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.

An Expert System Scenario for Computer-Aided Control Engineering

Abstract: We propose using expert systems to create third-generation man/machine environments for Computer-Aided Control Engineering (CACE). We believe that this approach promises to provide a high-level design environment that is powerful, supportive, flexible, broad in scope, and readily accessible to non-expert users. This paper focuses on illustrating the capabilities of such an expert system via an extended transaction (sample interaction between the expert system and a user).

Robust Nonlinear Control System Synthesis Method for Electro-Mechanical Pointing Systems with Flexible Modes

Abstract: This paper describes the design of a robust control system for a gun turret testbed called the ATB1000. The control system incorporates a control scheme based on sinusoidal-input describing function (SIDF) models of the testbed's drive subsystem to reduce the effects of backlash and nonlinear friction, and a dissipative control scheme to make the control system insensitive to the unmodeled dynamics and parameter imprecision associated with the flexible modes of the wheel/barrel subsystem.

Copropagating and counterpropagating pumps in second-order- pumped discrete fiber Raman amplifiers.

Abstract: The gains and noise figures of discrete second-order-pumped fiber Raman amplifiers utilizing copropagating and counterpropagating pump configurations were experimentally obtained, and the gain results were compared with computer simulations. It was found that the additional gain that is due to second-order Raman pumping is larger for the copropagating pumps than for the counterpropagating pumps, in agreement with simulations. In contrast to distributed second-order-pumped fiber Raman amplifiers, a slight increase in noise figure, by as much as ∼1 dB was observed relative to the single-pump scheme. However, the advantages of second-order pumping in discrete amplifiers include greater flexibility in design of the gain distribution along the fiber and the ability to spectrally distribute the pump powers to avoid undesired nonlinear effects.

Considerations on spatial crop load mapping

Abstract: Crop load, the ratio of vine size to mass of fruit harvested, is fundamental to viticulture. Measuring vine size and crop yield, the components of crop load, has historically been a labour intensive exercise that has limited the use of crop load information to improve management in vineyards. Recent advances in assessing vine vigour, size and yield using geo‐referenced sensors are starting to make high resolution crop load mapping possible. In this paper, the concept of crop load is revisited with an emphasis on how vine size and yield can be mapped in vineyards. Existing literature is reviewed on how vine size and yield vary spatially and temporally within vineyard blocks and the inference this has on the spatio‐temporal variability of crop load. An example of crop load mapping using sensor technology is presented to illustrate recent advances in sensor technology in viticulture. Finally, some emerging technology and knowledge gaps for implementing spatial crop load information into vineyard management are discussed.

Evaluation of a commercial grape yield monitor for use mid-season and at-harvest

Abstract: Aims: Yield monitors are becoming more common in North America. This research evaluates the precision and accuracy of a retro-fitted, commercially available grape yield monitor mid-season, for crop estimation and crop thinning applications, and at harvest for yield mapping.Methods and Results: Several grape yield monitors were mounted on the discharge conveyor belt of grape harvesters in both commercial and research vineyards in North America. Sensor response was compared to manual measurements at multiple masses, ranging from 20 kg to 28 Mg over the course of three seasons. Measurements were taken during crop thinning and estimation (mid-season) and at harvest. Results showed that the grape yield monitor performance was sufficient to generate good spatial maps of the relative variation in harvest yield and mid-season thinned yield. However, at harvest the sensor showed a shift in response between days of up to ±15%, such that the generation of absolute yield maps required a daily calibration against a known mass. Within a day (single harvest operation) the sensor response did not appear to drift. Mid-season applications required a different calibration to harvest applications.Conclusion: The yield sensor worked well for both mid-season and at harvest operations in North American vineyards but required a daily calibration to avoid drift issues. The mid-season yield calibrations were different between seasons; however, the harvest calibration factor was stable between seasons.Significance and Impact of study: The study showed that a commercial yield monitor with correct calibration was effective at even low fruit flow. This opens the possibility of using a harvest sensor mid-season to mechanically estimate fruit load from small point samples and to map the amount of fruit removed during fruit thinning operations. This will improve the quality of information available to viticulturist to understand fruit and crop load. The commercial yield monitor is suitable for use in North American vineyards.

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

Abstract: Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.

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.