This study is a preliminary investigation into a method for updating analytical models using actual vibration measurement data to improve the reliability of the seismic performance evaluations. The research was conducted on 26 models with various parameters, aiming to develop an optimal analytical model that closely matches the natural frequencies of the actual building. By identifying the dynamic characteristics of the target building through vibration measurements taken just before the demolition of the structure, the natural frequency analysis results of the analytical models were compared to the measured data. Based on this comparison, an optimized method for adjusting the parameters of the analytical models was derived. Throughout the analysis, various parameters were adjusted, and the eigenvalue analysis results were corrected by comparing them with vibration measurements. Among the comparative analytical models, the model with the lowest error rate was selected. The results showed that, in all cases, the analytical model with a concrete compressive strength of 16 MPa (based on actual measurements), pin boundary conditions, and an idealized strip footing cross-section had the closest match to the actual building's natural frequencies, with an average error of less than 8%.

이 논문은 한국산업경영시스템학회 연구윤리위원회 심의(2024.7.3.)결과, 중복게재가 확인되어 게재가 철회된 논문임. This study is to identify the maintenance service quality of eco-friendly cars, which are rapidly increasing recently. Research is conducted by synthesizing research from the perspectives of internal employees and external customers by using the service profit chain model. Specifically, it is to study the overall structural relationship between internal customer satisfaction, physical quality, interaction quality, outcome quality, external customer satisfaction and long-term orientation. For the study, 202 questionnaires were collected from internal employees and 204 questionnaires from external customers. The results of testing the research hypotheses targeting the internal employee group are as follows. As a result of testing hypothesis 1, internal customer satisfaction has a significant positive (+) effect on physical quality and interaction quality. As a result of testing hypothesis 2, the service quality of eco-friendly car maintenance has a significant positive (+) effect on each other. As a result of testing hypothesis 3, physical quality and outcome quality have a significant positive (+) effect on external customer satisfaction. The results of testing the research hypotheses targeting an external customer group are as follows. As a result of testing hypothesis 2, in the relationship between eco-friendly car maintenance service quality, physical quality has a significant positive (+) effect on interaction quality, and interaction quality has a significant positive (+) effect on outcome quality. As a result of testing hypothesis 3, interaction quality and outcome quality have a significant positive (+) effect on external customer satisfaction. As a result of testing Hypothesis 4, external customer satisfaction has a significant positive (+) effect only on intention to reuse. Finally, as a result of examining the difference in perception between the internal employee group and the external customer group in eco-friendly car maintenance service quality and external customer satisfaction, it was verified that there was a significant difference only in outcome quality and external customer satisfaction.

본 연구에서는 2 016년부터 2 02 0년까지 내륙 관측소 중 안개 최다발 지역인 안동을 대상으로 XGBoost-DART 머신러닝 알고리즘을 이용하여 1 시간 후 안개 유무를 예측하였다. 기상자료, 농업관측자료, 추가 파생자료와 각 자료 를 오버 샘플링한 확장자료, 총 6개의 데이터 세트를 사용하였다. 목측으로 획득한 기상현상번호와 시정계 관측으로 측 정된 시정거리 자료를 각각 안개 유[1]무[0]로 이진 범주화하였다. 총 12개의 머신러닝 모델링 실험을 설계하였고, 안개 가 사회와 지역사회에 미치는 유해성을 고려하여 모델의 성능은 재현율과 AUC-ROC를 중심으로 평가하였다. 전체적으 로, 오버샘플링한 기상자료와 기상현상번호 기반의 예측 목표를 조합한 실험이 최고 성능을 보였다. 이 연구 결과는 머 신러닝 알고리즘을 활용한 안개 예측에 있어서, 목측으로 획득한 기상현상번호의 중요성을 암시한다.

This study attempts a comparison between AHP(Analytic Hierarchy Process) in which the importance weight is structured by individual subjective values and regression model with importance weight based on statistical theory in determining the importance weight of casual model. The casual model is designed by for students’ satisfaction with university, and SERVQUAL modeling methodology is applied to derive factors affecting students’ satisfaction with university. By comparison of importance weights for regression model and AHP, the following characteristics are observed. 1) the lower the degree of satisfaction of the factor, the higher the importance weight of AHP, 2) the importance weight of AHP has tendency to decrease as the standard deviation(or p-value) increases. degree of decreases. the second sampling is conducted to double-check the above observations. This study empirically checks that the importance weight of AHP has a relationship with the mean and standard deviation(or p-value) of independence variables, but can not reveal how exactly the relationship is. Further research is needed to clarify the relationship with long-term perspective.

In this study, a welding heat source model was presented and verified during fiber laser welding. The multi-layered heat source model is a model that can cover most of existing studies and can be defined with a simple formula. It consists of a total of 12 parameters, and an optimization algorithm was used to find them. As optimization algorithms, adaptive simulated annealing, multi island genetic algorithm, and Hooke-Jeeves technique were applied for comparative analysis. The parameters were found by comparing the temperature distribution when the STS304L was bead on plate welding and the temperature distribution derived through finite element analysis, and all three models were able to derive a model with similar trends. However, there was a deviation between parameters, which was attributed to the many variables. It is expected that a more clear welding heat source model can be derived in subsequent studies by giving a guide to the relational expression and range between variables and increasing the temperature measurement point, which is the target value.

본 연구는 대륜 국화 ‘백마’의 생육 특성인 생체중, 건물중, 엽면적을 조사하여, 생장 및 기후요소에 따른 생장 예측모델 개발을 위하여 수행되었다. 정식후 일수 및 누적온도에 따른 국화의 건물중 및 엽면적 분석에 기반한 ‘백마’의 생장예측을 위한 시그모이드 회귀모델을 개발하였다. ‘백마’의 건물중 상대 생장률(RGR)은 재배기간 평균 0.084 g·g-1·d-1이었다. 정식 후 재배 기간에 따른 건물중에 대한 상대 생장률은 정식 초기부터 단일처리 전까지 높았으며 최고 0.133 g·g-1·d-1까지 증가하였고, 63일째 단일처리가 시작된 후 수확 시기에서는 0.030 g·g-1·d-1으로 감소하는 경향을 보였다. 누적온도에 따른 국화의 건물중, 엽면적에 대한 생장 모델(sigmoid 곡선)을 개발하였다. 정식 후 일수와 누적온도에 따른 ‘백마’의 건물중 및 엽면적은 지수함수적으로 증가하였으며, 건물중의 경우 63일(누적온도 1601℃)까지 평균 39.1%씩 증가하였고, 이후 평균 7.4%씩 증가하였다. 엽면적의 경우 정식 후 28일차 까지 평균 63.3%씩 증가하였고, 화아분화가 발생하기 전인 84일차까지 평균 6.5%씩 증가하였으며 화아 분화가 발생하 기 전 84일까지 평균 6.5%로 증가했고, 이후 수확 전까지 평균 10.6%씩 증가하는 경향을 보였다. 본 실험은 충남지역에 서 대륜 국화 ‘백마’의 재배관리 체계와 계획적 연중 생산 체계를 구축하는데 유용한 자료로 활용될 수 있다. 보다 정밀한 생육 예측 모델을 만들기 위해서는 누적 일사량을 포함한 다양한 기상자료를 바탕으로 하여 교정 및 검증이 필요하다.

목적 : 인공지능의 기계학습 또는 심층학습을 이용한 연구가 다양한 분야에서 시도되고 있다. 본 연구는 공공 시력데이터를 자동화 수집하고, 수집한 데이터를 기계학습에 적용 및 예측하였다. 다양한 학습모델간 성능을 비교 함으로써, 시과학분야에서 적용 가능한 기계학습 최적화모델을 제시함에 있다. 방법 : 국민건강보험(NHISS) 및 통계포털(KOSIS)에 발표된 국민 시력분포 현황관련 자료를 특정 색인을 포함하 는 자료검색기법인 크롤링(crawling)을 사용하여 검색 및 수집을 자동화하였다. 2011년부터 2018년까지 보고된 모든 자료를 수집하였으며, 데이터 학습을 위해 Linear Regression, LASSO, Ridge, Elastic Net, Huber Regression, LASSO/LARS, Passive Aggressive Regressor 그리고 Pansacregressor 총 8개 모델을 사용하여 각각 데이터 학습 하였다. 결과 : 수집한 데이터를 기반으로 기계학습 모델을 통해 2018년을 예측하였다. 각 모델간 2018년도 실제-예측데 이터 차이를 MAE(Mean Absolute Error)와 RMSE(Root Mean Square Error) 점수로 각각 나타냈다. 학습모델 별 차이 중 MAE 평가결과 모델간 우/좌 Linear Regression(0.22/0.22), LASSO(0.83/0.81), RIDGE(0.31/0.31), Elastic Net(0.86/0.84), Huber Regression(0.14/0.07), LASSO/LARS(0.15/0.14), Passive Aggressive Regressor (0.29/0.18) 그리고 RANSA Regressor(0.22/0.22)를 보였다. RMSE에서 Linear Regression(0.40/0.40), LASSO (1.08/1.06), Ridge(0.54/0.54), Elastic Net(1.19/1.17), Huber Regression(0.20/0.20), LASSO/LARS(0.24/0.23), Passive Aggressive Regressor(0.21/0.58) 그리고 RANSA Regressor(0.40/0.40) 각각 나타냈다. 결론 : 본 연구는 자동화 자료검색 및 수집을 위한 크롤링 기법을 이용하여 데이터를 수집하였다. 이를 기반으 로 고전 선형모델을 기계학습에 적용할 수 있도록 하고, 데이터 학습을 위한 8개 학습모델들 간 성능을 비교하였다.

A conventional lumped-mass stick model is based on the tributary area method to determine the masses lumped at each node and used in earthquake engineering due to its simplicity in the modeling of structures. However the natural frequencies of the conventional model are normally not identical to those of the actual structure. To solve this problem, recently an updated lumped-mass stick model is developed to provide the natural frequencies identical to actual structure. The present study is to investigate the seismic response accuracy of the updated lumped-mass stick model, comparing with the response results of the shaking table test. For the test, a small size four-story steel frame structure is prepared and tested on shaking table applying five earthquake ground motions. From the comparison with shaking table test results, the updated model shows an average error of 3.65% in the peak displacement response and 9.68% in the peak acceleration response. On the other hand, the conventional model shows an average error of 5.15% and 27.41% for each response.

Shiga toxin 2e (Stx2e) has a pivotal role in the colonization and enterotoxicity of F18+Shiga toxin-producing Escherichia coli (STEC), which causes porcine edema disease (ED). In this study, a Stx2eA mutant, which has a Glu167Gln mutation in Stx2eA that inactivates N-glycosidase activity, was genetically engineered to evaluate its potential immunogenicity and protective efficacy. A significant increase in serum IgG1 (a Th2 indicator) was shown in mice immunized with the mutated Stx2eA. However, only 56% of the mice immunized with the toxoid (5 μg) survived following a challenge with a lethal dose 50 (LD50) of a virulent F18+STEC strain (JOL654), while mice immunized with Salmonella ghosts delivering selected antigens of F18+STEC showed an 86% survival rate. The results suggest that sole use of the mutated Stx2eA toxoid may not be an effective preventive strategy for the control of porcine ED.

In order to remove the inefficient cost occurred among companies, the cooperation among companies are required. The single vendor-single buyer integrated model is often studied for that purpose. Buyer’s demand follows normal distribution, and vendor’s inventory policy use a continuous inventory review policy. If buyer places order, then vendor begins to make products and transfer those products to the buyer several times. In real situation, the size of company’s warehouse is restricted, space limit constraint is considered. There are three approaches for the single vendor-single buyer integrated model with space limit: equal batch shipment approach, increasing batch shipment approach, and mixed approach. In this paper, these approaches are compared one another and we discuss about the advantages and disadvantages of these approaches.

PURPOSES : This paper presents a comparison study between dynamic and static analyses of falling weight deflectometer (FWD) testing, which is a test used for evaluating layered material stiffness. METHODS: In this study, a forward model, based on nonlinear subgrade models, was developed via finite element analysis using ABAQUS. The subgrade material coefficients from granular and fine-grained soils were used to represent strong and weak subgrade stiffnesses, respectively. Furthermore, the nonlinearity in the analysis of multi-load FWD deflection measured from intact PCC slab was investigated using the deflection data obtained in this study. This pavement has a 14-inch-thick PCC slab over finegrained soil. RESULTS: From case studies related to the nonlinearity of FWD analysis measured from intact PCC slab, a nonlinear subgrade modelbased comparison study between the static and dynamic analyses of nondestructive FWD tests was shown to be effectively performed; this was achieved by investigating the primary difference in pavement responses between the static and dynamic analyses as based on the nonlinearity of soil model as well as the multi-load FWD deflection. CONCLUSIONS : In conclusion, a comparison between dynamic and static FEM analyses was conducted, as based on the FEM analysis performed on various pavement structures, in order to investigate the significance of the differences in pavement responses between the static and dynamic analyses.

PURPOSES : The aim of this study was to compare the performance of the QUEENSOD method and the gravity model in estimating Origin-Destination (O/D) tables for a large-sized microscopic traffic simulation network. METHODS : In this study, an expressway network was simulated using the microscopic traffic simulation model, VISSIM. The gravity model and QUEENSOD method were used to estimate the O/D pairs between internal and between external zones. RESULTS: After obtaining estimations of the O/D table by using both the gravity model and the QUEENSOD method, the value of the root mean square error (RMSE) for O/D pairs between internal zones were compared. For the gravity model and the QUEENSOD method, the RMSE obtained were 386.0 and 241.2, respectively. The O/D tables estimated using both methods were then entered into the VISSIM networks and calibrated with measured travel time. The resulting estimated travel times were then compared. For the gravity model and the QUEENSOD method, the estimated travel times showed 1.16% and 0.45% deviation from the surveyed travel time, respectively. CONCLUSIONS : In building a large-sized microscopic traffic simulation network, an O/D matrix is essential in order to produce reliable analysis results. When link counts from diverse ITS facilities are available, the QUEENSOD method outperforms the gravity model.

미국 AASHTO LRFD(AASHTO, 2012)나 국내의 도로교설계기준(2012)의 차랑충돌에 대한 교각설계기준을 참조하면 교각 설계 시 차량충돌에 대해 정적인 하중을 고려하도록 제시하고 있다. 한편 2003년 미국 네브래스카 주에 트럭이 교각에 충돌하여 교각 및 교량 상부구조가 붕괴되는 사고가 발생하는 등 차량충돌에 의한 교량붕괴사고는 홍수에 의한 교량붕괴사고에 이은 두 번째 요인으로 분류되기도 한다. 화물차량의 대형화와 도로시스템의 개선으로 인하여 이러한 사고가 발생할 가능성이 중가하고 있다고 볼 수 있다. 본 연구에서는 교각 설계시 차량충돌에 대한 동적 해석을 수행하게 되면 많은 비용과 시간이 소요되어 실용적인 측면에서 연구결과가 쉽게 반영되지 못하고 있으므로 충돌해석 비용과 시간을 저감할 수 있는 모델축소법(model reduction)을 이용한 해석방법을 개발하였으며 그 효용성을 최종변위에 대해 직접충돌해석결과와 비교함으로써 평가하였다.

Estimation approaches for casual relation model with high-order factors have strict restrictions or limits. In the case of ML (Maximum Likelihood), a strong assumption which data must show a normal distribution is required and factors of exponentiation is impossible due to the uncertainty of factors. To overcome this limitation many PLS (Partial Least Squares) approaches are introduced to estimate the structural equation model including high-order factors. However, it is possible to yield biased estimates if there are some differences in the number of measurement variables connected to each latent variable. In addition, any approach does not exist to deal with general cases not having any measurement variable of high-order factors. This study compare several approaches including the repeated measures approach which are used to estimate the casual relation model including high-order factors by using PLS (Partial Least Squares), and suggest the best estimation approach. In other words, the study proposes the best approach through the research on the existing studies related to the casual relation model including high-order factors by using PLS and approach comparison using a virtual model.

목화진딧물 (Aphis gossypii)의 온도에 따른 발육시험을 실내 15, 18, 21, 24, 27, 30℃의 6개 항온, 광주기 14L:10D, 상대습도 50∼60% 조건과 오이 비닐하우스에서 3월 23일부터 8월 20일까지 6회 접종하여 수행하였다. 실내사망률은 저온에서는 2~3령충의 사망률이 높았고 온도가 증가할수록 3~4령충의 사망률이 높았으며 고온에서 전체 사망률이 높았다. 전체 약충의 발육기간은 실내에서 15℃에서 12.2일로 가장 짧았으며 변온의 28.5℃에서 4.09일로 가장 짧았다. 온도와 발육율과의 관계를 보기위해 선형 및 3개의 비선형 모형(Briere 1, Lactin 2, Logan 6)을 이용하여 분석한 결과, 선형모형을 이용하여 전체약충의 발육영점온도는 6.8℃였으며 발육유효적산온도는 각각 111.1DD였다. 3가지 비선형 모형중 Logan-6 모형이 전약충, 후약충 전체약충 단계에서 AIC와 BIC 값이 가장 적어 온도와 발육율과의 관계를 잘 설명하였으며, 발육단계별 발육완료분포는 3-parameter Weibull 함수를 사용하였으며 전약충, 후약충, 전체약충에서 r2 값이 0.88~0.91로 높은 값을 보여 양호한 모형 적합성을 보였으며 정식시기별 성충 발생 예측치와 포장 조사치가 일치하여 방제적기 추정에 유용하게 사용할 수 있을 것이다.

건전성 예측은 구조물의 고장이 발생될 때까지 남은 시간인 잔존유효수명을 예측하는 것으로, 이는 안전 및 정비 계획과 직접적으로 연관되기 때문에 매우 중요하다. 건전성 예측방법에는 물리모델 기반방법, 데이터 기반방법과 두 방법의 장점 을 통합하는 방법이 있으며, 본 연구에서는 잔존수명 예측의 정확도가 모델변수 추정과 직접적으로 관련되는 물리모델 기 반 건전성 예측에 초점을 맞춘다. 물리모델기반 건전성 예측에서는 모델변수 추정을 통해 시스템 상태의 장기 예측이 가능 하지만, 대부분의 실제 구조물들의 상태모델은 여러 개의 모델변수를 포함함은 물론이고, 그 변수들이 서로 상관되어 있기 때문에 모델변수를 추정하는 일은 간단한 문제가 아니다. 본 연구에서는 물리모델 기반 건전성 예측을 위한 세 가지 변수 추정방법들의 차이를 논한다. 이 세 가지 방법들은 파티클 필터, 전반적인 베이지안 접근법, 그리고 순차적인 베이지안 접 근법으로 모두 베이지안 추론이라는 하나의 이론적 바탕에 기반하지만, 샘플링 방법이나 갱신 절차 등에서 차이가 있다. 균열성장을 표현하는 Paris 모델의 변수 추정을 통해 세 가지 방법의 차이점이 논해지고, 건전성 예측 메트릭을 이용하여 정량적 차이를 표현한다. 파티클 필터방법이 건전성 예측 메트릭 측면에서 가장 높은 성능을 나타내었지만, 전반적인 베이 지안 방법은 파티클 필터방법과 근소한 차이를 보이면서도 데이터가 집단으로 존재할 때에는 가장 효율적인 방법으로 나 타났다.