• Journal of Climate Change Research
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• v.3 no.2
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• pp.117-128
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• 2012

The Government of South Korea has continued its effort to fixate virtuous circle of economic growth and climate change response to cope with international demands and pressure to commitment for greenhouse gas reduction effectively. Nationally, Korean Government has established "Enforcement of the Framework Act on Low carbon, Green Growth"(2010. 4. 13) to implement national mid-term GHG mitigation goal(30% reduction by 2020 compare to BAU), which established the foundation for phased GHG mitigation by setting up the sectoral and industrial goal, adopting GHG and Energy Target Management System. Also, follow-up measures are taken such as planning and control of mid-term and short-term mitigation target by detailed analysis of potential mitigation of sector and industry, building up the infrastructure for periodic and systematic analysis of target management. Likewise, it is required to establish more accurate, reliable and detailed sectoral GHG inventory for successfully establishment and implement the frame act. In comparison to the $CO_2$ emission, Especially fluorinated greenhouse gases (HFCs, PFCs, $SF_6$) are lacking research to build the greenhouse gas inventories to identify emissions sources and collection of the applicable collection activities data. In this study, with the refrigeration and air conditioning sector being used to fluorine refrigerant(HFCs) as the center, greenhouse gas emission estimation methodology for evaluating the feasibility of using this methodology look over and mobile air conditioning, fixed air conditioning, household refrigeration equipment, commercial refrigeration equipment for the greenhouse gas emissions were calculated. First look at in terms of methodology, refrigeration and air conditioning sector GHG emissions in developing country-specific emission factors and activity data of the industrial sector the construction of the DB is not enough, it's 2006 IPCC Guidelines Tier 2a (emission factor approach) rather than the Tier 2b (mass balance approach) deems appropriate, and each detail by process, sectoral activity data more accurate, if DB is built Tier 2a (emission factor approach) can be applied will also be judged. Refrigeration and air conditioning sector in 2009 due to the use of refrigerant greenhouse gas emissions ($CO_2eq.$) assessment results, portable air conditioner 1,974,646 ton to year, fixed-mount air conditioner 1,011,754 ton to year, household refrigeration unit 4,396 ton to year, commercial refrigeration equipment 1,263 ton to year was estimated to total 2,992,037 tons.

• Journal of Korean Society of Forest Science
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• v.100 no.3
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• pp.522-534
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• 2011

The aim of this study is to draw forth the tasks for establishing the right of native biology in Korea through the case study on 'Access on genetic resources and Benefit Sharing'. For this purpose, this study decided on its research subject by selecting Hoodia, on which ABS treaty was made the most recently, through the examination of the representative ABS precedents on plant species. This study analyzed the process background of ABS on Hoodia, and compared & analyzed the ABS procedures of 'Bonn Guidelines' adopted by the 6th Conference of the Parties of the Convention on Biological Diversity in 2002 and Hoodia case. Together with the ABS major issues in common drawn as a result of this analysis, and "Nagoya Protocol" adopted by the 10th Conference of the Parties of the Convention on Biological Diversity, this study intended to shed a light on the impending tasks which Korea faces at present and its role relationship. The research results are as follows: 1. It is required that species habitats should be divided based on biological classification and its subsequent community should be established with the development of infrastructure such as a community's independent production, management and monitoring of bio-species. 2. There needs to be a designation of ABS National Focal Point for sharing of ABS-related general information, boosting of implementation of the relevant convention. 3. There needs to be the establishment of ABS convention system consequent on legislative, administrative, political procedures, and designation of the Competent National Authorities for the provision of the format of Prior Informed Consent (PIC) and Mutually Agreed Terms (MAT) and their contents assessment and confirmation. 4. There should be the establishment of integrated management system of ABS-related research and development of forest biological resources and its relevant research projects. 5. There should be information development through the distribution of responsibility and role between the ministries and offices concerned according to bio-resources, and there needs to be efforts in aiming for opening a working group of academic-industrial institutions for developing a mutually interchangeable system. 6. It's required that the efficient access between industrial circles and the people should be promoted by setting up ABS support center of biological resources in ministry and office's charge. 7. There should be a selection of a national supervisory organization for securement of the right of a local community and monitoring of ABS convention implementation, and a countermeasure system for preventing outflow of forest bioresources. Conclusively, it's judged that it will be possible to inquire into the countermeasures for the establishment of the native forest biology dominion through such research results.

• Korean small business review
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• v.42 no.2
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• pp.69-92
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• 2020

This study is aimed at the growth and development of family businesses that greatly contribute to Korea's economic development, but the specific research purpose is to firstly examine the research trends and current status of Korean family businesses and compare them with those of developed countries such as the United States. Second, I would like to look at the future research for revitalizing Korean family business research. In addition, we intend to contribute to increasing the interest in this field and the number of researchers involved. The research target of this paper is 212 papers published in professional academic journals for 13 years from 2006 to 2018 when family businesses began to be fully researched in Korea, 112 master's and doctoral dissertations (graduate schools), and 324 totals. As a result of empirical analysis, the number of published papers is increasing more than the initial ones, but it has been on the decline recently. In addition, 57.5% of the journals are papers that do not have specific definitions or simply list the claims of several scholars by analyzing content. Thesis was 33.9%. As for the type of research, qualitative research, which is a conceptual research, is a small number, and empirical research occupies most of the research topics. Research topics and academic dissertations also have a large proportion of management, management strategy, succession, financial accounting, and business performance. In other words, it can be said that the research on family business in Korea corresponds to the early childhood of the United States. First of all, in the future, we need to put more effort into increasing the qualitative research, starting with the definition of a family business, which is an essential problem, in addition to the theory building of family business. Second, as an analysis level of research, we should make family an important level of analysis for existing individuals, groups, and organizations. Third, the research subject and research area should be expanded. It is desperately necessary to study large companies including chaebols, mainly from small and medium-sized companies, which are the existing research areas of family business. In addition, it is considered that it is necessary to appropriately introduce various theories suitable for the interdisciplinary study, which is the characteristic of the family business, for example, theories of family science, psychology, and sociology. Fourth, it should build the research infrastructure.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.5
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• pp.40-53
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• 2022

This study carried out to provide the methodology and basic status material of using Korean national statistics needed to find the actual state of the landscape architecture industry. The landscape architecture industry was classified into 'Design', 'Construction Management', 'construction', 'Maintenance & Management', 'Materials', 'Research', 'Education', and 'Administration' areas. In each field, business types were systemized and associated in accordance with Korean standard industrial classification and legislations pertinent to construction. Among them, the business types directly defined in the construction related legislations under the Ministry of Land, Infrastructure and Transport were focused on, and the establishment, association, integration, distribution, duplication, and omission of national statistics were analyzed. As a result, the business types of statistical analysis were selected. In order for commonality of statistical items and minimized error of interpretation, semantic analysis was conducted. Finally, the number of registered business types, the number of workers, and sales were selected. Based on them, the analysis framework applicable to fundamental analysis and evaluation of the actual state of the industry was proposed. Actual national statical data were applied for analysis and evaluation. In 2019, the number of registered business types related to the landscape architecture industry was 12,160, the number of workers by business type was 106,296, and the sales by business type were 8,308.5 billion KRW. The number of registered business types and the number of workers had been on the rise from 2017, whereas the sales had been on the decrease. It is required to come up with a plan for industrial development. This study was conducted with the national statistics established by multiple public institutions, so that there are limitations in securing consistency and reliability. Therefore, it is necessary to establish systematic and consistent national statistics in accordance with 「Landscaping Promotion Act」. In the future, it will planned to research application and development plans of national statistics according to subjects including park and green.

• Korean Association of Arts Management
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• no.51
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• pp.5-34
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• 2019

This study is a qualitative study conducted to identify environmental factors that impede emerging artists' ongoing creative activities, focusing on the local cultural ecosystem that they are part of. By doing so, we tried to understand the dynamics between key stake holders in the ecosystem that these young artists interact with and how they build and perceive their own, local cultural environment. The central research question of this study is: what factors impede the continuous creative activities of young artists and what causes them to leave local art scenes? The research was conducted thoroughly on the basis of emerging artists' experience and perspectives and applied to Creative Sector Holistic Model for analysis. The data of this research were collected based on two national-funding projects to support young artists from 2016 to 2018. The main research method of this study was interviews: official and casual interviews were executed with 29 young artists aged 20-34 who work in the fields of painting, literature, sculpture, video, korean traditional music, visual design and crafts. For the analysis of the data, the Creative Sector Holistic Model(Wyszomirski, 2008), which had applied the ecological logic to the creative industries, was applied. The result of this study shows that economic difficulties were not the only hindering factor in their sustainable art-making process. Various impeding factors derived from the local cultural ecosystem have been identified within the Holistic Model, demonstrating that these factors are all intertwined and connected. Thus, analyzing and understanding one's local cultural ecosystem can provide keys to long-term and lasting impacts when a local authorities wish to support young artists for the future of local cultural environment.

• Korean Journal of Construction Engineering and Management
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• v.24 no.6
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• pp.24-35
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• 2023

Recently, the Ministry of Land, Infrastructure and Transport presented the '6th Construction Technology Promotion Basic Plan' and 'Smart Construction Revitalization Plan (2022.7.20)'. Off-Site Construction (OSC), which involves construction and production of PC (Precast Concrete) and Modular, etc., has advantages in shortening the construction period, reducing costs, improving quality, reducing construction waste, and reducing safety accidents. However, the construction cost is high compared to the traditional RC construction method, which has hindered its utilization and spread. In this study, OSC utilization was improved. An economic analysis indicator and methodology that can support decision-making in the planning and design stages for multi-unit housing were proposed. The factors used in the economic analysis of OSC (based on the PC method) of apartment houses were reviewed. As for the indicators used in the cost and benefit section, 'Construction Period', 'Disaster Occurrence', 'Waste Generation', and 'Greenhouse gas Emission', which reflect the technical advantages of OSC, were derived. In addition, a scenario analysis was conducted based on actual apartment housing case data for the presented economic analysis indicators and benefit calculation standards. The level of benefit that offsets the difference between the existing RC construction method and the construction cost was reviewed. In future studies, it will be necessary to conduct additional case studies to apply the measurement criteria for detailed indicators and supplement the benefit indicators.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1173-1183
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• 2023

Over the past 20 years, Korea's overall import and export cargo volume has increased at an average annual rate of approximately 5.3%. About 99% of the cargo is still being transported by sea. Due to recent increases in maritime cargo volume, congestion in maritime logistics has become challenging due to factors such as the COVID-19 pandemic and conflicts. Continuous monitoring of ports has become crucial. Various ground observation systems and Automatic Identification System (AIS) data have been utilized for monitoring ports and conducting numerous preliminary studies for the efficient operation of container terminals and cargo volume prediction. However, small and developing countries' ports face difficulties in monitoring due to environmental issues and aging infrastructure compared to large ports. Recently, with the increasing utility of artificial satellites, preliminary studies have been conducted using satellite imagery for continuous maritime cargo data collection and establishing ocean monitoring systems in vast and hard-to-reach areas. This study aims to visually detect ships docked at berths in the Busan New Port using high-resolution satellite imagery and quantitatively evaluate berth utilization rates. By utilizing high-resolution satellite imagery from Compact Advanced Satellite 500-1 (CAS500-1), Korea Multi-Purpose satellite-3 (KOMPSAT-3), PlanetScope, and Sentinel-2A, ships docked within the port berths were visually detected. The berth utilization rate was calculated using the total number of ships that could be docked at the berths. The results showed variations in berth utilization rates on June 2, 2022, with values of 0.67, 0.7, and 0.59, indicating fluctuations based on the time of satellite image capture. On June 3, 2022, the value remained at 0.7, signifying a consistent berth utilization rate despite changes in ship types. A higher berth utilization rate indicates active operations at the berth. This information can assist in basic planning for new ship operation schedules, as congested berths can lead to longer waiting times for ships in anchorages, potentially resulting in increased freight rates. The duration of operations at berths can vary from several hours to several days. The results of calculating changes in ships at berths based on differences in satellite image capture times, even with a time difference of 4 minutes and 49 seconds, demonstrated variations in ship presence. With short observation intervals and the utilization of high-resolution satellite imagery, continuous monitoring within ports can be achieved. Additionally, utilizing satellite imagery to monitor changes in ships at berths in minute increments could prove useful for small and developing country ports where harbor management is not well-established, offering valuable insights and solutions.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.37-50
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• 2023

Venture capital invests the necessary capital and supports management and technology in promising small and medium-sized venture companies in the early stages of start-up with promising technology and excellent manpower. It plays a role as a key player in the venture ecosystem that realizes profits by collecting the investment through various means after growth. Venture capital's job is to recruit various investors(LPs) to invest in small and medium-sized venture companies with growth potential through the formation of venture investment funds, and to collect investment as companies grow, distribute and reinvest. The main tasks of venture capitalists, which play the most important role in venture investment, are finding promising companies, corporate analysis and evaluation, investment screening, follow-up management, and investment recovery. Venture capital's success indicators are fund formation and return on investment, and venture capitalists are rewarded with annual salary, performance-based incentive, and promotion with work performance such as investment, exit, and fund formation. Compared to the recent rapidly growing venture investment market, investment manpower is insufficient, and venture capital is making great efforts to foster manpower and establish infrastructure and systems for long-term service, but research has been conducted mainly from a quantitative perspective. Accordingly, this study aims to empirically analyzed the impact of investment experience, delegation of authority, job fit, and peer relationships on fund formation and return on investment according to the characteristics of the venture capital industry. The results of these empirical studies suggested that future venture capital needs a job environment and manpower operation strategy so that venture capitalists with high job fit and investment experience can work for a long time.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.