mine safety and health administration’s (MSHA’s) Accident Injuries and Violations datasets. The available datasets were cleaned and organized by mine type and commodity, then divided into separate subsets for training, validating, and testing. Different models, cutoff metrics, learning rates, number of hidden layers, data processing methods, data processing divisions, number of points observed …

Computation-Efficient Deep Learning Models For Computer Vision And Multimodal Vision-Language Tasks Via Network Pruning, Abir Mohammad Hadi

Electronic Theses and Dissertations

With the rapid evolution of deep neural networks over the past decade, the demand for efficient, generalizable, and task-adaptable models, especially in computer vision, has increased significantly. To address the computational and deployment challenges posed by overparameterized models, the research community has extensively explored model compression techniques such as pruning, quantization, and distillation. These approaches aim to enhance model efficiency without compromising performance, particularly when adapting to domain-specific tasks under limited resources. This dissertation investigates several underexplored yet critical aspects of task-aware deep learning model compression, spanning both convolutional and vision-language architectures. In the early part of this work, we …

Video Comprehension Score (Vcs): A Metric For Long-Form Video Description Evaluation, Harsh Dubey

Electronic Theses and Dissertations

Existing video description evaluation metrics fail to capture the long-range chronology and semantic alignment essential for long-form descriptions. An effective evaluation metric for long-form descriptions must (i) assess global thematic alignment, (ii) measure local semantic alignment, and (iii) evaluate chronological alignment while detecting corrupted content. We introduce Video Comprehension Score (VCS), a reference-based metric, which directly addresses these evaluation requirements through three components: Global Alignment Score for thematic alignment, Local Alignment Score for local semantic alignment, and Narrative Alignment Score for chronological alignment with adjustable tolerance. We evaluate VCS on two large-scale synthetic datasets designed to test corruption detection and …

Predicting Biomechanical Risk Factors For Division - I Women’S Basketball Athletes, Aayushi Shah, Vanaja Agarwal, Dhairya Shah, Harman Jani, Sristi Sharma, Kaya Tolga, Christopher Taber, Mehul Raval

School of Computer Science & Engineering Faculty Publications

Collegiate basketball is characterized by high-impact movements such as jump landings, making athletes more susceptible to injuries. Critical biomechanical factors like knee flexion, lateral trunk flexion, and foot landing asymmetry are strongly associated with injury risk. This study aims to predict six biomechanical risk factors in the landing error scoring system (LESS). The dataset comprises 8600 video frames of counter-movement jumps (CMJs) from 17 NCAA Division I female basketball athletes, recorded from frontal and lateral perspectives and annotated using a customized error annotation algorithm. The study uses the You Only Look Once (YOLOv5nu) model to analyze the basketball athletes’ CMJ …

Sogar: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition, Naga Venkata Sai Raviteja Chappa, Pha Nguyen, Alexander H. Nelson, Han-Seok Seo, Xin Li, Page Daniels Dobbs, Khoa Luu

Electrical Engineering and Computer Science Faculty Publications and Presentations

Social group activity recognition is crucial for various applications including surveillance, human-robot interaction, and behavioral analysis. Current approaches often require extensive manual annotations and rely heavily on pre-trained detectors, limiting their practical applications. Additionally, existing methods struggle to effectively model long-term spatiotemporal relationships in group activities. This paper introduces a novel approach to Social Group Activity Recognition (SoGAR) using Self-supervised Transformers network that can effectively utilize unlabeled video data. To extract spatio-temporal information, we create local and global views with varying frame rates. Our self-supervised objective ensures that features extracted from contrasting views of the same video are consistent across …

Are We Ready For Synchronous Conceptual Modeling In Augmented Reality? A Usability Study On Causal Maps With Hololens 2, Anish Shrestha, Philippe J. Giabbanelli

VMASC Publications

(1) Background: Participatory modeling requires combining individual views to create a shared conceptual model. While remote collaboration tools have enabled synchronous online modeling, they are limited to desktop settings. Augmented reality (AR) offers a new approach by potentially providing the sense of presence found in physical collaboration, which may better support participants in achieving the sense of presence found in physical locations, thus supporting them in negotiating meaning and building a shared model. (2) Methods: Building on prior works that developed technology, we performed a usability study with pairs of modelers to examine their ability at performing key conceptual modeling …

Heuristic Approaches For Coordination Of Heterogeneous Robotic Systems In Harvesting Automation With Size Constraints, Hyeseon Lee

Dissertations, Master's Theses and Master's Reports

This thesis presents the development of path planning algorithms for the coordination of heterogeneous robotic systems while considering size constraints. The objective is to generate practical and efficient solutions for real-world applications. The use of heterogeneous collaborative robots is beneficial in many applications, such as transportation operations in warehouses or manufacturing environments, surveillance, and monitoring, and task allocation and path planning are critical techniques that need to be addressed to deploy in real-world applications. This research focuses on automating lavender harvesting, where robots with varying capabilities must collaboratively navigate complex field layouts to efficiently complete harvesting tasks.

The problem considers …

Exploring Research And Tools In Ai Security: A Systematic Mapping Study, Sidhant Narula, Mohammad Ghasemigol, Javier Carnerero-Cano, Amanda Minnich, Emil Lupu, Daniel Takabi

School of Cybersecurity Faculty Publications

With the pervasive integration of artificial intelligence (AI) in various facets of modern technology, the importance of AI security has been thrust into the spotlight. The field is rapidly evolving, with new challenges and solutions emerging at a swift pace. However, the breadth and depth of AI security research have not been comprehensively mapped in recent times, presenting a crucial need for an extensive review and synthesis of existing literature. Given the increasing reliance on AI in critical domains such as healthcare, finance, and national security, ensuring the resilience and trustworthiness of these systems is imperative. This survey fulfills the …

Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory, Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli, Timothy Pennington

School of Cybersecurity Faculty Publications

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue …

Zero Trust Architecture As A Risk Countermeasure In Small-Medium Enterprises And Advanced Technology Systems, Ahmed M. Abdelmagid, Rafael Diaz

Engineering Management & Systems Engineering Faculty Publications

The growing sophistication of cyberattacks exposes small- and medium-sized businesses (SMBs) to a widening range of security risks. As these threats evolve in complexity, the need for advanced security measures becomes increasingly pressing. This necessitates a proactive approach to defending against potential cyber intrusions. Emerging technologies, such as blockchain, artificial intelligence, and Zero Trust security framework, offer crucial tools for strengthening the digital infrastructure of SMBs. The Zero Trust architecture (ZTA) holds significant promise as a critical strategy for protecting SMBs. While existing literature explores the implementation of ZTA in various business settings, discussions specifically addressing the financial, human resource, …

Security Enhancement In Uav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Unmanned Aerial Vehicles (UAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These UAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

Security Enhancement In Aav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Autonomous Aerial Vehicles (AAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These AAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

Tl-Convlstm: A Transfer-Learning-Based Convolutional Lstm To Identify And Forecast Traffic In The Nextg Environments, Bikash Chandra Singh, Peter Foytik, Rafael Diaz, Sachin Shetty

School of Cybersecurity Faculty Publications

Forecasting and categorizing cellular traffic flows and their types are essential functions in intelligent network systems to ensure efficient network optimization. The ever-evolving nature of 5G networks results in fluctuations in traffic patterns over time, leading to a phenomenon known as model drift. Consequently, accurately predicting and identifying cellular traffic patterns becomes a complex task. To tackle this challenge, this article introduces an innovative approach called TL-ConvLSTM, which combines transfer learning with convolutional long short-term memory (ConvLSTM) to effectively combat model drift and provide precise forecasting and recognition of cellular traffic within the network. To accomplish this, we initiate the …

Leaf-Based Varietal Categorization Of Sweetpotato (Ipomoea Batatas L. Lam.), A Potentially Healthful Vegetable, Using Image Processing And K-Means Clustering, Shahidul Islam, Md Towfiqur Rahman, Md Hamidul Rahman, Abdul Momin

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Sweetpotato (Ipomoea batatas Lam) leaves contain higher concentrations of phenolic compounds, flavonoids, and carotenoids that are remarkable in health promotion. However, the nutrient content in sweetpotato leaves varies from variety to variety, and leaf shape and color are the key identifying factors for the varietal classification of sweetpotatoes. So, detecting sweetpotato leaves is essential for the in-situ identification of sweetpotato varieties and for developing intelligent agricultural systems. This study aimed to create a leaf-shape-based varietal classification technique for sweetpotato using image processing techniques coupled with a K-means clustering algorithm. 38 leaf images (RGB) of two sweetpotato cultivars were collected …

In-Season Nitrogen Mmanagement: Leveraging Data Visualization For Precision Agriculture, Chathurika Harshani Narayana

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Effective nitrogen management is vital for sustainable agriculture, impacting both crop yield and environmental health. Traditional methods often use fixed application rates set before planting, which do not adapt to changing crop needs during the season. This can lead to over- or under-application, reducing efficiency and sustainability. While modern tools like sensors, satellites, and UAVs provide valuable real-time data on crop and field conditions, integrating and using this data to guide timely nitrogen decisions remains a major challenge. In-season nitrogen management offers a solution by allowing for dynamic adjustments to nitrogen applications, addressing crop needs as they arise. This approach …

Development Of An Ecg-Based Deep Learning Model For Pediatric Congenital Heart Disease (Chd) Diagnosis, Annbar Mekouar

Selected Full-Text Master Theses 2021-

Congenital heart disease (CHD) stands as the leading congenital anomaly which affects pediatric populations throughout the world. The effectiveness of treatment depends on both early diagnosis and accurate identification but echocardiography requires manual interpretation which proves time-consuming and inconsistent especially when examining pediatric patients with their distinct cardiac systems. The research aims to create a deep learning-based diagnostic framework which uses ECG data to identify coronary artery disease subtypes in pediatric patients. The model uses high-quality datasets from Dr. Ignacio Lugones to extract R-R intervals and QRS durations through convolutional neural networks (CNNs). The system addresses pediatric-specific challenges while enhancing …

An Analytical Model Of Motion Artifacts In A Measured Arterial Pulse Signal—Part I: Accelerometers And Ppg Sensors, Md Mahfuzur Rahman, Subodh Toraskar, Mamun Hasan, Zhili Hao

Mechanical & Aerospace Engineering Faculty Publications

This paper, the first of two parts, presents an analytical model of motion artifacts (MAs) in measured pulse signals by accelerometers and photoplethysmography (PPG) sensors. As the transmission path from the true pulse signal in an artery to the sensor output (measured pulse signal), the tissue-contact-sensor (TCS) stack is modeled as a 1DOF (degree-of-freedom) system. MAs cause baseline drift of the mass and simultaneously time-varying system parameters (TVSPs) of the TCS stack. With arterial wall displacement and pulsatile pressure serving separately as the true pulse signal, an analytical model is developed to mathematically relate baseline drift and TVSP to a …

From Cyclones To Cybersecurity: A Call For Convergence In Risk And Crisis Communications Research, Ann Marie Reinhold, Ross J. Gore, Barry Ezell, Clemente I. Izurieta, Elizabeth A. Shanahan

VMASC Publications

Effective risk and crisis communication can improve health and safety and reduce harmful effects of hazards and disasters. A robust body of literature investigates mechanisms for improving risk and crisis communication. While effective risk and crisis communication strategies are equally desired across different hazard types (e.g., natural hazards, cyber security), the extent to which risk and crisis communication experts utilize the “lessons learned” from scientific domains outside their own is suspect. Therefore, we hypothesized that risk and crisis communication research is siloed according to academic disciplines at the detriment to the advancement of the field of risk communications research writ …

Applying Transfer Learning For Street-Scale Nuisance Flood Forecasting In Coastal-Urban Cities, Binata Roy, Jonathan L. Goodall, Diana Mcspadden, Chetan Kumar, Steven Goldenberg, Yidi Wang, Malachi Schram

VMASC Publications

An important challenge with Machine Learning (ML) is its transferability; i.e., whether a ML model trained on one set of data can be applied to a second set of data without requiring full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained for one …

Ai-Generated Messaging For Life Events Using Structured Prompts: A Comparative Study Of Gpt With Human Experts And Machine Learning, Christopher Lynch, Erik Jensen, Ross Gore, Virginia Zamponi, Kevin O'Brien, Brandon Feldhaus, Katherine Smith, Joseph Martínez, Madison H. Munro, Timur E. Ozkose, Tugce B. Gundogdu, Ann Marie Reinhold, Hamdi Kavak, Barry Ezell

VMASC Publications

Large Language Models (LLMs) play an increasingly integrated and pivotal role in generating diverse types of texts, such as social media messages, emails, narratives, and technical reports, among other textual communication forms. As AI-generated messaging filters into human communication, a systematic exploration of their effectiveness for mimicking human-like communication of life events is needed. In this study, we employ a zero-shot structured narrative prompt to generate 24,000 life event messages for birth, death, hiring, and firing events using OpenAI's GPT-4. From this dataset, we manually classify 2880 messages and evaluate their validity in conveying these life events through the form …

Age Of Information-Based Optimal Scheduling With Energy Cost Trade-Off For Smart Warehouse: A Deep Reinforcement Learning-Based Approach, Sandip Roy, Abhishek Bisht, Ashok Kumar Das, Sachin Shetty

VMASC Publications

Recent advances in the integration of high-speed mobile networks and real-time IoT devices have facilitated in building of smart warehouses, where a set of beacons and Internet of Things (IoT) devices (or source nodes) can monitor the status of various physical processes in a time-critical way. In real-time status monitoring systems, like smart warehouses, quantifying the freshness of the Internet of Things (IoT) data based on the age of information (AoI) metrics becomes quite crucial. As source nodes are battery-constrained, a balanced trade-off between AoI minimization and preservation of source node battery energy is essential. In this paper, in a …

Faithful Narratives From Complex Conceptual Models: Should Modelers Or Large Language Models Simplify Causal Maps, Tyler J. Gandee, Philippe J. Giabbanelli

VMASC Publications

(1) Background: Comprehensive conceptual models can result in complex artifacts, consisting of many concepts that interact through multiple mechanisms. This complexity can be acceptable and even expected when generating rich models, for instance to support ensuing analyses that find central concepts or decompose models into parts that can be managed by different actors. However, complexity can become a barrier when the conceptual model is used directly by individuals. A ‘transparent’ model can support learning among stakeholders (e.g., in group model building) and it can motivate the adoption of specific interventions (i.e., using a model as evidence base). Although advances in …

Flexible Hybrid Self-Powered Piezo-Triboelectric Nanogenerator Based On Bto-Pvdf/Pdms Nanocomposites For Human Machine Interaction, Wentao Dong, Mengyun Li, Chang Chen, Kun Xie, Jinhua Hong, Lin Yang

Civil & Environmental Engineering Faculty Publications

As flexible and wearable electronics play more and more important role in smart watches, smart glass and virtual reality, and the power supply to the wearable electronics have been revealed more attentions for long-term usage and continuous healthy monitoring. To overcome the challenge, flexible self-powered BTO-PVDF/PDMS piezoelectric-triboelectric electric hybrid generators (BPP-HNG) are developed to human gesture monitoring and human machine interaction (HMI) application without external power supply. BPP-HNG based on BTO-PVDF and PDMS films are prepared by sol-gel and spin-coating method. When the BTO content is 20 wt.%, BPP-HNG exhibits better electrical performance with an output voltage of 20.51 V. …

A Global Application Programming Interface-Enabled Earthquake Ground Motion Relational Database For Engineering Applications, Tristan E. Buckreis, Chukwuebuka C. Nweke, Pengfei Wang, Scott J. Brandenberg, Maria E. Ramos-Sepúlveda, Rashid Shams, Shako Mohammed, Renmin Pretell, Silvia Mazzoni, Paolo Zimmaro, Jonathan P. Steward

Civil & Environmental Engineering Faculty Publications

We present a application programming interface (API)-enabled relational database of global earthquake ground motion intensity measures, associated metadata, and processed time-series data. Raw ground motion records were processed by the authors using either manual or semi-automated processing procedures, and every processed record has passed a quality review by a trained analyst. Computed intensity measures include peak acceleration and velocity, pseudo-spectral acceleration response spectra, cumulative absolute velocity, Arias Intensity, and Fourier amplitude spectra. The processed time-series data, associated metadata, and ground motion intensity measures were organized into a web-served relational database consisting of 32 tables connected by primary/foreign key pairs. Ground …

A Novel Intelligent Thermal Feedback Framework For Electric Motor Protection In Embedded Robotic Systems, Mohamed Shili, Salah Hammedi, Hicham Chaoui, Khaled Nouri

Electrical & Computer Engineering Faculty Publications

As robotic systems advance in autonomy and sophistication while being used in uncertain environments, the challenge of building reliable and robust electric motors that are embedded into robotic systems has never been a more important engineering problem. Thermal distress caused by extended operation or excessive loading can negatively affect a motor’s performance and efficiency and lead to catastrophic hardware failure. This paper proposes a novel intelligent control framework that includes real-time thermal feedback for hybrid electric motors that are embedded into robotic systems. The framework relies on adaptive control techniques and lightweight machine learning techniques to estimate internal motor temperatures …

Physics-Informed Deep Learning With Kalman Filter Mixture For Traffic State Prediction, Niharika Deshpande, Hyoshin (John) Park

Engineering Management & Systems Engineering Faculty Publications

Accurate traffic forecasting is crucial for understanding and managing congestion for efficient transportation planning. However, conventional approaches often neglect epistemic uncertainty, which arises from incomplete knowledge across different spatiotemporal scales. This study addresses this challenge by introducing a novel methodology to establish dynamic spatiotemporal correlations that captures the unobserved heterogeneity in travel time through distinct peaks in probability density functions, guided by physics-based principles. We propose an innovative approach to modifying both prediction and correction steps of the Kalman Filter (KF) algorithm by leveraging established spatiotemporal correlations. Central to our approach is the development of a novel deep learning model …

Predictive Maintenance In Naval Vessel Propulsion Systems For Enhanced Marine Operations Using A Bigmm-Hmm Framework With Divergence-Based Clustering, Farshid Javadnejad, Hyoshin John Park, Samuel Kovacic, Andres Sousa-Poza

Engineering Management & Systems Engineering Faculty Publications

This study introduces a BiGMM-HMM Integration Framework designed to improve predictive maintenance strategies for naval vessel propulsion systems, addressing the need for efficient and reliable operation in marine engineering applications. The framework effectively manages multimodal sensor data by leveraging a unique combination of Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) in a bidirectional architecture. It analyses the dynamic interactions between sensors and subsystems. Two preprocessing methods are evaluated: Method 1 focuses on subsystem interactions, employing divergence-based root cause analysis to identify key sensor variables by clustering of sensors and subsystems. In contrast, Method 2 processes the entire dataset …