• Journal of Communications and Networks
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• v.12 no.1
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• pp.52-66
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• 2010

Since large objects consume substantial resources, web proxy caching incurs a fundamental trade-off between performance (i.e., hit-ratio and latency) and overhead (i.e., resource usage), in terms of caching and relaying large objects to users. This paper investigates how and to what extent the current dedicated-server based web proxy caching scheme is affected by large file transfers in a high bandwidth campus network environment. We use a series of trace-based performance analyses and profiling of various resource components in our experimental squid proxy cache server. Large file transfers often overwhelm our cache server. This causes a bottleneck in a web network, by saturating the network bandwidth of the cache server. Due to the requests for large objects, response times required for delivery of concurrently requested small objects increase, by a factor as high as a few million, in the worst cases. We argue that this cache bandwidth bottleneck problem is due to the fundamental limitations of the current centralized web proxy caching model that scales poorly when there are a limited amount of dedicated resources. This is a serious threat to the viability of the current web proxy caching model, particularly in a high bandwidth access network, since it leads to sporadic disconnections of the downstream access network from the global web network. We propose a peer-to-peer cooperative web caching scheme to address the cache bandwidth bottleneck problem. We show that it performs the task of caching and delivery of large objects in an efficient and cost-effective manner, without generating significant overheads for participating peers.

• Clinical and Experimental Pediatrics
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• v.52 no.11
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• pp.1249-1259
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• 2009

Purpose:From a societal perspective, we evaluated the cost-effectiveness of a novel sustained-release injection of recombinant human growth hormone (GH) administered on a weekly basis compared with that of the present daily GH injection for the treatment of children with GH deficiency. Methods:Health-related utility for GH therapy was measured based on the visual analogue scale. During July 2008, caregivers of 149 children receiving GH therapy form 2 study sites participated in a web-based questionnaire survey. The survey required the caregivers to rate their current subjective utility with daily GH injections or expected utility of weekly GH injections. Because there was no difference in the costs of the daily and weekly therapies, for the purposes of this study, only drug acquisition costs were considered. Results:Switching from daily to weekly injection of GH increased the utility from 0.584 to 0.784 and incurred an extra cost of 4,060,811 Korean won (KW) per year. The incremental cost-utility ratio (ICUR) for a base case was 20,305,055 KW per quality-adjusted life year (QALY) gained. Scenario analyses showed that the ICUR ranged from 15,751,198 to 25,489,929 KW per QALY. Conclusion:The ICUR for a base case and worst case scenario analyses ranged from 0.85 to 1.37-times per capita gross domestic product of Korea, which is considered to be within the generally accepted willingness-to-pay threshold. Thus, it is concluded that switching from daily to weekly injection of GH would be cost-effective.

• Economic and Environmental Geology
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• v.38 no.3 s.172
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• pp.305-317
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• 2005

This study addresses the preliminary results of rock slope stability analyses including hazard assessments for slope failure conducted on the selected sections of rural road cut slope which are about 4 km long. The study area is located in the Mt. Chuntae northeast of Busan and mainly composed of Cretaceous rhyolitic ash-flow tuff', fallout tuff, rhyolitc and andesite. The volcanic rock mass in the area has a number of discontinuities that produce a potentially unstable slope, as the present cut slope is more than 70 degrees in most of the slope sections. Discontinuity geometry data were collected at selected 8 scanline sections and analyzed to estimate important discontinuity geometry parameters to perform rock slope kinematic and block theory analyses. Kinematic analysis for plane sliding has resulted in maximum safe slope angles greater than $65^{\circ}$ for most of the discontinuities. For most of the wedges, maximum safe cut slope angles greater than $45^{\circ}$ were obtained. Maximum safe slope angles greater than 80" were obtained fur most of the discontinuities in the toppling case. The block theory analysis resulted in the identification of potential key blocks (type II) in the SL4, SL5, SL6 and SL8 sections. The chance of sliding taking place through a type ll block under a combined gravitational and external loading is quite high in the investigated area. The results support in-field observations of a potentially unstable slope that could become hazardous under external forces. The results obtained through limit equilibrium slope stability analyses show how a stable slope can become an unstable slope as the water pressure acting on joints increases and how a stable slope under Barton's shear strength criterion can fail as the worst case scenario of using Mohr-Coulomb criterion.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.3D
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• pp.231-237
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• 2012

The interrelation of integrity degree and country competitive power was known as high. But, according to the CPI announcement of Transparency International, the domestic Corruption Perceptions Index ranked 5 points, as being corrupt. It was investigated that the corruption level of Korea was getting worse most in the construction sector. In this way, it is emergent and inevitable to improve the transparency in the construction industry, accounting for 25-54% of the total corruption cases in our society. Transparency International has opened to the public the source data, utilized in the CPI measures in 2010. In the case of Korea, the 9 data of 6 organizations were utilized. According to the PERC, the corruption level of the private sector in Korea has been estimated as the worst among the 16 countries. In this context, this paper analyses the corruption level of Korea by utilizing the source data of the Transparency International CPI. And it aims to comprehend structural problems in the construction industry and to suggest implicative countermeasures through out the anti-corruption activities in the world. It propose finally an improvement of the structural causes in the construction industry, a promotion of effective punishment against corrupt practices, a reinforcement of the transparency management in the construction sector, etc.

• Management & Information Systems Review
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• v.28 no.3
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• pp.69-98
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• 2009

This study investigates in-sample and out-of-sample predictive abilities of accruals and accruals components with respect to future cash flows using models developed by Barth et al.(2001). In tests, data collected fromda62 Korean KOSPI and KOSDAQ listed firms for ccr4-2007 are used. Results of in-sample prediction tests are similar with those of Barth et al.(2001). Their accrual components model is better than other three models(NI only model, CF only model and NI-total accruals model) in future cash flows predictive ability. That is, in the case of in-sample prediction, accrual components excluding amortization have additional information contents for future cash flows. But in out-of-sample tests, the results are different. The model including operational cash flows(CF only model) shows best out-of-sample predictive ability with respect to future cash flows among above four prediction models. The accrual components model of Barth et al.(2001) has worst out-of-sample predictive ability. The results are robust to sensitivity analyses. In conclusion, we can't find the evidence that accruals and accrual components have predictive ability with respect to future cash flows in out-of-sample prediction tests. This results are consistent with results of Lev et al.(2005), and inconsistent with the belief of accounting standards formulating organizations such as FASB and KASB.

• Journal of Hospice and Palliative Care
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• v.11 no.1
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• pp.42-50
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• 2008

Purpose: Undertreatment of canter pain, especially due to the differences in the perception of pain between the patients and caregivers, is a well recognized problem. The purpose of this study were to determine if there exist differences in communication about pain intensity scores between patients and their family caregivers in Korea. Methods: A total of 127 patient-family caregiver dyads who have experienced canter pain participated in this study at a hospital in Seoul for six months. The data were obtained by fare to face interview with a structured questionnaire based on Brief Pain Inventory-Korean version and other previous researches. The clinical information for all patients was compiled by reviewing their medical records. Results: Patients' 'worst-pain for 24-hour' and 'right-now-pain' scores estimated by family caregivers were significantly higher than those by patient themselves. The degree of agreement between patients and family caregivers in the estimate of patients' 'worst-pain for 24-hour' intensity categories was 78.7% for 'severe pain', 40% for 'no pain', 27.5% for 'mild pain' and 22.9% for 'moderate pain'. In case of 'right-now-pain' intensity categories, the agreement was 50% for 'severe pain', 47.2% for mild pain, 46.3% for 'no pain', and 26.3% for 'moderate pain'. Conclusion: This study demonstrates that the degree of agreement between patients and family caregivers in the estimate of patients 'pain intensity categories was less than 50% except for 'severe pain'. The results indicate that Korean family caregivers tend to overestimate the canter pain intensity of their caring patients, especially, when a lancer patient has 'moderate' or 'mild pain'. Health Providers are advised to educate patient-family caregiver dyads to use a pain measurement scale to promote their agreement in pain Intensity stores. Further analyses and studies are needed to identify the factors and differences that influence their communication about pain intensity scores between patients and their family caregivers.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.