• International Journal of Fluid Machinery and Systems
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• 제5권4호
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• pp.143-151
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• 2012

Predicting the future condition of machine and assessing the remaining useful life are the center of prognostics. This paper contributes a new prognostic method based on grey model and survival probability. The first step of the method is building a normal condition model then determining the error indicator. In the second step, the survival probability value is obtained based on the error indicator. Finally, grey model coupled with one-step-ahead forecasting technique are employed in the last step. This work has developed a modified grey model in order to improve the accuracy of prediction. For evaluating the proposed method, real trending data of low methane compressor acquired from condition monitoring routine were employed.

• 반도체디스플레이기술학회지
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• 제12권1호
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• pp.1-7
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• 2013

Grey-level statistical models have been widely used in many applications for object location and identification. However, conventional models yield some problems in model refinement when training images are not properly aligned, and have difficulties for real-time recognition of arbitrarily rotated models. This paper presents improved grey-level statistical models that align training images using image or feature matching to overcome problems in model refinement of conventional models, and that enable real-time recognition of arbitrarily rotated objects using efficient hierarchical search methods. Edges or features extracted from a mean training image are used for accurate alignment of models in the search image. On the aligned position and orientation, fitness measure based on grey-level statistical models is computed for object recognition. It is demonstrated in various experiments in PCB inspection that proposed methods are superior to conventional methods in recognition accuracy and speed.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2003년도 학술발표논문집
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• pp.535-538
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• 2003

A Grey model was developed to forecast short-term runoff from the Naju watershed in Korea. In calibration, the root mean square error(RMSE) of the simulated runoff of six hours ahead using Grey model ranged from 6.3 to $290.52m^3/s,\;R^2$ ranged from 0.91 to 0.99, compared to the observed data. In verification, the RMSE ranged from 75.7 to $218.9m^3/s,\;R^2$ ranged from 0.87 to 0.96, compared to the observed data. The results in this study demonstrate that the proposed model can reasonably forecast runoff one to six hours ahead.

• 한국수자원학회논문집
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• 제38권12호
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• pp.1061-1072
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• 2005

본 연구에서는 영산호의 상류에 위치한 나주유역의 홍수시 유출량을 실시간으로 예측하기 위하여 Grey홍수 유출모형을 개발하였다. 나주유역의 유출량은 나주수위관측소에서 실시간으로 측정하고 있으며, 이곳은 영산호의 유입홍수량을 예측과 홍수관리를 위한 주관측소이다. 모형의 지배방정식은 Grey시스템 이론에 근거하여 구성되었으며, 모형의 매개변수는 Grey 시스템매개변수의 조합으로 구성하였다. 모형의 차수는 실측자료와 모의결과를 비교하여 다른 차수 보다 양호한 결과를 나타내는 5차로 하였다. 모형의 보정시 예측결과와 실측치간의 RMSE는 $3.1\~290.5m^{3}/sec$를 나타냈으며, $R^{2}$는 $0.909\~0.999$를 나타냈다. 모형의 검정시 예측결과와 실측치간의 RMSE는 $20.6\~147.4m^{3}/sec$를 나타냈으며, $R^{2}는\;0.940\~0.998$를 나타냈다. 매개변수가 추정된 모형을 이용하여 담수호의 유입량을 하천수위 상태에 따라 예측한 결과, 하천수위가 상승할 경우와 하강할 경우의 예측 홍수량은 예측시간이 증가할수록 커지는 경향을 나타냈다. 또한, 하천수위가 첨두에 가까운 시기의 홍수량은 예측시간에 관계없이 실측자료와 비슷한 결과를 나타냈다. 이와 같은 결과는 Grey 홍수유출모형을 홍수시 담수호 유입량을 실시간으로 정확하게 예측하는데 적용할 수 있음을 나타낸다.

• 한국수자원학회논문집
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• 제42권1호
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• pp.61-73
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• 2009

본 연구에서는 다목적댐의 효율적인 홍수관리와 조기 홍수 경보시스템의 정확성을 향상시키기 위하여 두 가지 모형이 제안되었다. 두 모형은 상류 유입 홍수량과 하류 하천의 홍수량을 실시간으로 예측할 수 있는 능력을 각각 가지고 있다. 이들 모형은 남강댐 상류와 하류 홍수량의 실측치와 모의치를 비교하여 보정 및 검정되었으며, 실제 상황에서 모형의 홍수량 예측 능력이 평가되었다. 상류 유입량 예측 모형은 Grey 시스템 이론에 근거하였으며, 모형의 예측능력을 고려하여 6차 모형을 선정하였다. 서로 다른 자료 세트를 사용하여 보정된 모형들을 사용하여 예측한 홍수량과 실측자료를 비교하여 가장 적정한 모형이 선정되었으며, 검정 결과를 검토한 결과 선정된 모형이 양호한 예측결과를 제시하는 것으로 나타났다. 댐 하류 하천 홍수량 예측 모형은 Grey 모형과 수정 Muskingum 홍수 추적 모형을 병합하여 구성되었으며, 보정 및 검정을 통해서 모형의 예측 능력이 평가되었다. 제안된 모형들을 실시간 홍수량 예측에 적용한 결과, 비교적 양호한 예측결과를 나타냈다. 또한, 모형의 정확도를 향상시키기 위해서는 유출 단계를 고려한 모형의 보정 및 적용이 필요하다는 것이 밝혀졌다.

• 대한의용생체공학회:의공학회지
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• 제14권1호
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• pp.63-72
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• 1993

In this paper, the effect of background grey levels on the visual perception of target image displayed on CRT monitor has been investigated. The purpose of this study is to investigate the efficacy of CRT monitor as a display medium of image Information especially in medical imaging field. Tllree sets of experiments have been performed in this study : the first was to measure the luminance response of CRT monitor and to find the best fitting equation, and the second was the psychophysical experiment measuring the threshold grey level differences between the target image and the background required for visual discrimination (or various background grey levels, and the third was to develop a visual model that is predictable of the threshold grey level difference measured in the psychophysical experiment. The result of psycophysical experiment shows that the visual perception performance is significantly degraded in the range of grey levels lower than 50, which is turned out due to she low luminance change of CRT monitor in this range while human eye has been adapted lo relatively bright ambient illumination. And it Is also shown in the simulation study using the developed visual model that the dominant factor degrading the visual performance is the reflected light from the monitor surface by ambient light in general illumination condition.

• Journal of Information Processing Systems
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• 제18권4호
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• pp.457-469
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• 2022

Hydraulic concrete buildings in the northwest of China are often subject to the combined effects of low-temperature frost damage, during drying and wetting cycles, and salt erosion, so the study of concrete deterioration prediction is of major importance. The prediction model of the relative dynamic elastic modulus (RDEM) of four different kinds of modified concrete under the special environment in the northwest of China was established using Grey residual Markov theory. Based on the available test data, modified values of the dynamic elastic modulus were obtained based on the Grey GM(1,1) model and the residual GM(1,1) model, combined with the Markov sign correction, and the dynamic elastic modulus of concrete was predicted. The computational analysis showed that the maximum relative error of the corrected dynamic elastic modulus was significantly reduced, from 1.599% to 0.270% for the BS2 group. The analysis error showed that the model was more adjusted to the concrete mixed with fly ash and mineral powder, and its calculation error was significantly lower than that of the rest of the groups. The analysis of the data for each group proved that the model could predict the loss of dynamic elastic modulus of the deterioration of the concrete effectively, as well as the number of cycles when the concrete reached the damaged state.

• Geomechanics and Engineering
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• 제11권4호
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• pp.493-513
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• 2016

A modified grey clustering method is presented to systematically evaluate the risk of water inrush in karst tunnels. Based on the center triangle whitenization weight function and upper and lower limit measure whitenization weight function, the modified grey evaluation model doesn't have the crossing properties of grey cluster and meets the standard well. By adsorbing and integrating the previous research results, seven influence factors are selected as evaluation indexes. A couple of evaluation indexes are modified and quantitatively graded according to four risk grades through expert evaluation method. The weights of evaluation indexes are rationally distributed by the comprehensive assignment method. It is integrated by the subjective factors and the objective factors. Subjective weight is given based on analytical hierarchy process, and objective weight obtained from simple dependent function. The modified grey evaluation model is validated by Jigongling Tunnel. Finally, the water inrush risk of Shangjiawan Tunnel is evaluated by using the established model, and the evaluation result obtained from the proposed method is agrees well with practical situation. This risk assessment methodology provides a powerful tool with which planners and engineers can systematically assess the risk of water inrush in karst tunnels.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2004년도 학술발표회
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• pp.759-764
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• 2004

본 연구는 Deng(1989)이 제시한 Grey 모형을 이용하여 성진강댐의 월유입량을 예측하였고 그 방법을 제시하였다. Grey 모형은 시계열모형이나 다른 모형에 비해 비교적 적은 수의 자료를 이용하고, 간단할 수식으로 구성되어 있는 장점이 있으나, 적은 수의 자료로 인해 입력자료가 가지는 증감의 경향(trend)으로 오차가 발생하기 쉽다. 그러므로 예측오차를 극복하기 위해서 Fuzzy 시스템을 결합한 Fuzzy-Grey 모형을 구성하였고 Fuzzy 시스템에 필요한 매개변수를 추정하기 위해 최적화기법인 유전자 알고리즘(GA; Genetic Algorithm)을 이용하였다. Grey 모형과 결합된 Fuzzy 시스템은 현재의 입력자료가 가지는 패턴과 가장 유사한 패턴의 과거자료를 이용하여 현재의 입력자료의 예측오차를 추론해내는 기능을 가진다. 오차를 추론하기 위해서 과거 월유입량 자료중 현재 입력 자료와 유사한 패턴을 Grey 상관도를 이용하여 검색하고, 보다 높은 유사성을 가지는 패턴을 선별하고자 노름(norm)을 사용하였고, 유전자 알고리즘의 탐색공간을 제한하였다. 이렇게 구성한 Fuzzy-Grey 모형을 이용하여 전국적인 가뭄년도였던 1992년, 1988년, 2001년에 대해 섬진강댐의 월유입량을 예측하였다. 오차는 1982년, 2001년, 1988년 순으로 비슷한 크기의 오차가 발생하였는데 결과를 분석하여 보면, 급격한 월유입량의 변화가 있었던 경우에 오차가 크게 발생하였으나 가뭄년도에 대해 월유입량의 불확실성이 큼에도 불구하고 비교적 월유입량의 추세를 잘 예측한 것으로 판단된다. 본 연구에서 적용한 Fuzzy-Grey 모형은 적은 수의 자료를 이용하여 예측하고 예측결과를 다시 입력자료로 사용하는 업데이트 방식을 사용하기 때문에 예측결과의 오차가 완전하게 보정되지 않으면 다음 결과에 역시 오차를 주게 되어 오차보정이 상당히 중요하다는 것을 알 수 있었다. 오차를 보다 효과적으로 보정하기 위해서는 퍼지제어에 사용되는 퍼지규칙의 수를 늘리고, 유입량에 직접적인 영향을 주는 강우량과 연계한 2변수의 Fuzzy-Grey 모형을 이용한다면 보다 정확한 유입량 예측이 가능할 것으로 사료된다.

• Geomechanics and Engineering
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• 제18권5호
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• pp.515-525
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• 2019

A karst collapse, as a natural hazard, is totally different to a normal collapse. In recent years, karst collapses have caused substantial economic losses and even threatened human safety. A risk assessment model for karst collapse was developed based on the fuzzy analytic hierarchy process (FAHP) and grey relational analysis (GRA), which is a simple and effective mathematical algorithm. An evaluation index played an important role in the process of completing the risk assessment model. In this study, the proposed model was applied to Jiaobai village in southwest China. First, the main controlling factors were summarized as an evaluation index of the model based on an investigation and statistical analysis of the natural formation law of karst collapse. Second, the FAHP was used to determine the relative weights and GRA was used to calculate the grey relational coefficient among the indices. Finally, the relational sequence of evaluation objects was established by calculating the grey weighted relational degree. According to the maximum relational rule, the greater the relational degree the better the relational degree with the hierarchy set. The results showed that the model accurately simulated the field condition. It is also demonstrated the contribution of various control factors to the process of karst collapse and the degree of collapse in the study area.