James Taylor

Bio: James Taylor is an academic researcher from Newcastle University. The author has contributed to research in topics: Laser & Fiber laser. The author has an hindex of 95, co-authored 1161 publications receiving 39945 citations. Previous affiliations of James Taylor include Institut national de la recherche agronomique & European Spallation Source.

Evaluation of the Concord Crop Load Response for Current Commercial Production in New York

Abstract: Economic pressures in the New York Concord grape industry over the past 30 years have driven crop management practices toward less severe pruning to achieve larger crops. The purpose of this study was to investigate the effect of crop load on juice soluble solids and the seasonal change in vine pruning weight in New York Concord grapevines. Over a four-year period, vines were balanced pruned at two levels or fixed node pruned at two levels to give four pruning severities. For balanced pruning, vines were pruned to leave 33 or 66 fruiting nodes for the first 500 g pruning weight and an additional 11 nodes for each additional 500 g pruning weight. For fixed node pruning, vines were pruned to 100 or 120 fruiting nodes per vine. The 120-node vines were also manually cluster-thinned at 30 days after bloom to target 0, 25, or 50% crop removal. In a second study, the 120-node pruning with midseason fruit-thinning was repeated over 11 years to assess seasonal differences on the crop load response. Crop load was measured as the yield-to-pruning weight ratio (Y:PW) and ranged from 1 to 40 in this study. On average, the industry standard of 16 Brix was achieved at a Y:PW of 20, and no seasonal pruning weight change was observed at a Y:PW of 17.5. There was a positive linear relationship between seasonal growing degree days and the Y:PW needed to reach 16 Brix, as well as between seasonal precipitation and the Y:PW required to observe no seasonal pruning weight change. The results from this study were used to improve crop load management recommendations for New York Concord vineyards under current practices.

Potential--and potential pitfalls--of screening newborns for critical congenital heart disease.

Abstract: Before discharge from a newborn nursery, most US infants undergo screening for a diverse list of medical conditions. This article considers the issues around screening newborns for congenital heart disease.

Using Conditional Kernel Density Estimation for Wind Power Forecasting

Abstract: Of the various renewable energy resources, wind power is widely recognized as one of the most promising The management of wind farms and electricity systems can benefit greatly from the availability of estimates of the probability distribution of wind power generation However, most research has focused on point forecasting of wind power In this paper, we develop an approach to producing density forecasts for the wind power generated at individual wind farms Our interest is in intraday data and prediction from 1 to 72 hours ahead We model wind power in terms of wind speed and wind direction In this framework, there are two key uncertainties First, there is the inherent uncertainty in wind speed and direction, and we model this using a bivariate VARMA-GARCH model, with a Student t distribution, in the Cartesian space of wind speed and direction Second, there is the stochastic nature of the relationship of wind power to wind speed (described by the power curve), and to wind direction We model this using conditional kernel density (CKD) estimation, which enables a nonparametric modeling of the conditional density of wind power Using Monte Carlo simulation of the VARMA-GARCH model and CKD estimation, density forecasts of wind speed and direction are converted to wind power density forecasts Our work is novel in several respects: previous wind power studies have not modeled a stochastic power curve; to accommodate time evolution in the power curve, we incorporate a time decay factor within the CKD method; and the CKD method is conditional on a density, rather than a single value The new approach is evaluated using datasets from four Greek wind farms

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Using Multivariate Statistics

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The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Linear Matrix Inequalities in System and Control Theory

Abstract: Preface 1. Introduction Overview A Brief History of LMIs in Control Theory Notes on the Style of the Book Origin of the Book 2. Some Standard Problems Involving LMIs. Linear Matrix Inequalities Some Standard Problems Ellipsoid Algorithm Interior-Point Methods Strict and Nonstrict LMIs Miscellaneous Results on Matrix Inequalities Some LMI Problems with Analytic Solutions 3. Some Matrix Problems. Minimizing Condition Number by Scaling Minimizing Condition Number of a Positive-Definite Matrix Minimizing Norm by Scaling Rescaling a Matrix Positive-Definite Matrix Completion Problems Quadratic Approximation of a Polytopic Norm Ellipsoidal Approximation 4. Linear Differential Inclusions. Differential Inclusions Some Specific LDIs Nonlinear System Analysis via LDIs 5. Analysis of LDIs: State Properties. Quadratic Stability Invariant Ellipsoids 6. Analysis of LDIs: Input/Output Properties. Input-to-State Properties State-to-Output Properties Input-to-Output Properties 7. State-Feedback Synthesis for LDIs. Static State-Feedback Controllers State Properties Input-to-State Properties State-to-Output Properties Input-to-Output Properties Observer-Based Controllers for Nonlinear Systems 8. Lure and Multiplier Methods. Analysis of Lure Systems Integral Quadratic Constraints Multipliers for Systems with Unknown Parameters 9. Systems with Multiplicative Noise. Analysis of Systems with Multiplicative Noise State-Feedback Synthesis 10. Miscellaneous Problems. Optimization over an Affine Family of Linear Systems Analysis of Systems with LTI Perturbations Positive Orthant Stabilizability Linear Systems with Delays Interpolation Problems The Inverse Problem of Optimal Control System Realization Problems Multi-Criterion LQG Nonconvex Multi-Criterion Quadratic Problems Notation List of Acronyms Bibliography Index.