• Journal of Korea Trade
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• v.26 no.4
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• pp.1-22
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• 2022

Purpose - This paper empirically investigates the effect of a rise in the global value chain (GVC) on the industry-level efficiency of resource allocation (based on plant-level inefficiency measures) in Korea, with a focus on various channels through which a rise in the GVC can increase competition among firms and thus induce resources to be allocated more efficiently across firms. Design/methodology - We empirically investigate the relationship between the industry-specific importance of GVC and the industry-level allocative inefficiency that is measured as the dispersion of the plant-level marginal revenue of capital (MRK) as in Hsieh and Klenow's (2009) influential model. We compute MRK dispersion for industries sorted by various characteristics that are closely related to firm/industry sensitivity to the GVC. In other words, we compute the average industry-level MRK dispersion for industries sorted by industry-specific importance of GVC and compute the difference between the two groups of industries (higher vs. lower than the median GVC); we also calculate the difference between industries sorted by industry-specific export (import) intensity. This is our difference-in-difference estimate of the MRK dispersion associated with the GVC for the export (import)-intensive industry versus the non-export (non-import)-intensive industry. This difference-in-difference estimate of the MRK dispersion conditional vs. unconditional on firm-level productivity is then calculated further (triple-difference estimate). Findings - A rise in GVC is associated with a decrease in the MRK dispersion in the export-intensive industry compared to the non-export-intensive industry. The same is true for industries that rely heavily on imports versus those that do not (i.e., import intensive vs. non-intensive). Furthermore, the reduction in the MRK dispersion in the export-intensive industry associated with an increase in the GVC is disproportionately greater for high-productivity firms. In contrast, the negative relationship between GVC and MRK dispersion in the import-intensive industry is disproportionately smaller for high-productivity firms. Originality/value - Existing studies focus on the relationship between GVC and aggregate output, exports, and imports at the country level. We investigate detailed firm/industry-level mechanisms that determine the relationship between GVC, trade, and productivity. Using the plant-level data in South Korea, we investigate how GVC is related to the cross-firm MRK dispersion, an important measure of allocative inefficiency, based on Hsieh and Klenow's (2009) influential economic theory. This is the first study to provide plant-level evidence of how GVC affects MRK dispersion. Furthermore, we examine how the relationship between GVC and MRK-dispersion varies across export intensity, import intensity, and firm-level productivity, providing insight into how GVC can affect firms' exposure to competition in the global market differently depending on market conditions and thus generate trade-related productivity gains.

• Korean Journal of Veterinary Service
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• v.46 no.1
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• pp.15-27
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• 2023

Bordetella (B.) bronchiseptica, Mycoplasma (M.) cynos, and M. canis are the major bacterial pathogens that cause canine infectious respiratory disease complex (CIRDC). In this study, we developed a triplex real-time polymerase chain reaction (tqPCR) assay for the differential detection of these bacteria in a single reaction. The assay specifically amplified three bacterial genes with a detection limit of below 10 copies/reaction. The assay showed high repeatability and reproducibility, with coefficients of intra- and inter-assay variations of less than 1%. The diagnostic results of the assay using 94 clinical samples from household dogs with CIRDC clinical signs, the prevalence of B. bronchiseptica, M. cynos, and M. canis was 22.3%, 18.1%, and 20.2%, respectively, indicating that the diagnostic sensitivity was comparable to those of previously reported qPCR assays. The dual infection rate of B. bronchiseptica and M. cynos, B. bronchiseptica and M. canis, and M. cynos and M. canis was 5.3%, 7.4%, and 3.1%, respectively. Moreover, the triple infection rate of B. bronchiseptica, M. cynos, and M. canis was 2.1%. These results indicate that coinfections with B. bronchiseptica, M. cynos, and M. canis have frequently occurred in the Korean dog population. The newly developed tqPCR assay in the present study will be a useful tool for etiological and epidemiological studies on these three CIRDC-associated bacterial pathogens. The prevalence and coinfection data revealed through this study will contribute to expanding knowledge on the epidemiology of CIRDC in the recent Korean dog population.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.60.3-61
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• 2021

The Korean VLBI Network (KVN) consists of three 21m radio telescopes installed in Seoul, Ulsan, and Jeju Island with the world's first 4-channel receiver that can observe four different frequencies (e.g., 22, 43, 86, 129 GHz) simultaneously. This receiving system of KVN is particularly effective in millimeter-wavelength VLBI (mm-VLBI) observations by compensating fast atmospheric fluctuations effectively. This technology is now being enhanced with a compact triple-band receiver, becoming the world standard for a mm-VLBI system. In 2020, KVN supported 54 observing programs (KVN: 28, EAVN: 26) including the 2nd KVN Key Science Program (KSP) which supports 8Gbps data recording rate and the East Asian VLBI Network (EAVN) programs. KVN also participated in the European VLBI Network (EVN) and GMVA (Global Millimeter VLBI Array) sessions regularly. Here, we report current status and future propsects of KVN.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.38.2-39
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• 2021

I will presents the analysis of the gas properties of the protoplanetary disk surrounding the young low-mass (about 1.2M_sun) triple star, GG Tau A. This work makes use of ALMA observations of rotational lines of CO (¹²CO, ¹³CO and C¹⁸O) together NOEMA observations of a few dozens of other molecules. While the CO emission gives information on the molecular layer close to the disk atmosphere, its less abundant isotopologues ¹³CO and C¹⁸O bring information much deeper in the molecular layer. I will present the analysis of the morphology and kinematics of the gas disk using the CO isotopologues. A radiative transfer model of the ring in CO isotopologues will also be presented. The subtraction of this model from the original data reveals the weak emission of the molecular gas lying inside the cavity. Thus, I am able to evaluate the properties of the gas inside the cavity, such as the gas dynamics, excitation conditions, and the amount of mass in the cavity. High angular resolution observations of CO reveals sprials induced by embedded planet(s) located near the 3:2:1 mean-motion resonance that help to explain the special morphology of the circumbinary disk. I also discuss some chemical properties of the GG Tau A disk. I report the first detection of H2S and C₂S in a protoplanetary disk. The molecule abundance relative to ¹³CO of about twenties other molecules will also be given. In GG Tau A, the detections of rare molecules such as H₂S and C₂S have been probably possible because the disk is more massive (a factor about 3-5) than other disks where the molecules was searched. Such a large disk mass makes the system suitable to detect rare molecules and to study cold-chemistry in protoplanetary disks.

• The Journal of Korean Academy of Prosthodontics
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• v.61 no.4
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• pp.379-385
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• 2023

With the recent development of digital dentistry, fully digitalized methods for fabricating dentures, using intraoral scans and computer-aided design/computer-aided manufacturing (CAD-CAM), are getting popular. Digital methods have the advantage of simplifying the fabrication process in the clinic and laboratory, supplementing digital data. This case report shows a fully digital fabrication method for interim removable dentures in a patient with anterior tooth loss in which implant placement is impossible or delayed. Interim removable dentures were fabricated using two methods. One method is printing tooth and base parts separately and combining, and the other method is printing the whole denture at one time and coloring on the base part. Afterward, dentures were delivered and adaptation was evaluated using the triple scan technique. The extracted site was scanned intraorally (first scan) and the interim removable denture was digitally scanned both intraorally (second scan) and, after removal extraorally (Third scan). In both method, denture adaptation was shown favorable. We report this case report as both the patient and the operator were satisfied with a simplified process using a fully digital method in the clinic.

• Information Systems Review
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• v.18 no.3
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• pp.1-29
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• 2016

The radical changes in the business environment have motivated firms to exert serious efforts in managing sustainable development. This study identified the effects of sustainable development on the perceived performance of firms from the viewpoint of the balanced scorecard. Independent variables include economic efforts (of efficiency and ethic of accounting and fairness), environmental efforts (management and energy control), and social efforts (consumer protection and contribution for local community). The result from the analysis of data collected in this research suggests that sustainable development efforts are the critical sources of the incorporated performance of firms. The consumer protection efforts of the local community determine the competitiveness of a firm in managing social responsibility and creating value and social activities. Efforts to reconsider efficiency determine the competitiveness of a firm, becoming the critical factors that determine sustainable performance. Energy control facilitates value creation for the environment through cooperation and harmonization with nature, resulting in sustainable business performances through the vitalization of practical establishments and operations. Sustainable management needs to meet international standards, cooperation, and harmony. These standards are based on the economic, environmental, and social efforts that enable firms to adopt sustainable management efforts that are suitable for their own systems.

• Korean Journal of Radiology
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• v.21 no.6
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• pp.670-683
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• 2020

Objective: The presence of coagulative necrosis (CN) in clear cell renal cell carcinoma (ccRCC) indicates a poor prognosis, while the absence of CN indicates a good prognosis. The purpose of this study was to build and validate a radiomics signature based on preoperative CT imaging data to estimate CN status in ccRCC. Materials and Methods: Altogether, 105 patients with pathologically confirmed ccRCC were retrospectively enrolled in this study and then divided into training (n = 72) and validation (n = 33) sets. Thereafter, 385 radiomics features were extracted from the three-dimensional volumes of interest of each tumor, and 10 traditional features were assessed by two experienced radiologists using triple-phase CT-enhanced images. A multivariate logistic regression algorithm was used to build the radiomics score and traditional predictors in the training set, and their performance was assessed and then tested in the validation set. The radiomics signature to distinguish CN status was then developed by incorporating the radiomics score and the selected traditional predictors. The receiver operating characteristic (ROC) curve was plotted to evaluate the predictive performance. Results: The area under the ROC curve (AUC) of the radiomics score, which consisted of 7 radiomics features, was 0.855 in the training set and 0.885 in the validation set. The AUC of the traditional predictor, which consisted of 2 traditional features, was 0.843 in the training set and 0.858 in the validation set. The radiomics signature showed the best performance with an AUC of 0.942 in the training set, which was then confirmed with an AUC of 0.969 in the validation set. Conclusion: The CT-based radiomics signature that incorporated radiomics and traditional features has the potential to be used as a non-invasive tool for preoperative prediction of CN in ccRCC.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.26 no.12
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• pp.605-619
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• 2014

This article reviews the papers published in the Korean Journal of Air-Conditioning and Refrigeration Engineering during 2013. It is intended to understand the status of current research in the areas of heating, cooling, ventilation, sanitation, and indoor environments of buildings and plant facilities. Conclusions are as follows. (1) The research works on the thermal and fluid engineering have been reviewed as groups of fluid machinery, pipes and relative parts including orifices, dampers and ducts, fuel cells and power plants, cooling and air-conditioning, heat and mass transfer, two phase flow, and the flow around buildings and structures. Research issues dealing with home appliances, flows around buildings, nuclear power plant, and manufacturing processes are newly added in thermal and fluid engineering research area. (2) Research works on heat transfer area have been reviewed in the categories of heat transfer characteristics, pool boiling and condensing heat transfer and industrial heat exchangers. Researches on heat transfer characteristics included the results for general analytical model for desiccant wheels, the effects of water absorption on the thermal conductivity of insulation materials, thermal properties of Octadecane/xGnP shape-stabilized phase change materials and $CO_2$ and $CO_2$-Hydrate mixture, effect of ground source heat pump system, the heat flux meter location for the performance test of a refrigerator vacuum insulation panel, a parallel flow evaporator for a heat pump dryer, the condensation risk assessment of vacuum multi-layer glass and triple glass, optimization of a forced convection type PCM refrigeration module, surface temperature sensor using fluorescent nanoporous thin film. In the area of pool boiling and condensing heat transfer, researches on ammonia inside horizontal smooth small tube, R1234yf on various enhanced surfaces, HFC32/HFC152a on a plain surface, spray cooling up to critical heat flux on a low-fin enhanced surface were actively carried out. In the area of industrial heat exchangers, researches on a fin tube type adsorber, the mass-transfer kinetics of a fin-tube-type adsorption bed, fin-and-tube heat exchangers having sine wave fins and oval tubes, louvered fin heat exchanger were performed. (3) In the field of refrigeration, studies are categorized into three groups namely refrigeration cycle, refrigerant and modeling and control. In the category of refrigeration cycle, studies were focused on the enhancement or optimization of experimental or commercial systems including a R410a VRF(Various Refrigerant Flow) heat pump, a R134a 2-stage screw heat pump and a R134a double-heat source automotive air-conditioner system. In the category of refrigerant, studies were carried out for the application of alternative refrigerants or refrigeration technologies including $CO_2$ water heaters, a R1234yf automotive air-conditioner, a R436b water cooler and a thermoelectric refrigerator. In the category of modeling and control, theoretical and experimental studies were carried out to predict the performance of various thermal and control systems including the long-term energy analysis of a geo-thermal heat pump system coupled to cast-in-place energy piles, the dynamic simulation of a water heater-coupled hybrid heat pump and the numerical simulation of an integral optimum regulating controller for a system heat pump. (4) In building mechanical system research fields, twenty one studies were conducted to achieve effective design of the mechanical systems, and also to maximize the energy efficiency of buildings. The topics of the studies included heating and cooling, HVAC system, ventilation, and renewable energies in the buildings. Proposed designs, performance tests using numerical methods and experiments provide useful information and key data which can improve the energy efficiency of the buildings. (5) The field of architectural environment is mostly focused on indoor environment and building energy. The main researches of indoor environment are related to infiltration, ventilation, leak flow and airtightness performance in residential building. The subjects of building energy are worked on energy saving, operation method and optimum operation of building energy systems. The remained studies are related to the special facility such as cleanroom, internet data center and biosafety laboratory. water supply and drain system, defining standard input variables of BIM (Building Information Modeling) for facility management system, estimating capability and providing operation guidelines of subway station as shelter for refuge and evaluation of pollutant emissions from furniture-like products.