• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.130-132
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• 2018

후각과 관련된 연구가 활발해짐에 따라 응용 분야도 동시에 넓어지고 있다. 공기 중에 돌아다니는 자연적인 냄새뿐만 아니라 특정 약물의 화학적 성분을 분석하는 방식을 신경망 알고리즘을 이용해 구분하고 퍼지 추론 방식으로 농도를 측정하고 경로 탐색 알고리즘과 DIY드론을 이용하여 약물의 위치를 탐지하게 하는 것이 최종 목표이다.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.401-408
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• 2000

In this paper, we proposed a fuzzy controller that founded by the general feedback control with the new adjustment method when it's tuning. The general feedback controller is operated that supply to the plant making the control input multiplying the appropriate gain of controller on the error between the output of the plant and the reference, But proposed feedback fuzzy controller consist of three loops. The inner loop consists of plant and an ordinary feedback controller. The fuzzy inference of controller performed by the outer loops, which is composed of a fuzzy modeling and inference. We can observe that the output of control system converges toward the reference. Also, the behaviour of feedback fuzzy system is converged from the transient. That is, we verified that designed fuzzy controllers was adapted effectively through the experiments in the hydraulic motor system using floating point DSP processor.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.1
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• pp.122-129
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• 2008

The direct torque control method of an inverter fed squirrel cage typed induction motor using fuzzy logic controller has been proposed. This method is suitable for the traction which requires a fast torque response during the star-up and step change. The fuzzy control algorithm based upon the control principles of conventional DSC(Direct Self Controller) is developed. The fuzzy algorithm is tarried out by defuzzification strategy of the fuzzy output extracted from the possibility distribution of an inferred fuzzy control rule. The flux and torque of an induction motor are estimated by the dynamic model of the rotor flux field-oriented scheme which has decoupling characteristics and excellent dynamic response over a wide speed range. The proposed controller shows a good dynamic response. Moreover, since the fuzzy controller possesses highly adaptive capability, the performance of fuzzy controller is quite robust and insensitive to the motor parameters and change of operation conditions.

• Journal of IKEEE
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• v.18 no.1
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• pp.57-63
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• 2014

This paper is to technically implement the sensing platform for Home-Wellness Robot. The self-Localization of indoor mobile robot is very important for the sophisticated trajectory control. In this paper, the robot's self-localization algorithm is designed by RF sensor network and fuzzy inference. The robot realizes its self-localization, using RFID sensors, through the collaboration algorithm which uses fuzzy inference for combining the strengths of triangulation and triangulation. For the triangulation self-Localization, RSSI is implemented. TOA method is used for realizing the triangulation self-localization. The final improved position is, through fuzzy inference, made by the fusion algorithm of the resultant coordinates from trilateration and triangulation in real time. In this paper, good performance of the proposed self-localization algorithm is confirmed through the results of a variety of experiments in the base of RFID sensor network and reader system.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.319-321
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• 2005

최근 들어 자동차 판매에 영향을 미치고 있는 것으로 보증수리기간과 품질을 들 수 있다. 보증수리기간과 품질을 중심으로 어떻게 판매에 영향을 주는지 그 유형을 미리 파악하여 보증수리기간을 결정함으로써 경제적인 리스크를 최소화할 수 있다. 적절한 접근 방식으로는 지식기반 시스템을 사용하는 것이다. 본 논문은 퍼지 추론과 관련된 전문가 시스템을 퍼지 전문가 응용 프로그램을 개발하여 기존의 자동차회사 통합시스템과 연결하여 개별 사용자로 하여금 자동차 판매 정책 활동에 대한 리스크를 최소화 시킬 수 있게 하는데 있다.

• The Transactions of the Korea Information Processing Society
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• v.4 no.8
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• pp.2070-2079
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• 1997

In this paper, we proposed the FNCAC (fuzzy neural call admission control) scheme of the ATM networks which used the benefits of fuzzy logic controller and the learning abilities of the neural network to solve the call admission control problems. The new call in ATM networks is connected if QoS(quality of service) of the current calls is not affected due to the connection of a new call. The neural network CAC(call admission control) system is predictable system because the neural network is able to learn by the input/output pattern. We applied the fuzzy inference on the learning rate and momentum constant for improving the learning speed of the fuzzy neural network. The excellence of the proposed algorithm was verified using measurement of learning numbers in the traditional neural network method and fuzzy neural network method by simulation. We found that the learning speed of the FNCAC based on the fuzzy learning rules is 5 times faster than that of the CAC method based on the traditional neural network theory.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.381-386
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• 2006

오늘날 분산 컴퓨팅 환경에서 운용되는 시스템이 증가함에 따라 시스템의 관리작업은 고수준(high-level)의 자동화에 대한 요구가 증가하고 있다. 이에 따라 시스템 관리방식이 전통적인 관리자 중심의 방식에서 시스템 스스로가 자신의 문제를 인식하고 상황을 분석하여 해결하는 자율 컴퓨팅 방식으로 변화하고 있으며, 이에 대한 연구가 많은 연구기관에서 다양한 방법으로 이루어지고 있다. 그러나 이러한 대부분의 기존 연구들은 문제가 발생한 이후의 치유에 주로 초점이 맞추어져 있다. 이러한 문제를 해결하기 위해서는 시스템 스스로가 동작환경을 인식하고 에러의 발생을 예측하기 위한 예측 모델이 필요하다. 따라서 본 논문에서는 자율 컴퓨팅환경에서 자가 치유를 지원하는 4가지의 예측 모델 설계 방법을 제안한다. 본 예측 모델은 ID3 알고리즘, 퍼지 추론, 퍼지 뉴럴 네트워크 그리고 베이지안 네트워크가 각 시스템 상황에 맞춰 적절하게 사용되는 방식이며, 이를 통해 보다 정확한 에러 예측이 가능해진다. 우리는 제안모델의 평가를 위해 본 예측모델을 자가치유 시스템에 적용하여 기존 연구와 예측의 효율을 비교하였으며, 그 결과를 통해 제안 모델의 유효성을 증명하였다.

• Journal of the Korea Society for Simulation
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• v.25 no.4
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• pp.127-135
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• 2016

Even thought modernized marine navigation devices help navigators, marine accidents has been often occurred and ship collision is one of the main types of the accidents. Various studies on the assessment method of collision risk have been reported, and studies using fuzzy theory are remarkable for the reason that reflect linguistic and ambiguous criteria for real situations. In these studies, collision risks were assessed on the assumption that the current state of navigation ship would be maintained. However, navigators ignore or turn off frequent alarms caused by the devices predicting collision risk, because they think that they can avoid the collisions in the most of situations. This paper proposes a model of predicting ship collision risk considering the general patterns of collision avoidance, and the approach is based on fuzzy inference and discrete event system specification (DEVS) formalism.

• Journal of the Korea Computer Industry Society
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• v.8 no.3
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• pp.147-154
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• 2007

Many studies have reported that each evaluation had a stronger effect on the development of a student's teaming ability. Nevertheless, in reality schools rely on the results of summative evaluation after the lesson only for the purpose of learning evaluation. Such a method of evaluation is a result-oriented learning evaluation, with no consideration of developing process of loaming ability of each student. Existing learning evaluation has been considered difficult to process learning performance ability in a clearer manner, as it examines teaming performance ability by diagnostic evaluation and learning ability improvement by formative evaluation, separately. Therefore, this paper proposes a learning evaluation method incorporating diagnostic and formative evaluation, using a Fuzzy inference, for a more objective assessment of performance ability. The proposed method assessed teaming ability based on different weight values, in order to reflect the level of diagnostic and formative evaluation.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.2
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• pp.131-138
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• 2016

In order to improve the performance of an extended Kalman filter, a simplified indirect inference method (SIIM) fuzzy logic system (FLS) is proposed. The proposed FLS is composed of two fuzzy input variables, four fuzzy rules and one fuzzy output. Two normalized fuzzy input variables are the variance between the trace of a prior and a posterior covariance matrix, and the residual error of a Kalman algorithm. One fuzzy output variable is the weighting factor to adjust for the Kalman gain. There is no need to decide the number and the membership function of input variables, because we employ the normalized monotone increasing/decreasing function. The single parameter to be determined is the magnitude of a universe of discourse in the output variable. The structure of the proposed FLS is simple and easy to apply to various nonlinear state estimation problems. The simulation results show that the proposed FLS has strong adaptability to estimate the states of the incoming/outgoing moving objects, and outperforms the conventional extended Kalman filter algorithm by providing solutions that are more accurate.