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

There is an increasing demand for dynamic systems to become safer and more reliable This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital It is clear that fault diagnosis is becoming an important subject in modern control theory and practice Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework It contains many important topics and methods; however, total coverage and completeness is not the primary concern The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications The first two chapters are of tutorial value and provide a starting point for newcomers to this field The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications This will certainly appeal to experts in this field Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed Although this is a research monograph, it will be an important text for postgraduate research students world-wide The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world

Robust Model-Based Fault Diagnosis for Dynamic Systems

비만아 부모의 돌봄 경험: q 방법론

The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

This paper describes a control method for mobile robots represented by a nonlinear dynamical system, which is subjected to an output deviation caused by drastically changed disturbances. We here propose some controllers in the framework of neuro-interface. It is assumed that a neural network (NN)-based feedforward controller is construcetd by following the concept of virtual master-slave robot, in which a virtual master robot as a feedforward controller is used to control the slave (i.e., actual) robot. The whole system of the present neuro-interface consists of an NN-based feedforward controller, a feedback PD controller and an adaptive fuzzy feedback compensator. The NN-based feedforward controller is trained offline by using a gradient method, the gains of the PD controller are to be chosen constant, and the adaptive fuzzy compensator is constructed with a simplified fuzzy reasoning. Some simulations are presented to confirm the validity of the present approach, where a nonholonomic mobile robot with two independent driving wheels is assmued to have a disturbance due to the change of mass for the robot.

A Neuro-interface with fuzzy compensator for controlling nonholonomic mobile robots

An optimal structurally partitioned filtering theory is presented for continuous-time linear stochastic systems. A necessary and sufficient condition is given for the possibility of constructing such a partitioned filter, which consists of the well known reduced-order Kalman-type nominal filter and a reduced-order fixed point smoother, applying the Lainiotis' partition theorem. It is then shown that in an incompletely disturbable system, three kinds of partitioned filter can always be synthesized through the Kalman's disturbable canonical model. It is also indicated that the so called Friedland's bias correcting estimator can be obtained as an identity structurally partitioned filter. Furthermore, a partitioned filter based on the Jordan's canonical form is given for the case where there exists no system noise, i.e. a completely un-disturbable system. In comparison with the usual Kalman filtering method, the partitioned approach proposed here is able to deal with the lower order Riccati equation and hence...

Optimal structurally partitioned filter for undisturbable stochastic systems Part I. Basic theory

In the conventional SLAM problem, a laser range scanner that can obtain relative angle and distance between a robot and a landmark is used as an external sensor. However, such a sensor is expensive, so that a bearing-only SLAM is studied recently by using a cheap CCD camera. For such a bearing-only SLAM problem, there is a problem that an objective environment has to be measured at multiple distinct poses of the robot, and since there is a lack of information it is also called to be not easy for assuring that the self-position of the robot and the landmarks are estimated with a high accuracy. In this paper, we focus on unscented smoothers that can improve the estimation accuracy for a general SLAM using unscented Kalman filters and apply it to design a bearing-only unscented smoother for a visual SLAM problem. Simulations are presented to check the usefulness of the proposed method.

Bearing-only unscented smoothers for a visual SLAM

An iterative learning control scheme is described for linear discrete-time systems. A weighted least-squares criterion of learning error is optimized to obtain a unique control gain for a case when the number of sampling is relatively small. It is then shown that algorithmic convergence can be readily guaranteed, because the present learning rule consists of a steady-state Kalman filter. By paying attention to the sparse system structure for the system's impulse response model, we further derive a suboptimal iterative learning control for a practical case when the number of sampling is large.

An interative learning control scheme using the weighted least-squares method

Human beings are fascinating creatures. Their behavior and appearance cannot be compared with any other living organism in the world. They have two distinct features with compared to any other living being; unique physical nature and emotions / feelings. Anybody who studies on humans or tries to construct human like machines should consider these two vital facts. When robots are interacting with humans and other objects, they certainly have a safe distance between them and the object. Some of the problems in concern are how can this distance be optimized when interacting with humans; will there be any advantages over achieving this; will it help to improve the condition of robots; can it be a mere constant distance; how will the humans react, etc. In order to “humanize” robots, they (robots) should also have certain understanding of such emotions that we, humans have. The present main objective is to “teach” one such human understanding, commonly known as “personal space” to autonomous mobile robots. As Simmons et al., 1997 described, recent research in mobile robot navigation has utilized autonomous mobile robots in service fields. To operate them in an environment with people, it requires more than just localization and navigation. The robots should recognize and act according to human social behavior to share the resources without conflict (Nakauchi & Simmons, 2002). Sometimes, even when humans interact with each other, it leads to resource conflict. At such times humans use social rules to maintain order (Sack, 1986). The comfort level of the humans for which the robot is working will be very important if the robot is to do its job effectively. Extensive research is being performed in the area of robotics to improve the conditions of the robots, advancing the abilities to do specific tasks, motion planning, etc. However, very little work has been performed in trying to understand how people would interact with a robot, how to make them comfortable, factors that make uncomfortable or threatening, methods or ways for robots to indicate their feelings, etc. to analyze the aesthetic qualities of the behavior patterns of robots (Nakauchi & Simmons, 2002). As in the very beginning of the mobile robotic systems, there had been some kind of distance or space between the robots to any other object in the vicinity. This was just a mere distance for safety for easy maneuvering and for collision avoidance. As Stentz, 1996 and O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m

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Keigo Watanabe

Papers

A Neuro-interface with fuzzy compensator for controlling nonholonomic mobile robots

Optimal structurally partitioned filter for undisturbable stochastic systems Part I. Basic theory

Bearing-only unscented smoothers for a visual SLAM

An interative learning control scheme using the weighted least-squares method

Adaptive Personal Space for Humanizing Mobile Robots