• The Journal of the Korea Contents Association
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• v.20 no.10
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• pp.45-56
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• 2020

This study is about the scale to evaluate the data of the countermeasures in the 'simulation based government exercise' (tentative name). The criteria and weighting of the scale is a standard for evaluating the data of each agency input. This maintains the objectivity and consistency of the evaluation, which complements the subjective evaluation of the observer. Therefore, the criteria should accommodate the purpose of the government exercise evaluation, the possibility of measurement in the simulation environment, the objectivity, no-duplication and causality between criteria, and the grade applying the evaluation results, and the weighting should be set. In this study, three major criteria of timeliness, accuracy, and appropriateness is set up for the evaluation scale and it consists of sub-criteria classified into three classes below respectively and the concept of it was defined. The weighting was calculated by AHP analysis considering the hierarchical structure of it. When this evaluation scale is applied to the 'simulation based government exercise' in the future, it will be possible to secure consistency and objectivity of the evaluation of measures by integrating the quantified evaluation into the observation evaluation and to conduct AAR by scientific techniques.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.5
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• pp.17-26
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• 2022

In this study, in order to establish the criteria for evaluation of importance by the type of facility specialized for agricultural reservoirs, an expert group consisting of a total of 167 members who were in management, or specialized in the fields of design, research, and diagnosis were organized, and the importance for facilities was set with application of the AHP technique. The importance of dam body, spillway, and intake structure composing a reservoir were set at 59%, 24%, and 17%, and the importance of dam crest, upstream slope, and downstream slope constituting a dam body was set at 32%, 31%, and 37%, respectively. In addition, the importance of approach channel, regulated channel, chute channel, and stilling basin consisting a spillway was set at 15%, 44%, 26%, and 15%, and the importance of inclined conduit and outlet conduit consisting an intake structure was set at 35% and 65%, respectively. The safety grade of the reservoirs evaluated by applying the newly presented importance values in this study showed the rearrangement of the grades with a change of 11% compared to the previous grades. In this way, the newly established criteria are expected to be utilized as basic data with strategic importance in reservoir safety management and disaster prevention as well as the operation of systems in the future.

• Proceedings of the KIEE Conference
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• 2002.06a
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• pp.175-177
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• 2002

This paper presents a methodology for analyzing individual differences on Kansei evaluation for a set of product samples. This analysis divides subjects into several groups by each subject's Kansei evaluation data according to what kinds of Kansei are related on what kinds of design elements. The basic idea is to classify the results of cluster analysis in individual subject's ranges. A similarity matrix of subject is computed by comparing dendrogram of each subjects. The methodology is applied to analyzing evaluation data of cellular phone design.

• Journal of Korean Society on Water Environment
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• v.35 no.5
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• pp.432-438
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• 2019

The purpose of this study is to predict Trophic Diatom Index (TDI) in tributaries of the Han River watershed using the random forest algorithm. The one year (2017) and supplied aquatic ecology health data were used. The data includes water quality(BOD, T-N, $NH_3-N$, T-P, $PO_4-P$, water temperature, DO, pH, conductivity, turbidity), hydraulic factors(water width, average water depth, average velocity of water), and TDI score. Seven factors including water temperature, BOD, T-N, $NH_3-N$, T-P, $PO_4-P$, and average water depth are selected by the Correlation Feature Selection. A TDI prediction model was generated by random forest using the seven factors. To evaluate this model, 2017 data set was used first. As a result of the evaluation, $R^2$, % Difference, NSE(Nash-Sutcliffe Efficiency), RMSE(Root Mean Square Error) and accuracy rate show that this model is compatible with predicting TDI. To be more concrete, $R^2$ is 0.93, % Difference is -0.37, NSE is 0.89, RMSE is 8.22 and accuracy rate is 70.4%. Also, additional evaluation using data set more than 17 times the measured point was performed. The results were similar when the 2017 data set were used. The Wilcoxon Signed Ranks Test shows there was no statistically significant difference between actual and predicted data for the 2017 data set. These results can specify the elements which probably affect aquatic ecology health. Also, these will provide direction relative to water quality management for a watershed that must be continuously preserved.

• Journal of Power System Engineering
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• v.4 no.3
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• pp.72-80
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• 2000

In order to design a stable robust controller, nominal model, and the upper bound about the uncertainty which is the error of the model are needed. The problem to estimate the nominal model of controlled system and the upper bound of uncertainty at the same time is called robust identification. When the nominal model of controlled system and the upper bound of uncertainty in relation to robust identification are given, the evaluation of the validity of the model and the upper bound makes it possible to distinguish whether there is a model which explains observation data including disturbance among the model set. This paper suggests a method to identity the uncertainty which removes disturbance and expounds observation data by giving a probable postulation and plural data set to disturbance. It also examines the suggested method through a numerical computation simulation and validates its effectiveness.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2690-2692
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• 2000

In order to design a stable robust controller, nominal model, and the upper bound about the uncertainty which is the error of the model are needed. The problem to estimate the nominal model of controlled system and the upper bound of uncertainty at the same time is called robust identifcation. When the nominal model of controlled system and the upper bound of uncertainty in relation to robust identifcation are given, the evaluation of the validity of the model and the upper bound makes it possible to distinguish whether there is a model which explains observation data including disturbance among the model set. This paper suggests a method to identify the uncertainty which removes disturbance and expounds observation data by giving a probable postulation and plural data set to disturbance. It also examines the suggested method through a numerical computation simulation and validates its effectiveness.

• Journal of Biomedical Engineering Research
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• v.43 no.5
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• pp.341-352
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• 2022

Medical image segmentation is the most important task in radiation therapy. Especially, when segmenting medical images, the liver is one of the most difficult organs to segment because it has various shapes and is close to other organs. Therefore, automatic segmentation of the liver in computed tomography (CT) images is a difficult task. Since tumors also have low contrast in surrounding tissues, and the shape, location, size, and number of tumors vary from patient to patient, accurate tumor segmentation takes a long time. In this study, we propose a method algorithm for automatically segmenting the liver and tumor for this purpose. As an advantage of setting the boundaries of the tumor, the liver and tumor were automatically segmented from the CT image using the 2D CoordConv DeepLab V3+ model using the CoordConv layer. For tumors, only cropped liver images were used to improve accuracy. Additionally, to increase the segmentation accuracy, augmentation, preprocess, loss function, and hyperparameter were used to find optimal values. We compared the CoordConv DeepLab v3+ model using the CoordConv layer and the DeepLab V3+ model without the CoordConv layer to determine whether they affected the segmentation accuracy. The data sets used included 131 hepatic tumor segmentation (LiTS) challenge data sets (100 train sets, 16 validation sets, and 15 test sets). Additional learned data were tested using 15 clinical data from Seoul St. Mary's Hospital. The evaluation was compared with the study results learned with a two-dimensional deep learning-based model. Dice values without the CoordConv layer achieved 0.965 ± 0.01 for liver segmentation and 0.925 ± 0.04 for tumor segmentation using the LiTS data set. Results from the clinical data set achieved 0.927 ± 0.02 for liver division and 0.903 ± 0.05 for tumor division. The dice values using the CoordConv layer achieved 0.989 ± 0.02 for liver segmentation and 0.937 ± 0.07 for tumor segmentation using the LiTS data set. Results from the clinical data set achieved 0.944 ± 0.02 for liver division and 0.916 ± 0.18 for tumor division. The use of CoordConv layers improves the segmentation accuracy. The highest of the most recently published values were 0.960 and 0.749 for liver and tumor division, respectively. However, better performance was achieved with 0.989 and 0.937 results for liver and tumor, which would have been used with the algorithm proposed in this study. The algorithm proposed in this study can play a useful role in treatment planning by improving contouring accuracy and reducing time when segmentation evaluation of liver and tumor is performed. And accurate identification of liver anatomy in medical imaging applications, such as surgical planning, as well as radiotherapy, which can leverage the findings of this study, can help clinical evaluation of the risks and benefits of liver intervention.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.2 no.2 s.3
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• pp.55-64
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• 2003

This research is about executing the suitability evaluation of the data through wireless communication system in comparison with the data through the wire system used in National Highway Traffic Management System. The communication system for the evaluation was established on Peoyngteak-Anjung section(about 19 Km) at Route 38 in southern National Highway of the National Capital region where image detectors with the communication by wire were set up. In the situation, the comparison of the data transmission rates between the wire system and the proposed system was accomplished. Before we evaluate the possibility of application about the proposed system, the elements of wireless systems performed the spot investigations. Traffic data transmission test among the equipments was accomplished on the section of the Route 38 where wireless communication equipments were set up. And then, the test of traffic data transmission between the field equipment and the data collective center of wireless communication system was carried out. It evaluated the reliability of wireless communication data for the suitability evaluation of cable/wireless data collected while 73 days.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.3
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• pp.132-137
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• 2019

The amount of data generated from medical images is increasingly exceeding the limits of professional visual analysis, and the need for automated medical image analysis is increasing. For this reason, this study evaluated the classification and accuracy according to the presence or absence of tumor using Inception V3 deep learning model, using MRI medical images showing normal and tumor findings. As a result, the accuracy of the deep learning model was 90% for the training data set and 86% for the validation data set. The loss rate was 0.56 for the training data set and 1.28 for the validation data set. In future studies, it is necessary to secure the data of publicly available medical images to improve the performance of the deep learning model and to ensure the reliability of the evaluation, and to implement modeling by improving the accuracy of labeling through labeling classification.

• The Korean Journal of Archival Studies
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• no.70
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• pp.41-83
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• 2021

After the revision of the Enforcement Decree of the Public Records Act, the archives created a management standard table for data set records management and performed management and control. Therefore, in this study, the data set record identification procedure and evaluation index were developed for systematic data set record management of archives. By applying this, a management standard table was prepared after identifying the records of 8 datasets in kr's electronic procurement system, and the evaluation was carried out according to the evaluation index, and the retention period, transfer, and collection were determined. It is hoped that this case study will be of practical use to the archives at a time when concrete examples of procedures for the management of dataset records are lacking.