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조윤상(Yoon Sang Cho),남보우(Bo Woo Nam),홍사영(Sa Young Hong),김진하(Jin Ha Kim),김현조(Hyun Jo Kim) 한국해양공학회 2014 韓國海洋工學會誌 Vol.28 No.6
A numerical method to investigate the non-linear motion characteristics of a TLP is established. A time domain simulation that includes the memory effect using the convolution integral is used to consider the transient effect of TLP motion. The hydrodynamic coefficients and wave force are calculated using a potential flow model based on the HOBEM(higher order boundary element method). The viscous drag force acting on the platform and tendons is also considered by using Morison’s drag. The results of the present numerical method are compared with experimental data. The focus is the nonlinear effect due to the viscous drag force on the TLP motion. The ringing, springing, and drift motion are due to the drag force based on Morison’s formula.
강원도 벼 주산지에서 생태형별 이앙시기가 현미 품질에 미치는 영향
조윤상 ( Youn-sang Cho ),이지우 ( Ji-woo Lee ),윤예지 ( Ye-ji Yoon ),김용복 ( Yong-bok Kim ),정정수 ( Jung-su Jung ) 강원대학교 농업생명과학연구원 2022 강원 농업생명환경연구 Vol.34 No.0
강원도의 벼 주산지인 중부평야지(춘천), 동해안지(강릉), 중북부평야지(철원)에서 시험기간 중 출수 후 40일간 평균기온 기준 최적 등숙적온은 중부평야지 23.7°C, 동해안지 21.8°C, 중북부평야지 22.4°C였다. 중부평야지에서 벼생태형에 따른 품질은 조기보다 만기이앙이 좋았으나, 중북부평야지 및 동해안지의 중만생종 품종은 등숙기 적산온도가 부족하여 현미 품질이 급격히 저하되었다. 동해안지에서 종생종은 5월 20일 이앙시 품질이 가장 높았고, 중만생종은 6월 10일 이앙은 미숙립 등 품질이 낮아 안전재배에 적당하지 않았다. 중북부평야지에서 조생종은 5월 10일에서 5월 30일까지 이앙시 품질의 차이는 크지 않았으나 만기 이앙인 6월 10일 이앙에서 현미 품질이 급격히 저하되었다. 특히 중만생종은 현미 품질이 매우 낮아 재배는 권장할 수 없었다. 최근 기후변화나 이상기후 발생 증가에 따른 벼주산지에서 최적 품종 선발 및 이앙시기 재설정으로 재배안정성과 농가소득 향상에 기여할 수 있을 것으로 판단된다. Recent changes in the climate of Korea show that the average rate of temperature increase is above 1.5°C, which is more than twice the global average temperature increase rate of 0.74°C, indicating that the rate of warming here is faster than that in other regions. These rapid climate changes demand a response strategy that include the development of adaptive varieties and cultivation techniques in agricultural ecosystems. The major rice producing regions of Gangwon-do are diverse and located in the central plains, northern plains, and east coast. To obtain the basic data necessary for resetting the cultivation methods to adapt to climate change, the yield quantity and yield components of rice were analyzed based on different ecological types and their transplanting period. In the central plain, the yield of early and middle maturing varieties increased with the increase in average temperature. The yields were not stable for the high-quality cultivation of mid-late varieties. However, the temperature rise did not affect the yields of the early, middle, and mid-late maturing varieties in the east coast region. In the central and northern plains, cultivation stability was confirmed only for the early varieties. Therefore, cultivation of the middle and mid-late varieties is not recommended for high-quality rice production and maintaining cultivation-stability. In the climate change scenario, quality and stability of cultivation are the priority factors that must be considered in rice cultivation areas.
조윤상(Yoon Sang Cho),이상민(Sang Min Lee),김해중(Hae Joong Kim),김성범(Seoung Bum Kim) 대한산업공학회 2018 대한산업공학회지 Vol.44 No.5
Maintenance of quality level is essential in semiconductor manufacturing that contains a series of complicated processes and equipment. Recently, the problem of abnormal quality caused by the inadequate process sequence has become an important quality management issue. Furthermore, characterization of the sequential combination of the process and equipment that cause the abnormal quality is a crucial task in manufacturing systems. In this study, we propose the methodology that detects the sequence of faulty equipment based on hidden Markov models. The effectiveness and applicability of the proposed approach were demonstrated through the experiments using a simulator that reflects the actual manufacturing conditions.
예측 모델 기반 섀플리 값을 이용한 타이어 설계 인자 분석
황석철(Seok Cheol Hwang),조윤상(Yoon Sang Cho),김성범(Seoung Bum Kim) 대한산업공학회 2022 대한산업공학회지 Vol.48 No.5
In the tire manufacturing industry, factor analysis plays a crucial role in deriving design candidates that can increase tire performance by deriving design factors, including variable importance and directions of design change. Typically, expert knowledge and finite element analysis (FEA) are necessary to derive design factors. However, they required a high computation load. Although several studies have been performed using machine learning and variable selection methods, they have limitations in recommending design change directions for each variable. In this study, we propose a predictive model coupled with Shapley additive explanations (SHAP) for tire design factor analysis. We first construct a predictive model using tire design data, and then applied SHAP for deriving important variables and directions of design change. To demonstrate the effectiveness of our methodology, we evaluate the design candidates using FEA. Results show that our methodology is effective for a tire factor analysis.
Mycobacterium bovis에 의한 소 및 사슴 결핵의 폐 병리조직학적 소견 비교연구
진영화,노인순,이경현,이경우,조윤상,주이석,Jean, Young Hwa,Roh, In Soon,Lee, Kyung Hyun,Lee, Kyung Woo,Cho, Yoon Sang,Joo, Yi Seok 대한수의학회 2008 大韓獸醫學會誌 Vol.48 No.2
Comparative studies of histopathologic lesions from 23 purified protein derivative (PPD) positive cattle, 21 slaughter cattle found with tuberculosis, and 11 tuberculosis-positive elk (Cervus elaphus) were performed. PPD positive cattle did not show specific histopathologic lesions in all 23 heads that were no visible lesion reactor. Slaughter cattle found with tuberculosis revealed microscopically classical granulomatous lesion (tubercle) with central caseous necrosis surrounded by mantle of epithelioid cells and Langhan's giant cells capsuled by connective tissue in lung. Elk was noted with some different lesion patterns with classical granulomatous lesion and suppurative abscesses that was composed of fibrin, degenerated cells without having connective tissue. In addition, many Langhan's giant cells infiltration in alveoli at peripheral lesion were observed in some cases of classical granulomatous lesion and suppurative abscesses.
이영재(Young Jae Lee),조윤상(Yoon Sang Cho),최재한(Jae Han Choi),김성범(Seoung Bum Kim) 대한산업공학회 2020 대한산업공학회지 Vol.46 No.5
Recently, machine learning algorithms have been widely applied in the automotive industry. In particular, it is important to characterize tire quality that can determine the reliability of product design. However, the previous studies are insufficient to explain tire quality because they are based mainly on experimental design methods. In this study, we propose using convolutional neural networks (CNN) and class activation map (CAM) to predict tire quality and perform cause analysis. To properly reflect the location information of a car, we convert the structured data into the image data. We compare the proposed CNN+CAM with other machine learning methods including random forest, gradient boosting machine, adaboost, linear regression with feature selection, support vector regression, partial least square regression, and deep neural networks. The results indicate that the CNN+ CAM yields higher prediction accuracy than other methods. This implies that the proposed CNN+CAM can identify important variables that play an important role in predicting tire quality.
다중 혈중 성분 농도 예측을 위한 비침습형 센서 기반 멀티 아웃풋 네트워크
유이경(Leekyung Yoo),조윤상(Yoon Sang Cho),목충협(Chunghyup Mok),배진수(Jinsoo Bae),정기원(Keewon Jeong),김성범(Seoung Bum Kim) 대한산업공학회 2022 대한산업공학회지 Vol.48 No.5
In healthcare industries, non-invasive sensor technology has played an important role in gathering biometric information without blood sampling for each patient. Existing studies have attempted to predict blood component levels based on non-invasive sensor coupled with machine learning models. However, they focused on constructing a single output model that predicts only one blood component level. In this study, we propose a multi-output predictive model that can predict the multiple blood components levels simultaneously based on non-invasive impedance sensor data. Results show that our method improves predictive performance compared to the single output models. Furthermore, we use Shapley additive explanation to identify important sensor variables that achieve efficient sensor design reducing the cost of data collection. To the best of our knowledge, this study is the first attempt to use non-invasive impedance sensor data to predict multiple blood components levels.