• Journal of Korean Society for Geospatial Information Science
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• v.25 no.2
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• pp.13-19
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• 2017

This paper analyzed MODIS data from 2006 to 2013 to determine relationship between meteorological changes and vegetation index. The experimental area was divided into the northern, central and southern regions according to the regional characteristics, and the smoothed MODIS LAI and NDVI were obtained using Timesat. In the case of precipitation, MODIS NDVI had correlation coefficients of 0.66, 0.44 and 0.35 in the northern, central and southern regions and the correlation was the highest in the northern region. In the case of temperature, MODIS LAI had correlation coefficients of 0.66, 0.64 and 0.68, and MODIS NDVI had 0.89, 0.89 and 0.80. The correlation of MODIS NDVI was higher and showed similar positive correlation regardless of region. In addition, The accuracy between Timesat plant seasonal start and actual plant seasonal start in MODIS NDVI was higher than MODIS LAI. The average error in MODIS LAI was 19 days in the central region and 20 days in the southern region. And the average error in MODIS NDVI was 6 days in the central region and 8 days in the southern region.

• Journal of Distribution Science
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• v.16 no.1
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• pp.87-99
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• 2018

Purpose - The Export Infrastructure Strengthening Program(EISP) is a project to expand exports of agri-food products through providing customized export information to food distribution companies and supporting overseas information activities. A total of 39.6 billion won was provided by 2016. So, the purpose of this study is to analyze whether EISP is effective for expanding exports of agri-food products. Research design, data, and methodology - A simple average difference between the export performance of the policy beneficiaries and the non-policy beneficiaries can be biased if the export capacity or inherent characteristics of the enterprise are not taken into consideration. In order to solve the problem of such a bias, the propensity score matching(PSM) method has been employed in this study. PSM is a method of converting the characteristics of an export company into an index through logit analysis and then reducing the matching to one dimension to improve the accuracy of the performance measurement. Results - The balancing test was conducted to determine how the characteristics of the policy beneficiary group and the matched policy non-beneficiary group corresponded to each other. As a result of the test, we could not reject the null hypothesis that there was no difference between the two groups, so that after the matching, the two groups were similar and the explanatory variables were well controlled. Using the nearest neighbor matching with propensity score estimating through logit analysis, we estimated average treatment effect on the treated(ATT). The food companies participating the EISP had the effect of increasing the exports of $ 5.88 million. As a result, the number of export contracts increased by 11.77, the number of exporting countries by 7.52, the number of export items by 47.51, and the number of buyers' consultation by 3.50. And overseas marketing expenses increased by 35.92 million won. Except for the number of export contracts, other export performance results showed statistically significant results. Conclusions - As the EISP has a positive effect on the expansion of agro-food exports, efforts should be made to find out the limitations or problems of the policy in the future and to make a greater contribution to the increase of exports.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.40 no.3
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• pp.127-138
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• 2016

Diesel vehicles should be equipped with urea-selective catalytic reduction(SCR) system as a high-performance catalyst, in order to reduce harmful nitrogen oxide emissions. In this study, a three-dimensional Eulerian-Lagrangian CFD analysis was used to numerically predict the multiphase flow characteristics of the urea-SCR system, coupled with the chemical reactions of the system's transport phenomena. Then, the numerical spray structure was modified by comparing the results with the measured values from spray visualization, such as the injection velocity, penentration length, spray radius, and sauter mean diameter. In addition, the analysis results were verified by comparison with the removal efficiency of the nitrogen oxide emissions during engine and chassis tests, resulting in accuracy of the relative error of less than 5%. Finally, a verified CFD analysis was used to calculate the interanl flow of the urea-SCR system, thereby analyzing the characteristics of pressure drop and velocity increase, and predicting the uniformity index and overdistribution positions of ammonia.

• The KIPS Transactions:PartA
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• v.13A no.5 s.102
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• pp.465-472
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• 2006

Recently, many suggestions have been made in image segmentation methods for extracting human organs or disease affected area from huge amounts of medical image datasets. However, images from some areas, such as brain, which have multiple structures with ambiruous structural borders, have limitations in their structural segmentation. To address this problem, clustering technique which classifies voxels into finite number of clusters is often employed. This, however, has its drawback, the influence from noise, which is caused from voxel by voxel operations. Therefore, applying image enhancing method to minimize the influence from noise and to make clearer image borders would allow more robust structural segmentation. This research proposes an efficient structural segmentation method by filtering based clustering to extract detail structures such as white matter, gray matter and cerebrospinal fluid from brain MR. First, coherence enhancing diffusion filtering is adopted to make clearer borders between structures and to reduce the noises in them. To the enhanced images from this process, fuzzy c-means clustering method was applied, conducting structural segmentation by assigning corresponding cluster index to the structure containing each voxel. The suggested structural segmentation method, in comparison with existing ones with clustering using Gaussian or general anisotropic diffusion filtering, showed enhanced accuracy which was determined by how much it agreed with the manual segmentation results. Moreover, by suggesting fine segmentation method on the border area with reproducible results and minimized manual task, it provides efficient diagnostic support for morphological abnormalities in brain.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.4
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• pp.137-144
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• 2020

Delirium is among the most common mental disorders encountered in patients with a temporary cognitive impairment such as consciousness disorder, attention disorder, and poor speech, particularly among those who are older. Delirium is distressing for patients and families, can interfere with the management of symptoms such as pain, and is associated with increased elderly mortality. The purpose of this paper is to generate useful clinical knowledge that can be used to distinguish the outcomes of patients with delirium in long-term care facilities. For this purpose, we extracted the clinical classification knowledge associated with delirium using a local covering rule acquisition approach with the rough lower approximation region. The clinical applicability of the proposed method was verified using data collected from a prospective cohort study. From the results of this study, we found six useful clinical pieces of evidence that the duration of delirium could more than 12 days. Also, we confirmed eight factors such as BMI, Charlson Comorbidity Index, hospitalization path, nutrition deficiency, infection, sleep disturbance, bed scores, and diaper use are important in distinguishing the outcomes of delirium patients. The classification performance of the proposed method was verified by comparison with three benchmarking models, ANN, SVM with RBF kernel, and Random Forest, using a statistical five-fold cross-validation method. The proposed method showed an improved average performance of 0.6% and 2.7% in both accuracy and AUC criteria when compared with the SVM model with the highest classification performance of the three models respectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.846-851
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• 2005

In this paper, we propose Genetic Algorithms(GAs)-based Optimized Polynomial Neural Networks(PNN). The proposed algorithm is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional neural networks and can be generated in a dynamic manner. As each node of PNN structure, we use several types of high-order polynomial such as linear, quadratic and modified quadratic, and it is connected as various kinds of multi-variable inputs. The conventional PNN depends on the experience of a designer that select the number of input variables, input variable and polynomial type. Therefore it is very difficult to organize optimized network. The proposed algorithm leads to identify and select the number of input variables, input variable and polynomial type by using Genetic Algorithms(GAs). The aggregate performance index with weighting factor is proposed as well. The study is illustrated with tile NOx omission process data of gas turbine power plant for application to nonlinear process. In the sequel the proposed model shows not only superb predictability but also high accuracy in comparison to the existing intelligent models.

• Journal of the Korean Society of Radiology
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• v.12 no.2
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• pp.277-287
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• 2018

Pelvic floor muscle is the main sub-system that maintains urinary continence. The weakness of pelvic floor muscles causes the stress urinary incontinence, and therefore the degree of functioning of pelvic floor muscles could be used as an index to assess the degree of stress urinary incontinence. In this study, the quantitative diagnosis algorithm was proposed to estimate the degree of stress urinary incontinence (SUI) by measuring the contraction pressure of pelvic floor muscle. For these reason, the contraction pressure measurement system from pelvic floor muscle was developed, and the measuring protocol was suggested to analysis the obtained data. As the results of clinical test, the proposed diagnosis algorithm shows the 80% of accuracy, and 20% of false positive diagnosis. On the other hand, false negative results were not confirmed. Consequentially, we thought that the proposed urinary incontinence diagnosis algorithm can quantitatively diagnose the progression of the stress urinary incontinence and it can be used for the development of the incontinence diagnosis system.

• Journal of Korea Water Resources Association
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• v.45 no.6
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• pp.607-616
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• 2012

The objective of this study is to evaluate the valid forecast lead time and the accuracy when AWS observed rainfall data are used for real-time river flow forecast. For this, Namhan river basin is selected as study area and SURF model is constructed during flood seasons in 2006~2009. The simulated flow with and without the assimilation of the observed flow data are well fitted. Effectiveness index (EI) is used to evaluate amount of improvement for the assimilation. EI at Chungju, Dalcheon, Hoengsung and Yeoju sites as evaluation points show 32.08%, 51.53%, 39.70% and 18.23% improved, respectively. In the results of the forecasted values using the limited observed rainfall data in each forecast time before peak flow occur, the peak flow under the 20% tolerance range of relative error at Chungju, Dalcheon, Hoengsung and Yeoju sites can be simulated in forecast time-11h, 2h, 3h and 5h and the flow volume in the same condition at those sites can be simulated in forecast time-13h, 2h, 4h and 9h, respectively. From this results, observed rainfall data can be used for real-time peak flow forecast because of basin lag time.

• The Korean Journal of Nuclear Medicine
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• v.24 no.2
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• pp.237-243
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• 1990

Pulmonary embolism demands rapid and accurate diagnosis. And ventilation imaging has greatly improved the diagnostic accuracy of pulmonary embolism in addition to perfusion imaging. Agents currently used include xenon-133, krypton-81m and technetium-99m radioaerosols. However radioactive gases are compromised by availability and cost for krypton-81m, radiation dose, gamma energy and non?physiologic behaviour for xenon-133. Radioaerosols of technetium-99m componds are rapidly cleared from the lung after inhalation, and their relative low effeciency (specific radioactivity) and wide distribution of particle sizes make them also suboptimum. A new ventilation agent, Technegas is a suspension of structured graphite ellipsoids with diameter below 20nm, labelled with $^{99m}Tc$ in a carrier gas of Argon. This report describes the authors' clinical experience with Technegas. This is the first reported clinical study of this agent in Korea. A comparison of Technegas and $^{99m}Tc-DTPA$ aerosol was performed in 12 patients with various pulmonary diseases such as COPD, pulmonary tuberculosis and pleural effusion. All patients were studied with $^{99m}Tc-DTPA$ aerosol inhalation and Technegas ventilation. In both studies image quality was assessed (1) semiquantitatively by scoring bronchial and gastric activity, (2) subjectively by direct visual comparison of peripheral lung images and (3) quantitatively by computing the peripheral penetration index(PI) for each lungs. The bronchial activites were seen in 7 out of 12 cases with $^{99m}Tc-DTPA$ aerosol and in 5/12 with Technegas. The gastric activities were seen in 5/12 and 1/12 cases respectively. The average values of PI were 61.26% with $^{99m}Tc-DTPA$ aerosol and 69.20% with Technegas (p>0.05). Using $^{99m}Tc-DTPA$ aerosol, COPD patients showed deposition in the central airways with poor visualization of the peripheral areas of the lungs. In Technegas studies these phenomena were less prominent, and the examination is well tolerated by pateients and requires only a minimum of patient cooperation. With superiority of easy availability and handling, better physical characteristics and favorable Image quality, Technegas is a Promising agent for lung ventilation scanning.

• Journal of Korean Physical Therapy Science
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• v.27 no.2
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• pp.25-35
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• 2020

Background: There are many situations where walking in an actual community needs to change direction along with walking on a straight path, and this situation needs to be reflected in assessing walking ability of the community. Therefore, in this study, we tried to determine whether the assessments can distinguish the level of walking in the community. Design: Retrospective cohort study. Methods: Fifty-two survivors with chronic stroke have participated in the study. According to the evaluation result of 10mWT, the subjects of 0.8m/s and above were classified as the group who could walk in the community (n=22), and the subjects of 0.4m/s~0.8m/s were classified into the group who could not walk in the community (n=30). Modified Rivermead Mobility Index, Postural Assessment Scale for Stroke, Fugl-Meyer Assessment, Berg Balance Scale, 10-meter Walk Test (10mWT) were used to evaluate the motor skills. Furthermore, Activities-specific Balance Confidence Scale was used to evaluate psychological factors, and Timed Up & Go Test (TUG), Figure-of-Eight Walk Test (F8WT), Four Square Step Test (FSST), Step Test (ST) were applied to evaluate dynamic balance and mobility. Results: As a result for distinguishing walking levels in the community, TUG was 14.25 seconds, F8WT was 13.34 seconds, FST was 19.43 seconds, and ST of affected side and non-affected side were 6.5 points and 7.5 points, respectively. TUG (AUC=0.923), F8WT (AUC=0.905), and FST (AUC=0.941) were highly accurate, but the ST of affected side and non-affected side (AUC=0.806, 0.705) showed the accuracy of the median degree, respectively. Conclusion: To distinguish walking levels in the community of survivors with chronic stroke, TUG and FSST have been found to be the best assessment tool, and in particular, FSST could be very valuable in clinical use as the most important assessment tool to distinguish walking levels in the community.