Author
James Taylor
Other affiliations: Institut national de la recherche agronomique, European Spallation Source, Children's Hospital of Philadelphia ...read more
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.
Topics: Laser, Fiber laser, Optical fiber, Picosecond, Photonic-crystal fiber
Papers published on a yearly basis
Papers
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01 Oct 2007TL;DR: The autonomy, communications, and artificial intelligence (AI) requirements of the component agents of such a system are defined and the software implementation of such agents are discussed.
Abstract: This paper addresses a practical intelligent multi-agent system for asset management for the petroleum industry, which is crucial for profitable oil and gas facilities operations and maintenance. A research project was initiated to study the feasibility of an intelligent asset management system. Having proposed a conceptual model, architecture, and implementation plan for such a system in previous work [1], [2], [3], we define the autonomy, communications, and artificial intelligence (AI) requirements of the component agents of such a system. We also discuss the software implementation of such agents. Furthermore, we describe a simple system prototype, and conduct a real time simulation experiment to analyze the prototype performance. Simulation results reveal that MATLAB can be used to build high performance real-time multi-agent systems, which can be used for many applications.
16 citations
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TL;DR: In this paper, the passive modelocking of a cw rhodamine B dye laser was reported, and a simple linear cavity with no dispersion correction was achieved over the spectral range 616-658 nm using 1,3'-diethyl-4,2'-quinolylthiacarbocyanine iodide (DQTCI) as the saturable absorber.
16 citations
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TL;DR: In this article, the passive modelocking of a cw rhodamine 6G dye laser over the spectral range 570 to 600 nm using 2-(p-dimethylaminostyryl)- benzthiazolylethyliodide (DASBTI) as the saturable absorber is reported.
15 citations
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15 Jun 1988TL;DR: A CACE environment with EDBM as an integral part is defined and implemented, which can accommodate several control engineers working on a single project, with a project lead engineer having responsibility and control of the entire data base.
Abstract: There has been substantial progress made in the past decade in the development of analysis and design software for computer-aided control engineering (CACE). Engineering data-base management (EDBM) for support of CACE has not received much attention until recently, however. As CACE environments become more comprehensive and more powerful, the need for keeping track of the models, simulations, analysis results, control system designs, and validation study results over the control system design cycle becomes more pressing and the lack of EDBM support becomes more of an impediment to effective controls engineering. We have defined and implemented a CACE environment with EDBM as an integral part. The data base is organized in a hierarchical famework having the levels Project, Sub-project, Model, Attribute, and Element. The levels Project and Sub-project accommodate several control engineers working on a single project, with a project lead engineer having responsibility and control of the entire data base. Within a project, Models (plant models, control system models, etc.) are the main focus. Each model has two attributes, a Description and a Result_set. Fundamental properties plus component models (representations of a plant, compensator, sensor, etc.) comprise the elements of a Description; elements of a Result_set include any data generated with the model, such as a time-history, frequency response, etc. One factor that complicates the CACE DBM problem is that system models used for simulation, analysis, and design activity usually evolve as a project progresses.
15 citations
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TL;DR: In this paper, the second-harmonic generation in periodically poled potassium titanyl phosphate waveguides was used to generate a 1.12μm pump in the case of the ytterbium-doped fiber pump.
Abstract: Compact yellow (560nm) and green (532nm) picosecond pulse sources are demonstrated that utilize second-harmonic generation in periodically poled potassium titanyl phosphate waveguides. Both systems employ ytterbium-doped fiber pump sources. In the yellow case, efficient single-pass Raman scattering in 25m of dispersion-compensating fiber was additionally used to generate the 1.12μm pump. Raman gain could similarly be used in compact configurations to generate other pump wavelengths for use in frequency upconversion schemes.
15 citations
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TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
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 …
33,785 citations
01 Jan 2016
TL;DR: The using multivariate statistics is universally compatible with any devices to read, allowing you to get the most less latency time to download any of the authors' books like this one.
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.
14,604 citations
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TL;DR: 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.
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
13,333 citations
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TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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.).
13,246 citations
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01 Jan 1994
TL;DR: In this paper, the authors present a brief history of LMIs in control theory and discuss some of the standard problems involved in LMIs, such as linear matrix inequalities, linear differential inequalities, and matrix problems with analytic solutions.
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.
11,085 citations