ASRSM: A Sequential Experimental Design for Response Surface Optimization

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96749/1/qre1306.pd

Self learning strategies for experimental design and response surface optimization

Most preset RSM designs offer ease of implementation and good performance over a wide range of process and design optimization applications. These designs often lack the ability to adapt the design based on the characteristics of application and experimental space so as to reduce the number of experiments necessary. Hence, they are not cost effective for applications where the cost of experimentation is high or when the experimentation resources are limited. In this dissertation, we present a number of self-learning strategies for optimization of different types of response surfaces for industrial experiments with noise, high experimentation cost, and requiring high design optimization performance. The proposed approach is a sequential adaptive experimentation approach which combines concepts from nonlinear optimization, non-parametric regression, statistical analysis, and response surface optimization. The proposed strategies uses the information gained from the previous experiments to design the subsequent experiment by simultaneously reducing the region of interest and identifying factor combinations for new experiments. Its major advantage is the experimentation efficiency such that, for a given response target, it identifies the input factor combination (or containing region) in less number of experiments than the classical designs. Through extensive simulated experiments and real-world case studies, we show that the proposed ASRSM method clearly outperforms the classical CCD and BBD methods, works superior to optimal A- D- and V- optimal designs on average and compares favorably with global optimizations methods including Gaussian Process and RBF

On The Development of a Dynamic Contrast-Enhanced Near-Infrared Technique to Measure Cerebral Blood Flow in the Neurocritical Care Unit

A dynamic contrast-enhanced (DCE) near-infrared (NIR) method to measure cerebral blood flow (CBF) in the neurocritical care unit (NCU) is described. A primary concern in managing patients with acquired brain injury (ABI) is onset of delayed ischemic injury (DII) caused by complications during the days to weeks following the initial insult, resulting in reduced CBF and impaired oxygen delivery. The development of a safe, portable, and quantitative DCE-NIR method for measuring CBF in NCU patients is addressed by focusing on four main areas: designing a clinically compatible instrument, developing an appropriate analytical framework, creating a relevant ABI animal model, and validating the method against CT perfusion. In Chapter 2, depth-resolved continuous-wave NIR recovered values of CBF in a juvenile pig show strong correlation with CT perfusion CBF during mild ischemia and hyperemia (r=0.84, p\u3c0.001). In particular, subject-specific light propagation modeling reduces the variability caused by extracerebral layer contamination. In Chapter 3, time-resolved (TR) NIR improves the signal sensitivity to brain tissue, and a relative CBF index is be both sensitive and specific to flow changes in the brain. In particular, when compared with the change in CBF measured with CT perfusion during hypocapnia, the deconvolution-based index has an error of 0.8%, compared to 21.8% with the time-to-peak method. To enable measurement of absolute CBF, a method for characterizing the AIF is described in Chapter 4, and the theoretical basis for an advanced analytical framework—the kinetic deconvolution optical reconstruction (KDOR)—is provided in Chapter 5. Finally, a multichannel TR-NIR system is combined with KDOR to quantify CBF in an adult pig model of ischemia (Chapter 6). In this final study, measurements of CBF obtained with the DCE-NIR technique show strong agreement with CT perfusion measurements of CBF in mild and moderate ischemia (r=0.86, p\u3c0.001). The principle conclusion of this thesis is that the DCE-NIR method, combining multidistance TR instrumentation with the KDOR analytical framework, can recover CBF values that are in strong agreement with CT perfusion values of CBF. Ultimately, bedside CBF measurements could improve clinical management of ABI by detecting delayed ischemia before permanent brain damage occurs

Advancing efficiency and robustness of neural networks for imaging

Enabling machines to see and analyze the world is a longstanding research objective. Advances in computer vision have the potential of influencing many aspects of our lives as they can enable machines to tackle a variety of tasks. Great progress in computer vision has been made, catalyzed by recent progress in machine learning and especially the breakthroughs achieved by deep artificial neural networks. Goal of this work is to alleviate limitations of deep neural networks that hinder their large-scale adoption for real-world applications. To this end, it investigates methodologies for constructing and training deep neural networks with low computational requirements. Moreover, it explores strategies for achieving robust performance on unseen data. Of particular interest is the application of segmenting volumetric medical scans because of the technical challenges it imposes, as well as its clinical importance. The developed methodologies are generic and of relevance to a broader computer vision and machine learning audience. More specifically, this work introduces an efficient 3D convolutional neural network architecture, which achieves high performance for segmentation of volumetric medical images, an application previously hindered by high computational requirements of 3D networks. It then investigates sensitivity of network performance on hyper-parameter configuration, which we interpret as overfitting the model configuration to the data available during development. It is shown that ensembling a set of models with diverse configurations mitigates this and improves generalization. The thesis then explores how to utilize unlabelled data for learning representations that generalize better. It investigates domain adaptation and introduces an architecture for adversarial networks tailored for adaptation of segmentation networks. Finally, a novel semi-supervised learning method is proposed that introduces a graph in the latent space of a neural network to capture relations between labelled and unlabelled samples. It then regularizes the embedding to form a compact cluster per class, which improves generalization.Open Acces

Proceedings, MSVSCC 2015

The Virginia Modeling, Analysis and Simulation Center (VMASC) of Old Dominion University hosted the 2015 Modeling, Simulation, & Visualization Student capstone Conference on April 16th. The Capstone Conference features students in Modeling and Simulation, undergraduates and graduate degree programs, and fields from many colleges and/or universities. Students present their research to an audience of fellow students, faculty, judges, and other distinguished guests. For the students, these presentations afford them the opportunity to impart their innovative research to members of the M&S community from academic, industry, and government backgrounds. Also participating in the conference are faculty and judges who have volunteered their time to impart direct support to their students’ research, facilitate the various conference tracks, serve as judges for each of the tracks, and provide overall assistance to this conference. 2015 marks the ninth year of the VMASC Capstone Conference for Modeling, Simulation and Visualization. This year our conference attracted a number of fine student written papers and presentations, resulting in a total of 51 research works that were presented. This year’s conference had record attendance thanks to the support from the various different departments at Old Dominion University, other local Universities, and the United States Military Academy, at West Point. We greatly appreciated all of the work and energy that has gone into this year’s conference, it truly was a highly collaborative effort that has resulted in a very successful symposium for the M&S community and all of those involved. Below you will find a brief summary of the best papers and best presentations with some simple statistics of the overall conference contribution. Followed by that is a table of contents that breaks down by conference track category with a copy of each included body of work. Thank you again for your time and your contribution as this conference is designed to continuously evolve and adapt to better suit the authors and M&S supporters. Dr.Yuzhong Shen Graduate Program Director, MSVE Capstone Conference Chair John ShullGraduate Student, MSVE Capstone Conference Student Chai

A Small-Scale 3D Imaging Platform for Algorithm Performance Evaluation

In recent years, world events have expedited the need for the design and application of rapidly deployable airborne surveillance systems in urban environments. Fast and effective use of the surveillance images requires accurate modeling of the terrain being surveyed. The process of accurately modeling buildings, landmarks, or other items of interest on the surface of the earth, within a short lead time, has proven to be a challenging task. One approach of high importance for countering this challenge and accurately reconstructing 3D objects is through the employment of airborne 3D image acquisition platforms. While developments in this arena have significantly risen, there remains a wide gap in the verification of accuracy between the acquired data and the actual ground-truth data. In addition, the time and cost of verifying the accuracy of the acquired data on airborne imaging platforms has also increased. This thesis investigation proposes to design and test a small-scale 3D imaging platform to aid in the verification of current image acquisition, registration and processing algorithms at a lower cost in a controlled lab environment. A rich data set of images will be acquired and the use of such data will be explored

2020 Student Symposium Research and Creative Activity Book of Abstracts

The UMaine Student Symposium (UMSS) is an annual event that celebrates undergraduate and graduate student research and creative work. Students from a variety of disciplines present their achievements with video presentations. It’s the ideal occasion for the community to see how UMaine students’ work impacts locally – and beyond. The 2020 Student Symposium Research and Creative Activity Book of Abstracts includes a complete list of student presenters as well as abstracts related to their works

Optimizing sensory stimulation in humans after spinal cord injury

Sensory stimulation has shown promise in improving human walking after spinal cord injury (SCI). Previous studies have demonstrated some improvement with open-loop, non-individualized sensory stimulation, but after SCI, there are many unique, individual changes in sensorimotor processing. These changes make a priori identification of the best sensory stimulation pattern difficult for any given individual. Real-time optimization provides a solution to this individuality problem, through optimizing sensory stimulation parameters for a given subject in on-line (in real-time). In this research, I developed an approach to optimize sensory stimulation to maximally assist human walking after incomplete SCI. To do so, I had to develop and validate a novel optimization algorithm for globally-optimizing noisy, time-variant, black-box systems, while maximizing the information gained from each test (experiment). I optimized sensory stimulation across a range of SCI subjects, across multiple sensory stimulation sites, and with different stimulation parameterizations. In all subjects and stimulation sites, the optimal stimulation protocol produced better walking (i.e. less external force assistance was required) than three alternative stimulation protocols: an industry-standard stimulation protocol, a no-stimulation protocol, and a random-stimulation protocol. The optimization approach minimized the total force required from an assistive orthosis, and post-hoc analysis of the optimization sessions produced a better understanding of how stimulation parameters affected specific gait features (e.g. hip forces during swing). Transcutaneous spinal cord stimulation (TSCS) frequency had divergent effects on the stance and swing phases – high frequencies tended to assist with swing, but low frequencies tended to assist with stance. For the two peripheral nerve stimulation sites (posterior tibial and common peroneal nerves), the optimal gait-phase for stimulation was generally after mid-stance and before early swing. There was some variability within this time-range depending on the specific feature under study. Experimental history (i.e. time spent walking/time spent being stimulated) proved to be as important a predictor as any of the stimulation parameters.Ph.D