• Asia-Pacific Journal of Business
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• v.13 no.4
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• pp.91-107
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• 2022

Purpose - This study quantitatively examines the level of financial inclusion of a microfinance institution in each region and how this is changing recently, and examines the level of financial inclusion by region and various financial characteristic factors related to it. It was empirically verified what kind of significant impact actually has on the institution's major management performance indicators (stability, profitability, efficiency, and public interest). Design/methodology/approach - It was confirmed that the institution's financial inclusion index declined rapidly after 2015 as a whole, although there were some differences by region depending on regional characteristics. However, considering the fact that the number of branches per 100,000 adult population is steadily increasing nationwide, it was found that, contrary to what is known, the simple decrease in the number of branches of the institution was not the main cause. Findings - The analysis results of this study show that the institution's efforts for financial inclusion have a positive impact on profitability, stability, efficiency, and public interest, and that the institution pursues profitability, efficiency, stability, and public interest. showed that some trade-offs exist. In other words, overall, it was analyzed that profitability of the institution has a positive effect on efficiency, and efficiency has a positive effect on stability and public interest. Research implications or Originality - Since the institution's efforts to improve its profitability do not have a negative impact on its stability and public interest, it is judged that it is important to take a strategic stance, so excessive loan supply that exceeds the scope of the institution's own control needs to be avoided as much as possible. More detailed financial supply strategies and business management capabilities that enhance the asset soundness and management efficiency of safes need to be demonstrated.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.11
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• pp.369-382
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(quality of service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to activate only the minimum number of servers needed to handle current user requests. Previous studies on energy aware server cluster put efforts to reduce power consumption or heat dissipation, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management method to improve not only performance per watt but also QoS of the existing server power mode control method based on autonomous learning. Our proposed method is to adjust server power mode based on a hybrid approach of autonomous learning method with multi level thresholds and power consumption prediction method. Autonomous learning method with multi level thresholds is applied under normal load situation whereas power consumption prediction method is applied under abnormal load situation. The decision on whether current load is normal or abnormal depends on the ratio of the number of current user requests over the average number of user requests during recent past few minutes. Also, a dynamic shutdown method is additionally applied to shorten the time delay to make servers off. We performed experiments with a cluster of 16 servers using three different kinds of load patterns. The multi-threshold based learning method with prediction and dynamic shutdown shows the best result in terms of normalized QoS and performance per watt (valid responses). For banking load pattern, real load pattern, and virtual load pattern, the numbers of good response per watt in the proposed method increase by 1.66%, 2.9% and 3.84%, respectively, whereas QoS in the proposed method increase by 0.45%, 1.33% and 8.82%, respectively, compared to those in the existing autonomous learning method with single level threshold.

• Journal of Korea Spatial Information System Society
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• v.7 no.1 s.13
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• pp.25-37
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• 2005

Recently, with the development of wireless communications and mobile computing, interest about mobile computing is rising. Mobile computing can be regarded as an environment where a user carries mobile devices, such as a PDA or a notebook, and shares resources with a server computer via wireless communications. A mobile database refers to a database which is used in these mobile devices. The mobile database can be used in the fields of insurance business, banking business, medical treatment, and so on. Especially, LBS(Location Based Service) which utilizes location information of users becomes an essential field of mobile computing. In order to support LBS in the mobile environment, there must be an Embedded Spatial MMDBMS(Main-Memory Database Management System) that can efficiently manage large spatial data in spatial mobile devices. Therefore, in this paper, we designed and implemented the Embedded Spatial MMDBMS, extended from the HSQLDB which is an existing MMDBMS for PC, to manage spatial data efficiently in spatial mobile devices. The Embedded Spatial MMDBMS adopted the spatial data model proposed by ISO(International Organization for Standardization), provided the arithmetic coding method that is suitable for spatial data, and supported the efficient spatial index which uses the MBR compression and hashing method suitable for spatial mobile devices. In addition, the system offered the spatial data display capability in low-performance processors of spatial mobile devices and supported the data caching and synchronization capability for performance improvement of spatial data import/export between the Embedded Spatial MMDBMS and the GIS server.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.4
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• pp.259-267
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• 2013

GIS-based web map service is all the more accessible to the public. Among others, location query services are most frequently utilized, which are currently restricted to only one keyword search. Although there increases the demand for the service for querying multiple keywords corresponding to sequential activities(banking, having lunch, watching movie, and other activities) in various locations POI, such service is yet to be provided. The objective of the paper is to develop the k-IPS algorithm for quickly and accurately querying multiple POIs that internet users input and locating the search outcomes on a web map. The algorithm is developed by utilizing hierarchical tree structure of $R^*$-tree indexing technique to produce overlapped geometric regions. By using recursive $R^*$-tree index based spatial join process, the performance of the current spatial join operation was improved. The performance of the algorithm is tested by applying 2, 3, and 4 multiple POIs for spatial query selected from 159 keyword set. About 90% of the test outcomes are produced within 0.1 second. The algorithm proposed in this paper is expected to be utilized for providing a variety of location-based query services, of which demand increases to conveniently support for citizens' daily activities.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.275-284
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• 2008

With increases in recent security requirements, biometric technology such as fingerprints, faces and iris recognitions have been widely used in many applications including door access control, personal authentication for computers, internet banking, automatic teller machines and border-crossing controls. Finger vein recognition uses the unique patterns of finger veins in order to identify individuals at a high level of accuracy. This paper proposes new device and methods for touchless finger vein recognition. This research presents the following five advantages compared to previous works. First, by using a minimal guiding structure for the finger tip, side and the back of finger, we were able to obtain touchless finger vein images without causing much inconvenience to user. Second, by using a hot mirror, which was slanted at the angle of 45 degrees in front of the camera, we were able to reduce the depth of the capturing device. Consequently, it would be possible to use the device in many applications having size limitations such as mobile phones. Third, we used the holistic texture information of the finger veins based on a LBP (Local Binary Pattern) without needing to extract accurate finger vein regions. By using this method, we were able to reduce the effect of non-uniform illumination including shaded and highly saturated areas. Fourth, we enhanced recognition performance by excluding non-finger vein regions. Fifth, when matching the extracted finger vein code with the enrolled one, by using the bit-shift in both the horizontal and vertical directions, we could reduce the authentic variations caused by the translation and rotation of finger. Experimental results showed that the EER (Equal Error Rate) was 0.07423% and the total processing time was 91.4ms.

• Journal of Wetlands Research
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• v.19 no.1
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• pp.45-51
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• 2017

Nepal is a landlocked mountainous country in South Asia, located between China to the north and India to the south, east, and west. As such, wastewater management has become one of the most significant problems in urban area of Nepal. In Nepal, the centralized wastewater treatment systems were dysfunctional due to high cost of operation, discontinuous power supply, lack of proper maintenance and proper technical workforce to address the issues. As such, constructed wetlands (CW) were applied to treat various secondary wastewater as alternative to wastewater treatment facilities. Generally, efficiency and sustainability of CW technology depends on proper operation and maintenance and active community involvement. This study summarizes information about 26 CW in Nepal. Specifically, factors including data banking, removal efficiency, quality of discharged water, compliance to water quality standard of Nepal and operation and maintenance were investigated. Considering removal efficiency per pollutant, Ka-1 achieved the greatest reduction for most pollutant followed by B-1, L-3, Ka-5 and K-1. Nepal has practiced CW technology for more than 2 decades but currently, development of technology was interrupted by the inefficient performance of existing facilities. Public awareness about the technology, natural disaster, unavailability of specified substrate materials, lack of fund for further research and experiments has hindered the expansion of technology. In spite of these concerns, CW was still proven as an alternative solution to the present wastewater problems in urban areas of Nepal.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.8
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• pp.1551-1559
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• 2015

Recently, the performance of smart devices is almost similar to that of the existing PCs, thus the users of smart devices can perform similar works such as messengers, SNSs(Social Network Services), smart banking, etc. originally performed in PC environment using smart devices. Although the development of smart devices has led to positive impacts, it has caused negative changes such as an increase in security threat aimed at mobile environment. Specifically, the threats of mobile devices, such as leaking private information, generating unfair billing and performing DDoS(Distributed Denial of Service) attacks has continuously increased. Over 80% of the mobile devices use android platform, thus, the number of damage caused by mobile malware in android platform is also increasing. In this paper, we propose android based malware detection mechanism using time-series analysis, which is one of statistical-based detection methods.We use auto-regressive moving-average model which is extracting accurate predictive values based on existing data among time-series model. We also use fast and exact malware detection method by extracting possible malware data through Z-Score. We validate the proposed methods through the experiment results.

• Journal of Service Research and Studies
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• v.7 no.4
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• pp.51-68
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• 2017

Financial technology (FinTech) is an emerging financial service sector include innovations in financial literacy and investment, retail banking, education, and crypto-currencies like bitcoin. One of the crucial branch of financial technology-third-party payment (TPP) is undergoing rapid growth, with online/mobile systems replacing offline financial systems. System quality and user attitudes are key perceptions driving third-party payment usage, the importance of these perceptions, however, may be different with countries as users' thinking varies from country to country. Thus, the purpose of this study is to elaborate how factors differ from China to Korea by drawing on the unified theory of acceptance and use of technology (UTAUT2). Additionally, this study also aims to propose a multi-attribute evaluation of the third-party online payment system based on analytic hierarchy process (AHP), fuzzy sets and technique for order performance by similarity to ideal solution (TOPSIS), to examine the relative importance of the perceptions influencing new technology adoption intention. The results showed that the price value has the most significant influence on Chinese perceptions, while the perceived credibility has the most significant effect on Korean perceptions. Sub-criteria also performs different results to Chinese and Korean third-party online payment system.

• Journal of Venture Innovation
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• v.4 no.2
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• pp.77-95
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• 2021

In the midst of the progress of the 4th industrial revolution, the Corona19 Pandemic was forming giant double wave. Companies riding this wave can win, but companies that do not will fall into the wave and struggle. In connection with the 4th industrial revolution, various technologies are emerging and commercialized. At this point, consumers, especially digital natives, who have been with digital since birth, tried to find out what factors affect the intention to use these technologies and which factors have the most important influence. For this purpose, data were collected through a survey on factors affecting the intention to use FinTech technology and AR technology for 150 digital natives in their 20s. Based on this, statistical analysis was conducted and the following results were obtained. As a result of the overall analysis regardless of the type of technology, it was found that performance expectancy, effort expectancy, social influence, and habits have a positive (+) effect on digital natives' intention to use the 4th industrial technology. On the other hand, a significant influence relationship between the facilitating conditions, hedonic motivation and intention to use the 4th industrial technology was not tested. It was found that the influence was greatly influenced by social influence and habits. In the case of FinTech and AR, which were further subdivided into this study, different aspects were revealed as a result of separate analysis. In the case of FinTech technology that emphasizes utilitarian value, performance expectancy, effort expectancy, social influence, and habits had a positive (+) effect on intention to use. It was found that the influence was greatly influenced by habits and social influence. In the case of AR, which emphasizes the hedonic value, all the variables adopted in this study had a positive (+) effect on the intention to use the technology. It was found that hedonic motivation and social influence had a great influence. Combining the results of the analysis, social influence was found to be an important influence variable regardless of the type of 4th industrial technology. FinTech technologies such as mobile banking, where services are becoming more common, are habits, and in the case of AR, which has not yet been universalized and is provided mainly for entertainment, hedonic motivation was found to be an important factor. This study was able to present academic and practical implications based on the above confirmation of factors affecting digital natives' acceptance and use of the 4th industry technology.

• 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.