Computer Sciences Commons^™

Development Of A Collaborative Research Platform For Efficient Data Management And Visualization Of Qubit Control, Devanshu Brahmbhatt

Computer Science and Engineering Theses - Archive

This thesis introduces QubiCSV, a pioneering open-source platform for quantum computing field. With an emphasis on collaborative research, QubiCSV addresses the critical need for specialized data management and visualization tools in qubit control. The platform is crafted to overcome the challenges posed by the high costs and complexities associated with quantum experimental setups. It emphasizes efficient utilization of resources through shared ideas, data, and implementation strategies. One of the primary obstacles in quantum computing research has been the ineffective management of extensive calibration data and the inability to visualize complex quantum experiment outcomes effectively. QubiCSV fills this gap by offering …

Minerrouter : Effective Message Routing Using Contact-Graphs And Location Prediction In Underground Mine, Abhay Goyal, Sanjay Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Location-based distributed communication in underground mines has been a hard problem to solve due to unreliable centralized architecture such as leaky feeder systems, high attenuation, and the unavailability of GPS signals. Delay Tolerant Networks (DTN) enable decentralized message routing using the store-carry-forward method that can help in creating situational awareness needed to handle emergency and disaster scenarios. The ability to predict where the DTN nodes (miner) might have been at/are headed to (with respect to the mine regions and pillars) at different times, combined with contact-based routing and intelligent handling of buffer, can be used for better delivery of messages. …

Asem 4.0/5.0 - Evolving The Engineering Management Profession Through Industry 4.0/5.0 Collaborative Networks, T. Steven Cotter, Faisal Mahmud, Ziniya Zahedi

Engineering Management & Systems Engineering Faculty Publications

The American Society for Engineering Management was created and matured under Industry 3.0 automation. The emergence of Industry 4.0 and 5.0 are forcing all organizational sectors to rethink their long-term strategy with respect to emerging horizontal/vertical cyber-physical systems integration. This leaves open the question of the directions in which ASEM should evolve into the 21st century. This paper reports an initial mapping of Industry 4.0 and 5.0 technologies and initiatives as goal-oriented, long-term strategic collaborative networks. The research method began with the Boston Consulting Group nine technologies of Industry 4.0 (2015) and the Industry 5.0 technologies within its human-centric, sustainability, …

An Algorithm Based On Priority Rules For Solving A Multi-Drone Routing Problem In Hazardous Waste Collection, Youssef Harrath Dr., Jihene Kaabi Dr.

Research & Publications

This research investigates the problem of assigning pre-scheduled trips to multiple drones to collect hazardous waste from different sites in the minimum time. Each drone is subject to essential restrictions: maximum flying capacity and recharge operation. The goal is to assign the trips to the drones so that the waste is collected in the minimum time. This is done if the total flying time is equally distributed among the drones. An algorithm was developed to solve the problem. The algorithm is based on two main ideas: sort the trips according to a given priority rule and assign the current trip …

Privacy And Security Of The Windows Registry, Edward L. Amoruso

Graduate Thesis and Dissertation 2023-2024

The Windows registry serves as a valuable resource for both digital forensics experts and security researchers. This information is invaluable for reconstructing a user's activity timeline, aiding forensic investigations, and revealing other sensitive information. Furthermore, this data abundance in the Windows registry can be effortlessly tapped into and compiled to form a comprehensive digital profile of the user. Within this dissertation, we've developed specialized applications to streamline the retrieval and presentation of user activities, culminating in the creation of their digital profile. The first application, named "SeeShells," using the Windows registry shellbags, offers investigators an accessible tool for scrutinizing and …

Reinforcement Learning-Based Constrained Optimal Control Of Strict-Feedback Nonlinear Systems: Application To Autonomous Underwater Vehicles, Behzad Farzanegan, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper addresses a constrained neural network (NN)-based optimal tracking scheme for a class of uncertain nonlinear discrete-time systems in strict-feedback form by using a control barrier function (CBF). First, a modified barrier-type cost function is introduced for each subsystem, guiding the actual system trajectory toward the safe set or desired trajectory while avoiding unwanted sets. To address the tracking problem, an augmented system is employed to convert the time-varying optimal tracking to a time-invariant optimal regulation. Then, an actor-critic framework is employed with the backstepping technique to obtain both virtual and actual optimal control policies for each subsystem to …

Relative Altitude Estimation Of Infrared Thermal Uav Images Using Sift Features, Shirin Nasr Esfahani, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

Unmanned Aerial Vehicles (UAVs) have become indispensable in various applications, including surveillance, urban scene analysis, and agricultural monitoring. Accurate altitude estimation is critical for UAV operations, especially in environments where traditional sensors like GPS, pressure altimeters, and radar may fail. This paper explores the use of infrared and thermal imaging for relative altitude estimation of UAVs, highlighting their significant advantages over traditional RGB images. Infrared and thermal imaging offer superior performance in low-light and adverse weather conditions, providing clearer visibility and more reliable feature detection. By leveraging the Scale-Invariant Feature Transform (SIFT) features, this approach utilizes the inherent benefits of …

แบบจำลองการจัดลำดับความสำคัญของความเสี่ยงของยูสเซอร์สตอรีสำหรับการจัดลำดับความสำคัญโดยใช้ตรรกะคลุมเครือ, เปรม สุนทรภาส

Chulalongkorn University Theses and Dissertations (Chula ETD)

ในกระบวนการพัฒนาซอฟต์แวร์แบบแอไจล์ ยูสเซอร์สตอรีเป็นองค์ประกอบสำคัญที่สะท้อนความต้องการของผู้ใช้ แต่หากยูสเซอร์สตอรีมีคุณภาพไม่เพียงพอ อาจก่อให้เกิดความเสี่ยงที่ส่งผลกระทบต่อความสำเร็จของโครงการอย่างมีนัยสำคัญ ดังนั้นงานวิจัยนี้จึงนำเสนอแบบจำลองการจัดลำดับความสำคัญของความเสี่ยงของยูสเซอร์สตอรีโดยใช้ตรรกะคลุมเครือ เพื่อช่วยให้สามารถจัดการกับความไม่แน่นอนในการตัดสินใจได้อย่างมีประสิทธิภาพ โดยเริ่มจากการนิยามความเสี่ยงของยูสเซอร์สตอรีจำนวน 12 ประเภท พร้อมทั้งกำหนดเกณฑ์การประเมินใน 2 ด้าน ได้แก่ ด้านความพยายามในการบริหารจัดการ และด้านข้อจำกัดในการบริหารจัดการ จากนั้นจึงประยุกต์ใช้ตรรกะคลุมเครือร่วมกับตัวแปรภาษาและฟังก์ชันความเป็นสมาชิกในการคำนวณค่าคะแนนความเสี่ยง และจัดลำดับความสำคัญผ่านแบบจำลองที่พัฒนาขึ้น ผลการประเมินการใช้งานแบบจำลองกับผู้มีประสบการณ์ในโครงการแอไจล์พบว่า แบบจำลองสามารถช่วยในการจำแนกความเสี่ยงได้อย่างเหมาะสมและมีความสอดคล้องกับความคิดเห็นของผู้ประเมิน ซึ่งผลการประเมินสะท้อนถึงความสมเหตุสมผลของแบบจำลองและความสามารถในการประยุกต์ใช้จริง ดังนั้นงานวิจัยนี้จึงเป็นประโยชน์ต่อทีมพัฒนาในการประเมินและจัดลำดับความเสี่ยงของยูสเซอร์สตอรีได้อย่างเป็นระบบ และเพิ่มประสิทธิภาพในการบริหารความเสี่ยงในโครงการพัฒนาซอฟต์แวร์แบบแอไจล์ได้ดียิ่งขึ้น

The Feasibility Of Motion Tracking Camera System For Magnetic Suspension Wind Tunnel Tests, Hisham M. Shehata, David Cox, Mark Schoenenberger, Colin Britcher, Eli Shellabarger, Timothy Schott, Brendan Mcgovern

Mechanical & Aerospace Engineering Faculty Publications

The Entry Systems Modeling (ESM) Program at NASA has actively participated in the re-development of the Magnetic Suspension Balance System (MSBS) at the six-inch subsonic wind tunnel at NASA Langley Research Center. This initiative aims to enhance the MSBS system's capabilities, enabling the testing of stingless entry vehicle models at supersonic speeds. To achieve this, control algorithms are required to ensure magnetic levitation control and stability for models during free-oscillation dynamic responses. Currently, the system relies on electromagnetic position sensors to provide real-time 3 degrees of freedom control of a rigid body. While this approach has proven successful for subsonic …

Development Of A Two-Finger Haptic Robotic Hand With Novel Stiffness Detection And Impedance Control, Vahid Mohammadi, Ramin Shahbad, Mojtaba Hosseini, Mohammad Hossein Gholampour, Saeed Shiry Ghidary, Farshid Najafi, Ahad Behboodi

Mechanical & Aerospace Engineering Faculty Publications

Haptic hands and grippers, designed to enable skillful object manipulation, are pivotal for high-precision interaction with environments. These technologies are particularly vital in fields such as minimally invasive surgery, where they enhance surgical accuracy and tactile feedback: in the development of advanced prosthetic limbs, offering users improved functionality and a more natural sense of touch, and within industrial automation and manufacturing, they contribute to more efficient, safe, and flexible production processes. This paper presents the development of a two-finger robotic hand that employs simple yet precise strategies to manipulate objects without damaging or dropping them. Our innovative approach fused force-sensitive …

Kinodynamic Motion Planning For A System With Squid Dynamics, Logan E. Beaver, Cong Wei, Wei-Kuo Yen

Mechanical & Aerospace Engineering Faculty Publications

This paper introduces a path planning algorithm for a system with squid dynamics in a cluttered environment. We capture the complex interactions of fin, arms, and body patterning by analyzing experimental data collected from observing squid motion. We extract nine motion primitives to build the control sequence for a time-optimal trajectory. This task is formulated as a mixed-integer program, and we generate the minimum-time trajectory using a sample-based approach. Numerical simulations illustrate the efficacy of this strategy and motivate ongoing and future efforts to exploration of squid motion features, improvement of the modeling, and experimental demonstrations of the motion planning …

Improving Question Answering Retrieval System Through Multi-Result Ranking Model, Danupat Khamnuansin

Chulalongkorn University Theses and Dissertations (Chula ETD)

Recent trends in various industries involve integrating artificial intelligence (AI) systems to enhance operational efficiency. Among these advancements, AI-supported question-answering (QA) systems have gained significant attention. These systems typically employ a two-stage process, integrating QA system capabilities with Information Retrieval (IR) methods. The introduction of the Retrieval Question Answering (ReQA) has further refined this process, offering a more practical solution, aiming to improve real-world applicability. Considering the wide availability of diverse QA retrieval models, employing a combination of multiple systems presents as a viable solution. However, the approach to combining QA retrieval systems remains relatively limited. We propose a method …

การออกแบบและพัฒนาระบบการจัดการความรู้สำหรับการพัฒนาซอฟต์แวร์แบบสกรัมตามมาตรฐาน Iso/Iec 12207, กีรติกา ห่อเกียรติ

Chulalongkorn University Theses and Dissertations (Chula ETD)

โครงงานมหาบัณฑิตนี้มีวัตถุประสงค์ในการปรับปรุงกระบวนการจัดการความรู้ของหน่วยงานแห่งหนึ่งให้สอดคล้องกับมาตรฐานไอเอสโอ/ไออีซี 12207 กระบวนการจัดการความรู้นี้นำเสนอสำหรับโครงการที่ใช้ระเบียบวิธีแบบเอจายล์ตามกรอบงานสกรัม พร้อมทั้งได้ออกแบบและพัฒนาระบบการจัดการความรู้ เพื่อสนับสนุนการประยุกต์ใช้กระบวนการจัดการความรู้ภายในองค์กร ระบบที่พัฒนาสามารถให้บริการในการจัดเก็บ ค้นหา และแบ่งปันความรู้จากโครงการต่าง ๆ ขององค์กร อีกทั้งสนับสนุนให้มีการแบ่งปันความรู้เพื่อให้มีการพัฒนาซอฟต์แวร์ที่ความสอดคล้องตามความต้องการของผู้ใช้ และช่วยเพิ่มสมรรถนะในการทำงานของทีมพัฒนาซอฟต์แวร์ การดำเนินงานเริ่มจากการศึกษาแนวคิดและมาตรฐานที่เกี่ยวข้องกับการจัดการความรู้ รวมถึงการวิเคราะห์มาตรฐาน ไอเอสโอ/ไออีซี 12207 เพื่อประเมินและปรับปรุงกระบวนการจัดการความรู้ในปัจจุบันของ 2 กิจกรรม ได้แก่ “กิจกรรมที่ 3) การแบ่งปันสินทรัพย์ความรู้ทั่วทั้งองค์กร” และ “กิจกรรมที่ 4) การจัดการความรู้ ทักษะ และสินทรัพย์ความรู้” ในการปรับปรุงกระบวนการนั้นได้นำเสนอ โครงสร้างพื้นฐานกระบวนการ และการนิยามกระบวนการที่ปรับปรุง จากนั้นได้ทำการพัฒนาระบบการจัดการความรู้ ที่สามารถรองรับกระบวนการจัดการความรู้ที่ปรับปรุงแล้ว รวมถึงทวนสอบระบบกับความต้องการเชิงฟังก์ชัน และเพื่อยืนยันว่าระบบสามารถสนับสนุนกระบวนการทำงานของทีมสกรัมตามการนิยามกระบวนการได้อย่างมีประสิทธิภาพ ผลลัพธ์ของโครงงานนี้แสดงว่า ได้ช่วยเพิ่มประสิทธิภาพในการจัดการความรู้ภายในองค์กรตัวอย่าง และสามารถใช้เป็นแนวทางสำหรับองค์กรอื่น ๆ ที่ต้องการปรับปรุงกระบวนการจัดการความรู้ในบริบทของการพัฒนาซอฟต์แวร์แบบสกรัม

Enhancing Multilingual Sentence Representation Learning For The Job Recruitment Domain, Napat Laosaengpha

Chulalongkorn University Theses and Dissertations (Chula ETD)

With the advancement in natural language processing (NLP), there has been significant development in multilingual pretraining sentence encoder. Typically, these pretraining models are trained on large-scale datasets that consist of general text data from various sources such as Wikipedia. However, the general proposed models aren't enough to understand contexts in such a domain-specific, especially in the job recruitment domain. It is due to its niche nature and the lack of readily available related information. To enhance the existing multilingual pretraining sentence encoder and mitigate the aforementioned problems, we first propose multi-task dual-encoder framework to improve the sentence encoder for general-purpose …

Adaptable And Trustworthy Machine Learning For Human Activity Recognition From Bioelectric Signals, Morgan S. Stuart

Theses and Dissertations

Enabling machines to learn measures of human activity from bioelectric signals has many applications in human-machine interaction and healthcare. However, labeled activity recognition datasets are costly to collect and highly varied, which challenges machine learning techniques that rely on large datasets. Furthermore, activity recognition in practice needs to account for user trust - models are motivated to enable interpretability, usability, and information privacy. The objective of this dissertation is to improve adaptability and trustworthiness of machine learning models for human activity recognition from bioelectric signals. We improve adaptability by developing pretraining techniques that initialize models for later specialization to unseen …

Predicting Quality Of Life In Driving Scene Using Image Recognition Techniques And User Group Information, Ployrada Suvarnakuta

Chulalongkorn University Theses and Dissertations (Chula ETD)

This study presents a machine learning approach for predicting perceived urban Quality of Life (QoL) by integrating visual features from street-level imagery with personal attributes, including demographic, socioeconomic, and travel behavior data. Using datasets from Bangkok and London, we trained supervised models—Support Vector Machines and Multilayer Perceptrons—under multiple input configurations to evaluate the contribution of each data type. Results show that combining visual and personal features improves prediction accuracy compared to using visual features alone. Statistical feature selection identified income, education, housing stability, and travel patterns as consistently important predictors, with some variation across urban contexts. These findings underscore the …

Using Ontological Methods To Compare Cybersecurity Maturity Model Certification 2.0 And Cobit 19, Aaron Marshall Ramey

CCAC Theses and Dissertations

Cybersecurity frameworks developed by a variety of organizations and implemented by a much larger collection of organizations differ in their focus and application. Whether designed by a private or government organization, the primary goal is to provide a framework to assess and reduce risk. The Department of Defense (DoD) has recently implemented the second version of the Cybersecurity Maturity Model Certification (CMMC 2.0). In some situations, compliance with CMMC 2.0 has already become mandatory for the Defense Industrial Base (DIB). Compliance will soon be required for all Large Businesses (LB) and Small Businesses (SB) within the DIB. While COBIT 19 …

Combating Disinformation On Social Media Networks With Media And Information Literacy Training For Social Media Network Users, Oscar Kwok Chao Ho

CCAC Theses and Dissertations

In the Internet age, social media networks (SMNs), such as Facebook (FB), Instagram (IG), and Twitter (TW), have gained popularity and become an essential part of human life. SMNs provide ease of connection to family, friends, and communities; however, they increase the chances social media network users (SMN users) will disclose private information (PI), causing critical harm to SMN users’ information privacy (IP). Furthermore, SMN users are exposed to significant amounts of disinformation, misinformation, or fake news, which they share without realizing the information is untrustworthy.

The goal of this developmental research was to investigate, examine, and understand the effects …

A Technique For Visualization Of Multivariate Categorical Data, Janice James

CCAC Theses and Dissertations

Multivariate Categorical Data (MCD) plays a significant role in many industries, and the ability to understand the data is critical for insight and decision making. Visualization is a key tool for understanding the data. This dissertation designed and implemented a novel technique for visualizing MCD called Pivoting Parallel Charts (PPC). The design of PPC was informed by studying several existing MCD visualization techniques.

PPC visualizes MCD as a sequence of parallel axes with affixed bar charts. A user-specified axis, called the pivot, acts as the crucial point of consideration for all data relationships. The bar charts are color-coded by the …

Enhancing Iot Security: Optimizing Anomaly Detection Through Machine Learning, Maria Balega, Waleed Farag, Xin-Wen Wu, Soundarararjan Ezekiel, Zaryn Good

Computer Science Articles

As the Internet of Things (IoT) continues to evolve, securing IoT networks and devices remains a continuing challenge. Anomaly detection is a crucial procedure in protecting the IoT. A promising way to perform anomaly detection in the IoT is through the use of machine learning (ML) algorithms. There is a lack of studies in the literature identifying optimal (with regard to both effectiveness and efficiency) anomaly detection models for the IoT. To fill the gap, this work thoroughly investigated the effectiveness and efficiency of IoT anomaly detection enabled by several representative machine learning models, namely Extreme Gradient Boosting (XGBoost), Support …

A Robust Form Understanding System Using Graph-Based Neural Network, Chavin Chuangchaichatchavarn

Chulalongkorn University Theses and Dissertations (Chula ETD)

In this work, we address the challenge of form understanding in real-world documents affected by OCR noise and layout uncertainty. We introduce TONDFU, a bilingual Thai and English dataset consisting of official documents such as vehicle registrations and utility bills, annotated for entity labelling and entity linking. We also introduce a noisy character feature extractor that captures lexical and spatial patterns to improve the model's robustness against noisy textual content. This feature is integrated with geometric, visual, and semantic features in the graph-based model. Experiments show that the noisy character feature outperforms the frequency histogram baseline, and with pretraining on …

The Hazard Prediction Problem, Mary E. Helander, Brendan Smith, Sylvia Charchut, Erika Swiatowy, Calvin Nau, Gregory Cavaretta, Timothy Schuler, Adam Schunk, Héctor Ortiz-Peña

Social Science - All Scholarship

This work formulates the hazard prediction problem while addressing the research question: Can machine learning create a model to automatically recognize patterns that correspond to hazard state conditions during a mission-critical operation? Supervised learning models were trained and tested on data observed from mission simulators, which allowed for safe observation of dynamic system states and undesirable casualty events. The prediction task was formulated as a binary classification problem, producing the probability of being in a hazard state at time t and providing situational awareness of a possible imminent loss. Several modeling architectures were investigated: neural networks, logistic regression, a support …

An Efficient And Trusted Deep Learning Framework For Real-Time Ppe Detection In Secure Iomt Environment, Anusha Verma

Browse all Theses and Dissertations

Occupationally-acquired infections impact thousands of healthcare workers (HCWs) in the U.S., with many cases preventable through proper use of personal protective equipment (PPE). This study seeks to develop a robust system to enhance PPE compliance and reduce infection risks among HCWs. The objectives of this thesis are twofold: (1) to create a hybrid machine learning model that combines object detection and keypoint detection to ensure correct donning and doffing of PPE, and (2) to design a real-time feedback system using LED indicators and a display interface to offer actionable guidance to HCWs during PPE usage. The goal is to optimize …

An Enhanced Real-Time Object Detection Of Helmets And License Plates Using A Lightweight Yolov8 Deep Learning Model, Mounika Thatikonda

Browse all Theses and Dissertations

Traffic surveillance and enforcement heavily depend on the real-time detection of helmets and license plates, particularly in high-density urban environments. This study presents a dynamic and optimized lightweight model, the proposed G-YOLOv8n, designed for resource constrained edge devices like the Raspberry Pi. By integrating the GhostNet module into the YOLOv8n architecture, this research achieves a nearly 50% reduction in model size and computational load, while maintaining comparable detection accuracy to the original YOLOv8n. These enhancements enable real-time processing capabilities crucial for traffic monitoring operations. The growing demand for real-time, low-power solutions in intelligent transportation systems necessitates lightweight, efficient detection models. …

Thai-English Supported Automatic Speech Recognition For Endoscopic Reporting, Arpanant Saeng-Xuto

Chulalongkorn University Theses and Dissertations (Chula ETD)

This thesis presents the automatic speech recognition (ASR) system for endoscopic reporting that supports Thai-English code-switching. During endoscopic procedures, gastroenterologists are required to use both hands to handle instruments, thereby complicating the real-time documentation of abnormal findings. While recent advances in speech recognition offer promising solutions, existing models face difficulties with Thai-English code-switching and tend to overfit when fine-tuned on limited datasets. To overcome these limitations, we propose an ASR model enhanced with the Mixture of Experts (MoE) technique to improve transcription accuracy. Furthermore, the Named Entity Recognition (NER) model extracts gastrointestinal (GI) terminology from the transcriptions and classifies its …

Enhancing Large Language Models For Thai Legal Chatbots, Supachoke Hanwiboonwat

Chulalongkorn University Theses and Dissertations (Chula ETD)

Currently, developing a Thai legal question-answering system for the general public is highly challenging due to the complex, difficult-to-understand language and the extensive content of legal codes. This research proposes a Thai legal question-answering system designed for the public, aiming to establish best practices for developing effective legal QA systems. To improve performance, we created our own Thai legal QA dataset and incorporated data from various sources. We conducted comparative experiments to identify the most suitable language model for Thai legal contexts, and fine-tuned the models with diverse datasets for enhanced capabilities in legal QA and legal examinations. Additionally, we …

End-To-End Development Of Mandible Reconstruction Using Machine Learning, Nattapon Kamboonsri

Chulalongkorn University Theses and Dissertations (Chula ETD)

Virtual surgical planning (VSP) is a critical step in mandible reconstruction surgery, which involves preoperative planning and implant design to restore mandibular defects. This study focuses on two essential components of VSP: (1) mandible segmentation, specifically the separation of healthy and defective regions, which currently relies on manual annotation, and (2) generation of the complete mandible, where traditional approaches such as mirroring the contralateral side often fail in scenarios involving midline-crossing defects. While recent automated methods have addressed mandible segmentation from CT scans, they typically focus only on binary segmentation and often utilize conventional UNet-based architectures that suffer from limited …

Automated Cecum Identification In Colonoscopy Using Deep Learning Approach, Kittipoom Sutthinuntakorn

Chulalongkorn University Theses and Dissertations (Chula ETD)

Colonoscopy is essential for the early detection and prevention of colorectal cancer. Identifying the cecum is a vital element of this process. However, most existing automated methods rely on still images or temporal cues alone, without incorporating camera motion awareness, and are rarely suitable for real-time use. In this paper, we present a real-time cecum detection method that integrates spatial features, temporal modeling, and camera motion cues. We deploy ConvNeXtV2 for spatial feature extraction, LTContext for temporal modeling, and Depth Anything in Robotic Endoscopic Surgery (DARES) to enhance understanding of endoscope positioning and camera motion. The dataset used in this …

แบบจำลองวุฒิภาวะด้านการวิเคราะห์ผลป้อนกลับของผู้ใช้สำหรับการบำรุงรักษาซอฟต์แวร์, ภัทรพงศ์ วิโรจน์ปกรณ์

Chulalongkorn University Theses and Dissertations (Chula ETD)

การวิเคราะห์ผลป้อนกลับของผู้ใช้ซอฟต์แวร์เป็นองค์ประกอบสำคัญของการบำรุงรักษาซอฟต์แวร์ เนื่องจากช่วยให้เข้าใจโดยตรงถึงวิธีที่ผู้ใช้จริงมีปฏิสัมพันธ์กับระบบ อุปสรรคที่ผู้ใช้เผชิญ และการปรับปรุงที่ผู้ใช้ต้องการ องค์กรที่พัฒนาซอฟต์แวร์อาจมีช่องทางหลากหลายในการรวบรวมผลป้อนกลับของผู้ใช้ แต่ประสิทธิภาพขององค์กรในการนำผลป้อนกลับเหล่านั้นไปใช้อาจมีความแตกต่างกันอย่างมาก งานวิจัยนี้เสนอแบบจำลองวุฒิภาวะด้านการวิเคราะห์ผลป้อนกลับของผู้ใช้ซอฟต์แวร์ (SFAMM) ซึ่งเป็นกรอบแนวคิดสำหรับประเมินระดับวุฒิภาวะของกระบวนการวิเคราะห์ผลป้อนกลับของผู้ใช้ซอฟต์แวร์ (SFAP) ขององค์กรพัฒนาซอฟต์แวร์ โดย SFAMM แบ่งระดับวุฒิภาวะออกเป็น 6 ระดับ ซึ่งแต่ละระดับประกอบด้วย 4 ปัจจัยหลักและปัจจัยย่อยของแต่ละปัจจัยหลัก SFAMM ได้รับการทวนสอบความถูกต้องผ่านการเชื่อมโยงกับมาตรฐาน ISO/IEC 15504 ซึ่งเป็นมาตรฐานสำหรับการประเมินกระบวนการ นอกจากนี้ SFAMM ยังมาพร้อมกับแบบสอบถามสำหรับประเมินวุฒิภาวะด้านการวิเคราะห์ผลป้อนกลับของผู้ใช้ซอฟต์แวร์ (SFAA) เพื่อให้องค์กรสามารถประเมินตนเองได้ว่าอยู่ในระดับใด และสามารถกำหนดแนวทางในการปรับปรุงกระบวนการวิเคราะห์ผลป้อนกลับของผู้ใช้ งานวิจัยยังได้เสนอผลสำรวจวุฒิภาวะด้านการวิเคราะห์ผลป้อนกลับของผู้ใช้ซอฟต์แวร์ขององค์กรพัฒนาซอฟต์แวร์ในประเทศไทย

Cattle Identification By Muzzle Patterns Using Few-Shot Learning And Two-Branch Feature Extraction, Kunanon Sereewatanapong

Chulalongkorn University Theses and Dissertations (Chula ETD)

Cattle identification is a critical component of modern farm management systems, particularly in ensuring traceability, disease control, and improved production efficiency. Traditional identification methods such as ear tags or microchips often face challenges related to cost, durability, and animal welfare concerns. To address these limitations, this study proposes TBPN-ACEM (Two-Branch Prototype Network with Adaptive-Color Local Binary Pattern and Enhanced Margin Prototype Loss), a lightweight and few-shot learning framework designed for cattle identification using muzzle patterns, a unique and permanent biometric trait analogous to human fingerprints. The TBPN-ACEM architecture consists of three core components: (1) a two-branch structure that jointly extracts …