• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.35 no.4
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• pp.98-109
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• 2017

The results of this study attempting to graft to the space by cognizing and categorizing the characteristic of the under-toned private property to the tablets of the Zhuozhengyuan are as follows. The tablet of Zhuozhengyuan was typed into three groups. In the A group, the frequency of artificial elements including the top, ship, and head was high, and the main emphasis was on the elegance and integrity spirit that the Sunbi(classical scholar) had to have. It has a characteristic of good architects such as pavilion and Nu(樓) is included and as it is adjacent to the water surface the one side of the building is integrated to the water surface. The B group is characterized mainly by the fact that the scenery is portrayed through the climatic factors including rain, wind, and snow, or the scene atmosphere and it is located at the top of the summit which is good for viewing the scenery. In the C group, plant elements such as lotus, magnolia, plum, and bamboo appeared frequently, and various buildings such as pavilion, Kwan(館), Gak(閣) and Dang(堂) belong to this structure. The structure has the characteristic of integrating directly with the water surface or putting the terrace between and scattering throughout the whole garden. Furthermore, this study identified area characteristics based on the frequency of the tablet type. In the west area, Group A is mostly distributed and the humor and concept implied in the tablet, combined with the shape and location of the scenery, viewpoint of the viewer, and the composition of the landscape, improved the orderliness of the space, which led to expand the scope of the enjoyment to the entire space. Meanwhile, in the middle area, tablets of Group C are mostly distributed, and the unique characteristics and shapes of the woody plants that were used as the name of the tablet were connected with various buildings including pavilion, Dang, Kuan, and Gak, improving the unique identity of the space and forming connection with the world of ideas through unity and independence.

• The Korean Journal of Archival Studies
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• no.16
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• pp.147-200
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• 2007

Archives provide users with easier and more convenient access to and use of archival data through their Internet websites. Now, archival websites function not only as a tool of information provision or marketing, or as a gateway of Internet-based materials, but also as cyber space for all services of archives, providing users with information and knowledge and direct accessibility to the services in the archives. At present, Korean archives are proceeding with establishing websites or upgrading existing websites, and the use rates and reliability of web services by users are becoming higher. However, although there have been various studies for the evaluation of general websites, few are found with regard to the evaluation of archival websites. It is necessary for archival websites that provide information service to users of every stripe to make more efforts and have more interest in user-centered convenience, right to know, and information provision-centered service. Accordingly, needless to say, it is necessary to study evaluation criteria of websites so that high quality archival websites can be established. With this background, this study establishes evaluation criteria for archival websites, which are appropriate to their objectives and functions and directly evaluates archives, presenting ways to establish and redevelop archival websites. More detailed purposes are as follows: First, analyzes existing theories of evaluation through reviews on previous literature and elicits evaluation criteria for websites, which are appropriate to archives; Second, based on the elicited evaluation criteria for archival websites, examines the current state of domestic archival websites through analytic evaluation; and Third, presents ways to improve archival websites that may be helpful in establishing or improving them in the future. The expectancy effects of this study are as follows: First, it will be helpful when one wishes to identify the current state of archival websites and to improve or redevelop existing websites, or to develop online service through website; Second, it will function as a checklist when a developer who is to establish an archival website wishes to develop evaluation criteria; and Third, it may be used as an inspection tool when an archives contracts out the establishment of its website.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.2
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• pp.115-128
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• 2022

This research aims to analyze of the Effect of Army Start-up Support Program (hereinafter, ASSP) on the level of discharged or scheduled to be discharged soldier's satisfaction of their military service. In addition, this paper also seeks to elaborate on how those Army-supported programs, such as Start-up competitions, mentoring, camp, and school, can increase the level of military service satisfaction. By participating in those programs, service members can leverage their time in the military to make opportunity for their future career rather than consider it as a career break, increasing their military service satisfaction. Finally, on the basis of findings, this paper advocates for expanding Service Members startup Support Program of government agencies and municipalities and enhancing younger population's individual capabilities through ASSP. This research used ASSPs, such as Army Start-up competitions, mentoring, camp, and school, as independent variables. For controlled variables, this paper takes demographic elements such as status and career path of the participants. Our empirical analysis reveals that ASSP has a direct impact on the military service satisfaction, supported by the statistics from the hierarchical regression analysis. However, the research also suggests that individual' military service satisfaction from ASSP may differ from one another in accordance with the controlled variables such as status and career of the participants. This paper proves that ASSP can play a substantial role in enhancing service members' capabilities during their time in military and that Army fosters talent and connects itself to the nation and society. In addition, this research could contribute to increasing employment rate of and expanding Start-up support program of government agencies and municipalities for younger population by providing empirical results which advocates the latter's necessity.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Journal of Chest Surgery
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• v.41 no.1
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• pp.1-11
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• 2008

Background: The Damus-Kaye-Stansel (DKS) procedure is a proximal MPA-ascending aorta anastomosis used to relieve systemic ventricular outflow tract obstructions (SVOTO) and pulmonary hypertension. The purpose of this study was to review the indications and outcomes of the DKS procedure, including the DKS pathway and semilunar valve function. Material and Method: A retrospective review of 28 patients who underwent a DKS procedure between May 1994 and April 2006 was performed. The median age at operation was 5.3 months ($13\;days{\sim}38.1\;months$) and body weight was 5.0 kg ($2.9{\sim}13.5\;kg$). Preoperative pressure gradients were $25.3{\pm}15.7\;mmHg$ ($10{\sim}60\;mmHg$). Eighteen patients underwent a preliminary pulmonary artery banding as an initial palliation. Preoperative main diagnoses were double outlet right ventricle in 9 patients, double inlet left ventricle with ventriculoarterial discordance in 6,. another functional univentricular heart in 5, Criss-cross heart in 4, complete atrioventricular septal defect in 3, and hypoplastic left heart variant in 1. DKS techniques included end-to-side anastomosis with patch augmentation in 14 patients, classical end-to-side anastomosis in 6, Lamberti method (double-barrel) in 3, and others in 5. The bidirectional cavopulmonary shunt and Fontan procedure were concomitantly performed in 6 and 2 patients, respectively. Result: There were 4 hospital deaths (14.3%), and 3 late deaths (12.5%) with a follow-up duration of $62.7{\pm}38.9$ months ($3.3{\sim}128.1$ months). Kaplan-Meier estimated actuarial survival was $71.9%{\pm}9.3%$ at 10 years. Multivariate analysis showed right ventricle type single ventricle (hazard ratio=13.960, p=0.004) and the DKS procedure as initial operation (hazard ratio=6.767, p=0.042) as significant mortality risk factors. Four patients underwent staged biventricular repair and 13 received Fontan completion. No SVOTO was detected after the procedure by either cardiac catheterization or echocardiography except in one patient. There was no semiulnar valve regurgitation (>Gr II) or semilunar valve-related reoperation, but one patient (3.6%) who underwent classical end-to-side anastomosis needed reoperation for pulmonary artery stenosis caused by compression of the enlarged DKS pathway. The freedom from reoperation for the DKS pathway and semilunar valve was 87.5% at 10 years after operation. Conclusion: The DKS procedure can improve the management of SVOTO, and facilitate the selected patients who are high risk for biventricular repair just after birth to undergo successful staged biventricular repair. Preliminary pulmonary artery banding is a safe and effective procedure that improves the likelihood of successful DKS by decreasing pulmonary vascular resistance. The long-term outcome of the DKS procedure for semilunar valve function, DKS pathway, and relief of SVOTO is satisfactory.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.191-207
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• 2021

Up to this day, mobile communications have evolved rapidly over the decades, mainly focusing on speed-up to meet the growing data demands of 2G to 5G. And with the start of the 5G era, efforts are being made to provide such various services to customers, as IoT, V2X, robots, artificial intelligence, augmented virtual reality, and smart cities, which are expected to change the environment of our lives and industries as a whole. In a bid to provide those services, on top of high speed data, reduced latency and reliability are critical for real-time services. Thus, 5G has paved the way for service delivery through maximum speed of 20Gbps, a delay of 1ms, and a connecting device of 106/㎢ In particular, in intelligent traffic control systems and services using various vehicle-based Vehicle to X (V2X), such as traffic control, in addition to high-speed data speed, reduction of delay and reliability for real-time services are very important. 5G communication uses high frequencies of 3.5Ghz and 28Ghz. These high-frequency waves can go with high-speed thanks to their straightness while their short wavelength and small diffraction angle limit their reach to distance and prevent them from penetrating walls, causing restrictions on their use indoors. Therefore, under existing networks it's difficult to overcome these constraints. The underlying centralized SDN also has a limited capability in offering delay-sensitive services because communication with many nodes creates overload in its processing. Basically, SDN, which means a structure that separates signals from the control plane from packets in the data plane, requires control of the delay-related tree structure available in the event of an emergency during autonomous driving. In these scenarios, the network architecture that handles in-vehicle information is a major variable of delay. Since SDNs in general centralized structures are difficult to meet the desired delay level, studies on the optimal size of SDNs for information processing should be conducted. Thus, SDNs need to be separated on a certain scale and construct a new type of network, which can efficiently respond to dynamically changing traffic and provide high-quality, flexible services. Moreover, the structure of these networks is closely related to ultra-low latency, high confidence, and hyper-connectivity and should be based on a new form of split SDN rather than an existing centralized SDN structure, even in the case of the worst condition. And in these SDN structural networks, where automobiles pass through small 5G cells very quickly, the information change cycle, round trip delay (RTD), and the data processing time of SDN are highly correlated with the delay. Of these, RDT is not a significant factor because it has sufficient speed and less than 1 ms of delay, but the information change cycle and data processing time of SDN are factors that greatly affect the delay. Especially, in an emergency of self-driving environment linked to an ITS(Intelligent Traffic System) that requires low latency and high reliability, information should be transmitted and processed very quickly. That is a case in point where delay plays a very sensitive role. In this paper, we study the SDN architecture in emergencies during autonomous driving and conduct analysis through simulation of the correlation with the cell layer in which the vehicle should request relevant information according to the information flow. For simulation: As the Data Rate of 5G is high enough, we can assume the information for neighbor vehicle support to the car without errors. Furthermore, we assumed 5G small cells within 50 ~ 250 m in cell radius, and the maximum speed of the vehicle was considered as a 30km ~ 200 km/hour in order to examine the network architecture to minimize the delay.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.30 no.2
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• pp.263-278
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• 2004

Ursolic acid (UA) and Oleanolic acid (ONA), known as urson, micromerol and malol, are pentacyclic triterpenoid compounds which naturally occur in a large number of vegetarian foods, medicinal herbs, and plants. They may occur in their free acid form or as aglycones for triterpenoid saponins, which are comprised of a triterpenoid aglycone, linked to one or more sugar moieties. Therefore UA and ONA are similar in pharmacological activity. Lately scientific research, which led to the identification of UA and ONA, revealed that several pharmacological effects, such as antitumor, hepato-protective, anti-inflammatory, anticarcinogenic, antimicrobial, and anti-hyperlipidemic could be attributed to UA and ONA. Here, we introduced the effect of UA and ONA on acutely barrier disrupted and normal hairless mouse skin. To evaluate the effects of UA and ONA on epidermal permeability barrier recovery, both flanks of 8-12 week-old hairless mice were topically treated with either 0.01-0.1mg/mL UA or 0.1-1mg/mL ONA after tape stripping, and TEWL (transepidermal water loss) was measured. The recovery rate increased in those UA or ONA treated groups (0.1mg/mL UA and 0.5mg/mL ONA) at 6h more than 20% compared to vehicle treated group (p < 0.05). Here, we introduced the effects of UA and ONA on acute barrier disruption and normal epidermal permeability barrier function. For verifying the effects of UA and ONA on normal epidermal barrier, hydration and TEWL were measured for 1 and 3 weeks after UA and ONA applications (2mg/mL per day). We also investigated the features of epidermis and dermis using electron microscopy (EM) and light microscopy (LM). Both samples increased hydration compared to vehicle group from 1 week without TEWL alteration (p < 0.005). EM examination using RuO4 and OsO4 fixation revealed that secretion and numbers of lamellar bodies and complete formation of lipid bilayers were most prominent (ONA=UA > vehicle). LM finding showed that thickness of stratum corneum (SC) was slightly increased and especially epidermal thickening and flattening was observed (UA > ONA > vehicle). We also observed that UA and ONA stimulate epidermal keratinocyte differentiation via PPAR Protein expression of involucrin, loricrin, and filaggrin increased at least 2 and 3 fold in HaCaT cells treated with either ONA (10${\mu}$M) or UA (10${\mu}$M) for 24 h respectively. This result suggested that the UA and ONA can improve epidermal permeability barrier function and induce the epidermal keratinocyte differentiation via PPAR Using Masson-trichrome and elastic fiber staining, we observed collagen thickening and elastic fiber elongation by UA and ONA treatments. In vitro results of collagen and elastin synthesis and elastase inhibitory activity measurements were also confirmed in vivo findings. These data suggested that the effects of UA and ONA related to not only epidermal permeability barrier functions but also dermal collagen and elastic fiber synthesis. Taken together, UA and ONA can be relevant candidates to improve epidermal and dermal functions and pertinent agents for cosmeseutical applications.