Computer Engineering Commons^™

Data Science Applied To Discover Ancient Minoan-Indus Valley Trade Routes Implied By Commonweight Measures, Peter Revesz

CSE Conference and Workshop Papers

This paper applies data mining of weight measures to discover possible long-distance trade routes among Bronze Age civilizations from the Mediterranean area to India. As a result, a new northern route via the Black Sea is discovered between the Minoan and the Indus Valley civilizations. This discovery enhances the growing set of evidence for a strong and vibrant connection among Bronze Age civilizations.

Applications Of Supervised Machine Learning In Autism Spectrum Disorder Research: A Review, Kayleigh K. Hyde, Marlena N. Novack, Nicholas Lahaye, Chelsea Parlett-Pelleriti, Raymond Anden, Dennis R. Dixon, Erik Linstead

Engineering Faculty Articles and Research

Autism spectrum disorder (ASD) research has yet to leverage "big data" on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as …

Domain-Specific Use Cases For Knowledge-Enabled Social Media Analysis, Soon Jye Kho, Swati Padhee, Goonmeet Bajaj, Krishnaprasad Thirunarayan, Amit Sheth

Publications

No abstract provided.

Using The K-Means Clustering Algorithm To Classify Features For Choropleth Maps, Mark Polczynski, Michael Polczynski

Electrical and Computer Engineering Faculty Research and Publications

Common methods for classifying choropleth map features typically form classes based on a single feature attribute. This technical note reviews the use of the k-means clustering algorithm to perform feature classification using multiple feature attributes. The k-means clustering algorithm is described and compared to other common classification methods, and two examples of choropleth maps prepared using k-means clustering are provided.

A Knowledge-Based Clinical Toxicology Consultant For Diagnosing Multiple Exposures, Joel D. Schipper, Douglas D. Dankel Ii, A. Antonio Arroyo, Jay L. Schauben

Publications

Objective: This paper presents continued research toward the development of a knowledge-based system for the diagnosis of human toxic exposures. In particular, this research focuses on the challenging task of diagnosing exposures to multiple toxins. Although only 10% of toxic exposures in the United States involve multiple toxins, multiple exposures account for more than half of all toxin-related fatalities. Using simple medical mathematics, we seek to produce a practical decision support system capable of supplying useful information to aid in the diagnosis of complex cases involving multiple unknown substances.

Methods: The system is automatically trained using data mining …

Knowledge Discovery And Analysis In Manufacturing, Mark Polczynski, Andzrej Kochanski

Electrical and Computer Engineering Faculty Research and Publications

The quality and reliability requirements for next-generation manufacturing are reviewed, and current approaches are cited. The potential for augmenting current quality/reliability technology is described, and characteristics of potential future directions are postulated. Methods based on knowledge discovery and analysis in manufacturing (KDAM) are reviewed, and related successful applications in business and social fields are discussed. Typical KDAM applications are noted, along with general functions and specific KDAM-related technologies. A systematic knowledge discovery process model is reviewed, and examples of current work are given, including description of successful applications of KDAM to creation of rules for optimizing gas porosity in sand …

Diagnostics Of Eccentricities And Bar/End-Ring Connector Breakages In Polyphase Induction Motors Through A Combination Of Time-Series Data Mining And Time-Stepping Coupled Fe-State Space Techniques, John F. Bangura, Richard J. Povinelli, Nabeel Demerdash, Ronald H. Brown

Electrical and Computer Engineering Faculty Research and Publications

This paper develops the foundations of a technique for detection and categorization of dynamic/static eccentricities and bar/end-ring connector breakages in squirrel-cage induction motors that is not based on the traditional Fourier transform frequency-domain spectral analysis concepts. Hence, this approach can distinguish between the "fault signatures" of each of the following faults: eccentricities, broken bars, and broken end-ring connectors in such induction motors. Furthermore, the techniques presented here can extensively and economically predict and characterize faults from the induction machine adjustable-speed drive design data without the need to have had actual fault data from field experience. This is done through the …

A New Temporal Pattern Identification Method For Characterization And Prediction Of Complex Time Series Events, Richard J. Povinelli, Xin Feng

Electrical and Computer Engineering Faculty Research and Publications

A new method for analyzing time series data is introduced in this paper. Inspired by data mining, the new method employs time-delayed embedding and identifies temporal patterns in the resulting phase spaces. An optimization method is applied to search the phase spaces for optimal heterogeneous temporal pattern clusters that reveal hidden temporal patterns, which are characteristic and predictive of time series events. The fundamental concepts and framework of the method are explained in detail. The method is then applied to the characterization and prediction, with a high degree of accuracy, of the release of metal droplets from a welder. The …

Diagnostics Of Bar And End-Ring Connector Breakage Faults In Polyphase Induction Motors Through A Novel Dual Track Of Time-Series Data Mining And Time-Stepping Coupled Fe-State Space Modeling, Richard J. Povinelli, John F. Bangura, Nabeel Demerdash, Ronald H. Brown

Electrical and Computer Engineering Faculty Research and Publications

This paper develops the fundamental foundations of a technique for detection of faults in induction motors that is not based on the traditional Fourier transform frequency domain approach. The technique can extensively and economically characterize and predict faults from the induction machine adjustable speed drive design data. This is done through the development of dual-track proof-of-principle studies of fault simulation and identification. These studies are performed using our proven Time Stepping Coupled Finite Element-State Space method to generate fault case data. Then, the fault cases are classified by their inherent characteristics, so-called “signatures” or “fingerprints.” These fault signatures are extracted …