2,858 research outputs found

    Change-point Problem and Regression: An Annotated Bibliography

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    The problems of identifying changes at unknown times and of estimating the location of changes in stochastic processes are referred to as the change-point problem or, in the Eastern literature, as disorder . The change-point problem, first introduced in the quality control context, has since developed into a fundamental problem in the areas of statistical control theory, stationarity of a stochastic process, estimation of the current position of a time series, testing and estimation of change in the patterns of a regression model, and most recently in the comparison and matching of DNA sequences in microarray data analysis. Numerous methodological approaches have been implemented in examining change-point models. Maximum-likelihood estimation, Bayesian estimation, isotonic regression, piecewise regression, quasi-likelihood and non-parametric regression are among the methods which have been applied to resolving challenges in change-point problems. Grid-searching approaches have also been used to examine the change-point problem. Statistical analysis of change-point problems depends on the method of data collection. If the data collection is ongoing until some random time, then the appropriate statistical procedure is called sequential. If, however, a large finite set of data is collected with the purpose of determining if at least one change-point occurred, then this may be referred to as non-sequential. Not surprisingly, both the former and the latter have a rich literature with much of the earlier work focusing on sequential methods inspired by applications in quality control for industrial processes. In the regression literature, the change-point model is also referred to as two- or multiple-phase regression, switching regression, segmented regression, two-stage least squares (Shaban, 1980), or broken-line regression. The area of the change-point problem has been the subject of intensive research in the past half-century. The subject has evolved considerably and found applications in many different areas. It seems rather impossible to summarize all of the research carried out over the past 50 years on the change-point problem. We have therefore confined ourselves to those articles on change-point problems which pertain to regression. The important branch of sequential procedures in change-point problems has been left out entirely. We refer the readers to the seminal review papers by Lai (1995, 2001). The so called structural change models, which occupy a considerable portion of the research in the area of change-point, particularly among econometricians, have not been fully considered. We refer the reader to Perron (2005) for an updated review in this area. Articles on change-point in time series are considered only if the methodologies presented in the paper pertain to regression analysis

    Introduction to machine and deep learning for medical physicists

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    Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155469/1/mp14140_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155469/2/mp14140.pd

    Joint University Program for Air Transportation Research, 1988-1989

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    The research conducted during 1988 to 1989 under the NASA/FAA-sponsored Joint University Program for Air Transportation Research is summarized. The Joint University Program is a coordinated set of three grants sponsored by NASA Langley Research Center and the Federal Aviation Administration, one each with the Massachusetts Institute of Technology, Ohio University, and Princeton University. Completed works, status reports, and annotated bibliographies are presented for research topics, which include computer science, guidance and control theory and practice, aircraft performance, flight dynamics, and applied experimental psychology. An overview of the year's activities for each university is also presented

    Joint University Program for Air Transportation Research, 1991-1992

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    This report summarizes the research conducted during the academic year 1991-1992 under the FAA/NASA sponsored Joint University Program for Air Transportation Research. The year end review was held at Ohio University, Athens, Ohio, June 18-19, 1992. The Joint University Program is a coordinated set of three grants sponsored by the Federal Aviation Administration and NASA Langley Research Center, one each with the Massachusetts Institute of Technology (NGL-22-009-640), Ohio University (NGR-36-009-017), and Princeton University (NGL-31-001-252). Completed works, status reports, and annotated bibliographies are presented for research topics, which include navigation, guidance and control theory and practice, intelligent flight control, flight dynamics, human factors, and air traffic control processes. An overview of the year's activities for each university is also presented

    Modeling and Optimal Operation of Hydraulic, Wind and Photovoltaic Power Generation Systems

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    The transition to 100% renewable energy in the future is one of the most important ways of achieving "carbon peaking and carbon neutrality" and of reducing the adverse effects of climate change. In this process, the safe, stable and economical operation of renewable energy generation systems, represented by hydro-, wind and solar power, is particularly important, and has naturally become a key concern for researchers and engineers. Therefore, this book focuses on the fundamental and applied research on the modeling, control, monitoring and diagnosis of renewable energy generation systems, especially hydropower energy systems, and aims to provide some theoretical reference for researchers, power generation departments or government agencies

    Uncertainty of Measurement in Medical Laboratories

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    The “Guide to the Expression of Uncertainty in Measurement” (GUM) is not systematically used in medical laboratories, for what the laboratorian should understand the Uncertainty Approach and its importance to recognize the level of realism of results. This chapter presents, discusses, and recommends the models fulfilling GUM principles. An example is given to a single test for an easier understanding of the determination of measurement uncertainty. All the practice uses a freeware. Results with larger measurement uncertainty intervals have a significant probability of being unrealistic, arising a high risk of the uncorrected clinical decision. A flow chart to the selection of models for the determination of measurement uncertainty in a medical laboratory is recommended

    Deterministic Artificial Intelligence

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    Kirchhoff’s laws give a mathematical description of electromechanics. Similarly, translational motion mechanics obey Newton’s laws, while rotational motion mechanics comply with Euler’s moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research culminating here with a text on the ability to make rigid bodies in rotation become self-aware, and even learn. This book is meant for basic scientifically inclined readers commencing with a first chapter on the basics of stochastic artificial intelligence to bridge readers to very advanced topics of deterministic artificial intelligence, espoused in the book with applications to both electromechanics (e.g. the forced van der Pol equation) and also motion mechanics (i.e. Euler’s moment equations). The reader will learn how to bestow self-awareness and express optimal learning methods for the self-aware object (e.g. robot) that require no tuning and no interaction with humans for autonomous operation. The topics learned from reading this text will prepare students and faculty to investigate interesting problems of mechanics. It is the fondest hope of the editor and authors that readers enjoy the book
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