Research Presentations

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The modern manufacturing industry is investing in new technologies such as the Internet of Things (IoT), big data analytics, cloud computing and cyber security to cope with system complexity, increase information visibility, improve production performance, and gain competitive advantage in the global market. These advances are rapidly enabling a new generation of smart manufacturing that “enable all information about the manufacturing process to be available whenever it is needed, wherever it is needed, and in an easily comprehensible form across the enterprise and among interconnected enterprises”. Smart manufacturing goes beyond the automation of manufacturing shop floors but rather depends on data-driven innovations to realize high levels of autonomy and optimization of manufacturing enterprises. This webinar will review the Internet of Things (IoT) for smart manufacturing that help you:
- Understand the evolution of IoT technology and its applications in the manufacturing domain
- Develop the strategy to implement IoT technology for smart manufacturing
- Understand the technology of cloud computing and fog computing for IoT data analytics
- Realize full potentials of big data through new analytical methods and tools for smarter manufacturing.

H. Yang, S. Kumara, S. Bukkapatnam, and F. Tsung, “The internet of things for smart manufacturing – a review,” IISE Transactions, 2018. DOI: 10.1080/24725854.2018.1555383

Abstract: Cardiac electrical activities are varying in both space and time. Human heart consists of a fractal network of muscle cells, Purkinje fibers, arteries and veins. Whole-heart modeling of electrical wave conduction and propagation involves a greater level of complexity. Our previous work developed a computer model of the anatomically realistic heart and simulated the electrical conduction with the use of cellular automata and parallel computing. However, simplistic assumptions and rules limit its ability to provide an accurate approximation of real-world dynamics on the complex heart surface, due to sensitive dependence of nonlinear dynamical systems on initial conditions. In this paper, we propose new reaction-diffusion methods and pattern recognition tools to simulate and model spatiotemporal dynamics of electrical wave conduction and propagation on the complex heart surface, which include (i) whole heart model; (ii) 2D isometric graphing of 3D heart geometry; (iii) reaction diffusion modeling of electrical waves in 2D graph, and (iv) spatiotemporal pattern recognition. Experimental results show that the proposed numerical solution has strong potentials to model the space-time dynamics of electrical wave conduction in the whole heart, thereby achieving a better understanding of disease-altered cardiac mechanisms.

Click here for presentation slides    DOI: 10.13140/RG.2.2.24336.51200

Abstract: Nonlinear dynamics arise whenever multifarious entities of a system cooperate, compete, or interfere. Effective monitoring and control of nonlinear dynamics will increase system quality and integrity, thereby leading to significant economic and societal impacts. In order to cope with system complexity and increase information visibility, modern industries are investing in a variety of sensor networks and dedicated data centers. Real-time sensing gives rise to “big data”. Realizing the full potential of “big data” for advanced quality control requires fundamentally new methodologies to harness and exploit complexity. This talk will present novel nonlinear methodologies that mine dynamic recurrences from in-process big data for real-time system informatics, monitoring, and control. Recurrence (i.e., approximate repetitions of a certain event) is one of the most common phenomena in natural and engineering systems. For examples, the human heart is near-periodically beating to maintain vital living organs. Stamping machines are cyclically forming sheet metals during production. Process monitoring of dynamic transitions in complex systems (e.g., disease conditions or manufacturing quality) is more concerned about aperiodic recurrences and heterogeneous recurrence variations. However, little has been done to investigate heterogeneous recurrence variations and link with the objectives of process monitoring and anomaly detection. This talk will present the state of art in nonlinear recurrence analysis and a new heterogeneous recurrence methodology for monitoring and control of nonlinear stochastic processes. Specifically, the developed methodologies will be demonstrated in both manufacturing and healthcare applications. The proposed methodology is generally applicable to a variety of complex systems exhibiting nonlinear dynamics, e.g., precision machining, sleep apnea, aging study, nanomanufacturing, biomanufacturing. In the end, future research directions will be discussed. 

Click here for presentation slides    DOI: 10.13140/RG.2.2.17625.62566

Abstract: Industry in the 21st century is investing in a variety of sensor networks and dedicated data centers to increase information visibility. Advanced sensing captures a wealth of information on the condition and status of a product, process or a system. As a result, we are facing spatially and temporally data-rich environments. The next major challenge is in harnessing the big data to bring substantial improvements to the design and operations, particular in quality and integrity assurance, of complex systems. This talk will present an integral approach of physics-based modeling and sensor-based informatics to advance knowledge discovery and innovation in complex systems. First, I will describe a new physical-statistical modeling approach for efficient and effective computer experiments and optimization of cardiac models. Computer modeling and experiments provide physiologists and cardiologists an indispensable tool to characterize and model cardiac functions in health and in disease, as well as to optimize medical decision making. In this study, an easy-to-evaluate surrogate model is developed for faster approximation and calibration of cardiac models to investigate glycosylation-altered kinetics of Na+ ion channels and the causal effects on cardiac cells. Second, I will talk about a new approach of sparse particle filtering for modeling spatiotemporal dynamics of big data in distributed sensor networks. Distributed sensing gives rise to spatially-temporally big data. Realizing the full potentials of distributed sensing calls for space-time data fusion in the dynamically-evolving physical environment. In this study, we developed a sparse particle filtering model to recursively estimate and update latent state variables for predicting nonlinear stochastic dynamics and modeling close interactions between spatial and temporal processes in distributed sensor networks. In the end, future research directions will be discussed.

Click here for presentation slides    DOI: 10.13140/RG.2.2.14873.11362

Abstract: Rapid advancement of distributed sensing and imaging technology brings the proliferation of high-dimensional spatiotemporal data, i.e., y(s; t) and x(s; t) in manufacturing and healthcare systems. Traditional regression is not generally applicable for predictive modeling in these complex structured systems. For example, infrared cameras are commonly used to capture dynamic thermal images of 3D parts in additive manufacturing. The temperature distribution within parts enables engineers to investigate how process conditions impact the strength, residual stress and microstructures of fabricated products. The ECG sensor network is placed on the body surface to acquire the distribution of electric potentials y(s; t), also named body surface potential mapping (BSPM). Medical scientists call for the estimation of electric potentials x(s; t) on the heart surface from BSPM y(s; t) so as to investigate cardiac pathological activities (e.g., tissue damages in the heart). However, spatiotemporally varying data and complex geometries (e.g., human heart or mechanical parts) defy traditional regression modeling and regularization methods. This talk will present a novel physics-driven spatiotemporal regularization (STRE) method for high-dimensional predictive modeling in complex manufacturing and healthcare systems. This model not only captures the physics-based interrelationship between time-varying explanatory and response variables that are distributed in the space, but also addresses the spatial and temporal regularizations to improve the prediction performance. In the end, we will introduce our lab at Penn State and future research directions will also be discussed.

Click here for presentation slides    DOI: 10.13140/RG.2.2.10914.73920

Abstract: Rapid advancement of sensing and information technology brings the big data, which presents a gold mine of the 21st century to advance knowledge discovery. However, big data also brings significant challenges for data-driven decision making. In particular, it is common that a large number of variables (or predictors, features) underlie the big data. Complex interdependence structures among variables challenge the traditional framework of predictive modeling. This paper presents a new methodology of self-organizing network for variable clustering and predictive modeling. Specifically, we developed a new approach, namely nonlinear coupling analysis to measure nonlinear interdependence structures among variables. Further, all the variables are embedded as nodes in a complex network. Nonlinear-coupling forces move these nodes to derive a self-organizing topology of network. As such, variables are clustered as sub-network communities in the space. Experimental results demonstrated that the proposed method not only outperforms traditional variable clustering algorithms such as hierarchical clustering and oblique principal component analysis, but also effectively identify interdependent structures among variables and further improves the performance of predictive modeling. The proposed new methodology of self-organizing variable clustering is generally applicable for data-driven decision making in many disciplines that involve a large number of highly-redundant variables.

Click here for presentation slides    DOI: 10.13140/RG.2.2.17946.75205
Invited keynote talk in the NERCCS 2018: The First Northeast Regional Conference on Complex Systems, April 11-14, 2018
Nonlinear dynamics arise whenever multifarious entities of a system cooperate, compete, or interfere. Effective monitoring and control of nonlinear dynamics will increase system quality and integrity, thereby leading to significant economic and societal impacts. In order to cope with system complexity and increase information visibility, modern industries are investing in a variety of sensor networks and dedicated data centers. Real-time sensing gives rise to “big data”. Realizing the full potential of “big data” for advanced quality control requires fundamentally new methodologies to harness and exploit complexity. This talk will present novel sensor-based nonlinear dynamical methodologies for real-time system informatics, monitoring, and control. Specifically, the developed methodologies will be demonstrated in both advanced manufacturing and smart health applications. The proposed methodology is generally applicable to a variety of complex systems exhibiting nonlinear dynamics, e.g., additive manufacturing, cardiovascular systems, precision machining, sleep apnea, biomanufacturing. In the end, future research directions will be discussed.
Abstract: Wireless sensor network has emerged as a key technology for monitoring space-time dynamics of complex systems, e.g., environmental sensor network, battlefield surveillance network, and body area sensor network. However, sensor failures are not uncommon in traditional sensing systems. As such, we propose the design of stochastic sensor networks to allow a subset of sensors at varying locations within the network to transmit dynamic information intermittently. Realizing the full potential of stochastic sensor network hinges on the development of novel information-processing algorithms to support the design and exploit the uncertain information for decision making. This paper presents a new approach of sparse particle filtering to model spatiotemporal dynamics of big data in the stochastic sensor network. Notably, we developed a sparse kernel-weighted regression model to achieve a parsimonious representation of spatial patterns. Further, the parameters of spatial model are transformed into a reduced-dimension space, and thereby sequentially updated with the recursive Bayesian estimation when new sensor observations are available over time. Therefore, spatial and temporal processes closely interact with each other. Experimental results on real-world data and different scenarios of stochastic sensor networks (i.e., spatially, temporally, and spatiotemporally dynamic networks) demonstrated the effectiveness of sparse particle filtering to support the stochastic design and harness the uncertain information for modeling space-time dynamics of complex systems.
Click here for presentation slides
H. Yang, One-day workshop on "Nonlinear Dynamics, Recurrence Analysis, and Complex Networks", Georgia Institute of Technology – IsyE Department, Atlanta, GA, Nov. 9, 2012. The topics are listed as follows:
Part I – Nonlinear dynamical systems and chaos
Part II – Recurrence analysis of complex systems
Part III – Multiscale recurrence analysis
Part IV – Recurrence and complex networks
Matlab Toolboxes
(1) Toolbox of recurrence plot and recurrence quantification analysis
(2) Toolbox of heterogeneous recurrence analysis
Abstract: Additive manufacturing (AM) provides a greater level of flexibility to produce a 3D part with complex geometries directly from the design. However, the widespread application of AM is currently hampered by technical challenges in process repeatability and quality control. To enhance the in-process information visibility, advanced sensing is increasingly invested for real-time AM process monitoring. The proliferation of in-situ sensing data calls for the development of analytical methods for the extraction of features sensitive to layerwise defects, and the exploitation of pertinent knowledge about defects for in-process quality control of AM builds. As a result, there are increasing interests and rapid development of sensor-based models for the characterization and estimation of layerwise defects in the past few years. However, very little has been done to go from sensor-based modeling of defects to the suggestion of in-situ corrective actions for quality control of AM builds. In this talk, we present a new sequential decision-making framework for in-situ control of AM processes through the constrained Markov decision process (CMDP), which jointly considers the conflicting objectives of both total cost (i.e., energy or time) and build quality. Experimental results show that the CMDP formulation provides an effective policy for executing corrective actions to repair and counteract incipient defects in AM before completion of the build.
Click here for presentation slides

Abstract: Advanced manufacturing is moving towards a new paradigm of ‘low-volume-high-mix’ production. There is an urgent need to develop effective representations of real-world 3D objects and further enable the matching and retrieval of engineering designs. This paper presents a new self-organizing network representation of 3D objects. Each voxel of the 3D object is a node in a network, and the edge is dependent on node closeness in space. Then, the network is self-organized by balancing attractive and repulsive forces between the nodes. Experimental results show the effectiveness of network representation by reassembling the geometry of 3D objects.

Hui Yang, Runsang Liu, Soundar Kumara, “Self-organizing network modelling of 3D objects,” CIRP Annals, 2020. DOI: https://doi.org/10.1016/j.cirp.2020.04.099
Abstract: Physics-based principles (e.g., heat transfer, bioelectromagnetism theorems) generally help predict complex dynamics in manufacturing and healthcare systems. For example, mechanical engineers leverage infrared cameras and heat-transfer physics to predict thermal distribution within 3D builds in additive manufacturing. Medical scientists use bioelectromagnetism physics to predict heart-surface electric potentials and investigate cardiac pathological activities (e.g., tissue damages in the heart). However, physics-based principles do not account for real-world uncertainties, thereby often generating predictions that have discrepancies from sensor observations. Such uncertainties may be introduced by simplified physical assumptions, geometric variations, measurement noises, and other extraneous factors. This talk will present a novel physics-driven spatiotemporal regularization (STRE) method for high-dimensional predictive modeling in complex manufacturing and healthcare systems. This model not only captures the physics-based interrelationship between time-varying explanatory and response variables that are distributed in the space, but also addresses the spatial and temporal regularizations to improve the prediction performance.

Click here for presentation slides
B. Yao, and H. Yang, “Spatiotemporal Regularization for Inverse ECG Modeling,” IISE Transactions Health Systems Engineering, 2020. DOI: https://doi.org/10.1080/24725579.2020.1823531
B. Yao, R. Zhu, and H. Yang, “Characterizing the Location and Extent of Myocardial Infarctions with Inverse ECG Modeling and Spatiotemporal Regularization,” IEEE Journal of Biomedical and Health Informatics, page 1-11, 2017, DOI: https://doi.org/10.1109/JBHI.2017.2768534
B. Yao and H. Yang, “Physics-driven spatiotemporal regularization for high-dimensional predictive modeling,” Nature - Scientific Reports 6, 39012, 2016. DOI: https://www.nature.com/articles/srep39012
B, Yao and H. Yang, “Mesh Resolution Impacts the Accuracy of Inverse and Forward ECG problems,” Proceedings of 2016 IEEE Engineering in Medicine and Biology Society Conference (EMBC), August 16-20, 2016, Orlando, FL, United States. DOI: https://doi.org/10.1109/EMBC.2016.7591615
Penn State College of Medicine Researach Quality Assurance Seminar
RQACOMseminar
The Industrial Internet of Things (IIoT) has revolutionized the way manufacturers across the country are using data and thinking about their operations. But, as the connectivity grows, so does the risk for cyber attacks. How can cloud computing and AI help keep that data secure? Join MxD, University of West Florida, and Penn State University on February 25 as we discuss current research in the space. Attendees will have the opportunity to ask questions and engage with the speakers about their current efforts!
mxd022521

If a talk looks interesting, please feel free to contact me for a presentation or seminar in person.

Biography: Dr. Hui Yang is a Professor in the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at The Pennsylvania State University, University Park, PA. Currently, he serves as the PI and site director of NSF Center for Health Organization Transformation (CHOT). He obtained his BS and MS degrees from China University of Mining and Technology (Beijing), PhD from Oklahoma State University. He has been at the Pennsylvania State University since 2015. Prior to joining Penn State in 2015, he was an Assistant Professor in the Department of Industrial and Management Systems Engineering at the University of South Florida from 2009 to 2015. He is a recipient of 2015 Outstanding Faculty Award at the University of South Florida.

Dr. Yang's research interests focus on sensor-based modeling and analysis of complex systems for process monitoring, process control, system diagnostics, condition prognostics, quality improvement, and performance optimization. His research program is supported by National Science Foundation (including the prestigious NSF CAREER award), National Institute of Standards and Technology (NIST), Lockheed Martin, NSF center for e-Design, Susan Koman Cancer Foundation, NSF Center for Healthcare Organization Transformation, Institute of Cyberscience, James A. Harley Veterans Hospital, and Florida James and Esther King Biomedical research program. His research group received a number of best paper awards and best poster awards from IISE Annual Conference, IEEE EMBC, IEEE CASE, and INFORMS.

Dr. Yang is the president (2017-2018) of IISE Data Analytics and Information Systems Society, the president (2015-2016) of INFORMS Quality, Statistics and Reliability (QSR) society, and the program chair of 2016 Industrial and Systems Engineering Research Conference (ISERC). He is also an associate editor for IISE Transactions, IISE Transactions Healthcare Systems Engineering, IEEE Journal of Biomedical and Health Informatics (JBHI), IEEE Transactions on Automation Science and Engineering (TASE), IEEE Robotics and Automation Letters (RA-L), Quality Technology & Quantitative Management, and an Associate Editor for the Proceedings of IEEE CASE, IEEE EMBC, and IEEE BHI. He serves as a referee for a diverse set of top tier research journals such as Physical Review, IEEE Transactions on Biomedical Engineering, IEEE Journal of Biomedical and Health Informatics, Biophysical Journal, IIE Transactions, Technometrics, and IEEE Transactions on Automation Science and Engineering. He is a professional member of IEEE, IEEE EMBS, INFORMS, IIE, ASEE and American Heart Association (AHA).