• Smart Structures and Systems
• /
• 제22권4호
• /
• pp.399-411
• /
• 2018

The difficulty in modeling complex nonlinear structures lies in the presence of significant sources of uncertainties mainly attributed to sudden changes in the structure's behavior caused by regular aging factors or extreme events. Quantifying these uncertainties and accurately representing them within the complex mathematical framework of Structural Health Monitoring (SHM) are significantly essential for system identification and damage detection purposes. This study highlights the importance of uncertainty quantification in SHM frameworks, and presents a comparative analysis between intrusive and non-intrusive techniques in quantifying uncertainties for SHM purposes through two different variations of the Kalman Filter (KF) method, the Ensemble Kalman filter (EnKF) and the Polynomial Chaos Kalman Filter (PCKF). The comparative analysis is based on a numerical example that consists of a four degrees-of-freedom (DOF) system, comprising Bouc-Wen hysteretic behavior and subjected to El-Centro earthquake excitation. The comparison is based on the ability of each technique to quantify the different sources of uncertainty for SHM purposes and to accurately approximate the system state and parameters when compared to the true state with the least computational burden. While the results show that both filters are able to locate the damage in space and time and to accurately estimate the system responses and unknown parameters, the computational cost of PCKF is shown to be less than that of EnKF for a similar level of numerical accuracy.

• 디지털산업정보학회논문지
• /
• 제18권4호
• /
• pp.67-75
• /
• 2022

Recently, e-learning has been attracting significant attention due to COVID-19. However, while e-learning has many advantages, it has disadvantages as well. One of the main disadvantages of e-learning is that it is difficult for teachers to continuously and systematically monitor learners. Although services such as personalized e-learning are provided to compensate for the shortcoming, systematic monitoring of learners' concentration is insufficient. This study suggests a method to evaluate the learner's concentration by applying machine learning techniques. In this study, emotion and gaze data were extracted from 184 videos of 92 participants. First, the learners' concentration was labeled by experts. Then, statistical-based status indicators were preprocessed from the data. Random Forests (RF), Support Vector Machines (SVMs), Multilayer Perceptron (MLP), and an ensemble model have been used in the experiment. Long Short-Term Memory (LSTM) has also been used for comparison. As a result, it was possible to predict e-learners' concentration with an accuracy of 90.54%. This study is expected to improve learners' immersion by providing a customized educational curriculum according to the learner's concentration level.

• The Journal of Asian Finance, Economics and Business
• /
• 제9권8호
• /
• pp.89-97
• /
• 2022

The e-commerce market faces significant credit risks due to the complexity of the industry and information asymmetries. Therefore, credit risk has started to stymie the growth of e-commerce. However, there is no reliable system for evaluating the creditworthiness of e-commerce companies. Therefore, this paper constructs a credit risk evaluation index system that comprehensively considers the online and offline behavior of online retail enterprises, including 15 indicators that reflect online credit risk and 15 indicators that reflect offline credit risk. This paper establishes an integration method based on a fuzzy integral support vector machine, which takes the factor analysis results of the credit risk evaluation index system of online retail enterprises as the input and the credit risk evaluation results of online retail enterprises as the output. The classification results of each sub-classifier and the importance of each sub-classifier decision to the final decision have been taken into account in this method. Select the sample data of 1500 online retail loan customers from a bank to test the model. The empirical results demonstrate that the proposed method outperforms a single SVM and traditional SVMs aggregation technique via majority voting in terms of classification accuracy, which provides a basis for banks to establish a reliable evaluation system.

• ETRI Journal
• /
• 제45권3호
• /
• pp.448-461
• /
• 2023

This article performs a detailed data scrutiny on a chronic kidney disease (CKD) dataset to select efficient instances and relevant features. Data relevancy is investigated using feature extraction, hybrid outlier detection, and handling of missing values. Data instances that do not influence the target are removed using data envelopment analysis to enable reduction of rows. Column reduction is achieved by ranking the attributes through feature selection methodologies, namely, extra-trees classifier, recursive feature elimination, chi-squared test, analysis of variance, and mutual information. These methodologies are ranked via Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) using weight optimization to identify the optimal features for model building from the CKD dataset to facilitate better prediction while diagnosing the severity of the disease. An efficient hybrid ensemble and novel similarity-based classifiers are built using the pruned dataset, and the results are thereafter compared with random forest, AdaBoost, naive Bayes, k-nearest neighbors, and support vector machines. The hybrid ensemble classifier yields a better prediction accuracy of 98.31% for the features selected by extra tree classifier (ETC), which is ranked as the best by TOPSIS.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2021년도 학술발표회
• /
• pp.205-205
• /
• 2021

Surface soil moisture, which governs the partitioning of precipitation into infiltration and runoff, plays an important role in the hydrological cycle. The assimilation of satellite soil moisture retrievals into a land surface model or hydrological model has been shown to improve the predictive skill of hydrological variables. This study aims to improve streamflow prediction with Weather Research and Forecasting model-Hydrological modeling system (WRF-Hydro) by assimilating Soil Moisture Active and Passive (SMAP) data at 3 km and analyze its impacts on hydrological components. We applied Cumulative Distribution Function (CDF) technique to remove the bias of SMAP data and assimilate SMAP data (April to July 2015-2019) into WRF-Hydro by using an Ensemble Kalman Filter (EnKF) with a total 12 ensembles. Daily inflow and soil moisture estimates of major dams (Soyanggang, Chungju, Sumjin dam) of South Korea were evaluated. We investigated how hydrologic variables such as runoff, evaporation and soil moisture were better simulated with the data assimilation than without the data assimilation. The result shows that the correlation coefficient of topsoil moisture can be improved, however a change of dam inflow was not outstanding. It may attribute to the fact that soil moisture memory and the respective memory of runoff play on different time scales. These findings demonstrate that the assimilation of satellite soil moisture retrievals can improve the predictive skill of hydrological variables for a better understanding of the water cycle.

• 콘크리트학회논문집
• /
• 제15권6호
• /
• pp.924-929
• /
• 2003

A majority of infrastructures has been deteriorated over time. Therefore, it is very important to verify the quality of construction, and the level of structural deterioration in existing structures, to ensure their safety and functionality. Many researchers have studied non-destructive testing (NDT) methods to identify structural problems in existing structures. The impact echo technique is one of the widely used NDT techniques. The impact echo technique has several inherent problems, including the difficulties in P-wave velocity evaluation due to inhomogeneous concrete properties, deterioration of evaluation accuracy where multiple reflection boundaries exist, and the influence of the receiver location in evaluating the thickness of the tested structures. Therefore, the objective of this paper is to propose an enhanced impact echo technique that can reduce the aforementioned problems and develop a Virtual Instrument for the application via a thickness evaluation technique which has same technical background to find deterioration in concrete structures. In the proposed impact echo technique, transfer function from dual channel system analysis is used, and coherence is improved to achieve reliable data. Also an averaged signal -ensemble- is used to achieve more reliable results. From the analysis of transfer function, the thickness is effectively identified.

• 대한토목학회논문집
• /
• 제34권3호
• /
• pp.795-804
• /
• 2014

본 연구에서는 앙상블유량예측기법과 SWAT 모형을 이용하여 안동댐의 2011년 7월~9월의 각 댐유입량 예측을 실행하였으며 월별 및 순별 분석을 수행하였다. 또한 정확한 분석을 위해 기상청의 월별 및 순별 강우예보자료를 이용한 가중값 부여방법을 사용하였다. 분석 결과 기상청에서 발표한 강우 예측 구간이 실제 강우 구간과 동일하면 PDF-Ratio 가중값 부여방법이 가장 높은 정확성을 보이며, 과거 강우발생 구간 통계 중 높은 구간이 실제 강우 구간과 동일하다면 수정 PDF-Ratio 가중값 부여방법이 가장 높은 정확성을 보였다. 이는 기상청 예측이 맞지 않은 경우에도 과거 강우발생 구간의 빈도에 따라 정확성을 높일 수 있을 것으로 판단된다. 반대로 기상청의 예측이 실제와 다르면서 과거 강우발생 구간 통계에서도 낮은 구간의 강우가 발생하면 균일 가중값 부여방법의 정확성이 가장 높게 분석되었다.

• 한국농공학회논문집
• /
• 제51권4호
• /
• pp.57-66
• /
• 2009

수학적 모델은 수량과 수질의 예측을 위해 현장 조사의 대안으로 사용되어지며 이러한 모델의 사용과 실측에 불확실성이 존재하게 된다. 불확실성에 대한 많은 연구들이 진행되어 왔으나 시나리오에 의한 모델링 과정에서 발생하는 불확실성에 대한 연구는 미흡한 실정이다. 본 연구에서는 산림이 농경지와 목초지로의 변화에 따른 시나리오를 설계한 후 시나리오 적용에 따른 SWAT (Soil and Water Assessment Tool) 매개변수의 불확실성을 분석하고자 하였다. 몬테카를로 기법 (Monte Carlo simulation)을 이용하여 각 매개변수별 1,000개의 난수를 발생하였으며 앙상블 유량모의 기법을 이용하여 미국 Alabama주 카하바강 상류 (50,967ha)를 대상으로 각 난수별 100개의 유량을 통해 불확실성을 분석하였다. 분석 결과 산림지역이 농경지와 목초지로 변화 되었을 때 유출량이 증가하는 것으로 분석되었으며, 임야가 목초지 보다 농경지로 변화되었을 때 유출량은 더욱 증가하는 것으로 나타났다. 각 시나리오별 SWAT 매개변수의 불확실성은 AWC (Available water capacity), CN (Curve number), GWREVAP (groundwater re-evaporation coeffeicient), REVAPMN (minimum depth of water in shallow aquifer for re-evaporation to occur)순으로 크게 나타났으며, Ksat (Saturated hydraulic conductivity)와 ESCO(Soil evaporation compensation factor)는 유출량의 변화에 큰 영향을 미치지 못하는 것으로 분석되었다. 토지피복별 산림 면적이 클 경우 불확실성이 크게 나타나 산림이 목초지와 농경지로 변함에 따라 불확실성은 감소하는 것으로 나타났다.

• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2021년도 추계학술대회
• /
• pp.455-457
• /
• 2021

앱(App) 또는 어플리케이션이라고 부르는 응용 프로그램은 스마트폰이나 스마트TV와 같은 스마트 기기에서 사용되고 있다. 당연하게도 앱에도 악성코드가 있는데, 악성코드의 유무에 따라 정상앱과 악성앱으로 나눌 수 있다. 악성코드는 많고 종류가 다양하기 때문에 사람이 직접 탐지하기 어렵다는 단점이 있어 AI를 활용하여 악성앱을 탐지하는 방안을 제안한다. 기존 방법에서는 악성앱에서 Feature를 추출하여 악성앱을 탐지하는 방법이 대부분이었다. 하지만 종류와 수가 기하급수적으로 늘어 일일이 탐지할 수도 없는 상황이다. 따라서 기존 대부분의 악성앱에서 Feature을 추출하여 악성앱을 탐지하는 방안 외에 두 가지를 더 제안하려 한다. 첫 번째 방안은 기존 악성앱 학습을 하여 악성앱을 탐지하는 방법과 는 반대로 정상앱을 공부하여 Feature를 추출하여 학습한 후 정상에서 거리가 먼, 다시 말해 비정상(악성앱)을 찾는 것이다. 두 번째 제안하는 방안은 기존 방안과 첫 번째로 제안한 방안을 결합한 '앙상블 기법'이다. 이 두 기법은 향후 앱 환경에서 활용될 수 있도록 연구를 진행할 필요가 있다.

• 유체기계공업학회:학술대회논문집
• /
• 유체기계공업학회 1998년도 유체기계 연구개발 발표회 논문집
• /
• pp.123-128
• /
• 1998

This study presents the measurement techniques on the periodic fluctuating flow such as the discharge flow of a centrifugal impeller in unstable operating region. During rotating stall, the flow at the exit of a centrifugal compressor impeller fluctuates periodically with lower frequency than that of the blade passing. To observe the blade-to-blade flow characteristics during rotating stall, the phases of all the sampled data sets should be adjusted to those of the reference signals with two processes, in these processes, DPLEAT (Double Phase-Locked Ensemble Averaging Technique) can be used. From these measurement and data processing techniques, the characteristics not only on the blade-to-blade flow with high frequency, but also on the periodic rotating stall flow with low frequency at the centrifugal impeller exit can be clearly observed.