• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.17-25
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• 2011

Green IT, composed term with Green and Information Technology(IT), use IT for energy savings and carbon emission reductions. Green IT went beyond the scope of greening IT, and recently it's concept is expanded as far as counterplan of climate change including greening other industries by IT. 85% of total greenhouse gas emissions from the energy sector and 20% of them comes from transport parts, so it is time to research IT for automotive industry. In this paper, we take up the knowledge based fuzzy logic to provide life cycle analysis associated with greenhouse gas emissions for industry produced warranty claims frequently such as automobile industry. We propose a analysis method of warranty claims using expert knowledge about the warranty in car exhaust systems related to greenhouse gas emissions, past test results of malfunction, analysis of past field data, and warranty data. Furthermore, we propose life knowledge-based GWS (Greenery Warranty System). We demonstrate the applicability of IT in eco-friendly automotive industry by implementing knowledge-based fuzzy logic and applying.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.2
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• pp.105-112
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• 2014

The International Maritime Organization has determined that more than 80% of maritime accidents are caused by human error. A variety of methods have been considered to reduce maritime accidents caused by such human error. Navigators operate by observing surrounding maritime situations and analyzing information using various navigational devices. This study proposes a system to ensure safe navigation by assisting navigators through the delivery of maritime safety information (MSI) between land and sea. In the future, supplementing the system through long-term on-the-ship tests is necessary by defining MSI in relation to maritime service portfolio regions.

• IE interfaces
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• v.13 no.1
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• pp.92-99
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• 2000

Conflicts and interruptions caused by resource failures and rush orders require a nonlinear dynamic production management. Generally the PP&C systems used in industry presently do not meet these requirements because of their rigid concepts. Starting with the grasp of the disadvantages of current approaches, this paper presents a control structure that enables system to react to various malfunctions using a planning tolerance concept. Also, production processes are modeled by using Fuzzy-Petri-Net modeling tool in other to handle the complexity of job allocation and the existence of many disparities. On the basis of this model the developed system support the short-term shop control by rule based decision.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.221-227
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• 2007

This paper introduces an on-line particle-filtering-based framework for failure prognosis in nonlinear, non-Gaussian systems. This framework uses a nonlinear state-space model of the plant(with unknown time-varying parameters) and a particle filtering(PF) algorithm to estimate the probability density function(pdf) of the state in real-time. The state pdf estimate is then used to predict the evolution in time of the fault indicator, obtaining as a result the pdf of the remaining useful life(RUL) for the faulty subsystem. This approach provides information about the precision and accuracy of long-term predictions, RUL expectations, and 95% confidence intervals for the condition under study. Data from a seeded fault test for a UH-60 planetary carrier plate are used to validate the proposed methodology.

• Proceedings of the KIEE Conference
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• 2007.11b
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• pp.176-179
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• 2007

This paper proposes a fuzzy linear programming based solution approach fur the long-term generation mix with multi-stages (years) considering air pollution constraints on $CO_2$ emissions, under uncertain circumstances as like as ambiguities of budget and reliability criterion level. This paper approaches to generation mix problem for 2030 year in Korea eventually. The proposed approach may give more flexible solution rather than too robust plan. The effectiveness of the proposed approach is demonstrated by applying it to solve the multi-years best generation mix problem on the Korea power system which contains nuclear, coal, LNG, oil and pumped-storage hydro plants.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.102-106
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• 2000

본 논문은 뉴로-퍼지 모델의 구조학습을 이용하여 한 시간 앞의 전력 수요를 예측하는 체계적인 방법을 제안한다. 제안된 예측시스템은 시간 단위로 뉴로-퍼지 모델을 재학습하기 위해서 필요한 초기 구조를 요일 유형과 시간 별로 미리 생성하고, 이를 초기 구조 뱅크에 저장한다. 예측이 수행되는 시점의 요일 유형에 따라 선택된 초기 구조를 이용하여 뉴로-퍼지 모델을 초기화하고, 학습하고, 예측을 수행한다. 제안된 방법의 실효성을 검증하기 위해 1996년과 1997년의 실제 전력 수요 데이터를 이용하여 모의 실험을 수행한다. 실험결과 제안된 방법은 기존의 다층 퍼셉트론을 이용한 방법과 비교하여 예측의 정확도 측면과 신뢰도 측면에서 모두 향상된 결과를 얻는다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.20-28
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• 2012

We define a language $\mathcal{RS}$, a subclass of the scheduling language $\mathcal{RS}V$ (resource constrained project scheduling with variant processes). $\mathcal{RS}$ involves the determination of the starting times for ground activities of a project satisfying precedence and resource constraints, in order to minimize the total project duration. In $\mathcal{RS}$ ground activities and two structural symbols (operators) 'seq' and 'pll' are used to construct activity-terms representing scheduling problems. We consider three different variants for formalizing the $\mathcal{RS}$-scheduling problem, the optimizing variant, the number variant and the decision variant. Using the decision variant we show that the problem $\mathcal{RS}$ is $\mathcal{NP}$-complete. Further we show that the optimizing variant (or number variant) of the $\mathcal{RS}$-problem is computable in polynomial time iff the decision variant is computable in polynomial time.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.15-19
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• 2012

A Domain Dictionary generation algorithm based on pseudo feedback model is presented in this paper. This algorithm can increase the precision of domain dictionary generation algorithm. The generation of Domain Dictionary is regarded as a domain term retrieval process: Assume that top N strings in the original retrieval result set are relevant to C, append these strings into the dictionary, retrieval again. Iterate the process until a predefined number of domain terms have been generated. Experiments upon corpus show that the precision of pseudo feedback model based algorithm is much higher than existing algorithms.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.182-186
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• 2004

This paper describes an advanced compensation for non-linear functions designed to remove steering aberrations from phased array antennas. This system alters the steering command applied to the antenna in a way that the appropriate angle commands are given to the array steering software for the antenna to point to the desired position instead of squinting. Artificial neural networks are used to develop the inverse function necessary to correct the aberration. Also a straightforward antenna steering function is implemented with neural networks for the 9-term polynomials of forward steering function. In all cases the aberration is removed resulting in small RMS angular errors across the operational angle space when the actual antenna position is compared with the desired position. The use of neural network model provides a method of producing a non-linear system that can correct antenna performance and demonstrates the feasibility of generating an inverse steering algorithm.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.323-326
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• 2001

본 논문은 뉴로-퍼지 모델의 구조 학습을 이용하여 단기 전력 수요 예측시스템을 개발하기 위한 체계적인 방법을 제안한다. 제안된 단기 수요 예측시스템은 1시간, 24시간, 168시간의 예측 리드 타임을 갖고 예측을 수행하기 위해서 요일 유형과 시간 별로 총 96개의 초기 구조를 미리 생성하고, 이를 초기 구조 뱅크에 저장한다. 예측이 수행되는 시점에 해당하는 초기 구조를 선택하여 뉴로-퍼지 모델을 초기화하고, 학습하고, 예측을 수행한다. 제안된 예측시스템은 단지 2개의 입력 변수만을 이용하기 때문에 간단한 모델 구조를 가질 뿐 아니라 학습된 퍼지 규칙을 해석하는 것이 매우 용이하다는 장점을 갖는다. 제안된 방법의 실효성을 검증하기 위해 1996년과 1997년의 한국전력의 실제 전력 수요 데이터를 이용하여 1시간, 24시간 168시간 앞의 전력 수요를 예측하는 모의 실험을 수행한다. 실험 결과 제안된 방법은 단지 2개의 입력 변수를 사용함에도 불구하고 기존의 예측 방법과 비교하여 예측의 정확도와 신뢰도 측면에서 우수한 성능을 얻는다.