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Articles 4381 - 4410 of 36803

Full-Text Articles in Engineering

A Multistep Fusion Matcher Approach For Large Scale Latent Fingerprint/Palmprint Recognition, İsmai̇l Kilinç, Yusuf Oğuzhan Artan, Emre Başeski̇ Mar 2023

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 …

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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 Mar 2023

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. …

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Solving Turkish Math Word Problems By Sequence-To-Sequence Encoder-Decoder Models, Esi̇n Gedi̇k, Tunga Güngör Mar 2023

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 …

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Reconstructing Dynamic Human Shapes From Sparse Silhouettes Via Latent Space Optimization Of Parametric Shape Models, Kanika Singla, Parma Nand Mar 2023

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 …

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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 Mar 2023

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 …

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Spayk: An Environment For Spiking Neural Network Simulation, Aykut Görkem Gelen, Ayten Atasoy Mar 2023

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 …

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Unmanned-Aircraft-System-Assisted Early Wildfire Detection With Air Quality Sensors †, Doaa Rjoub, Ahmad Alsharoa, Ala'eddin Masadeh Mar 2023

Unmanned-Aircraft-System-Assisted Early Wildfire Detection With Air Quality Sensors †, Doaa Rjoub, Ahmad Alsharoa, Ala'eddin Masadeh

Electrical and Computer Engineering Faculty Research & Creative Works

Numerous Hectares of Land Are Destroyed by Wildfires Every Year, Causing Harm to the Environment, the Economy, and the Ecology. More Than Fifty Million Acres Have Burned in Several States as a Result of Recent Forest Fires in the Western United States and Australia. According to Scientific Predictions, as the Climate Warms and Dries, Wildfires Will Become More Intense and Frequent, as Well as More Dangerous. These Unavoidable Catastrophes Emphasize How Important Early Wildfire Detection and Prevention Are. the Energy Management System Described in This Paper Uses an Unmanned Aircraft System (UAS) with Air Quality Sensors (AQSs) to Monitor Spot …

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Deep Learning To Predict The Hydration And Performance Of Fly Ash-Containing Cementitious Binders, Taihao Han, Rohan Bhat, Sai Akshay Ponduru, Amit Sarkar, Jie Huang, Gaurav Sant, Hongyan Ma, Narayanan Neithalath, Aditya Kumar Mar 2023

Deep Learning To Predict The Hydration And Performance Of Fly Ash-Containing Cementitious Binders, Taihao Han, Rohan Bhat, Sai Akshay Ponduru, Amit Sarkar, Jie Huang, Gaurav Sant, Hongyan Ma, Narayanan Neithalath, Aditya Kumar

Electrical and Computer Engineering Faculty Research & Creative Works

Fly ash (FA) – an industrial byproduct – is used to partially substitute Portland cement (PC) in concrete to mitigate concrete's environmental impact. Chemical composition and structure of FAs significantly impact hydration kinetics and compressive strength of concrete. Due to the substantial diversity in these physicochemical attributes of FAs, it has been challenging to develop a generic theoretical framework – and, therefore, theory-Based analytical models – that could produce reliable, a priori predictions of properties of [PC + FA] binders. in recent years, machine learning (ML) – which is purely data-driven, as opposed to being derived from theorical underpinnings – …

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Deep Learning To Predict The Hydration And Performance Of Fly Ash-Containing Cementitious Binders, Taihao Han, Rohan Bhat, Sai Akshay Ponduru, Amit Sarkar, Jie Huang, Gaurav Sant, Hongyan Ma, Narayanan Neithalath, Aditya Kumar Mar 2023

Deep Learning To Predict The Hydration And Performance Of Fly Ash-Containing Cementitious Binders, Taihao Han, Rohan Bhat, Sai Akshay Ponduru, Amit Sarkar, Jie Huang, Gaurav Sant, Hongyan Ma, Narayanan Neithalath, Aditya Kumar

Electrical and Computer Engineering Faculty Research & Creative Works

Fly ash (FA) – an industrial byproduct – is used to partially substitute Portland cement (PC) in concrete to mitigate concrete's environmental impact. Chemical composition and structure of FAs significantly impact hydration kinetics and compressive strength of concrete. Due to the substantial diversity in these physicochemical attributes of FAs, it has been challenging to develop a generic theoretical framework – and, therefore, theory-based analytical models – that could produce reliable, a priori predictions of properties of [PC + FA] binders. In recent years, machine learning (ML) – which is purely data-driven, as opposed to being derived from theorical underpinnings – …

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Phone Microwave, Khanh Kim Hoang, Emily Zhou Mar 2023

Phone Microwave, Khanh Kim Hoang, Emily Zhou

Computer Engineering

This project involves the installation of remote-control capabilities in an antique 1980s microwave, effectively turning the microwave into a “smart” device. While preserving the original functionality of the microwave, a combination of software and hardware components allows for remote microwave operations. The microwave can be remotely operated by calling the built-in number, and more advanced settings and options can be utilized by texting. The microwave is also secured against unauthorized use with the addition of a PIN code that is required to operate the device.

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Validation Of Bottom-Up Gnss Radio Occultation Method To Measure D- And E-Region Electron Density, Dylan J. Shaver Mar 2023

Validation Of Bottom-Up Gnss Radio Occultation Method To Measure D- And E-Region Electron Density, Dylan J. Shaver

Theses and Dissertations

An in-depth validation of a new bottom-up approach using GNSS Radio Occultation (GNSS-RO) data to generate electron density profiles in the D- and E-region ionosphere. This comparison was completed using daytime ionosonde profiles when sporadic-E (Es) was not present, and corresponding FIRI profiles. The average GNSS-RO profile is a few kilometers higher in altitude than the ionosonde profiles at the minimum frequency, f min. When the ionosonde profiles are shifted so that the altitudes match at f min, they are in good agreement up to the E-region peak altitude, hmE. Below f min, the …

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Fragility Of The Florida Panhandle's Electrical Transmission Grid To Hurricanes, Zachary D. Schumann Mar 2023

Fragility Of The Florida Panhandle's Electrical Transmission Grid To Hurricanes, Zachary D. Schumann

Theses and Dissertations

The increased frequency and intensity of extreme weather events from climate change necessitates understanding impacts on critical infrastructure, particularly electrical transmission grids. One of the foundational concepts of a grid’s resilience is its robustness to extreme weather events, such as hurricanes. Resilience of the electric grid to high wind speeds is predicated upon the location and physical characteristics of the system components. Previous modeling assessments of electric grid failure were done at the systems level with assumptions on location and type of specific components. To facilitate more explicit adaptation metrics, accurate component-level information is needed. In this study, we build …

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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 Mar 2023

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 …

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Time-Domain Evaluation Of Atmospheric Ducting Effects On X-Band Propagation Over Water, Jack A. Gallegos Mar 2023

Time-Domain Evaluation Of Atmospheric Ducting Effects On X-Band Propagation Over Water, Jack A. Gallegos

Master's Theses

The marine atmospheric boundary layer (MABL) is the region of atmosphere that interacts with the ocean surface. The atmospheric variability (i.e. temperature and relative humidity) in this region can result in rapid changes in the refractive index with increasing height from the sea surface. The complex region can result in non-standard propagation of electromagnetic (EM) waves beyond the horizon under atmospheric ducting conditions. However, when ducting layers are not present, EM waves are limited to line-of-sight transmission. Atmospheric ducting research is typically conducted using radio frequencies in the X-band (around 8-12 GHz) due to its impact on performance of marine …

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Design Of A Miniaturized Cubesat Tt&C Patch Antenna, Thomas A. Butterick Mar 2023

Design Of A Miniaturized Cubesat Tt&C Patch Antenna, Thomas A. Butterick

Theses and Dissertations

This effort explored the design of miniaturized rectangular microstrip patch antennas for application as a telemetry, tracking, and control (TT&C) on a CubeSat. The motivation for this research was the Grissom-P CubeSat mission planned by AFIT. The TT&C antenna selected for the mission exceeded the allotted size by over a factor of two. The studies performed included analyses of simply reducing aperture size, altering substrate permittivity, and layered approaches to antenna miniaturization. The primary approach to miniaturization was based on a virtually shorted patch antenna. Other approaches included an edge-shorted, slotted patch and the use of corner truncations for improved …

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Characterizing Location-Based Electromagnetic Leakage Of Computing Devices Using Convolutional Neural Networks To Increase The Effectiveness Of Side-Channel Analysis Attacks, Ian C. Heffron Mar 2023

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 …

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The Electromagnetic Bayonet: Development Of A Scientific Computing Method For Aperture Antenna Optimization, Michael P. Ingold Mar 2023

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 …

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Classification Tradeoffs In Multispectral Polarimetric Ladar Architectures, Connor B. Martin Mar 2023

Classification Tradeoffs In Multispectral Polarimetric Ladar Architectures, Connor B. Martin

Theses and Dissertations

An end-to-end LADAR system is modeled at the waveform level to perform material classification at a per-pixel basis. A K-Nearest Neighbors machine learning algorithm is chosen to make predictions using polarimetric material characteristics as features. A variable receiver design is modeled to allow for the use of multiple configurations of Polarization State Analyzers. This research investigates the inclusion of multiple wavelengths in the transmitted laser pulse to improve classification accuracy. Additionally, the effects of lowering the receiver’s detector bandwidth are investigated. Through the classification process, transmitting a multispectral laser pulse is shown to improve classification and may improve future LADAR …

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Effects Of Calibration Errors On Dropped-Channel Polarimetric Synthetic Aperture Radar, Jacob C. Morrison Mar 2023

Effects Of Calibration Errors On Dropped-Channel Polarimetric Synthetic Aperture Radar, Jacob C. Morrison

Theses and Dissertations

Compressed Sensing (CS) is a mathematical technique that can be applied to sparse data sets to allow for sub-Nyquist sampling. DCPCS is a CS technique that recovers the signal from unmeasured polarisation channels due to antenna crosstalk coupling the information onto the remaining channels. DCPCS reduces data storage/transmission and receiver hardware requirements. This thesis examines the robustness of DCPCS to calibration errors on the antenna crosstalk matrix. Although the antenna design problem is relaxed to a large region of acceptable crosstalk values, very accurate calibration may be required in a monostatic radar. This thesis also looks at the importance of …

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Robust Feature Matching For Visual-Based Aerial Navigation Using Registered Imagery, Luis Montenegro Mar 2023

Robust Feature Matching For Visual-Based Aerial Navigation Using Registered Imagery, Luis Montenegro

Theses and Dissertations

This work leverages the SIFT detector along with known robust feature matching techniques for vision-aided sUAS navigation solutions. The proposed algorithm focuses on a sufficient number of features extracted, their quality and their distribution.

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Classifying Open-Air Target Measurements Using Simulation-Trained Convolutional Neural Networks, Matthew M. Rofrano Mar 2023

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 …

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Detection And Identification Of Covert Devices Using Infrared And Stacked Optics Detection, Michael A. Reyneke Mar 2023

Detection And Identification Of Covert Devices Using Infrared And Stacked Optics Detection, Michael A. Reyneke

Theses and Dissertations

This work investigates stacked optics detection methodologies to successfully detect and identify observational systems with a cyber-physical sensing tool, ODIN (Observational Device Identification Network). ODIN successfully detected the presence of stacked optics and LiDAR systems using night-vision devices with a 96.32% average accuracy rating, both overt and covertly placed, with objective lens diameters ranging from 17 mm to 50 mm at distances between 1 m to 5 m with and without commonly employed anti-reflective countermeasures. ODIN provides a foundation for counter- measure capabilities of NIR devices and stacked optical systems in stationary environments. Additionally, a pilot study on smartphone LiDAR …

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The Wideband Tunability Of Double Gunn Diodes, Latarence Butts, Samir M. El-Ghazaly Mar 2023

The Wideband Tunability Of Double Gunn Diodes, Latarence Butts, Samir M. El-Ghazaly

Electrical Engineering Faculty Publications and Presentations

Gunn diodes use the Ridley–Watkins–Hilsum effect to generate low noise millimeter wave signals for communication and sensing applications. However, Monte Carlo calculations indicate that the velocity–field characteristics of compensated GaAs contain a second peak due to phonon scattering. A novel device that leverages this double Ridley–Watkins–Hilsum effect to achieve ultra-wideband tunability of low-noise millimeter waves is proposed in this article. The second region of negative differential mobility is gradually introduced in seven mobility profiles, which are each simulated in eight device lengths. It is found that a 2.5 μm Gunn diode with a compensation ratio of 0.6 operating at …

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Special Section On Local And Distributed Electricity Markets, Rui Bo, Linquan Bai, Antonio J. Conejo, Jianzhong Wu, Tao Jiang, Fei Ding, Babak Enayati Mar 2023

Special Section On Local And Distributed Electricity Markets, Rui Bo, Linquan Bai, Antonio J. Conejo, Jianzhong Wu, Tao Jiang, Fei Ding, Babak Enayati

Electrical and Computer Engineering Faculty Research & Creative Works

Driven by the Goals of Clean Energy and Zero Carbon Emissions, the Power Industry is Undergoing Significant Transformations. the Rapid Growth of Diverse Distributed Energy Resources (DERs) at Grid Edge Such as Rooftop Photovoltaics (PVs) and Electric Vehicles is Transforming the Traditional Centralized Power Grid Management to a Decentralized, Bottom-Up, and Localized Control Paradigm. Establishing Local and Distribution-Level Electricity Markets Provides an Effective Solution to Managing Large Amounts of Small-Scale DERs. New Regulations Such as the Recent FERC Order 2222 in the U.S. Open the Door to DERs in the Wholesale Markets. through Coordinating the Local and Distribution-Level Markets with …

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Description Of Reaction And Vibrational Energetics Of Co2-Nh3interaction Using Quantum Computing Algorithms, Manh Tien Nguyen, Yueh-Lin Lee, Dominic Alfonso, Qing Shao, Yuhua Duan Mar 2023

Description Of Reaction And Vibrational Energetics Of Co2-Nh3interaction Using Quantum Computing Algorithms, Manh Tien Nguyen, Yueh-Lin Lee, Dominic Alfonso, Qing Shao, Yuhua Duan

Markey Cancer Center Faculty Publications

CO2 capture is critical to solving global warming. Amine-based solvents are extensively used to chemically absorb CO2. Thus, it is crucial to study the chemical absorption of CO2 by amine-based solvents to better understand and optimize CO2 capture processes. Here, we use quantum computing algorithms to quantify molecular vibrational energies and reaction pathways between CO2 and a simplified amine-based solvent model—NH3. Molecular vibrational properties are important to understanding kinetics of reactions. However, the molecule size correlates with the strength of anharmonicity effect on vibrational properties, which can be challenging to address using classical computing. Quantum computing can help enhance molecular …

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Evolution Of Coronal Magnetic Field Parameters During X5.4 Solar Flare, Seth H. Garland, Benjamin F. Akers, Vasyl B. Yurchyshyn, Robert D. Loper, Daniel J. Emmons Mar 2023

Evolution Of Coronal Magnetic Field Parameters During X5.4 Solar Flare, Seth H. Garland, Benjamin F. Akers, Vasyl B. Yurchyshyn, Robert D. Loper, Daniel J. Emmons

Faculty Publications

The coronal magnetic field over NOAA Active Region 11,429 during a X5.4 solar flare on 7 March 2012 is modeled using optimization based Non-Linear Force-Free Field extrapolation. Specifically, 3D magnetic fields were modeled for 11 timesteps using the 12-min cadence Solar Dynamics Observatory (SDO) Helioseismic and Magnetic Imager photospheric vector magnetic field data, spanning a time period of 1 hour before through 1 hour after the start of the flare. Using the modeled coronal magnetic field data, seven different magnetic field parameters were calculated for 3 separate regions: areas with surface |Bz| ≥ 300 G, areas of flare brightening seen …

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Magnetic Anomaly Mapping For Navigation, Luke T. Bergeron Mar 2023

Magnetic Anomaly Mapping For Navigation, Luke T. Bergeron

Theses and Dissertations

Magnetic navigation (MagNav) has the potential to provide a global form of navigation that uses magnetic measurements of the Earth’s anomaly magnetic field and compares those measurements to a magnetic anomaly map in order to determine the user’s position. Widespread use of MagNav will require a database of fully-sampled, low-altitude magnetic anomaly maps. Existing magnetic anomaly map databases usually come from under- or poorly-sampled surveys. In this work, we provide an easy to follow MagNav anomaly map generation framework and set of survey collection metrics/requirements in an effort to help facilitate and standardize the creation of such a database. We …

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Improved Object Re-Identification Via More Efficient Embeddings, Ertugrul Bayraktar Mar 2023

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 …

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A Modified Space Vector Modulation Based Rotor Flux Oriented Control Of Six-Phase Asymmetrical Induction Motor Drive, Krunal Shah, Rakesh Maurya Mar 2023

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 …

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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 Mar 2023

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 …

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