• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.5
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• pp.87-97
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• 2016

This work proposes a time-shared 10b DAC based on a two-step resistor string to minimize the effective area of a DAC channel for driving each AMOLED display column. The proposed DAC shows a lower effective DAC area per unit column driver and a faster conversion speed than the conventional DACs by employing a time-shared DEMUX and a ROM-based two-step decoder of 6b and 4b in the first and second resistor string. In the second-stage 4b floating resistor string, a simple current source rather than a unity-gain buffer decreases the loading effect and chip area of a DAC channel and eliminates offset mismatch between channels caused by buffer amplifiers. The proposed 1-to-24 DEMUX enables a single DAC channel to drive 24 columns sequentially with a single-phase clock and a 5b binary counter. A 0.9pF sampling capacitor and a small-sized source follower in the input stage of each column-driving buffer amplifier decrease the effect due to channel charge injection and improve the output settling accuracy of the buffer amplifier while using the top-plate sampling scheme in the proposed DAC. The proposed DAC in a $0.18{\mu}m$ CMOS shows a signal settling time of 62.5ns during code transitions from '$000_{16}$' to '$3FF_{16}$'. The prototype DAC occupies a unit channel area of $0.058mm^2$ and an effective unit channel area of $0.002mm^2$ while consuming 6.08mW with analog and digital power supplies of 3.3V and 1.8V, respectively.

• Korean Journal of Remote Sensing
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• v.34 no.2_2
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• pp.377-391
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• 2018

In order to efficiently monitor red tide over a wide range, the need for red tide detection using remote sensing is increasing. However, the previous studies focus on the development of red tide detection algorithm for ocean colour sensor. In this study, we propose the use of multi-sensor to improve the inaccuracy for red tide detection and remote sensing data in coastal areas with high turbidity, which are pointed out as limitations of satellite-based red tide monitoring. The study area were selected based on the red tide information provided by National Institute of Fisheries Science, and spatial fusion and spectral-based fusion were attempted using GOCI image as ocean colour sensor and Landsat OLI image as terrestrial sensor. Through spatial fusion of the two images, both the red tide of the coastal area and the outer sea areas, where the quality of Landsat OLI image was low, which were impossible to observe in GOCI images, showed improved detection results. As a result of spectral-based fusion performed by feature-level and rawdata-level, there was no significant difference in red tide distribution patterns derived from the two methods. However, in the feature-level method, the red tide area tends to overestimated as spatial resolution of the image low. As a result of pixel segmentation by linear spectral unmixing method, the difference in the red tide area was found to increase as the number of pixels with low red tide ratio increased. For rawdata-level, Gram-Schmidt sharpening method estimated a somewhat larger area than PC spectral sharpening method, but no significant difference was observed. In this study, it is shown that coastal red tide with high turbidity as well as outer sea areas can be detected through spatial fusion of ocean colour and terrestrial sensor. Also, by presenting various spectral-based fusion methods, more accurate red tide area estimation method is suggested. It is expected that this result will provide more precise detection of red tide around the Korean peninsula and accurate red tide area information needed to determine countermeasure to effectively control red tide.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.1
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• pp.51-60
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• 2014

In this study, ICT medical service provider's level of knowledge fire fighting safety and methods on coping with fires in the regions of Gwangju and Jeonam Province of Korea were investigated to determine the elements affecting such levels and provide basic information on the manuals for educating how to cope with the fire fighting safety in medical facilities. The data were analyzed using SPSS Win 14.0. The scores of level of knowledge fire fighting safety of ICT medical service provider's were 7.06(10 point scale), and the scores of level of recognizing how to cope with fire fighting safety were 6.61(11 point scale). level of recognizing how to cope with fire fighting safety were significantly different according to gender(t=4.12, p<.001), age(${\chi}^2$=17.24, p<.001), length of career(${\chi}^2$=22.76, p<.001), experience with fire fighting safety education(t=6.10, p<.001), level of subjective knowledge on fire fighting safety(${\chi}^2$=53.83, p<.001). In order to enhance the level of understanding of fire fighting safety and methods of coping by the ICT medical service providers it is found that: self-directed learning through avoiding the education just conveying knowledge by lecture tailored learning for individuals fire fighting education focused on experiencing actual work by developing various contents emphasizing cooperative learning deploying patients by classification systems using simulations and a study on the implementation of digital anti-fire monitoring system with multipoint communication protocol, a design and development of the smoke detection system using infra-red laser for fire detection in the wide space, video based fire detection algorithm using gaussian mixture mode developing an education manual for coping with fire fighting safety through multi learning approach at the medical facilities are required.

• Journal of radiological science and technology
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• v.31 no.1
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• pp.71-81
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• 2008

Our objective was to evaluate the image of spatial domain filtering as an alternative to additional image reconstruction using different kernels in MDCT. Derived from thin collimated source images were generated using water phantom and abdomen B10(very smooth), B20(smooth), B30(medium smooth), B40 (medium), B50(medium sharp), B60(sharp), B70(very sharp) and B80(ultra sharp) kernels. MTF and spatial resolution measured with various convolution kernels. Quantitative CT attenuation coefficient and noise measurements provided comparable HU(Hounsfield) units in this respect. CT attenuation coefficient(mean HU) values in the water were values in the water were $1.1{\sim}1.8\;HU$, air($-998{\sim}-1000\;HU$) and noise in the water($5.4{\sim}44.8\;HU$), air($3.6{\sim}31.4\;HU$). In the abdominal fat a CT attenuation coefficient($-2.2{\sim}0.8\;HU$) and noise($10.1{\sim}82.4\;HU$) was measured. In the abdominal was CT attenuation coefficient($53.3{\sim}54.3\;HU$) and noise($10.4{\sim}70.7\;HU$) in the muscle and in the liver parenchyma of CT attenuation coefficient($60.4{\sim}62.2\;HU$) and noise ($7.6{\sim}63.8\;HU$) in the liver parenchyma. Image reconstructed with a convolution kernel led to an increase in noise, whereas the results for CT attenuation coefficient were comparable. Image scanned with a high convolution kernel(B80) led to an increase in noise, whereas the results for CT attenuation coefficient were comparable. Image medications of image sharpness and noise eliminate the need for reconstruction using different kernels in the future. Adjusting CT various kernels, which should be adjusted to take into account the kernels of the CT undergoing the examination, may control CT images increase the diagnostic accuracy.

• The Korean Journal of Pesticide Science
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• v.20 no.4
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• pp.319-325
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• 2016

This study was conducted to investigate residual organochlorine pesticides in green house soil and oriental melon, green pepper, and lettuce. The majority of them were designated as persistent organic pollutants (POPs) by the international community at the Stockholm Convention on Persistent Organic Pollutant. Extraction and clean-up method were developed using the QuEChERS method for residual organochlorine pesticides (OCPs) in soil and oriental melon, green pepper and lettuce. Recovery of OCPs in greenhouse soil and oriental melon, green pepper, and lettuce ranged from 73.3-110.6%. Limit of detection (LOD) of OCPs in soil and 3 crops were 0.01-0.08 and $0.11-0.17{\mu}g/kg$. The residues of OCPs in oriental melon, green pepper and lettuce greenhouse soil were analyzed by the developed method, and dieldrin, ${\beta}-endosulfan$ and endosulfan sulfate were detected at 1.4-72.5, 0.1-78.7 and $0.0-214.1{\mu}g/kg$, respectively. The detection frequency of 3 compounds in soils were 52 (29.7%), 34 (19.4%) and 57 (32.6%) among 175 samples, respectively. However, these compounds were not detected in all crop samples. The residue level in 3 crops were lower than 1/58.8 of maximum residue level of them. These results showed that the OCPs residue in oriental melon, green pepper, and lettuce greenhouse soil were not as high as crop safety threatening.

• Journal of Korea Water Resources Association
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• v.47 no.4
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• pp.371-384
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• 2014

This study developed a new algorithm of extreme rainfall extraction based on the Communication, Ocean and Meteorological Satellite (COMS) and the Tropical Rainfall Measurement Mission (TRMM) Satellite image data and evaluated its applicability for the heavy rainfall event in July-2011 in Seoul, South Korea. The power-series-regression-based Z-R relationship was employed for taking into account for empirical relationships between TRMM/PR, TRMM/VIRS, COMS, and Automatic Weather System(AWS) at each elevation. The estimated Z-R relationship ($Z=303R^{0.72}$) agreed well with observation from AWS (correlation coefficient=0.57). The estimated 10-minute rainfall intensities from the COMS satellite using the Z-R relationship generated underestimated rainfall intensities. For a small rainfall event the Z-R relationship tended to overestimated rainfall intensities. However, the overall patterns of estimated rainfall were very comparable with the observed data. The correlation coefficients and the Root Mean Square Error (RMSE) of 10-minute rainfall series from COMS and AWS gave 0.517, and 3.146, respectively. In addition, the averaged error value of the spatial correlation matrix ranged from -0.530 to -0.228, indicating negative correlation. To reduce the error by extreme rainfall estimation using satellite datasets it is required to take into more extreme factors and improve the algorithm through further study. This study showed the potential utility of multi-geostationary satellite data for building up sub-daily rainfall and establishing the real-time flood alert system in ungauged watersheds.

• Journal of dental hygiene science
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• v.13 no.1
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• pp.62-70
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• 2013

The aim of this study was to analyze factors related to xerostomia in adults aged 30 years and over. The data were analyzed using the t-test, one-way ANOVA and multiple linear regression analysis in the SPSS version 12.0 program. The results were as follow. 1. The respondents who were older than 51 years old, unemployed and had less than 3 million won of average monthly income were more aware of xerostomia (p<0.05). 2. The respondents who answered poor and moderate for their general and oral health and the group with duplicate medication and comorbidity were more aware of xerostomia (p<0.05). 3. The respondents that had problems in chewing, communication, ordinary activities and complained of pain discomfort and suffered from anxiety depression were more aware of xerostomia (p<0.001). 4. The respondents that answered frequent dryness on their skin, eyes, lips, and nasal mucosa were more aware of xerostomia (p<0.001). 5. Xerostomia showed highest correlation with quality of life ($\beta$=0.436) followed by the number of medications ($\beta$=0.239), sense of entire body dryness ($\beta$=0.200), feeling of hopelessness ($\beta$=0.160) and number of oral mucosa disease symptoms ($\beta$=0.099) (p<0.05). According to the results of the study, xerostomia may cause deterioration in quality of life. Thus, it is advised to improve the patient management system among dental professions to prevent various complications caused by xerostomia and conduct regular health education on the cause and management method of xerostomia.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.5
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• pp.137-150
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• 2015

This study investigated the influence of Entrepreneurship, Start-up Competency on Entrepreneurial Satisfaction and the moderating effect of Flow experience. The goal of this investigation is to empirically prove that despite the benefits of educational and financial support, mentoring and consulting which selected applicants received as a part of supporting business for start-up companies provided by national and local government and public organizations, there is a gap between individual entrepreneurs' satisfaction after actual foundation of the company. For the purpose of this investigation, data was collected through a survey with 320 entrepreneurs who were beneficiaries of government's supporting business for start-up companies and have founded their companies less than 5 years ago. For entrepreneurship variables, three factors of innovativeness, proactiveness and risk-taking were reviewed, and also reviewed the three variables network capability, market orientation and marketing capacity as Start-up competency. The collected data was used to perform a multiple regression analysis, and the moderating effect of flow experience was analysed using moderated regression analysis. The result of analysis shows that innovativeness, proactiveness and risk-taking of entrepreneurship positively affected the entrepreneurial satisfaction, and network capability, market orientation and marketing capability also positively affected the entrepreneurial satisfaction. As a result of analysing the moderating effect of flow experience, it appeared that there is a positive moderating effect between entrepreneurship, start-up competency, and the entrepreneurial satisfaction. This result concludes that evaluation items of entrepreneurship and start-up competency as applicant selecting criteria in supporting business for start-up companies, are valid. It is also concluded that start-up supports such as education, funds and consulting are also important, but it is necessary to make changes such as construction of start-up ecosystem and reinforcement of networking support, so that entrepreneurs can have the flow experience themselves in the process after founding a company.

• Korean Journal of Psychosomatic Medicine
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• v.27 no.2
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• pp.101-110
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• 2019

Objectives : It is not clear which clinical variables are most closely associated with delirium in the Intensive Care Unit (ICU). By comparing clinical data of ICU delirium and non-delirium patients, we sought to identify variables that most effectively differentiate delirium from non-delirium. Methods : Medical records of 6,386 ICU patients were reviewed. Random Subset Feature Selection and Principal Component Analysis were utilized to select a set of clinical variables with the highest discriminatory capacity. Statistical analyses were employed to determine the separation capacity of two models-one using just the selected few clinical variables and the other using all clinical variables associated with delirium. Results : There was a significant difference between delirium and non-delirium individuals across 32 clinical variables. Richmond Agitation Sedation Scale (RASS), urinary catheterization, vascular catheterization, Hamilton Anxiety Rating Scale (HAM-A), Blood urea nitrogen, and Acute Physiology and Chronic Health Examination II most effectively differentiated delirium from non-delirium. Multivariable logistic regression analysis showed that, with the exception of vascular catheterization, these clinical variables were independent risk factors associated with delirium. Separation capacity of the logistic regression model using just 6 clinical variables was measured with Receiver Operating Characteristic curve, with Area Under the Curve (AUC) of 0.818. Same analyses were performed using all 32 clinical variables;the AUC was 0.881, denoting a very high separation capacity. Conclusions : The six aforementioned variables most effectively separate delirium from non-delirium. This highlights the importance of close monitoring of patients who received invasive medical procedures and were rated with very low RASS and HAM-A scores.