Papers by Satya Aditya Akundi
Application of Automated Quality Control in Smart Factories - A Deep Learning-based Approach
is a Professor and Chair of Industrial, Manufacturing and Systems Engineering at UTEP. His resear... more is a Professor and Chair of Industrial, Manufacturing and Systems Engineering at UTEP. His research focuses on the computational intelligence, data mining, bio-informatics and advanced manufacturing. Dr. Tseng published in many refereed journals such as IEEE Transactions, IIE Transaction, Journal of Manufacturing Systems and others. He has been serving as a principle investigator of many research projects, funded by NSF, NASA, DoEd, KSEF and LMC. He is currently serving as an editor of Journal of Computer Standards & Interfaces.
Model based systems engineering—A text mining based structured comprehensive overview
Systems Engineering, Sep 6, 2021
An observed increase in systems scale and complexity has led to a significant momentum in explori... more An observed increase in systems scale and complexity has led to a significant momentum in exploring, identifying, and adopting model based systems engineering (MBSE) tools and techniques amongst research communities and industry practitioners. Several attempts to transform systems design and engineering practices through the use of MBSE in academia and industry has led to a considerable increase in the number of articles published containing the keyword “MBSE.” This growth serves as the motivation in this paper to explore the MBSE landscape with the help of text mining techniques to identify the most often used key terms, tools, and languages, in the context of research in MBSE and the thematic aspects defining the use of MBSE by researchers and practitioners. The objective of this paper is to provide a structured comprehensive overview of research contributions across the MBSE landscape by employing text mining techniques for: (a) identifying the concepts and methodologies inferred upon in relation to MBSE, and (b) classifying the literature published to identify commonalities across academic researchers and practitioners using MBSE tools and methods. For this purpose, the abstracts of 2380 relevant articles published in the period of the last two decades from five different databases are mined. It is found that the terms “SysML,” “Cyber Physical Systems,” and “Production” are the most used terms among researchers across the MBSE landscape with SysML being the most widely used modeling language. Further, six major thematic topics are identified that classify articles from over the last two decades with an increasing interest observed in the use of MBSE to support manufacturing and production engineering activities, especially in the cyber physical systems domain. The contributions of this paper provide a leeway on using text mining techniques to understand the research directions that are currently of interest in the field of MBSE and thereby identify potential future research directions.
He earned a Master of Science in Electrical and Computer Engineering at the University of Texas a... more He earned a Master of Science in Electrical and Computer Engineering at the University of Texas at El Paso (UTEP). Intrigued by Systems Engineering , he earned a Ph.D in Electrical and Computer Engineering, with a concentration in Industrial and Systems Engineering (ISE) at Unniversity of Texas in 2016. His research is focused on undersanding Complex Technical and Socio-Technical Systems from an Infromation Theortic approach. He has worked on a number of projects in the field of Electrical & Computer Engineering, Systems Engineering, Additive Manufacturing and Green Energy Manufacturing. His research interests are in Systems Engineering & Architecture, Complex systems, Systems testing and Application of Entropy to Complex Systems.
is a Professor and Chair of Industrial, Manufacturing and Systems Engineering at UTEP. His resear... more is a Professor and Chair of Industrial, Manufacturing and Systems Engineering at UTEP. His research focuses on the computational intelligence, data mining, bio-informatics and advanced manufacturing. Dr. Tseng published in many refereed journals such as IEEE Transactions, IIE Transaction, Journal of Manufacturing Systems and others. He has been serving as a principle investigator of many research projects, funded by NSF, NASA, DoEd, KSEF and LMC. He is currently serving as an editor of Journal of Computer Standards & Interfaces.
is a Professor and Chair of Industrial, Manufacturing and Systems Engineering at UTEP. His resear... more is a Professor and Chair of Industrial, Manufacturing and Systems Engineering at UTEP. His research focuses on the computational intelligence, data mining, bio-informatics and advanced manufacturing. Dr. Tseng published in many refereed journals such as IEEE Transactions, IIE Transaction, Journal of Manufacturing Systems and others. He has been serving as a principle investigator of many research projects, funded by NSF, NASA, DoEd, KSEF and LMC. He is currently serving as an editor of Journal of Computer Standards & Interfaces.
Modern instances of disease emergence have shown that human subjective reactions to a novel disea... more Modern instances of disease emergence have shown that human subjective reactions to a novel disease can be as important as the objective reality of the disease spread. Therefore, this work introduces human cognitive heuristics and biases into epidemiological modeling. Human subjective perception and reaction to the presence of disease is represented in the difference between the objective and subjective probability of contagion. It is assumed that humans within a disease spread situation will have either limited or full information about the objective probability of contagion. From this information, humans subjectively react, forming a subjective assessment of the probability of contagion. Although the translation from the objective to the subjective probability of contagion is rooted in a biological basis, the translation has been adequately determined by previous research in Prospect Theory as developed by Daniel Kahneman and Amos Tversky. The formulation of Lotka-Volterra epidemiology models with parameters for perceived probability of contagion was followed by numerical experimentation and sensitivity analyses that determined values of the parameters that create cyclic population behavior, whether growing or dampened, as well as acyclic behavior. The results show that the model is capable of capturing stable as well as unstable behavior, and is able to model key epidemiological disease behaviors and states, such as epidemic and endemic conditions.
Engineering (ISE) track. He earned a Master of Science in Electrical and Computer Engineering at ... more Engineering (ISE) track. He earned a Master of Science in Electrical and Computer Engineering at the University of Texas at El Paso (UTEP) in 2012. He has worked on a number of projects in the field of Electrical & Computer Engineering, Systems Engineering, Additive Manufacturing and Green Energy Manufacturing. He is the current president of INCOSE UTEP student chapter along with being involved in UTEP Green Fund committee. His research interests are in Systems Engineering & Architecture, Complex systems, Systems testing and Application of Entropy to Complex Systems.
2018 ASEE Annual Conference & Exposition Proceedings, Sep 10, 2020
is a Professor and Chair of Industrial, Manufacturing and Systems Engineering at UTEP. His resear... more is a Professor and Chair of Industrial, Manufacturing and Systems Engineering at UTEP. His research focuses on the computational intelligence, data mining, bio-informatics and advanced manufacturing. Dr. Tseng published in many refereed journals such as IEEE Transactions, IIE Transaction, Journal of Manufacturing Systems and others. He has been serving as a principle investigator of many research projects, funded by NSF, NASA, DoEd, KSEF and LMC. He is currently serving as an editor of Journal of Computer Standards & Interfaces.
Information Entropy as a Basis for Classroom Structural Assessment
Procedia Computer Science, 2021
Quality control (QC) in manufacturing processes is critical to ensuring consumers receive product... more Quality control (QC) in manufacturing processes is critical to ensuring consumers receive products with proper functionality and reliability. Faulty products can lead to additional costs for the manufacturer and damage trust in a brand. A growing trend in QC is the use of machine vision (MV) systems because of their noncontact inspection, high repeatability, and relatively low cost. This paper presents a robust MV system developed to perform comparative dimensional inspection on diversely shaped samples, including additive manufacturing products. The algorithm used performs dimensional inspection on a base product considered to have acceptable dimensions. The perimeter, area, rectangularity, and circularity of the base product are determined using blob analysis on a calibrated camera. These parameters are then used as the standard with which to judge additional products. Each product following is similarly inspected and compared to the base product parameters. A likeness score is calculated for each product, which provides a single value tracking all parameter differences. Finally, the likeness score is considered on whether it is within a threshold, and the product is considered to be acceptable or defective. The proposed MV system has achieved satisfactory results, as discussed in the results section, that would allow it to serve as a dependable and accurate QC inspection system in industrial settings.
is a Professor and Chair of Industrial, Manufacturing and Systems Engineering at UTEP. His resear... more is a Professor and Chair of Industrial, Manufacturing and Systems Engineering at UTEP. His research focuses on the computational intelligence, data mining, bio-informatics and advanced manufacturing. Dr. Tseng published in many refereed journals such as IEEE Transactions, IIE Transaction, Journal of Manufacturing Systems and others. He has been serving as a principle investigator of many research projects, funded by NSF, NASA, DoEd, KSEF and LMC. He is currently serving as an editor of Journal of Computer Standards & Interfaces.
The professional Master of Science in Systems Engineering (MSSE) program at UTEP was approved in ... more The professional Master of Science in Systems Engineering (MSSE) program at UTEP was approved in 2009, before the development of the Graduate Reference Curriculum for System Engineering (GRCSE) v0.25 released in December 2010. GRCSE v1.0 (released December 2012) is part of the Body of Knowledge and Curriculum to Advance Systems Engineering (BKCASE) project of the Systems Engineering Research Center (SERC). GRCSE is a set of recommendations, from invited experts from industry, government, academia, and various professional organizations, for a systems-centric masters' level graduate program in system engineering, together with implementation guidance for a university to satisfy the suggested requirements. GRCSE includes recommendations for a program architecture and core body of knowledge, as well as for objectives, student outcomes, and assessment methodologies. This paper investigates the correspondence between the MSSE Program and the GRCSE in terms of curricula, architecture, and core body of knowledge, including features which were deemed crucial for the success of this particular program application. The paper also reports MSSE Program outcomes and objectives attainment, as well as achievements during the first four years of the program. Based on alumni and employer feedback, enhancements that will increase MSSE Program alignment with the GRCSE are possible, and are discussed. Finally, the paper presents insights from the authors' experience with the MSSE which may inform the further development of GRCSE.
Social media and pandemic events: challenges for alert-warning systems
He earned a Master of Science in Electrical and Computer Engineering at the University of Texas a... more He earned a Master of Science in Electrical and Computer Engineering at the University of Texas at El Paso (UTEP). Intrigued by Systems Engineering , he earned a Ph.D in Electrical and Computer Engineering, with a concentration in Industrial and Systems Engineering (ISE) at Unniversity of Texas in 2016. His research is focused on undersanding Complex Technical and Socio-Technical Systems from an Infromation Theortic approach. He has worked on a number of projects in the field of Electrical & Computer Engineering, Systems Engineering, Additive Manufacturing and Green Energy Manufacturing. His research interests are in Systems Engineering & Architecture, Complex systems, Systems testing and Application of Entropy to Complex Systems.
INCOSE SE handbook v3.2 and v4.0 analysis of context diagrams set
Growing interest observed in the Associate Systems Engineering Professional (ASEP) certification ... more Growing interest observed in the Associate Systems Engineering Professional (ASEP) certification among students and faculty in the Systems Engineering Programs has led the authors to analyze the SE Handbook. In this paper, an effort in the analysis of The International Council on Systems Engineering (INCOSE) Systems Engineering (SE) Handbook versions 3.2 and 4.0 using network abstractions to understand and identify central concepts of SE processes is portrayed by examining only the Context Diagram figures, each of which depicts a set of Activities (a Process) that transforms inputs (Inputs, Controls, and Enablers) into outputs (Outputs). The network representation of the said helped in detecting key life cycle processes and information transferors of SE handbook versions 3.2 and 4.0, along with identifying central communities of SE Handbook.
2018 ASEE Annual Conference & Exposition Proceedings, Sep 10, 2020
He earned a Master of Science in Electrical and Computer Engineering at the University of Texas a... more He earned a Master of Science in Electrical and Computer Engineering at the University of Texas at El Paso (UTEP). Intrigued by Systems Engineering , he earned a Ph.D in Electrical and Computer Engineering, with a concentration in Industrial and Systems Engineering (ISE) at Unniversity of Texas in 2016. His research is focused on undersanding Complex Technical and Socio-Technical Systems from an Infromation Theortic approach. He has worked on a number of projects in the field of Electrical & Computer Engineering, Systems Engineering, Additive Manufacturing and Green Energy Manufacturing. His research interests are in Systems Engineering & Architecture, Complex systems, Systems testing and Application of Entropy to Complex Systems.
is a Professor and Chair of Industrial, Manufacturing and Systems Engineering at UTEP. His resear... more is a Professor and Chair of Industrial, Manufacturing and Systems Engineering at UTEP. His research focuses on the computational intelligence, data mining, bio-informatics and advanced manufacturing. Dr. Tseng published in many refereed journals such as IEEE Transactions, IIE Transaction, Journal of Manufacturing Systems and others. He has been serving as a principle investigator of many research projects, funded by NSF, NASA, DoEd, KSEF and LMC. He is currently serving as an editor of Journal of Computer Standards & Interfaces.
Understanding the Trends of Autonomous Systems Over the Last Decade — A Work in Progress
2020 IEEE International Systems Conference (SysCon), Aug 24, 2020
Research communities are characterized by their vocabulary, which directly maps to the concepts u... more Research communities are characterized by their vocabulary, which directly maps to the concepts utilized by the community at a point in time. The Autonomous Systems community is characterized by the published vocabulary of approximately the last decade. To understand the concepts utilized by the autonomous systems community, in this paper we analyze the abstracts of the published articles from ACM Transactions on Autonomous and Adaptive Systems over the last decade. The data shows the increasing and decreasing presence of conceptual group “Topics” year by year. The results indicate the rising importance of big data, data mining, and feature extraction driven artificial intelligence.
is a Professor and Chair of Industrial, Manufacturing and Systems Engineering at UTEP. His resear... more is a Professor and Chair of Industrial, Manufacturing and Systems Engineering at UTEP. His research focuses on the computational intelligence, data mining, bio-informatics and advanced manufacturing. Dr. Tseng published in many refereed journals such as IEEE Transactions, IIE Transaction, Journal of Manufacturing Systems and others. He has been serving as a principle investigator of many research projects, funded by NSF, NASA, DoEd, KSEF and LMC. He is currently serving as an editor of Journal of Computer Standards & Interfaces.
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Papers by Satya Aditya Akundi