• Childhood Kidney Diseases
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• v.9 no.1
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• pp.8-14
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• 2005

Purpose : This study was peformed to study normative blood pressure data in full-term neonates that may be used to facilitate Identification of neonatal hypertension.Methods : 383 newborns born in our hospital from May 2003 to January 2004 were enrolled in this study Using an oscillometric device(BP-88 NEXT, COLIN Corp.), their blood pressures were measured more than one time within a week after birth. According to each clinical variable such as sex, delivery mode, birth weight, gestational age and presence of maternal disease or perinatal problems, we divided the population into groups and calculated the mean blood pressures of each group. We compared mean blood pressures between the divided groups according to each clinical variable statistically.Results : Mean systolic and diastolic blood pressure of the Population was 70.8$\pm$ 10.9 mmHg and 43.4 $\pm$ 8.0 mmHg, respectively. There was no satistically significant difference in blood pressure according to clinical variables. Mean systolic pressure showed positive correlation with birth weight and gestational age(r=0.1420, 0.03130).Conclusion : Our results are almost in agreement with Zubrow's data from 695 newborns in U.S.A, 1995. Our data may be helpful for early detection and management of neonatal hypertension, thereby maintaining renal function ,and preventing possible complications of renal disease.

• Journal of IKEEE
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• v.25 no.3
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• pp.493-500
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• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.

• The Journal of the Korea Contents Association
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• v.11 no.9
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• pp.26-44
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• 2011

The Study was carreid out to see whether the intensity of electromagnetic waves in each floor of a building where the sharing base station has been established is harmful to a human body, and to expect the intensity of the waves in the building. The investigate was performed on both of sharing base station either with many scatterers or without any of them. To satisfy the international standard and the domestic TTA standard, rms for each of the electromagnetic wave of every floor in the building with the station was measured from 3 location of 3 heights(1.1m,1.5m, and 1.7m). Max of the measured rsm from the each of the frequencies in the nine location was confirmed to be 48.12%(the rooftop measured value) at most, compared to the human body protection standard. The value was confirmed to satisfy the human body protection standard for each frequency. And the total value of the calculated exposure indexes for each frequency was determined to be more than 7 times lower at most, which was 0.1445, compared to the 1 standard. Since P value in both of 868MHz and 2.14GHz electromagnetic waves intensity for each base station and floor was less than 0.05, it was revealed to be meaningful, and since R-Sq(adj) value showed a value more than 50%, the regression equation was determined to fully absorb the data information. However, although the P value of both of 868MHz and 2.14GHz electromagnetic waves intensities under the integrating terms of the base station data and the floor data was showed to be less than 0,05, since R-Sq(adj) value of 868MHz electromagnetic waves intensity presented a value smaller than 50%(34.15%), it was determined that the 868MHz electromagnetic waves intensity is very much influenced by an environment with a base station. Because the electromagnetic waves intensity of 2.14GHz show R-Sq(adj) value bigger then 50%(51.8%), The regression equation model of 2.14GHz electromagnetic waves intensity was confirmed to be proper. It also turned out not to be effected by the surrounding environment near a building with the base station and the intensity of electromagnetic waves for each floor of such building was expectable by the regression equation.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.5
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• pp.171-180
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• 2023

The three major sleep disorders in Korea are snoring, sleep apnea, and insomnia. Lack of sleep is the root of all diseases. Some of the most serious potential problems associated with sleep deprivation are cardiovascular problems, cognitive impairment, obesity, diabetes, colitis, prostate cancer, etc. To solve these problems, the Korean government provided low-cost national health insurance benefits for polysomnography tests in July 2018. However, insomnia patients still have problems getting treated in terms of time, space, and economic perspectives. Therefore, it would be better for insomnia patients to be allowed to test at home. The measuring device can measure six biosignals (eye movement, tossing and turning, body temperature, oxygen saturation, heart rate, and audio). A gyroscope sensor (MPU9250, InvenSense, USA) was used for eye movement, tossing, and turning. The input range of the sensor was in 258°/sec to 460°/sec, and the data range was in the input range. Body temperature, oxygen saturation range, and heart rate were measured by a sensor (MAX30102, Analog Devices, USA). The body temperature was measured in 30 ℃ to 45 ℃, and the oxygen saturation range was 0% for the unused state and 20 % to 90 % for the used state. The heart rate measurement range was in 40 bpm to 180 bpm. The measurement of audio signal was performed by an audio sensor (AMM2742-T-R, PUIaudio, USA). The was -42 dB ±1 dB frequency range was 20 Hz to 20 kHz. The measured data was successfully received in wireless network conditions. The system configuration was consisted of a PC and a mobile app for bio-signal measurement and data collection. The measured data was collected by mobile phones and desktops. The data collected can be used as preliminary data to determine the stage of sleep and perform the screening function for sleep induction and sleep disturbances. In the future, this convenient sleep measurement device could be beneficial for treating insomnia.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.301-313
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• 2020

This study is a study on the interlocking method between emergency preparedness simulation model and military exercise war game model. The national emergency preparedness government exercises are being carried out by a message exercise and technology development for simulation models is being carried out to create a situation similar to the actual practice. In order to create a situation similar to the actual war, the military situation must be reflected and to do so, a link with the military exercise war game model is needed. The military exercise war game model applies HLA/RTI, which is a standardized interlocking method for various models such as Taegeuk JOS, and it is necessary to apply HLA/RTI linkage method to link with these military exercise war game models. In addition, since the emergency preparedness simulation model requires limited information such as enemy location and enemy attack situation on major facilities in the military exercise model, a method of interlocking that can select and link information is required. Therefore, in this study, the interlocking interface design plan is presented in order to selectively link the interlocking method and information between the emergency preparedness simulation model and the military exercise war game model. The main functions of interlocking interface include federation synchronization, storage and recovery, object management service, time management, and data filtering functions.

• Journal of IKEEE
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• v.15 no.1
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• pp.76-85
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• 2011

In this paper, we analysed RSS characteristics by external factors and presented an efficient algorithm for real-time location tracking and its hardware system. The proposed algorithm enhanced the ranging accuracy using Kalman Filter based on the RSS DB. The location tracking system that consists of the tag, AP(Access Point), a data collector(Data Receiver) with IEEE 802.15.4(ZigBee) network environment, and location tracking application that reveal locations of each tag is implemented for the test environment. The location tracking system presented in this paper is implemented with MSP430 microprocessor manufactured by TI(Texas Instrument), CC2420 RF chipset and the location tracking application. With the results of the experiment, the proposed algorithm and the system can achieve the efficiency and the accuracy of location tracking with the average error of 19.12cm, and its standard deviation of 5.31cm in outdoor circumstance. Also, the experimental result shows that exact tracking of position in indoor circumstance cannot achieve because of vulnerable RSS with external circumstance.

• Journal of Dental Rehabilitation and Applied Science
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• v.36 no.1
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• pp.12-20
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• 2020

Purpose: This study was designed to analyze the relationship between the presence and severity of depression and chewing problems (CPs) in a representative sample of the general population. Materials and Methods: Health surveys and examinations were conducted on a nationally representative sample (n = 8150) of Korean was conducted. CPs was determined by a simple survey response concerning "Do you feel uncomfortable about chewing your food because of problems with your mouth such as teeth, dentures and gums?" Depression was defined as individuals with a total score ≥ 10 on the Patient Health Questionnaire (PHQ)-9 survey. Data regarding demographics, socioeconomic history and comorbid health conditions were used to analyze adjusted logistic regression models. Results: In the Korean population, the prevalence of depression was significantly greater in individuals with CP (17.2%) than in those without CP (10.2%). On multivariate logistic regression analysis, the presence of depression was significantly associated with CPs (adjusted odd ratio [aOR]: 1.90, P < 0.001). The risk of CPs increased with increasing severity of depression as follows: severe depression (OR: 2.62, P < 0.001), moderately severe depression (OR: 2.19, P < 0.001). Conclusion: The presence of depression was significantly associated with CPs, especially in severely depressed individuals. Depression screening should be considered in treating CP patients.

• Korean Journal of Construction Engineering and Management
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• v.16 no.4
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• pp.50-59
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• 2015

While the international construction industry for Korean companies have grown in market size exponentially in the recent years, the profit rate of small and medium sized construction companies (SMCCs) are incomparably lower than those of large construction companies. Furthermore, small and medium size companies, especially subcontractor, lacks the judgement of project involvement appropriateness, which leads to an unpredictable profit rate. Therefore, this research aims to create a profit rate prediction model for the international construction project focusing on SMCCs. First, the factors that influence the profit rate and the area of profit zone are defined by using a total of 8,637 projects since the year 1965. Seconds, an extensive literature review is conducted to derive 10 influencing factors. Multiple regression analysis and corresponding judgement technique are used to derive the weight of each factor. Third, cased based reasoning (CBR) methodology is applied to develop the model for profit rate analysis in the project participation review stage. Using 120 validation data set, the developed model showed 11% (14 data sets) of error rate for type 1 and type 2 error. In utilizing the result, project decision makers are able to make decision based on authentic results instead of intuitive based decisions. The model additionally give guidance to the Korean subcontractors when advancing into the international construction based on the model result that shows the profit distribution and checks in advance for the quality of the project to secure a sound profit in each project.

• Journal of Intelligence and Information Systems
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• v.13 no.3
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• pp.119-140
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• 2007

Recommender systems enable a user to decide which information is interesting and valuable in our world of information overload. As the recent studies of distributed computing environment have been progressing actively, recommender systems, most of which were centralized, have changed toward a peer-to-peer approach. Collaborative Filtering (CF), one of the most successful technologies in recommender systems, presents several limitations, namely sparsity, scalability, cold start, and the shilling problem, in spite of its popularity. The move from centralized systems to distributed approaches can partially improve the issues; distrust of recommendation and abuses of personal information. However, distributed systems can be vulnerable to attackers, who may inject biased profiles to force systems to adapt their objectives. In this paper, we consider both effective CF in P2P environment in order to improve overall performance of system and efficient solution of the problems related to abuses of personal data and attacks of malicious users. To deal with these issues, we propose a multi-agent framework for a distributed CF focusing on the trust relationships between individuals, i.e. web of trust. We employ an agent-based approach to improve the efficiency of distributed computing and propagate trust information among users with effect. The experimental evaluation shows that the proposed method brings significant improvement in terms of the distributed computing of similarity model building and the robustness of system against malicious attacks. Finally, we are planning to study trust propagation mechanisms by taking trust decay problem into consideration.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.12
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• pp.511-522
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• 2014

During the development of the software, a variety of bugs are reported. Several bug tracking systems, such as, Bugzilla, MantisBT, Trac, JIRA, are used to deal with reported bug information in many open source development projects. Bug reports in bug tracking system would be triaged to manage bugs and determine developer who is responsible for resolving the bug report. As the size of the software is increasingly growing and bug reports tend to be duplicated, bug triage becomes more and more complex and difficult. In this paper, we present an approach to assign bug reports to appropriate developers, which is a main part of bug triage task. At first, words which have been included the resolved bug reports are classified according to each developer. Second, words in newly bug reports are selected. After first and second steps, vectors whose items are the selected words are generated. At the third step, TF-IDF(Term frequency - Inverse document frequency) of the each selected words are computed, which is the weight value of each vector item. Finally, the developers are recommended based on the similarity between the developer's word vector and the vector of new bug report. We conducted an experiment on Eclipse JDT and CDT project to show the applicability of the proposed approach. We also compared the proposed approach with an existing study which is based on machine learning. The experimental results show that the proposed approach is superior to existing method.