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Full-Text Articles in Computer Sciences

Big Data With Cloud Computing: Discussions And Challenges, Amanpreet Kaur Sandhu Mar 2022

Big Data With Cloud Computing: Discussions And Challenges, Amanpreet Kaur Sandhu

Big Data Mining and Analytics

With the recent advancements in computer technologies, the amount of data available is increasing day by day. However, excessive amounts of data create great challenges for users. Meanwhile, cloud computing services provide a powerful environment to store large volumes of data. They eliminate various requirements, such as dedicated space and maintenance of expensive computer hardware and software. Handling big data is a time-consuming task that requires large computational clusters to ensure successful data storage and processing. In this work, the definition, classification, and characteristics of big data are discussed, along with various cloud services, such as Microsoft Azure, Google Cloud, …

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Exploiting More Associations Between Slots For Multi-Domain Dialog State Tracking, Hui Bai, Yan Yang, Jie Wang Mar 2022

Exploiting More Associations Between Slots For Multi-Domain Dialog State Tracking, Hui Bai, Yan Yang, Jie Wang

Big Data Mining and Analytics

Dialog State Tracking (DST) aims to extract the current state from the conversation and plays an important role in dialog systems. Existing methods usually predict the value of each slot independently and do not consider the correlations among slots, which will exacerbate the data sparsity problem because of the increased number of candidate values. In this paper, we propose a multi-domain DST model that integrates slot-relevant information. In particular, certain connections may exist among slots in different domains, and their corresponding values can be obtained through explicit or implicit reasoning. Therefore, we use the graph adjacency matrix to determine the …

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Sampling With Prior Knowledge For High-Dimensional Gravitational Wave Data Analysis, He Wang, Zhoujian Cao, Yue Zhou, Zong-Kuan Guo, Zhixiang Ren Mar 2022

Sampling With Prior Knowledge For High-Dimensional Gravitational Wave Data Analysis, He Wang, Zhoujian Cao, Yue Zhou, Zong-Kuan Guo, Zhixiang Ren

Big Data Mining and Analytics

Extracting knowledge from high-dimensional data has been notoriously difficult, primarily due to the so-called "curse of dimensionality" and the complex joint distributions of these dimensions. This is a particularly profound issue for high-dimensional gravitational wave data analysis where one requires to conduct Bayesian inference and estimate joint posterior distributions. In this study, we incorporate prior physical knowledge by sampling from desired interim distributions to develop the training dataset. Accordingly, the more relevant regions of the high-dimensional feature space are covered by additional data points, such that the model can learn the subtle but important details. We adapt the normalizing flow …

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Toward Intelligent Financial Advisors For Identifying Potential Clients: A Multitask Perspective, Qixiang Shao, Runlong Yu, Hongke Zhao, Chunli Liu, Mengyi Zhang, Hongmei Song, Qi Liu Mar 2022

Toward Intelligent Financial Advisors For Identifying Potential Clients: A Multitask Perspective, Qixiang Shao, Runlong Yu, Hongke Zhao, Chunli Liu, Mengyi Zhang, Hongmei Song, Qi Liu

Big Data Mining and Analytics

Intelligent Financial Advisors (IFAs) in online financial applications (apps) have brought new life to personal investment by providing appropriate and high-quality portfolios for users. In real-world scenarios, identifying potential clients is a crucial issue for IFAs, i.e., identifying users who are willing to purchase the portfolios. Thus, extracting useful information from various characteristics of users and further predicting their purchase inclination are urgent. However, two critical problems encountered in real practice make this prediction task challenging, i.e., sample selection bias and data sparsity. In this study, we formalize a potential conversion relationship, i.e., user→activated user→client and decompose this relationship into …

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A Comparison Of Computational Approaches For Intron Retention Detection, Jiantao Zheng, Cuixiang Lin, Zhenpeng Wu, Hong-Dong Li Mar 2022

A Comparison Of Computational Approaches For Intron Retention Detection, Jiantao Zheng, Cuixiang Lin, Zhenpeng Wu, Hong-Dong Li

Big Data Mining and Analytics

Intron Retention (IR) is an alternative splicing mode through which introns are retained in mature RNAs rather than being spliced in most cases. IR has been gaining increasing attention in recent years because of its recognized association with gene expression regulation and complex diseases. Continuous efforts have been dedicated to the development of IR detection methods. These methods differ in their metrics to quantify retention propensity, performance to detect IR events, functional enrichment of detected IRs, and computational speed. A systematic experimental comparison would be valuable to the selection and use of existing methods. In this work, we conduct an …

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Reducing Loading On The Contralateral Limb Using Human-In-The-Loop Optimization, Siena Senatore Mar 2022

Reducing Loading On The Contralateral Limb Using Human-In-The-Loop Optimization, Siena Senatore

UNO Student Research and Creative Activity Fair

In most everyday activities, we head towards a specific goal by updating our choices for a more direct path. However, there are specific clinical tasks where taking the direct path is more challenging. Clinical investigations of optimizing a prosthesis involve the assessment of multiple parameter settings through trial and error rather than goal-directed optimization. We investigate if a human-in-the-loop optimization algorithm can guide manual alterations to a prosthesis-simulating device to reduce the ground reaction force on the contralateral limb. In most participants, the optimal condition reduced the loading rate on the contralateral limb compared to the initial condition tested. These …

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Design And Development Of Software With A Graphical User Interface To Display And Convert Multiple Microscopic Histology Images, Sayed Ahmadreza Razian, Majid Jadidi, Alexey Kamenskiy Mar 2022

Design And Development Of Software With A Graphical User Interface To Display And Convert Multiple Microscopic Histology Images, Sayed Ahmadreza Razian, Majid Jadidi, Alexey Kamenskiy

UNO Student Research and Creative Activity Fair

Histological images are widely used to assess the microscopic anatomy of biological tissues. Recent advancements in image analysis allow the identification of structural features on histological sections that can help advance medical device development, brain and cancer research, drug discovery, vascular mechanobiology, and many other fields. Histological slide scanners create images in SVS and TIFF formats that were designed to archive image blocks and high-resolution textual information. Because these formats were primarily intended for storage, they are often not compatible with conventional image analysis software and require conversion before they can be used in research. We have developed a user-friendly …

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Three Wave Mixing In Epsilon-Near-Zero Plasmonic Waveguides For Signal Regeneration, Nicholas Mirchandani, Mark C. Harrison Mar 2022

Three Wave Mixing In Epsilon-Near-Zero Plasmonic Waveguides For Signal Regeneration, Nicholas Mirchandani, Mark C. Harrison

Engineering Faculty Articles and Research

Vast improvements in communications technology are possible if the conversion of digital information from optical to electric and back can be removed. Plasmonic devices offer one solution due to optical computing’s potential for increased bandwidth, which would enable increased throughput and enhanced security. Plasmonic devices have small footprints and interface with electronics easily, but these potential improvements are offset by the large device footprints of conventional signal regeneration schemes, since surface plasmon polaritons (SPPs) are incredibly lossy. As such, there is a need for novel regeneration schemes. The continuous, uniform, and unambiguous digital information encoding method is phase-shift-keying (PSK), so …

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An Interactive Game With Virtual Reality Immersion To Improve Cultural Sensitivity In Healthcare, Paul J. Hershberger, Yong Pei, Timothy N. Crawford, Sabrina M. Neeley, Thomas Wischgoll, Dixit B. Patel, Miteshkumar M. Vasoya, Angie Castle, Sankalp Mishra, Lahari Surapaneni, Aman A. Pogaku, Aishwarya Bositty, Todd Pavlack Mar 2022

An Interactive Game With Virtual Reality Immersion To Improve Cultural Sensitivity In Healthcare, Paul J. Hershberger, Yong Pei, Timothy N. Crawford, Sabrina M. Neeley, Thomas Wischgoll, Dixit B. Patel, Miteshkumar M. Vasoya, Angie Castle, Sankalp Mishra, Lahari Surapaneni, Aman A. Pogaku, Aishwarya Bositty, Todd Pavlack

Computer Science and Engineering Faculty Publications

Purpose: Biased perceptions of individuals who are not part of one’s in-groups tend to be negative and habitual. Because health care professionals are no less susceptible to biases than are others, the adverse impact of biases on marginalized populations in health care warrants continued attention and amelioration. Method: Two characters, a Syrian refugee with limited English proficiency and a black pregnant woman with a history of opioid use disorder, were developed for an online training simulation that includes an interactive life course experience focused on social determinants of health, and a clinical encounter in a community health center utilizing virtual …

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Ad-Corre: Adaptive Correlation-Based Loss For Facial Expression Recognition In The Wild, Ali Pourramezan Fard, Mohammad H. Mahoor Mar 2022

Ad-Corre: Adaptive Correlation-Based Loss For Facial Expression Recognition In The Wild, Ali Pourramezan Fard, Mohammad H. Mahoor

Electrical and Computer Engineering: Faculty Scholarship

Automated Facial Expression Recognition (FER) in the wild using deep neural networks is still challenging due to intra-class variations and inter-class similarities in facial images. Deep Metric Learning (DML) is among the widely used methods to deal with these issues by improving the discriminative power of the learned embedded features. This paper proposes an Adaptive Correlation (Ad-Corre) Loss to guide the network towards generating embedded feature vectors with high correlation for within-class samples and less correlation for between-class samples. Ad-Corre consists of 3 components called Feature Discriminator, Mean Discriminator, and Embedding Discriminator. We design the Feature Discriminator component to guide …

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Technical Analysis Of Thanos Ransomware, Ikuromor Ogiriki, Christopher Beck, Vahid Heydari Mar 2022

Technical Analysis Of Thanos Ransomware, Ikuromor Ogiriki, Christopher Beck, Vahid Heydari

College of Science & Mathematics Departmental Research

Ransomware is a developing menace that encrypts users’ files and holds the decryption key hostage until the victim pays a ransom. This particular class of malware has been in charge of extortion hundreds of millions of dollars every year. Adding to the problem, generating new variations is cheap. Therefore, new malware can detect antivirus and intrusion detection systems and evade them or manifest in ways to make themselves undetectable. We must first understand the characteristics and behavior of various varieties of ransomware to create and construct effective security mechanisms to combat them. This research presents a novel dynamic and behavioral …

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Malware Binary Image Classification Using Convolutional Neural Networks, John Kiger, Shen-Shyang Ho, Vahid Heydari Mar 2022

Malware Binary Image Classification Using Convolutional Neural Networks, John Kiger, Shen-Shyang Ho, Vahid Heydari

College of Science & Mathematics Departmental Research

The persistent shortage of cybersecurity professionals combined with enterprise networks tasked with processing more data than ever before has led many cybersecurity experts to consider automating some of the most common and time-consuming security tasks using machine learning. One of these cybersecurity tasks where machine learning may prove advantageous is malware analysis and classification. To evade traditional detection techniques, malware developers are creating more complex malware. This is achieved through more advanced methods of code obfuscation and conducting more sophisticated attacks. This can make the manual process of analyzing malware an infinitely more complex task. Furthermore, the proliferation of malicious …

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Adaptive Output Tracking Of Distributed Parameter Systems, İhsan Berk Altiner, Mustafa Doğan, Janset Daşdemi̇r Mar 2022

Adaptive Output Tracking Of Distributed Parameter Systems, İhsan Berk Altiner, Mustafa Doğan, Janset Daşdemi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we consider the unknown trajectory tracking problem for stable distributed parameter systems. The main assumptions are that trajectory signals are generated by an unknown finite-dimensional exosystem that is a marginally stable system and the tracking error is available for measurement. In order to achieve perfect error regulation, a frequency estimator scheme is proposed to estimate unknown exosystem parameters, and the control law that is designed based on geometric output regulation theory is revisited. The success of the proposed method is demonstrated on a parabolic heat equation and a first-order hyperbolic partial differential equation.

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Identifying Characteristics For Success Of Robotic Process Automations, Charles M. Unkrich Mar 2022

Identifying Characteristics For Success Of Robotic Process Automations, Charles M. Unkrich

Theses and Dissertations

In the pursuit of digital transformation, the Air Force creates digital airmen. Digital airmen are robotic process automations designed to eliminate the repetitive high-volume low-cognitive tasks that absorb so much of our Airmen's time. The automation product results in more time to focus on tasks that machines cannot sufficiently perform data analytics and improving the Air Force's informed decision-making. This research investigates the assessment of potential automation cases to ensure that we choose viable tasks for automation and applies multivariate analysis to determine which factors indicate successful projects. The data is insufficient to provide significant insights.

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The Application Of Virtual Reality In Firefighting Training, Dylan A. Gagnon Mar 2022

The Application Of Virtual Reality In Firefighting Training, Dylan A. Gagnon

Theses and Dissertations

Immersive simulations such as virtual reality is becoming more prevalent for use in training environments for many professions. United States Air Force firefighters may benefit from incorporating VR technology into their training program to increase organizational commitment, job satisfaction, self-efficacy, and job performance. With implementing a new training platform, it is also important to understand the relationship between these variables and the perceived benefits and efficacy of the VR training, which has not yet been studied in previous research. This study addresses this issue by gathering data from fire departments currently fielding a VR fire training platform.

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Delaunay Walk For Fast Nearest Neighbor: Accelerating Correspondence Matching For Icp, James D. Anderson, Ryan M. Raettig, Josh Larson, Scott L. Nykl, Clark N. Taylor, Thomas Wischgoll Mar 2022

Delaunay Walk For Fast Nearest Neighbor: Accelerating Correspondence Matching For Icp, James D. Anderson, Ryan M. Raettig, Josh Larson, Scott L. Nykl, Clark N. Taylor, Thomas Wischgoll

Computer Science and Engineering Faculty Publications

Point set registration algorithms such as Iterative Closest Point (ICP) are commonly utilized in time-constrained environments like robotics. Finding the nearest neighbor of a point in a reference 3D point set is a common operation in ICP and frequently consumes at least 90% of the computation time. We introduce a novel approach to performing the distance-based nearest neighbor step based on Delaunay triangulation. This greedy algorithm finds the nearest neighbor of a query point by traversing the edges of the Delaunay triangulation created from a reference 3D point set. Our work integrates the Delaunay traversal into the correspondences search of …

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Microwave Hyperthermia Application With Bioimplant Single Slot Coaxial Antennadesign For Mouse Breast Cancer Treatment, Ahmet Ri̇fat Görgün, Cem Baytöre, Selçuk Çömlekçi̇, Mehmet İbrahi̇m Tuğlu, Adnan Kaya Mar 2022

Microwave Hyperthermia Application With Bioimplant Single Slot Coaxial Antennadesign For Mouse Breast Cancer Treatment, Ahmet Ri̇fat Görgün, Cem Baytöre, Selçuk Çömlekçi̇, Mehmet İbrahi̇m Tuğlu, Adnan Kaya

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, a novel animal model for the breast cancer treatment which contains hyperthermia is proposed. For this main purpose a low cost, interstitial, bioimplant antenna by short ended single slot design is proposed to heat the cancerous tissues. Both the theoretical background of the proposed system and the simulation and measurement results of antenna design are presented. An artificial tissue phantom model has been created under laboratory conditions and then the utility of the proposed antenna has been tested on this model. Artificial tissues have been heated by 25W to (41-44 ? C) in a short time like …

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Identification And Mitigation Of Non-Line-Of-Sight Path Effect Using Repeater Forhybrid Ultra-Wideband Positioning And Networking System, Gwo Chin Chung, Mohd. Aqmal Syafiq Kamarudin, It Ee Lee, Soo Fun Tan Mar 2022

Identification And Mitigation Of Non-Line-Of-Sight Path Effect Using Repeater Forhybrid Ultra-Wideband Positioning And Networking System, Gwo Chin Chung, Mohd. Aqmal Syafiq Kamarudin, It Ee Lee, Soo Fun Tan

Turkish Journal of Electrical Engineering and Computer Sciences

At least two decades ago, various applications have been proposed for the implementation of ultra-wideband (UWB) technology, but only a few of them are being realised such as radar detection, home networking, and indoor positioning. Although UWB positioning offers precise locality tracking, the accuracy of the estimation is greatly affected by the non-line-of-sight (NLOS) path effect. In this paper, we propose a hybrid indoor UWB positioning and networking system that utilises the existing repeater of the data network to eliminate the NLOS paths. A switching algorithm is written to identify the existence of NLOS paths based on received signal strength …

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A Factor Graph Optimization Mapping Based On Normaldistributions Transform, Kedi Zhong, Yuansheng Liu, Jiansuo Yang, Ming Lu, Jun Zhang Mar 2022

A Factor Graph Optimization Mapping Based On Normaldistributions Transform, Kedi Zhong, Yuansheng Liu, Jiansuo Yang, Ming Lu, Jun Zhang

Turkish Journal of Electrical Engineering and Computer Sciences

This paper aims to achieve highly accurate mapping results and real time pose estimation of autonomous vehicle by using the normal distribution transform (NDT) algoritm. A factor graph optimization algorithm (FGO-NDT) is proposed to address the poor real-time performance and pose drift errors of the NDT algorithm. Smooth point cloud data are obtained by multisensor calibration and data preprocessing. NDT registration is then used for lidar odometry and feature matching. The global navigation satellite system (GNSS) data and loop detection results are added to the factor graph framework as the pose constraint factors to optimize the pose trajectory and eliminate …

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Independent Closed Loop Control Of Di/Dt And Dv/Dt For High Power Igbts, Osman Tanriverdi̇, Deni̇z Yildirim Mar 2022

Independent Closed Loop Control Of Di/Dt And Dv/Dt For High Power Igbts, Osman Tanriverdi̇, Deni̇z Yildirim

Turkish Journal of Electrical Engineering and Computer Sciences

As the insulated gate bipolar transistor (IGBT) modules have their own specific characteristic switching forms, their turn-on and turn-off times are changed according to practical applications. For the conventional gate drives, gate resistors are used to adjust the turn-on and turn-off times which change switching losses that have a significant amount in total losses. Collector current rate of change, $di_{C}/ dt$ and collector-emitter rate of change, $dv_{CE}/ dt$ are dependent on each other and they affect operating parameters in high power converters. Relations between current and voltages during the switching transitions are given and effects of the changes in electrical …

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Clustering With Density Based Initialization And Bhattacharyya Based Merging, Erdem Köse, Ali̇ Köksal Hocaoğlu Mar 2022

Clustering With Density Based Initialization And Bhattacharyya Based Merging, Erdem Köse, Ali̇ Köksal Hocaoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Centroid based clustering approaches, such as k-means, are relatively fast but inaccurate for arbitrary shape clusters. Fuzzy c-means with Mahalanobis distance can accurately identify clusters if data set can be modelled by a mixture of Gaussian distributions. However, they require number of clusters apriori and a bad initialization can cause poor results. Density based clustering methods, such as DBSCAN, overcome these disadvantages. However, they may perform poorly when the dataset is imbalanced. This paper proposes a clustering method, named clustering with density initialization and Bhattacharyya based merging based on the fuzzy clustering. The initialization is carried out by density estimation …

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Calculating Influence Based On The Fusion Of Interest Similarity And Informationdissemination Ability, Shulin Cheng, Ziming Wang, Meng Qian, Shan Yang, Xin Zheng Mar 2022

Calculating Influence Based On The Fusion Of Interest Similarity And Informationdissemination Ability, Shulin Cheng, Ziming Wang, Meng Qian, Shan Yang, Xin Zheng

Turkish Journal of Electrical Engineering and Computer Sciences

With the popularization, in-depth development and application of the Internet, microblogs have become a mainstream social network platform. Several studies on social networks have conducted researches, and user influence evaluation is an important research hotspot. Most of the existing studies calculate user influence by improving PageRank and have achieved certain results. However, these studies ignored the fusion of users' interest theme similarity and information dissemination ability, and the analysis of interaction behaviors among users is not comprehensive. To address these issues, we propose a new microblog user influence algorithm called microblog user influence based on interest similarity and information dissemination …

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Photonic Integrated Circuit-Assisted Optical Time-Domainreflectometer System, Mehmet Cengi̇z Onbaşli Mar 2022

Photonic Integrated Circuit-Assisted Optical Time-Domainreflectometer System, Mehmet Cengi̇z Onbaşli

Turkish Journal of Electrical Engineering and Computer Sciences

Optical time-domain reflectometers (OTDR) are photonic systems that consist of an interrogator, a receiver and a fiber optical cable and have applications in telecommunications, security, environmental monitoring, distributed temperature and strain sensing. Since OTDR systems are bulk optical setups that consume multiple Watts of power, have large mass and volume footprint and are vulnerable to thermal drift, deployment of OTDR systems in the field is expensive, complicated and may not necessarily yield accurate sensing results. Thus, a compact, low-power, inexpensive and thermal drift-free OTDR system needs to be developed for improving the accuracy and the viability of OTDR in the …

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Critical Speed Calculation Of A Refurbishment Of 11mw Hydro Power Plant Unit, Ahmet Seli̇m Pehli̇van, Dario Kraljevic, Ivan Triplat, Beste Bahçeci̇ Mar 2022

Critical Speed Calculation Of A Refurbishment Of 11mw Hydro Power Plant Unit, Ahmet Seli̇m Pehli̇van, Dario Kraljevic, Ivan Triplat, Beste Bahçeci̇

Turkish Journal of Electrical Engineering and Computer Sciences

Hydro generator design is a significant issue in terms of safety, efficiency, and energy production sustainability. One of the most crucial issues about design criteria is to satisfy the needs of the project?s critical speed. In this work, the critical speed calculation of an 11 MW hydro power plant was investigated with several design steps. Numerical solution methodologies were implemented using ARMD?. A generator design was developed, and the implementation of the rotor was carried out in Antalya, Turkey. Vibration and displacements of the hydro generator are adequate. The generator unit is active for 3 years and have never encountered …

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Determining Allowable Parametric Uncertainty In An Uncommon Quadrotormodel For Closed Loop Stability, Mehmet Baskin, Mehmet Kemal Leblebi̇ci̇oğlu Mar 2022

Determining Allowable Parametric Uncertainty In An Uncommon Quadrotormodel For Closed Loop Stability, Mehmet Baskin, Mehmet Kemal Leblebi̇ci̇oğlu

Turkish Journal of Electrical Engineering and Computer Sciences

In this article, control oriented uncertainty modeling of an uncommon quadrotor in hover is discussed. This quadrotor consists of two counter-rotating big rotors on longitudinal axis and two counter-rotating small tilt rotors on lateral axis. Firstly, approximate linear model of this vehicle around hover is obtained by using Newton--Euler formulation. Secondly, specific uncertainty is assigned to each parameter. Resulting uncertain model is converted into a linear fractional transformation framework for robustness analysis. Next, the most critical uncertain parameters in terms of robust stability in a proposed quadrotor model are investigated using $ \mu $ sensitivities. Finally, skewed-$ \mu $ analysis …

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Performance Analysis And Feature Selection For Network-Based Intrusion Detectionwith Deep Learning, Serhat Caner, Nesli̇ Erdoğmuş, Yusuf Murat Erten Mar 2022

Performance Analysis And Feature Selection For Network-Based Intrusion Detectionwith Deep Learning, Serhat Caner, Nesli̇ Erdoğmuş, Yusuf Murat Erten

Turkish Journal of Electrical Engineering and Computer Sciences

An intrusion detection system is an automated monitoring tool that analyzes network traffic and detects malicious activities by looking out either for known patterns of attacks or for an anomaly. In this study, intrusion detection and classification performances of different deep learning based systems are examined. For this purpose, 24 deep neural networks with four different architectures are trained and evaluated on CICIDS2017 dataset. Furthermore, the best performing model is utilized to inspect raw network traffic features and rank them with respect to their contributions to success rates. By selecting features with respect to their ranks, sets of varying size …

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Modeling And Evaluation Of Soc-Based Coordinated Ev Charging For Powermanagement In A Distribution System, Murat Akil, Emrah Dokur, Ramazan Bayindir Mar 2022

Modeling And Evaluation Of Soc-Based Coordinated Ev Charging For Powermanagement In A Distribution System, Murat Akil, Emrah Dokur, Ramazan Bayindir

Turkish Journal of Electrical Engineering and Computer Sciences

The importance of using clean energy in electrical energy generation and transportation network planning has recently increased due to carbon footprint rising. In this direction, the use of electric vehicles (EV), known as ultra-low carbon emission vehicles, has become widespread in addition to renewable energy sources (RES) such as wind and photovoltaic (PV) power generations. The trend of EVs to be preferred the primary means of transport has revealed the effects of charging an additional load on the grid. There is a need to create coordinated charging methods by considering the approaches for real-time charging models of EVs. In this …

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Forecasting Tv Ratings Of Turkish Television Series Using A Two-Level Machinelearning Framework, Büşranur Akgül, Tayfun Küçükyilmaz Mar 2022

Forecasting Tv Ratings Of Turkish Television Series Using A Two-Level Machinelearning Framework, Büşranur Akgül, Tayfun Küçükyilmaz

Turkish Journal of Electrical Engineering and Computer Sciences

TV rating is a numeric estimate of the popularity of television programs. Forecasting TV ratings is considered an important asset for investment planning of media due to its potential of reducing the risks of future ventures. The aim of this study is to develop a machine learning model capable of efficiently forecasting the TV ratings of Turkish TV series in a practical manner. To this end, two prediction models were proposed for forecasting the TV ratings of television series, facilitating an extensive set of features. A contribution of this study is the inclusion of social media-based features using search trends …

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Predictive Optimization Of Sliding Mode Control Using Recurrent Neural Paradigmfor Nonlinear Dfig-Wpgs During Distorted Voltage, Omar Busati, Xiangjie Liu Mar 2022

Predictive Optimization Of Sliding Mode Control Using Recurrent Neural Paradigmfor Nonlinear Dfig-Wpgs During Distorted Voltage, Omar Busati, Xiangjie Liu

Turkish Journal of Electrical Engineering and Computer Sciences

Dynamic characteristics of the doubly-fed induction generator (DFIG)-based wind power generation (WPGS) are fully nonlinear. Therefore, issues such as stability and achieving high efficiency, especially under harmonics behavior, are challenges that assess the control strategy reliability to find the perfect dynamic solution. This discussion offers a control strategy for the separated stator-port power using a predictive sliding mode strategy with a resonant function (PSMC-R) based on a deep recurrent neural network (DRNN). DRNN is formed as a low-order Taylor series formula. PSMC-R predicts the perfect switching surface path and regulates the distorted nonlinear DFIG with several dynamic aims. This approach …

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Scattering Analyses Of Arbitrary Roughness From 2-D Perfectly Conductiveperiodic Surfaces With Moments Method, Yunus Emre Yamaç, Ahmet Kizilay Mar 2022

Scattering Analyses Of Arbitrary Roughness From 2-D Perfectly Conductiveperiodic Surfaces With Moments Method, Yunus Emre Yamaç, Ahmet Kizilay

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, a periodic-MoM-based code with high accuracy performance is developed to calculate electromagnetic scattering from a periodic conductive surface in two dimensions with any degree of roughness. Firstly, the existing separate methods in the literature are reviewed step by step to compose a periodic-MoM solution for 2-D periodic surfaces. Then, the dynamic selection of optimal formulation of the periodic-MoM solutions created using these existing methods is evaluated to reduce solution time and obtain high accuracy. In this study, the performance parameters of the existing methods are investigated in solving a real 3-D scattering problem by a periodic-MoM for …

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