• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Journal of Food Hygiene and Safety
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• v.34 no.2
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• pp.124-134
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• 2019

An analytical method was developed for the determination of broflanilide and its metabolites in agricultural products. Sample preparation was conducted using the QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) method and LC-MS/MS (liquid chromatograph-tandem mass spectrometer). The analytes were extracted with acetonitrile and cleaned up using d-SPE (dispersive solid phase extraction) sorbents such as anhydrous magnesium sulfate, primary secondary amine (PSA) and octadecyl ($C_{18}$). The limit of detection (LOD) and quantification (LOQ) were 0.004 and 0.01 mg/kg, respectively. The recovery results for broflanilide, DM-8007 and S(PFP-OH)-8007 ranged between 90.7 to 113.7%, 88.2 to 109.7% and 79.8 to 97.8% at different concentration levels (LOQ, 10LOQ, 50LOQ) with relative standard deviation (RSD) less than 8.8%. The inter-laboratory study recovery results for broflanilide and DM-8007 and S (PFP-OH)-8007 ranged between 86.3 to 109.1%, 87.8 to 109.7% and 78.8 to 102.1%, and RSD values were also below 21%. All values were consistent with the criteria ranges requested in the Codex guidelines (CAC/GL 40-1993, 2003) and the Food and Drug Safety Evaluation guidelines (2016). Therefore, the proposed analytical method was accurate, effective and sensitive for broflanilide determination in agricultural commodities.

• Journal of Preventive Medicine and Public Health
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• v.30 no.3 s.58
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• pp.630-642
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• 1997

The purpose of this study was to examine the mortality risk associated with cognitive impairment among the rural elderly. The subjective of study was 558 of 'A Study on the Depression and Cognitive Impairment in the Rural Elderly' of Jung Ae Rhee and Hyang Gyun Jung's study(1993). Cognitive impairment and other social and health factors were assessed in 558 elderly rural community residents. For this study, a Korean version of the Mini-Mental State Examination(MMSEK) was used as a global indicator of cognitive functioning. And mortality risk factors for each cognitive impairment subgroup were identified by univariate and multivariate Cox regression analysis. At baseline 22.6% of the sample were mildly impaired and 14.2% were severely impaired. As the age increased, the cognitive function was more impaired. Sexual difference was existed in the cognitive function level. Also the variables such as smoking habits, physical disorders had the significant relationship with cognitive function impairment. Across a 3-year observation period the mortality rate was 8.5% for the cognitively unimpaired, 11.1% for the mildly impaired, and 16.5% for the severly impaired respendents. And the survival probability was .92 for the cognitively unimpaired, .90 for the mildly impaired, and .86 for the severly impaired respondents. Compared to survival curve for the cognitively unimpaired group, each survival curve for the mildly and the severely impaired group was not significantly different. When adjustments models were not made for the effects of other health and social covariates, each hazard ratio of death of mildly and severely impaired persons was not significantly different as compared with the cognitively unimpaired. But, as MMSEK score increased, significantly hazard ratio of death decreased. Employing Cox univariate proportional hazards model, statistically other significant variables were age, monthly income, smoking habits, physical disorders. Also when adjustments were made for the effects of other health and social covariates, there was no difference in hazard ratio of death between those with severe or mild impairment and unimpaired persons. And as MMSEK score increased, significantly hazard ratio of death did not decrease. Employing Cox multivariate proportional hazards model, statistically other significant variables were age, monthly income, physical disorders. Employing Cox multivariate proportional hazards model by sex, at men and women statistically significant variable was only age. For both men and women, also cognitive impairment was not a significant risk factor. Other investigators have found that cognitive impairment is a significant predictor of mortality. But we didn't find that it is a significant predictor of mortality. Even though the conclusions of our study were not related to cognitive impairment and mortality, early detection of impaired cognition and attention to associated health problems could improve the quality of life of these older adults and perhaps extend their survival.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.1
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• pp.29-38
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• 2008

Purpose: The aim of this study was to investigate the feasibility of 3 ' -[F-18]fluoro-3 ' -deoxythymidine positron emission tomography(FLT-PET) for the detection of locally advanced breast cancer and to compare the degree of FLT and 2' -deoxy-2 ' -[F-18]fluoro-d-glucose(FDG) uptake in primary tumor, lymph nodes and other normal organs. Material & Methods: The study subjects consisted of 22 female patients (mean age; $42{\pm}6$ years) with biopsy-confirmed infiltrating ductal carcinoma between Aug 2005 and Nov 2006. We performed conventional imaging workup, FDG-PET and FLT PET/CT. Average tumor size measured by MRI was $7.2{\pm}3.4$ cm. With visual analysis, Tumor and Lymph node uptakes of FLT and FDG were determined by calculation of standardized uptake value (SUV) and tumor to background (TB) ratio. We compared FLT tumor uptake with FDG tumor uptake. We also investigated the correlation between FLT tumor uptake and FDG tumor uptake and the concordant rate with lymph node uptakes of FLT and FDG. FLT and FDG uptakes of bone marrow and liver were measured to compare the biodistribution of each other. Results: All tumor lesions were visually detected in both FLT-PET and FDG-PET. There was no significant correlation between maximal tumor size by MRI and SUVmax of FLT-PET or FDG-PET (p>0.05). SUVmax and $$SUV_{75} (average SUV within volume of interest using 75% isocontour) of FLT-PET were significantly lower than those of FDG-PET in primary tumor (SUVmax; $6.3{\pm}5.2\;vs\;8.3{\pm}4.9$, p=0.02 /$SUV_{75};\;5.3{\pm}4.3\;vs\;6.9{\pm}4.2$, p=0.02). There is significant moderate correlation between uptake of FLT and FDG in primary tumor (SUVmax; rho=0.450, p=0.04 / SUV75; rho=0.472, p=0.03). But, TB ratio of FLT-PET was higher than that of FDG-PET($11.7{\pm}7.7\;vs\;6.3{\pm}3.8$, p=0.001). The concordant rate between FLT and FDG uptake of lymph node was reasonably good (33/34). The FLT SUVs of liver and bone marrow were $4.2{\pm}1.2\;and\;8.3{\pm}4.9$. The FDG SUVs of liver and bone marrow were $1.8{\pm}0.4\;and\;1.6{\pm}0.4$. Conclusion: The uptakes of FLT were lower than those of FDG, but all patients of this study revealed good FLT uptakes of tumor and lymph node. Because FLT-PET revealed high TB ratio and concordant rate with lymph node uptakes of FDG-PET, FLT-PET could be a useful diagnostic tool in locally advanced breast cancer. But, physiological uptake and individual variation of FLT in bone marrow and liver will limit the diagnosis of bone and liver metastases.

• Radiation Oncology Journal
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• v.28 no.3
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• pp.155-165
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• 2010

Purpose: In order to evaluate the positional uncertainty of internal organs during radiation therapy for treatment of liver cancer, we measured differences in inter- and intra-fractional variation of the tumor position and tidal amplitude using 4-dimentional computed radiograph (DCT) images and gated orthogonal setup kilovolt (KV) images taken on every treatment using the on board imaging (OBI) and real time position management (RPM) system. Materials and Methods: Twenty consecutive patients who underwent 3-dimensional (3D) conformal radiation therapy for treatment of liver cancer participated in this study. All patients received a 4DCT simulation with an RT16 scanner and an RPM system. Lipiodol, which was updated near the target volume after transarterial chemoembolization or diaphragm was chosen as a surrogate for the evaluation of the position difference of internal organs. Two reference orthogonal (anterior and lateral) digital reconstructed radiograph (DRR) images were generated using CT image sets of 0% and 50% into the respiratory phases. The maximum tidal amplitude of the surrogate was measured from 3D conformal treatment planning. After setting the patient up with laser markings on the skin, orthogonal gated setup images at 50% into the respiratory phase were acquired at each treatment session with OBI and registered on reference DRR images by setting each beam center. Online inter-fractional variation was determined with the surrogate. After adjusting the patient setup error, orthogonal setup images at 0% and 50% into the respiratory phases were obtained and tidal amplitude of the surrogate was measured. Measured tidal amplitude was compared with data from 4DCT. For evaluation of intra-fractional variation, an orthogonal gated setup image at 50% into the respiratory phase was promptly acquired after treatment and compared with the same image taken just before treatment. In addition, a statistical analysis for the quantitative evaluation was performed. Results: Medians of inter-fractional variation for twenty patients were 0.00 cm (range, -0.50 to 0.90 cm), 0.00 cm (range, -2.40 to 1.60 cm), and 0.00 cm (range, -1.10 to 0.50 cm) in the X (transaxial), Y (superior-inferior), and Z (anterior-posterior) directions, respectively. Significant inter-fractional variations over 0.5 cm were observed in four patients. Min addition, the median tidal amplitude differences between 4DCTs and the gated orthogonal setup images were -0.05 cm (range, -0.83 to 0.60 cm), -0.15 cm (range, -2.58 to 1.18 cm), and -0.02 cm (range, -1.37 to 0.59 cm) in the X, Y, and Z directions, respectively. Large differences of over 1 cm were detected in 3 patients in the Y direction, while differences of more than 0.5 but less than 1 cm were observed in 5 patients in Y and Z directions. Median intra-fractional variation was 0.00 cm (range, -0.30 to 0.40 cm), -0.03 cm (range, -1.14 to 0.50 cm), 0.05 cm (range, -0.30 to 0.50 cm) in the X, Y, and Z directions, respectively. Significant intra-fractional variation of over 1 cm was observed in 2 patients in Y direction. Conclusion: Gated setup images provided a clear image quality for the detection of organ motion without a motion artifact. Significant intra- and inter-fractional variation and tidal amplitude differences between 4DCT and gated setup images were detected in some patients during the radiation treatment period, and therefore, should be considered when setting up the target margin. Monitoring of positional uncertainty and its adaptive feedback system can enhance the accuracy of treatments.

• The Korean Journal of Nuclear Medicine
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• v.38 no.1
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• pp.85-98
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• 2004

Purpose: Cellular uptake of $^{99}mTc$-sestamibi (MIBI) and $^{99}mTc$-tetrofosmin (TF) is low in cancer cells expressing multidrug resistance(MDR) by p-glycoprotein(Pgp) or multidrug related protein(MRP). Verapamil is known to increase cellular uptake of MIBI in MDR cancer cells, but is recently reported to have different effects on tracer uptake in certain cancer cells. This study was prepared to evaluate effects of verapamil on cellular uptake of MIBI and TF in several cancer cells. Materials and Methods: Celluar uptakes of Tc-99m MIBI and TF were measured in erythroleukermia K562 cell, breast cancer MCF7 cell, and human ovarian cancer SK-OV-3 cells, and data were compared with those of doxorubicin-resistant K562(Ad) cells. RT-PCR and Western blot analysis were used for the detection of mdr1 mRNA and Pgp expression, and to observe changes in isotypes of PKC enzyme. Effects of verapamil on MIBI and TF uptake were evaluated at different concentrations upto $200{\mu}M\;at\;1{\times}10^6\;cells/ml\;at\;37^{\circ}C$. Radioactivity in supernatant and pellet was measured with gamma counter to calculate cellular uptake ratio. Toxicity of verapamil was measured with MTT assay. Results: Cellular uptakes of MIBI and TF were increased by time in four cancer cells studied. Co-incubation with verapamil resulted in an increase in uptake of MIBI and TF in K562(Adr) cell at a concentration of $100{\mu}M$ and the maximal increase at $50{\mu}M$ was 10-times to baseline. In contrast, uptakes of MIBI and TF in K562, MCF7, SK-OV3 cells were decreased with verapamil treatment at a concentration over $1{\mu}M$. With a concentration of $200{\mu}M$ verapamil, MIBI and TF uptakes un K562 cells were decreased to 1.5 % and 2.7% of those without verapamil, respectively. Cellular uptakes of MIBI and TF in MCF7 and SK-OV-3 cells were not changed with $10{\mu}M$, but were also decreased with verapamil higher than $10{\mu}M$, resulting 40% and 5% of baseline at $50{\mu}M$. MTT assay of four cells revealed that K562, MCF7, SK-OV3 were not damaged with verapamil at $200{\mu}M$. Conclusion: Although verapamil increases uptake of MIBI and TF in MDR cancer cells, cellular uptakes were further decreased with verapamil in certain cancer cells, which is not related to cytotoxicity of drug. These results suggest that cellular uptakes of both tracers might differ among different cells, and interpretation of changes in tracer uptake with verapamil in vitro should be different when different cell lines are used.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Childhood Kidney Diseases
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• v.3 no.2
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• pp.161-169
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• 1999

Purpose : The urinary mass screening program for the detection of proteinuria in school aged population has been performed in Seoul since 1981. Systematic evaluation in corporation with the Seoul School Health Center for students with proteinuria identified in the mass screening has been performed from 1987. The results of urinary mass screening up to 1994 was reported. I report here the results of urinary mass screening from 1995 to 1997 and compare them with previous results and attempt to reveal the significance of urinary mass screening. Objects and Methods : In the 3-year period between 1995 and 1997, annually about 460,000 students comprising 3 different age groups; 5th grade of elementary school, 2nd grade of middle school and 2nd grade of high school were chosen, corresponding to the approximate ages of 11, 14, and 17 years, respectively. These subjects accounted for 26% of total school aged children in Seoul. The screening program was carried out in 3 steps. The 1st test was performed with dipstick at school and the 2nd at the Seoul School Health Center. Those students who showed proteinuria in the 1st and 2nd tests were referred to the hospital. Laboratory examinations including renal biopsies were performed to those students with pathologic proteinuria to clarify the incipient renal diseases. Results : 1) The prevalence of asymptomatic proteinuria was 0.28% in the 1st test. It peaked at the group of 14 years old as 0.34%, compared with 0.26% at the group of 11 years old and 0.24% at the group of 17 years old. It reached to 0.26% in male and 0.30% in female. 2) 25 percent of those having proteinuria at the first test were positive at the second test. 3) The proportion of patients with proteinuria by 3rd test were as follows; 25% of transient proteinuria, 55% of orthostatic proteinuria, 6% of constant proteinuria, 12% of proteinuria with hematuria, and 2% of transient proteinuria with isolated hematuria. Pathologic proteinuria were totaled as 20%. The prevalence of renal diseases among the age group of 7-18 years old was estimated to be 1.4 per 10,000. 4) Renal biopsy performed on 38 children with proteinuria at the third test revealed IgA nephropathy in 17(44%), focal segmental glomerusclerosis in 5(13%), minimal change disease in 4(11%), membranoproliferative glomeronephritis in 3(8%), $Henoch-Sch\"{o}nlein$ purpura nephritis in 3(8%), and others in 6(16%). Therefore, the prevalence of IgA nephropathy among the age group of 7-18 years old was estimated to be 0.64 per 10,000. 5) The prevalence of chronic renal failure was estimated to be 5.7 per 1 million of 7 to 18 years age group. Conclusions : 1) The prevalence of proteinuria in the first screening test was 0.28% and finally only 5% of them showed the pathologic proteinuria at the third test. 2) The prevalence of IgA nephropathy and chronic renal failure were 0.63 per 10,000 and 5.7 per 1 million, respectively among school-aged children in Seoul.

• Childhood Kidney Diseases
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• v.7 no.2
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• pp.142-149
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• 2003

Purpose : The urinary mass screening program for the detection of urinary abnormalities in school aged population has been performed in Seoul since 1981. Nation-wide urinary mass screening program was also performed since 1998. The aim of this study was to analyze the cause and nature of isolated proteinuria detected by chance on the urinary mass screening test in Busan and Kyungsangnam-do Province Methods : The medical records of 44 cases of isolated proteinuria detected by chance on the urinary mass screening test in Busan and Kyungsangnam-do Province, and evaluated for urinary abnormalities at the pediatrics outpatients renal clinics of Busan Paik Hospital from April 2002 to August 2003 were reviewed prospectively. Results : The cause and incidence of isolated proteinuria were as follows; transient proteinuria 4 cases(9.1%), orthostatic proteinuria 36 cases(81.8%) and persistent proteinuria 4 cases (9.1%). The total protein amount of the 24 hour urine were $121.0{\pm}136.4\;mg$ in transient proteinuria, $179.1{\pm}130.0\;mg$ in orthostatic proteinuria and $1532.8{\pm}982.5\;mg$ in persistent proteinuria. In the orthostatic proteinuria group, the total protein amount of the 24 hour urine was in the range of 40-616 mg. Spot urine protein/creatinine ratio(PCR) were $0.10{\pm}0.01$ in transient proteinuria, $0.61{\pm}0.61$ in orthostatic proteinuria and $4.35{\pm}4.04$ in persistent proteinuria. In the orthostatic proteinuria group, spot me PCR was in the range of 0.09-2.32. Renal biopsy was peformed in 4 children of the persisitent proteinuria group. They showed minimal change in 1 case, membranoproliferatiye glomerulonephritis in 2 cases and secondary renal amyloidosis in 1 case. Conclusion : The majority of isolated proteinuria which was detected by chance on school urinary mass screening were transient or orthostatic proteinuria. Even though the incidence of persistent proteinuria was much lower, it is necessary to take care of these children regularly and continuously, because persistent proteinuria itself is a useful marker of the progressive renal problems.