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

Fuzzy Coach-Player Systems for Controlling a Robot Manipulator

Many studies have been performed on the position/force control of robot manipulators. Since the desired position and force required to realize certain tasks are usually designated in the operational space, the controller should adapt itself to an environment and generate the control force vector in the operational space. On the other hand, the friction of each joint of a robot manipulator is a serious problem since it impedes control accuracy. Therefore, the friction should be effectively compensated for in order to realize precise control of robot manipulators. Recently, soft computing techniques (fuzzy reasoning, neural networks and genetic algorithms) have been playing an important role in the control of robots. Applying the fuzzy-neuro approach (a combination of fuzzy reasoning and neural networks), learning/adaptation ability and human knowledge can be incorporated into a robot controller. In this paper, we propose a two-stage adaptive robot manipulator position/force control method in which the unce...

Two-stage adaptive robot position/force control using fuzzy reasoning and neural networks

Evolution strategies, based on the natural evolution, are the algorithms to solve the parameter optimization problems numerically. In this paper, a new evolution strategy (ES) is proposed to solve optimal control problems. With a view to making a balance between exploration and exploitation, a competing subpopulation based arithmetical crossover technique is proposed. The effectiveness of the proposed ES is illustrated by some simulations for the push-cart (a discrete-time optimal control model) control problem.

An evolution strategy with competing subpopulations

A method for the identification of complex nonlinear dynamics of a multi-link robot manipulator using Runge-Kutta-Gill neural networks (RKGNNs) in the absence of input torque information is proposed. The RKGNNs constructed using shape adaptive radial basis functions are trained by an evolutionary algorithm. Due to the fact that the main function network is divided into sub-networks to represent the dynamic properties of the manipulator, the neural networks have greater information, processing capacity and can be tested for properties such as positive definiteness of the inertia matrix. Dynamics of a three-link manipulator are identified using only their input-output position and velocity data, and promising control results are obtained to prove the effectiveness of the proposed method in capturing highly nonlinear dynamics of a multi-link manipulator.

Controlling multi-link manipulators by fuzzy selection of dynamic models

This paper proposes a method for evaluating fuzzy voice commands (FVCs) by internal rehearsal. The robot's experience on previous movements is very useful for understanding fuzzy linguistic information. Therefore, an internal rehearsal system is introduced to acquire the robot's experience on previous movements. Such acquisition enhances the robot's capability to interpret fuzzy linguistic information in FVCs according to the current environmental context. The proposed system is used to evaluate the fuzzy linguistic information related to the primitive movements of a robot manipulator. Those primitive movements are realized by behavior evaluation network (BEN) with the guidance of the internal rehearsal system. Mainly, the system is utilized to understand fuzzy voice motion commands (FVMCs) and fuzzy voice joint commands (FVJCs) for controlling the end-effector movements and the single joint movements of the robot manipulator. The proposed idea is demonstrated with a PA-10 robot manipulator.

Keigo Watanabe

Papers

Fuzzy Coach-Player Systems for Controlling a Robot Manipulator

Two-stage adaptive robot position/force control using fuzzy reasoning and neural networks

An evolution strategy with competing subpopulations

Controlling multi-link manipulators by fuzzy selection of dynamic models

Evaluating fuzzy voice commands by internal rehearsal for controlling a robot manipulator