• Land and Housing Review
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• v.12 no.4
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• pp.41-59
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• 2021

This study reviews theoretical considerations and past studies about real estate prices, macroeconomic variables, and real estate policies. Monthly data from January 2003 to June 2021 are used, and a VEC model, the most widely used multivariate time series analysis method, is employed for analysis. Through the model, the effects of macroeconomic variables and real estate regulatory policies on real estate prices in Seoul are analyzed. Findings are summarized as follows. First, macroeconomic variables such as money supply and interest rates do not have a significant impact on Seoul's apartment prices. Due to the high demand for housing and insufficient supply, there is a demand for buying a home regardless of macroeconomic booms or recessions. Second, tax and financial regulatory policies have an initial impact on the rise in apartment prices in Seoul, and their influence diminishes over time. Third, anti-speculation zones are expected to decrease apartment prices through the suppression of demand. However, these zones cause a rise in apartment prices. This could be understood as a lock-in effect due to the strengthening of capital gains tax. Fourth, the price ceiling did not decrease apartment prices. These findings propose that, in Seoul, where demand is high and supply is insufficient, the supply of high-quality and sufficient housing should be prioritized over various regulations such as tax regulations, financial regulations, anti-speculation zones, and price caps. Moreover, the findings provide an implication that city-specific real estate policies should be implemented for Seoul rather than regulation-oriented approaches in public policy.

• International Area Studies Review
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• v.20 no.1
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• pp.105-125
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• 2016

The expansionary monetary policy was practiced after 2001 in Japan to treat the deflation spiral, and reduced only the nominal interest rates and domestic household demand. One of the most serious factors for this failure was the change of private sector's expectancy. This paper has studied the effect of Japanese monetary policy in 21c., with empirical research based on a renewed macroeconomic model and the VAR. The empirical analysis shows that the effect of monetary policy on the national income during 2001.01-2015.03 is weaker than that of 1985.01-1994.04. Money volume has a diminutive effect on the growth of GDP within a short term after 2001. The change in the expectations of the private sectors might have been the cause of ineffectiveness of the expansive monetary policy. Economic agents learned from the past Japanese financial crisis that an expansive monetary policy increased the inflation rate and caused the 'bubbles to burst' afterwards. The VAR analysis says that the effectiveness of monetary policy on the economic depression declined over the past 20 years and the expansion of money volume has no influence on exchange rate and net export. This means that the expansive monetary policy lost its effect on net export and national income steadily. Monetary policy makers have to recognize this fact, and to consider another anti-cycle political instrument, i.e. the fiscal policy with government debt.

• Korea Trade Review
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• v.43 no.4
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• pp.155-175
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• 2018

This study estimates the technical efficiency and total factor productivity(TFP) of and analyzes the relationship between TFP and exports for Korean manufacturing companies from 2000 to 2016. Specially, TFP is decomposed into Technical Change(TC), Technical Efficiency Change (TEC), and Sale Effect(SE), and compared between large and small enterprises. First, in the case of technical efficiency, the Korean economy has been very vulnerable to external shocks, such as the sharp decline following the 2008 financial crisis. The efficiency of the electronics, automobile, and machinery sectors is low and needs to be improved. In addition, the technological efficiency of large enterprises is higher than that of SMEs in most manufacturing sub-sectors except for non-ferrous metals. In the case of TFP, most changes are due to TC, and the effective combination of labor, capital and the effect of scale have little effect, suggesting that improvement of internal structure is urgent. In addition, volatility due to the impact of the financial crisis in 2008 was much larger in SMEs than in large companies, so external economic impacts are more greater for SMEs than large enterprises. The relationship between TFP decomposition factors and exports shows that TC has a positive effect only on exports of SMEs. Therefore, in order to increase exports, in the case of SMEs, R&D support to promote technological development is needed. In the case of large companies, it is necessary to establish differentiated strategies for each export market, competitor company, and item to link efficiency and scale effect of exports.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.87-95
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• 2016

This study examined the characteristics of the hourly demand of city gas in Korea and proposed multiple regression models to obtain precise estimates of the hourly demand of city gas. Forecasting the hourly demand of city gas with accuracy is essential in terms of safety and cost. If underestimated, the pipeline pressure needs to be increased sharply to meet the demand, when safety matters. In the opposite case, unnecessary inventory and operation costs are incurred. Data analysis showed that the hourly demand of city gas has a very high autocorrelation and that the 24-hour demand pattern of a day follows the previous 24-hour demand pattern of the same day. That is, there is a weekly cycle pattern. In addition, some conditions that temperature affects the hourly demand level were found. That is, the absolute value of the correlation coefficient between the hourly demand and temperature is about 0.853 on average, while the absolute value of the correlation coefficient on a specific day improves to 0.861 at worst and 0.965 at best. Based on this analysis, this paper proposes a multiple regression model incorporating the hourly demand ahead of 24 hours and the hourly demand ahead of 168 hours, and another multiple regression model with temperature as an additional independent variable. To show the performance of the proposed models, computational experiments were carried out using real data of the domestic city gas demand from 2009 to 2013. The test results showed that the first regression model exhibits a forecasting accuracy of MAPE (Mean Absolute Percentage Error) around 4.5% over the past five years from 2009 to 2013, while the second regression model exhibits 5.13% of MAPE for the same period.

• Journal of Korea Port Economic Association
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• v.37 no.2
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• pp.73-89
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• 2021

The handysize bulk carriers are capable of transporting a variety of cargo that cannot be transported by mid-large size ship, and the spot chartering market is active, and it is a market that is independent of mid-large size market, and is more risky due to market conditions and charterage variability. In this study, Granger causality test, the Impulse Response Function(IRF) and Forecast Error Variance Decomposition(FEVD) were performed using monthly time series data. As a result of Granger causality test, coal price for coke making, Japan steel plate commodity price, hot rolled steel sheet price, fleet volume and bunker price have causality to Baltic Handysize Index(BHSI) and charterage. After confirming the appropriate lag and stability of the Vector Autoregressive model(VAR), IRF and FEVD were analyzed. As a result of IRF, the three variables of coal price for coke making, hot rolled steel sheet price and bunker price were found to have significant at both upper and lower limit of the confidence interval. Among them, the impulse of hot rolled steel sheet price was found to have the most significant effect. As a result of FEVD, the explanatory power that affects BHSI and charterage is the same in the order of hot rolled steel sheet price, coal price for coke making, bunker price, Japan steel plate price, and fleet volume. It was found that it gradually increased, affecting BHSI by 30% and charterage by 26%. In order to differentiate from previous studies and to find out the effect of short term lag, analysis was performed using monthly price data of major cargoes for Handysize bulk carriers, and meaningful results were derived that can predict monthly market conditions. This study can be helpful in predicting the short term market conditions for shipping companies that operate Handysize bulk carriers and concerned parties in the handysize chartering market.

• Journal of Korea Port Economic Association
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• v.36 no.4
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• pp.17-30
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• 2020

In the shipping industry, it is essential to engage in the preemptive prediction of freight rate volatility through market monitoring. Considering that freight rates have already started to fall, the loss of shipping companies will soon be uncontrollable. Therefore, in this study, factors affecting the freight rates of bulk carriers, which have relatively large freight rate volatility as compared to container freight rates, were quantified and analyzed. In doing so, we intended to contribute to future shipping market monitoring. We performed an analysis using a vector error correction model and estimated the influence of six independent variables on the charter rates of bulk carriers by Handy Size, Supramax, Panamax, and Cape Size. The six independent variables included the bulk carrier fleet volume, iron ore traffic volume, ribo interest rate, bunker oil price, and Euro-Dollar exchange rate. The dependent variables were handy size (32,000 DWT) spot charter rates, Supramax 6 T/C average charter rates, Pana Max (75,000 DWT) spot charter, and Cape Size (170,000 DWT) spot charter. The study examined charter rates by size of bulk carriers, which was different from studies on existing specific types of ships or fares in oil tankers and chemical carriers other than bulk carriers. Findings revealed that influencing factors differed for each ship size. The Libo interest rate had a significant effect on all four ship types, and the iron ore traffic volume had a significant effect on three ship types. The Ribo rate showed a negative (-) relationship with Handy Size, Supramax, Panamax, and Cape Size. Iron ore traffic influenced three types of linearity, except for Panamax. The size of shipping companies differed depending on their characteristics. These findings are expected to contribute to the establishment of a management strategy for shipping companies by analyzing the factors influencing changes in the freight rates of charterers, which have a profound effect on the management performance of shipping companies.

• Journal of Korea Port Economic Association
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• v.37 no.4
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• pp.161-173
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• 2021

The shipping industry plummeted and was depressed due to the global economic crisis caused by the bankruptcy of Lehman Brothers in the US in 2008. In 2020, the shipping market also suffered from a collapse in the unstable global economic situation due to the COVID-19 pandemic, but unexpectedly, it changed to an upward trend from the end of 2020, and in 2021, it exceeded the market of the boom period of 2008. According to the Clarksons report published in May 2021, the decrease in cargo volume due to the COVID-19 pandemic in 2020 has returned to the pre-corona level by the end of 2020, and the tramper bulk carrier capacity of 103~104% of the Panamax has been in the ports due to congestion. Earnings across the bulker segments have risen to ten-year highs in recent months. In this study, as factors affecting BDI, the capacity and congestion ratio of Cape and Panamax ships on the supply side, iron ore and coal seaborne tonnge on the demand side and Granger causality test, IRF(Impulse Response Function) and FEVD(Forecast Error Variance Decomposition) were performed using VAR model to analyze the impact on BDI by congestion caused by strengthen quarantine at the port due to the COVID-19 pandemic and the loading and discharging operation delay due to the infection of the stevedore, etc and to predict the shipping market after the pandemic. As a result of the Granger causality test of variables and BDI using time series data from January 2016 to July 2021, causality was found in the Fleet and Congestion variables, and as a result of the Impulse Response Function, Congestion variable was found to have significant at both upper and lower limit of the confidence interval. As a result of the Forecast Error Variance Decomposition, Congestion variable showed an explanatory power upto 25% for the change in BDI. If the congestion in ports decreases after With Corona, it is expected that there is down-risk in the shipping market. The COVID-19 pandemic occurred not from economic factors but from an ecological factor by the pandemic is different from the past economic crisis. It is necessary to analyze from a different point of view than the past economic crisis. This study has meaningful to analyze the causality and explanatory power of Congestion factor by pandemic.

• The Korean Journal of Nuclear Medicine
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• v.4 no.1
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• pp.27-36
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• 1970

적혈구(赤血球) 수명의 측정에는 $^{51}Cr$-표지적혈구법(標識赤血球法)이 임상적(臨床的)으로 이용(利用)되고 있으며 이는 이론상(理論上) steady state 즉(卽) 측정기간(測定期間)동안 순환(循環) $^{51}Cr$량(量)-적혈구량(赤血球量)이 일정(一定)할 때에 한(限)하여 유효(有效)하며 unsteady state 때는 true red cell survival을 알기 위하여서는 측정치에 영향을 주는 요인(要因)에 대하여 각각(各各) 교정(校正)해 줄 필요(必要)가 있다. 이 요인(要因)중에 특히 실혈(失血)로 인(因)한 영향에 관(關)하여는 계통적인 연구(硏究)가 적다. 이에 저자(著者)들은 $^{51}Cr$표지적혈구법(標識赤血球法)을 이용(利用)하여 실혈(失血)이 적혈구(赤血球) 수명측정(測定)에 미치는 영향을 인체(人體)에서 실험 관찰하여 몇가지 성적을 얻었다. 연구대상(硏究對象)은 총(總) 56명(名)의 청장년(靑壯年)으로 급성실혈군(急性失血群)과 만성실혈군(慢性失血群)으로 구분(區分)하여 급성실혈군(急性失血群)은 위장출혈등(胃腸出血等)이 없는 2대(代)의 의대생(醫大生)으로 $^{51}Cr$표지적혈구법(標識赤血球法)을 사용하여 적혈구(赤血球) 수명을 측정하는 동안($10{\sim}14$ 일간(日間)) 1일당(日當) 10ml(6명(名)), 25ml(4명(名)), 50ml(4명(名)), 75ml(4명(名)), 100ml(6명(名))를 각각(各各) 사혈(瀉血)한 군(群)과 10일간(日間) 1,000ml를 사혈한 군(群) 즉 200ml씩 5회(回)(4명(名)), 500ml씩 2회(回)(4명(名))로 세분(細分)하였으며 만성실혈군(慢性失血群)은 직업적인 공혈자(供血者)로 반복사혈로 생긴 9명(名)의 빈혈자와 십이지장충증(十二指腸蟲症)에 감염(感染)되어 구충(驅蟲)한 중등도(中等度)의 철결핍성 빈혈환자 7명(名)으로 나누어 관찰하였다. 측정(測定) 방법(方法)으로는 Gray 및 Sterling법(法)을 개설한 방법(方法)으로 $^{51}Cr$표지적혈구(標識赤血球)의 계측시료(計測試料)로서 전혈(全血) 및 적혈구(赤血球)를 사용(使用)하였다. 실험(實驗)성적은 1. 1일당(日當) 실혈량(失血量)이 증가(增加)할수록 적혈구(赤血球)수명($T\frac{1}{2}$)은 짧아짐을 알 수 있었다. 즉(卽) 1일당(日當) $20{\sim}50ml$ 사혈군에서는 $T\frac{1}{2}$이 현저히 짧아지는 rapid phase을 나타내고 1일당(日當) 50ml이상(以上) 사혈군에서는 짧아지는 정도(程度)가 완만한 slow phase을 나타낸다(Fig. 6). 2. 1일량(日量) 10ml 및 25ml식(式) 사혈한 군(群)의 적혈구수명(赤血球壽命)을 측정(測定)하는데 있어 적혈구(赤血球)를 사용하였을 때에는 $T\frac{1}{2}$측 정치에 유의한 차(差)가 없었으며 이 범위 내에서는 Hct., Hb. 및 혈청철치(血淸鐵値)도 역시 유의한 차(差)가 없었다. 3. 1일량(日量) 50ml 및 75ml, 100ml씩 사혈한 군(群)에서는 적혈구(赤血球)만을 사용(使用)하였을 때와 전혈(全血)을 시료(試料)로 하였을 때 사이에 $T\frac{1}{2}$의 측정치에 유의한 차(差)가 있었으며 이 때는 Hct., Hb. 및 혈청철치(血淸鐵値)에도 변화(變化)가 있었다. 즉(卽), 전혈(全血)을 사용한 적혈구(赤血球) 수명($T\frac{1}{2}$)의 측정치가 적혈구(赤血球)만를 사용(使用)한 적혈구(赤血球) 수명($T\frac{1}{2}$)의 측정치 보다 짧았다. 4. 일정(一定)기간(10 일(日)) 사혈의 총량(1000ml)이 같을 매는 200ml를 5회(回) 사혈한 군(群)이나 500ml를 2회(回) 사혈한 군(群) 사이에 적혈구(赤血球) 수명($T\frac{1}{2}$)에 유의(有義)한 차(差)를 볼 수 없었다. 5. 직업적 공혈자의 반복사혈로 인(因)한 만성(慢性) 빈혈환자 9명(名)에서의 $^{51}Cr$적혈구(赤血球)수명($T\frac{1}{2}$) 측정치는 평균(平均) 19.2일(日)로 짧아져 있으나 적혈구수명측정전후(赤血球壽命測定前後)에 충분(充分)한 철제(鐵劑)를 투여(投與)하여 Hct., Hb. 및 혈청철치(血淸鐵値)를 증가(增加)시켰으며 이때 볼 수 있었든 Hct치(値)를 규준(規準)하여 교정한 적혈구(赤血球)수명($T\frac{1}{2}$)은 거의 정상(正常)범위 안에 있어(27.6일(日)) 이러한 인자(因子)를 고려하지 않으면 잘못 이해할 수가 있다. 6. 구충자충(鉤蟲仔蟲)을 구충한 7명(名)의 중등도(中等度) 철(鐵)결핍성 빈혈환자에서의 적혈구(赤血球)수명($T\frac{1}{2}$) 측정치는 25일(日)$\sim$31일(日)로 평균(平均) 28일(日)이었으며, 이때 장 출혈량은 1일(日) $1.0{\sim}3.5ml$이었다. 단시일내의 급성실혈시에는 이와같은 소량의 실혈(失血)도 적혈구(赤血球)수명($T\frac{1}{2}$) 측정치에 영향을 보여 줌을 알 수 있었다. 따라서 이러한 정도의 실혈은 실험오차에 기인하는 것인지 아니면 장기 출혈에서는 이러한 소량의 실혈이 적혈구(赤血球)수명($T\frac{1}{2}$) 측정에 영향을 미치지 않는 것인지는 아직 확실히 말할 수 없다. 8. $^{51}Cr$-표지적혈구(標識赤血球)로 측정한 적혈구(赤血球)수명($T\frac{1}{2}$)은 측정시의 실혈량(失血量)에 큰 영향을 받음을 알 수 있으며 저자(著者)들은 $^{51}Cr$표지적혈구(標識赤血球)를 이용(利用)한 적혈구(赤血球) 수명 측정때 검사기간중 실혈량이 적혈구수명치(赤血球壽命値)에 미치는 관계를 상술(上述)한 실험치(實驗値)를 기초(基礎)로 하여 다음과 같을 교정식(校正式)을 고찰(考察)해 보았다. $^{51}Cr\;T\frac{1}{2}=17.0e^{-0.0495}+18.4e^{-0.000924x}$ 단(但) X : 1일(日) 실혈량(失血量)(단위(單位) ml)

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• The Korean Journal of Nuclear Medicine
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• v.8 no.1_2
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• pp.57-62
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• 1974

이 논문(論文)은 1973년(年) ICSH주최로 열린 panel에서 적혈구파괴(赤血球破壞) 장소(場所)를 결정(決定)하기 위한 생체(生體) 체표계측법(醫表計測法)의 표준화(標準化)에 관(關)한 토론(討論) 결과(結果)를 초록(抄錄)한 것이다. 체표계측(體表計測)은 체외(體外)에서 계측기(計測器)를 이용(利用)하여 각(各) 장기(臟器)에서의 방사표지물질(放射標識物質)의 분포(分布) 및 시간경과(時間經過)에 따른 변화(變化)를 측정(測定)하는 것으로서 $^{51}Cr$를 사용(使用)하여 적혈구수명(赤血球壽命)을 측정(測定)할 때 간(肝), 비(脾), 심장(心臟)의 방사능(放射能)을 계측(計測)한다. 이 방법(方法)은 각(各) 장기(臟器)에서의 적혈구파괴(赤血球破壞)의 정도(程度)를 예측할 수 있다. 특(特)히 용혈성(溶血性) 빈혈환자(貧血患者)에서 비장적출(脾臟摘出) 여부를 결정(決定)하는데 도움이 된다. 이 panel에서는 주(主)로 오차(誤差)의 원인(原因)이 되는 여러가지 요인(要因)에 대(對)하여 토론(討論)하였으며 일반적으로 다음과 같은 것에 의견(意見)의 일치(一致)를 보았다. 즉(卽) 비장(脾臟)의 위치(位置)는 $^{99m}Tc$로 비주사(脾走査)를 실시하여 결정(決定)하는것이 좋고, $^{51}Cr$은 체중(體重) 1kg당 $1.5{\mu}Ci$를 사용하여, 계측기(計測器)는 NaI crystal(직경이 5cm이상, 두께가 3.75cm이상)의 scintillation doctor를 사용하고, 계측(計測)은 $^{51}Cr$로 표지(標識)된 적혈구(赤血球) 주입후(注入後) 15분(分) 이후(以後)에 하고 다음날 계측(計測)한 후(後) 2주(週) 동안에 적어도 6번 계측(計測)한다. Data 처리는 excess count법(法)과 비(脾)와 간(肝)의 비(比)로서하는 것이 좋다.定値)에 차이(差異)가 있어 그 결과(結果)의 해석(解釋) 및 비교(比較) 검토(檢討)에 적지않은 난점(難點)이 생겨 표준화(標準化)된 공통적(共通的)인 방법(方法)의 사용(使用)이 중요(重要)하다는 사실(事實)이 인식(認識)되게 되었다. 1966년(年) 호주(濠洲)의 Sydney에서 개최(開催)되었든 제11차(第11次) 국제혈액학회(國際血學會)때 열린 제4차(第4次) International Committee for Standardization in Haematology(ICSH)에서 Diagnostic Applications of Radioisotopes in Haematology에 관(關)한 expert panel을 갖을것을 의결(議決)하여 다음과 같은 12명(名)의 위원(委員)이 결정(決定)되었으며 위원회(委員會)의 의장(議長)에 Dr. Szur, 총무(總務)에 Dr. Glass가 각각(各各) 선임(選任)되었다. 그간(間) 1967년(年) 영경(英京) London에서 첫 회합(會合)이 있은후(後) New York, Vienna(IAEA후원(後援)) Brthesda(NIH후원(後援))에서 전문위원회(專門委員會)를 갖고 적혈구수명측정법(赤血球壽命測定法)에 관(關)한 의견(意見)의 일치(一致)를 보았다. ICSH와 국제혈액학회(國際血學會)에서는 이번에 결정(決定)된 적혈구수명측정법(赤血球壽命測定法)을 널리 소개(紹介)하며, 측정법(測定法)과 얻어진 결과(結果)의 해석(解釋)에 표준화(標準化)를 기(期)할 목적(目的)으로 이에 연관성(聯關性)있는 전문지(專門誌)에 게재(揭載)할 것을 요청(要請) 받었기에 이에 전문(全文)을 소개(紹介)하는 바이다. 이들은 방사성(放射性) chromium 법(法)의 모든 세부적(細部的)인 면(面)을 표준화(標準化)하고 있으며 그간(間) 가장 논란(論難)의 대상(對象)이 되었던, $^{51}Cr$-표지방법(標識方法)에 있어서의 세가지 변법(變法),