2016 INFORMS Annual Meeting Program

WB66

INFORMS Nashville – 2016

2 - Justification And Justice Matter: Organizational Attention To Ideas On Digital Innovation Platforms Inchan Kim, University of New Hampshire, Durham, NH, United States, i.kim@unh.edu, John Qi Dong Digital innovation platforms allow crowdsourcing of ideas, in effect generating big data for organizations. Then, to which ideas do organizations direct their focus? Based on the six logics of justification together with the four justice principles, we use qualitative comparative analysis (QCA) to analyze the configurational impacts of justification and justice reflected in ideas from MyStarbucksIdea.com on organizational allocation of attention. 3 - Domain Specific Lexicon For Clinical Trial Subject Eligibility Analysis Euisung Jung, University of Toledo, Euisung.Jung@utoledo.edu It is well understood that an NLP application requires sophisticated lexical resources to support its processing goals. Different solutions have been proposed to identify multi-gram disease named entities in the healthcare informatics literature. In this study, we develop a domain-specific lexicon for n-gram Named Entity Recognition (NER) in the breast cancer domain. The domain-specific dictionary was evaluated by comparing it with Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT). The results showed that it add significant number of new terms which is very useful in effective natural language processing. WB66 Mockingbird 2- Omni Graph Analytics for Complex Systems - II Sponsored: Quality, Statistics and Reliability Sponsored Session Chair: Hoang Tran, Texas A&M University, Address, College Station, TX, United States, tran@tamu.edu Co-Chair: Satish Bukkapatnam, Texas A&M University, College Station, Calibration refers to the process of adjusting parameters of a computer simulation so that the simulation responses match the corresponding physical responses. Calibration can be interpreted as a curve to surface matching problem. We propose a graph-theoretic non-isometric matching approach to solve this problem using the graph shortest path algorithm in one-dimensional spaces. For higher dimensional spaces, we introduce the generalized shortest path concept to solve the matching problem. 2 - Spectral Graph Theoretic Sensor Fusion Prahalad Rao, Binghamton University, prao@binghamton.edu The central theme of this talk is motivated from the compelling need for sensor- based in situ quality assurance approaches in complex systems and processes, such as additive manufacturing (AM). The key research question is: how to fuse information from multidimensional sensor signals for monitoring and prognosis? The proposed approach maps a multidimensional signal as an un-weighted undirected network graph. Through this talk, it is demonstrated that graph theoretic signal processing has the potential to monitor complex systems in a data rich environment. 3 - Learning Data Association Graph We presents a general formulation for a minimum cost data association problem which associates data features via one-to-one, m-to-one and one-to-n links with minimum total cost of the links. A motivating example is a problem of tracking multiple interacting targets imaged on video frames. Many existing multitarget tracking methods are capable of tracking non-interacting targets or tracking interacting targets of restricted degrees of interactions. The proposed formulation solves a multitarget tracking problem for general degrees of inter-object interactions. Chiwoo Park, Florida State University, 2525 Pottsdamer St., Tallahassee, FL, 32310, United States, cpark5@fsu.edu, Taylor J. Woehl, James E. Evans, Nigel Browning College Station, TX, United States, satish@tamu.edu 1 - Generalized Graph Shortest Path For Calibration Of Computer Simulations. Babak Farmanesh, Oklahoma State University, farmane@ostatemail.okstate.edu

4 - Measuring Redundancy Of State Estimators In Large Networks By Combining L1-minimziation And Integer Programming Vishnu Vijayaraghavan, Texas A&M University, College Station, TX, 77843, United States, vishnunitr@tamu.edu, Kiavash Kianfar, Yu Ding, Hamid Parsaei Finding the degree of redundancy for structured linear systems is proven to be NP-hard. Bound-and-decompose, 0-1 mixed integer programming (MIP) and hybrid algorithms embedding 0-1 mixed integer programming within a bound- and-decompose framework have all been studied and compared in the literature. In this paper we take advantage of the computational efficiency of linear programs to present an l1 minimization approach combined with mixed integer programming to address this problem. This approach proves to be very effective in measuring redundancy of state estimators in large networks. WB67 Mockingbird 3- Omni Joint Session QSR/DM: Process Monitoring for Diverse Types of Data Sponsored: Quality, Statistics and Reliability Sponsored Session Chair: Youngseon Jeong, Annandale, VA, United States, youngseonjeong@gmail.com Co-Chair: Myong K Jeong, Rutgers University, Piscataway, NJ, United States, mjeong@rci.rutgers.edu 1 - Bayesian Based Distribution Free Procedure For Fault Identification Mehmet Turkoz, Rutgers University, 16 Rachel Terrace, Piscataway, NJ, 08854, United States, turkoz@scarletmail.rutgers.edu, Sangahn Kim, Youngseon Jeong, Myong K Jeong, Elsayed A. Elsayed, A.M.S. Hamouda, Khalifa Al-Khalifa Many real life process control problems do not follow multivariate normal distribution. In a process with unknown underlying distribution, identifying fault variables of an out-of-control signal is a challenging issue for quality problems. In this research, we present a new Bayesian fault identification method that does not assume any specific probability distribution. 2 - Modeling And Shape Control Of Large Composite Components For Section-to-fuselage Joints Yuchen Wen, Gatech, ycwen@gatech.edu Shape control of large composite components is important in aerospace industry. The current method of shape control has limitations of low efficiency and non- optimal. We propose a surrogate-model based optimal shape control strategy in order to achieve dimensional variation reduction and efficient shape adjustment in large composite parts assembly process. The objective is accomplished by (i) Investigating a surrogate model to achieve good prediction performance; (ii) Conducting multi-objective optimization to determine the control actions from the Pareto solutions; (iii) Implementing the sensitivity analysis to determine the best number and positions of the locators. 3 - Process Tracking And Monitoring Based On Discrete Jumping Model Chao Wang, University of Wisconsin-Madison, Madison, WI, United States, cwang436@wisc.edu, Shiyu Zhou Jumping model has been used as an effective tool in tracking and detecting changes for continuous statistics in various applications. In this paper, we extend the current jumping model from the continuous case to the discrete case to track and monitor the changes in attribute data. The jumping model based posterior distribution of the process mean is constructed with attribute data and prior knowledge of the process. Using the posterior distribution, a control chart is then developed to monitor the attribute data process. 4 - A New Bayesian Classification Model For Uncertain Data Young-Seon Jeong, Chonnam National University, Gwangju, Korea, Republic of, youngseonjeong@gmail.com, Byunghoon Kim, Myong K Jeong, Jeongsub Choi, Soonmok Kwon, Jihoon Kang This talk presents a new Bayesian classification model which considers the correlation among uncertain features. Even though several classifiers for uncertain data have been developed, they did not consider the dependency among uncertain features, which have a critical effect on classification accuracy. Experimental results with simulated data and real-life data show that the proposed approach for uncertain data is more accurate than existing approaches.

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