• Journal of Korean Tunnelling and Underground Space Association
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• v.6 no.1
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• pp.71-83
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• 2004

The purpose of this study is to present an analysis method for the prediction of the changes m ground conditions. To this end, three dimensional convergence displacements are analyzed in several ways to estimate the trend of displacement changes. Three-dimensional arching effect is occurred around the unsupported excavation surface including tunnel face when a tunnel is excavated in a stable rock mass. If the ground condition ahead of tunnel face changes or a weak zone exists, a diagnostic trend of displacement change is observed by the 3 dimensional measurement and numerical analysis. Therefore, the change of ground condition and the existence of a weak zone ahead of tunnel face can be predicted by monitoring 3-dimensional absolute displacements during excavation, and applying the methodology (the ratio of L/C, $C/C_o$, etc.) presented in this study.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.1
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• pp.107-114
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• 2017

Iris diagnosis is an alternative medicine to diagnose the disease of the patient by using different of the iris pattern, color and other characteristics. This paper proposed a disease prediction algorithm that using the iris regions that analyze iris change to using differential image of iris image. this method utilize as patient's health examination according to iris change. Because most of previous studies only find a sign pattern in a iris image, it's not enough to be used for a iris diagnosis system. We're developed an iris diagnosis system based on a iris images processing approach, It's presents the extraction algorithms of 8 major iris signs and correction manually for improving the accuracy of analysis. As a result, PNSR of applied edge detection image is about 132, and pattern matching area recognition presented practical use possibility by automatic diagnostic that presume situation of human body by iris about 91%.

• Journal of Environmental Science International
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• v.15 no.1
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• pp.9-19
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• 2006

The purpose of this research is development of radar data assimilation observed at Jindo S-band radar The accurate observational data assimilation system is one of the important factors to meteorological numerical prediction of the region scale. Diagnostic analysis system LAPS(Local Analysis and Prediction System) developed by US FSL(Forecast Systems Laboratory) is adopted assimilation system of the Honam district forecasting system. The LAPS system was adjusted in calculation environment in the Honam district. And the improvement in the predictability by the application of the LAPS system was confirmed by the experiment applied to Honam district local severe rain case of generating 22 July 2003. The results are as follows: 1) Precipitation amounts of Gwangju is strong associated with the strong in lower level from analysis of aerological data. This indicated the circulation field especially, 850hPa layer, acts important role to precipitation in Homan area. 2) Wind in coastal area tends to be stronger than inland area and radar data show the strong wind in conversions zone around front. 3) Radar data assimilation make the precipitation area be extended and maximum amount of precipitation be smaller. 4) In respect to contribution rate of different height wind field on precipitation variation, radar data assimilation of upper level is smaller than that of lower level.

• Genomics & Informatics
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• v.4 no.4
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• pp.161-166
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• 2006

The establishment of DNA microarray technology has enabled high-throughput analysis and molecular profiling of various types of cancers. By using the gene expression data from microarray analysis we are able to investigate diagnostic applications at the molecular level. The most important step in the application of microarray technology to cancer diagnostics is the selection of specific markers from gene expression profiles. In order to select markers of Immortalization and transformation we used c-myc and $H-ras^{V12}$ oncogene-transfected NIH3T3 cells as our model system. We have identified 8751 differentially expressed genes in the immortalization/transformation model by multivariate permutation F-test (95% confidence, FDR<0.01). Using the support vector machine algorithm, we selected 13 discriminative genes which could be used to predict immortalization and transformation with perfect accuracy. We assayed $H-ras^{V12}$-transfected 'transformed' cells to validate our immortalization/transformation dassification system. The selected molecular markers generated valuable additional information for tumor diagnosis, prognosis and therapy development.

• Clinical and Experimental Pediatrics
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• v.58 no.7
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• pp.251-255
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• 2015

Purpose: The widespread introduction of bacterial conjugate vaccines has decreased the risk of cerebrospinal fluid (CSF) pleocytosis due to bacterial meningitis (BM) in children. However, most patients with CSF pleocytosis are hospitalized and treated with parenteral antibiotics for several days. The bacterial meningitis score (BMS) is a validated multivariate model derived from a pediatric population in the postconjugate vaccine era and has been evaluated in several studies. In the present study, we examined the usefulness of BMS in South Korean patients. Methods: This study included 1,063 patients with CSF pleocytosis aged between 2 months and 18 years. The BMS was calculated for all patients, and the sensitivity and negative predictive value (NPV) of the test were evaluated. Results: Of 1,063 patients, 1,059 (99.6%) had aseptic meningitis (AM). Only four patients (0.4%) had BM. The majority of patients (98%) had a BMS of ${\leq}1$, indicating a diagnosis of AM. The BMS was 0 in 635 patients (60%) and 1 in 405 patients (38%). All four BM patients had a BMS of ${\geq}4$. Conclusion: To our knowledge, this is the first study to investigate the diagnostic strength of the BMS in South Korea. In our study, the BMS showed 100% sensitivity and 100% NPV. Therefore, we believe that the BMS is a good clinical prediction rule to identify children with CSF pleocytosis who are at a risk of BM.

• Journal of the Korean Solar Energy Society
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• v.39 no.6
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• pp.55-65
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• 2019

With the rapid development of the national industry, the importance of electrical safety was recognized because of a lot of new electrical equipment are installing and the electrical accidents have been occurring annually. Today, the electrical equipments is inspect by using the portable Infrared thermal imaging camera. but the most negative element of using the camera is inspected for only state of heating, the reliable diagnosis is depended with inspector's knowledge, and real-time monitoring is impossible. This paper present the infrared thermal imaging safety diagnosis system. This system is able to monitor in real time, predict the state of fault, and diagnose the state with analysis of thermal and power data. The system consists of a main processor, an infrared camera module, the power data acquisition board, and a server. The diagnostic algorithm is based on a mathematical model designed by analyzing the Pearson's Correlation Coefficient between temperature and power data. To test the prediction algorithm, the simulations were performed by damaging the terminals or cables on the switchboard to generate a large amount of heat. Utilizing these simulations, the developed prediction algorithm was verified.

• Journal of Korean Society on Water Environment
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• v.40 no.3
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• pp.121-129
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• 2024

Harmful cyanobacterial blooms (HCBs) are caused by the rapid proliferation of cyanobacteria and are believed to be exacerbated by climate change. However, the extent to which HCBs will be stimulated in the future due to increased temperature remains uncertain. This study aims to predict the future occurrence of cyanobacteria in the Nakdong River, which has the highest incidence of HCBs in South Korea, based on temperature rise scenarios. Representative Concentration Pathways (RCPs) were used as the basis for these scenarios. Data-driven model simulations were conducted, and out of the four machine learning techniques tested (multiple linear regression, support vector regressor, decision tree, and random forest), the random forest model was selected for its relatively high prediction accuracy. The random forest model was used to predict the occurrence of cyanobacteria. The results of boxplot and time-series analyses showed that under the worst-case scenario (RCP8.5 (2100)), where temperature increases significantly, cyanobacterial abundance across all study areas was greatly stimulated. The study also found that the frequencies of HCB occurrences exceeding certain thresholds (100,000 and 1,000,000 cells/mL) increased under both the best-case scenario (RCP2.6 (2050)) and worst-case scenario (RCP8.5 (2100)). These findings suggest that the frequency of HCB occurrences surpassing a certain threshold level can serve as a useful diagnostic indicator of vulnerability to temperature increases caused by climate change. Additionally, this study highlights that water bodies currently susceptible to HCBs are likely to become even more vulnerable with climate change compared to those that are currently less susceptible.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.12
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• pp.4933-4938
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• 2014

Background: Although various tumor markers have been utilized in management of stomach cancer (SC), only a few reports have described relevance of examples such as CYFRA 21-1 and neuron-specific enolase (NSE). The purpose of this study was to evaluate the potential diagnostic performance of carcinoembryonic antigen (CEA), CA 19-9, CA72-4, CYFRA 21-1 and NSE in patients with SC. Materials and Methods: Ninety-six SC patients with pathologic confirmation between 2012 and 2013 were enrolled. Serum levels of five tumor markers were analyzed using a solid-phase immunoradiometric assay. Receiver operating characteristic (ROC) curves were plotted for the five tumor markers to investigate their diagnostic powers and adjusted cutoff values derived from analysis of ROC curves were evaluated to calculate the sensitivity of each for SC with recommended cutoff values. Results: Based on two different cutoff values (recommended and adjusted), CYFRA 21-1 (${\geq}2.0$ and 1.2 ng/ml) had a respective sensitivity of 50% and 78.1%, compared with 8.3% and 18.8% for CEA (${\geq}7.0$ and 3.9 ng/ml), 15.6% and 18.8% for CA 19-9 (${\geq}37$ and 26.7 ng/ml), 28.1% and 9.6% for CA 72-4 (${\geq}4.0$ and 13 ng/ml) and 7.3% and 7.3% for NSE (${\geq}14.7$ and 15.0 ng/ml) in the initial staging of primary SC. The area under the curve (AUC) for CYFRA 21-1, with a value of 0.978 (95% confidence interval, 0.964-0.991) was comparatively the highest. Univariate analysis revealed significant relationships between tumor marker level and lymph node involvement, metastasis and staging with CYFRA 21-1, CA 72-4 and NSE. Conclusions: CYFRA 21-1 was the most sensitive tumor marker and showed the most powerful diagnostic performance among the five SC tumor markers. NSE and CA 72-4 are significantly related to lymph node involvement, metastasis or stage. Further evaluations are warranted to clarify the clinical usefulness and prognostic prediction of these markers in SC.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.5
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• pp.825-833
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• 2020

Currently, the K-Water uses various valves that can be remotely controlled for optimal water management. Valve system fault can be classified into rotor defects, stator defects, bearing defects, and gear defects of induction motors. If the valve cannot be operated due to a gear fault, the water management operation can be greatly affected. For effective water management, there is an urgent need for preemptive repairs to determine whether gear is damaged through failure prediction diagnosis.. Recently, deep learning algorithms are being applied for valve failure diagnosis. However, the method currently applied has a disadvantage of attaching a vibration sensor to the valve. In this paper, propose a new algorithm to determine whether a fault exists using a convolutional neural network (CNN) based on the voltage and current information of the valve without additional sensor mounting. In particular, a normalized Lisasjous diagram was used to maximize the fault classification performance in the CNN-based diagnostic system.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.1
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• pp.135-143
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• 2006

The objective of this study is to develop PSCF (potential source contribution function) program and determine the optimal values of control parameters to enhance the prediction of PSCF modeling. This study provides an important information and methodologies that can be used to get better results of locating influencing sources, especially unknown and fugitive sources. To determine proper values of control parameters in PSCF model, the diagnostic assessment on the results obtained by the various input conditions was carried out. PSCF model has created and improved from version 1.0 to version 7.0 since 200 I and the measured data (at least > 100) of receptor, and the values of control input parameters should be arranged and determined to obtain reliable results in PSCF modeling. The size of modeling domain must be determined to include enough trajectories to get reliable results. And the size of grid is recommended to be 2.5 $\sim$ 5 degrees for global scale, 0.2 $\sim$ 1 degrees for regional scale and 0.05 degree for local scale.