• Smart Structures and Systems
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• v.30 no.1
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• pp.63-74
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• 2022

The deformation coordination between a rock/soil mass and an optical sensing cable is an important issue for accurate deformation monitoring. A stress-controlled triaxial apparatus was retrofitted by introducing an optical fiber into the soil specimen. High spatial resolution optical frequency domain reflectometry (OFDR) was used for monitoring the strain distribution along the axial direction of the specimen. The results were compared with those measured by a displacement meter. The strain measured by the optical sensing cable has a good linear relationship with the strain calculated by the displacement meter for different confining pressures, which indicates that distributed optical fiber sensing technology is feasible for soil deformation monitoring. The performance of deformation coordination between the sensing cable and the soil during unloading is higher than that during loading based on the strain transfer coefficients. Three hypothetical strain distributions of the triaxial specimen are proposed, based on which theoretical models of the strain transfer coefficients are established. It appears that the parabolic distribution of specimen strain should be more reasonable by comparison. Nevertheless, the strain transfer coefficients obtained by the theoretical models are higher than the measured coefficients. On this basis, a strain transfer model considering slippage at the interface of the sensing cable and the soil is discussed.

• International Journal of Advanced Culture Technology
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• v.7 no.1
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• pp.8-13
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• 2019

Despite technical advances in healthcare, the rates of hospital-acquired pressure injury (HAPI) are still high although many are potentially preventable. The purpose of this study was to determine whether tree-based prediction modeling is suitable for assessing the risk of HAPI in ICU patients. Retrospective cohort study has been carried out. A decision tree model was constructed with Age, Weight, eTube, diabetes, Braden score, Isolation, and Number of comorbid conditions as decision nodes. We used RStudio for model training and testing. Correct prediction rate of the final prediction model was 92.4 and the Area Under the ROC curve (AUC) was 0.699, which means there is about 70% chance that the model is able to distinguish between HAPI and non-HAPI. The results of this study has limited generalizability as the data were from a single academic institution. Our research finding shows that the data-driven tree-based prediction modeling may potentially support ICU sensitive risk assessment for HAPI prevention.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.32-42
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• 2023

There are serious problems worldwide, such as a pandemic due to an unprecedented infection caused by COVID-19. On previous approaches, they invented medical vaccines and preemptive testing tools for medical engineering. However, it is difficult to access poor medical systems and medical institutions due to disparities between countries and regions. In advanced nations, the damage was even greater due to high medical and examination costs because they did not go to the hospital. Therefore, from a software engineering-based perspective, we propose a learning model for determining coronavirus infection through symptom data-based software prediction models and tools. After a comparative analysis of various models (decision tree, Naive Bayes, KNN, multi-perceptron neural network), we decide to choose an appropriate decision tree model. Due to a lack of data, additional survey data and overseas symptom data are applied and built into the judgment model. To protect from thiswe also adapt human normalization approach with traditional Korean medicin approach. We expect to be possible to determine coronavirus, flu, allergy, and cold without medical examination and diagnosis tools through data collection and analysis by applying decision trees.

• International Commerce and Information Review
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• v.8 no.2
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• pp.59-78
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• 2006

Considering rapid development of Internet Portal Site Contents in Korea, it is an important issue to analyze consumers' satisfaction and e-loyalty. This research develops, and empirically test a model for explaining/predicting the satisfaction and e-loyalty with internet-based Electronic Trade Site. This paper describes a theoretical model for investigating the Satisfaction and e-Loyalty of the Electronic Trade Site. This study investigates the concept of the satisfaction, e-loyalty in Electronic Trade Site and its determinants, and tries to establish e-loyalty analyzing model. The model of the satisfaction, e-loyalty electronic trade site is tested here using data from 158 samples. Based on the research model, a comprehensive set of hypotheses is formulated and a methodology for testing them is outlined. some of the hypotheses are tested empirically to demonstrate the applicability of the theoretical model. In examining the relationships of the determinants factors, service quality satisfaction show in significantly by Reliability, Information Provided, Trustworthiness, Convenience, International of contents but that show in not significantly by Interaction. In addition, Electronic Trade site retention shows indirect effect between customer satisfaction and referral. Although our research can not show the determinants of e-loyalty in the electronic trade site the empirical result give both theoretical and managerial implication for managing the consumers' satisfaction and e-loyalty in electronic trade site.

• Computers and Concrete
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• v.13 no.2
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• pp.209-220
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• 2014

Vibration based damage detection is very popular in the civil engineering area. Especially, special structures like dams, long-span bridges and high-rise buildings, need continues monitoring in terms of mechanical properties of material, static and dynamic behavior. It has been stated in the International Commission on Large Dams that more than half of the large concrete dams were constructed more than 50 years ago and the old dams have subjected to repeating loads such as earthquake, overflow, blast, etc.,. So, some unexpected failures may occur and catastrophic damages may be taken place because of theloss of strength, stiffness and other physical properties of concrete. Therefore, these dams need repairs provided with global damage evaluation in order to preserve structural integrity. The paper aims to show the effectiveness of the model updating method for global damage detection on a laboratory arch dam model. Ambient vibration test is used in order to determine the experimental dynamic characteristics. The initial finite element model is updated according to the experimentally determined natural frequencies and mode shapes. The web thickness is selected as updating parameter in the damage evaluation. It is observed from the study that the damage case is revealed with high accuracy and a good match is attained between the estimated and the real damage cases by model updating method.

• Journal of Korea Trade
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• v.26 no.1
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• pp.99-112
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• 2022

Purpose - This paper empirically investigates via a spatial lag model from the perspective of space economy to find the influencing factors of South Korea's OFDI along with 60 countries. Design/methodology - In the study of regional economic phenomena, we must first test the corresponding spatial correlation, and on this basis, complete the construction of the spatial model. For the target research object, after testing the spatial correlation, if there is spatial correlation, a spatial measurement model is needed. This paper uses the global Moran's I index for calculation. Based on the characteristics and research needs of the research object, this paper selects the spatial lag model to verify the existence of the spatial effect and factors affecting OFDI. Findings - Our results show that export scale, infrastructure, technology level, political stability, resource endowment, market size, distance and labor cost have a certain impact on Korea's OFDI, but at present the distance and market size factors are the most important influencing factors for South Korea's OFDI, The technical level and political stability have little effect on South Korea's OFDI, and are not main factors determining South Korea's OFDI. Originality/value - Through spatial measurement verification, it was found that the spatial effect has a significant impact on OFDI, along with more than 60 countries. On this basis, relevant suggestions are put forward, which have strong practical significance for South Korea's OFDI to achieve healthy and sustainable development.

• Journal of Korea Water Resources Association
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• v.53 no.6
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• pp.395-408
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• 2020

This paper aims to develop and apply three different machine learning approaches (i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and wavelet-based neural networks (WNN)) combined with an evolutionary optimization algorithm and the k-fold cross validation for multi-step (days) streamflow forecasting at the catchment located in Algeria, North Africa. The ANN and ANFIS models yielded similar performances, based on four different statistical indices (i.e., root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and peak flow criteria (PFC)) for training and testing phases. The values of RMSE and PFC for the WNN model (e.g., RMSE = 8.590 ㎥/sec, PFC = 0.252 for (t+1) day, testing phase) were lower than those of ANN (e.g., RMSE = 19.120 ㎥/sec, PFC = 0.446 for (t+1) day, testing phase) and ANFIS (e.g., RMSE = 18.520 ㎥/sec, PFC = 0.444 for (t+1) day, testing phase) models, while the values of NSE and R for WNN model were higher than those of ANNs and ANFIS models. Therefore, the new approach can be a robust tool for multi-step (days) streamflow forecasting in the Seybous River, Algeria.

• Journal of The Korean Astronomical Society
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• v.52 no.6
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• pp.217-225
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• 2019

We apply a modified Convolutional Neural Network (CNN) model in conjunction with transfer learning to predict whether an active region (AR) would produce a ≥C-class or ≥M-class flare within the next 24 hours. We collect line-of-sight magnetogram samples of ARs provided by the SHARP from May 2010 to September 2018, which is a new data product from the HMI onboard the SDO. Based on these AR samples, we adopt the approach of shuffle-and-split cross-validation (CV) to build a database that includes 10 separate data sets. Each of the 10 data sets is segregated by NOAA AR number into a training and a testing data set. After training, validating, and testing our model, we compare the results with previous studies using predictive performance metrics, with a focus on the true skill statistic (TSS). The main results from this study are summarized as follows. First, to the best of our knowledge, this is the first time that the CNN model with transfer learning is used in solar physics to make binary class predictions for both ≥C-class and ≥M-class flares, without manually engineered features extracted from the observational data. Second, our model achieves relatively high scores of TSS = 0.640±0.075 and TSS = 0.526±0.052 for ≥M-class prediction and ≥C-class prediction, respectively, which is comparable to that of previous models. Third, our model also obtains quite good scores in five other metrics for both ≥C-class and ≥M-class flare prediction. Our results demonstrate that our modified CNN model with transfer learning is an effective method for flare forecasting with reasonable prediction performance.

• Journal of Korean Academy of Nursing Administration
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• v.12 no.1
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• pp.41-51
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• 2006

Purpose: The purposed of this study was to develop and evaluate a Web-based for medical terminology educational program. Method: For the development of this program, the NBISD model was applied as a basic model and WBI ID Process Action model was applied. It was executed for 15 weeks along with off-line lectures. After the operation of this program to 210 nursing students, learners' responses were analyzed twice(at 5th week and 15th week). Result: The satisfaction with this program was slightly above the center point. But the structure of system and composition of design were high score. The results of the final evaluation were more positive than those of the interim one. This program have the advantage of easy accessibility and easy understandability with picture and testing. Conclusions: This study proved the importance of the suitability of learning contents and learning form in developing WBI programs and showed possibility for applying WBI to medical terminology. In order to enhance the effects of Web-based medical terminology educational program for, it is necessary to perform analytic comparison with the effect of WBI based on self-directed learning.

• Journal of Information Processing Systems
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• v.7 no.4
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• pp.717-732
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• 2011

Classification in biomedical applications is an important task that predicts or classifies an outcome based on a given set of input variables such as diagnostic tests or the symptoms of a patient. Traditionally the classification algorithms would have to digest a stationary set of historical data in order to train up a decision-tree model and the learned model could then be used for testing new samples. However, a new breed of classification called stream-based classification can handle continuous data streams, which are ever evolving, unbound, and unstructured, for instance--biosignal live feeds. These emerging algorithms can potentially be used for real-time classification over biosignal data streams like EEG and ECG, etc. This paper presents a pioneer effort that studies the feasibility of classification algorithms for analyzing biosignals in the forms of infinite data streams. First, a performance comparison is made between traditional and stream-based classification. The results show that accuracy declines intermittently for traditional classification due to the requirement of model re-learning as new data arrives. Second, we show by a simulation that biosignal data streams can be processed with a satisfactory level of performance in terms of accuracy, memory requirement, and speed, by using a collection of stream-mining algorithms called Optimized Very Fast Decision Trees. The algorithms can effectively serve as a corner-stone technology for real-time classification in future biomedical applications.