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A Deep Learning Graphical User Interface Application on MATLAB

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Conference

2018 ASEE Annual Conference & Exposition

Location

Salt Lake City, Utah

Publication Date

June 23, 2018

Start Date

June 23, 2018

End Date

July 27, 2018

Conference Session

Manufacturing Curriculum and Course Innovations

Tagged Division

Manufacturing

Page Count

8

Permanent URL

https://peer.asee.org/29674

Download Count

205

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Paper Authors

biography

Aditya Akundi University of Texas, El Paso Orcid 16x16 orcid.org/0000-0002-8656-7002

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Aditya Akundi is currently affiliated to Industrial Manufacturing and Systems Engineering department, and Research Institute for Manufacturing and Engineering Systems at University of Texas, ElPaso.

He earned a Bachelor of Technology in Electronics and Communication Engineering from Jawaharlal Nehru Technological University, India. 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.

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biography

Tzu-Liang Bill Tseng University of Texas, El Paso

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Dr. Tseng 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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Zejing Cao The University of Texas at El Paso

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Hoejin Kim University of Texas, El Paso Orcid 16x16 orcid.org/0000-0002-0139-9098

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Mr. Hoejin Kim is currently a Ph.D. student in Department of Mechanical Engineering at the University of Texas at El Paso. He received a bachelor degree in materials engineering technology at Korea Tech in 2008 and a master degree in manufacturing engineering technology at Oregon Institute of Technology in 2014. His research interests are focused on 3D printing of piezo-, pyro-, and dielectric materials for pressure and temperature sensors and quality control of 3D printed product using big data mining.

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Abstract

Deep learning, a new era of machine learning technique, shows the ability to generalize computational model through hierarchical layers by learning the features from a large amount of training data without any human intervention. Deep learning was mainly implemented in areas of computer vision, audio, and speech processing. Especially deep learning has greatly improved state of art in image classification. Image-based classification, the most classic but significant research topic, has been critically improved using deep learning architecture and outperformed the majority of state-of-the-art achievements. No bells and whistles, deep learning becomes the most popular research area. However, in order to train a comprehensive classifier, a large amount of data is inescapable, which apparently needs lots of computational time so that it requires fast GPUs to accomplish fast training. In general, powerful GPUs are expensive and the process of solving compatibility problems in both hardware and software are reluctant as a consequence, universities only have very limited access to GPUs’ implementations. The main purpose of this paper is in identifying how to propagate principles of deep learning to students and build the bridge to make them learn and use deep learning easier or more accessible. In this paper, we develop a Graphic User Interface (GUI) deep learning for beginners to test classification results through CPU without massive training on computation. We use the high-performance GPU to train a model, once finished, we save the parameters of training model and load it to the GUI and provide a platform for students to do the testing.

Akundi, A., & Tseng, T. B., & Cao, Z., & Kim, H. (2018, June), A Deep Learning Graphical User Interface Application on MATLAB Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. https://peer.asee.org/29674

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