• Tunnel and Underground Space
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• v.24 no.4
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• pp.282-288
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• 2014

Stoping method has been acknowledged as one of the typical metalliferous underground mining methods. Notwithstanding with the popularity of the method, the majority of stoping mines are suffering from excessive unplanned dilution which often becomes as the main cause of mine closure. Thus a reliable unplanned dilution management system is imperatively needed. In this study, reliable unplanned dilution prediction system is introduced by adopting artificial neural network (ANN) based on data investigated from one underground stoping mine in Western Australia. In addition, contributions of input parameters were analysed by connection weight algorithm (CWA). To validate the reliability of the proposed ANN, correlation coefficient (R) was calculated in the training and test stage which shown relatively high correlation of 0.9641 in training and 0.7933 in test stage. As results of CWA application, BHL (Length of blast hole) and SFJ (Safety factor of Joint orientation) show comparatively high contribution of 18.78% and 19.77% which imply that these are somewhat critical influential parameter of unplanned dilution.

• Journal of Korean Society for Geospatial Information Science
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• v.12 no.2 s.29
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• pp.17-28
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• 2004

This study has tried to develop the modified DRASTIC Model by supplying the parameters, such as structural lineament density and land-use, into conventional DRASTIC model, and to predict the potential of groundwater contamination using GIS in Hwanam 2 District, Gyeonggi Province, Korea. Since the aquifers in Korea is generally through the joints of rock-mass in hydrogeological environment, lineament density affects to the behavior of groundwater and contaminated plumes directly, and land-use reflect the effect of point or non-point source of contamination indirectly. For the statistical analysis, lattice-layers of each parameter were generated, and then level of confidence was assessed by analyzing each correlation coefficient. Groundwater contamination potential map was achieved as a final result by comparing modified DRASTIC potential and the amount of pollutant load logically. The result suggest the predictability of contamination potential in a specified area in the respects of hydrogeological aspect and water quality.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.477-482
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• 2015

Linear regressions and evolutionary nonlinear regression based compensation techniques for the short-range prediction of wind speed are investigated. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, but a linear regression based MOS is hard to manage an irregular nature of weather prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP(Genetic Programming) is suggested for a development of MOS for wind speed prediction. The proposed method is compared to various linear regression methods for prediction of wind speed. Also, statistical analysis of distribution for UM elements for each method is executed. experiments are performed for KLAPS(Korea Local Analysis and Prediction System) re-analysis data from 2007 to 2013 year for Jeju Island and Busan area in South Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.201-201
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• 2023

일반적으로 하천의 수질은 산업화, 인구증가 등으로 인해 여러 종류의 오염물질이 유입되어 악화된다. 수질 악화의 대표적인 현상은 부영양화이며 이를 일으키는 주요 원인 물질은 통상 영양염류라고 말하는 질소와 인으로 알려져 있다. T-N이 다량 수계로 유입되면 식물성 플랑크톤 등이 대량 번식하여 녹조 현상등 수질 악화를 발생시켜 관리가 필요하다. 현재 많은 수자원 관리 부서에서 모니터링 포인트를 설정하여 수질 변화를 관찰하고 있다. 기존의 T-N 분석방법은 (1) 자외선 흡광광도법 (2) 카드뮴 환원법 (3) 환원증류-킬달법등이 있다. 그러나 이러한 방법들은 실험실 기반의 정량적 분석으로 시간과 비용이 크게 소요되어 발생하는 문제에 대해 초기대응을 하기 힘들다. 따라서 T-N을 효과적으로 측정할 수 있는 방법이 필요하다. 국내에서는 수질자료를 통한 연관된 수질 인자를 찾아내어 머신러닝 알고리즘을 활용해 Chl-a 농도를 추정한 연구사례가 있다. 국외에서는 TN과 센서 측정 지표 간의 물리적, 화학적 관계를 기반으로 센서 감지의 적시성과 지능형 알고리즘의 정확도를 결합하여 실시간 총질소(TN) 측정 방법 연구 사례가 있다. 따라서 본 연구에서는 머신러닝을 활용하여 국내에 적합한 T-N 예측 모델을 만들고자한다. 본 연구에서는 센서기반으로 측정가능한 수질항목들과 T-N의 상관성 분석을 통해 주요 수질인자를 도출하였다. 도출된 인자와 Python 기반의 머신러닝을 활용하여 T-N을 추정하였다. 그 후, T-N 추정값과 실측값을 비교하여 머신러닝 성능을 평가하고 실제 적용 가능성에 대해서 검증하였다. 본 연구는 기존 T-N 측정에 소모되는 시간과 비용의 감소에 기여하고 이를 통해 앞으로 더 정확한 수질 예측이 가능해질 것으로 기대된다.

• Journal of the Korean Society of Radiology
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• v.81 no.2
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• pp.395-408
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• 2020

Purpose To evaluate the value of clinicopathologic factors and imaging features of primary breast cancer in predicting early recurrence after the primary treatment. Materials and Methods We enrolled 480 patients who had been followed-up after breast-conserving surgery and adjuvant therapy from January 2010 to December 2014 at our hospital. Early recurrence was defined as recurrence within 3 years after completion of primary treatment, and univariate and multivariate logistic regression analyses were performed to determine the clinicopathologic and imaging predictive factors of early recurrence. Results In the univariate analysis, among the clinicopathologic factors, advanced stage (p = 0.021), high histologic grade (p < 0.001), estrogen receptor negative (p = 0.002), high Ki-67 proliferation index (p = 0.017), and triple-negative breast cancer (p = 0.019), and among the imaging features, multifocality (p < 0.001), vessels in the rim on Doppler ultrasonography (US) (p = 0.012), and rim enhancement (p < 0.001) on magnetic resonance imaging of the breast were significantly associated with early recurrence. In the multivariate analysis, advanced stage [odds ratio (OR) = 3.47; 95% confidence interval (CI) 1.12-10.73; p = 0.031] and vessels in the rim on Doppler US (OR = 3.32; 95% CI 1.38-8.02; p = 0.008) were the independent predictive factors of early recurrence. Conclusion Vascular findings in the rim of the primary breast cancer on Doppler US before treatment is a radiologic independent predictive factor of early recurrence after the primary treatment.

• Korean Chemical Engineering Research
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• v.45 no.2
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• pp.133-142
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• 2007

As the environmental pollution caused by excessive uses of chemical fertilizers and pesticides is aggravated, organic farming using pasture and livestock manure is gaining an increased necessity. The application rate of the organic farming materials to the field is determined as a function of crops and soil types, weather and cultivation surroundings. When livestock manure is used for organic farming materials, the volatilization of ammonia from field-spread animal manure is a major source of atmospheric pollution and leads to a significant reduction in the fertilizer value of the manure. Therefore, an ammonia emission model should be presented to reduce the ammonia emission and to know appropriate application rate of manure. In this study, the ammonia emission rate from field-applied pig manure is predicted using an artificial neural network (ANN) method, where the Michaelis-Menten equation is employed for the ammonia emission rate model. Two model parameters (total loss of ammonia emission rate and time to reach the half of the total emission rate) of the model are predicted using a feedforward-backpropagation ANN on the basis of the ALFAM (Ammonia Loss from Field-applied Animal Manure) database in Europe. The relative importance among 15 input variables influencing ammonia loss is identified using the weight partitioning method. As a result, the ammonia emission is influenced mush by the weather and the manure state.

• Korean Journal of Soil Science and Fertilizer
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• v.35 no.2
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• pp.87-92
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• 2002

Soil erosion is detrimental to sustain soil productivity in north Korea, since agriculture of this country depends largely upon the slope land in mountainous area. Taking any measure for protection from erosion should be based on prediction of soil loss. Estimation of rainfall factor, R, in north Korea for the Universal Soil Loss Equation was attempted. The monthly precipitation data of the twenty six locations provided by the Korean Meteorological Adminstration were used. From the relationship between II_30 and the July-August precipitation concentration percents, the regional adjustment factor was obtained. The rainfall factor was calculated with the monthly precipitation data and the regional adjustment factor. The annual precipitation in north Korea ranged from 606 to 1,520mm, and the July-August precipitation concentration percents were 34.4 to 53.8. The regional adjustment factor ranged from 0.53 to 1.33 showing lower value in the highland and east coastal region than in the mid mountainous inland and west region. The R-factor value estimated from the monthly precipitation and the regional adjustment factor ranged from 107 to 483, which was lower than average value in south Korea.

• Clinical and Experimental Pediatrics
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• v.50 no.5
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• pp.484-488
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• 2007

Purpose : The purpose of this study was to determine the incidence and potential predictors of weight gain in older children and teens treated with valproate (VPA) for epilepsy. Methods : Sixty-five subjects aged 8 to 17 years of age, who began VPA treatment between January 1, 2001, and December 31, 2004, and who had documented weight and height measurements at medication initiation and at least one follow-up visit were retrospectively identified. Exclusion criteria were follow-up <6 months, discontinuation of VPA within 6 months, and concurrent therapy with medication known to affect weight (such as topiramate, carbamazepin). Body mass index (BMI) was calculated at initiation and either discontinuation of VPA or last follow-up and stratified into four categories: group 1, underweight <5%; group 2, appropriate 5-85%; group 3, potentially overweight 85-95%; group 4, overweight >95%. Results : Twenty-eight subjects (77.8%) remained within their same category and eight (22.2%) moved up at least one category. Weight gain (increase in BMI difference) was observed in 72.2% of the 36 subjects treated with VPA. Three factors, neurocognitive status (P=0.017), seizure type (P=0.001) and duration of VPA treatment (P=0.035) were identified to be significant predictors of BMI difference. Conclusion : VPA induces weight gain in children and teens with epilepsy. These factors which are normal neurocognitive status, primary generalized type and duration of VPA treatment over the 12 months were predictors for an increase of weight gain. Therefore potential weight gain should be discussed with patients before the initiation of therapy and BMI should be monitored closely.

• Journal of the Korean Geotechnical Society
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• v.27 no.11
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• pp.71-81
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• 2011

Adfreeze bond strength is a primary design parameter, which determines bearing capacity of pile foundation in frozen ground. It is reported that adfreeze bond strength is influenced by various affecting factors like freezing temperature, confining pressure, characteristics of pile surface, soil type, etc. However, several limited researches have been performed to obtain adfreeze bond strength, for past studies considered only few affecting factors such as freezing temperature and type of pile structures. Therefore, there exists a limitation of estimating the design parameter of pile foundation with various factors in frozen ground. In this study, artificial neural network algorithm was involved to predict adfreeze bond strength with various affecting factors. From past five studies, 137 data for various experimental conditions were collected. It was divided by 100 training data and 37 testing data in random manner. Based on the analysis result, it was found that it is necessary to consider various affecting factors for the prediction of adfreeze bond strength and the prediction with artificial neural network algorithm provides enough reliability. In addition, the result of parametric study showed that temperature and pile type are primary affecting factors for adfreeze bond strength. And it was also shown that vertical stress influences only certain temperature zone, and various soil types and loading speeds might cause the change of evolution trend for adfreeze bond strength.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.355-357
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• 2004

소프트웨어의 측정은 소프트웨어의 생명주기 초기에 분석되어 규모와 비용의 예측에 도움을 주어야 한다. 초기에는 정확한 LOC 기반 규모예측이 어려워 기능 점수 기반의 측정에 의하여 예측할 수 있다. 그러나 현재의 기능 점수 기반 측정은 모든 시스템에 획일화되어 있어 시스템의 특성을 반영할 수 없으며, IFPUG에서 제시한 일반 시스템 특성은 웹 기반 어플리케이션에서 사용자의 인지도가 매우 낮다. 본 논문에서는 웹 기반 어플리케이션 중에 동적 웹사이트에 특화된 시스템 특성 집합 및 영향도 측정 방법을 제시한다. 이 요소를 근거로 동적 웹사이트의 시스템 특성을 분석하여 기능 정수의 값 조정 인자에 반영하고 실제 시스템을 대상으로 조정된 기능 점수를 산정한다.