In this study, in order of to reflect the mold deformation in the injection molding process to design of mold, the mold deformation was analyzed by performing flow and structural analysis. The 5 inch LGP(light guide plate) mold, platen and tie bar were modeled and applied to the analysis. The result of melt pressure from flow analysis was extracted for use as boundary conditions acting on the mold surface in the structural analysis. In order to evaluate the accuracy of simulation analysis results, injection molding was performed under the process conditions of simulation. As a results, the mold deformation during injection molding tends to be similar that of injection pressure, and it is confirmed that it shows the behavior and properties of melt resins. Compared with the simulation and experiment, the error of the maximum mold deformation in the injection phase was 4.20%.

태풍 내습 시 신속하고 정확한 해일고 예측은, 연안재해 대응에 필수적인 요소이다. 이러한 해일고의 예측을 위해서 기존에는 태풍예측정보를 수치모델에 적용하여 예측자료를 생산하는 것이 대부분이였다. 이러한 방법은 대용량의 컴퓨팅 자원과 시간이 소요된다는 단점이 있다. 최근에는 인공지능 기반으로 신속하게 예측자료를 생산하는 연구가 다양한 분야에서 진행되고 있으며, 본 연구에서는 인공지능 기반 해일고 예측을 수행하였다. 인공지능 적용을 위해서는 많은 수의 학습자료가 필요하게 되며, 기왕 발생태풍은 개수가 한정되어 있어 본 연구에서는 TCRM(Tropical Cyclone Risk Model)을 통하여 합성태풍을 생성하고, 이를 폭풍해일 모델에 적용하여 해일고 자료를 생성한 후, 학습자료로 활용하였다. 인공지능으로 예측한 해일고와 실제 발생 태풍에 대한 비교 결과, RMSE(Root Mean Square Error)는 0.09 ~ 0.30 m, CC(Correlation Coefficient)는 0.65 ~ 0.94, 최대 해일고의 ARE(Absolute Relative Error)는 1.0 ~ 52.5 %로 분석되었다. 특정 태풍/지점에 서는 다소 오차가 크게 나타나고 있으나, 향후 학습자료의 최적화 등을 통하여 정확도를 개선할 수 있을 것으로 기대된다.

효과적인 보호구역의 보전 관리를 위해서는 외래종의 정착 모니터링 및 확산 위험에 대한 저감 노력이 수반되어야 한다. 본 연구는 울진에 위치한 산림유전자원보호구역(2,274ha)에서 조사된 외래식물 분포 정보를 대상으로 활용도가 높은 세가지 종분포모형(Bioclim, GLM, MaxEnt)을 활용하여 외래식물의 잠재출현지역을 모의하였고, 모의 결과를 비교하여 지역적 지리 및 생태 관리 특성이 반영된 현실성 및 적합성 높은 종분포모형을 선발하였다. 분석에서 예측된 외래식물의 출현지역은 실제 분포와 같이 도로 같은 선형 경관 요소를 따라 분포하는 경향이었으며, 일부 벌채지가 포함되었다. 본 연구에서 적용한 각 모형의 예측력과 정확도를 통계적으로 비교한 결과, GLM과 MaxEnt 모형은 대체로 높은 예측력과 정확도를 보였지만, Bioclim 모형은 낮았다. Bioclim은 가장 넓은 면적을 출현예상지역으로 계산하였고, GLM, 그리고 MaxEnt 순으로 면적이 작았다. 모의 결과의 현상학적 검토에서는 GLM과 Bioclim 모형은 표본 수에 따라 예측력이 크게 영향을 받는 것으로 나타났고, 표본 수와 관계없이 가장 일관성 높은 모형은 MaxEnt로 평가되었다. 종합적으로, 본 연구에 사용된 모형 중 외래식물 분포 예측을 위한 최적 모형은 MaxEnt 모형인 것으로 판단되었다. 본 연구에서 제시한 정밀 생물종 분포 자료 기반의 모델 선발 접근 방식은 산림생태계 보호구역의 보전 관리 및 지역 특성이 반영된 현실적이고 정교한 모델 발굴 연구에 도움이 될 것이다.

Rye, whole-crop barley and Italian Ryegrass are major winter forage species in Korea, and yield monitoring of winter forage species is important to improve forage productivity by precision management of forage. Forage monitoring using Unmanned Aerial Vehicle (UAV) has offered cost effective and real-time applications for site-specific data collection. To monitor forage crop by multispectral camera with UAV, we tested four types of vegetation index (Normalized Difference Vegetation Index; NDVI, Green Normalized Difference Vegetation Index; GNDVI, Normalized Green Red Difference Index; NGRDI and Normalized Difference Red Edge Index; NDREI). Field measurements were conducted on paddy field at Naju City, Jeollanam-do, Korea between February to April 2019. Aerial photos were obtained by an UAV system and NDVI, GNDVI, NGRDI and NDREI were calculated from aerial photos. About rye, whole-crop barley and Italian Ryegrass, regression analysis showed that the correlation coefficients between dry matter and NDVI were 0.91∼0.92, GNDVI were 0.92∼0.94, NGRDI were 0.71∼0.85 and NDREI were 0.84∼0.91. Therefore, GNDVI were the best effective vegetation index to predict dry matter of rye, wholecrop barley and Italian Ryegrass by UAV system.

유리섬유강화 모르타르 관을 구성하는 보강섬유는 직교이방성 부재로 간주되며 재료의 성질은 서로 직각을 이루는 두 개의 축을 기준으로 정의된다. 유리섬유 모르타르 관의 구조적 거동 해석을 수행하기 위해서 길이방향과 원주방향의 재료의 역학적 성질, 즉 탄성계수, 전단탄성계수, 포아송비 등이 필요하며 각각의 성질들은 실험을 통해 결정하였다. 이 실험으로부터 구한 각각의 역학적 성질을 적용하여 간소화된 유한요소해석방법을 제안하기 위해 적층판 이론으로부터 유리섬유강화 모르타르 관의 탄성계수를 계산하고, 계산된 탄성계수를 적용하여 유한요소 해석을 수행하였다. 또한, 유한요소해석과 편평시험을 통해 구한 하중-변위 관계를 비교하였으며 ASTM D2412에서 제시하고 하고 있는 관의 강성 값을 유한요소해석과 실험을 통해 예측하여 비교하였다.

In the process of cutting large aircraft parts, the tool may be abnormally worn or damaged due to various factors such as mechanical vibration, disturbances such as chips, and physical properties of the workpiece, which may result in deterioration of the surface quality of the workpiece. Because workpieces used for large aircrafts parts are expensive and require strict processing quality, a maintenance plan is required to minimize the deterioration of the workpiece quality that can be caused by unexpected abnormalities of the tool and take maintenance measures at an earlier stage that does not adversely affect the machining. In this paper, we propose a method to indirectly monitor the tool condition that can affect the machining quality of large aircraft parts through real-time monitoring of the current signal applied to the spindle motor during machining by comparing whether the monitored current shows an abnormal pattern during actual machining by using this as a reference pattern. First, 30 types of tools are used for machining large aircraft parts, and three tools with relatively frequent breakages among these tools were selected as monitoring targets by reflecting the opinions of processing experts in the field. Second, when creating the CNC machining program, the M code, which is a CNC auxiliary function, is inserted at the starting and ending positions of the tool to be monitored using the editing tool, so that monitoring start and end times can be notified. Third, the monitoring program was run with the M code signal notified from the CNC controller by using the DAQ (Data Acquisition) device, and the machine learning algorithms for detecting abnormality of the current signal received in real time could be used to determine whether there was an abnormality. Fourth, through the implementation of the prototype system, the feasibility of the method proposed in this paper was shown and verified through an actual example.

This research explores how imported automobile companies can develop their strategies to improve the outcome of their recalls. For this, the researchers analyzed patterns of recall demand, classified recall types based on the demand patterns and examined response strategies, considering plans on how to procure parts and induce customers to visit workshops, recall execution capacity and costs. As a result, recalls are classified into four types: U-type, reverse U-type, L- type and reverse L-type. Also, as determinants of the types, the following factors are further categorized into four types and 12 sub-types of recalls: the height of maximum demand, which indicates the volatility of recall demand; the number of peaks, which are the patterns of demand variations; and the tail length of the demand curve, which indicates the speed of recalls. The classification resulted in the following: L-type, or customer-driven recall, is the most common type of recalls, taking up 25 out of the total 36 cases, followed by five U-type, four reverse L-type, and two reverse U-type cases. Prior studies show that the types of recalls are determined by factors influencing recall execution rates: severity, the number of cars to be recalled, recall execution rate, government policies, time since model launch, and recall costs, etc. As a component demand forecast model for automobile recalls, this study estimated the ARIMA model. ARIMA models were shown in three models: ARIMA (1,0,0), ARIMA (0,0,1) and ARIMA (0,0,0). These all three ARIMA models appear to be significant for all recall patterns, indicating that the ARIMA model is very valid as a predictive model for car recall patterns. Based on the classification of recall types, we drew some strategic implications for recall response according to types of recalls. The conclusion section of this research suggests the implications for several aspects: how to improve the recall outcome (execution rate), customer satisfaction, brand image, recall costs, and response to the regulatory authority.

Postal logistics organizations are characterized as having high labor intensity and short response times. These characteristics, along with rapid change in mail volume, make load scheduling a fundamental concern. Load analysis of major postal infrastructures such as post offices, sorting centers, exchange centers, and delivery stations is required for optimal postal logistics operation. In particular, the performance of mail traffic forecasting is essential for optimizing the resource operation by accurate load analysis. This paper addresses a traffic forecast problem of postal parcel that arises at delivery stations of Korea Post. The main purpose of this paper is to describe a method for predicting short-term traffic of postal parcel based on self-similarity analysis and to introduce an application of the traffic prediction model to postal logistics system. The proposed scheme develops multiple regression models by the clusters resulted from feature engineering and individual models for delivery stations to reinforce prediction accuracy. The experiment with data supplied by main postal delivery stations shows the advantage in terms of prediction performance. Comparing with other technique, experimental results show that the proposed method improves the accuracy up to 45.8%.

In this study, we consider the problem of forecasting the number of inbound foreigners visiting Korea. Forecasting tourism demand is an essential decision to plan related facilities and staffs, thus many studies have been carried out, mainly focusing on the number of inbound or outbound tourists. In order to forecast tourism demand, we use a seasonal ARIMA (SARIMA) model, as well as a SARIMAX model which additionally comprises an exogenous variable affecting the dependent variable, i.e., tourism demand. For constructing the forecasting model, we use a search procedure that can be used to determine the values of the orders of the SARIMA and SARIMAX. For the exogenous variable, we introduce factors that could cause the tourism demand reduction, such as the 9/11 attack, the SARS and MERS epidemic, and the deployment of THAAD. In this study, we propose a procedure, called Measuring Impact on Demand (MID), where the impact of each factor on tourism demand is measured and the value of the exogenous variable corresponding to the factor is determined based on the measurement. To show the performance of the proposed forecasting method, an empirical analysis was conducted where the monthly number of foreign visitors in 2019 were forecasted. It was shown that the proposed method can find more accurate forecasts than other benchmarks in terms of the mean absolute percentage error (MAPE).

There has been increasing interest in UHPC (Ultra-High Performance Concrete) materials in recent years. Owing to the superior mechanical properties and durability, the UHPC has been widely used for the design of various types of structures. In this paper, machine learning based compressive strength prediction methods of the UHPC are proposed. Various regression-based machine learning models were built to train dataset. For train and validation, 110 data samples collected from the literatures were used. Because the proportion between the compressive strength and its composition is a highly nonlinear, more advanced regression models are demanded to obtain better results. The complex relationship between mixture proportion and concrete compressive strength can be predicted by using the selected regression method.

Spot welding is a representative process in automotive welding and the application of intelligent systems is accelerating. In particular, in the case of welding electrode management, the timing of electrode wear and dressing was determined by continuous spot welding evaluation, however there is concerned that errors in welding equipment or processes may work in a complex manner. In this study, a dynamic resistance waveform sensing and image measurement system that greatly affects the nugget formation, which is important to the quality of spot welding, was fabricated and used. Based on the experimental data of the galvanized steel sheet, an electrode life prediction algorithm for electrode wear was derived through CNN(Convolutional Neural Network) model of machine learning training.

본 연구에서는 농산물에서 오염 가능성이 있는 병원성 식중독 균 L. monocytogenes에 대해 신선편의 샐러드, 파인애플, 냉동망고에서 예측 모델을 개발하고, 본 연구에서 개발된 예측 모델을 다른 제품에서 적용 여부를 검증하였다. 시료에 L. monocytogenes를 접종하여 각각의 저장 온도에 보관 시 샐러드는 13oC, 파인애플은 10oC 이상에서 성장하였으며, 두 식품 중 파인애플에서 L. monocytogenes 가 더 빠르게 성장하는 것으로 확인 되었다. 또한, 냉동망 고에 접종한 L. monocytogenes는 -2, -10, -18oC의 저장온 도에서 온도가 낮아질수록 delta 값이 커지며 생존력이 높아지는 양상을 보였다. 본 실험 검증을 통해 같은 신선편 의 과일, 채소 식품 그룹에 속하더라도 식품 각각의 특성에 따라 L. monocytogenes의 성장 패턴은 일정하지 않으며 각기 다른 행동 패턴을 보이는 것으로 확인하였다. 신 선편의 샐러드 및 절단된 과일류는 냉장유통 되며 추가세 척 없이 소비되는 제품 특성상 공정과정에서 L. monocytogenes에 의한 오염이 일어나지 않도록 위생관리 에 주의하고 유통과정에서 온도 남용이 되지 않도록 유통 온도 관리에도 유의해야할 것으로 사료된다.

트랙터의 보급이 늘어나는 만큼 올바른 사용법 및 점검이 중요하다. 특히 타이어 공기압에 따른 토양다짐 현상, 연료의 과소비, 안전사고를 예방하기 위해 간편한 측정기술이 필요하다. 따라서 본 연구에서는 대중적 모바일 영상 취득 장치인 스마트폰 카메라를 활용해 획득한 이미지 데이터로 타이어 압력을 예측하였다. 전통적 캘리브레이션을 응용하여 왜곡률을 보정하였다. 트랙터 타이어에 공기압을 0 kPa에서 300 kPa까지 주입하면서 구간별 타이어 촬영을 하였으며, 타이어 중심을 기준으로 상, 하, 좌, 우의 픽셀을 측정하였다. 하중을 받는 타이어의 기하학적 특성을 고려하여 중심과 바닥의 픽셀을 보정식을 통해 보정한 뒤 압력에 따른 픽셀의 변화를 도출하였다.