• 한국정밀공학회지
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• 제12권10호
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• pp.138-148
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• 1995

While Model-based Monitoring systems based on state observer theory have shown much promise in the laboratory, they have not been widely accepted by industry because, inpractice, these systems often have poor performance with respect to accuracy, band-width, reliability(false alarms), and robustness. In this paper, the linitations of the deterministic discrete-time state observer are investigated quantitatively from the machine monitoring viewpoint. The limitations in the transient and steady-state observer performance are quantified as estimation error bounds from which performance indices are selected. Each index represents the conditioning of the corresponding performance. By utilizing matrix norm theory, an unified main index is determined, that dominates all the indices. This index could from the basis for an observer design methodology that should improve the performance of model-based monitoring systems.

• 대한공간정보학회지
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• 제21권2호
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• pp.63-69
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• 2013

변화탐지 기법은 위성영상의 활용 및 국토 모니터링에 있어서 필수적인 알고리즘이다. 그러나, 변화탐지 기법을 고해상도 위성영상에 적용할 경우, 다시기 영상 간의 기하학적 차이 등에 의하여 변화탐지 정확도가 저하될 수 있다. 본 연구에서는 효과적인 위성영상의 변화탐지를 위하여 기존의 융합 영상 평가지수를 활용하고자 한다. 또한, 기존의 다시기 위성영상을 활용한 일반적인 변화탐지 기법과 교차융합영상을 이용한 변화탐지 결과를 비교하여, 다시기 고해상도 위성영상에 적합한 변화탐지 기법을 제안하고자 한다. 이를 위해, 융합영상 평가 지수인 ERGAS, UIQI, SAM를 무감독 변화탐지 기법에 적용하고 기존의 CVA를 이용한 변화탐지 기법의 결과와 비교하였다. 또한, 영상융합 기법에 따른 고해상도 위성영상 변화탐지 정확도를 평가하여 고해상도 위성영상의 무감독 변화탐지에서 발생할 수 있는 기하학적 오차를 최소화할 수 있는 방법을 분석하였다. 실험결과, 교차융합영상과 ERGAS 지수를 활용한 변화탐지 기법이 기존 기법과 비교하여 상대적으로 높은 변화지역 탐지 가능성을 가지는 것을 확인할 수 있었다.

• 치과방사선
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• 제29권1호
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• pp.149-159
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• 1999

Purpose: The purpose of this study was to evaluate cervical lymph node metastasis of oral squamous cell carcinoma patients by MRI film and neural network system. Materials and Methods: The oral squamous cell carcinoma patients(21 patients. 59 lymph nodes) who have visited SNU hospital and been taken by MRI. were included in this study. Neck dissection operations were done and all of the cervical lymph nodes were confirmed with biopsy. In MR images. each lymph node were evaluated by using 6 MR imaging criteria(size. roundness. heterogeneity. rim enhancement. central necrosis, grouping) respectively. Positive predictive value. negative predictive value. and accuracy of each MR imaging criteria were calculated. At neural network system. the layers of neural network system consisted of 10 input layer units. 10 hidden layer units and 1 output layer unit. 6 MR imaging criteria previously described and 4 MR imaging criteria (site I-node level II and submandibular area. site II-other node level. shape I-oval. shape II-bean) were included for input layer units. The training files were made of 39 lymph nodes(24 metastatic lymph nodes. 10 non-metastatic lymph nodes) and the testing files were made of other 20 lymph nodes(10 metastatic lymph nodes. 10 non-metastatic lymph nodes). The neural network system was trained with training files and the output level (metastatic index) of testing files were acquired. Diagnosis was decided according to 4 different standard metastatic index-68. 78. 88. 98 respectively and positive predictive values. negative predictive values and accuracy of each standard metastatic index were calculated. Results: In the diagnosis of using single MR imaging criteria. the rim enhancement criteria had highest positive predictive value (0.95) and the size criteria had highest negative predictive value (0.77). In the diagnosis of using single MR imaging criteria. the highest accurate criteria was heterogeneity (accuracy: 0.81) and the lowest one was central necrosis (accuracy: 0.59). In the diagnosis of using neural network systems. the highest accurate standard metastatic index was 78. and that time. the accuracy was 0.90. Neural network system was more accurate than any other single MR imaging criteria in evaluating cervical lymph node metastasis. Conclusion: Neural network system has been shown to be more useful than any other single MR imaging criteria. In future. Neural network system will be powerful aiding tool in evaluating cervical node metastasis.

• Smart Structures and Systems
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• 제20권4호
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• pp.427-440
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• 2017

One of the suitable structural damage detection methods using vibrational characteristics are damage-index-based methods. In this study, a damage index for identifying damages in plate structures using frequency response function (FRF) data has been provided. One of the significant challenges of identifying the damages in plate structures is high number of degrees of freedom resulting in decreased damage identifying accuracy. On the other hand, FRF data are of high volume and this dramatically decreases the computing speed and increases the memory necessary to store the data, which makes the use of this method difficult. In this study, FRF data are compressed using two-dimensional principal component analysis (2D-PCA), and then converted into damage index vectors. The damage indices, each of which represents a specific condition of intact or damaged structures are stored in a database. After computing damage index of structure with unknown damage and using algorithm of lookup tables, the structural damage including the severity and location of the damage will be identified. In this study, damage detection accuracy using the proposed damage index in square-shaped structural plates with dimensions of 3, 7 and 10 meters and with boundary conditions of four simply supported edges (4S), three clamped edges (3C), and four clamped edges (4C) under various single and multiple-element damage scenarios have been studied. Furthermore, in order to model uncertainties of measurement, insensitivity of this method to noises in the data measured by applying values of 5, 10, 15 and 20 percent of normal Gaussian noise to FRF values is discussed.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2008년도 추계학술대회 논문집 Vol.21
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• pp.321-321
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• 2008

To obtaingood resolution in PDP, one of the important factors is to achieve the accuracy of barrier ribs. The photolithographic process can be used to form patterns of barrier rib with high accuracy and a high aspect ratio. The composition for photolithography is based on the $B_2O_3-SiO_2-Al_2O_3$ glass system including additives such as alkali oxides and alkali earth oxides. The refractive index and thermal properties in glass system are changed by amount of alkali oxides and alkali earth oxides. Therefore, it is important that additives are controlled to have proper refractive index and thermal properties. The additives are contributed to non-bridging oxygen within the glass network, causing a change of density. In addition to a change of the structural cross-link density, the refractive index, dielectric and thermal properties glass are correlated with ionic radius and polarizability of cations. In this study, we investigated the refractive index and the thermal properties such as glass transition temperature, glass softening temperature and coefficient of thermal expansion by changing composition in the $B_2O_3-SiO_2-Al_2O_3$ glass system.

• Earthquakes and Structures
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• 제18권1호
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• pp.73-82
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• 2020

With the coal mining depth increasing, both stress and gas pressure rapidly enhance, causing coal and gas outburst risk to become more complex and severe. The conventional method for prediction of coal and gas outburst adopts one prediction index and corresponding critical value to forecast and cannot reflect all the factors impacting coal and gas outburst, thus it is characteristic of false and missing forecasts and poor accuracy. For the reason, based on analyses of both the prediction indicators and the factors impacting coal and gas outburst at the test site, this work carefully selected 6 prediction indicators such as the index of gas desorption from drill cuttings Δh₂, the amount of drill cuttings S, gas content W, the gas initial diffusion velocity index ΔP, the intensity of electromagnetic radiation E and its number of pulse N, constructed the Bayes discriminant analysis (BDA) index system, studied the BDA-based multi-index comprehensive model for forecast of coal and gas outburst risk, and used the established discriminant model to conduct coal and gas outburst prediction. Results showed that the BDA - based multi-index comprehensive model for prediction of coal and gas outburst has an 100% of prediction accuracy, without wrong and omitted predictions, can also accurately forecast the outburst risk even for the low indicators outburst. The prediction method set up by this study has a broad application prospect in the prediction of coal and gas outburst risk.

• 한국측량학회지
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• 제38권6호
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• pp.671-679
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• 2020

경지면적의 작물 상황에 대한 모니터링 및 분석은 식량자급율을 높이기 위한 가장 중요한 요소이지만, 기존의 모니터링 방법은 노동 집약적이며 시간이 많이 들어 식량자급율을 높이기 위한 방안으로 그 활용성이 떨어진다. 이와같은 단점을 극복하기 위하여 국내에 다수 존재하고 있는 소규모 농지에서의 복합 작물 정보를 모니터링 하기위한 효율적인 방법을 개발할 필요가 있다. 본 연구에서는 복합작물의 분류 정확도를 높이기 위하여 무인항공기에서 취득된 RGB영상과 이를 이용한 식생지수를 딥러닝 입력데이터로 적용하고 복합 밭작물을 분류하였다. 각각의 입력데이터 분류 결과 RGB 영상을 이용한 분류는 전체정확도 80.23%, Kappa 계수 0.65가 나타났고, RGB영상과 식생지수를 이용한 방법의 경우 식생지수 3개(ExG,ExR,VDVI) 추가 데이터는 전체정확도 89.51%, Kappa 계수 0.80이며, 식생지수 6개(ExG,ExR,VDVI,RGRI,NGRDI,ExGR)는 90.35%, Kappa 계수 0.82로 분석되었다. 분류결과 RGB영상만을 이용한 방법에 비하여 식생지수를 추가한 결과 값이 비교적 높게 분석되었으며, 복합작물을 분류하는데 있어 식생지수를 추가한 데이터가 더 좋은 결과를 나타내었다.

• 한국측량학회지
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• 제40권2호
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• pp.135-144
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• 2022

식생은 인간에게 다양한 생태공간을 제공하고 수자원 및 기후환경 측면에서도 매우 중요하기 때문에 근적외선 센서 기반의 식생지수를 활용한 식생 모니터링 연구가 많이 수행되어 왔다. 따라서 근적외선 센서를 구비하지 못할 경우 식생 모니터링 연구가 현실적으로 어려운 문제가 있었다. 본 연구에서는 이러한 문제를 개선하고자 NDVI 식생지수를 기준자료로 하여 광학센서 기반의 식생지수 정확도를 평가하였다. 먼저 현장에서 조사한 식생조사 지점과 NDVI 식생지수와의 중첩을 통해 Kappa 계수를 계산하였으며, 그 결과 Kappa 계수가 0.930으로 가장 높게 나타난 0.6 이상의 임계값을 갖는 식생영역을 광학센서 기반의 식생지수 정확도 평가의 기준자료로 선정할 수 있었다. NDVI 식생지수를 기준자료로 선정하여 광학센서 기반의 식생지수와 비교한 결과, 0.04, 0.08, 0.30 이상의 임계값 구간에서 Kappa 계수가 각각 0.713, 0.713, 0.828로 가장 높게 분석되었다. 특히 RGBVI 식생지수의 경우 Kappa 계수가 0.828로 높게 나타났으며, 따라서 근적외선 센서를 활용하지 못하는 환경에서도 광학센서를 활용한 식생 모니터링 연구가 가능함을 알 수 있었다.

• Journal of Animal Science and Technology
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• 제65권4호
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• pp.720-734
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• 2023

In Korea, Korea Proven Bulls (KPN) program has been well-developed. Breeding and evaluation of cows are also an essential factor to increase earnings and genetic gain. This study aimed to evaluate the accuracy of cow breeding value by using three methods (pedigree index [PI], pedigree-based best linear unbiased prediction [PBLUP], and genomic-BLUP [GBLUP]). The reference population (n = 16,971) was used to estimate breeding values for 481 females as a test population. The accuracy of GBLUP was 0.63, 0.66, 0.62 and 0.63 for carcass weight (CWT), eye muscle area (EMA), back-fat thickness (BFT), and marbling score (MS), respectively. As for the PBLUP method, accuracy of prediction was 0.43 for CWT, 0.45 for EMA, 0.43 for MS, and 0.44 for BFT. Accuracy of PI method was the lowest (0.28 to 0.29 for carcass traits). The increase by approximate 20% in accuracy of GBLUP method than other methods could be because genomic information may explain Mendelian sampling error that pedigree information cannot detect. Bias can cause reducing accuracy of estimated breeding value (EBV) for selected animals. Regression coefficient between true breeding value (TBV) and GBLUP EBV, PBLUP EBV, and PI EBV were 0.78, 0.625, and 0.35, respectively for CWT. This showed that genomic EBV (GEBV) is less biased than PBLUP and PI EBV in this study. In addition, number of effective chromosome segments (M_e) statistic that indicates the independent loci is one of the important factors affecting the accuracy of BLUP. The correlation between M_e and the accuracy of GBLUP is related to the genetic relationship between reference and test population. The correlations between M_e and accuracy were -0.74 in CWT, -0.75 in EMA, -0.73 in MS, and -0.75 in BF, which were strongly negative. These results proved that the estimation of genetic ability using genomic data is the most effective, and the smaller the M_e, the higher the accuracy of EBV.

• International Journal of Reliability and Applications
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• 제3권1호
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• pp.37-50
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• 2002

Simultaneous estimation of accuracy and probability corresponding to a prediction interval is considered in this study. Traditional application of confidence interval forecasting consists in evaluation of interval limits for a given significance level. The wider is this interval, the higher is probability and the lower is the forecast precision. In this paper a measure of stochastic forecast accuracy is introduced, and a procedure for balanced estimation of both the predicting accuracy and confidence probability is elaborated. Solution can be obtained in an optimizing approach. Suggested method is applied to constructing confidence intervals for parameters estimated by normal and t distributions