• Journal of the Korean Society of Food Culture
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• v.23 no.1
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• pp.1-9
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• 2008

This study aimed to offer some basic data for nutrition education to housewives by comparing behaviors of eating out targeted on housewives who live in Daejon surveyed by classification of regions and by family forms. As for their ages, 79 housewives (16.0%) are less than 30, 179 housewives (36.8%) are between 30 and 39, 156 housewives (35.9%) are between 40 and 49 and 74 housewives (15.2%) are more than 50, and as for their final academic careers, 204 housewives (41.9%) graduated from universities, which was the most and as for religions, 172 housewives (35.3%) had no religion, which was the most, and in the case that they had religions, 137 housewives (27.5%) believed in Christianity. And as for occupations, 327 housewives (69.0%) had no job, and in case they had jobs, 123 housewives (25.3%) that occupied the most were professional. As for husbands’ occupations, profession was the most for 143 housewives (29.4%) and as for monthly income, $2\; million\;won{\sim}3\;million$ won was most as 40.0%. As for times of eating out, ‘4 times a year’ was said by 131 housewives (26.9%), which was the most, and as for the times of food home service, ‘seldom’ was said by 221 housewives (45.4%), and as to the expense for eating out a month, ‘less than 100,000 won’ was said by 219 housewives (45.0%), which was the most, and as for the reasons of their eating out, ‘for harmony in a family’ was said by 119 housewives (24.4%). As for times of eating out by general items of housewives, it was appeared that the younger, the higher in education, less in religion, more in occupation, the more professional in their husbands’ jobs, the more nuclear in family forms, fewer in number of family members and the more average family income per a month, they had more times of eating out. Regarding the factors influencing on eating out, ‘high price’, ‘nutritive imbalance’ and ‘use of MSG’ were pointed by 204 housewives (41.9%), 83 housewives (17.0%) and 63 housewives (12.9%) respectively as the problems of food service shops. And as for the question why their went to their regular restaurants, 185 housewives (38.0%) said ‘taste of food’, and the most considerable thing when they selected menu at a restaurant was ‘taste of food’ for 167 housewives (34.3%). The survey showed that a lot of housewives thought the prices at restaurants were high, and they valued taste of food most, therefore food service shops should improve the taste and nutrition of food.

• Journal of Korea Entertainment Industry Association
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• v.14 no.6
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• pp.125-137
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• 2020

The purpose of this study is to analyze new words in Korean and Chinese based on changes in popular culture. As China and Korea embrace increasingly close communication in recent years, their languages have influenced each other. A lot of new Korean and Chinese words have been discovered to have the same linguistic characteristics. New words are considered as new developments of a language. They are welcomed and widely used by young people in Korea and China. Therefore, in terms of the communicative function of languages, it is worthwhile to understand new words in Korean and Chinese from the perspective of academic research. This study takes Chinese words created in 2018 as the research object. Firstly, a morphological and semantic comparison of Chinese words created in 2018 and those created in 2017 is carried out to extract the characteristic indicators of Chinese words created in 2018, with emphasis on compound words, abbreviations, substitutions, patters and rhetorical expressions. Secondly, the similarities and differences of these Chinese words with Korean words created in 2018 in terms of morphology are analyzed. Finally, after conducting sample classification and comparison, the characteristics of new Chinese and Korean words and the interaction mechanism under mutual influence are concluded. According to the study, the majority of the new words are created on the basis of existing words. Thus, it is important to explore the morphology of new words as a standard language.

• Herbal Formula Science
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• v.11 no.2
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• pp.1-18
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• 2003

The Theory for Monarch, Minister, Adjuvant and Dispatcher (or the Theory of Principal, Assistant, Adjuvant and Guiding Korean Oriental Herbal Medicines) has served as a standard principle for newly developed prescription formulas as well as established ones. Despite its significance, however, this theory hasn't been thoroughly studied and covered in the academic journals of Korean Oriental Herbal Medicines (KOHM) yet. This paper inquires into the origin of the theory while presenting the definitions and functions of Principal, Assistant, Adjuvant, and Guiding KOHM. In the end, the recommended doses and number of the KOHM comprising each of Principal, Assistant, Adjuvant, and Guiding KOHM are suggested. The compatibility theory of Principal, Assistant, Adjuvant, and Guiding KOHM can be traced back to the Warring States Period during which it was recorded in the treatise of the various schools of thoughts and their exponents. The theory was firmly established as a full system in ${\ulcorner}Shinnong's\;Pharmacopoeia{\lrcorner}\;and\;{\ulcorner}Yellow\;Emperor's\;Cannon\;of\;Internal\;Medicine{\lrcorner}$. While ${\ulcorner}Shinnong's\;Pharmacopoeia{\lrcorner}$ focuses on the classification of the properties of KOHM, ${\ulcorner}Yellow\;Emperor's\;Cannon\;of\;Internal\;Medicine{\lrcorner}$ mainly deals with the principles for writing prescriptions. In this regard, it is ${\ulcorner}Yellow\;Emperor's\;Cannon\;of\;Internal\;Medicine{\lrcorner}$ that systemized the Theory of Principal, Assistant, Adjuvant, and Guiding KOHM in a real sense. Principal KOHM aims at the causes of diseases and treat main symptoms. The doses are greater than Assistant, Adjuvant and Guiding KOHM. With their comprehensive effects, Principal KOHM is a leading ingredient of any prescription formula. Assistant KOHM are similar to Principal KOHM in its natures and flavors. Although its natures, flavors as well as efficacies may slightly differ from those of Principal KOHM, Assistant KOHM strengthens the therapeutic effects, jointly working with Principal KOHM. They mainly treat accompanying diseases and symptoms. Adjuvant KOHM is divided into two types: facilitator and inhibitor. Facilitators with the similar properties to those of Principal and Assistant KOHM help strengthen the therapeutic effects. Since they usually treat accompanying symptoms or secondary accompanying symptoms (minor accompanying symptoms), there are two kinds of facilitators. (1) The first kind of facilitators assists Principal KOHM, targeting accompanying symptoms. (2) The second ones supporting Assistant KOHM are for accompanying or secondary accompanying symptoms (or minor accompanying symptoms). Inhibitors counteract and thereby complement Principal and Assistant KOHM. Some of them inhibit the side effects or toxicity of Principal KOHM for the sake of the safety of the whole prescription formula while the others generate induced interactions. Guiding KOHM can be used for two purposes: guiding and mediating. The Guiding KOHM for the former purpose leads the other KOHM in a prescription formula to the lesion. But, the Guiding KOHM for mediating coodinate and harmonize all the ingredients in a prescription formula. The number of KOHM for those Principal, Assistant, Adjuvant and Guiding KOHM and their doses are different, depending on the types of prescriptions: classical prescriptions, prescriptions after ${\ulcorner}$Treatise of Cold-Induced Diseases${\lrcorner}$ and prescriptions of Sasang Constitutions Medicines. In the case of the prescriptions after ${\ulcorner}$Treatise of Cold-Induced Diseases${\lrcorner}$, it is highly recommended to follow the view of ${\ulcorner}$Thesaurus of Korean Oriental Medicine Doctors in Chosun Dynasty${\lrcorner}$ for the number of KOHM to be used. For the doses, however, ${\ulcorner}$Elementary Course for Medicine${\lrcorner}$, is found to be more accurate. The most appropriate number of KOHM per prescription is 11-13. To be more specific, for one prescription formula, it is recommended to administer one kind of KOHM for Principal KOHM, 2-3 for Assistant KOHM, 3-4 for Adjuvant KOHM and 5 for Guiding KOHM. As for the proportion of the doses, when 10 units are to be administered for Principal KOHM in a formula, the doses for the other three should be 7-8 units for Assistant KOHM, 5-6 for Adjuvant KOHM and 3-4 for Guiding KOHM. The doses of the KOHM added to or taken out of the prescription correspond to those of Adjuvant and Guiding KOHM.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.91-104
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• 2023

The records system is believed to have started in Italy in the 14th century in line with trade developments in Europe. In 1491, Luca Pacioli, a mathematician, and an Italian Franciscan monk wrote the first book that described double-entry accounting processes. In many countries, including Korea, the government accounting standards used single-entry bookkeeping rather than double-entry bookkeeping that can be aggregated by account subject. The cash-based and single-entry bookkeeping used by the government in the past had limitations in providing clear information on financial status and establishing a performance-oriented financial management system. Accordingly, the National Accounting Act (promulgated in October 2007) stipulated the introduction of double-entry bookkeeping and accrual accounting systems in the government sector from January 1, 2009. Furthermore, the Korean government has also introduced International Financial Reporting Standards (IFRS), and the System of National Accounts (SNA). Since 2014, Korea owned five national accounts. In Korea, valuation began with the 1968 National Wealth Statistics Survey. The academic origins of the valuation of national wealth statistics which had been investigated by due diligence every 10 years since 1968 are based on the 'Engineering Valuation' of professor Marston in the Department of Industrial Engineering at Iowa State University in the 1930s. This field has spread to economics, etc. In economics, it became the basis of capital stock estimation for positive economics such as econometrics. The valuation by the National Wealth Statistics Survey contributed greatly to converting the book value of accounting data into vintage data. And in 2000 National Statistical Office collected actual disposal data for the 1-digit asset class and obtained the ASL(average service life) by Iowa curve. Then, with the data on fixed capital formation centered on the National B/S Team of the Bank of Korea, the national wealth statistics were prepared by the Permanent Inventory Method(PIM). The asset classification was also classified into 59 types, including 2 types of residential buildings, 4 types of non-residential buildings, 14 types of structures, 9 types of transportation equipment, 28 types of machinery, and 2 types of intangible fixed assets. Tables of useful lives of tangible fixed assets published by the Korea Appraisal Board in 1999 and 2013 were made by the Iowa curve method. In Korea, the Iowa curve method has been adopted as a method of ASL estimation. There are three types of the Iowa curve method. The retirement rate method of the three types is the best because it is based on the collection and compilation of the data of all properties in service during a period of recent years, both properties retired and that are still in service. We hope the retirement rate method instead of the individual unit method is used in the estimation of ASL. Recently Korean government's accounting system has been developed. When revenue expenditure and capital expenditure were mixed in the past single-entry bookkeeping we would like to suggest that BOK and National Statistical Office have accumulated knowledge of a rational difference between revenue expenditure and capital expenditure. In particular, it is important when it is estimated capital stock by PIM. Korea also needs an empirical study on economic depreciation like Hulten & Wykoff Catalog A of the US BEA.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.37 no.1
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• pp.56-67
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• 2019

In order to investigate the issue with the proper name of eshel(Tamarix aphylla) mentioned in the Bible, analysis of morphological taxonomy features of plants, studies on the symbolism of the Tamarix genus, analysis of examples in Korean classics and Chinese classics, and studies on the problems found in translations of Korean, Chinese and Japanese Bibles. The results are as follows. According to plant taxonomy, similar species of the Tamarix genus are differentiated by the leaf and flower, and because the size is very small about 2-4mm, it is difficult to differentiate by the naked eye. However, T. aphylla found in the plains of Israel and T. chinensis of China and Korea have distinctive differences in terms of the shape of the branch that droops and its blooming period. The Tamarix genus is a very precious tree that was planted in royal courtyards of ancient Mesopotamia and the Han(漢) Dynasty of China, and in ancient Egypt, it was said to be a tree that gave life to the dead. In the Bible, it was used as a sign of the covenant that God was with Abraham, and it also symbolized the prophet Samuel and the court of Samuel. When examining the example in Korean classics, the Tamarix genus was used as a common term in the Joseon Dynasty and it was often used as the medical term '$Ch{\bar{e}}ngli{\check{u}}$(檉柳)'. Meanwhile, the term 'wiseonglyu(渭城柳)' was used as a literary term. Upon researching the period and name of literature related to $Ch{\bar{e}}ngli{\check{u}}$(檉柳) among Chinese medicinal herb books, a total of 16 terms were used and among these terms, the term Chuísīliǔ(垂絲柳) used in the Chinese Bible cannot be found. There was no word called 'wiseonglyu(渭城柳)' that originated from the poem by Wang Wei(699-759) of Tang(唐) Dynasty and in fact, the word 'halyu(河柳)' that was related to Zhou(周) China. But when investigating the academic terms of China currently used, the words Chuísīliǔ(垂絲柳) and $Ch{\bar{e}}ngli{\check{u}}$(檉柳) are used equally, and therefore, it appears that the translation of eshel in the Chinese Bible as either Chuísīliǔ (垂絲柳) or $Ch{\bar{e}}ngli{\check{u}}$(檉柳) both appear to be of no issue. There were errors translating tamarix into 'やなぎ(willow)' in the Meiji Testaments(舊新約全書 1887), and translated correctly 'ぎょりゅう(檉柳)' since the Colloquial Japanese Bible(口語譯 聖書 1955). However, there are claims that 'gyoryu(ぎょりゅう 檉柳)' is not an indigenous species but an exotics species in the Edo Period, so it is necessary to reconsider the terminology. As apparent in the Korean classics examples analysis, there is high possibility that Korea's T. chinensis were grown in the Korean Peninsula for medicinal and gardening purposes. Therefore, the use of the medicinal term $Ch{\bar{e}}ngli{\check{u}}$(檉柳) or literary term 'wiseonglyu' in the Korean Bible may not be a big issue. However, the term 'wiseonglyu' is used very rarely even in China and as this may be connected to the admiration of China and Chinese things by literary persons of the Joseon Dynasty, so the use of this term should be reviewed carefully. Therefore, rather than using terms that may be of issue in the Bible, it is more feasible to transliterate the Hebrew word and call it eshel.