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Economic Rationale of Compensating Balance Requirements and Its Impact on Money Supply (「꺾기」의 경제학(經濟學)과 통화량(通貨量) 효과분석(效果分析))

  • Jwa, Sung-hee
    • KDI Journal of Economic Policy
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    • v.14 no.1
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    • pp.89-119
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    • 1992
  • This paper purports to analyze the economic rationale of compensating balance requirements and its impact on money supply. This practice has recently been severely criticized for artificially increasing the money supply and, therefore, limiting the nation's aggregate lending policy under the tight constraint of the given money supply target. A review of the existing literature implies that compensating balance requirements is a banking practice which leads to corrections in the distortion of financial resource allocation due to the imperfection of financial market stemming from asymmetric information and/or financial regulations on deposit and lending rates. Therefore, the economic rationale of this practice is deemed to improve the efficiency of financial resource allocation. On the other hand, the macroeconomic impact of compensating balance requirements on the money supply depends on the impact on the money multiplier, which in turn depends on the desired ratio of deposit that people wish to maintain on the money borrowed from the banking system, and on the desired reserve ratio that the banking system would like to hold for deposit withdrawal. If the compensating balance requirements could increase the desired ratio of deposit to borrowing (bank lending), it will increase the available amount of total reserve within the banking system and, in turn, the money multiplier. However, this channel has not been fully analyzed in the literature, and the direction of the effect is ambiguous. If the practice could reduce the turn-over rate of deposit and, thereby, reduce the desired reserve ratio of the banking system, then it will also increase the money multiplier. While this channel operates unambiguously toward increasing the money multiplier, this effect will be limited by the extent that the banking system holds the excess reserve over the required reserve because the excess reserve will set the maximum amount for the desired reserve to fall. This paper tries to determine the effect on the money supply by empirically estimating the multiplier and the desired ratio of deposit to lending equations as functions of the ratio of compensating balance to the related lending, which is not observable and is estimated for the regression purpose. The results suggest that the effect of compensating balance requirements on the money supply in Korea does not exist or is very tenuous even if it could operate. Therefore, this paper concludes that the well publicized policy of cross cancelling the compensating balance and the related lending will not be effective at controlling the money supply and increasing the amount of loans without expanding the money supply.

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A Study on Shaker's Free Design from Fashion (유행(流行)으로부터 자유로운 세이커(Shaker) 디자인에 대한 고찰)

  • Choi, Sung-Woon;Huh, Jin
    • Archives of design research
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    • v.20 no.3 s.71
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    • pp.279-288
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    • 2007
  • Today, design is not free from fashion, which emerges and vanishes temporarily, and aims at equalization. As a result, products quickly become obsolete because of fashion. This means that the span of products is determined by a social concept, which is not clarified, regardless of their functions. Usable products will gradually disappear from us and it will cause serious environmental problems, unless we can find out measures against fashion. As such, it is important to study the characteristics of the shaker's design in this circumstance. The Shaker's community has a distinguishable difference from other general societies. Temporary fashion and misled information cannot interfere with their consciousness. Religion, the life and the principle of design have developed on the same level in their community. Especially, any decoration or the difference of materials is not allowed in shaker's design. It reflects their thinking that all people are equal in the sight of God. Therefore, any decoration for social and economical superiority can not be used. Through this consciousness, they can be free from fashion or decoration. They, also, believe that they can reach perfection through practicality and simplicity. The reason why shaker's design is not disturbed by fashion is that their belief is involved in their design. Consequently, if religious or conscious contents are primarily set up, design can be free from fashion and products can be used for a long time.

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A Study on the Necessity of Making Online Marketplace for the Korean Animation Industry (국내 애니메이션 산업의 온라인 마켓플레이스 구축 필요성 연구)

  • Han, Sang-Gyun
    • Cartoon and Animation Studies
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    • s.24
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    • pp.223-246
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    • 2011
  • Today, cultural content industry could be defined to service business rather than manufacturing business because of its own trait. Also, it has the realistic restriction that it can't hold the dominant position in the market competition when it can't provide consumers satisfaction regardless of its quality or degree of completion. In other word, it can only expect great success when the business plan and the activities get the perfect balance with its best quality and perfect of completion. As the result, it emphasizes the importance of business competition in the global market. In briefly, there is no doubt that the creativeness of content is very important in the cultural content industry but in the future, making system to maintain the distribution process and share the profits fairly will be taken more important role. Especially, animation genre has the feature, which compares to other genres, such as film or TV drama, would be free from cultural barriers, and it is a great advantage. So to speak, animation can get little influence from cultural discount. However, Korean animation can't use the advantage properly for the foreign distribution because of its poor infrastructure and short of professional human resources. For those reasons, it has been needed to set up the realistic and specific action plan to overcome the situation. Therefore, considering those needs and the situations of Korean animation facing, making B2B online marketplace could be a great solution. The online marketplace stands for taking more efficient and broad distribution channel instead of the passive way, which we have now. If we have the B2B online marketplace, we can share all the information about the Korean animation with the potential customers whom live outside of Korea at real time. It also could be use to the windows of multiple distribution, which can make additional profits and activate the optional markets for the Korean animation. Through the method, Korean animation would be expected to get the higher international competitiveness, and it would be developed in quality and quantity of the business. Finally, it would be a great chance to Korean animation, which can get the unique brand power by improving the backward distribution circumstances.

Underweight Related Factors in School-Aged Children in Daegu (대구지역 초등학생의 저체중 현황파악 및 관련요인 분석)

  • Yun, Young-Hee;Park, Kyong
    • Journal of the Korean Society of Food Science and Nutrition
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    • v.42 no.10
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    • pp.1592-1599
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    • 2013
  • Despite numerous studies regarding overweight or obese children, only a limited number of studies have investigated the effect of underweight. The purpose of this study is to investigate the determinants of underweight among school-aged children. A total of 493 students (86 underweight and 407 normal weight students) aged 11 to 13 years were included in our study. Socio-demographic characteristics, eating habits, health information, self-perception of weight, weight-control efforts and birth-related information were collected by using survey questionnaires for children and parents. Dietary information was obtained by two 24-hour food records, which were completed by both children and their parents. The prevalence of underweight was significantly higher in girls than boys, and the frequency of medical treatment and flu symptoms were higher in underweight children than normal ones. Overall, girls tended to overestimate their own weight; this misclassification was greater among underweight girls. Birthweight was positively correlated with current weight (P<0.05) and height (P<0.01) in girls, but these correlations were not seen in boys. In conclusion, underweight girls had inappropriate self-perception of weight, and underweight in girls may be related with birthweight and inadequate dietary intakes. Therefore, it is important to build a well-designed framework that integrates efforts of home, school, and community to maintain a healthy weight with balanced diet and exercise throughout the lifetime.

Evaluation of Image Quality Using CT Attenuation Correction in SPECT/CT (SPECT/CT에서 CT감쇠보정에 따른 영상의 질 평가)

  • Cho, Sung Wook;Kim, Gye Hwan;Sung, Yong Joon;Lee, Hyung Jin;Kim, Jin Eui
    • The Korean Journal of Nuclear Medicine Technology
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    • v.17 no.2
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    • pp.78-83
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    • 2013
  • Purpose: SPECT/CT, a combination of SPECT and CT, is capable of expressing the results of attenuation correction on images biased by automatic program. As a result, this research evaluates the usefulness of images with CT attenuation correction, using various phantoms and images of patients. Materials and Methods: From July of 2012 to September of 2012, this research was conducted on the contrast, spatial resolution, and images of patients. We studied the contrast with IEC body phantom and Jaszczak phantom, while the spatial resolution was evaluated with NEMA triple line phantom. Further, a comparative study was carried out on the quality of the images, on the difference between the images before and after the CT attenuation correction. Results: Compared the differences between the contrast before and after the CT attenuation correction in IEC body phantom. The contrast was improved by 33.6% at minimum, 89.8% at maximum. In case of Jaszczak Phantom, the contrast was enhanced by 9.9% at minimum, 27.8% at maximum. In NEMA Triple line phantom, the resolution was raised by 4.5% in average: 4.4% in horizontal, 4.5% in vertical. In Anthropomorphic Torso Phantom, the perfusion score of the interior wall with the most severe attenuation was measured to be 29.4%. In the experiment carried out on myocardial perfusion SPECT/CT patients, 9% improvement was discovered in the interior wall, where the most dramatic attenuation occurred, after the CT attenuation correction. Conclusion: SPECT/CT proved its clinical usefulness by enabling the acquisition of images with enhanced contrast and spatial resolution compare to the ones resulted from SPECT.

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RPC Correction of KOMPSAT-3A Satellite Image through Automatic Matching Point Extraction Using Unmanned AerialVehicle Imagery (무인항공기 영상 활용 자동 정합점 추출을 통한 KOMPSAT-3A 위성영상의 RPC 보정)

  • Park, Jueon;Kim, Taeheon;Lee, Changhui;Han, Youkyung
    • Korean Journal of Remote Sensing
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    • v.37 no.5_1
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    • pp.1135-1147
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    • 2021
  • In order to geometrically correct high-resolution satellite imagery, the sensor modeling process that restores the geometric relationship between the satellite sensor and the ground surface at the image acquisition time is required. In general, high-resolution satellites provide RPC (Rational Polynomial Coefficient) information, but the vendor-provided RPC includes geometric distortion caused by the position and orientation of the satellite sensor. GCP (Ground Control Point) is generally used to correct the RPC errors. The representative method of acquiring GCP is field survey to obtain accurate ground coordinates. However, it is difficult to find the GCP in the satellite image due to the quality of the image, land cover change, relief displacement, etc. By using image maps acquired from various sensors as reference data, it is possible to automate the collection of GCP through the image matching algorithm. In this study, the RPC of KOMPSAT-3A satellite image was corrected through the extracted matching point using the UAV (Unmanned Aerial Vehichle) imagery. We propose a pre-porocessing method for the extraction of matching points between the UAV imagery and KOMPSAT-3A satellite image. To this end, the characteristics of matching points extracted by independently applying the SURF (Speeded-Up Robust Features) and the phase correlation, which are representative feature-based matching method and area-based matching method, respectively, were compared. The RPC adjustment parameters were calculated using the matching points extracted through each algorithm. In order to verify the performance and usability of the proposed method, it was compared with the GCP-based RPC correction result. The GCP-based method showed an improvement of correction accuracy by 2.14 pixels for the sample and 5.43 pixelsfor the line compared to the vendor-provided RPC. In the proposed method using SURF and phase correlation methods, the accuracy of sample was improved by 0.83 pixels and 1.49 pixels, and that of line wasimproved by 4.81 pixels and 5.19 pixels, respectively, compared to the vendor-provided RPC. Through the experimental results, the proposed method using the UAV imagery presented the possibility as an alternative to the GCP-based method for the RPC correction.

Ensemble Learning with Support Vector Machines for Bond Rating (회사채 신용등급 예측을 위한 SVM 앙상블학습)

  • Kim, Myoung-Jong
    • Journal of Intelligence and Information Systems
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    • v.18 no.2
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    • pp.29-45
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    • 2012
  • Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.