• KDI Journal of Economic Policy
• /
• v.26 no.2
• /
• pp.281-314
• /
• 2004

This paper theoretically formulated and empirically explored the relationship between exchange rate pass-through (ERPT) for (average) market price and an individual country's price, using steel products data in the US market, with special reference to two major steel exporting countries, Korea and Japan. It was found that the direction of market ERPT can be different from that of individual ERPT that each exporter experiences, due to strategic interactions among producers and different parameters. Vector error correction (VEC) models and impulse response analysis were used with the statistical inference based on the bootstrap-after- bootstrap of Kilian (1998) for short-run, and the fully modified estimation of Phillips and Hansen (1990) was used for long-run. Empirical results indicate that market ERPT in the US market due to changes in Korea-US exchange rates is different from those due to changes in Japan-US exchange rates. The framework developed in this study indicates that this phenomenon is attributed to either (i) the two countries have individual ERPTs of different magnitudes and directions for the products in the US market, or (ii) the pricing strategies of the other exporters' (to the US steel market) respond differently depending on whether the price of the product from Korea changes or that from Japan does. As each exporter's ERPT can be significantly different, and market response to each country's ERPT can be also different, this study concludes that it is crucial for an exporter to understand how competitors in the market respond to changes in its price, as well as to understand how its price changes when the relevant exchange rate fluctuates.

• Clinical and Experimental Pediatrics
• /
• v.60 no.7
• /
• pp.227-231
• /
• 2017

Purpose: This study aimed to verify the safety of low-dose topiramate on language development in pediatric patients with migraine. Methods: Thirty newly diagnosed pediatric patients with migraine who needed topiramate were enrolled and assessed twice with standard language tests, including the Test of Language Problem Solving Abilities (TOPs), Receptive and Expressive Vocabulary Test, Urimal Test of Articulation and Phonology, and computerized speech laboratory analysis. Data were collected before treatment, and topiramate as monotherapy was sustained for at least 3 months. The mean follow-up period was $4.3{\pm}2.7months$. The mean topiramate dosage was 0.9 mg/kg/day. Results: The patient's mean age was $144.1{\pm}42.3months$ (male-to-female ratio, 9:21). The values of all the language parameters of the TOPs were not changed significantly after the topiramate treatment as follows: Determine cause, from $15.0{\pm}4.4$ to $15.4{\pm}4.8$ (P>0.05); making inference, from $17.6{\pm}5.6$ to $17.5{\pm}6.6$ (P>0.05); predicting, from $11.5{\pm}4.5$ to $12.3{\pm}4.0$ (P>0.05); and total TOPs score, from $44.1{\pm}13.4$ to $45.3{\pm}13.6$ (P>0.05). The total mean length of utterance in words during the test decreased from $44.1{\pm}13.4$ to $45.3{\pm}13.6$ (P<0.05). The Receptive and Expressive Vocabulary Test results decreased from $97.7{\pm}22.1$ to $96.3{\pm}19.9months$, and from $81.8{\pm}23.4$ to $82.3{\pm}25.4months$, respectively (P>0.05). In the articulation and phonology validation in both groups, speech pitch and energy were not significant, and all the vowel test results showed no other significant values. Conclusion: No significant difference was found in the language-speaking ability between the patients; however, the number of vocabularies used decreased. Therefore, topiramate should be used cautiously for children with migraine.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2007.05a
• /
• pp.532-536
• /
• 2007

급속한 도시화 및 지구온난화로 인한 집중호우로 홍수피해가 해마다 증가하고 있다. 홍수피해를 최소화하기 위하여 4대강 중심의 홍수예경보시스템이 구축되는 등 다양한 제도적 장치가 마련되고 있으나 중소하천이 분포되어 있는 도시유역에서의 홍수예측기능은 부족한 실정이다. 본 연구에서는 중소 도시하천에 적용 가능한 실시간 도시홍수예측서비스 시스템(Realtime Urban Flood Forecasting Service, U-FFS)을 개발하였다. 경기도 성남에 위치한 탄천을 대상유역으로 선정하고 실시간 강우 및 수위관측소를 설치하여 수문데이타를 수집하였으며 이를 바탕으로 수위예측모형을 구축하였다. 모형구축에는 이미 국내외 학계에서 그 정확도가 입증된 바 있는 Data-driven 모델의 일종인 ANFIS(Adaptive Neuro-Fuzzy Inference System)를 이용하였다. 개발된 수위예측모형은 지정된 시간에 자동으로 작동 가능한 실행파일로 프로그래밍되어 최종적으로 홍수예측 웹서비스와 연동된다. U-FFS는 집중호우 발생 시 최종 유출구의 30분, 1시간, 2시간 후의 수위 예측값을 웹 상을 통해 제공함으로써 언제 어디서나 홍수예측 정보를 누구나 손쉽게 획득할 수 있는 장점이 있다. 시범운영 결과, 30분 및 1시간 후의 수위 예측은 정확도가 매우 뛰어났으며 2시간 후의 수위 예측의 정확성은 다소 떨어지는 것으로 확인되었으나 전반적인 홍수예측 판단에는 무리가 없을 것으로 예상된다. 본 시스템의 홍수예측모형은 생성 및 수정이 간편하여 그 활용성이 매우 높을 것으로 기대된다. 특히 안전함을 지향하는 각종 U-City나 홍수피해가 빈번한 도시유역에 적용하면 기존 시스템과 차별화된 실시간 홍수예측 서비스가 가능해져 홍수피해를 최소화할 수 있을 것이다. 취수구 직경 D의 3.3배를 벗어나지 않는다는 결과를 도출할 수 있었다.링 목적으로 사용될 수 있다. 본 연구에서 개발한 영상수위계는 한강홍수통제소 관할의 전류, 청담대교 등 4개소 낙동강 홍수통제소 2개소, 지자체 등에 적용되었으며, 적용 결과 비교적 안정적이면서 정확하게 수위를 측정하는 것으로 나타났다. 한편 기존 CCD 카메라 이외에 CCTV를 이용한 영상수위계를 개발하여 영상의 화질 개선뿐 아니라 하천화상 감시 기능을 강화하였다.소류의 섭취율은 높았다. 집단간의 상관도를 보면 교육별로 김치, 장아찌, 콩이 각각 p>0.5 수준에서 유의한 차가 없었고, 나머지는 유의한 차가 있었다. 연령별로는 멸치가 유의한 차가 없었고(p>0.5), 수입별로는 콩이 유의한 차가 없었다(p>0.5). 4. 영양지식(營養知識) 검토 가정생활(家庭生活)에 필요(必要)한 일반적(一般的)인 영양지식(營養知識)은 대체적으로 낮은 편이었다. 어린이 영양, 편식의 해로움, 비만증의 해로움, 임신부 그리고 수유부 영양에 대하여는 일반적으로 알고 있다고 하였으며, 그다음으로 이유기 영양, 어린이 발육에 필요한 식품, 식품과 영양소와의 관계, 우유의 성분, 노인영양에 대하여 잘 알고 있는 비율이 낮았으며, 인체의 영양소, 식단작성여부, 간식의 이론, 식품감별법에 대하여는 가장 낮은 비율을 나타냈다. 각 영양지식은 교육정도가 높을수록 영양지식이 높았고, 교육별 집단간의 유의한 차가 나타났다. (0.001

• Food Science of Animal Resources
• /
• v.28 no.2
• /
• pp.187-195
• /
• 2008

The purpose of this study was to establish proper shipping weight and backfat thickness by applying the growth model to backfat thickness, measured by means of not only body weight, but also ultrasonography, and predicting the changes by age. Three breeds, i.e. Duroc, Landrace, and Yorkshie, were analyzed, and the Gompertz, logistic, and Von Bertalanffy model were used for inference with the parameter of the growth model being sex. As a result, both body weight and backfat thickness showed different growth curve parameters and characteristics at inflection points depending on model selection and sex. As for backfat thickness, in estimating the inflection point, unlike the case of body weight, the inflection ages of the boars of the Duroc breed was earlier than that of sows, whereas the inflection ages of the sows of the Landrace and Yorkshire breeds was earlier than that of boars. More than anything else, in the analysis of the changes in backfat thickness according to body weight, as the body weight reached 145kg, the backfat thickness showed much variation as great as 1.7-3.2 cm in each breed and sex. In addition, unlike the other breeds, the boars of the Landrace breed showed an exponential type of relationship between body weight and backfat thickness. As they grow to become 100 kg or heavier, abrupt change in back fat thickness was confirmed. If the growth of body weight and backfat thickness is understood and the genetic relationship is taken advantage of like this, it would be possible to set desired body weight and backfat thickness, and thus help effectively set the shipping time. If not only the phenotype, but also genetic parameters about growth characteristics are estimated and analyzed additionally, more effective data can be generated.

• School Mathematics
• /
• v.15 no.3
• /
• pp.603-617
• /
• 2013

The major purpose of this article is to examine what kind of gap exists between mathematically gifted students' probability knowledge and the reality actually applying that knowledge and then analyze the cause of the gap. To attain the goal, 23 elementary mathematically gifted students at the highest level from G region were provided with problem situations internalizing a probability and expectation, and the problems are in series in which conditions change one by one. The study task is in a gaming situation where there can be the most reasonable answer mathematically, but the choice may differ by how much they consider a certain condition. To collect data, the students' individual worksheets are collected, and all the class procedures are recorded with a camcorder, and the researcher writes a class observation report. The biggest reason why the students do not make a decision solely based on their own mathematical knowledge is because of 'impracticality', one of the properties of probability, that in reality, all things are not realized according to the mathematical calculation and are impossible to be anticipated and also their own psychological disposition to 'avoid loss' about their entry fee paid. In order to provide desirable probability education, we should not be limited to having learners master probability knowledge included in the textbook by solving the problems based on algorithmic knowledge but provide them with plenty of experience to apply probabilistic inference with which they should make their own choice in diverse situations having context.

• Korea journal of population studies
• /
• v.21 no.1
• /
• pp.162-183
• /
• 1998

This study examines the sampling bias that may have resulted from the large number of missing observations. Despite well-designed and reliable sampling procedures, the observed sample values in DSFH(Demographic Survey on Changes in Family and Household Structure, Japan) included many missing observations. The head administerd survey method of DSFH resulted in a large number of missing observations regarding characteristics of elderly non-head parents and their children. In addition, the response probability of a particular item in DSFH significantly differs by characteristics of elderly parents and their children. Furthermore, missing observations of many items occurred simultaneously. This complex pattern of missing observations critically limits the ability to produce an unbiased analysis. First, the large number of missing observations is likely to cause a misleading estimate of the standard error. Even worse, the possible dependency of missing observations on their latent values is likely to produce biased estimates of covariates. Two models are employed to solve the possible inference biases. First, EM algorithm is used to infer the missing values based on the knowledge of the association between the observed values and other covariates. Second, a selection model was employed given the suspicion that the probability of missing observations of proximity depends on its unobserved outcome.

• KIPS Transactions on Software and Data Engineering
• /
• v.9 no.5
• /
• pp.169-176
• /
• 2020

Recently, deep neural networks (DNNs) have brought revolutions to many mobile and embedded devices by providing human-level machine intelligence for various applications. However, high inference accuracy of such DNNs comes at high computational costs, and, hence, there have been significant efforts to reduce computational overheads of DNNs either by compressing off-the-shelf models or by designing a new small footprint DNN architecture tailored to resource constrained devices. One notable recent paradigm in designing small footprint DNN models is sharing parameters in several layers. However, in previous approaches, the parameter-sharing techniques have been applied to large deep networks, such as ResNet, that are known to have high redundancy. In this paper, we propose a parameter-sharing method for already parameter-efficient small networks such as ShuffleNetV2. In our approach, small templates are combined with small layer-specific parameters to generate weights. Our experiment results on ImageNet and CIFAR100 datasets show that our approach can reduce the size of parameters by 15%-35% of ShuffleNetV2 while achieving smaller drops in accuracies compared to previous parameter-sharing and pruning approaches. We further show that the proposed approach is efficient in terms of latency and energy consumption on modern embedded devices.

• Journal of the Society of Naval Architects of Korea
• /
• v.34 no.1
• /
• pp.122-129
• /
• 1997

Thermo-elastic-plastic analyses used in solving plate forming process are often computationally expensive. To obtain an optimal process of line heating typically requires numerous iterations between the simulation and a finite element analysis. This process often becomes prohibitive due to the amount of computer time required for numerical simulation of line heating process. Therefore, a new techniques that could significantly reduce the computer time required to solve a complex analysis problem would be beneficial. In this paper, we considered factors that influence the bending effect by line heating and developed inference engine by using the concept of artificial neural network. To verify the validity of the neural network, we used results obtained from numerical analysis. We trained the neural network with the data made from numerical analysis and experiments varying the structure of neural network, in other words varying the number of hidden layers and the number of neurons in each hidden layers. From that we concluded that if the number of neurons in each hidden layers is large enough neural network having two hidden layers can be trained easily and errors between exact value and results obtained from trained network are not so large. Consequently, if there are enough number of training pairs, artificial neural network can infer similar results. Based on the numerical results, we applied the artificial neural network technique to deal with mechanical behavior of line heating at simulation stage effectively.

• The Korean Journal of Physiology
• /
• v.15 no.2
• /
• pp.91-96
• /
• 1981

The wormwood is one of the plants which occur widely throughout the world. Though the precise data on the entire chemical composition of mugwort leaves are not available, the major principles which have been found so far include inulin, alkaloid, thujon, sesquiterpene and several vitamins. Santonin, a parasiticide, is one of the glucosides extracted from the limited species of wormwood. It has long been known in herb medicine that the plants of this family has not only strong hemostatic, analgesic and parasiticidal actions but also therapeutic effects for diarrhea, stomachache and asthma. In recent pharmaceutical botany the wormwood is introduced to have antipyretic and astringent actions also. The mugwort(Artemisia asiatica Nakai) is the most common species of wormwood that occurs in Korea. The usage of this edible leaves of mugwort is rather various. It is used not only for wormwood bath but also as forage, moxa and medicinal agents. Recently Kim et al reported from their study on the effect of mugwort on the motility of isolated intestine of rabbits that tonus and motility were markedly enhanced by mugwort but this effect of mugwort on intestinal motility was almost completely blocked by atropine suggesting that activity of mugwort was exerted through its cholinergic effect. It was the findings of Kim et al that prompted the authors to do the present experiment. The present study was undertaken to investigate effects of mugwort(Artemisia asiatica Nakai) juice on the respiration and blood pressure in cats. And also studied was the mechanism of depressor action of Artemisia asiatica Nakai Juice (AAJ). The results obtained are as follows; 1) It was observed that mean arterial blood pressure and heart rate were decreased markedly by AAJ. Following administration of 0.15 ml/kg and 0.3 ml/kg AAJ into cats the maximum depressor responses observed were $77.5{\pm}2.2\;mmHg$ and $94.0{\pm}3.7\;mmHg$ respectively. 2) Depressor responses to AAJ were blocked markedly by atropine whereas the responses were not affected by propranolol and dibenamine. Therefore it is strongly inferred that depressor action of AAJ results mainly from its cholinergic effect. This inference was further substantiated by the fact that heart rate change which invariably accompanies depressor responses to AAJ was almost completely abolished by atropinization. 3) After administration of AAJ into cats frequency of respiration was markedly increased while depth of respiration decreased during first 2-3 seconds.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.6
• /
• pp.749-754
• /
• 2011

In this study, we propose the design of optimized pRBFNNs-based face recognition system using two-dimensional Image and ASM algorithm. usually the existing 2 dimensional face recognition methods have the effects of the scale change of the image, position variation or the backgrounds of an image. In this paper, the face region information obtained from the detected face region is used for the compensation of these defects. In this paper, we use a CCD camera to obtain a picture frame directly. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. AdaBoost algorithm is used for the detection of face image between face and non-face image area. We can butt up personal profile by extracting the both face contour and shape using ASM(Active Shape Model) and then reduce dimension of image data using PCA. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of RBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to real-time face image database and then demonstrated from viewpoint of the output performance and recognition rate.