• Journal of Mechanical Science and Technology
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• 제17권8호
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• pp.1148-1155
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• 2003

The finite element method is one of the methods widely applied for predicting vibration in mechanical structures. In this paper, the effect of the mesh size of the finite element model on the accuracy of the numerical solutions of the structural vibration problems is investigated with particular focus on obtaining the optimal mesh size with respect to the solution accuracy and computational cost. The vibration response parameters of the natural frequency, modal density, and driving point mobility are discussed. For accurate driving point mobility calculation, the decay method is employed to experimentally determine the internal damping. A uniform plate simply supported at four corners is examined in detail, in which the response parameters are calculated by constructing finite element models with different mesh sizes. The accuracy of the finite element solutions of these parameters is evaluated by comparing with the analytical results as well as estimations based on the statistical energy analysis, or if not available, by testing the numerical convergence. As the mesh size becomes smaller than one quarter of the wavelength of the highest frequency of interest, the solution accuracy improvement is found to be negligible, while the computational cost rapidly increases. For mechanical structures, the finite element analysis with the mesh size of the order of quarter wavelength, combined with the use of the decay method for obtaining internal damping, is found to provide satisfactory predictions for vibration responses.

• 응용통계연구
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• 제28권5호
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• pp.871-883
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• 2015

벡터자기회귀모형은 다차원의 시계열 자료간의 선형종속 관계를 연구하는데 효율적인 모형이다. 하지만 차원이 높아질 경우 추정해야할 모수가 급격히 증가하여 추정이 불안정해지고 예측력의 저하 및 해석의 어려움을 동반하는 문제를 가지고 있다. 이를 보완하기 위해서 많은 계수를 0으로 두는 희박벡터자기회귀모형이 제안되었고 고차원 시계열 분석에서 유용함이 밝혀졌다. 이 논문에서는 희박벡터자기회귀모형 추정에 있어서 어떠한 계수를 0으로 두어야 하는지를 판단해주는 한 쌍의 변수에 대한 상관 정도를 추정해주는 커플링 측도를 제안한다. 먼저 이 논문에서는 부분 스펙트럼 일관성에 기반을 둔 커플링 측도를 사용한 변수 선택의 경우 다른 변수의 효과를 제거한 잔차에 기반을 두었기에 좋은 효율성을 보임을 밝힌다. 하지만 부분 스펙트럼 일관성의 경우 벡터자기회귀모형 계수의 비대칭성을 고려하지 못한다는 단점이 있어 이를 보완하고자 필터링을 통해 다른 변수의 효과를 제거한 잔차에 기반을 둔 동시에 비대칭성을 가지는 커플링 측도들, 필터링된 잔차를 이용한 교차 상관성과 그래인저 인과관계를 제안한다. 모의실험을 통해 우리가 제안한 방법론들이 두터운 꼬리를 가지거나 높은 차수의 희박벡터자기회귀모형의 경우에도 매우 정확하게 0이 아닌 변수를 선택함을 보인다.

• 한국해양공학회지
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• 제33권5호
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• pp.427-434
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• 2019

Artificial intelligence (AI)-aided research currently enjoys active use in a wide array of fields thanks to the rapid development of computing capability and the use of Big Data. Until now, forecasting methods were primarily based on physics models and statistical studies. Today, AI is utilized in disaster prevention forecasts by studying the relationships between physical factors and their characteristics. Current studies also involve combining AI and physics models to supplement the strengths and weaknesses of each aspect. However, prior to these studies, an optimization algorithm for the AI model should be developed and its applicability should be studied. This study aimed to improve the forecast performance by constructing a model for neural network optimization. An artificial neural network (ANN) followed the ever-changing path of a typhoon to produce similar typhoon predictions, while the optimization achieved by the neural network algorithm was examined by evaluating the activation function, hidden layer composition, and dropouts. A learning and test dataset was constructed from the available digital data of one typhoon that affected Korea throughout the record period (1951-2018). As a result of neural network optimization, assessments showed a higher degree of forecast accuracy.

• 한국축산식품학회지
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• 제39권2호
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• pp.222-228
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• 2019

This research was motivated by our encounter with the situation where an optimization was done based on statistically non-significant models having poor fits. Such a situation took place in a research to optimize manufacturing conditions for improving storage stability of coffee-supplemented milk beverage by using response surface methodology, where two responses are $Y_1$=particle size and $Y_2$=zeta-potential, two factors are $F_1$=speed of primary homogenization (rpm) and $F_2$=concentration of emulsifier (%), and the optimization objective is to simultaneously minimize $Y_1$ and maximize $Y_2$. For response surface analysis, practically, the second-order polynomial model is almost solely used. But, there exists the cases in which the second-order model fails to provide a good fit, to which remedies are seldom known to researchers. Thus, as an alternative to a failed second-order model, we present the heterogeneous third-order model, which can be used when the experimental plan is a two-factor central composite design having -1, 0, and 1 as the coded levels of factors. And, for multi-response optimization, we suggest a modified desirability function technique. Using these two methods, we have obtained statistical models with improved fits and multi-response optimization results with the predictions better than those in the previous research. Our predicted optimum combination of conditions is ($F_1$, $F_2$)=(5,000, 0.295), which is different from the previous combination. This research is expected to help improve the quality of response surface analysis in experimental sciences including food science of animal resources.

• Animal Bioscience
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• 제34권4호
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• pp.770-782
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• 2021

Objective: The aim of the present study was to compare the effectiveness of three approaches (the seasonal auto-regressive integrated moving average [SARIMA] model, the nonlinear autoregressive exogenous [NARX] artificial neural networks and Wood's model) to the prediction of milk yield during lactation. Methods: The dataset comprised monthly test-day records from 965 Polish Holstein-Friesian Black-and-White primiparous cows. The milk yields from cows in their first lactation (from 5 to 305 days in milk) were used. Each lactation was divided into ten lactation stages of approximately 30 days. Two age groups and four calving seasons were distinguished. The records collected between 2009 and 2015 were used for model fitting and those from 2016 for the verification of predictive performance. Results: No significant differences between the predicted and the real values were found. The predictions generated by SARIMA were slightly more accurate, although they did not differ significantly from those produced by the NARX and Wood's models. SARIMA had a slightly better performance, especially in the initial periods, whereas the NARX and Wood's models in the later ones. Conclusion: The use of SARIMA was more time-consuming than that of NARX and Wood's model. The application of the SARIMA, NARX and Wood's models (after their implementation in a user-friendly software) may allow farmers to estimate milk yield of cows that begin production for the first time.

• 한국산학기술학회논문지
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• 제22권3호
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• pp.20-29
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• 2021

4차산업혁명 시대에는 대량의 데이터를 학습하여 예측과 분류의 정확성을 향상시킬 수 있는 인공지능의 활용이 핵심적이다. 그러나, 기존 이상탐지를 위한 방법은 제한된 데이터를 다루는 전통적인 통계 방법에 의존하고 있어, 정확한 이상탐지가 어렵다. 그러므로, 본 연구는 인공지능 기반 이상탐지 방법을 제시하여 예측 정확도를 높이고, 새로운 데이터 패턴을 정의하는 것을 목적으로 한다. 특히, 자동차의 경우 공회전 기간의 센서 데이터가 이상 탐지에 활용될 수 있다는 관점에서 데이터를 수집하고 분석하였다. 이를 위해, 예측 모델에 입력되는 데이터의 적정 시간 길이를 결정하고, 공회전 기간 데이터와 전체 운행 데이터의 분석 결과를 비교하며, 다양한 센서 데이터 조합에 의한 최적 예측 방법을 도출하였다. 또한, 인공지능 방법으로 선택된 CNN의 예측 정확성을 검증하기 위해 LSTM 결과와 비교하였다. 분석 결과, 공회전 데이터를 이용하고, 공회전 기간보다 1.5배 많은 기간의 데이터를 이용하며 LSTM보다는 CNN을 활용하는 것이 더 좋은 예측결과를 보였다.

• 대한의용생체공학회:의공학회지
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• 제43권1호
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• pp.27-34
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• 2022

Drunk driving defines a driver as unable to drive a vehicle safely due to drinking. To crack down on drunk driving, alcohol concentration evaluates through breathing and crack down on drinking using S-shaped courses. A method for assessing drunk driving without using BAC or BrAC is measurement via biosignal. Depending on the individual specificity of drinking, alcohol evaluation studies through various biosignals need to be conducted. In this study, we measure biosignals that are related to alcohol concentration, predict BrAC through SVM, and verify the effectiveness of the S-shaped course. Participants were 8 men who have a driving license. Subjects conducted a d2 test and a scenario evaluation of driving an S-shaped course when they attained BrAC's certain criteria. We utilized SVR to predict BrAC via biosignals. Statistical analysis used a one-way Anova test. Depending on the amount of drinking, there was a tendency to increase pupil size, HR, normLF, skin conductivity, body temperature, SE, and speed, while normHF tended to decrease. There was no apparent change in the respiratory rate and TN-E. The result of the D2 test tended to increase from 0.03% and decrease from 0.08%. Measured biosignals have enabled BrAC predictions using SVR models to obtain high Figs in primary and secondary cross-validations. In this study, we were able to predict BrAC through changes in biosignals and SVMs depending on alcohol concentration and verified the effectiveness of the S-shaped course drinking control method.

• 산업경영시스템학회지
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• 제45권2호
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• pp.65-76
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• 2022

Recently, not only traditional statistical techniques but also machine learning algorithms have been used to make more accurate bankruptcy predictions. But the insolvency rate of companies dealing with financial institutions is very low, resulting in a data imbalance problem. In particular, since data imbalance negatively affects the performance of artificial intelligence models, it is necessary to first perform the data imbalance process. In additional, as artificial intelligence algorithms are advanced for precise decision-making, regulatory pressure related to securing transparency of Artificial Intelligence models is gradually increasing, such as mandating the installation of explanation functions for Artificial Intelligence models. Therefore, this study aims to present guidelines for eXplainable Artificial Intelligence-based corporate bankruptcy prediction methodology applying SMOTE techniques and LIME algorithms to solve a data imbalance problem and model transparency problem in predicting corporate bankruptcy. The implications of this study are as follows. First, it was confirmed that SMOTE can effectively solve the data imbalance issue, a problem that can be easily overlooked in predicting corporate bankruptcy. Second, through the LIME algorithm, the basis for predicting bankruptcy of the machine learning model was visualized, and derive improvement priorities of financial variables that increase the possibility of bankruptcy of companies. Third, the scope of application of the algorithm in future research was expanded by confirming the possibility of using SMOTE and LIME through case application.

• 한국융합학회논문지
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• 제12권11호
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• pp.27-33
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• 2021

최근 인공지능, 딥러닝, 빅데이터 등 4차 산업의 핵심 분야에 대한 관심이 커지면서 기존의 의사결정 문제를 전통적인 방법론의 한계점을 최소화하는 과학적 접근 방식이 대두되고 있다. 특히 이런 과학적인 기법들은 주로 금융 상품의 방향성을 예측하는데 사용되는데 본 연구에서는 사회적으로 관심이 높은 아파트 가격의 요인을 자기조직화지도를 통해 분석하고자 한다. 이를 위해 아파트 가격의 실질 가격을 추출하고 아파트 가격에 영향을 주는 총 16개의 입력 변수를 선정한다. 실험 기간은 1986년 1월부터 2021년 6월까지이며 아파트 가격의 상승 및 횡보 구간을 나눠 각 구간 별 변수들의 특징을 살펴본 결과, 상승 구간과 횡보 구간의 입력 변수의 통계적 성향이 뚜렷하게 구분되는 것을 알 수 있었다. 더불어 U1~U3 구간이 N1~N3 구간에 비해서 변수들의 표준편차가 상대적으로 크게 나왔다. 본 연구는 중장기적으로 상승과 하락이라는 큰 주기를 갖고 있는 부동산에 대해서 현재 시점의 현황을 정량적으로 분석한 것에 의미가 있으며 향후 이미지 학습을 통해 미래 방향성을 예측하는 연구에 도움이 되기를 기대한다.

• Computers and Concrete
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• 제31권3호
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• pp.253-265
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• 2023

In this research, the gene expression programming (GEP) technique was employed to provide a new model for predicting the maximum loading capacity of concrete-encased steel (CES) columns. This model was developed based on 96 CES column specimens available in the literature. The six main parameters used in the model were the compressive strength of concrete (f_c), yield stress of structural steel (f_ys), yield stress of steel rebar (f_yr), and cross-sectional areas of concrete, structural steel, and steel rebar (A_c, A_s and A_r respectively). The performance of the prediction model for the ultimate load-carrying capacity was investigated using different statistical indicators such as root mean square error (RMSE), correlation coefficient (R), mean absolute error (MAE), and relative square error (RSE), the corresponding values of which for the proposed model were 620.28, 0.99, 411.8, and 0.01, respectively. Here, the predictions of the model and those of available codes including ACI ITG, AS 3600, CSA-A23, EN 1994, JGJ 138, and NZS 3101 were compared for further model assessment. The obtained results showed that the proposed model had the highest correlation with the experimental data and the lowest error. In addition, to see if the developed model matched engineering realities and corresponded to the previously developed models, a parametric study and sensitivity analysis were carried out. The sensitivity analysis results indicated that the concrete cross-sectional area (A_c) has the greatest effect on the model, while parameter (f_yr) has a negligible effect.