• Proceedings of the Korea Information Processing Society Conference
• /
• 2022.11a
• /
• pp.258-260
• /
• 2022

대체 불가능 토큰 (NFT, non-fungible token)은 고유하고 더 이상 분할할 수 없는 특성을 가지고 있다. NFT 는 디지털 콘텐츠에 대한 소유권을 증명해 주지만 현재 1) 소유권 증명 이상의 유틸리티가 명확하지 않고, 2) 토큰이지만 유동성이 거의 없으며, 3) 가격이 예측 불가능하다. 특히, 부동산의 경우 가격이 매우 높은 특징으로 인하여 투자 진입장벽이 매우 높다. NFT 분할을 하면 유동성의 증가, 그리고 접근성 증가에 따른 커뮤니티 볼륨의 증가를 기대해 볼 수 있다. 이러한 특성을 활용하여 기존에 투자하기 어려웠던 부동산을 다양한 기술을 활용하여 쉽게 투자를 할 수 있게 된다. 또한, Black Litterman 모델을 활용하여 보다 여러 종류의 NFT 들에 대한 최적 포트폴리오를 구성할 수 있는 알고리즘을 설계하고 구현하였다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.220-228
• /
• 2023

사이버 공격과 위협은 예측 불가능한 수준으로 높아지고 있어 해킹 위협을 완벽히 차단하고 예방하는 것은 현실적으로 불가능하다. 따라서 사이버 공간의 공격이 발생했을 경우 신속한 대응 및 시스템의 생존성 보장을 위해서 사이버 복원력이 필요하다. 우리는 정부, 공공기관, 기업이 사이버 복원력개념을 도입하고 내재화를 위한 평가모델을 연구하였다.

• Proceedings of the Korea Contents Association Conference
• /
• 2012.05a
• /
• pp.111-112
• /
• 2012

하수관거시스템(sewer system)의 효율적인 관리를 위해서는 관거 내의 유출, 수질, 불명수 및 CSOs(Combine Sewer Overflows)등에 대한 지속적인 모니터링이 필요하다. 그런데 하나의 유역 하수관거시스템에서 모든 지점에 대한 모니터링은 예산의 제약으로 인하여 불가능하다. 따라서 모니터링 지점들은 주어진 예산 내에서 최대의 효율적인 자료의 획득이 가능한 지점들로 선정되어야한다. 그럼에도 불구하고 모니터링의 지점의 선정에 대한 명확한 기준 및 선정된 모니터링 지점에서 획득된 자료에 대한 정량화된 평가방법에 대한 연구는 미흡한 실정이다. 따라서 본 연구에서는 엔트로피 방법과 선형회귀식을 이용하여 상류 유출을 통한 하류 유출을 예측할 수 있는 모니터링 지점을 선정하는 방법을 제시하였다. 검증결과 제시된 회귀식은 안정적으로 하류 유출을 예측할 수 있는 것으로 나타났다. 본 연구에서 산정한 회귀식을 사용하여 하류 유출의 사전 예측이 가능할 것으로 판단된다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2009.05a
• /
• pp.1-5
• /
• 2009

하천의 수위와 유량에 대한 정확한 정보는 이수, 치수와 같은 수자원 관리에 있어서 가장 기본 물리량이며, 각종 물이용 분쟁 해결, 수공구조물의 설계, 하천의 유사량 산정 및 수리 수문모형의 개발, 검증을 위한 기초자료로 이용된다. 그러나 유량의 직접 계측은 많은 비용이 소요되며, 홍수시에는 계측이 불가능하다. 지속적인 유량자료의 실측은 얻는 것은 매우 어렵다. 따라서 최근 수치 모형을 이용하여 수위-유량 곡선을 예측하고자 하는 연구가 진행되고 있다. 본 연구에서는 복단면 및 불규칙한 하상을 갖는 개수로의 수위-유량 곡선 및 단위유량 예측모형을 개발하고자 한다. 수심 적분된 2차원 운동량 방정식으로부터 정상류와 등류 조건을 가정하여 지배방정식을 구성하였으며, Manning 조도계수를 사용하여 자갈 및 모래와 같은 하상재료에 의한 전단력을 산정한다. 또한 식생항력을 이용하여 홍수터 및 제방의 식생이 수위-유량에 미치는 영향을 분석하였다.

• Journal of Chest Surgery
• /
• v.35 no.9
• /
• pp.659-663
• /
• 2002

The objective of this study was to assess the usefulness of bone scans in routine preoperative examinations of patients with newly diagnosed non-small cell lung carcinoma. Material and Method: We reviewed the medical records of 258 patients who were newly diagnosed with non-small cell lung cancer in our hospital between January 2000 and December 2000. More than half of the patients (132) were deemed to be inoperable due to their advanced stage based on the CT scans. The remaining 126 patients were considered potentially operable. For these patients, clinical evaluation including the presence of bone pain, serum alkaline phosphatase, and calcium levels was used as clinical predictors of bone metastasis. All patients received bone scans. Bone X-rays, MRI or bone biopsy were performed to confirm the presence of bone metastasis. The usefulness of the bone scan was evaluated by comparing its power of predicting bone metastasis to that of the clinical information. Result: In all patients, the positive and negative predictive values of bone scans for the bone metastasis were 44%, and 99%, respectively. Those of the clinical information were 38% , and 94%. However, in potentially operable patients, the negative predictive value of the clinical information was as high as 99%. Conclusion: If newly diagnosed non-small cell lung cancer patients are presented as potentially operable on the basis of CT scan with no clinical evidence of distant metastases, curative resection could be considered without performing routine bone scans because of the low probability of bone metastasis. However, if there are positive clinical findings, further evaluations, including bone scan should be followed as metastasis will be documented in more than 30% of patients.

• Journal of KIISE:Computer Systems and Theory
• /
• v.37 no.6
• /
• pp.354-367
• /
• 2010

There are many methods for analyzing patterns in time series data. Although stock data represents a time series, there are few studies on stock pattern analysis and prediction. Since people believe that stock price changes randomly we cannot predict stock prices using a scientific method. In this paper, we measured the degree of the randomness of stock prices using Kolmogorov complexity, and we showed that there is a strong correlation between the degree and the accuracy of stock price prediction using our semi-global alignment method. We transformed the stock price data to quantized string sequences. Then we measured randomness of stock prices using Kolmogorov complexity of the string sequences. We use KOSPI 690 stock data during 28 years for our experiments and to evaluate our methodology. When a high Kolmogorov complexity, the stock price cannot be predicted, when a low complexity, the stock price can be predicted, but the prediction ratio of stock price changes of interest to investors, is 12% prediction ratio for short-term predictions and a 54% prediction ratio for long-term predictions.

• KIISE Transactions on Computing Practices
• /
• v.23 no.11
• /
• pp.625-631
• /
• 2017

The stock price prediction for stock markets remains an unsolved problem. Although there have been various overtures and studies to predict the price of stocks scientifically, it is impossible to predict the future precisely. However, stock price predictions have been a subject of interest in a variety of related fields such as economics, mathematics, physics, and computer science. In this paper, we will study fluctuation patterns of stock prices and predict future trends using the Deep learning. Therefore, this study presents the three deep learning models using Tensorflow, an open source framework in which each learning model accepts different input features. We expand the previous study that used simple price data. We measured the performance of three predictive models increasing the number of priced-based input features. Through this experiment, we measured the performance change of the predictive model depending on the price-based input features. Finally, we compared and analyzed the experiment result to evaluate the impact of the price-based input features in stock price prediction.

• The Korean Journal of Financial Management
• /
• v.20 no.2
• /
• pp.235-261
• /
• 2003

We examine the relationship between the trading activities of Korea Stock Price Index (KOSPI) 200 futures contract and its underlying stock market volatility for about six years from May 1996 when the futures contract was introduced. The trading activities of the futures contracts are proxied by the volume and open interest, which are divided into expected and unexpected portions by using the previous data. The daily, intradilay, and overnight cash volatility is estimated by the GJR-GARCH model. We find a positive contemporaneous relationship between the intradaily stock market volatility and the unexpected futures volume while the relationship between the volatility and expected futures volume is weakly negative or non-existent. We also find that the unexpected futures volume strongly causes intradaily cash volatility. On the other hand, the overnight cash volatility causes the unexpected futures volume. The impulse responses between these variables are all positive. The result implies that during a trading time futures trading tends to increase the cash volatility while the unexpected overnight changes in cash volatility tends to increase the futures trading activities. We, however, find no association between the cash volatility and futures maturities.

• Journal of Space Technology and Applications
• /
• v.1 no.1
• /
• pp.23-32
• /
• 2021

The high-level Space Situational Awareness (SSA) objective is to provide to the users dependable, accurate and timely information in order to support risk management on orbit and during re-entry and support safe and secure operation of space assets and related services. Therefore the risk assessment for the re-entry of space objects should be managed nationally. In this research, the Software for Re-Entry Prediction of space objects (SREP) was developed for national SSA system. In particular, the rate of change of the drag coefficient is estimated through a newly proposed Drag Scale Factor Estimation (DSFE), and is used for high-precision orbit propagator (HPOP) up to an altitude of 100 km to predict the re-entry time and position of the space object. The effectiveness of this re-entry prediction is shown through the re-entry time window and ground track of space objects falling in real events, Grace-1, Grace-2, Tiangong-1, and Chang Zheng-5B Rocket body. As a result, through analysis 12 hours before the final re-entry time, it is shown that the re-entry time window and crash time can be accurately predicted with an error of less than 20 minutes.

• Journal of Life Science
• /
• v.31 no.11
• /
• pp.995-1003
• /
• 2021

Chromatin remodeling regulates gene expression through epigenetic mechanisms. Aberrations in histone modification have been associated with depression-like behaviors in animal models. Additionally, growing evidence also indicates that epigenetic modification is associated with depression. p11 (S100A10) has been implicated in the pathophysiology of depression both in human and rodent models. In the present study, we investigated alterations in histone acetylation and methylation at the promoter of the p11 gene in the hippocampus of mice subjected to chronic unpredictable stress (CUS). C57BL/6 mice were exposed to CUS daily for 3 weeks. Depression-like behaviors were measured with the forced swimming test (FST). The levels of hippocampal p11 expression were analyzed by quantitative real-time polymerase chain reaction (PCR) and Western blotting. The levels of acetylated and methylated histone H3 at the promoter of p11 were measured by chromatin immunoprecipitation followed by real-time PCR. CUS-exposed mice displayed depression-like behaviors with prolonged immobility in FST. CUS led to significant decreases in the expression of p11 at both protein and mRNA levels. Meanwhile, there was a decrease in histone H3 acetylation (Ac-H3) and H3-K4 trimethylation (H3K4met3) and an increase in H3-K27 trimethylation (H3K27met3) at the p11 promoter. These results indicate that chronic stress causes the epigenetic suppression of p11 expression in the hippocampus.