• 대한전기학회논문지:시스템및제어부문D
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• 제53권6호
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• pp.415-424
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• 2004

In this paper, we introduce and investigate a new category of rule-based fuzzy inference system based on Information Granulation(IG). The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of “If..., then...” statements, and exploits the theory of system optimization and fuzzy implication rules. The form of the fuzzy rules comes with three types of fuzzy inferences: a simplified one that involves conclusions that are fixed numeric values, a linear one where the conclusion part is viewed as a linear function of inputs, and a regression polynomial one as the extended type of the linear one. By the nature of the rule-based fuzzy systems, these fuzzy models are geared toward capturing relationships between information granules. The form of the information granules themselves becomes an important design features of the fuzzy model. Information granulation with the aid of HCM(Hard C-Means) clustering algorithm hell)s determine the initial parameters of rule-based fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial function being used in the Premise and consequence Part of the fuzzy rules. And then the initial Parameters are tuned (adjusted) effectively with the aid of the improved complex method(ICM) and the standard least square method(LSM). In the sequel, the ICM and LSM lead to fine-tuning of the parameters of premise membership functions and consequent polynomial functions in the rules of fuzzy model. An aggregate objective function with a weighting factor is proposed in order to achieve a balance between performance of the fuzzy model. Numerical examples are included to evaluate the performance of the proposed model. They are also contrasted with the performance of the fuzzy models existing in the literature.

• 한국산학기술학회논문지
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• 제15권2호
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• pp.1036-1043
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• 2014

환경오염과 에너지위기 문제를 해결하기 위하여 세계적으로 태양광전원의 설치가 매년 증가하고 있다. 하지만, 설치된 태양광모듈은 경년열화로 인한 성능저하와 운용상의 다양한 장애요소로 발전량 손실이 발생하여, 태양광모듈의 효율적인 운용을 위한 발전량예측과 상태진단 기술이 요구되고 있다. 기존의 발전량 예측 방법은 많은 파라미터를 고려해야하기 때문에 계산이 복잡하며, 표준시험 조건의 모듈 특성데이터를 사용하기 때문에 오차가 크게 발생한다. 따라서 본 논문에서는 태양광모듈에서 발생하고 있는 문제점을 분석하고 이에 대한 대책을 제시하기 위하여, 선형회귀분석법을 이용한 발전량 예측 알고리즘과 태양광모듈의 상태를 진단하는 알고리즘을 제안하였다. 또한, 이를 바탕으로 태양광모듈의 상태진단 평가시스템을 구현하여 시뮬레이션을 수행한 결과, 기존의 방법에 비하여 제안한 방법이 계산하기 편리하고 예측 오차도 감소함을 확인하였으며, 이상모듈의 상태와 위치를 신속하게 파악할 수 있어, 태양광모듈의 운용효율 향상에 유용함을 확인하였다.

• 디지털융복합연구
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• 제12권4호
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• pp.277-283
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• 2014

퍼지모델링은 일반적으로 주어진 데이터를 이용하고 퍼지규칙은 입력변수를 선정하고 각 입력변수에 대한 입력공간을 분할함으로써 입력변수 및 공간분할에 의해 확립된다. 퍼지규칙의 전반부는 입력변수, 공간분할 수 및 소속 함수를 선정하고 본 논문에서 후반부는 선형추론 및 변형된 이차식에 의해 다항식함수의 형태로 나타낸다. 전반부 파라미터의 동정은 입출력 데이터의 최소값과 최대값을 이용하는 최소-최대 방법 및 입출력 데이터를 군집으로 형성하는 C-Means 클러스터링 알고리즘을 사용하여 입력공간을 분할한다. 각 규칙의 후반부 파라미터들, 즉 다항식의 계수들의 동정은 표준최소자승법에 의해 수행된다. 본 논문에서 전반부 소속 함수는 사다리꼴형 멤버쉽 함수를 사용하여 입력공간을 분할하고 비선형공정에서 널리 이용되는 가스로데이터를 사용하여 성능을 평가한다.

• 한국작물학회지
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• 제51권spc1호
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• pp.42-46
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• 2006

쌀의 기계적 식미치 측정용으로 최근 많이 사용되고 있는 도요 미도메타의 미도치를 근적외선 분광분석 기를 이용 신속 간편하게 측정할 수 있는지를 검토하고자 실험 하였던바 그 결과는 다음과 같다. 1. 수집된 브랜드 쌀의 도요 미도치는 최저 62.9, 최고 84.2까지의 비교적 넓은 범위를 보였으며, 샘플의 분포 양상도 정규분포에 가까웠다. 2. MPLS(Modified Partial Least Square) 방식에 의한 검량식 작성시 도요 미도치와 근적외선 스펙트럼 간 결정계수 $(R^2)$는 0.94, 표준오차(SEC)는 0.95정도로 비교적 높은 상관성을 보였다. 3. 검량식 검증 표준오차는 1.64, 검증시 상관정도는 0.81로서 근적외선 분광분석기로 도요 미도치를 비 파괴적으로 손쉽게 측정할 수 있는 가능성을 제시 할 수 있었다.

• 방송공학회논문지
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• 제16권6호
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• pp.977-985
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• 2011

최근 다중사용자 전송 기술에 관한 연구를 통하여 단일 사용자 MIMO 시스템에서의 선형적 용량증가가 다중사용자 MIMO 시스템에도 적용 될 수 있다는 것을 보여 주었다. 본 논문에서는 다중사용자 하향 채널에서 벡터 섭동기법을 활용하여 시스템의 신뢰도를 향상시키기 위한 기법을 제안한다. 제안하는 기법에서는 최대 다수의 사용자를 통신에 참여시키는 대신에, 이 중 일부를 다른 사용자의 서비스 품질(QoS)을 향상시키기 위하여 활용하는 것을 특징으로 하고 있다. 희생되는 사용자의 원 신호정보 및 섭동벡터를 적절히 이용할 때 비트에러율(BER)의 이득을 얻을 수 있다. 모의실험을 통해 표준화된 벡터섭동 기법에 비하여 제안하는 기법이 상당한 성능이득을 가져오는 것을 확인할 수 있었다.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1258-1258
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• 2001

Estimation of sugar and reducing sugar content in molasses is very important task in sugar refineries. Conventional methods of determination of sugar content in molasses samples are highly time consuming and employ hazardous chemicals. Due to the physical properties of molasses, probability of error in conventional analytical techniques is high. These methods have proven to be inefficient for a process control in any sugar industry. Hence development of a rapid, inexpensive, physical and also accurate method for sugar determination in molasses will be highly useful. Near Infrared spectroscopy is being widely used worldwide as an analytical technique in food industry. The technique offers the advantage of being non-destructive and rapid. The present paper highlights the potential of near infrared reflectance spectroscopy as a rapid and automated analytical technique for determination of sugar and reducing sugar content in molasses. A number of molasses samples were collected during and after the sugar season from Havana Sugar Industry, Havana. The samples were chosen so as to obtain a wide range of concentration of sugar and reducing sugars. This was done in order to achieve a good calibration curve with widely spread data points. These samples were scanned in the region of 1100 - 2500 nm in diffuse reflectance mode. An indigenous ELICO NIR spectrophotometer, modified according to the requirements of sugar industry was used for this purpose. Each sample was also analyzed simultaneously by standard chemical methods. Chemical values were taken as reference for near infrared analysis. In order to obtain the most accurate calibration for the set of samples, various mathematical treatments were employed. Partial Least Square method was found to be most suitable for the analysis. A comparison is made between the actual values (chemical values) and the predicted values (NIR values). The actual values agree very well with the predicted values showing the accuracy of the technique. The validity of the technique is checked by predicting the concentration of sugar in unknown molasses samples using the calibration curve. The present investigation assesses the feasibility of the technique for on-line monitoring of sugars present in molasses in sugar industries.

• Journal of Biosystems Engineering
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• 제27권3호
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• pp.259-266
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• 2002

It is crucial to know spatial soil variability for precision farming. However, it is time-consuming, and difficult to measure spatial soil properties. Therefore, there are needs fur sensing technology to estimate spatial soil variability, and for electronic mapping technology to store, manipulate and process the sampled data. This research was conducted to develop a real-time soil organic matter sensor and an electronic mapping system. A soil organic matter sensor was developed with a spectrophotometer in the 900∼1,700 nm range. It was designed in a penetrator type to measure reflectance of soil at 15cm depth. The signal was calibrated with organic matter content (OMC) of the soil which was sampled in the field. The OMC was measured by the Walkeley-Black method. The soil OMCs were ranged from 0.07 to 7.96%. Statistical partial least square and principle component regression analyses were used as calibration methods. Coefficient of determination, standard error prediction and bias were 0.85 0.72 and -0.13, respectively. The electronic mapping system was consisted of the soil OMC sensor, a DGPS, a database and a makeshift vehicle. An algorithm was developed to acquire data on sampling position and its OMC and to store the data in the database. Fifty samples in fields were taken to make an N-fertilizer dosage map. Mean absolute error of these data was 0.59. The Kring method was used to interpolate data between sampling nodes. The interpolated data was used to make a soil OMC map. Also an N-fertilizer dosage map was drawn using the soil OMC map. The N-fertilizer dosage was determined by the fertilizing equation recommended by National Institute of Agricultural Science and Technology in Korea. Use of the N-fertilizer dosage map would increase precision fertilization up to 91% compared with conventional fertilization. Therefore, the developed electronic mapping system was feasible to not only precision determination of N-fertilizer dosage, but also reduction of environmental pollution.

• 한국해양학회지
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• 제28권2호
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• pp.114-120
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• 1993

입도분포의 특징분석에 종래의 조직특성치 대신 수치해석적인 분해법의 적용을 시 도하였다. 그 결과 이 방법이 제주해협의 대륙붕 표층퇴적물과 같은 복모드형 입 도분포의 분석에 매우 유용함이 입증 되었다. 복모드형 입도분포 퇴적물은 제주해협 대륙붕에서는 86%의 높은 비율을 점한다. 종래의 입도특성치(평균, 표준편차, 왜도, 첨도)는 복모드형 입도분포에 대해서는 왜곡된 값을 보인다. 따라서 입도분포의 모드 에 대체로 대응되는 정규성분으로 분해해서 각 정규성분의 특성치를 해석함으로써 입 도특성치에서와 같은 특징의 누락이나 왜곡을 피할 수 있다. 제주해협 대륙붕의 167개 퇴적물 입도분포곡선을 비선형 최소자승법을 사용하여 정규성분으로 분해해서 총 387 개의 정규성분을 얻었다. 정규성분의 평균은 1-3 phi 8-9 phi에 집중되는 것이 많다. 이중 조립질 정규성분의 평균치는 복잡하고 특징적인 지리적 분포를 보인다. 이러한 분포는 퇴적물 총층후 분포와 매우 유사하며 해저면의 지질과 지형을 면밀하게 반영하 고 있다. 해저면을 형성하는 퇴적층은 플라이스토세 후기의 해침성 모래층이며 해저지 형은 플라이스토세말 빙하기 저해수면시기의 침식에 의해 형성된 것으로 보인다.

• International Journal of Control, Automation, and Systems
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• 제3권2호
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• pp.183-194
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• 2005

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the premise part of the rule-based structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. We distinguish between two types of the linear fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, the models are experimented with a representative numerical example. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when comparing with other neurofuzzy models.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.346-348
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• 2004

In this rarer, we introduce a new Fuzzy Polynomial Neural Networks (FPNNs)-like structure whose neuron is based on the Fuzzy Set-based Fuzzy Inference System (FS-FIS) and is different from that of FPNNs based on the Fuzzy relation-based Fuzzy Inference System (FR-FIS) and discuss the ability of the new FPNNs-like structurenamed Fuzzy Set-based Polynomial Neural Networks (FSPNN). The premise parts of their fuzzy rules are not identical, while the consequent parts of the both Networks (such as FPNN and FSPNN) are identical. This difference results from the angle of a viewpoint of partition of input space of system. In other word, from a point of view of FS-FIS, the input variables are mutually independent under input space of system, while from a viewpoint of FR-FIS they are related each other. In considering the structures of FPNN-like networks such as FPNN and FSPNN, they are almost similar. Therefore they have the same shortcomings as well as the same virtues on structural side. The proposed design procedure for networks' architecture involves the selection of appropriate nodes with specific local characteristics such as the number of input variables, the order of the polynomial that is constant, linear, quadratic, or modified quadratic functions being viewed as the consequent part of fuzzy rules, and a collection of the specific subset of input variables. On the parameter optimization phase, we adopt Information Granulation (IG) based on HCM clustering algorithm and a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized FSPNN (gFSPNN), the model is experimented with using gas furnace process dataset.