• Journal of Korea Water Resources Association
• /
• v.53 no.11
• /
• pp.929-938
• /
• 2020

In this study, we estimated missing evapotranspiration (ET) data at a eddy-covariance flux tower in the Cheongmicheon farmland site using the Artificial Neural Network (ANN). The ANN showed excellent performance in numerical analysis and is expanding in various fields. To evaluate the performance the ANN-based gap-filling, ET was calculated using the existing gap-filling methods of Mean Diagnostic Variation (MDV) and Food and Aggregation Organization Penman-Monteith (FAO-PM). Then ET was evaluated by time series method and statistical analysis (coefficient of determination, index of agreement (IOA), root mean squared error (RMSE) and mean absolute error (MAE). For the validation of each gap-filling model, we used 30 minutes of data in 2015. Of the 121 missing values, the ANN method showed the best performance by supplementing 70, 53 and 84 missing values, respectively, in the order of MDV, FAO-PM, and ANN methods. Analysis of the coefficient of determination (MDV, FAO-PM, and ANN methods followed by 0.673, 0.784, and 0.841, respectively.) and the IOA (The MDV, FAO-PM, and ANN methods followed by 0.899, 0.890, and 0.951 respectively.) indicated that, all three methods were highly correlated and considered to be fully utilized, and among them, ANN models showed the highest performance and suitability. Based on this study, it could be used more appropriately in the study of gap-filling method of flux tower data using machine learning method.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.50 no.8
• /
• pp.531-540
• /
• 2022

In this paper, we present an algorithm that identifies artillery type and rapidly predicts the impact point based on the IMM filter. The ballistic trajectory equation is used as a system model, and three models with different ballistic coefficient values are used. Acceleration was divided into three components of gravity, air resistance, and lift. And lift acceleration was added as a new state variable. The kinematic condition that the velocity vector and lift acceleration are perpendicular was used as a pseudo-measurement value. The impact point was predicted based on the state variable estimated through the IMM filter and the ballistic coefficient of the model with the highest mode probability. Instead of the commonly used Runge-Kutta numerical integration for impact point prediction, a semi-analytic method was used to predict impact point with a small amount of calculation. Finally, a state variable initialization method using the least-square method was proposed. An integrated algorithm including artillery type identification, impact point prediction and initialization was presented, and the validity of the proposed method was verified through simulation.

• Journal of Korean Public Health Nursing
• /
• v.28 no.1
• /
• pp.57-70
• /
• 2014

Purpose: This study is a methodological research study for development of a tool for measurement of care satisfaction of home health nursing in patients who have received care through home health nursing and to test the validity and reliability of the tool. Method: Based on Watson's theory of caring and its constituent factors, the literature related to home health nursing was reviewed, and the standard of home health nursing was compared and analyzed. For verification of the validation and reliability of the final tool which has been developed, data were collected from 166 people who were receiving home health nursing care. Result: According to results of analysis of the factors based on Kaiser and scree test, 10 factors with an eigenvalue greater than 1.0 were extracted, and they explained 72.26% of the total variance. In addition, the factor loadings of 56 questions were greater than .30. For verification of reliability, Cronbach's alpha for all 56 items was .98 and Guttman split half reliability coefficient was .90. Conclusion: This tool based on Watson's theory of caring contributed to development and application of the nursing theory through verification of the theory.

• Journal of Biosystems Engineering
• /
• v.24 no.3
• /
• pp.225-234
• /
• 1999

This study was performed as a basic research for establishing an objective quality evaluation method on whole bulbs of garlic. The size of a whole bulb of garlic, the number and the uniformity of complete individual garlics, and the existence of bad individual garlics in the whole bulb of garlic were selected as quality grading factors. Quality discrimination algorithms with machine vision techniques were developed and verified for the four factors based on morphological and physical features of whole bulbs of garlic. Based on the results, the size discrimination by the projected area of a whole bulbs of garlic suggested four grading levels and the algorithm for predicting the number of complete individual garlics based on the peaks on its projected boundary showed ${\pm}$0.78 prediction error. In addition, the uniformity represented by coefficient of variation could be divided into four levels, but the algorithm for discriminating the existence of bad individual garlics in a whole bulb of garlic was not effective.

• Journal of East-West Nursing Research
• /
• v.20 no.2
• /
• pp.154-159
• /
• 2014

Purpose: The purpose of this study was to examine the effects of team-based learning (TBL) program on problem solving ability, critical thinking disposition and self-directed learning in junior nursing students caring for patients with respiratory diseases. Methods: One-group pretest-posttest design was used. TBL program was carried out for 2 hours a week for 6 weeks. Data were collected by questionnaires from 167 nursing students from March 6 to June 5, 2013. Data were analyzed by paired t-test and Pearson correlation coefficient. Results: At the completion of TBL program, significant improvement was found in problem solving ability (t=-6.04, p<.001), critical thinking disposition (t=5.02, p<.001) and self-directed learning (t=5.96, p<.001). There was a significant positive correlation among problem solving ability, critical thinking disposition and self-directed learning. Conclusion: In conclusion, TBL is a useful teaching and learning method on nursing students. We suggest that it is needed to measure the educational effects of TBL against other teaching methods in the future studies.

• Journal of Korean Academy of Fundamentals of Nursing
• /
• v.17 no.1
• /
• pp.82-89
• /
• 2010

Purpose: The purpose of this study was to compare nursing students before using problem-based learning and after the experience in: class satisfaction, tendency to critical thinking, learning attitude and motivation. Method: The data were collected on March 20 and June 5, 2008. The PBL study was given for 15 weeks from March through June involving 216 junior nursing students. To test effects of PBL, a one group pretest-posttest design was used. Statistical analysis was performed with SPSS 13.0. Paired t-test, $x^2$-test, and Pearson correlation coefficient were performed. Results: The results are summarized as follows: Following PBL, the students scored significantly higher on the class satisfaction (t=-3.321, p= .001), tendency to critical thinking (t=-2.218, p= .034), learning attitude (t=-2.910, p= .004) and motivation (t=-4.407, p<.001). The Pearson correlation coefficients among the three variables were significantly positive. Conclusion: This study contributes to our understanding of outcomes from the PBL approach. The students undertaking PBL showed that they developed a more positive attitude with their educational experience. Also, students' tendency to think critically improved through the use of the PBL approach.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.12
• /
• pp.2520-2528
• /
• 2009

In this paper, we proposed tracking detection methodology using information granulation-based fuzzy radial basis function neural networks (IG-FRBFNN). According to IEC 60112, tracking device is manufactured and utilized for experiment. We consider 12 features that can be used to decide whether tracking phenomenon happened or not. These features are considered by signal processing methods such as filtering, Fast Fourier Transform(FFT) and Wavelet. Such some effective features are used as the inputs of the IG-FRBFNN, the tracking phenomenon is confirmed by using the IG-FRBFNN. The learning of the premise and the consequent part of rules in the IG-FRBFNN is carried out by Fuzzy C-Means (FCM) clustering algorithm and weighted least squares method (WLSE), respectively. Also, Hierarchical Fair Competition-based Parallel Genetic Algorithm (HFC-PGA) is exploited to optimize the IG-FRBFNN. Effective features to be selected and the number of fuzzy rules, the order of polynomial of fuzzy rules, the fuzzification coefficient used in FCM are optimized by the HFC-PGA. Tracking inference engine is implemented by using the LabVIEW and loaded into embedded system. We show the superb performance and feasibility of the tracking detection system through some experiments.

• Geomechanics and Engineering
• /
• v.22 no.4
• /
• pp.305-318
• /
• 2020

Stone columns are widely used to treat soft clay ground. Optimizing the design of stone columns based on cost-effectiveness is always an attractive subject in the practice of ground treatment. In this paper, the design of stone columns is optimized using the concept of robust geotechnical design. Standard deviation of failure probability, which is a system response of concern of the stone column-reinforced foundation, is used as a measure of the design robustness due to the uncertainty in the coefficient of variation (COV) of the noise factors in practice. The failure probability of a stone column-reinforced foundation can be readily determined using Monte Carlo simulation (MCS) based on the settlements of the stone column-reinforced foundation, which are evaluated by a deterministic method. A framework based on the concept of robust geotechnical design is proposed for determining the most preferred design of stone columns considering multiple objectives including safety, cost and design robustness. This framework is illustrated with an example, a stone column-reinforced foundation under embankment loading. Based on the outcome of this study, the most preferred design of stone columns is obtained.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.4
• /
• pp.1724-1731
• /
• 2018

The conventional polynomial neural network (PNN) is a classical flexible neural structure and self-organizing network, however it is not free from the limitation of overfitting problem. In this study, we propose a space search-optimized polynomial neural network (ssPNN) structure to alleviate this problem. Ranking selection is realized by means of ranking selection-based performance index (RS_PI) which is combined with conventional performance index (PI) and coefficients based performance index (CPI) (viz. the sum of squared coefficient). Unlike the conventional PNN, L2-norm regularization method for estimating the polynomial coefficients is also used when designing the ssPNN. Furthermore, space search optimization (SSO) is exploited here to optimize the parameters of ssPNN (viz. the number of input variables, which variables will be selected as input variables, and the type of polynomial). Experimental results show that the proposed ranking selection-based polynomial neural network gives rise to better performance in comparison with the neuron fuzzy models reported in the literatures.

• Smart Media Journal
• /
• v.5 no.2
• /
• pp.84-89
• /
• 2016

For a place-recommendation model based on user's behavior and multi-attribute attitude in this thesis. We focus groups that show similar patterns of visiting restaurants and then compare one and the other. We make use of The Fishbein Equation, Pearson's Correlation Coefficient to calculate multi-attribute attitude scores. Furthermore, We also make use of Preference Prediction Algorithm and Distance based method named "Euclidean Distance" to provide accurate results. We can demonstrate how excellent this system is through several experiments carried out with actual data.