• KDI Journal of Economic Policy
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• v.12 no.4
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• pp.47-69
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• 1990

Using an aggregator model, we look into the possibilities for substitution between Korea's exports, imports, domestic sales and domestic inputs (particularly labor), and substitution between disaggregated export and import components. Our approach heavily draws on an economy-wide GNP function that is similar to Samuelson's, modeling trade functions as derived from an integrated production system. Under the condition of homotheticity and weak separability, the GNP function would facilitate consistent aggregation that retains certain properties of the production structure. It would also be useful for a two-stage optimization process that enables us to obtain not only the net output price elasticities of the first-level aggregator functions, but also those of the second-level individual components of exports and imports. For the implementation of the model, we apply the Symmetric Generalized McFadden (SGM) function developed by Diewert and Wales to both stages of estimation. The first stage of the estimation procedure is to estimate the unit quantity equations of the second-level exports and imports that comprise four components each. The parameter estimates obtained in the first stage are utilized in the derivation of instrumental variables for the aggregate export and import prices being employed in the upper model. In the second stage, the net output supply equations derived from the GNP function are used in the estimation of the price elasticities of the first-level variables: exports, imports, domestic sales and labor. With these estimates in hand, we can come up with various elasticities of both the net output supply functions and the individual components of exports and imports. At the aggregate level (first-level), exports appear to be substitutable with domestic sales, while labor is complementary with imports. An increase in the price of exports would reduce the amount of the domestic sales supply, and a decrease in the wage rate would boost the demand for imports. On the other hand, labor and imports are complementary with exports and domestic sales in the input-output structure. At the disaggregate level (second-level), the price elasticities of the export and import components obtained indicate that both substitution and complement possibilities exist between them. Although these elasticities are interesting in their own right, they would be more usefully applied as inputs to the computational general equilibrium model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.2
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• pp.1200-1206
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• 2015

The major target for drug discovery, G-protein coupled receptor (GPCR) is involved in many physiological activities and related to various diseases and disorders. Among experimental techniques relating to the GPCR drug discovery process, various cell-based screening methods are influenced by cell conditions used in the overall process. Recently, the utilization of frozen cells is suggested in terms of reducing data variation and cost-effectiveness. The aim of this study is to evaluate various conditions in cell freezing such as temperature conditions and storage terms. The stable cell lines for calcium sensing receptor and urotensin receptor were established followed by storing cultured cells at $-80^{\circ}C$ up to 4 weeks. To compare with cell stored at liquid nitrogen, agonist and antagonist responses were recorded based on the luminescence detection by the calcium induced photoprotein activation. Cell signals were reduced as the storage period was increased without the changes in $EC_{50}$ and $IC_{50}$ values $EC_{50}:3.46{\pm}1.36mM$, $IC_{50}:0.49{\pm}0.15{\mu}M$). In case of cells stored in liquid nitrogen, cell responses were decreased comparing to those in live cells, however changes by storage periods and significant variations of $EC_{50}/IC_{50}$ values were not detected. The decrease of cell signals in various frozen cells may be due to the increase of cell damages. From these results, the best way for a long-term cryopreservation is the use of liquid nitrogen condition, and for the purpose of short-term storage within a month, $-80^{\circ}C$ storage condition can be possible to adopt. As a conclusion, the active implementation of frozen cells may contribute to decrease variations of experimental data during the initial cell-based screening process.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.5
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• pp.3-16
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• 2008

A Wireless Sensor Network (WSN for short) is a wireless network consisting of distributed small devices which are called sensor nodes or motes. Recently, there has been an extensive research on WSN and also on its security. For secure storage and secure transmission of the sensed information, sensor nodes should be equipped with cryptographic algorithms. Moreover, these algorithms should be efficiently implemented since sensor nodes are highly resource-constrained devices. There are already some existing algorithms applicable to sensor nodes, including public key ciphers such as TinyECC and standard block ciphers such as AES. Stream ciphers, however, are still to be analyzed, since they were only recently standardized in the eSTREAM project. In this paper, we implement over the MicaZ platform nine software-based stream ciphers out of the ten in the second and final phases of the eSTREAM project, and we evaluate their performance. Especially, we apply several optimization techniques to six ciphers including SOSEMANUK, Salsa20 and Rabbit, which have survived after the final phase of the eSTREAM project. We also present the implementation results of hardware-oriented stream ciphers and AES-CFB fur reference. According to our experiment, the encryption speeds of these software-based stream ciphers are in the range of 31-406Kbps, thus most of these ciphers are fairly acceptable fur sensor nodes. In particular, the survivors, SOSEMANUK, Salsa20 and Rabbit, show the throughputs of 406Kbps, 176Kbps and 121Kbps using 70KB, 14KB and 22KB of ROM and 2811B, 799B and 755B of RAM, respectively. From the viewpoint of encryption speed, the performances of these ciphers are much better than that of the software-based AES, which shows the speed of 106Kbps.

• 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.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.