Fuzzy Control for Automatic Train Operation System

Yasunobu, S.; Miyamoto, Shigenobu; Ihara, Hirokazu

doi:10.1016/b978-0-08-029365-3.50010-9

Cited by 45 publications

(19 citation statements)

References 1 publication

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“…벡터장 방법은 이동 로봇의 연속적인 움직임을 나타내지만 다양한 환경에 적용하는데 한계가 있다 [5]. 퍼지 논리는 확실성과 정확성의 정도를 수치화할 수 있다는 장점이 있지만, 입력 변수의 증가에 따라 연산량이 크게 증가하는 단점이 있다 [6]. 신경망은 수학적으로 해결 불가능한 문제를 분석하는데 유용하고 경로 예측이나 분류 등에 있어 효과적인 방법이다 [7].…”

Section: 서 론 로봇이 실생활에 미치는 영향이 점차 커짐에 따라 로봇unclassified

Autonomous Navigation for a Mobile Robot Using Navigation Guidance Direction and Fuzzy Control

Park¹,

Shin²

2014

The Transactions of The Korean Institute of Electrical Engineer

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-This paper proposes a generation method of a navigation guidance direction and a fuzzy controller to achieve the autonomous navigation of a mobile robot using a particle swarm optimization(PSO) scheme in unknown environments. The proposed navigation guidance direction is the direction that leads a mobile robot to arrive a target point simultaneously with avoiding obstacles efficiently according to the surrounding local informations. It is generated by selecting the most suitable direction of the many directions in the surrounding environment using a particle swarm optimization scheme. Also, a robot can reach a target point with avoiding the various obstacles by controlling the robot so that it can move from its current orientation to the navigation guidance direction using the proposed fuzzy controller. Simulation results are presented to show the feasibility and validity of the proposed robot navigation scheme.

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Section: 서 론 로봇이 실생활에 미치는 영향이 점차 커짐에 따라 로봇unclassified

Autonomous Navigation for a Mobile Robot Using Navigation Guidance Direction and Fuzzy Control

Park¹,

Shin²

2014

The Transactions of The Korean Institute of Electrical Engineer

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“…system. The fuzzy logic and modeling has been very successful in dealing with similar nonlinear and complex systems such as in control and decision-making [21] [22], [23], [24], [25] and [26]. The following sections shows how the fuzzy modeling and reasoning logic are used to model and process E.I.…”

Section: A Dealing With Uncertainties In Assessment Of Ei Skillsmentioning

confidence: 99%

A fuzzy logic-based emotional intelligence framework for evaluating and orienting new students at HCT Dubai colleges

Bouslama¹,

Housley

Steele

2014

2014 14th International Conference on Hybrid Intelligent Systems

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Academic institutions across the world face the challenge of providing new intakes of students with appropriate orientation and counseling services to better help students cope with the changes and challenges of life at university or college. These sessions are often based on measurements of the students' technical skills or Intelligence Quotient (IQ) levels such as mathematical computation and communications abilities. However, Emotional Intelligence (E.I.) tests, which have become an essential tool and an integral part of the recruiting, orientation, and counseling strategies of many individuals and organizations, are not often part of these evaluation schemes. At some academic institutions, a partial test of those skills is conducted but may not provide a holistic view of the emotional intelligence of each individual. In this paper, a set of EI tests covering four general areas of EI is proposed to evaluate the emotional intelligence of the new intakes at the HCT Dubai Colleges. These tests will help identify students who lack experience with non-cognitive capabilities including competencies and skills that may influence their abilities to succeed in coping with educational environmental demands and pressures. A fuzzy-based emotional intelligence modeling and processing framework is also proposed to better model and capture uncertainties in surveys of new intakes, and which will deal well with the complexities of the classification system. This new system is expected to help the HCT Dubai Colleges better design and prepare orientation and counseling interventions which will help students develop their abilities to perceive, to access and generate emotions to promote their emotional and intellectual growth.

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“…To allow for speed changes, we perform a search according to the current speed -one faster, and one slower -as well as a special fast forward speed which enables better starting performance. This is similar to the strategy used in the Sendai city train system controller [38]. Because the controller only adjusts one speed level per frame (10 Hz), it can require a full second to come up to speed otherwise.…”

Section: Model Predictive Controlmentioning

confidence: 99%

Issues in the convergence of control with communication and computing: proliferation, architecture, design, services, and middleware

Graham

Baliga

Kumar

2004

2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)

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We anticipate the next wave in the information technology revolution to be the convergence of control, i.e., sensing and actuation, with communication and computing. This dissertation addresses the broad set of issues that we believe to be important to the design, implementation, and proliferation of such systems. In particular, we expound on the topics of the architecture of such systems, methodologies for design, distributed time, services, and middleware. We describe our design and implementation of a testbed.iii To my family ACKNOWLEDGMENTS

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Fuzzy Control for Automatic Train Operation System

Cited by 45 publications

References 1 publication

Autonomous Navigation for a Mobile Robot Using Navigation Guidance Direction and Fuzzy Control

Autonomous Navigation for a Mobile Robot Using Navigation Guidance Direction and Fuzzy Control

A fuzzy logic-based emotional intelligence framework for evaluating and orienting new students at HCT Dubai colleges

Issues in the convergence of control with communication and computing: proliferation, architecture, design, services, and middleware

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