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Articles 2851 - 2880 of 17324
Full-Text Articles in Engineering
A Multistep Fusion Matcher Approach For Large Scale Latent Fingerprint/Palmprint Recognition, İsmai̇l Kilinç, Yusuf Oğuzhan Artan, Emre Başeski̇
A Multistep Fusion Matcher Approach For Large Scale Latent Fingerprint/Palmprint Recognition, İsmai̇l Kilinç, Yusuf Oğuzhan Artan, Emre Başeski̇
Turkish Journal of Electrical Engineering and Computer Sciences
Latent fingerprints are ubiquitously used as forensic evidence by law enforcement agencies in solving crimes. However, due to deformations and artifacts within latent fingerprint images, performance of the automated latent recognition systems are far from desired levels. A basic matcher specifically designed for clean fingerprints using a minutiae-based matching algorithm can have high speed and accuracy in a sensor-to-sensor matching task, but low accuracy in matching latent prints, due to scale, rotation and quality differences between latent and sensor images. In this study, we propose a unique multistep fusion matcher (FM) on top of a base matcher that would utilize …
Study Of Helical Antenna Endowing Short Wire Length And Compact Structure For High-Frequency Operations And Its Exclusive Manufacturing Process, Meli̇h Aslan, Kaan Şik, İzzet Güzelkara, İbrahi̇m Tuna Özdür, Veli̇ Tayfun Kiliç
Study Of Helical Antenna Endowing Short Wire Length And Compact Structure For High-Frequency Operations And Its Exclusive Manufacturing Process, Meli̇h Aslan, Kaan Şik, İzzet Güzelkara, İbrahi̇m Tuna Özdür, Veli̇ Tayfun Kiliç
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper a study of a helical antenna resonating at high-frequency (HF) band with a very compact structure is reported. The designed antenna's S11 parameter magnitude change with frequency was calculated for different geometrical parameters. For each case, first, only a single parameter was changed. Then for a fair comparison, multiple parameters were changed simultaneously while the total wire length was set to be constant. Also, shifts in resonance frequencies and variations in -10 dB bandwidths were investigated. Our results show that resonance behaviour changes distinctively with the geometrical parameters and it allows shortening of the antenna wire length. …
Solving Turkish Math Word Problems By Sequence-To-Sequence Encoder-Decoder Models, Esi̇n Gedi̇k, Tunga Güngör
Solving Turkish Math Word Problems By Sequence-To-Sequence Encoder-Decoder Models, Esi̇n Gedi̇k, Tunga Güngör
Turkish Journal of Electrical Engineering and Computer Sciences
Solving math word problems (MWP) is a challenging task due to the semantic gap between natural language texts and mathematical equations. The main purpose of the task is to take a written math problem as input and produce a proper equation as output for solving that problem. This paper describes a sequence-to-sequence (seq2seq) neural model for automatically solving Turkish MWPs based on their semantic meanings in the text. It comprises a bidirectional encoder to comprehend the semantics of the problem by encoding the input sequence and a decoder with attention to extract the equation by tracking the semantic meanings of …
Reconstructing Dynamic Human Shapes From Sparse Silhouettes Via Latent Space Optimization Of Parametric Shape Models, Kanika Singla, Parma Nand
Reconstructing Dynamic Human Shapes From Sparse Silhouettes Via Latent Space Optimization Of Parametric Shape Models, Kanika Singla, Parma Nand
Turkish Journal of Electrical Engineering and Computer Sciences
The problem of dynamic 3D reconstruction has gained popularity over the last few years with most approaches relying on data driven learning and optimization methods. However this is quite a challenging task because of the need for tracking different features in both space and time?that too of deformable objects-where such robust tracking may not always be possible. A common way to better ground the problem is by using some forms of regularizations primarily on the shape representations. Over the years, mesh-based linear blend skinning models have been the standard for fitting templates of humans to the observed time series data …
An Exploratory Study On The Effect Of Applying Various Artificial Neural Networks To The Classification Of Lower Limb Injury, Rachel Yun, May Salama, Lamiaa Elrefaei
An Exploratory Study On The Effect Of Applying Various Artificial Neural Networks To The Classification Of Lower Limb Injury, Rachel Yun, May Salama, Lamiaa Elrefaei
Turkish Journal of Electrical Engineering and Computer Sciences
This paper explores the application of a deep neural network (DNN) framework to human gait analysis for injury classification. The paper aims to identify whether a subject is healthy or has an injury of the ankle, knee, hip, or heel solely based on ground reaction force plate measurements. We consider how three DNNs-the multi-layer perceptron (MLP), fully convolutional network (FCN), and residual network (ResNet)-can be applied to gait analysis when the number of trainable network parameters far exceeds the number of training samples, and benchmark their performance in this context against that of shallow neural networks. The DNN architectures outperformed …
Spayk: An Environment For Spiking Neural Network Simulation, Aykut Görkem Gelen, Ayten Atasoy
Spayk: An Environment For Spiking Neural Network Simulation, Aykut Görkem Gelen, Ayten Atasoy
Turkish Journal of Electrical Engineering and Computer Sciences
In research areas such as mobile robotics and computer vision, energy and computational efficiency have become critical. This has greatly increased interest in high-efficiency neuromorphic hardware and spiking neural networks. Because neuromorphic hardware is not yet widely available, spiking neural network studies are conducted by simulations. There are numerous simulators available today, each designed for a specific purpose. In this paper, a novel and open source package (SPAYK) for simulating spiking neural networks is presented. SPAYK has been proposed to speed up spiking neural network research. In the majority of simulators, networks are expressed with differential equations and require advanced …
Bayesian Recurrent Neural Networks For Real Time Object Detection, Stephen Z. Kimatian
Bayesian Recurrent Neural Networks For Real Time Object Detection, Stephen Z. Kimatian
Theses and Dissertations
Neural networks have become increasingly popular in real time object detection algorithms. A major concern with these algorithms is their ability to quantify their own uncertainty, leading to many high profile failures. This research proposes three novel real time detection algorithms. The first of leveraging Bayesian convolutional neural layers producing a predictive distribution, the second leveraging predictions from previous frames, and the third model combining these two techniques together. These augmentations seek to mitigate the calibration problem of modern detection algorithms. These three models are compared to the state of the art YOLO architecture; with the strongest contending model achieving …
Simulation And Analysis Of Dynamic Threat Avoidance Routing In An Anti-Access Area Denial (A2ad) Environment, Dante C. Reid
Simulation And Analysis Of Dynamic Threat Avoidance Routing In An Anti-Access Area Denial (A2ad) Environment, Dante C. Reid
Theses and Dissertations
This research modeled and analyzed the effectiveness of different routing algorithms for penetration assets in an A2AD environment. AFSIM was used with different configurations of SAMs locations and numbers to compare the performance of AFSIM’s internal zone and shrink algorithm routers with a Dijkstra algorithm router. Route performance was analyzed through computational and operational metrics, including computational complexity, run-time, mission survivability, and simulation duration. This research also analyzed the impact of the penetration asset’s ingress altitude on those factors. Additionally, an excursion was conducted to analyze the Dijkstra algorithm router’s grid density holding altitude constant to understand its impact on …
A Novel Covid-19 Herd Immunity-Based Optimizer For Optimal Accommodation Of Solar Pv With Battery Energy Storage Systems Including Variation In Load And Generation, Sumanth Pemmada, Nita Patne, Divyesh Kumar, Ashwini Manchalwar
A Novel Covid-19 Herd Immunity-Based Optimizer For Optimal Accommodation Of Solar Pv With Battery Energy Storage Systems Including Variation In Load And Generation, Sumanth Pemmada, Nita Patne, Divyesh Kumar, Ashwini Manchalwar
Turkish Journal of Electrical Engineering and Computer Sciences
The world has now looked towards installing more renewable energy sources type distributed generation (DG), such as solar photovoltaic DG (SPVDG), because of its advantages to the environment and the quality of power supply it produces. However, these sources' optimal placement and size are determined before their accommodation in the power distribution system (PDS). This is to avoid an increase in power loss and deviations in the voltage profile. Furthermore, in this article, solar PV is integrated with battery energy storage systems (BESS) to compensate for the shortcomings of SPVDG as well as the reduction in peak demand. This paper …
Classification And Analysis Of Twitter Bot And Troll Accounts, Callan P. Mccormick
Classification And Analysis Of Twitter Bot And Troll Accounts, Callan P. Mccormick
Theses and Dissertations
This research trains, tests, and analyzes bot and troll classification models using publicly available, open source datasets. Specifically, it applies decision tree, random forest, feed forward neural networks, and long-short term memory neural networks with hyperparameters tuned via designed experiment to five labeled bot datasets created between 2011 and 2020 and one dataset labeling state-sponsored disinformation accounts or trolls. The first three models utilize account profile features, while the last model applies natural language processing techniques, specifically GloVe embedding, to analyze a user’s Tweet history. Results indicate that the random forest model outperforms the other three models with an average …
A Reinforcement Learning Approach To A Beyond Visual Range Air Combat Maneuvering Problem, Caleb A. Taylor
A Reinforcement Learning Approach To A Beyond Visual Range Air Combat Maneuvering Problem, Caleb A. Taylor
Theses and Dissertations
A one-versus-one air combat maneuvering problem is considered wherein a friendly autonomous aircraft must engage and defeat an adversary autonomous aircraft in a beyond visual range environment. The Advanced Framework for Simulation, Integration, and Modeling (AFSIM) is leveraged to model the complex and interdependent operations of aircraft, sensors, and weapons utilized in beyond visual range air combat. We formulate a Markov decision process to obtain high-quality decision policies wherein our autonomous aircraft makes maneuvering and missile firing decisions. We utilize a reinforcement learning solution procedure that implements a linear value function approximation to represent state-decision pairs due to the high …
Characterizing Location-Based Electromagnetic Leakage Of Computing Devices Using Convolutional Neural Networks To Increase The Effectiveness Of Side-Channel Analysis Attacks, Ian C. Heffron
Theses and Dissertations
SCA attacks aim to recover some sort of secret information, often in the form of a cipher key, from a target device. Some of these attacks focus on either power-based leakage, or EM-based leakage. Neural networks have recently gained in popularity as tools in SCA attacks. Near-field EM probes with high-spatial resolution enable attackers to isolate physical locations above a processor. This enables attackers to exploit the spatial dependencies of algorithms running on said processor. These spatial dependencies result in different physical locations above a chip emanating different signal strengths. The strengths of different locations can be mapped using the …
The Electromagnetic Bayonet: Development Of A Scientific Computing Method For Aperture Antenna Optimization, Michael P. Ingold
The Electromagnetic Bayonet: Development Of A Scientific Computing Method For Aperture Antenna Optimization, Michael P. Ingold
Theses and Dissertations
The quiet zone of a radar range is the region over which a transmitted EM field approximates a uniform plane wave to within some finite error tolerance. Any target to be measured must physically fit within this quiet zone to prevent excess measurement error. Compact radar ranges offer significant operational advantages for performing RCS measurements but their quiet zone sizes are constrained by space limitations. In this work, a scientific computing approach is used to investigate whether equivalent-current transmitters can be designed that generate larger quiet zones than a conventional version at short range. A time-domain near-field solver, JefimenkoModels, was …
Classifying Open-Air Target Measurements Using Simulation-Trained Convolutional Neural Networks, Matthew M. Rofrano
Classifying Open-Air Target Measurements Using Simulation-Trained Convolutional Neural Networks, Matthew M. Rofrano
Theses and Dissertations
This research focuses on the development of machine learning networks that can identify and classify airborne targets using their radar cross section response. Simulation and measurement data for five targets was collected using Altair's CadFEKO software, and the Air Force Institute's Compact Radar Range. Three machine learning models were trained using simulation data, and evaluated using the collected measurement data. Variability is introduced to the training data by applying random gaussian noise to simulation results. Gaussian noise is added to the measurement data prior to evaluation in-order to model "hostile noise jamming." Network performance is measured against a baseline performance …
Using Embedded Systems And Augmented Reality For Automated Aerial Refueling, Nathaniel A. Wilson
Using Embedded Systems And Augmented Reality For Automated Aerial Refueling, Nathaniel A. Wilson
Theses and Dissertations
The goal of automated aerial refueling (AAR) is to extend the range of unmanned aircraft. Control latency prevents a human from remotely controlling the receiving aircraft as it approaches a tanker. To conform with the size, weight, and power constraints of a small unmanned aircraft, an AAR system must execute in real-time on an embedded platform. This thesis explores the timing and computational performance of a NVIDIA Jetson AGX Orin to a state-of-the-art general-purpose computer using existing AAR algorithms. It also constructs an augmented reality framework as an intermediate step for testing vision-based AAR algorithms between virtual testing and expensive …
Hierarchical Federated Learning On Healthcare Data: An Application To Parkinson's Disease, Brandon J. Harvill
Hierarchical Federated Learning On Healthcare Data: An Application To Parkinson's Disease, Brandon J. Harvill
Theses and Dissertations
Federated learning (FL) is a budding machine learning (ML) technique that seeks to keep sensitive data private, while overcoming the difficulties of Big Data. Specifically, FL trains machine learning models over a distributed network of devices, while keeping the data local to each device. We apply FL to a Parkinson’s Disease (PD) telemonitoring dataset where physiological data is gathered from various modalities to determine the PD severity level in patients. We seek to optimally combine the information across multiple modalities to assess the accuracy of our FL approach, and compare to traditional ”centralized” statistical and deep learning models.
Predicting Success Of Pilot Training Candidates Using Interpretable Machine Learning, Alexandra S. King
Predicting Success Of Pilot Training Candidates Using Interpretable Machine Learning, Alexandra S. King
Theses and Dissertations
The United States Air Force (USAF) has struggled with a sustained pilot shortage over the past several years; senior military and government leaders have been working towards a solution to the problem, with no noticeable improvements. Both attrition of more experienced pilots as well as wash out rates within pilot training contribute to this issue. This research focuses on pilot training attrition. Improving the process for selecting pilot candidates can reduce the number of candidates who fail. This research uses historical specialized undergraduate pilot training (SUPT) data and leverages select machine learning techniques to determine which factors are associated with …
Air Force Cadet To Career Field Matching Problem, Ian P. Macdonald
Air Force Cadet To Career Field Matching Problem, Ian P. Macdonald
Theses and Dissertations
This research examines the Cadet to Air Force Specialty Code (AFSC) Matching Problem (CAMP). Currently, the matching problem occurs annually at the Air Force Personnel Center (AFPC) using an integer program and value focused thinking approach. This paper presents a novel method to match cadets with AFSCs using a generalized structure of the Hospitals Residents problem with special emphasis on lower quotas. This paper also examines the United States Army Matching problem and compares it to the techniques and constraints applied to solve the CAMP. The research culminates in the presentation of three algorithms created to solve the CAMP and …
Analysis And Optimization Of Contract Data Schema, Franklin Sun
Analysis And Optimization Of Contract Data Schema, Franklin Sun
Theses and Dissertations
agement, development, and growth of U.S Air Force assets demand extensive organizational communication and structuring. These interactions yield substantial amounts of contracting and administrative information. Over 4 million such contracts as a means towards obtaining valuable insights on Department of Defense resource usage. This set of contracting data is largely not optimized for backend service in an analytics environment. To this end, the following research evaluates the efficiency and performance of various data structuring methods. Evaluated designs include a baseline unstructured schema, a Data Mart schema, and a snowflake schema. Overall design success metrics include ease of use by end …
Automated Registration Of Titanium Metal Imaging Of Aircraft Components Using Deep Learning Techniques, Nathan A. Johnston
Automated Registration Of Titanium Metal Imaging Of Aircraft Components Using Deep Learning Techniques, Nathan A. Johnston
Theses and Dissertations
Studies have shown a connection between early catastrophic engine failures with microtexture regions (MTRs) of a specific size and orientation on the titanium metal engine components. The MTRs can be identified through the use of Electron Backscatter Diffraction (EBSD) however doing so is costly and requires destruction of the metal component being tested. A new methodology of characterizing MTRs is needed to properly evaluate the reliability of engine components on live aircraft. The Air Force Research Lab Materials Directorate (AFRL/RX) proposed a solution of supplementing EBSD with two non-destructive modalities, Eddy Current Testing (ECT) and Scanning Acoustic Microscopy (SAM). Doing …
A Review On Learning To Solve Combinatorial Optimisation Problems In Manufacturing, Cong Zhang, Yaoxin Wu, Yining Ma, Wen Song, Zhang Le, Zhiguang Cao, Jie Zhang
A Review On Learning To Solve Combinatorial Optimisation Problems In Manufacturing, Cong Zhang, Yaoxin Wu, Yining Ma, Wen Song, Zhang Le, Zhiguang Cao, Jie Zhang
Research Collection School Of Computing and Information Systems
An efficient manufacturing system is key to maintaining a healthy economy today. With the rapid development of science and technology and the progress of human society, the modern manufacturing system is becoming increasingly complex, posing new challenges to both academia and industry. Ever since the beginning of industrialisation, leaps in manufacturing technology have always accompanied technological breakthroughs from other fields, for example, mechanics, physics, and computational science. Recently, machine learning (ML) technology, one of the crucial subjects of artificial intelligence, has made remarkable progress in many areas. This study thoroughly reviews how ML, specifically deep (reinforcement) learning, motivates new ideas …
Improved Object Re-Identification Via More Efficient Embeddings, Ertugrul Bayraktar
Improved Object Re-Identification Via More Efficient Embeddings, Ertugrul Bayraktar
Turkish Journal of Electrical Engineering and Computer Sciences
Object reidentification (ReID) in cluttered rigid scenes is a challenging problem especially when same-looking objects coexist in the scene. ReID is accepted to be one of the most powerful tools for matching the correct identities to each individual object when issues such as occlusion, missed detections, multiple same-looking objects coexisting in the same scene, and disappearance of objects from the view and/or revisiting the same region arise. We propose a novel framework towards more efficient object ReID, improved object reidentification (IO-ReID), to perform object ReID in challenging scenes with real-time processing in mind. The proposed approach achieves distinctive and efficient …
A Modified Space Vector Modulation Based Rotor Flux Oriented Control Of Six-Phase Asymmetrical Induction Motor Drive, Krunal Shah, Rakesh Maurya
A Modified Space Vector Modulation Based Rotor Flux Oriented Control Of Six-Phase Asymmetrical Induction Motor Drive, Krunal Shah, Rakesh Maurya
Turkish Journal of Electrical Engineering and Computer Sciences
In view of the attractive features like improved torque density, reduction torque pulsation, superior fault tolerance, reduced power rating of voltage source converter, and sterling noise characteristics of six-phase asymmetrical induction motor (SPAIM) as compared to its three-phase counterpart, the SPAIM is considered for the study. In this paper, mathematical modelling of SPAIM is carried out in the synchronous reference frame and then indirect rotor field-oriented control (IRFOC) of SPAIM using a modified carrier wave-based space vector modulation (SVM) scheme is developed. A Simulink model of the proposed system configuration is developed and a simulation study is carried out. In …
Machine Learning Techniques For Stock Price Prediction And Graphic Signal Recognition, Junde Chen, Yuxin Wen, Y. A. Nanehkaran, M. D. Suzauddola, Weirong Chen, Defu Zhang
Machine Learning Techniques For Stock Price Prediction And Graphic Signal Recognition, Junde Chen, Yuxin Wen, Y. A. Nanehkaran, M. D. Suzauddola, Weirong Chen, Defu Zhang
Engineering Faculty Articles and Research
Stock market analysis is extremely important for investors because knowing the future trend and grasping the changing characteristics of stock prices will decrease the risk of investing capital for profit. Thereupon, the prediction of stock prices and identifying the graphic signals of candlestick charts, which are two crucial tasks in stock price analysis, attract much attention from investors owing to the returns and risks that coexist in financial markets. To introduce a reliable approach for addressing these challenges, this paper proposes the modeling strategies based on machine learning (ML) techniques. A vector autoregression (VAR)-based rolling prediction model is proposed for …
Separation Axioms In Neutrosophic Topological Spaces, Sudeep Dey, Gautam Chandra Ray
Separation Axioms In Neutrosophic Topological Spaces, Sudeep Dey, Gautam Chandra Ray
Neutrosophic Systems with Applications
In this article, we first establish some results based on single-valued neutrosophic sets. Next, we define a subspace topology in a neutrosophic topological space and investigate some properties. We then define the neutrosophic T0, T1, T2-spaces and study their various properties, offering adequate examples.
Separation Axioms In Neutrosophic Topological Spaces, Sudeep Dey, Gautam Chandra Ray
Separation Axioms In Neutrosophic Topological Spaces, Sudeep Dey, Gautam Chandra Ray
Neutrosophic Systems with Applications
In this article, we first establish some results based on single-valued neutrosophic sets. Next, we define a subspace topology in a neutrosophic topological space and investigate some properties. We then define the neutrosophic T0, T1, T2-spaces and study their various properties, offering adequate examples.
Rank And Analysis Several Solutions Of Healthcare Waste To Achieve Cost Effectiveness And Sustainability Using Neutrosophic Mcdm Model, Ahmed Abdelhafeez, Hoda K. Mohamed, Nariman A. Khalil
Rank And Analysis Several Solutions Of Healthcare Waste To Achieve Cost Effectiveness And Sustainability Using Neutrosophic Mcdm Model, Ahmed Abdelhafeez, Hoda K. Mohamed, Nariman A. Khalil
Neutrosophic Systems with Applications
Managing healthcare waste (HCWTT) from healthcare facilities is difficult. It's high up on the list of health concerns. This growth in HCWTT has been especially visible in recent years, as the quantity of medical services available has increased. Because of the potential danger, this garbage poses to people and the planet, it must be properly disposed of. Because of ineffective waste management practices, inadequate financial resources, and a lack of adequate facilities, HCWT administration is especially crucial in developing nations. Reducing HCWT via appropriate treatment is important for the area's financial and environmental health. In order to solve single-valued neutrosophic …
Rank And Analysis Several Solutions Of Healthcare Waste To Achieve Cost Effectiveness And Sustainability Using Neutrosophic Mcdm Model, Ahmed Abdelhafeez, Hoda K. Mohamed, Nariman A. Khalil
Rank And Analysis Several Solutions Of Healthcare Waste To Achieve Cost Effectiveness And Sustainability Using Neutrosophic Mcdm Model, Ahmed Abdelhafeez, Hoda K. Mohamed, Nariman A. Khalil
Neutrosophic Systems with Applications
Managing healthcare waste (HCWTT) from healthcare facilities is difficult. It's high up on the list of health concerns. This growth in HCWTT has been especially visible in recent years, as the quantity of medical services available has increased. Because of the potential danger, this garbage poses to people and the planet, it must be properly disposed of. Because of ineffective waste management practices, inadequate financial resources, and a lack of adequate facilities, HCWT administration is especially crucial in developing nations. Reducing HCWT via appropriate treatment is important for the area's financial and environmental health. In order to solve single-valued neutrosophic …
Hybrid Modeling For Electrochemical Systems, Luis Alejandro Briceno-Mena
Hybrid Modeling For Electrochemical Systems, Luis Alejandro Briceno-Mena
LSU Doctoral Dissertations
The discovery of new materials like catalysts, polymeric films, and biomolecules, is driven by industrial needs such as improving reaction or separation selectivity, enhancing therapeutic effects on medical treatments, or reducing costs of replacement. However, deployment of these advances in industrial applications is often hindered by the lack of models needed for design and optimization. Due to the novelty of materials and devices, experimental data and first principles' knowledge are scarce, making it hard to build models either via data-driven or knowledge based approaches. In this context, a way to efficiently combine domain knowledge with data could provide a pathway …
Neutrosophic Critic Mcdm Methodology For Ranking Factors And Needs Of Customers In Product's Target Demographic In Virtual Reality Metaverse, Ahmed Sleem, Nehal Mostafa, Ibrahim Elhenawy
Neutrosophic Critic Mcdm Methodology For Ranking Factors And Needs Of Customers In Product's Target Demographic In Virtual Reality Metaverse, Ahmed Sleem, Nehal Mostafa, Ibrahim Elhenawy
Neutrosophic Systems with Applications
Affective design has come to place a premium on customer-centric creativity, which ultimately results in the creation of a product tailored to the requirements of a certain demographic. Designing a fresh and original item that appeals to clients remains challenging, despite the abundance of literature on customer-centric creativity and impact product design. This is so because optimizing technological and aesthetical design aspects and variables for a group of consumers is highly complicated, and for the same reasons that it is hard to know a customer's choice, enable the product's functioning, etc. One of the most important parts of creating cutting-edge …