FRP 시트(Sheet)를 활용한 보강 공법은 제작 과정에서의 간편함과 시공의 용이성으로 현장에서 다수 적용되고 있으며, 기존 연구자들은 FRP 시트로 보강한 철근콘크리트의 휨강도를 예측하기 위한 연구를 진행하였다. 그러나 이는 주로 탄소 섬유와 유리 섬유에 한정되어 있었다. 이 연구에서는 바잘트 섬유시트의 역학적 성질을 파악하기 위하여 물성 시험을 수행하였으며, 바잘트 섬유시트로 보강한 철근콘크리트 보의 휨실험을 수행하였다. 또한 그 결과 값을 비교 분석하여 기존 연구를 바탕 으로 바잘트 섬유 시트로 보강한 철근콘크리트 보의 휨모멘트 예측식을 제안하였다. 강도설계법, ACI440.2R (2017) 그리고 Park et al. (2005)의 예측값을 검토한 결과, 강도설계법은 실험값과 예측값의 비가 0.88로 나타났으며, ACI440.2R (2017) 설계식은 0.92, Park et al. (2005)은 0.97로 나타나 기존의 해석 방법은 휨모멘트를 과대평가하는 것으로 나타났다. 본 연구의 제안식은 실험값과 예측값의 비가 1.00으로 나타나 휨모멘트를 안전측으로 예측하는 것으로 나타났다.

Because the inner environment of greenhouse has a direct impact on crop production, many studies have been performed to develop technologies for controlling the environment in the greenhouse. However, it is difficult to apply the technology developed to all greenhouses because those studies were conducted through empirical experiments in specific greenhouses. It takes a lot of time and cost to develop the models that can be applicable to all greenhouse in real situation. Therefore studies are underway to solve this problem using computer-based simulation techniques. In this study, a model was developed to predict the inner environment of glass greenhouse using CFD simulation method. The developed model was validated using primary and secondary heating experiment and daytime greenhouse inner temperature data. As a result of comparing the measured and predicted value, the mean temperature and uniformity were 2.62°C and 2.92%p higher in the predicted value, respectively. R2 was 0.9628, confirming that the measured and the predicted values showed similar tendency. In the future, the model needs to improve by applying the shape of the greenhouse and the position of the inner heat exchanger for efficient thermal energy management of the greenhouse.

본 연구에서는 인공지능기법을 이용하여 진동만의 용존산소량 예측을 하였다. 관측자료에 존재하는 결측 구간을 보간하기 위해 양방향재귀신경망(BRITS, Bidirectional Recurrent Imputation for Time Series) 딥러닝 알고리즘을 이용하였고, 대표적 시계열 예측 선형모델인 ARIMA(Auto-Regressive Integrated Moving Average)과 비선형모델 중 가장 많이 이용되고 있는 LSTM(Long Short-Term Memory) 모델을 이용 하여 진동만의 용존산소량을 예측하고 그 성능을 평가했다. 결측 구간 보정 실험은 표층에서 높은 정확도로 보정이 가능했으나, 저층에서는 그 정확도가 낮았으며, 중층에서는 실험조건에 따라 정확도가 불안정하게 나타났다. 실험조건에 따라 정확도가 불안정하게 나타났다. 결과로부터 LSTM 모델이 중층과 저층에서 ARIMA 모델보다 우세한 정확도를 보였으나, 표층에서는 ARIMA모델의 정확도가 약간 높은 것으로 나타났다.

우리 사회에 출생률 감소와 급속한 고령화, 일자리 감소와 소득 양극화 등 사회·경제적으로 부정적인 여건이 대두되고 지속되면서 앞으로의 경제전망은 더욱 어두워지고 있다. 이러한 상황에서 정부는 경제 활성화를 위하여 미래 혁신성장동력 확보, 규제혁신 등 여러 경제정책을 종합하여 추진하고 있다. 본 연구는 일반적으로 미래 성장동력으로 쉽게 여겨지지 않는 농식품분야에서 유망한 대표적인 신산업을 대상으로 분석을 실시하였다. 고령친화식품산업과 펫푸드(Pet Food)산업에 대하여 산업 활성화를 위한 규제개선의 과제로 품질인증제 도입을 제시하고 이로 인한 경제적 효과도 예시적으로 계측하였으며, 마지막으로 이것이 갖는 경제학적 의미를 논의하였다. 두 농식품분야 신산업에서의 품질인증제 시행은 식품의 안전성을 강화하는 동시에 사회 전체 후생도 향상시킬 수 있는 방안으로 기대된다.

Perineural invasion (PNI) is the underestimated metastatic pathway and has been widely recognized as a negative prognostic factor in many human cancers. L1CAM is one of members of the immunoglobulin-like cell adhesion molecule (CAM) family, which play a role in neural development. Moreover, a new role of L1CAM outside the nervous system has been revealed. Overexpression of L1CAM was involved in the tumor progression and LN metastasis in various malignancies. In the present study, presence of PNI and L1CAM expression were examined to define their prognostic values in OSCC. In addition, association of L1CAM expression with presence of PNI was assessed to define the value as a candidate molecule supporting the diagnosis of PNI. We found that presence of PNI significantly correlated with LN metastasis and advanced clinical stage. L1CAM expression also significantly correlated with differentiation, lymph node metastasis, advanced clinical stage, as well as presence of PNI. Our results suggest that L1CAM seems to play a role in tumor progression, possibly through the PNI-related mechanism and could be a molecular marker for supporting the presence of PNI and predicting clinical outcome in OSCC.

해체 원전에서 총 폐기물의 약 70~80%에 해당하는 많은 양의 콘크리트 폐기물은 해체 폐기물의 대부분을 차지한다. 해체 시 발생된 콘크리트 폐기물은 핵종별 농도에 따라 규제해제 폐기물과 방사성폐기물로 정의할 수 있다. 따라서, 방사성 콘크리 트 폐기물의 처분 비용을 저감하기 위하여 자체 처분 및 제한적 재활용을 위한 제염 작업의 수행이 중요하다. 그러므로 콘크리트 폐기물의 효율적인 제염 작업을 위해 내부 방사능 분포를 예측하는 것이 필수적이다. 본 연구는 원전 해체 시, 발생되는 콘크리트 폐기물의 내부 방사능 분포를 예측하기 위하여 다양한 컴프턴 영상 재구성 방법의 성능을 비교하였다. 다양한 컴프턴 영상 재구성 방법으로 단순 역투사(SBP), 필터 후 역투사(FBP), 최대우도 기댓값 최대화 방법(MLEM), 그리고 기존 의 MLEM의 시스템 반응 함수에 에너지 정보가 결합되어 확률적으로 계산하는 최대우도 기댓값 최대화 방법(E-MLEM)이 사용되었다. 재구성된 영상을 획득한 후, 정량적인 분석 방법을 이용하여 재구성된 영상의 성능을 정량적으로 비교 및 평가하였다. MLEM 및 E-MLEM 영상 재구성 방법은 각각 재구성된 영상에서 높은 이미지 분해능과 신호 대 잡음비를 유지하는데 있어 가장 좋은 성능을 보여주었다. 본 연구에서 도출된 결과들은 원자력 시설 해체 시 방사성 콘크리트 폐기물의 내부 방사능 분포를 예측하기 위한 수단으로 컴프턴 영상을 사용할 수 있는 가능성을 보여주었다.

Various elements of Fabrication (FAB), mass production of existing products, new product development and process improvement evaluation might increase the complexity of production process when products are produced at the same time. As a result, complex production operation makes it difficult to predict production capacity of facilities. In this environment, production forecasting is the basic information used for production plan, preventive maintenance, yield management, and new product development. In this paper, we tried to develop a multiple linear regression analysis model in order to improve the existing production capacity forecasting method, which is to estimate production capacity by using a simple trend analysis during short time periods. Specifically, we defined overall equipment effectiveness of facility as a performance measure to represent production capacity. Then, we considered the production capacities of interrelated facilities in the FAB production process during past several weeks as independent regression variables in order to reflect the impact of facility maintenance cycles and production sequences. By applying variable selection methods and selecting only some significant variables, we developed a multiple linear regression forecasting model. Through a numerical experiment, we showed the superiority of the proposed method by obtaining the mean residual error of 3.98%, and improving the previous one by 7.9%.

Supply chain managers seek to achieve global optimization by solving problems in the supply chain's business process. However, companies in the supply chain hide the adverse information and inform only the beneficial information, so the information is distorted and cannot be the information that describes the entire supply chain. In this case, supply chain managers can directly collect and analyze supply chain activity data to find and manage the companies described by the data. Therefore, this study proposes a method to collect the order-inventory information from each company in the supply chain and detect the companies whose data characteristics are explained through deep learning. The supply chain consists of Manufacturer, Distributor, Wholesaler, Retailer, and training and testing data uses 600 weeks of time series inventory information. The purpose of the experiment is to improve the detection accuracy by adjusting the parameter values of the deep learning network, and the parameters for comparison are set by learning rate (lr = 0.001, 0.01, 0.1) and batch size (bs = 1, 5). Experimental results show that the detection accuracy is improved by adjusting the values of the parameters, but the values of the parameters depend on data and model characteristics.

Recently, a study of prognosis and health management (PHM) was conducted to diagnose failure and predict the life of air craft engine parts using sensor data. PHM is a framework that provides individualized solutions for managing system health. This study predicted the remaining useful life (RUL) of aeroengine using degradation data collected by sensors provided by the IEEE 2008 PHM Conference Challenge. There are 218 engine sensor data that has initial wear and production deviations. It was difficult to determine the characteristics of the engine parts since the system and domain-specific information was not provided. Each engine has a different cycle, making it difficult to use time series models. Therefore, this analysis was performed using machine learning algorithms rather than statistical time series models. The machine learning algorithms used were a random forest, gradient boost tree analysis and XG boost. A sliding window was applied to develop RUL predictions. We compared model performance before and after applying the sliding window, and proposed a data preprocessing method to develop RUL predictions. The model was evaluated by R-square scores and root mean squares error (RMSE). It was shown that the XG boost model of the random split method using the sliding window preprocessing approach has the best predictive performance.

Recently, deep learning that is the most popular and effective class of machine learning algorithms is widely applied to various industrial areas. A number of research on various topics about structural engineering was performed by using artificial neural networks, such as structural design optimization, vibration control and system identification etc. When nonlinear semi-active structural control devices are applied to building structure, a lot of computational effort is required to predict dynamic structural responses of finite element method (FEM) model for development of control algorithm. To solve this problem, an artificial neural network model was developed in this study. Among various deep learning algorithms, a recurrent neural network (RNN) was used to make the time history response prediction model. An RNN can retain state from one iteration to the next by using its own output as input for the next step. An eleven-story building structure with semi-active tuned mass damper (TMD) was used as an example structure. The semi-active TMD was composed of magnetorheological damper. Five historical earthquakes and five artificial ground motions were used as ground excitations for training of an RNN model. Another artificial ground motion that was not used for training was used for verification of the developed RNN model. Parametric studies on various hyper-parameters including number of hidden layers, sequence length, number of LSTM cells, etc. After appropriate training iteration of the RNN model with proper hyper-parameters, the RNN model for prediction of seismic responses of the building structure with semi-active TMD was developed. The developed RNN model can effectively provide very accurate seismic responses compared to the FEM model.

The objectives of this study were to estimate genetic parameters and breeding values of four carcass traits of the Hanwoo cattle breed: carcass weight (CWT), back fat thickness (BFT), eye-muscle area (EMA), and marbling score (MAR). Genetic parameters and breeding values were estimated based on data (“estimating dataset”) collected from September 2004 to March 2019. Predictability of parental breeding value estimates (EBVs) for the performances of progeny of the control group was evaluated on another dataset (“testing dataset”) using linear model equations involving parental EBVs classified into sex and age groups. The parental EBVs of animals in the testing dataset were traced by pedigree relationships of animals in the estimating dataset. Heritability estimates of CWT, BFT, EMA, and MAR were 0.53, 0.43, 0.38, and 0.54, respectively. Genetic correlation coefficients of CWT with BFT, EMA, and MAR were +0.32, +0.59, and +0.11, respectively. Environmental correlation coefficients of CWT with BFT, EMA, and MAR were +0.46, +0.55, and +0.29, respectively. In the testing dataset, partial regression coefficients of phenotypic values of progeny on sire EBVs ranged from +0.43 to +0.60 depending on traits fit into the models, while those on dam EBVs ranged from +0.54 to +0.67. All partial regression coefficients were statistically significant and were approximated to the expected value of +0.5. Together, these values validate the use of parental EBVs for predicting progeny carcass phenotypes in the Hanwoo herd.

There have been a lot of studies in the past for the method of predicting the failure of a machine, and recently, a lot of researches and applications have been generated to diagnose the physical condition of the machine and the parts and to calculate the remaining life through various methods. Survival models are also used to predict plant failures based on past anomaly cycles. In particular, special machine that reflect the fluid flow and process characteristics of chemical plants are connected to hundreds or thousands of sensors, so there are not many factors that need to be considered, such as process and material data as well as application of derivative variables. In this paper, the data were preprocessed through time series anomaly detection based on unsupervised learning to predict the abnormalities of these special machine. Next, clustering results reflecting clustering-based data characteristics were applied to produce additional variables, and a learning data set was created based on the history of past facility abnormalities. Finally, the prediction methodology based on the supervised learning algorithm was applied, and the model update was confirmed to improve the accuracy of the prediction of facility failure. Through this, it is expected to improve the efficiency of facility operation by flexibly replacing the maintenance time and parts supply and demand by predicting abnormalities of machine and extracting key factors.

본 연구에서는 당동만을 중심으로 빈산소가 발생하는 물리적 해양환경 특성을 파악하고, 로지스틱 회귀분석을 이용해 빈 산소 발생확률을 예측하였다. 관측 자료를 분석한 결과, 브런트-바이살라 주파수는 수심이 깊은 만 입구보다 수심이 얕은 만 내측에서 더 크게 나타났다. 이는 당동만 내측에서 담수 유입으로 인해 표층 염분이 낮아져 강한 밀도 성층이 형성되었기 때문이다. 시간적으로 는 6월 ~ 9월까지 리차드슨 수와 브런트 바이살라 주파수가 매우 높게 나타났고, 9월 2일 이후로는 성층이 완화되어 감소하는 경향을 보였다. 당동만에서 관측된 용존산소 및 수온, 염분 자료를 분석한 결과, 저층의 용존산소 농도는 공통적으로 표층과 저층의 수온차에 가장 큰 영향을 받는 것으로 나타났다. 한편, 수심차(dz)를 고정된 변수로 두고, 수온차(dt)의 변화에 의한 빈산소의 발생 확률의 변화 를 계산한 결과, 수심차(dz)가 각각 5 m, 10 m, 15 m, 20 m일 경우, 수온차(dt)는 8℃, 7℃, 5℃, 3℃일 때 빈산소 발생확률이 70 %를 상회 하는 것으로 나타났다. 이는 당동만에서 수심차(dz)가 커질수록 빈산소 발생에 필요한 수온차(dt)는 작아지게 된다는 것을 뜻하며, 특 히 당동만에서 수심차(dz)가 20 m 내외인 지역은 빈산소가 발생하기 매우 쉬운 환경이라는 것을 알 수 있었다.