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

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

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

Data Mining Practical Machine Learning Tools and Techniques

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

An inevitable collision state for a robotic system can be defined as a state for which, no matter what the future trajectory followed by the system is, a collision with an obstacle eventually occurs. An inevitable collision state takes into account the dynamics of both the system and the obstacles, fixed or moving. The main contribution of this paper is to lay down and explore this novel concept (and the companion concept of inevitable collision obstacle). Formal definitions of the inevitable collision states and obstacles are given. Properties fundamental for their characterization are established. This concept is very general, and can be useful both for navigation and motion planning purposes (for its own safety, a robotic system should never find itself in an inevitable collision state). To illustrate the interest of this concept, it is applied to a problem of safe motion planning for a robotic system subject to sensing constraints in a partially known environment (i.e. that may contain unexpected obst...

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Inevitable collision states - a step towards safer robots?

Great interest is now focused on distributed autonomous robotic systems (DARS) as a new strategy for the realization of flexible, robust, and intelligent robots. Inspired by autonomous, decentralized, and self-organizing biological systems, the field of DARS encompasses broad interdisciplinary technologies related not only to robotics and computer engineering but also to biology and psychology. The rapidly growing interest in this new area of research was manifest in the first volume of Distributed Autonomous Robotic Systems, published in 1994. This second volume in the series presents the most recent work by eminent researchers and includes such topics as multirobot control, distributed robotic systems design, self-organizing systems, and sensing and navigation for cooperative robots. Distributed Autonomous Robotic Systems 2 is a valuable source for those whose work involves robotics and will be of great interest to those in the fields of artificial intelligence, self-organizing systems, artificial life, and computer science.

Distributed Autonomous Robotic Systems

Constrained Model Predictive Control

A new driving mechanism for holonomic omnidirectional mobile robots is designed, which enables 3 DOF motion control by three correspondent actuators in a decoupled manner with no redundancy A prototype of the omnidirectional mobile robot with the driving mechanism is developed including a parallel link suspension mechanism The kinematics of the omnidirectional mobile robot is also analyzed, and simulation for velocity control of the robot is performed by a method for velocity modulation with interpolation to achieve the given target position and velocity

Development of an omni-directional mobile robot with 3 DOF decoupling drive mechanism

This paper presents the design method for a humanoid which has a network based modular structure and a standard PC architecture. Based on the proposed method, we developed DARwIn-OP which meets the requirements for an open humanoid platform. DARwIn-OP has an expandable system structure, high performance, simple maintenance, familiar development environment, and affordable prices. All resources of DARwIn-OP including source codes, circuit diagrams, mechanical CAD files, and parts information will be opened to the public.

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Hajime Asama

Papers

Inevitable collision states - a step towards safer robots?

Distributed Autonomous Robotic Systems

Constrained Model Predictive Control

Development of an omni-directional mobile robot with 3 DOF decoupling drive mechanism

Development of open humanoid platform DARwIn-OP