• Journal of Teachers' Pension
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• v.3
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• pp.69-105
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• 2018

저출산과 고령화 이슈는 우리사회의 경제적 문제뿐만 아니라 공적연금의 재정지속가능성 여부와도 맞물려 있다. 실제로 우리나라 모든 공적연금은 사회보험역설(social insurance paradox)이 지속되기 힘든 새로운 도전에 직면하였다. 즉, 재정지속가능성은 제도 내적 연금개혁 혹은 제도 외적 재정지원이 없다면 항시적 수지불균형 상태가 누적될 것으로 예측된다. 이에 정부는 직접 고용과 관련된 공무원연금과 군인연금에 대해서만 연금충당부채를 산출하도록 규정하고 있다. 발생주의회계를 채택한 국제회계기준(종업원급여)을 참조하여 연금충당부채 산출을 위한 연금회계준칙(2011.8.3. 제정; 2011.1.1. 시행) 그리고 '연금회계 평가 및 공시 지침(2011.8.3. 고시 : 이하 편의상 연금회계지침이라 함)'을 신설하였다. 사학연금에 적용성 여부 논의에 앞서, 이들의 산출방법상의 문제점을 먼저 살펴보았다. 첫째, 공적연금은 공통적으로 세대 간 합의에 의해 운영되는 사회계약에 해당하므로 제도의 연속성을 전제로 한다. 하지만 연금회계준칙 및 지침은 제도의 청산을 전제로 현재 가입자(연금 미수령자, 연금 수령자)에 대해서 연금충당부채를 산출하는 폐쇄형측정(closed group valuation)을 채택하고 있다. 즉, 폐쇄형은 제도의 연속성 속성을 반영하고 있지 못하고 있어 기본 전제와 모순된다. 둘째, 공무원연금과 군인연금은 이미 기금 소진(최소한의 유동성기금만 보유함)이 되었고 정부의 보전금에 의해 수지 균형이 유지되는 순수부과방식 체계로 전환되었다. 따라서 연금충당부채는 해당 적립기금의 과소 여부를 판정하는 재정상태 기준 값에 해당하므로 기금소진이 진행된 현 상황에서는 산출의 목적, 필요성을 찾기가 힘들다. 부언하면, 제도 외적 재정지원(보전금)에 의한 수지균형방식이라면 발생주의회계보다는 현금주의회계가 회계의 목적적합성이 높다. 마지막으로 연금충당부채 산출에 있어 가장 민감한 할인율 설정 권한을 기재부장관에게 위임한 내용은 산출의 객관성, 일관성을 확보하기 힘들다고 판단된다. 이를 해소하기 위한 방안으로 본 연구에서는 5년마다 실시하고 있는 장기재정계산에서 예측된 명목 기금투자수익률을 연도별로 적용할 것을 권고하고 있다. 현행 정부회계기준을 사학연금제도에 그대로 적용하기에는 상당한 무리가 있다. 그 이유와 공시방안에 대해 살펴본다. 현재 사학연금은 기금소진 이슈로부터 상당부분 벗어나기 위해 2015년 연금개혁을 단행한 바가 있고 이를 통해 상당기간 부분적립방식 체계가 유지될 것이다. 물론 제도 외적 재정지원은 사학연금법 제53조의7에서 정부지원의 가능성만을 열어 놓은 상태이므로 미래기금소진의 가능성은 상존한다고 볼 수 있다. 먼 미래에는 순수부과방식 체계로 전환될 개연성이 높다. 이러한 재정의 양면성을 본 연구에서는 이중재정방식(dual financing system)이라고 한다. 이러한 속성을 고려하여 연금충당부채(연금채무라는 표현이 적합할 것으로 사료됨)를 산출하고 공시하여야 한다. 그 주요 연구 결과는 다음과 같이 요약된다. 먼저 현행 부분적립방식의 재정상태 검증을 위해 연금채무를 산정할 필요성이 있다. 이를 위해 본 연구에서는 기발생주의(예측단위방식 적용)에 근거한 폐쇄형 측정I(제도 종료를 전제로 현 가입자의 잠재연금채무(IPD) 산출에 초점을 둠) 그리고 미래발생주의(가입연령방식 적용)에 근거한 폐쇄형 측정II(추가적으로 현 가입자의 일정기간 급여 및 기여 발생 허용)을 제안하고 있다. 이를 통해 미적립채무의 규모 그리고 이를 해소하기 위한 상각부담률을 산출할 수 있다. 최종적으로 미래 가입자들까지 포함하고 기금소진 가능성까지 고려하는 개방형측정(open group valuation)을 다루고 있다. 단, 본 연구에서는 공무원연금처럼 기금부족분에 대해서 향후 정부보전금이 있다는 가정 하에 공시 방법을 제시하고 있다. 요약하면, 현행 사학연금제도는 현재와 미래의 재정 양면성을 모두 고려하여 연금채무 및 미적립채무를 공시하여야 한다. 부언하면, 현재 부분적립방식 재정상태를 반영하는 연금채무는 발생주의회계를 적용하고 미래에 도래할 순수부과방식 재정상태는 현금주의회계를 적용할 것을 최종 결론으로 도출하고 있다. 마지막으로 본 연구의 한계는 정부보전금의 가능성에 대한 법률적 해석과 병행하여 책임준비금 범위의 안정적 확대를 전제로 한 공시 논의 그리고 보전금의 책임한도 범위에 따른 공시 논의 등은 다루고 있지 않다는 점이다. 이러한 논의 사항은 향후 연구과제로 두고자 한다.

• The Journal of Korean Academy of Prosthodontics
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• v.57 no.1
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• pp.8-17
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• 2019

Purpose: The study analyzed the prevalence of peri-implantitis and factors which may have affected the disease. Materials and methods: This study based on medical records and radiographs of 422 patients (853 implant cases) who visited Ewha Womans University Mokdong Hospital Dental Center from January 1, 2012 to December 31, 2016. Generalized estimation equations (GEE) was utilized to determine the statistical relationship between peri-implantitis and each element, and the cumulative prevalence of peri-implantitis during the observation period was obtained by using the Kaplan Meier Method. Results: The prevalence rate of peri-implantitis at the patient level resulted in 7.3% (31 patients out of a total of 422 patients), and at the implant level 5.5% (47 implants out of a total of 853 implants). Sex, GBR, guided bone regeneration (GBR) and functional loading periods had statistical significance with the occurrence of peri-implantitis. Upon analysis of the cumulative prevalence of peri-implantitis in terms of implant follow-up period, the first case of peri-implantitis occurred at 9 months after the placement of an implant, and the prevalence of peri-implantitis showed a non-linear rise over time without a hint of a critical point. Conclusion: The prevalence of peri-implantitis at the patient level and the implant were 7.3% and 5.5%, respectively. Male, implant installed with GBR and longer Functional Loading Periods were related with the risk of peri-implantitis.

• The Journal of Korean Society for Radiation Therapy
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• v.22 no.2
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• pp.123-129
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• 2010

Purpose: The purpose of this study was to evaluate dosimetric characteristics of Optically stimulated luminescent dosimeters (OSLD) for dosimetry Materials and Methods: InLight/OSL $NanoDot^{TM}$ dosimeters was used including $Inlight^{TM}MicroStar$ Reader, Solid Water Phantom, and Linear accelerator ($TRYLOGY^{(R)}$) OSLDs were placed at a Dmax in a solid water phantom and were irradiated with 100 cGy of 6 MV X-rays. Most irradiations were carried out using an SSD set up 100 cm, $10{\times}10\;cm^2$ field and 300 MU/min. The time dependence were measured at 10 minute intervals. The dose dependence were measured from 50 cGy to 600 cGy. The energy dependence was measured for nominal photon beam energies of 6, 15 MV and electron beam energies of 4-20 MeV. The dose rate dependence were also measured for dose rates of 100-1,000 MU/min. Finally, the PDD was measured by OSLDs and Ion-chamber. Results: The reproducibility of OSLD according to the Time flow was evaluated within ${\pm}2.5%$. The result of Linearity of OSLD, the dose was increased linearly up to about the 300 cGy and increased supralinearly above the 300 cGy. Energy and dose rate dependence of the response of OSL detectors were evaluated within ${\pm}2%$ and ${\pm}3%$. $PDD_{10}$ and PDD20 which were measured by OSLD was 66.7%, 38.4% and $PDD_{10}$ and $PDD_{20}$ which were measured by Ion-chamber was 66.6%, 38.3% Conclusion: As a result of analyzing characteration of OSLD, OSLD was evaluated within ${\pm}3%$ according to the change of the time, enregy and dose rate. The $PDD_{10}$ and $PDD_{20}$ are measured by OSLD and ion-chamber were evaluated within 0.3%. The OSL response is linear with a dose in the range 50~300 cGy. It was possible to repeat measurement many times and progress of the measurement of reading is easy. So the stability of the system and linear dose response relationship make it a good for dosimetry.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1413-1424
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• 2013

Recently, the frequency of severe storms increases in Korea. Severe storms occurring in a short time cause huge losses of both life and property. A considerable research has been performed for the flood control system development based on an accurate stream discharge prediction. A physical model is mainly used for flood forecasting and warning. Physical rainfall-runoff models used for the conventional flood forecasting process require extensive information and data, and include uncertainties which can possibly accumulate errors during modelling processes. ANFIS, a data driven model combining neural network and fuzzy technique, can decrease the amount of physical data required for the construction of a conventional physical models and easily construct and evaluate a flood forecasting model by utilizing only rainfall and water level data. A data driven model, however, has a disadvantage that it does not provide the mathematical and physical correlations between input and output data of the model. The characteristics of a data driven model according to functional options and input data such as the change of clustering radius and training data length used in the ANFIS model were analyzed in this study. In addition, the applicability of ANFIS was evaluated through comparison with the results of HEC-HMS which is widely used for rainfall-runoff model in Korea. The neuro-fuzzy technique was applied to a Cheongmicheon Basin in the South Han River using the observed precipitation and stream level data from 2007 to 2011.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.404-414
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• 2023

In this study, digital climate maps with high-resolution (1km, daily) for the period of 1981 to 2020 were produced for the use as reference data within the procedures for statistical downscaling of climate change scenarios. Grid data for the six climate variables including maximum temperature, minimum temperature, precipitation, wind speed, relative humidity, solar radiation was created over Korean Peninsula using statistical downscaling model, so-called IGISRM (Improved GIS-based Regression Model), using global reanalysis data and in-situ observation. The digital climate data reflects topographical effects well in terms of representing general behaviors of observation. In terms of Correlation Coefficient, Slope of scatter plot, and Normalized Root Mean Square Error, temperature-related variables showed satisfactory performance while the other variables showed relatively lower reproducibility performance. These digital climate maps based on observation will be used to downscale future climate change scenario data as well as to get the information of gridded agricultural weather data over the whole Korean Peninsula including North Korea.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.147-170
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• 2022

Although the number of patent which is one of the core outputs of technological innovation continues to increase, the number of low-value patents also hugely increased. Therefore, efficient evaluation of patents has become important. Estimation of patent lifespan which represents private value of a patent, has been studied for a long time, but in most cases it relied on a linear model. Even if machine learning methods were used, interpretation or explanation of the relationship between explanatory variables and patent lifespan was insufficient. In this study, patent lifespan (number of renewals) is predicted based on the idea that patent lifespan represents the value of the patent. For the research, 4,033,414 patents applied between 1996 and 2017 and finally granted were collected from USPTO (US Patent and Trademark Office). To predict the patent lifespan, we use variables that can reflect the characteristics of the patent, the patent owner's characteristics, and the inventor's characteristics. We build four different models (Ridge Regression, Random Forest, Feed Forward Neural Network, Gradient Boosting Models) and perform hyperparameter tuning through 5-fold Cross Validation. Then, the performance of the generated models are evaluated, and the relative importance of predictors is also presented. In addition, based on the Gradient Boosting Model which have excellent performance, Accumulated Local Effects Plot is presented to visualize the relationship between predictors and patent lifespan. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the evaluation reason of individual patents, and discuss applicability to the patent evaluation system. This study has academic significance in that it cumulatively contributes to the existing patent life estimation research and supplements the limitations of existing patent life estimation studies based on linearity. It is academically meaningful that this study contributes cumulatively to the existing studies which estimate patent lifespan, and that it supplements the limitations of linear models. Also, it is practically meaningful to suggest a method for deriving the evaluation basis for individual patent value and examine the applicability to patent evaluation systems.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.789-795
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• 2020

Many deaths have been reported due to the worldwide pandemic of COVID-19. In order to prevent the further spread of COVID-19, it is necessary to quickly and accurately read images of suspected patients and take appropriate measures. To this end, this paper introduces a deep learning-based COVID-19 chest X-ray reading technique that can assist in image reading by providing medical staff whether a patient is infected. First of all, in order to learn the reading model, a sufficient dataset must be secured, but the currently provided COVID-19 open dataset does not have enough image data to ensure the accuracy of learning. Therefore, we solved the image data number imbalance problem that degrades AI learning performance by using a Stacked Generative Adversarial Network(StackGAN++). Next, the DenseNet-based classification model was trained using the augmented data set to develop the reading model. This classification model is a model for binary classification of normal chest X-ray and COVID-19 chest X-ray, and the performance of the model was evaluated using part of the actual image data as test data. Finally, the reliability of the model was secured by presenting the basis for judging the presence or absence of disease in the input image using Grad-CAM, one of the explainable artificial intelligence called XAI.

• Korean Journal of Ecology and Environment
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• v.45 no.4
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• pp.403-411
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• 2012

Acoustic telemetry is used to obtain a relatively continuous record of fish movement. This method has several advantages for studying migrating fish populations that are moving from large rivers. The Nakdong River is the longest river in South Korea and the main stream has faced a change, which consists of the installation of the large weirs. In this study, we applied acoustic telemetry to monitor the movement pattern of Erythroculter erythropterus (family Cyprinidae) and identified home range and movement distance in the Nakdong River. A total of fourteen individuals were released at three different locations and around 80 km section from the estuary barrage was investigated. Eight individuals were tagged and released at estuary barrage (N02) utilized up to 15.9 km (home range) upstream from the release site as home range. Four individuals were tagged and released at Samrangjin (N07), most fish moved and stayed within 9.7 km (home range) downstream area, except E12, which did not show any movement. Two individuals were tagged and released at Changnyeong-Haman weir (N10), and all individuals migrated downstream from the release site. Especially, E14 recorded the longest accumulated detected distance, 36.7 km downstream during 32 days after release. There was no correlation identified between movement (accumulated detected distance and home range) and standard length (Spearman rank correlation, p>0.05). Although, this technique could be an available method to monitor behavior and ecology of freshwater fish effectively, increment of number of receivers and tags are required for more detailed results of fish migration.

• Tunnel and Underground Space
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• v.30 no.5
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• pp.462-472
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• 2020

Rock classification is fundamental discipline of exploring geological and geotechnical features in a site, which, however, may not be easy works because of high diversity of rock shape and color according to its origin, geological history and so on. With the great success of convolutional neural networks (CNN) in many different image-based classification tasks, there has been increasing interest in taking advantage of CNN to classify geological material. In this study, a feasibility of the deep CNN is investigated for automatically and accurately identifying rock types, focusing on the condition of various shapes and colors even in the same rock type. It can be further developed to a mobile application for assisting geologist in classifying rocks in fieldwork. The structure of CNN model used in this study is based on a deep residual neural network (ResNet), which is an ultra-deep CNN using in object detection and classification. The proposed CNN was trained on 10 typical rock types with an overall accuracy of 84% on the test set. The result demonstrates that the proposed approach is not only able to classify rock type using images, but also represents an improvement as taking highly diverse rock image dataset as input.

• Journal of Korea Water Resources Association
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• v.48 no.2
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• pp.115-126
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• 2015

In recent years, debris flow disaster has occurred in multiple locations between high and low mountainous areas simultaneously with a flooding disaster in urban areas caused by heavy and torrential rainfall due to the changing global climate and environment. As a result, these disasters frequently lead to large-scale destruction of infrastructures or individual properties and cause psychological harm or human death. In order to mitigate these disasters more effectively, it is necessary to investigate what causes the damage with an integrated model of both disasters at once. The objectives of this study are to analyze the mechanism of debris flow for real basin, to determine the PMP and run-off discharge due to the DAD analysis, and to estimate the influence range of debris flow for fan area according to the scenario. To analyse the characteristics of debris flow at the real basin, the parameters such as the deposition pattern, deposit thickness, approaching velocity, occurrence of sediment volume and travel length are estimated from DAD analysis. As a results, the peak time precipitation is estimated by 135 mm/hr as torrential rainfall and maximum total amount of rainfall is estimated by 544 mm as typhoon related rainfall.