• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2002.02a
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• pp.282-288
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• 2002

본 연구는 상업용 규모의 쇄미선별공정에 사용할 수 있는 파이로트 규모 원통형 홈 선별기 시작기의 설계 및 제작에 필요한 기초 연구로서, 실험실용 원통형 홈 선별기를 이용하여 쇄미선별 실험을 실시하였다. 원통 회전속도, trough각도, indent 크기와 형상, 공급량을 요인으로 하여 완전립의 수거율과 순도, 쇄립의 수거율과 순도 및 선별효율을 조사하였다. 수행한 연구결과를 요약하면 다음과 같다. 1. 선별효율에 대한 원통 회전속도와 trough각도의 단독효과 및 교호작용은 1% 수준에서 유의성이 인정되었다. 원통 회전속도가 커지면trough각도 역시 그에 따라 적절히 증가시켜야만 선별효율의 저하가 방지되는 것으로 나타났다. 2. 최고 선별효율 값은 홈의 모양과 크기, 그리고 공급량에 관계없이 낮은 회전속도 (17rpm)와 중간 trough각도 (37.5$^{\circ}$또는 60$^{\circ}$)가 조합된 처리에서 나타났으며, 60~70% 범위의 높은 값을 보였다. 선별효율에 관한 원통 회전속도와 trough각도의 최적 조합은 17rpm, 37.5$^{\circ}$라고 판단된다. 3. 말발굽형 홈과 반구형 홈 간 선별효율의 차이는 없었다. 따라서, 실제 상업용 규모의 원통형 홈 쇄미선별기 개발에 있어서는 제작이 쉽고 유지.보수가 간편한 반구형 홈을 채택하는 것이 바람직할 것으로 생각된다. 길이 2.5mm이하의 미립인 쇄미의 선별에 사용할 홈의 크기는 2.5mm 보다는 약간 큰 3.0mm 정도가 되어야만 할 것으로 판단된다. 4. 공급량에 따른 선별효율의 차이는 1% 수준에서 유의성이 인정되었으며, 공급량이 작았을 때 전반적으로 선별효율이 높았다.타리 시프터를 채택, 사용하고 있었으며, 로타리시프터 사용상의 문제는 회전몸체를 지지하는 rod spring의 파손 등 구조와 관련된 것이었다. 로타리 시프터에 의한 쇄립의 선별과 제거정도는 만족할 만한 수준은 아니었다. 4. 국내 유통백미 완전립의 길이, 폭, 두께는 각각 5.02mm, 2.93mm, 2.03mm이었으며, 산물밀도와 천립중은 각각 745.3kg/m3 및 20.46g이었다. 5. RPC 백미제품의 품질경쟁력 향상을 유도하고자 현행 쇄미의 정의와 기준을 보다 강화하여 다음과 같은 쇄미 기준과 계급을 설정, 제시하였다. "완전립" - 길이가 3.75mm이상인 미립 "준완전립" - 길이가 2.5∼3.75mm인 미립 "쇄미" - 길이가 1.75∼2.5 mm인 미립 "이물" - 길이가 1.75mm이하인 미립.볼 때 상토 종자혼합비 6 : 1, 성형롤 회전속도 약 7 rpm으로 판단되며, 이 때 제조능력은 시간당 약 65 Kg(펠렛종자 약 39,000 개), 성형률 약 87 %, 종자손실률은 약 30 %, 펠렛종자 내 평균 종자수는 약 5.5 개, 완전 벼 종자 3개 이상 포함 펠렛종자 비율은 약 100 %가 될 것으로 보인다. 세포의 Androgen 생성을 촉진시키는 역할이 있는 것으로 보여진다 따라서 옻나무 유래 F는 포유동물의 생식기능에 중요하게 작용하는 것으로 사료된다.된다.정량 분석한 결과이다. 시편의 조성은 33.6 at% U, 66.4 at% O의 결과를 얻었다. 산화물 핵연료의 표면 관찰 및 정량 분석 시험시 시편 표면을 전도성 물질로 증착시키지 않고, Silver Paint 에 시편을 접착하는 방법으로도 만족한 시험 결과를 얻을 수 있었다.째, 회복기 중에 일어나는 입자들의 유입은 자기폭풍의 지속시간

• Korean Journal of Materials Research
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• v.10 no.10
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• pp.703-708
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• 2000

As the engine design changes to get high efficiency and performance of commercial diesel engine, surface w wear of the earn follower becomes an important issue as applied load increasing at the contact face between cam follower and cam. We developed the ceramic cam follower made of sili$\infty$n nitride ceramic which was more wear resistant than the cast iron or sintered metal cam follower. Ceramic cam follower was made by direct brazing of thin ceramic disk to steel body using an active brazing alloy without the interlayer. In-situ crowning(R), resulted from the difference of thermal expansion coefficient between ceramic and carbon steel after direct brazing without any stress-relieving inter]ayer, could be controlled. When a earbon steel was heated above $A_{c1}$ point and then c$\infty$led, the expansion curve represented a hysteresis. Appropriate crowning was achieved below the $A_{c1}$ point(about $723^{\circ}C$) and crowning increased with brazing temperature exponentially above the $A_{c1}$ point. Optimum brazing temperature range was from 700 to $720^{\circ}C$. We developed successfully the ceramic cam follower having appropriate crowning and being inexpensive. Also we could successfully control the crowning of ceramic earn follower by hysteresis behavior of thermal expansion of earbon steel during direct brazing process.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.165-174
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• 2019

As a nonstationarity is observed in hydrological data, various studies on nonstationary frequency analysis for hydraulic structure design have been actively conducted. Although the inherent diversity in the atmosphere-ocean system is known to be related to the nonstationary phenomena, a nonstationary frequency analysis is generally performed based on the linear trend. In this study, a nonstationary frequency analysis was performed using climate indices as covariates to consider the climate variability and the long-term trend of the extreme rainfall. For 11 weather stations where the trend was detected, the long-term trend within the annual maximum rainfall data was extracted using the ensemble empirical mode decomposition. Then the correlation between the extracted data and various climate indices was analyzed. As a result, autumn-averaged AMM, autumn-averaged AMO, and summer-averaged NINO4 in the previous year significantly influenced the long-term trend of the annual maximum rainfall data at almost all stations. The selected seasonal climate indices were applied to the generalized extreme value (GEV) model and the best model was selected using the AIC. Using the model diagnosis for the selected model and the nonstationary GEV model with the linear trend, we identified that the selected model could compensate the underestimation of the rainfall quantiles.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.4
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• pp.33-41
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• 2006

RSA cryptosystem is of great use in systems such as IC card, mobile system, WPKI, electronic cash, SET, SSL and so on. RSA is performed through modular exponentiation. It is well known that the Montgomery multiplier is efficient in general. The critical path delay of the Montgomery multiplier depends on an addition of three operands, the problem that is taken over carry-propagation makes big influence at an efficiency of Montgomery Multiplier. Recently, the use of the Carry Save Adder(CSA) which has no carry propagation has worked McIvor et al. proposed a couple of Montgomery multiplication for an ideal exponentiation, the one and the other are made of 3 steps and 2 steps of CSA respectively. The latter one is more efficient than the first one in terms of the time complexity. In this paper, for faster operation than the latter one we use binary signed-digit(SD) number system which has no carry-propagation. We propose a new redundant binary adder(RBA) that performs the addition between two binary SD numbers and apply to Montgomery multiplier. Instead of the binary SD addition rule using in existing RBAs, we propose a new addition rule. And, we construct and simulate to the proposed adder using gates provided from SAMSUNG STD130 $0.18{\mu}m$ 1.8V CMOS Standard Cell Library. The result is faster by a minimum 12.46% in terms of the time complexity than McIvor's 2 method and existing RBAs.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.309-327
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• 2021

Dynamic treatment regimes (DTRs) are decision-making rules designed to provide personalized treatment to individuals in multi-stage randomized trials. Unlike classical methods, in which all individuals are prescribed the same type of treatment, DTRs prescribe patient-tailored treatments which take into account individual characteristics that may change over time. The Q-learning method, one of regression-based algorithms to figure out optimal treatment rules, becomes more popular as it can be easily implemented. However, the performance of the Q-learning algorithm heavily relies on the correct specification of the Q-function for response, especially in observational studies. In this article, we examine a number of double-robust weighted least-squares estimating methods for Q-learning in high-dimensional settings, where treatment models for propensity score and penalization for sparse estimation are also investigated. We further consider flexible ensemble machine learning methods for the treatment model to achieve double-robustness, so that optimal decision rule can be correctly estimated as long as at least one of the outcome model or treatment model is correct. Extensive simulation studies show that the proposed methods work well with practical sample sizes. The practical utility of the proposed methods is proven with real data example.

• Journal of Korea Water Resources Association
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• v.55 no.7
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• pp.545-556
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• 2022

It is recommended to use long-term hydrometeorological data for more than the service life of the hydraulic structures and water resource planning. For the purpose of expanding rainfall data, stochastic simulation models, such as Modified Bartlett-Lewis Rectangular Pulse (BLRP) and Neyman-Scott Rectangular Pulse (NSRP) models, have been widely used. The optimal parameters of the model can be estimated by repeatedly comparing the statistical moments defined through a combination of parameters of the probability distribution in the optimization context. However, parameter estimation using relatively small observed rainfall statistics corresponds to an ill-posed problem, leading to an increase in uncertainty in the parameter estimation process. In addition, as shown in previous studies, extreme values are underestimated because objective functions are typically defined by the first and second statistical moments (i.e., mean and variance). In this regard, this study estimated the parameters of the NSRP model using the objective function with the third moment and compared it with the existing approach based on the first and second moments in terms of estimation of extreme rainfall. It was found that the first and second moments did not show a significant difference depending on whether or not the skewness was considered in the objective function. However, the proposed model showed significantly improved performance in terms of estimation of design rainfalls.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.6
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• pp.128-138
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• 2022

In this study, in order to apply ultra-high-strength concrete beams of 100 MPa or more manufactured by centrifugal molding as the superstructure of the avalanche tunnel, the purpose is to verify the structural safety of the corner rigid joint in which the centrifugal molded beam is integrated with the substructure, which is the negative moment area. A full-size specimen was manufactured, and loading tests and analysis studies were performed. In order to expect the same effect that the maximum moment occurs in the corner joint part of the upper slab end when the standard model of the avalanche tunnel is designed with a load combination according to the specification, a modified cantilever type structural model specimen was manufactured and the corner rigid joint was fixedly connected. A study was performed to determine the performance of the method and the optimal connection construction method. The test results demonstrated that the proposed connection system outperforms others. Despite having differences in joint connection construction type, stable flexural behavior was shown in all the tested specimens. The proposed method also outperformed the behavior of centrifugally formed beams and upper slabs. The behavior of the corner rigid joint analysis model according to the F.E. analysis showed slightly greater stiffness compared to the results of the experiment, but the overall behavior was almost similar. Therefore, there is no structural problem in the construction of the corner rigid joint between the centrifugally formed beam and the wall developed in this study.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.4
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• pp.274-284
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• 2022

To use kenaf (Hibiscus cannabinus L.) as a fiber and livestock feed, a high-yielding variety needs to be identified. For this, accurate phenotyping of plant height is required for this breeding purpose due to the strong relationship between plant height and yield. Plant height can be estimated using RGB images from unmanned aerial vehicles (UAV-RGB) and photogrammetry based on Structure from Motion (SfM) algorithms. In kenaf, accurate measurement of height is limited because kenaf stems have high flexibility and its height is easily affected by wind, growing up to 3 ~ 4 m. Therefore, we aimed to identify a method suitable for the accurate estimation of plant height of kenaf and investigate the feasibility of using the UAV-RGB-derived plant height map. Height estimation derived from UAV-RGB was improved using multi-point calibration against the five different wooden structures with known heights (30, 60, 90, 120, and 150 cm). Using the proposed method, we analyzed the variation in temporal height of 23 kenaf cultivars. Our results demontrated that the actual and estimated heights were reliably comparable with the coefficient of determination (R²) of 0.80 and a slope of 0.94. This method enabled the effective identification of cultivars with significantly different heights at each growth stages.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.245-245
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• 2022

최근 국내 기후변화에 따른 국지성 집중호우로 인한 시간당 강우량의 증가로 도로부 유출량의 증가와 배수관거에서의 내수배제 불량에 따른 도심지 내수침수 피해가 증가함에 따라 이를 해결하기 위한 우수유출저감시설이 설치되고 있다. 그러나 대단위의 지하 저류시설의 지속적인 설치는 과밀화된 도심지에서 설치 지하공간의 구조적인 한계 및 적정 설치 위치의 미확보 등의 다양한 문제가 발생하여 저류시설의 침수저감 효과에 대한 추가적이고 새로운 저류시설에 대한 연구가 필요한 실정이다. 이에 내수 침수 저감 및 배수 능력 향상을 위한 도로 배수시설과 연계된 도로 측구부 저류시스템 구축이 필요하다. 이를 위해 역류 방지 및 노면수 저류 빗물받이에 적용되는 부력식 역류차단장치를 개발하였으며, 역류차단장치의 최적 형상 개발을 위해서 기존 빗물받이 연결관과의 통수능 비교 및 분석이 필요한 실정이다. 따라서 본 연구에서는 기존 빗물받이 연결관 및 연결관 내에 역류차단장치가 적용된 역류차단 빗물받이의 흐름분석을 위해 Fluent 모형을 이용하여 3차원 수치모의를 수행하였다. 수치모의 구성으로는 전체 형상을 40×50cm의 빗물받이 유입부와 50×50cm의 빗물받이로 결정하고 격자는 빗물받이 내부의 복잡한 3차원 흐름을 모의하기 위해 1.2~2mm 크기로 생성하였다. 다상유동 해석을 위해 VOF(Volume of Fluid)방법을 적용하였고, 수치해석 방법으로는 비정상류, 난류 모형으로는 SST k-𝜔모형을 적용하였다. 해석조건으로는 김정수(2021) 등이 제시한 4차선 기준 설계빈도별(5~30년) 빗물받이 유입유량을 산정하여 빗물받이 유입조건으로 선정하였으며, 빗물받이와 연결관에서의 통수능력 분석 조건으로는 빗물받이에 기존 연결관이 부착된 조건과 연결관 내에 역류차단장치가 설치되어 역류차단장치가 개방된 조건에서의 통수능을 비교하였으며, 역류상황을 가정한 연결관에서의 통수능을 비교하기 위하여 역류차단장치의 개폐정도를 15도(통수단면 33%감소) 닫힌 상태 및 30도(통수단면 67% 감소) 닫힌 상태 조건을 대상으로 빗물받이와 연결관에서의 흐름을 모의하였다. 수치모의 결과 역류차단장치의 계폐조건에 상관없이 5년 빈도유입량 조건에서는 완전 배수가 되었으며, 개폐조건 15도에서는 10년 빈도의 유입량에서는 완전 배수가 되었으나 20년 빈도 이상의 유입량 조건에서 빗물받이 유입부로의 역류가 발생하였으며, 개폐조건 30도에서는 5년 빈도 이상 유입량 조건에서 빗물받이 유입부로 역류가 발생하는 것으로 나타났다. 특히, 30년 빈도 이상의 유입량부터는 빗물받이 연결관 내에 역류차단장치 개페조건과 관계없이 빗물받이 유입부로의 역류로 인한 도로 침수가 발생하기 때문에 유휴공간인 도로 측구부를 저류공간으로 활용할 수 있는 도로 측구부 저류시스템의 구축은 필수적이라고 판단되며, 유량 조건에 따른 빗물받이 내부 와 흐름과 유출부에서의 유속 변화 특성을 확인하였다. 그러므로 측구 저류조 개발 형상과 연결한 3차원 흐름의 구현 및 분석에 Fluent 모형의 적용이 가능하다고 판단된다.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.