Digital Commons Network^™

Predicting Construction Labor Productivity With Bayesian Belief Networks, Ayoub Hazrati

Department of Construction Engineering and Management: Dissertations, Theses, and Student Research

Construction labor productivity plays an important role in labor intensive projects. Therefore, increasing construction labor productivity is a vital task to decrease a project’s cost (time). The primary goal of this research is to investigate the feasibility of developing a comprehensive causal model that can predict construction labor productivity for various project’s situations, such as existence of “Adverse Weather,” “Changes,” “Working Overtime,” etc., while considering uncertainty. It is found that Bayesian Belief Networks (BBNs) is the best approach that can model causal relationships among different factors while considering uncertainty, simultaneously.

Developing a BBNs model requires to extract its structure and, …

Incorporating Priors For Medical Image Segmentation Using A Genetic Algorithm, Payel Ghosh, Melanie Mitchell, James A. Tanyi, Arthur Y. Hung

Computer Science Faculty Publications and Presentations

Medical image segmentation is typically performed manually by a physician to delineate gross tumor volumes for treatment planning and diagnosis. Manual segmentation is performed by medical experts using prior knowledge of organ shapes and locations but is prone to reader subjectivity and inconsistency. Automating the process is challenging due to poor tissue contrast and ill-defined organ/tissue boundaries in medical images. This paper presents a genetic algorithm for combining representations of learned information such as known shapes, regional properties and relative position of objects into a single framework to perform automated three-dimensional segmentation. The algorithm has been tested for prostate segmentation …

Taxonomy Of Pathways To Dangerous Artificial Intelligence, Roman V. Yampolskiy

Faculty and Staff Scholarship

In order to properly handle a dangerous Artificially Intelligent (AI) system it is important to understand how the system came to be in such a state. In popular culture (science fiction movies/books) AIs/Robots became self-aware and as a result rebel against humanity and decide to destroy it. While it is one possible scenario, it is probably the least likely path to appearance of dangerous AI. In this work, we survey, classify and analyze a number of circumstances, which might lead to arrival of malicious AI. To the best of our knowledge, this is the first attempt to systematically classify types …

Acoustic Sequences In Non-Human Animals: A Tutorial Review And Prospectus, Arik Kershenbaum, Daniel T. Blumstein, Marie A. Roch, Çağlar Akçay, Gregory Backus, Mark A. Bee, Kirsten Bohn, Yan Cao, Gerald Carter, Cristiane Cäsar, Michael Coen, Stacy L. Deruiter, Laurance Doyle, Shimon Edelman, Ramon Ferreri Cancho, Todd M. Freeberg, Ellen C. Garland, Morgan Gustison, Heidi E. Harley, Chloé Huetz, Melissa Hughes, Julia Hyland Bruno, Amiyaal Ilany, Dezhe Z. Jin, Michael T. Johnson, Chenghui Ju, Jeremy Karnowski, Bernard Lohr, Marta B. Manser, Brenda Mccowan, Eduardo Mercado Iii, Peter M. Narins, Alex Piel, Megan Rice, Roberta Salmi, Kazutoshi Sasahara, Laela Sayigh, Yu Shiu, Charles Taylor, Edgar E. Vallejo, Sara Waller, Veronica Zamora Gutierrez

Electrical and Computer Engineering Faculty Research and Publications

Animal acoustic communication often takes the form of complex sequences, made up of multiple distinct acoustic units. Apart from the well-known example of birdsong, other animals such as insects, amphibians, and mammals (including bats, rodents, primates, and cetaceans) also generate complex acoustic sequences. Occasionally, such as with birdsong, the adaptive role of these sequences seems clear (e.g. mate attraction and territorial defence). More often however, researchers have only begun to characterise – let alone understand – the significance and meaning of acoustic sequences. Hypotheses abound, but there is little agreement as to how sequences should be defined and analysed. Our …

An Evaluation Of Gamification To Assess Students’ Learning On Their Understanding Of First Year Computer Science Programming Module, Daniel Gebremichael

Dissertations

This research examines the use of gamification to develop an assessment tool, to assess students’ learning of a first year computer science module. The students’ undertaking of the first semester Programming and Algorithms module in 2015 were assessed on their knowledge of the programming language Python. The incorporation of gamification when assessing students can have various potential benefits. The research aims to identify these benefits and issues. Assessments and games have almost opposite effects on opinions on people, as games are usually expected to have an entertainment value but this is not the case for assessments. The research examines if …

Intra- And Inter-Fractional Variation Prediction Of Lung Tumors Using Fuzzy Deep Learning, Seonyeong Park, Suk Jin Lee, Elisabeth Weiss, Yuichi Motai

Electrical and Computer Engineering Publications

Tumor movements should be accurately predicted to improve delivery accuracy and reduce unnecessary radiation exposure to healthy tissue during radiotherapy. The tumor movements pertaining to respiration are divided into intra-fractional variation occurring in a single treatment session and inter- fractional variation arising between different sessions. Most studies of patients' respiration movements deal with intra-fractional variation. Previous studies on inter-fractional variation are hardly mathematized and cannot predict movements well due to inconstant variation. Moreover, the computation time of the prediction should be reduced. To overcome these limitations, we propose a new predictor for intra- and inter-fractional data variation, called intra- and …

Fuzzy Control Of A Hyperloop Mass Transit System, Joseph D. Plantz

Browse all Theses and Dissertations

Fuzzy logic control of a Hyperloop is carried out in this thesis. Hyperloop is being described hypothetically as a fifth mode of mass transportation and is a registered trademark of Space Exploration Technologies Corporation (SpaceX). To inspire others to help in its development and make it a reality, the Hyperloop is being explored as open-source technology by SpaceX. In this thesis a near friction-less track is constructed and is fixed inside a tunnel. External fans are used to produce air pressure inside the tunnel to propel the vehicle down the track. Fuzzy Logic Control is used to stop the vehicle …

Knowledge Driven Search Intent Mining, Ashutosh Jadhav

Browse all Theses and Dissertations

Understanding users' latent intents behind search queries is essential for satisfying a user's search needs. Search intent mining can help search engines to enhance its ranking of search results, enabling new search features like instant answers, personalization, search result diversification, and the recommendation of more relevant ads. Hence, there has been increasing attention on studying how to effectively mine search intents by analyzing search engine query logs. While state-of-the-art techniques can identify the domain of the queries (e.g. sports, movies, health), identifying domain-specific intent is still an open problem. Among all the topics available on the Internet, health is one …

A Work System Front End For Object-Oriented Analysis And Design, Steven Alter, Narasimha Bolloju

Business Analytics and Information Systems

This paper proposes that basic ideas from the work system theory (WST) and the work system method (WSM) might serve as a front end to object-oriented analysis and design (OOAD), thereby providing a path from business-oriented descriptions to formal, technical specifications. After describing the background motivation and summarizing work system concepts, the paper uses a hiring system example to show how two tools from WSM can be used as a front end for OOAD, in effect, a step before creating use case diagrams and other types of Unified Modeling Language (UML) artifacts. Potential benefits of this approach stem from a …

Do We Have Compatible Concepts Of Epistemic Uncertainty?, Michael Beer, Scott Ferson, Vladik Kreinovich

Departmental Technical Reports (CS)

Epistemic uncertainties appear widely in civil engineering practice. There is a clear consensus that these epistemic uncertainties need to be taken into account for a realistic assessment of the performance and reliability of our structures and systems. However, there is no clearly defined procedure to meet this challenge. In this paper we discuss the phenomena that involve epistemic uncertainties in relation to modeling options. Particular attention is paid to set-theoretical approaches and imprecise probabilities. The respective concepts are categorized, and relationships are highlighted.

Bytewise Approximate Matching: The Good, The Bad, And The Unknown, Vikram S. Harichandran, Frank Breitinger, Ibrahim Baggili

Electrical & Computer Engineering and Computer Science Faculty Publications

Hash functions are established and well-known in digital forensics, where they are commonly used for proving integrity and file identification (i.e., hash all files on a seized device and compare the fingerprints against a reference database). However, with respect to the latter operation, an active adversary can easily overcome this approach because traditional hashes are designed to be sensitive to altering an input; output will significantly change if a single bit is flipped. Therefore, researchers developed approximate matching, which is a rather new, less prominent area but was conceived as a more robust counterpart to traditional hashing. Since the conception …

A Wavelet Transform Module For A Speech Recognition Virtual Machine, Euisung Kim

All Graduate Theses, Dissertations, and Other Capstone Projects

This work explores the trade-offs between time and frequency information during the feature extraction process of an automatic speech recognition (ASR) system using wavelet transform (WT) features instead of Mel-frequency cepstral coefficients (MFCCs) and the benefits of combining the WTs and the MFCCs as inputs to an ASR system. A virtual machine from the Speech Recognition Virtual Kitchen resource (www.speechkitchen.org) is used as the context for implementing a wavelet signal processing module in a speech recognition system. Contributions include a comparison of MFCCs and WT features on small and large vocabulary tasks, application of combined MFCC and WT features on …

Robust Fuzzy-Sliding Mode Based Upfc Controller For Transient Stability Analysis In Autonomous Wind-Diesel-Pv Hybrid System, Asit Mohanty, Sandipan Patra, Prakash K. Ray

Articles

This study presents a comparative study of transient stability and reactive power compensation issues in an autonomous wind–diesel-photovoltaic based hybrid system (HS) using robust fuzzy-sliding mode based unified power flow controller (UPFC). A linearised small-signal model of the different elements of the HS is considered for the transient stability analysis in the HS under varying loading conditions. An IEEE type 1 excitation system is considered for the synchronous generator in the HS, with negligible saturation characteristic, for detailed voltage stability analysis. It is noted from the simulation results that the performance of UPFC is superior to static VAR compensator and …

Improving Understandability And Uncertainty Modeling Of Data Using Fuzzy Logic Systems, Dumidu S. Wijayasekara

Theses and Dissertations

The need for automation, optimality and efficiency has made modern day control and monitoring systems extremely complex and data abundant. However, the complexity of the systems and the abundance of raw data has reduced the understandability and interpretability of data which results in a reduced state awareness of the system. Furthermore, different levels of uncertainty introduced by sensors and actuators make interpreting and accurately manipulating systems difficult. Classical mathematical methods lack the capability to capture human knowledge and increase understandability while modeling such uncertainty.

Fuzzy Logic has been shown to alleviate both these problems by introducing logic based on vague …

Multi-Type Display Calculus For Dynamic Epistemic Logic, Sabine Frittella, Giuseppe Greco, Alexander Kurz, Alessandra Palmigiano, Vlasta Sikimić

Engineering Faculty Articles and Research

In the present paper, we introduce a multi-type display calculus for dynamic epistemic logic, which we refer to as Dynamic Calculus. The displayapproach is suitable to modularly chart the space of dynamic epistemic logics on weaker-than-classical propositional base. The presence of types endows the language of the Dynamic Calculus with additional expressivity, allows for a smooth proof-theoretic treatment, and paves the way towards a general methodology for the design of proof systems for the generality of dynamic logics, and certainly beyond dynamic epistemic logic. We prove that the Dynamic Calculus adequately captures Baltag-Moss-Solecki’s dynamic epistemic logic, and enjoys Belnap-style cut …

Open Source Software Adoption Evaluation Through Feature Level Sentiment Analysis Using Twitter Data, Muhammad Touseef Ikram, Naveed Anwer Butt, Muhammad Tanvir Afzal

Turkish Journal of Electrical Engineering and Computer Sciences

Adopting open source software from the Internet, developers often encounter the problem of accessing the quality of candidate software. To efficiently adopt the system they need a sort of quality guarantee regarding software resources. To assist the developer in software adoption evaluation we have proposed a software adoption assessment approach based on user comments. In our proposed approach, we first collected the textual reviews regarding the software resource, assigned the sentiment polarity (positive or negative) to each comment, extracted the adoption aspect which the comment talks about, and then based on the adoption aspects of the software generated an aggregated …

Grange Stone Circle (B) – New Thoughts On An Old Monument, Frank Prendergast

Book/Book Chapter

No abstract provided.

Comparative Study Between Direct Load Control And Fuzzy Logic Control Based Demand Response, Chittesh Veni Chandran, Malabika Basu, Keith Sunderland

Conference papers

Demand response (DR), as part of Demand side management (DSM), is a method that modifies consumer side energy consumption. By actively controlling the time associated with electricity consumption, both energy and economic efficiencies are increased. There is however, depending on the algorithm employed, potential for consumer comfort levels to be adversely affected. Generally direct load (Boolean logic) control is employed, which solely considers demand reduction and not customer comfort. In comparison, fuzzy logic based programs can consider non-deterministic inputs such as consumer comfort level with ease and simplicity. This paper considers the evolution of DR through a comparison of the …

Towards A Semantic-Based Information Extraction System For Matching Résumés To Job Openings, Duygu Çeli̇k

Turkish Journal of Electrical Engineering and Computer Sciences

A curriculum vitae or a résumé, in general, consists of personal details, education, work experience, qualifications, and references. The overall objective of this study was to extract such data as experience, features, and business and education information from résumés stored in human resources repositories. In this article, we propose an ontology-driven information extraction system that is planned to operate on several million free-format textual résumés to convert them to a structured and semantically enriched version for use in semantic data mining of data essential in human resources processes. The architecture and working mechanism of the system, similarity of the concept …

The Classification Of Eeg Signals Using Discretization-Based Entropy And The Adaptive Neuro-Fuzzy Inference System, Mahmut Heki̇m

Turkish Journal of Electrical Engineering and Computer Sciences

A novel feature extraction called discretization-based entropy is proposed for use in the classification of EEG signals. To this end, EEG signals are decomposed into frequency subbands using the discrete wavelet transform (DWT), the coefficients of these subbands are discretized into the desired number of intervals using the discretization method, the entropy values of the discretized subbands are calculated using the Shannon entropy method, and these are then used as the inputs of the adaptive neuro-fuzzy inference system (ANFIS). The equal width discretization (EWD) and equal frequency discretization (EFD) methods are used for the discretization. In order to evaluate their …

A Generalized Particle Swarm Optimization Using Reverse Direction Information, Emre Çomak

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, an improved particle swarm optimization (PSO) algorithm, including 4 types of new velocity updating formulae (each is equal to the traditional PSO), was introduced. This algorithm was called the reverse direction supported particle swarm optimization (RDS-PSO) algorithm. The RDS-PSO algorithm has the potential to extend the diversity and generalization of traditional PSO by regulating the reverse direction information adaptively. To implement this extension, 2 new constants were added to the velocity update equation of the traditional PSO, and these constants were regulated through 2 alternative procedures, i.e. max--min-based and cosine amplitude-based diversity-evaluating procedures. The 4 most commonly …

A New Step-Size Searching Algorithm Based On Fuzzy Logic And Neural Networks For Lms Adaptive Beamforming Systems, Walter Orozco-Tupacyupanqui, Mariko Nakano-Miyatake, Hector Perez Meana

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, a novel algorithm based on fuzzy logic and neural networks is proposed to find an approximation of the optimal step size $\mu $ for least-mean-squares (LMS) adaptive beamforming systems. A new error ensemble learning (EEL) curve is generated based on the final prediction value of the ensemble-average learning curve of the LMS adaptive algorithm. This information is classified and fed into a back propagation neural network, which automatically generates membership functions for a fuzzy inference system. An estimate of the optimal step size is obtained using a group of linguistic rules and the corresponding defuzzification method. Computer …

A New Compression Algorithm For Fast Text Search, Aydin Carus, Altan Mesut

Turkish Journal of Electrical Engineering and Computer Sciences

We propose a new compression algorithm that compresses plain texts by using a dictionary-based model and a compressed string-matching approach that can be used with the compressed texts produced by this algorithm. The compression algorithm (CAFTS) can reduce the size of the texts to approximately 41% of their original sizes. The presented compressed string matching approach (SoCAFTS), which can be used with any of the known pattern matching algorithms, is compared with a powerful compressed string matching algorithm (ETDC) and a compressed string-matching tool (Lzgrep). Although the search speed of ETDC is very good in short patterns, it can only …

Applying Metaheuristic Optimization Methods To Design Novel Adaptive Pi-Type Fuzzy Logic Controllers For Load-Frequency Control In A Large-Scale Power Grid, Thimaiphuong Dao, Yaonan Wang, Ngockhoat Nguyen

Turkish Journal of Electrical Engineering and Computer Sciences

Due to the complexity and diversity of large-scale power systems in practice, designing load-frequency control (LFC) strategies against load variations faces big challenges to ensure the stability and economy of the network. The focus of this paper is to design a novel adaptive PI-type fuzzy logic (FL)-based LFC architecture for solving the LFC problem in such an interconnected electric power grid. Applying 2 biologically inspired optimization methods, namely particle swarm optimization method and a genetic algorithm, the membership functions and rule base of a basic PI-type FL model were parameterized and optimized simultaneously and successfully. An online self-tuning method was …

Fast Measurement Of Headlamps By Means Of A Developed Fuzzy Luxmeter Based On A Fuzzy Mapping Algorithm, Mustafa Şi̇nasi̇ Ayas, İsmai̇l Hakki Altaş, Turhan Alçeli̇k

Turkish Journal of Electrical Engineering and Computer Sciences

headlamps in the dark is inferable from the results of studies. Various regulations are available in order to arrange the required illuminance values at certain positions on the road. The designed and produced headlamps have to satisfy the requirements of these regulations. Manual measurement tools, such as the luxmeter (which are based on point-by-point measurements), have traditionally been used. This traditional way has been considered inefficient in terms of awkward setup procedures, complexity, the continuous intervention of human operators, and costs. In this study, in order to overcome these problems, a new approach for measuring the illuminance values is proposed. …

An Investigation Of Intelligent Controllers Based On Fuzzy Logic And Artificial Neural Network For Power System Frequency Maintenance, Ngockhoat Nguyen, Qi Huang, Thi-Mai-Phuong Dao

Turkish Journal of Electrical Engineering and Computer Sciences

No abstract provided.

Authentication Of Uncertain Data Based On K-Means Clustering, Levent Ünver, Taflan İmre Gündem

Turkish Journal of Electrical Engineering and Computer Sciences

No abstract provided.

Wind Farm Based On Dfig Entirely Interfaced With 14-Node Distribution Network: Power Control And Voltage Regulation, Babouri Rabah, Aouchenni Ounissa, Aouzellag Djamal, Ghedamsi Kaci

Turkish Journal of Electrical Engineering and Computer Sciences

power quality problems due to the fluctuating nature of the wind and the increasing complexity of the power system. This paper describes a novel approach to voltage and power control of a radial electrical distribution network, using a wind farm. The wind farm consists of seven 75 kW induction generators (DFIG) supplying two AC/DC converters. The feed structure of the DFIG allows operating the system conversion in a wide range of speed variations. That is why it is a recommended solution, due to its capacity to increase the generator power to twice its nominal power; consequently, the system's size and …

Scalable Sentiment Analytics, Aslan Baki̇rov, Kevser Nur Çoğalmiş, Ahmet Bulut

Turkish Journal of Electrical Engineering and Computer Sciences

Spark has become a widely popular analytics framework that provides an implementation of the equally popular MapReduce programming model. Hadoop is an Apache foundation framework that can be used for processing large datasets on a cluster of computers using the MapReduce programming model. Mahout is an Apache foundation project developed for building scalable machine learning libraries, which includes built-in machine learning classifiers. In this paper, we show how to build a simple text classifier on Spark, Apache Hadoop, and Apache Mahout for extracting out sentiments from a text collection containing millions of text documents. Using a collection of 7 million …

Identifying Acquisition Devices From Recorded Speech Signals Using Wavelet-Based Features, Ömer Eski̇dere

Turkish Journal of Electrical Engineering and Computer Sciences

Speech characteristics have played a critical role in media forensics, particularly in the investigation of evidence. This study proposes two wavelet-based feature extraction methods for the identification of acquisition devices from recorded speech. These methods are discrete wavelet-based coefficients (DWBCs) and wavelet packet-based coefficients, which are mainly based on a multiresolution analysis. These features' ability to capture characteristics of acquisition devices is compared to conventional mel frequency cepstral coefficients and subband-based coefficients. In the experiments, 14 different audio acquisition devices were trained and tested using support vector machines. Experimental results showed that DWBCs can effectively be used in source audio …