• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.4
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• pp.269-276
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• 2012

The objective of this short communication is to introduce several evaluation methods to crop model users because the evaluation of crop model performance is an important step to develop or select crop model. In this paper, mean error, mean absolute error, index of agreement, root mean square error, efficiency of model, accuracy factor and bias factor were explained and compared in terms of dimension and observed number. Efficiency of model and index of agreement are dimensionless and independent of number of observation. Relative root mean square, accuracy factor and bias factor are dimensionless and not independent of number of observation. Mean error and mean absolute error are affected by dimension and number of observation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6D
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• pp.727-736
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• 2009

Many of the researchers have tried to enhance the satisfaction of both users and owners of social infrastructures by applying the asset management methodology. This study is to develop more efficient asset management framework for bridge management. Based on various literature review, an asset management procedure for bridge management was suggested, and appropriate practices at each step were given. The suggested procedures include the determination of operation and maintenance strategy, level of service, performance measure, valuation of assets, and decision-makings. In addition, this study suggests an applicable decision-making process for the resource distribution based on the management strategy.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.761-766
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• 2023

In this paper, TI was measured to classify the presence or absence of mild left ventricular diastolic dysfunction. Of the total 306 data, 206 were used as training data and 100 were used as test data, and the machine learning models used for classification used SVM and KNN. As a result, it was confirmed that SVM showed relatively higher accuracy than KNN and was more useful in diagnosing the presence of left ventricular diastolic dysfunction. In future research, it is expected that classification performance can be further improved by adding various indicators that evaluate not only TI but also cardiac function and securing more data. Furthermore, it is expected to be used as basic data to predict and classify other diseases and solve the problem of insufficient medical manpower compared to the increasing number of tests.

• Journal of Korean Society of Environmental Engineers
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• v.29 no.3
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• pp.348-356
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• 2007

Statistical and probabilistic method was used in the analysis of data, which is the most effective one in describing the various natures, and the methodology relating the results with the design was developed. Influents and effluents of three treatment plants were analyzed and the focus was made on BOD, COD, SS, IN, TP The fluctuations of influent such as BOD, COD, SS were extremely large and their standard deviations(st.dev) were more than 10 mg/L. but those of TN, TP were small; the st.dev was 6.6 mg/L for TN, 0.6 mg/L for TP, respectively. But, effluent concentration showed consistent pattern regardless of the influent fluctuations, the st.dev was ranged between 0.28 and 4.48 mg/L. Effluent distributional characteristics were as follows; BOD, COD were distributed normally, but SS, TN, and TP, log-normally; unsymmetric and skewed to the right. The coefficient of reliability(COR) based on the results of statistics of data was introduced to evaluate the process performance an4 to reflect the process performance to the process design. The coefficient of reliability relates the design value(the goal) with the standards and it can be used in operating treatment facilities under a certain reliability level and/or in evaluating the reliability of the treatment facilities on operation. Each treated water quality of effluent showed the half of water quality standards in the level of 50% percentile and all treatment plant was achieved 100% probability of water quality standards. It was concluded that the variability of the process performance should be reflected to the design procedure and the standards through the analysis based on the statistics and the probability.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.1
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• pp.36-42
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• 2018

This paper considers the Marine Traffic Risk Assessment for fixed and moving targets, which threaten officers during a voyage. The Collision Risk Assessment Formula was calculated based on a dynamic ship domain considering the length, speed and maneuvering capability of a vessel. In particular, the Navigation Risk Assessment Model that is used to quantitatively index the effect of a ship's size, speed, etc. has been reviewed and improved using a hybrid combination of a vessel's dynamic area and the Collision Risk Assessment Formula. Accordingly, a new type of Marine Traffic Risk Assessment Model has been suggested giving consideration to the Speed Length Ratio, which was not sufficiently reflected in the existing Risk Assessment Model. The larger the Speed Length Ratio (dimensionless speed), the higher the CJ value. That is, the CJ value is presented well by the Speed Length Ratio. When the Speed Length Ratio is large, states ranging from [Caution], [Warning], [Dangerous] or [Very Dangerous] are presented from a greater distance than when the Speed Length Ratio is small. The results of this study, can be used for route and port development, including dangerous route avoidance, optimum route planning, breakwater width, bridge span, etc. as well as the development of costal navigation safety charts. This research is also applicable for the selection of optimum ship routing and the prevention of collisions for smart ships such as autonomous vessels.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1475-1490
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• 2021

Detecting oil spill area using statistical characteristics of SAR images has limitations in that classification algorithm is complicated and is greatly affected by outliers. To overcome these limitations, studies using neural networks to classify oil spills are recently investigated. However, the studies to evaluate whether the performance of model shows a consistent detection performance for various oil spill cases were insufficient. Therefore, in this study, two CNNs (Convolutional Neural Networks) with basic structures(Simple CNN and U-net) were used to discover whether there is a difference in detection performance according to the structure of CNN and distribution characteristics of oil spill. As a result, through the method proposed in this study, the Simple CNN with contracting path only detected oil spill with an F1 score of 86.24% and U-net, which has both contracting and expansive path showed an F1 score of 91.44%. Both models successfully detected oil spills, but detection performance of the U-net was higher than Simple CNN. Additionally, in order to compare the accuracy of models according to various oil spill cases, the cases were classified into four different categories according to the spatial distribution characteristics of the oil spill (presence of land near the oil spill area) and the clarity of border between oil and seawater. The Simple CNN had F1 score values of 85.71%, 87.43%, 86.50%, and 85.86% for each category, showing the maximum difference of 1.71%. In the case of U-net, the values for each category were 89.77%, 92.27%, 92.59%, and 92.66%, with the maximum difference of 2.90%. Such results indicate that neither model showed significant differences in detection performance by the characteristics of oil spill distribution. However, the difference in detection tendency was caused by the difference in the model structure and the oil spill distribution characteristics. In all four oil spill categories, the Simple CNN showed a tendency to overestimate the oil spill area and the U-net showed a tendency to underestimate it. These tendencies were emphasized when the border between oil and seawater was unclear.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.6
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• pp.493-500
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• 2019

In this study, sensitivity analysis of sediment transport scaling factors in Delft3D-Morphology was performed to examine the effect those parameters on simulation results of cross-shore profile changes. For numerical experiments, one-year wave time series data which were observed in 2018 on the Maengbang coast in Gangwon prefecture were applied as external force. Bathymetric data observed in January and October of the same year were used as initial bathymetric data and annual bathymetric change data, respectively. The simulation performance of the model was evaluated based on the Brier Skill Score index for each part by dividing an arbitrary cross section within the calculation domain into the onshore and offshore parts. As a result, it was found thet the f_BED variable has a slight effect on the simulation results. The f_BEDW and f_SUSW variables show good simulation performance in onshore part when the value less than 0.5 is applied and vice versa. Among the experimental conditions, the optimal combinations of variables are f_BED = 1.0, f_BEDW = 1.0, f_SUSW = 0.1 for the onshore region and f_BED = 1.0, f_BEDW = 1.0, f_SUSW = 0.5 for the offshore region. However, since these combinations were derived based on the observation data on Maengbang beach in 2018, users should be careful when applying those results to other areas.

• The Journal of the Acoustical Society of Korea
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• v.34 no.2
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• pp.146-156
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• 2015

This study investigated how the location of sound absorptive materials and sound diffusers affects the acoustic performance of small auditoriums. It was conducted for a standard model established with the averaged dimension of 36 auditoriums which had opened since 2000 in Daehak-ro, Seoul. In this study, the installation area of finishing materials was calculated upon a back wall which had the smallest installation effective area of finishing materials. To analyze the changes of acoustic performance according to installation location of finishing materials, experiments were carried out using the 1/10 down scale models for 8 cases which were made by classifying the installation location of ceiling and side wall into the front, middle and rear part.The used acoustic parameters were reverberation time (RT), early decay time (EDT), clarity (C80), definition (D50) and speech transmission index (STI). In result, the index related to the amount of reverberant sound (RT, EDT) showed the great changes when evaluating it through just noticeable difference (JND), but the one related to clarity (C80, D50, STI) hardly indicated the changes. In case to obtain short reverberation time, it was most effective to control reverberation time through the side walls when installing sound absorptive and diffusive materials, and side wall front was the location which could get the shortest reverberation time.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.3
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• pp.73-86
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• 2021

The primary purpose of this paper is to explore the advanced models that predict entrepreneurial innovativeness most accurately. For the first time in the field of entrepreneurship research, it presents a model that predicts entrepreneurial innovativeness based on machine learning corresponding to data scientific approaches. It uses 22,099 the Global Entrepreneurship Monitor (GEM) data from 62 countries to build predictive models. Based on the data set consisting of 27 explanatory variables, it builds predictive models that are traditional statistical methods such as multiple regression analysis and machine learning models such as regression tree, random forest, XG boost, and artificial neural networks. Then, it compares the performance of each model. It uses indicators such as root mean square error (RMSE), mean analysis error (MAE) and correlation to evaluate the performance of the model. The analysis of result is that all five machine learning models perform better than traditional methods, while the best predictive performance model was XG boost. In predicting it through XG boost, the variables with high contribution are entrepreneurial opportunities and cross-term variables of market expansion, which indicates that the type of entrepreneur who wants to acquire opportunities in new markets exhibits high innovativeness.

• Geophysics and Geophysical Exploration
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• v.26 no.3
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• pp.126-137
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• 2023

In this study, a recurrent neural network (RNN) was employed as a methodological approach to classify dolphin click signals derived from ocean monitoring data. To improve the accuracy of click signal classification, the single time series data were transformed into fractional domains using fractional Fourier transform to expand its features. Transformed data were used as input for three RNN models: long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (BiLSTM), which were compared to determine the optimal network for the classification of signals. Because the fractional Fourier transform displayed different characteristics depending on the chosen angle parameter, the optimal angle range for each RNN was first determined. To evaluate network performance, metrics such as accuracy, precision, recall, and F1-score were employed. Numerical experiments demonstrated that all three networks performed well, however, the BiLSTM network outperformed LSTM and GRU in terms of learning results. Furthermore, the BiLSTM network provided lower misclassification than the other networks and was deemed the most practically appliable to field data.