Engineering Commons^™

Low-Resource Automatic Speech Recognition Domain Adaptation – A Case-Study In Aviation Maintenance, Nadine Amin, Tracy L. Yother, Julia Rayz

Journal of Aviation/Aerospace Education & Research

With timeliness and efficiency being critical in the aviation maintenance industry, the need has been growing for smart technological solutions that optimize and streamline the different underlying tasks (Bergkvist & Sabbagh, 2021). One such task is the technical documentation of the performed maintenance operations (Chandola et al., 2022). Instead of manual documentation, voice tools that transcribe spoken logbook entries allow technicians to document their work right away in a hands-free and time efficient manner. However, an accurate automatic speech recognition (ASR) model requires large training corpora (Siyaev & Jo, 2021a), which are lacking in the domain of aviation maintenance. In …

The Impact Of Dissolved Organic Matter On Photodegradation Rates, Byproduct Formations, And Degradation Pathways For Two Neonicotinoid Insecticides In Simulated River Waters, Josephus F. Borsuah, Tiffany L. Messer, Daniel D. Snow, Steven D. Comfort, Shannon Bartelt-Hunt

UK CARES Faculty Publications

The influences of dissolved organic matter (DOM) on neonicotinoid photochemical degradation and product formation in natural waters remain unclear, potentially impacting the sustainability of river systems. Therefore, our overall objective was to investigate the photodegradation mechanisms and phototransformation byproducts of two neonicotinoid pesticides, imidacloprid and thiamethoxam, under simulated sunlight at the microcosm scale, to assess the implications of DOM for insecticide degradation in rivers. Direct and indirect photolysis were investigated using twelve water matrices to identify possible reaction pathways with two DOM sources and three quenching agents. Imidacloprid, thiamethoxam, and potential degradants were measured, and reaction pathways identified. The photodegradation …

A 3-Step, Open-Data, Ride-Hailing Ridership Model With Pricing Applications, Richard A. Mucci

Theses and Dissertations--Civil Engineering

Researchers and practitioners studied the effects ride-hailing had in cities before the covid-19 pandemic. Previous research found ride-hailing to produce negative externalities, such as reducing transit ridership and increasing congestion in various cities. Since the pandemic, ride-hailing ridership has nearly recovered to pre-pandemic levels in Chicago. Ride-hailing ridership has grown steadily since the pandemic while a rider’s willingness to share their trip stagnated. Ride-hailing ridership nearly recovering to pre-covid levels in Chicago suggests that transportation planners, and policy makers, will need to continue assessing the impacts ride-hailing trips have in their cities.

Pickup and drop off locations in the Chicago …

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 …

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 …

Lifelong Safe Optimal Adaptive Tracking Control Of Nonlinear Strict-Feedback Discrete-Time Systems, Behzad Farzanegan, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a comprehensive approach for achieving multi-task safe optimal adaptive tracking (MSOAT) for a class of nonlinear discrete-time systems, particularly those in strict-feedback form, utilizing a multi-layer neural network (MNN)-based framework. To begin, a cost function with a novel Barrier function (BF) term is introduced for each subsystem to address the weak safely reachable problem, serving as a crucial tool for guiding the system's trajectory toward the safe set while avoiding unwanted sets. To deal with the tracking problem, the Hamilton-Jacobi-Bellman (HJB) framework is used through the actor-critic MNN-based backstepping technique to estimate the solution of the value …

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 …

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 …

Adaptive Resilient Control For A Class Of Nonlinear Distributed Parameter Systems With Actuator Faults, Hasan Ferdowsi, Jia Cai, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a new model-based fault resilient control scheme for a class of nonlinear distributed parameter systems (DPS) represented by parabolic partial differential equations (PDE) in the presence of actuator faults. A Luenberger-like observer on the basis of nonlinear PDE representation of DPS is developed with boundary measurements. A detection residual is generated by taking the difference between the measured output of the DPS and the estimated one given by the observer. Once a fault is detected, an unknown actuator fault parameter vector together with a known basis function is utilized to adaptively estimate the fault dynamics. A novel …

Learning Social Fairness Preferences From Non-Expert Stakeholder Opinions In Kidney Placement, Mukund Telukunta, Sukruth Rao, Gabriella Stickney, Venkata Sriram Siddardh Nadendla, Casey I. Canfield

Computer Science Faculty Research & Creative Works

Modern kidney placement incorporates several intelligent recommendation systems which exhibit social discrimination due to biases inherited from training data. Although initial attempts were made in the literature to study algorithmic fairness in kidney placement, these methods replace true outcomes with surgeons' decisions due to the long delays involved in recording such outcomes reliably. However, the replacement of true outcomes with surgeons' decisions disregards expert stakeholders' biases as well as social opinions of other stakeholders who do not possess medical expertise. This paper alleviates the latter concern and designs a novel fairness feedback survey to evaluate an acceptance rate predictor (ARP) …

Digitalization Of Railway Transportation Through Ai-Powered Services: Digital Twin Trains, Salih Sarp, Murat Kuzlu, Vukica Jovanovic, Zekeriya Polat, Ozgur Guler

Engineering Technology Faculty Publications

Digitalization is a key concept that transformed the various industries through technologies like Internet of Things (IoT), Artificial Intelligence (AI), and Digital Twin (DT). Although innovations provided by the advancement of digitalization have paved the way for more efficient operations and products for transportation, the rail transportation sector struggles to keep up with the rest of the transportation industry, since trains are designed to last for decades, and the insufficient infrastructure investment leads to multiple railroad derailments across the globe. Therefore, the primary aim is to transform current railway systems into human-centric, adaptable, sustainable and future-proof networks, aligning with Industry …

Federated Learning: Overview, Strategies, Applications, Tools And Future Directions, Betul Yurdem, Murat Kuzlu, Mehmet Kemal Gullu, Maliha Tabassum

Engineering Technology Faculty Publications

Federated learning (FL) is a distributed machine learning process, which allows multiple nodes to work together to train a shared model without exchanging raw data. It offers several key advantages, such as data privacy, security, efficiency, and scalability, by keeping data local and only exchanging model updates through the communication network. This review paper provides a comprehensive overview of federated learning, including its principles, strategies, applications, and tools along with opportunities, challenges, and future research directions. The findings of this paper emphasize that federated learning strategies can significantly help overcome privacy and confidentiality concerns, particularly for high-risk applications.

Gnss Software Defined Radio: History, Current Developments, And Standardization Efforts, Thomas Pany, Dennis Akos, Javier Arribas, M. Zahidul H. Bhuiyan, Pau Closas, Fabio Dovis, Ignacio Fernandez-Hernandez, Carles Fernandez-Prades, Sanjeev Gunawardena, Todd Humphreys, Zaher M. Kassas, Jose A. Lopez Salcedo, Mario Nicola, Mario L. Psiaki, Alexander Rugamer, Yong-Jin Song, Jong-Hoon Won

Faculty Publications

Taking the work conducted by the global navigation satellite system (GNSS) software-defined radio (SDR) working group during the last decade as a seed, this contribution summarizes, for the first time, the history of GNSS SDR development. This report highlights selected SDR implementations and achievements that are available to the public or that influenced the general development of SDR. Aspects related to the standardization process of intermediate-frequency sample data and metadata are discussed, and an update of the Institute of Navigation SDR Standard is proposed. This work focuses on GNSS SDR implementations in general-purpose processors and leaves aside developments conducted on …

A Chinese Power Text Classification Algorithm Based On Deep Active Learning, Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu

Computer Science Faculty Publications

The construction of knowledge graph is beneficial for grid production, electrical safety protection, fault diagnosis and traceability in an observable and controllable way. Highly-precision text classification algorithm is crucial to build a professional knowledge graph in power system. Unfortunately, there are a large number of poorly described and specialized texts in the power business system, and the amount of data containing valid labels in these texts is low. This will bring great challenges to improve the precision of text classification models. To offset the gap, we propose a classification algorithm for Chinese text in the power system based on deep …

Mosaic: A Prune-And-Assemble Approach For Efficient Model Pruning In Privacy-Preserving Deep Learning, Yifei Cai, Qiao Zhang, Rui Ning, Chunsheng Xin, Hongyi Wu

Computer Science Faculty Publications

To enable common users to capitalize on the power of deep learning, Machine Learning as a Service (MLaaS) has been proposed in the literature, which opens powerful deep learning models of service providers to the public. To protect the data privacy of end users, as well as the model privacy of the server, several state-of-the-art privacy-preserving MLaaS frameworks have also been proposed. Nevertheless, despite the exquisite design of these frameworks to enhance computation efficiency, the computational cost remains expensive for practical applications. To improve the computation efficiency of deep learning (DL) models, model pruning has been adopted as a strategic …