• Proceedings of the Korean Geotechical Society Conference
• /
• 1998.05a
• /
• pp.83-114
• /
• 1998

The most widely accepted method for understanding the deformation mechanism of rock is from the use of computer simulation. However, if the changes in rock properties after excavation are significant this will prevent the computer simulation kent predicting the deformation with acceptable accuracy. If the deformations are, however, carefully measured in situ, the resulting data can be more useful far predicting the deformational behavior of underground openings, since the effect of the parameters which influence the deformational behavior are included in the measurement. In this study, extensive data analyses were carried out using the deformation measurements from the Waste Isolation Pilot Plant (WIPP), which is a permanent nuclear waste repository The results from computer simulations were compared with field measurements to evaluate the assumptions used in the computer simulations, For better description of the deformational behavior around underground excavations, several techniques were developed, namely: (a) the calculation of the zero strain boundary; (b) the evaluation of the influence of adjacent excavations on the deformational behavior of pre-excavated openings; (c) the description of the deformational behavior using in situ measurements; (d) the calculation of the shear stress distribution; and (e) the application of a Neural Network for the prediction of opening deformation.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.54 no.4
• /
• pp.274-283
• /
• 2005

Coronary Perfusion Pressure(CPP) is known for the most important parameter related to the Return of Spontaneous Circulation (ROSC), however, clinically measuring CPP is difficult either invasive or non-invaisive method. En this paper, we analyze the correlation between the extracted parameter from VF ECG wave and the CPP with the statistical method, and predict CPP value using the extracted parameters within significance level. the extracted parameters are median frequency(MF), peak frequency(PF), average segment amplitude(ASA), MSA(maximum segment amplitude), Two parameters, MF, and ASA are selected in order to predict CPP value with general regression neural network, and then we evaluated the agreement statistics between the simulated CPP and the measured CPP. In conclusion, the mean and variance of the difference between the simulated CPP and the measured CPP are 8.9716±1.3526 mmHg, and standard deviation 6.4815 mmHg with one hundred-times training and test results. the simulated CPP and the measured CPP are agreed with the overall accuracy $90.68\%$ and kappa coefficient $81.14\%$ as a discriminant parameter of ROSC.

• Journal of Platform Technology
• /
• v.6 no.2
• /
• pp.19-25
• /
• 2018

Existing Photovoltaic(PV) monitoring system monitors the current, past power generation, all values of environmental sensors. It is necessary to predict solar power generation for efficient operation and maintenance on the power plant. We propose a method for estimating the generation of PV data based PV monitoring system with data accumulation. Through this, we intend to find the failure prediction of the photovoltaic power plant in proportion to the predicted power generation. As a result, the administrator can predict the failure of the system it will be prepared in advance.

• Geomechanics and Engineering
• /
• v.10 no.3
• /
• pp.269-284
• /
• 2016

Simplified techniques based on in situ testing methods are commonly used to assess seismic liquefaction potential. Many of these simplified methods were developed by analyzing liquefaction case histories from which the liquefaction boundary (limit state) separating two categories (the occurrence or non-occurrence of liquefaction) is determined. As the liquefaction classification problem is highly nonlinear in nature, it is difficult to develop a comprehensive model using conventional modeling techniques that take into consideration all the independent variables, such as the seismic and soil properties. In this study, a modification of the Multivariate Adaptive Regression Splines (MARS) approach based on Logistic Regression (LR) LR_MARS is used to evaluate seismic liquefaction potential based on actual field records. Three different LR_MARS models were used to analyze three different field liquefaction databases and the results are compared with the neural network approaches. The developed spline functions and the limit state functions obtained reveal that the LR_MARS models can capture and describe the intrinsic, complex relationship between seismic parameters, soil parameters, and the liquefaction potential without having to make any assumptions about the underlying relationship between the various variables. Considering its computational efficiency, simplicity of interpretation, predictive accuracy, its data-driven and adaptive nature and its ability to map the interaction between variables, the use of LR_MARS model in assessing seismic liquefaction potential is promising.

• Annual Conference on Human and Language Technology
• /
• 2007.10a
• /
• pp.128-133
• /
• 2007

Focused crawling은 검색시스템의 구축을 위한 웹 문서 수집단계에서, 미리 정의된 토픽 집합들과 관련성을 가지는 웹 문서를 수집하기 위하여 제안되었다. 이러한 focused crawling 연구에서 보다 효과적인 웹 문서 수집을 위해 주어진 토픽에 대한 양질의 배경지식을 제공할 수 있도록 온톨로지가 활발히 활용되어왔다. 그러나 기존의 온톨로지 기반 focused crawling 연구는 토픽과 웹 문서 간의 관련성 측정을 위하여, 주어진 토픽과 관련있는 온톨로지 내 각 개념들에 직관에 의존한 가중치를 부여하여 활용하였다. 하지만 이러한 직관에 의존한 가중치부여 기법은 안정된 수집결과를 도출할 수 있는 최적화된 가중치 값을 얻기가 힘든 한계가 있다. 따라서 본 논문에서는 이러한 개념에 대한 가중치가 학습에 의하여 자동으로 결정되도록, 인공신경망을 적용한 온톨로지 기반 focused crawling 기법을 제안한다. 웹 상에서 제안된 시스템의 성능을 실험한 결과 기존의 온톨로지 기반 수집 기법에 비하여 보다 향상된 결과를 보임을 알 수 있었다.

• The Journal of the Acoustical Society of Korea
• /
• v.17 no.4
• /
• pp.3-13
• /
• 1998

본 논문은 음성 다이어링 시스템을 구현하기 위한 한국어 단독 숫자음 및 연속 숫 자음 인식에 관한 것이다. 단독 숫자음의 인식은 미지의 입력 음성을 재귀 신경망을 이용하 여 모델링된 각 모델에 인가하고, 신경 회로망의 출력 노드의 상태열을 검사하여 적절한 상 태 전이를 하며 최고의 확률값을 출력하는 모델을 인식된 결과로 출력한다. 연속 숫자음의 인식은 미지의 연속 숫자음을 재귀 신경 회로망을 이용한 연속 숫자음 모델에 입력하고, 신 경 회로망의 출력에 대하여 적절한 상태 전이에 대한 검사와 레벨 빌딩(Level Building)을 수행하여 최소의 오차를 가지는 모델열을 인식된 결과로 출력한다. 재귀 신경 회로망을 이 용하여 음절 모델을 만드는 과정에서 재귀 노드는 예상치가 주어지지 않으므로 신경 회로망 의 학습에서 제외되어 현저한 학습 속도의 저하를 가져온다. 따라서 본 논문에서는 재귀 신 경 회로망의 학습 속도를 향상시키기 위한 2가지 방법을 제안 한다. 첫 번째는 재귀 신경 회로망의 재귀 노드의 예상치를 실험적으로 주어줌으로써 학습 속도의 향상을 도모하였다. 두 번째는 음절 모델의 출력노드의 개수와 음절 모델의 세그먼트 경계를 알고리듬을 이용하 여 자동적으로 조절하였다. 실험결과, 단독어의 경우 음절 '에'에 포함하는 한국어 11개의 숫 자음에 대하여 화자 종속의 경우 97.3%, 화자 독립의 경우 80.5%의 인식률을 얻었으며, 연 속 숫자음의 경우는 21종류의 연속 숫자음에 대하여 화자 종속에서 88.2%, 화자 독립의 경 우 81.3%의 인식률을 얻을 수 있었다.

• Proceedings of the National Institute of Ecology of the Republic of Korea
• /
• v.1 no.1
• /
• pp.74-82
• /
• 2020

Predictions of suitable habitat areas can provide important information pertaining to the risk assessment and management of alien plants at early stage of their establishment. Here, we predict the invasion potential of Muhlenbergia capillaris (pink muhly) in South Korea using five bioclimatic variables. We adopt four models (generalized linear model, generalized additive model, random forest (RF), and artificial neural network) for projection based on 630 presence and 600 pseudo-absence data points. The RF model yielded the highest performance. The presence probability of M. capillaris was highest within an annual temperature range of 12 to 24℃ and with precipitation from 800 to 1,300 mm. The occurrence of M. capillaris was positively associated with the precipitation of the driest quarter. The projection map showed that suitable areas for M. capillaris are mainly concentrated in the southern coastal regions of South Korea, where temperatures and precipitation are higher than in other regions, especially in the winter season. We can conclude that M. capillaris is not considered to be invasive based on a habitat suitability map. However, there is a possibility that rising temperatures and increasing precipitation levels in winter can accelerate the expansion of this plant on the Korean Peninsula.

• Journal of Sasang Constitutional Medicine
• /
• v.32 no.2
• /
• pp.10-21
• /
• 2020

Objectives Genome-wide association studies(GWAS) is a useful method to identify genetic associations for various phenotypes. The purpose of this study was to develop predictive models for Sasang constitution types using genetic factors. Methods The genotypes of the 1,999 subjects was performed using Axiom Precision Medicine Research Array (PMRA) by Life Technologies. All participants were prescribed Sasang Constitution-specific herbal remedies for the treatment, and showed improvement of original symptoms as confirmed by Korean medicine doctor. The genotypes were imputed by using the IMPUTE program. Association analysis was conducted using a logistic regression model to discover Single Nucleotide Polymorphism (SNP), adjusting for age, sex, and BMI. Results & Conclusions We developed models to predict Korean medicine constitution types using identified genectic factors and sex, age, BMI using Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN). Each maximum Area Under the Curve (AUC) of Teaeum, Soeum, Soyang is 0.894, 0.868, 0.767, respectively. Each AUC of the models increased by 6~17% more than that of models except for genetic factors. By developing the predictive models, we confirmed usefulness of genetic factors related with types. It demonstrates a mechanism for more accurate prediction through genetic factors related with type.

• Proceedings of the KWS Conference
• /
• 1996.10a
• /
• pp.139-141
• /
• 1996

Among various welding parameters, the welding current which is inversely proportional to the tip-to-workpiece distance in GMAW is an essential parameter to monitor the GMAW process of horizontal fillet joints. For the case of weld defect such as overlap in horizontal fillet welding, therefore, the signal processing for process monitoring or automatic seam tracking should be modified by considering the weld pool surface geometry including the corresponding weld defect. In other words, the adequate signal processing algorithm is indispensible to improve the performance of the arc sensor. However, arc sensor algorithm already developed usually focus on weld seam tracing but do not considering the weld qualities. In this paper, various experiments were carried out to investigate the tendencies of the weld defects when weaving motion is added, and the experimental method based on 2$^n$ factorial design was proposed for deriving the mathematical model between the leg length and the various welding conditions. Moreover, a signal processing method based on the artificial neural network(Adaptive Resonance Theory) was proposed far discriminating the current signal of sound weld beads from that of weld beads with overlap. Finally, the algorithm for weld seam tracking combined with the mathematical modeling and the signal processing method was carried out to track the weld line in conjunction with the improvement of the weld qualities. The reliability of the proposed algorithms were evaluated through various experiments, which showed that the proposed algorithms could be effectively used for arc welding automation.

• The Journal of the Korea Contents Association
• /
• v.10 no.1
• /
• pp.10-18
• /
• 2010

Due to a large number of classes in Hangul character recognition, it is usual to use the six-type preclassification stage. After the preclassification, the first consonent, vowel, and last consonent can be classified separately. Though each of three components has a few of classes, classification errors occurs often due to shape similarity such as 'ㅔ' and 'ㅖ'. So this paper proposes a hierarchical recognition method which adopts multi-stage tree structures for each of 6-types. In addition, to reduce the interference among three components, the method uses the recognition results of first consonents and vowel as features of vowel classifier. The recognition accuracy for the test set of PHD08 database was 98.96%.