Engineering Commons^™

Two-Dimensional Quantum Material Identification Via Self-Attention And Soft-Labeling In Deep Learning, Xuan Bac Nguyen, Apoorva Bisht, Benjamin Thompson, Hugh O.H. Churchill, Khoa Luu, Samee U. Khan

Electrical Engineering and Computer Science Faculty Publications and Presentations

Detecting two-dimensional (2D) materials in silicon chips presents a significant challenge in the field of quantum machines due to the difficulty of data collection. Specifically, among thousands of flakes, not all flakes are useful or well-annotated, resulting in noisy and hard samples within the dataset, which challenges the deep neural network (DNN) to learn. To address this problem, we propose a novel method for identifying quantum 2D flakes even when there is a high rate of missing annotations in the input images. In particular, we first propose a new mechanism for automatically detecting false negative flakes that are missing annotations. …

Seismic Data Denoising Based On The Convolutional Neural Network With An Attention Mechanism In The Curvelet Domain, Bao Qianzong, Zhou Mei, Qiu Yi

Coal Geology & Exploration

[Objective] Noise in seismic data significantly affects the accurate interpretation of subsurface stratigraphic information. Given that effective signals with pronounced lateral correlations in seismic data are distributed in specific coefficients but random noise typically spreads uniformly over all coefficients in the curvelet domain, more effective separation of signals can be achieved. [Methods] The convolutional neural network based on the attention mechanism can adaptively extract key information by focusing on important features of images. Hence, this study proposed a noise attenuation method for seismic data using a convolutional neural network based on the curvelet transform and attention mechanism (Curvelet-AU-Net). First, the …

A Non-Uniform Interpolation Method For Seismic Data Based On A Diffusion Probabilistic Model, Chen Yao, Yu Siwei, Lin Rongzhi

Coal Geology & Exploration

Objective The non-uniform interpolation of seismic data is identified as a prolonged challenge in energy exploration. Since geophones cannot be precisely placed at positions corresponding to theoretical grid points, current uniform interpolation techniques frequently suffer deviations and detail distortion. Methods This study proposed a novel non-uniform interpolation method based on a diffusion probabilistic model, which is an emerging generative model in deep learning that involves the diffusion and generation processes. In the diffusion process, noise is added to the complete seismic data iteratively to train the denoising capability of the neural network. In the generation process, the neural network is …

A Petrophysical Modeling-Guided Method For Predicting Parameters Of Low-Permeability Reservoirs, Wang Rui, Li Fang, Liu Shiyou, Sun Wanyuan, Li Songling, Huang Sheng

Coal Geology & Exploration

Backgroud Accurately predicting reservoir parameters is significant for characterizing subsurface reservoirs, establishing gas accumulation patterns, releasing production capacity, and understanding fluid migration. The traditional approaches based on core measurement or mathematical-petrophysical modeling are limited by the strong multiplicity of solutions and low accuracy of elastic parameters inversion results, making it difficult to meet the demands of modern exploration.Objective and Methods To more effectively predict reservoir parameters, this study proposed a petrophysical modeling-guided method for predicting parameters of low-permeability reservoirs. With the convolutional neural network (CNN) as a deep learning framework, the proposed method can predict water saturation, clay content, …

Deep Learning For Improved Data-Driven Modeling Of Soft Robots For Model Predictive Control, Daniel Gray Cheney

Theses and Dissertations

The field of soft robotics is a growing research interest due to the unique benefits of soft robots over traditional robots, such as durability, compliance, and low-cost. These benefits allow soft robots to perform a variety of tasks that would be difficult for traditional robots, such as throwing, hammering, or whole-body manipulation. However, there is a significant variety of materials, actuators, and designs used in soft robotics. While helpful for completing more tasks, this variety also makes efficient modeling and control of soft robots difficult. In this thesis, an open-source repository, Modeling for Learned Dynamics (MoLDy), is presented. This repository …

Implementation Of Machine Learning Using Deep Neural Networks To Estimate The Failure Risk Caused By Leakage In Pressure Relief Devices, Adi Yudho Wijayanto, Yossi Andreano, M. Ali Yafi Rizky, Dedi Priadi

Journal of Materials Exploration and Findings

The primary objective of deploying Pressure Relief Device (PRD) equipment is to ensure the safety of pressure vessels within a pressurized system. Over time, PRD equipment may degrade and fail to perform its intended function, which must be identified as a failure mode. To mitigate potential risks associated with this, it is recommended that an approach such as risk-based inspection (RBI) be implemented. Despite the widespread adoption of RBI, the method relies on qualitative techniques, leading to significant variations in equipment risk assessments. This study proposes a novel risk analysis method that uses deep learning-based machine learning to develop a …

Modeling Advancement In Spacefaring Through Deep Learning, Peng-Hung Tsai

Theses and Dissertations

Predicting advancement in space exploration technology allows space agencies and companies to strategically allocate resources, prioritize missions, and explore collaborations on complex projects. This foresight acts as a roadmap for future exploration, making the best use of valuable resources. The present study employs spacecraft lifetimes as an exemplar of technological progress and proposes a novel forecasting model based on Long Short-Term Memory (LSTM) networks. Existing research often suggests an increasing exponential relationship between technology performance and time (the generalized Moore's Law). However, applying this model directly to spacecraft lifetimes has a limitation. Spacecraft lifespans are unknown at launch, and recent …

Pinn-Chk: Physics-Informed Neural Network For High-Fidelity Prediction Of Early-Age Cement Hydration Kinetics, Md Asif Rahman, Tianjie Zhang, Yang Lu

Civil Engineering Faculty Publications and Presentations

Cement hydration kinetics, characterized by heat generation in early-age concrete, poses a modeling challenge. This work proposes a physics-informed neural network (PINN) named PINN-CHK designed for cement hydration kinetics, to predict early-age temperature rises in cement paste. PINN-CHK leverages data-driven solutions to craft a high-fidelity prediction model, encompassing material properties and maturity functions in cement hydration. Trained on heated cement paste data, it simultaneously fits experimental results and underlying physics, yielding a mesh-free simulation. Incorporating governing partial differential equations (PDEs), and initial and boundary conditions into its loss function, PINN-CHK architecture undergoes rigorous benchmark testing, demonstrating unparalleled predictive accuracy compared …

Denoising And Super-Resolution Of In-Vitro 4e Flow Mri In A Stenotic Phantom Model Using Physics-Informed Neural Networks., Shrouk M. Wally

Electronic Theses and Dissertations

In recent years, the use of 4D flow MRI has revolutionized cardiovascular imag- ing by providing comprehensive data on blood flow dynamics over time. However, the limited spatial and temporal resolution of this imaging modality can hinder the accurate assessment of complex hemodynamic phenomena. This thesis explores the application of Physics-Informed Neural Networks (PINNs) to enhance the resolution of 4D flow MRI data, thereby improving its clinical utility. PINNs are a class of neural networks that integrate physical laws into their training process. By embedding these physics equations, PINNs can discover the underlying physics of fluid dynamics to produce more …

Reinforcement Learning Assisted Communication Resources Optimization In Advanced Air Mobility., Ruixuan Han

Electronic Theses and Dissertations

Advanced air mobility (AAM), which envisages a safe and efficient aviation transportation system, has drawn significant attention to support the increasing mobility demand in metropolitan areas. Communication services for AAM aerial vehicles (AVs) are crucial for ensuring flight safety. This dissertation explores three research topics on communication resource allocation problems in AAM applications. The first topic, addressed in Chapter II, investigates the joint velocity selection and spectrum allocation problem for AAM applications to enhance spectrum utilization efficiency (SUE). In the AAM scenario, multiple AVs travel along predefined paths for passenger and cargo deliveries. Given that AAM aims to provide fast …

Monitoring Vehicle Seat Occupancy Status And Tracking Human Passenger Activities Inside Vehicle Passenger Cabins, Rahul Prasanna Kumar

All Dissertations

An autonomous vehicle (AV) is a promising engineering innovation that can operate without a human driver. Nonetheless, to ensure passenger safety and comfort in the absence of human drivers, the vehicle must continuously monitor and predict how the cabin’s state will evolve in the immediate future. The two major factors influencing the cabin’s state are the occupancy statuses of vehicle seats and the activities of human passengers occupying them. Therefore, this dissertation presents a system employing a capacitance-sensing mat and a machine learning unit to monitor vehicle seat occupancy status and track passenger activities non-intrusively. While the mat integrates with …

Enrichment Of Turkish Question Answering Systems Using Knowledge Graphs, Okan Çi̇ftçi̇, Fati̇h Soygazi̇, Selma Teki̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Recent capabilities of large language models (LLMs) have transformed many tasks in Natural Language Processing (NLP), including question answering. The state-of-the-art systems do an excellent job of responding in a relevant, persuasive way but cannot guarantee factuality. Knowledge graphs, representing facts as triplets, can be valuable for avoiding errors and inconsistencies with real-world facts. This work introduces a knowledge graph-based approach to Turkish question answering. The proposed approach aims to develop a methodology capable of drawing inferences from a knowledge graph to answer complex multihop questions. We construct the Beyazperde Movie Knowledge Graph (BPMovieKG) and the Turkish Movie Question Answering …

Network Intrusion Detection Based On Machine Learning Strategies: Performance Comparisons On Imbalanced Wired, Wireless, And Software-Defined Networking (Sdn) Network Traffics, Hi̇lal Hacilar, Zafer Aydin, Vehbi̇ Çağri Güngör

Turkish Journal of Electrical Engineering and Computer Sciences

The rapid growth of computer networks emphasizes the urgency of addressing security issues. Organizations rely on network intrusion detection systems (NIDSs) to protect sensitive data from unauthorized access and theft. These systems analyze network traffic to detect suspicious activities, such as attempted breaches or cyberattacks. However, existing studies lack a thorough assessment of class imbalances and classification performance for different types of network intrusions: wired, wireless, and software-defined networking (SDN). This research aims to fill this gap by examining these networks’ imbalances, feature selection, and binary classification to enhance intrusion detection system efficiency. Various techniques such as SMOTE, ROS, ADASYN, …

Multi-Label Voice Disorder Classification Using Raw Waveforms, Gökay Di̇şken

Turkish Journal of Electrical Engineering and Computer Sciences

Automated voice disorder systems that distinguish pathological voices from healthy ones have been developed with the aid of machine learning methods. Both clinicians and patients can benefit from these systems as they provide many advantages, compared to the invasive techniques. These systems can produce binary (healthy/pathological) or multi-class (healthy/selected pathologies) decisions. However, multiple disorders might exist in an individual’s voice. Multi-label classification should be considered in such cases. By this time, only a single report is available on this topic, where hand-crafted features were used, and a data augmentation technique was utilized to overcome class imbalances. In this study, a …

From Cnns To Transformers In Multimodal Human Action Recognition: A Survey, Muhammad Bilal Shaikh, Douglas Chai, Syed Muhammad Shamsul Islam, Naveed Akhtar

Research outputs 2022 to 2026

Due to its widespread applications, human action recognition is one of the most widely studied research problems in Computer Vision. Recent studies have shown that addressing it using multimodal data leads to superior performance as compared to relying on a single data modality. During the adoption of deep learning for visual modelling in the past decade, action recognition approaches have mainly relied on Convolutional Neural Networks (CNNs). However, the recent rise of Transformers in visual modelling is now also causing a paradigm shift for the action recognition task. This survey captures this transition while focusing on Multimodal Human Action Recognition …

Harnessing Social Media For Disaster Response: Intelligent Identification Of Reliable Rescue Requests During Hurricanes, Wael Khallouli

Engineering Management & Systems Engineering Theses & Dissertations

Hurricanes pose a significant threat to both human lives and infrastructure. Decision-makers face substantial challenges during such events, as they must act quickly to address victims’ needs. Social media platforms provide a valuable source for quick and real-time information. Recent hurricane events have shown that people turn to social media to call for help when official communication channels, such as 911, are overwhelmed. However, extracting actionable information from the massive number of messages posted on social media is challenging. Furthermore, verifying social media messages posted by the public is a critical concern for disaster response practitioners, making them hesitant to …

Face Mask Detection Based On Deep Learning: A Review, Shahad Fadhil Abbas, Shaimaa Hameed Shaker, Firas. A. Abdullatif

Journal of Soft Computing and Computer Applications

The coronavirus disease 2019 outbreak caused widespread disruption. The World Health Organization has recommended wearing face masks, along with other public health measures, such as social distancing, following medical guidelines, and thermal scanning, to reduce transmission, reduce the burden on healthcare systems, and protect population groups. However, wearing a mask, which acts as a barrier or shield to reduce transmission of infection from infected individuals, hides most facial features, such as the nose, mouth, and chin, on which face detection systems depend, which leads to the weakness of these systems. This paper aims to provide essential insights for researchers and …

Strangeness Detection From Crowded Video Scenes By Hand-Crafted And Deep Learning Features, Ali A. Hussan, Shaimaa H. Shaker, Akbas Ezaldeen Ali

Journal of Soft Computing and Computer Applications

Video anomaly detection is one of the trickiest issues in intelligent video surveillance because of the complexity of real data and the hazy definition of anomalies. Since abnormal occurrences typically seem different from normal events and move differently. The global optical flow was determined with the maximum accuracy and speed using the Farneback approach for calculating the magnitudes. Two approaches have been used in this study to detect strangeness in the video. These approaches are Deep Learning (DL) and manuality. The first method uses the activity map's development of entropy to detect the oddity in the video using a particular …

A Comprehensive Analysis Of Deep Learning And Swarm Intelligence Techniques To Enhance Vehicular Ad-Hoc Network Performance, Hussein K. Abdul Atheem, Israa T. Ali, Faiz A. Al Alawy

Journal of Soft Computing and Computer Applications

The primary elements of Intelligent Transportation Systems (ITSs) have become Vehicular Ad-hoc NETworks (VANETs), allowing communication between the infrastructure environment and vehicles. The large amount of data gathered by connected vehicles has simplified how Deep Learning (DL) techniques are applied in VANETs. DL is a subfield of artificial intelligence that provides improved learning algorithms able to analyzing and process complex and heterogeneous data. This study explains the power of DL in VANETs, considering applications like decision-making, vehicle localization, anomaly detection, traffic prediction and intelligent routing, various types of DL, including Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs) are …

Transforming Organizational Cyber Security With Artificial Intelligence And Data-Driven Optimization, Soumyadeep Hore

USF Tampa Graduate Theses and Dissertations

This dissertation presents a comprehensive framework for enhancing organizational cybersecurity through data-driven intelligence. The research integrates multiple methodologies to tackle challenges in network intrusion detection and vulnerability management within cybersecurity operations centers (CSOCs). First, the research investigates vulnerability prioritization and mitigation techniques currently employed by CSOCs. To further streamline the vulnerability prioritization and mitigation process a machine learning (ML)-based Vulnerability Priority Scoring System (VPSS) is introduced, significantly improving the prioritization and mitigation of context-sensitive vulnerabilities. The VPSS outperforms traditional methods, reducing the cumulative vulnerability exposure score by up to 30% by considering both organizational context and vulnerability severity. Next, the …

Ocean Rain Detection And Wind Retrieval Through Deep Learning Architectures On Advanced Scatterometer Data, Matthew Yoshinori Otani Mckinney

Theses and Dissertations

The Advanced Scatterometer (ASCAT) is a satellite-based remote sensing instrument designed for measuring wind speed and direction over the Earth's oceans. This thesis aims to expand and improve the capabilities of ASCAT by adding rain detection and advancing wind retrieval. Additionally, this expansion to ASCAT serves as evidence of Artificial Intelligence (AI) techniques learning both novel and traditional methods in remote sensing. I apply semantic segmentation to ASCAT measurements to detect rain over the oceans, enhancing capabilities to monitor global precipitation. I use two common neural network architectures and train them on measurements from the Tropical Rainfall Measuring Mission (TRMM) …

Comparing Life-Cycle Dynamics Of Li-Ion Batteries (Libs) Clustered By Operating Conditions With Sindy, Kristen L. Hallas, Md Shahriar Forhad, Tamer Oraby, Benjamin Peters, Jianzhi Li

Manufacturing & Industrial Engineering Faculty Publications

Lithium-ion batteries (LIBs) play a big part in the vision of a net-zero emission economy, yet it is commonly reported that only a small percentage of LIBs are recycled worldwide. An outstanding barrier to making recycling LIBs economical throughout the supply chain pertains to the uncertainty surrounding their remaining useful life (RUL). How do operating conditions impact initial useful life of the battery? We applied sparse identification of nonlinear dynamics method (SINDy) to understand the life-cycle dynamics of LIBs with respect to sensor data observed for current, voltage, internal resistance and temperature. A dataset of 124 commercial lithium iron phosphate/graphite …

Heartfev1: A Mobile Electrocardiogram Based System For Inferring Forced Expiratory Volume In One Second From Patients With Chronic Obstructive Pulmonary Disease, Maria Nyamukuru

Dartmouth College Ph.D Dissertations

Chronic Obstructive Pulmonary Disease (COPD), characterized by chronic airway inflammation and airflow obstruction, is the third leading cause of death globally. Patients with COPD experience exacerbated symptoms like breathlessness and cough, significantly impacting their quality of life and leading to costly hospitalizations. Early detection of COPD exacerbations is crucial for mitigating these negative effects.

The most critical element for early detection of COPD exacerbations is daily monitoring of lung function, particularly forced expiratory volume in one second (FEV1), a key metric of lung function. By tracking declines in FEV1, COPD exacerbations can be predicted up to two weeks in advance, …

Creating Synthetic Energy Meter Data Using Conditional Diffusion And Building Metadata, Chun Fu, Hussain Kazmi, Matias Quintana, Clayton Miller

Research Collection College of Integrative Studies

Advances in machine learning and increased computational power have driven progress in energy-related research. However, limited access to private energy data from buildings hinders traditional regression models relying on historical data. While generative models offer a solution, previous studies have primarily focused on short-term generation periods (e.g., daily profiles) and a limited number of meters. Thus, the study proposes a conditional diffusion model for generating high-quality synthetic energy data using relevant metadata. Using a dataset comprising 1,828 power meters from various buildings and countries, this model is compared with traditional methods like Conditional Generative Adversarial Networks (CGAN) and Conditional Variational …

Deep Learning-Based Breast Cancer Diagnosis With Multiview Of Mammography Screening To Reduce False Positive Recall Rate, Meryem Altın Karagöz, Özkan Ufuk Nalbantoğlu, Derviş Karaboğa, Bahriye Akay, Alper Baştürk, Halil Ulutabanca, Serap Doğan, Damla Coşkun, Osman Demi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Breast cancer is the most prevalent and crucial cancer type that should be diagnosed early to reduce mortality. Therefore, mammography is essential for early diagnosis owing to high-resolution imaging and appropriate visualization. However, the major problem of mammography screening is the high false positive recall rate for breast cancer diagnosis. High false positive recall rates psychologically affect patients, leading to anxiety, depression, and stress. Moreover, false positive recalls increase costs and create an unnecessary expert workload. Thus, this study proposes a deep learning based breast cancer diagnosis model to reduce false positive and false negative rates. The proposed model has …

Text-To-Sql: A Methodical Review Of Challenges And Models, Ali Buğra Kanburoğlu, Faik Boray Tek

Turkish Journal of Electrical Engineering and Computer Sciences

This survey focuses on Text-to-SQL, automated translation of natural language queries into SQL queries. Initially, we describe the problem and its main challenges. Then, by following the PRISMA systematic review methodology, we survey the existing Text-to-SQL review papers in the literature. We apply the same method to extract proposed Text-to-SQL models and classify them with respect to used evaluation metrics and benchmarks. We highlight the accuracies achieved by various models on Text-to-SQL datasets and discuss execution-guided evaluation strategies. We present insights into model training times and implementations of different models. We also explore the availability of Text-to-SQL datasets in non-English …

Dpafy-Gcaps: Denoising Patch-And-Amplify Gabor Capsule Network For The Recognition Of Gastrointestinal Diseases, Henrietta Adjei Pokuaa, Adeboya Felix Adekoya, Benjamin Asubam Weyori, Owusu Nyarko-Boateng

Turkish Journal of Electrical Engineering and Computer Sciences

Deep learning (DL) models have performed tremendously well in image classification. This good performance can be attributed to the availability of massive data in most domains. However, some domains are known to have few datasets, especially the health sector. This makes it difficult to develop domain-specific high-performing DL algorithms for these fields. The field of health is critical and requires accurate detection of diseases. In the United States Gastrointestinal diseases are prevalent and affect 60 to 70 million people. Ulcerative colitis, polyps, and esophagitis are some gastrointestinal diseases. Colorectal polyps is the third most diagnosed malignancy in the world. This …

Enhancing Electrical Network Vulnerability Assessment With Machine Learning And Deep Learning Techniques, M Mishkatur Rahman, Ayman Sajjad Akash, Harun Pirim, Chau Le, Trung Le, Om Prakash Yadav

Northeast Journal of Complex Systems (NEJCS)

This research utilizes advanced machine learning techniques to evaluate node vul-
nerability in power grid networks. Utilizing the SciGRID and GridKit datasets, con-
sisting of 479, 16,167 nodes and 765, 20,539 edges respectively, the study employs
K-nearest neighbor and median imputation methods to address missing data. Cen-
trality metrics are integrated into a single comprehensive score for assessing node
criticality, categorizing nodes into four centrality levels informative of vulnerability.
This categorization informs the use of traditional machine learning (including XG-
Boost, SVM, Multilayer Perceptron) and Graph Neural Networks in the analysis.
The study not only benchmarks the capabilities of these …

Deep Learning Based Local Path Planning Method For Moving Robots, Zesen Liu, Sheng Bi, Chuanhong Guo, Yankui Wang, Min Dong

Journal of System Simulation

Abstract: In order to integrate visual information into the robot navigation process, improve the robot's recognition rate of various types of obstacles, and reduce the occurrence of dangerous events, a local path planning network based on two-dimensional CNN and LSTM is designed, and a local path planning approach based on deep learning is proposed. The network uses the image from camera and the global path to generate the current steering angle required for obstacle avoidance and navigation. A simulated indoor scene is built for training and validating the network. A path evaluation method that uses the total length and the …

Toward Intuitive 3d Interactions In Virtual Reality: A Deep Learning- Based Dual-Hand Gesture Recognition Approach, Trudi Di Qi, Franceli L. Cibrian, Meghna Raswan, Tyler Kay, Hector M. Camarillo-Abad, Yuxin Wen

Engineering Faculty Articles and Research

Dual-hand gesture recognition is crucial for intuitive 3D interactions in virtual reality (VR), allowing the user to interact with virtual objects naturally through gestures using both handheld controllers. While deep learning and sensor-based technology have proven effective in recognizing single-hand gestures for 3D interactions, research on dual-hand gesture recognition for VR interactions is still underexplored. In this work, we introduce CWT-CNN-TCN, a novel deep learning model that combines a 2D Convolution Neural Network (CNN) with Continuous Wavelet Transformation (CWT) and a Temporal Convolution Network (TCN). This model can simultaneously extract features from the time-frequency domain and capture long-term dependencies using …