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 …

I and i

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.).

Machine learning

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

1. Introduction. Control System Design Steps. Adaptive Control. A Brief History. 2. Models for Dynamic Systems. Introduction. State-Space Models. Input/Output Models. Plant Parametric Models. Problems. 3. Stability. Introduction. Preliminaries. Input/Output Stability. Lyapunov Stability. Positive Real Functions and Stability. Stability of LTI Feedback System. Problems. 4. On-Line Parameter Estimation. Introduction. Simple Examples. Adaptive Laws with Normalization. Adaptive Laws with Projection. Bilinear Parametric Model. Hybrid Adaptive Laws. Summary of Adaptive Laws. Parameter Convergence Proofs. Problems. 5. Parameter Identifiers and Adaptive Observers. Introduction. Parameter Identifiers. Adaptive Observers. Adaptive Observer with Auxiliary Input. Adaptive Observers for Nonminimal Plant Models. Parameter Convergence Proofs. Problems. 6. Model Reference Adaptive Control. Introduction. Simple Direct MRAC Schemes. MRC for SISO Plants. Direct MRAC with Unnormalized Adaptive Laws. Direct MRAC with Normalized Adaptive Laws. Indirect MRAC. Relaxation of Assumptions in MRAC. Stability Proofs in MRAC Schemes. Problems. 7. Adaptive Pole Placement Control. Introduction. Simple APPC Schemes. PPC: Known Plant Parameters. Indirect APPC Schemes. Hybrid APPC Schemes. Stabilizability Issues and Modified APPC. Stability Proofs. Problems. 8. Robust Adaptive Laws. Introduction. Plant Uncertainties and Robust Control. Instability Phenomena in Adaptive Systems. Modifications for Robustness: Simple Examples. Robust Adaptive Laws. Summary of Robust Adaptive Laws. Problems. 9. Robust Adaptive Control Schemes. Introduction. Robust Identifiers and Adaptive Observers. Robust MRAC. Performance Improvement of MRAC. Robust APPC Schemes. Adaptive Control of LTV Plants. Adaptive Control for Multivariable Plants. Stability Proofs of Robust MRAC Schemes. Stability Proofs of Robust APPC Schemes. Problems. Appendices. Swapping Lemmas. Optimization Techniques. Bibliography. Index. License Agreement and Limited Warranty.

Robust adaptive control

The possibility and desirability of damping the step response of stepper motors has been noted by many authors. A root locus interpretation is presented in this paper which shows concisely some aspects of the damping. A method for providing damping when using a Pulsewidth Modulation (PWM) chopper is presented and it is shown to be possible to introduce damping in "single" or "multi"-phase excitation modes. These ideas have been implemented by the authors on three different stepper motors and some practical conclusions are presented.

Electromagnetic Damping for Stepper Motors with Chopper Drives

Robust asymptotic model matching and its application to output synchronization of heterogeneous multi-agent systems

The Mekong Delta (MKD) region in Vietnam has experienced large-scale inter-provincial migration flows. This study employs agent-based modeling to explore migration dynamics across the provinces and cities in the MKD. An agent-based model, which incorporates the Theory of Planned Behaviour into the decision-making process to effectively break down migration intention into related components, is developed to simulate how an individual makes migration decisions considering different economic, social, and environmental circumstances. Outputs of the agent-based model are calibrated via real province-level data of in-, out-, and net-migration flows in the MKD region with the use of a data analytics approach, in an attempt to validate the results produced.

Agent-based Modeling of Inter-provincial Migration in the Mekong Delta, Vietnam: A Data Analytics Approach

In 2006 the Sony Corporation announced that production of the Aibo robot, which is the standard robot platform of the four-legged league of RoboCup, would be discontinued. This paper describes a prototype robot kit which could lead to a new standard platform for the four-legged league. The aims for development of the new platform can be summarized as follows: 1.) Continue and improve the concepts behind the Sony Aibo robot. 2.) Allow research into sophisticated quadruped soccer skills. 3.) Build an open systems robot to advance low level features and to support high level strategy planning and learning. 4.) Achieve faster and more exciting soccer games. 5.) Contribute to progress of the four-legged league, which to date has been a very successful league. 6.) Determine kit style hardware for year to year upgrades of selected modules. The robot kit allows for substantial new flexibility in hardware design in associated scientific challenges and research projects. The present paper addresses two possible versions of the proposed kit: terrier and bear.

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Proposal of a kit-style robot as the new standard platform for the four-legged league

The Standard Platform League (SPL) provides an environment that is essentially static; with the exception of other robots and the audience, the area in which a robot is expected to localise itself is quite favourable. However, a large number of the predefined landmarks in the given world model can be perceived as ambiguous in many scenarios, with the prime example being field line markings. In this paper a technique is presented that implicitly disambiguates these detected field line objects in order to use them for localization purposes.

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Richard H. Middleton

Papers

Electromagnetic Damping for Stepper Motors with Chopper Drives

Robust asymptotic model matching and its application to output synchronization of heterogeneous multi-agent systems

Agent-based Modeling of Inter-provincial Migration in the Mekong Delta, Vietnam: A Data Analytics Approach

Proposal of a kit-style robot as the new standard platform for the four-legged league

Line point registration: a technique for enhancing robot localization in a soccer environment