Computer Engineering Commons^™

Developing A Real-World Vehicle Trip Dataset Through Public Travel Surveys And Applying It To Battery Electric Vehicle Performance Study, Nizar Ali Khemri

Wayne State University Dissertations

Real-world second-by-second vehicle driving cycle data is very important for research and development of the traditional fuel-powered vehicles, the emerging electric vehicles, and the hybrid vehicles. A project solely dedicated to generating such information would be extremely costly and time-consuming. Alternatively, we introduce a method to develop such a database by utilizing two publicly available passenger vehicle travel surveys; the 2004-2006 Puget Sound Regional Commission (PSRC) Travel Survey and the 2011 Atlanta Regional Commission (ARC) Travel Survey. The two surveys complement each other – the former is in low time resolution but covers vehicle driving and non-driving operation for over …

Data-Driven Intelligent Scheduling For Long Running Workloads In Large-Scale Datacenters, Guoyao Xu

Wayne State University Dissertations

Cloud computing is becoming a fundamental facility of society today. Large-scale public or private cloud datacenters spreading millions of servers, as a warehouse-scale computer, are supporting most business of Fortune-500 companies and serving billions of users around the world. Unfortunately, modern industry-wide average datacenter utilization is as low as 6% to 12%. Low utilization not only negatively impacts operational and capital components of cost efficiency, but also becomes the scaling bottleneck due to the limits of electricity delivered by nearby utility. It is critical and challenge to improve multi-resource efficiency for global datacenters.

Additionally, with the great commercial success of …

Efficient Virtual Data Center Request Embedding Based On Row-Epitaxial And Batched Greedy Algorithms, Sivaranjani B, Surendran Doraiswamy

Turkish Journal of Electrical Engineering and Computer Sciences

Data centers are becoming the main backbone of and centralized repository for all cloud-accessible services in on-demand cloud computing environments. In particular, virtual data centers (VDCs) facilitate the virtualization of all data center resources such as computing, memory, storage, and networking equipment as a single unit. It is necessary to use the data center efficiently to improve its profitability. The essential factor that significantly influences efficiency is the average number of VDC requests serviced by the infrastructure provider, and the optimal allocation of requests improves the acceptance rate. In existing VDC request embedding algorithms, data center performance factors such as …

Hybrid Control Of Five-Phase Permanent Magnet Synchronous Machine Using Space Vector Modulation, Djamel Difi, Khaled Halbaoui, Djamel Boukhetala

Turkish Journal of Electrical Engineering and Computer Sciences

This paper aims to study the hybrid control of a five-phase permanent-magnet synchronous machine improved by the space vector modulation (SVM) technique. The torque ripples and currents will therefore be reduced. This control is based on the theory of hybrid dynamic systems (HDS), its discrete component is the voltage inverter which has a finite number of states controlling the continuous component that represents the machine. The results of the simulation made on MATLAB/Simulink are presented and discussed in order to check the performance of the strategy of the studied control. They show, in particular, the main advantages of this control …

Design Of A Portable And Low-Cost Mass-Sensitive Sensor With The Capability Of Measurements On Various Frequency Quartz Tuning Forks, Mehmet Altay Ünal, İsmai̇l Cengi̇z Koçum, Di̇lek Çökeli̇ler Serdaroğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Recently, sensor and biosensor applications have become widespread and are now significant tools in the biomedical field and other areas. Since quartz tuning fork (QTF) resonance frequency depends on the mass adsorbed to its prongs, it is generally used to measure minor mass change and detect target analyte in picogram levels. This study is undertaken to design and fabricate a sensor device for the measurement of QTF transducers. When QTF sensor studies were investigated, it was found that explanations on the details of instrumentation part were limited, and in addition, there was no compact commercial products. In this study, a …

Generation Rescheduling Using Multiobjective Bilevel Optimization, Kiran Babu Vakkapatla, Srinivasa Varma Pinni

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a new multiobjective optimization method that can be used for generation rescheduling in power systems. Generation rescheduling in restructured power systems is performed by the system operator for different operations like congestion management, day-ahead scheduling, and preventive maintenance. The nonlinear nature of the equations involved and the constraints on decision variables pose a challenge to find the global optimum. In order to find the global optimum using a genetic algorithm, a bilevel optimization method is proposed. In the proposed multiobjective optimization method, the objectives are classified as primary and secondary based on their relative importance. The best …

On The Output Regulation For Linear Fractional Systems, Jesus Alberto Meda Campana, Elba Cinthya Garcia Estrada, Julio Cesar Gomez Mancilla, Jose De Jesus Rubio Avila, Mario Ricardo Cruz Deviana, Ricardo Tapia Herrera

Turkish Journal of Electrical Engineering and Computer Sciences

In this work, the regulation problem is extended to the field of fractional-order linear systems considering the Caputo fractional derivative. The regulation equations are obtained on the basis of the Francis equations. It is also shown that the linear fractional regulator exists at $t=0$ only if the order of the plant is not greater than the order of the reference system.

The Adoption Of Collaborative Robots Toward Ubiquitous Diffusion: A Research Agenda, Yuhua (Jake) Liang, Seungcheol Austin Lee

Communication Faculty Articles and Research

This paper proposes a framework to study the adoption of collaborative robots (co-robots or cobots) as an innovation and their diffusion into the larger population. Collaborative robots are only starting to appear in our society, yet challenges such as fear and distrust may impede their further adoption. This paper discusses the foundational work necessary to understand collaborative robot adoption and the core elements to achieve ubiquitous diffusion, with a focus on human users and the communication processes.

Neuroengineering Of Clustering Algorithms, Leonardo Enzo Brito Da Silva

Doctoral Dissertations

"Cluster analysis can be broadly divided into multivariate data visualization, clustering algorithms, and cluster validation. This dissertation contributes neural network-based techniques to perform all three unsupervised learning tasks. Particularly, the first paper provides a comprehensive review on adaptive resonance theory (ART) models for engineering applications and provides context for the four subsequent papers. These papers are devoted to enhancements of ART-based clustering algorithms from (a) a practical perspective by exploiting the visual assessment of cluster tendency (VAT) sorting algorithm as a preprocessor for ART offline training, thus mitigating ordering effects; and (b) an engineering perspective by designing a family of …

Use Of Virtual Reality Technology In Medical Training And Patient Rehabilitation, Sankalp Mishra

Browse all Theses and Dissertations

Coaching patients to follow the rehabilitation routines correctly and timely after surgery is often a challenge due to the limited medical knowledge of patients and limited availability of clinicians. Similarly, it is also a challenge to train medical professionals with both the technical and communication skills required in their practices. The recent emergence of VR technologies shines the light on improving the current training practices. In this thesis research, I will look at the development and application of VR-based immersive training games for two particular cases: 1. Post hand surgery rehab; and, 2. Training for Social determinants of health (SDOH) …

Building A Classification Model Using Affinity Propagation, Christopher R. Klecker

College of Graduate Studies: Theses & Dissertations

Regular classification of data includes a training set and test set. For example for Naïve Bayes, Artificial Neural Networks, and Support Vector Machines, each classifier employs the whole training set to train itself. This thesis will explore the possibility of using a condensed form of the training set in order to get a comparable classification accuracy. The technique explored in this thesis will use a clustering algorithm to explore with data records can be labeled as exemplar, or a quality of multiple records. For example, is it possible to compress say 50 records into one single record? Can a single …

Should Robots Prosecute And Defend?, Stephen E. Henderson

Faculty Articles

Even when we achieve the ‘holy grail’ of artificial intelligence—machine intelligence that is at least as smart as a human being in every area of thought—there may be classes of decisions for which it is intrinsically important to retain a human in the loop. On the common account of American criminal adjudication, the role of prosecutor seems to include such decisions given the largely unreviewable declination authority, whereas the role of defense counsel would seem fully susceptible of automation. And even for the prosecutor, the benefits of automation might outweigh the intrinsic decision-making loss, given that the ultimate decision—by judge …

Active Recall Networks For Multiperspectivity Learning Through Shared Latent Space Optimization, Theus Aspiras, Ruixu Liu, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

Given that there are numerous amounts of unlabeled data available for usage in training neural networks, it is desirable to implement a neural network architecture and training paradigm to maximize the ability of the latent space representation. Through multiple perspectives of the latent space using adversarial learning and autoencoding, data requirements can be reduced, which improves learning ability across domains. The entire goal of the proposed work is not to train exhaustively, but to train with multiperspectivity. We propose a new neural network architecture called Active Recall Network (ARN) for learning with less labels by optimizing the latent space. This …

Blockchain-Based Healthcare: Three Successful Proof-Of-Concept Pilots Worth Considering, Rebecca Angeles

Journal of International Technology and Information Management

This paper features the use of blockchain technology in the healthcare industry, with special focus on healthcare data exchange and interoperability; drug supply chain integrity and remote auditing; and clinical trials and population health research. This study uses the research method of analyzing the published case studies, academic articles, trade articles, and videos on MEDRec, Patientory, and the AmerisourceBergen/Merck alliance with SAP/CryptoWerk. The “blockchain” concept was introduced around October 2008 when a proposal for the virtual currency, bitcoin, was offered. Blockchain is a much broader concept than bitcoin and has the following key attributes: distributed database; peer-to-peer transmission; transparency with …

Car Image Classification Using Deep Neural Networks, Mingchen Li

Honors Theses

Image classification is widely used in many fields of study. Deep neural networks are proven to be effective classifier structure due to its massive parameters and training capability. This paper outlines the development of Deep Neural Network in recent years and applied them on a Car image data set in order to compare their performances.

ระบบการจัดการมูลค่าข้อมูลจากเกมสู่เกมด้วยบล็อกเชน, ชานน ยาคล้าย

Chulalongkorn University Theses and Dissertations (Chula ETD)

แม้ว่าในปัจจุบันบล็อกเชนจะถูกนำมาใช้ประโยชน์ในหลายอุตสาหกรรม แต่ในอุตสาหกรรมเกมนั้น บล็อกเชนไม่ได้ถูกนำไปใช้อย่างกว้างขวางมากนัก นอกจากนี้ แม้ว่าในอุสาหกรรมเกมจะมีผู้เล่นอยู่เป็นจำนวนมาก แต่ก็ยังไม่มีเกมหรือแพลตฟอร์มใดที่ให้สิทธิผู้เล่นในการเป็นเจ้าของสินทรัพย์หรือข้อมูลภายในเกมอย่างแท้จริง โดยแม้จะมีความพยายามในการระดมทุนเพื่อทำเกมหรือแพลตฟอร์มที่ให้ผู้เล่นได้มีโอกาสเป็นเจ้าของสินทรัพย์หรือข้อมูลภายในเกมอยู่บ้าง แต่ก็ยังคงอยู่ในขั้นตอนการทดลองที่ยังไม่เสร็จสมบูรณ์ และผู้เล่นยังต้องพึ่งพาระบบนิเวศน์ของแพลตฟอร์มนั้น ๆ อีกด้วย ในวิทยานิพนธ์ฉบับนี้ ผู้วิจัยจึงประสงค์ที่จะนำเสนอสถาปัตยกรรมกลางที่ทำให้ผู้เล่นเกมสามารถเป็นเจ้าของเวลาที่ตนเองใช้ภายในเกมได้โดยใช้บล็อกเชนสาธารณะ ทั้งผู้เล่นยังสามารถนำเวลาดังกล่าวไปใช้ในเกมอื่นได้ด้วย โดยใช้มาตราฐานโทเคนดิจิทัล ERC-20 บนอีเธอเรี่ยม นอกจากนี้ รูปแบบสถาปัตยกรรมที่นำเสนอดังกล่าวยังสามารถประยุกต์ใช้ได้กับทุกบล็อกเชนสาธารณะ และยังเป็นประโยชน์ต่อทุกองค์ประกอบของระบบนิเวศน์ อาทิเช่น ผู้เล่น บล็อกเชนโหนด และผู้พัฒนาเกม โดยผลการทดลองในงานวิทยานิพนธ์นี้ ยังแสดงว่าแนวความคิดดังกล่าวทำให้ผู้เล่นใช้เวลาในการเล่นเกมนานขึ้น และมีแนวโน้มที่จะอยากเล่นเกมใหม่ๆ ที่สามารถนำมูลค่าในเกมเดิมไปใช้ได้ แต่ทั้งนี้ยังมีปัจจัยหลายอย่างที่มีผล อาทิเช่น ประเภทของเกม การแลกเปลี่ยนค่าของเวลาภายในเกม เป็นต้น

Thai Scene Text Recognition, Thananop Kobchaisawat

Chulalongkorn University Theses and Dissertations (Chula ETD)

Automatic scene text detection and recognition can benefit a large number of daily life applications such as reading signs and labels, and helping visually impaired persons. Reading scene text images becomes more challenging than reading scanned documents in many aspects due to many factors such as variations of font styles and unpredictable lighting conditions. The problem can be decomposed into two sub-problems: text localization and text recognition. The proposed scene text localization works at the pixel level combined with a new text representation and a fully-convolutional neural network. This method is capable of detecting arbitrary shape texts without language limitations. …

The Evaluation Of An Android Permission Management System Based On Crowdsourcing, Pulkit Rustgi

Theses and Dissertations

Mobile and web application security, particularly concerning the area of data privacy, has received much attention from the public in recent years. Most applications are installed without disclosing full information to users and clearly stating what they have access to. This often raises concerns when users become aware of unnecessary information being collected or stored. Unfortunately, most users have little to no technical knowledge in regard to what permissions should be granted and can only rely on their intuition and past experiences to make relatively uninformed decisions. DroidNet, a crowdsource based Android recommendation tool and framework, is a proposed avenue …

Enabling Space Time Division Multiple Access In Ietf 6tisch Protocol, Sedat Görmüş, Sercan Külcü

Turkish Journal of Electrical Engineering and Computer Sciences

IETF 6TiSCH standard aims to create reliable, deterministic, and low-power networks by scheduling bandwidth resources in time and frequency domains. The main emphasis of 6TiSCH protocol is that it creates Internet of things (IoT) networks with a deterministic and controllable delay. However, many of its benefits are tied to the ability of the 6TiSCH scheduler to optimally distribute radio resources among wireless nodes which may not be possible when the number of frequency resources are limited and several other wireless technologies share the same frequency band (e.g., WiFi, Bluetooth and IEEE 802.15.4). Here the integration of a low-complexity directional antenna …

Stegogis: A New Steganography Method Using The Geospatial Domain, Ömer Kurtuldu, Mehmet Demi̇rci̇

Turkish Journal of Electrical Engineering and Computer Sciences

Geographic data are used on a variety of computing devices for many different applications including navigation, tracking, location planning, and marketing. The prevalence of geographic data makes it possible to envision new useful applications. In this paper, we propose using geographic data as a medium for secret communication, or steganography. We develop a method called StegoGIS for hiding messages in geographic coordinates in the well-known binary of fast-moving objects and transmitting them secretly. We show that discovering this secret communication is practically impossible for third parties. We also show that a large amount of secret data can be transmitted this …

Improving Undersampling-Based Ensemble With Rotation Forest For Imbalanced Problem, Huaping Guo, Xiaoyu Diao, Hongbing Liu

Turkish Journal of Electrical Engineering and Computer Sciences

As one of the most challenging and attractive issues in pattern recognition and machine learning, the imbalanced problem has attracted increasing attention. For two-class data, imbalanced data are characterized by the size of one class (majority class) being much larger than that of the other class (minority class), which makes the constructed models focus more on the majority class and ignore or even misclassify the examples of the minority class. The undersampling-based ensemble, which learns individual classifiers from undersampled balanced data, is an effective method to cope with the class-imbalance data. The problem in this method is that the size …

Examination Of Adoption Theory On The Devops Practice Of Continuous Delivery, Andrew John Anderson

Walden Dissertations and Doctoral Studies

Many organizations have difficulty adopting advanced software development practices. Some software development project managers in large organizations are not aligned with the relationship between performance expectancy, effort expectancy, social influence, and facilitating conditions, as moderated by experience, with intent to adopt the DevOps practice of continuous delivery. The purpose of this study was to examine the statistical relationships between the independent variables—performance expectancy, effort expectancy, social influence, and facilitating conditions, as moderated by experience—and the dependent variable of behavioral intent to adopt a continuous delivery system. Venkatesh, Morris, Davis, and Davis's unified theory of acceptance and use of technology provided …

Evaluation Of Suas Education And Training Tools, Brent Terwilliger, Scott Burgess, James Solti, Kristine Kiernan, Christian Janke, Andrew Shepherd

Publications

The wide distribution and demographic composition of students seeking small unmanned aircraft system (sUAS) education presents a need to fully understand the capabilities, limitations, and dependencies of effective training tools. Concepts, practices, and technologies associated with modeling and simulation, immersive gaming, augmented and mixed-reality, and remote operation have demonstrated efficacy to support engaged student learning and objective satisfaction. Identification and comparison of key attributes critical to an aviation educational framework, such as competency-based training, enables educational designers to identify those tools with the highest potential to support successful learning. A series of factors, such as system performance, regulatory compliance, environmental …

Noise Reduction In Eeg Signals Using Convolutional Autoencoding Techniques, Conor Hanrahan

Dissertations

The presence of noise in electroencephalography (EEG) signals can significantly reduce the accuracy of the analysis of the signal. This study assesses to what extent stacked autoencoders designed using one-dimensional convolutional neural network layers can reduce noise in EEG signals. The EEG signals, obtained from 81 people, were processed by a two-layer one-dimensional convolutional autoencoder (CAE), whom performed 3 independent button pressing tasks. The signal-to-noise ratios (SNRs) of the signals before and after processing were calculated and the distributions of the SNRs were compared. The performance of the model was compared to noise reduction performance of Principal Component Analysis, with …

Exploring Age-Related Metamemory Differences Using Modified Brier Scores And Hierarchical Clustering, Chelsea Parlett-Pelleriti, Grace C. Lin, Masha R. Jones, Erik Linstead, Susanne M. Jaeggi

Engineering Faculty Articles and Research

Older adults (OAs) typically experience memory failures as they age. However, with some exceptions, studies of OAs’ ability to assess their own memory functions—Metamemory (MM)— find little evidence that this function is susceptible to age-related decline. Our study examines OAs’ and young adults’ (YAs) MM performance and strategy use. Groups of YAs (N = 138) and OAs (N = 79) performed a MM task that required participants to place bets on how likely they were to remember words in a list. Our analytical approach includes hierarchical clustering, and we introduce a new measure of MM—the modified Brier—in order to adjust …

Deep Temporal Convolutional Networks For Short-Term Traffic Flow Forecasting, Wentian Zhao, Yanyun Gao, Tingxiang Ji, Xili Wan, Feng Ye, Guangwei Bai

Electrical and Computer Engineering Faculty Publications

To reduce the increasingly congestion in cities, it is essential for intelligent transportation system (ITS) to accurately forecast the short-term traffic flow to identify the potential congestion sites. In recent years, the emerging deep learning method has been introduced to design traffic flow predictors, such as recurrent neural network (RNN) and long short-term memory (LSTM), which has demonstrated its promising results. In this paper, different from existing work, we study the temporal convolutional network (TCN) and propose a deep learning framework based on TCN model for short-term city-wide traffic forecast to accurately capture the temporal and spatial evolution of traffic …

Development Of Semantic Scene Conversion Model For Image-Based Localization At Night, Dongyoun Kim

Electronic Theses and Dissertations

Developing an autonomous vehicle navigation system invariant to illumination change is one of the biggest challenges in vision-based localization field due to the fact that the appearance of an image becomes inconsistent under different light conditions even with the same location. In particular, the night scene images have greatest change in appearance compared to the according day scenes. Moreover, the night images do not have enough information in Image-based localization. To deal with illumination change, image conversion methods have been researched. However, these methods could lose the detail of objects and add fake objects into the output images. In this …

Edge Heterogeneous Hardware Evaluation Based On Real Connected And Autonomous Vehicles (Cavs) Workloads, Mustafa Ahmad

Research Opportunities for Engineering Undergraduates (ROEU) Program 2018-19

There has recently been a wide expansion of hardware to assist in autonomous driving tasks. On this project, we focus on using some state-of-the-art deep learning workloads in connected autonomous vehicle (CAV) scenarios,such as object detection and object tracking to evaluate the heterogeneous hardware.

Pv-Based Off-Board Electric Vehicle Battery Charger Using Bidc, Ankita Paul, Krithiga Subramanian, Sujitha N

Turkish Journal of Electrical Engineering and Computer Sciences

In recent years, the use of renewable energy sources is increasing drastically in several sectors, which leads to its role in the automobile industry to charge electric vehicle (EV) batteries. In this paper, a photovoltaic (PV) array-fed off-board battery charging system using a bidirectional interleaved DC-DC converter (BIDC) is proposed for light-weight EVs. This off-board charging system is capable of operating in dual mode, thereby supplying power to the EV battery from the PV array in standstill conditions and driving the DC load by the EV battery during running conditions. This dual mode operation is accomplished by the use of …

A Control Scheme For Maximizing The Delivered Power To The Load In A Standalonewind Energy Conversion System, Saeed Heshmatian, Davood A. Khaburi, Mahyar Khosravi, Ahad Kazemi

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, a control scheme is proposed for maximum power point tracking (MPPT) in a variable speed standalone wind energy conversion system (WECS) with permanent magnet synchronous generator. A MPPT algorithm is designed trying to eliminate the main deficiency of the conventional perturbation and observation (P&O) method, which is the challenge of choosing a proper step size and the unwanted trade-off between accuracy and speed. The designed algorithm properly addresses this drawback and significantly improves the MPPT performance. Another important issue is to ensure fast and accurate tracking of the optimal reference point obtained from the MPPT algorithm and …