• Journal of the Korean Association of Geographic Information Studies
• /
• v.2 no.2
• /
• pp.27-44
• /
• 1999

Digital maps produced in Korea are various in scale and include a lot of geographic and attribute data. In this study, it is argued that, to reduce the production cost and the difficulties for renewal, it is necessary to establish the already nationally drawn 1:5,000 scale digital maps as the base maps and simplify them as much as the TIGER files in the U.S. The comprehensive data included in the digital maps in Korea are mostly land use information, which are supposed to be established separately from the digital maps. The land use information system could be maintained and updated cheaply and frequently at the local government level. In response to common needs, the land use information could be imported to GIS and used for analyses. As technologies and societies changes, the Census questions and methodologies should be changed for better uses. Along with GIS, the Census would be developed and processed more reliably and efficiently. Also, it is recommended for Korean government to develop the Census Tract and Block Group system. Current Eup, Myon, Dong as basic units for Census information may not be useful or effective for micro level urban analyses and public service planning activities because of their large population and land areas. It is recommended that optimum population of a Census Tract be 5,000 and a Block Groups 1,500, and one Census Tract includes 1~9 Block Groups. It is recommend that Census Tract and Block Group boundary lines be decided flexibly in light of population, physical features, socio-economic attributes, and tradition. For urban analyses using GIS, socio-economic census data, city government's information such as parcel data and building permit data, survey data, and satellite image data could also be used. The existence of Census Tracts and Block Groups as well as GIS could help for the data and methods to be useful for urban analyses and public service provisions.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.27 no.3
• /
• pp.127-143
• /
• 2022

Recently, many attempts to run numerical ocean models in cloud computing environments have been tried actively. A cloud computing environment can be an effective means to implement numerical ocean models requiring a large-scale resource or quickly preparing modeling environment for global or large-scale grids. Many commercial and private cloud computing systems provide technologies such as virtualization, high-performance CPUs and instances, ether-net based high-performance-networking, and remote direct memory access for High Performance Computing (HPC). These new features facilitate ocean modeling experimentation on commercial cloud computing systems. Many scientists and engineers expect cloud computing to become mainstream in the near future. Analysis of the performance and features of commercial cloud services for numerical modeling is essential in order to select appropriate systems as this can help to minimize execution time and the amount of resources utilized. The effect of cache memory is large in the processing structure of the ocean numerical model, which processes input/output of data in a multidimensional array structure, and the speed of the network is important due to the communication characteristics through which a large amount of data moves. In this study, the performance of the Regional Ocean Modeling System (ROMS), the High Performance Linpack (HPL) benchmarking software package, and STREAM, the memory benchmark were evaluated and compared on commercial cloud systems to provide information for the transition of other ocean models into cloud computing. Through analysis of actual performance data and configuration settings obtained from virtualization-based commercial clouds, we evaluated the efficiency of the computer resources for the various model grid sizes in the virtualization-based cloud systems. We found that cache hierarchy and capacity are crucial in the performance of ROMS using huge memory. The memory latency time is also important in the performance. Increasing the number of cores to reduce the running time for numerical modeling is more effective with large grid sizes than with small grid sizes. Our analysis results will be helpful as a reference for constructing the best computing system in the cloud to minimize time and cost for numerical ocean modeling.

• Journal of Internet Computing and Services
• /
• v.14 no.5
• /
• pp.1-10
• /
• 2013

RFID(Radio Frequency Identification) system is non-contact identification technology. A basic RFID system consists of a reader, and a set of tags. RFID tags can be divided into active and passive tags. Active tags with power source allows their own operation execution and passive tags are small and low-cost. So passive tags are more suitable for distribution industry than active tags. A reader processes the information receiving from tags. RFID system achieves a fast identification of multiple tags using radio frequency. RFID systems has been applied into a variety of fields such as distribution, logistics, transportation, inventory management, access control, finance and etc. To encourage the introduction of RFID systems, several problems (price, size, power consumption, security) should be resolved. In this paper, we proposed an algorithm to significantly alleviate the collision problem caused by simultaneous responses of multiple tags. In the RFID systems, in anti-collision schemes, there are three methods: probabilistic, deterministic, and hybrid. In this paper, we introduce ALOHA-based protocol as a probabilistic method, and Tree-based protocol as a deterministic one. In Aloha-based protocols, time is divided into multiple slots. Tags randomly select their own IDs and transmit it. But Aloha-based protocol cannot guarantee that all tags are identified because they are probabilistic methods. In contrast, Tree-based protocols guarantee that a reader identifies all tags within the transmission range of the reader. In Tree-based protocols, a reader sends a query, and tags respond it with their own IDs. When a reader sends a query and two or more tags respond, a collision occurs. Then the reader makes and sends a new query. Frequent collisions make the identification performance degrade. Therefore, to identify tags quickly, it is necessary to reduce collisions efficiently. Each RFID tag has an ID of 96bit EPC(Electronic Product Code). The tags in a company or manufacturer have similar tag IDs with the same prefix. Unnecessary collisions occur while identifying multiple tags using Query Tree protocol. It results in growth of query-responses and idle time, which the identification time significantly increases. To solve this problem, Collision Tree protocol and M-ary Query Tree protocol have been proposed. However, in Collision Tree protocol and Query Tree protocol, only one bit is identified during one query-response. And, when similar tag IDs exist, M-ary Query Tree Protocol generates unnecessary query-responses. In this paper, we propose Adaptive M-ary Query Tree protocol that improves the identification performance using m-bit recognition, collision information of tag IDs, and prediction technique. We compare our proposed scheme with other Tree-based protocols under the same conditions. We show that our proposed scheme outperforms others in terms of identification time and identification efficiency.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.28 no.2
• /
• pp.311-326
• /
• 2018

This paper has experimented using News sample video with QCIF ($176{\times}144$) resolution in JM v10.2 code of H.264/AVC-MPEG. The region of interest (ROI) to be encrypted occurred the drift by unnecessarily referring to each frame continuously in accordance with the characteristics of the motion prediction and compensation of the H.264 standard. In order to mitigate the drift, the latest related research method of re-inserting encrypted I-picture into a certain period leads to an increase in the amount of additional computation that becomes the factor increasing the bit-rate overhead of the entire video. Therefore, the reference search range of the block and the frame in the ROI to be encrypted is restricted in the motion prediction and compensation for each frame, and the reference search range in the non-ROI not to be encrypted is not restricted to maintain the normal encoding efficiency. In this way, after encoding the video with restricted reference search range, this article proposes a method of RC4 bit-stream encryption for the ROI such as the face to be able to identify in order to protect personal information in the video. Also, it is compared and analyzed the experimental results after implementing the unencrypted original video, the latest related research method, and the proposed method in the condition of the same environment. In contrast to the latest related research method, the bit-rate overhead of the proposed method is 2.35% higher than that of the original video and 14.93% lower than that of the latest related method, while mitigating temporal drift through the proposed method. These improved results have verified by experiments of this study.

• KIPS Transactions on Software and Data Engineering
• /
• v.2 no.2
• /
• pp.81-90
• /
• 2013

During the past decade, many changes and attempts have been tried and are continued developing new technologies in the computing area. The brick wall in computing area, especially power wall, changes computing paradigm from computing hardwares including processor and system architecture to programming environment and application usage. The high performance computing (HPC) area, especially, has been experienced catastrophic changes, and it is now considered as a key to the national competitiveness. In the late 2000's, many leading countries rushed to develop Exascale supercomputing systems, and as a results tens of PetaFLOPS system are prevalent now. In Korea, ICT is well developed and Korea is considered as a one of leading countries in the world, but not for supercomputing area. In this paper, we describe architecture design of MAHA supercomputing system which is aimed to develop 300 TeraFLOPS system for bio-informatics applications like human genome analysis and protein-protein docking. MAHA supercomputing system is consists of four major parts - computing hardware, file system, system software and bio-applications. MAHA supercomputing system is designed to utilize heterogeneous computing accelerators (co-processors like GPGPUs and MICs) to get more performance/$, performance/area, and performance/power. To provide high speed data movement and large capacity, MAHA file system is designed to have asymmetric cluster architecture, and consists of metadata server, data server, and client file system on top of SSD and MAID storage servers. MAHA system softwares are designed to provide user-friendliness and easy-to-use based on integrated system management component - like Bio Workflow management, Integrated Cluster management and Heterogeneous Resource management. MAHA supercomputing system was first installed in Dec., 2011. The theoretical performance of MAHA system was 50 TeraFLOPS and measured performance of 30.3 TeraFLOPS with 32 computing nodes. MAHA system will be upgraded to have 100 TeraFLOPS performance at Jan., 2013.

• Journal of KIISE:Databases
• /
• v.33 no.1
• /
• pp.102-116
• /
• 2006

Electronic catalogs (or e-catalogs) hold information about the goods and services offered or requested by the participants, and consequently, form the basis of an e-commerce transaction. Catalog management is complicated by a number of factors and product classification is at the core of these issues. Classification hierarchy is used for spend analysis, custom3 regulation, and product identification. Classification is the foundation on which product databases are designed, and plays a central role in almost all aspects of management and use of product information. However, product classification has received little formal treatment in terms of underlying model, operations, and semantics. We believe that the lack of a logical model for classification Introduces a number of problems not only for the classification itself but also for the product database in general. It needs to meet diverse user views to support efficient and convenient use of product information. It needs to be changed and evolved very often without breaking consistency in the cases of introduction of new products, extinction of existing products, class reorganization, and class specialization. It also needs to be merged and mapped with other classification schemes without information loss when B2B transactions occur. For these requirements, a classification scheme should be so dynamic that it takes in them within right time and cost. The existing classification schemes widely used today such as UNSPSC and eClass, however, have a lot of limitations to meet these requirements for dynamic features of classification. In this paper, we try to understand what it means to classify products and present how best to represent classification schemes so as to capture the semantics behind the classifications and facilitate mappings between them. Product information implies a plenty of semantics such as class attributes like material, time, place, etc., and integrity constraints. In this paper, we analyze the dynamic features of product databases and the limitation of existing code based classification schemes. And describe the semantic classification model, which satisfies the requirements for dynamic features oi product databases. It provides a means to explicitly and formally express more semantics for product classes and organizes class relationships into a graph. We believe the model proposed in this paper satisfies the requirements and challenges that have been raised by previous works.

• KIPS Transactions on Computer and Communication Systems
• /
• v.10 no.8
• /
• pp.223-230
• /
• 2021

The lightweight block cipher PIPO announced at ICISC'20 has been effectively implemented by applying the bit slice technique. In this paper, we propose a parallel optimal implementation of PIPO for ARM processors. The proposed implementation enables parallel encryption of 8-plaintexts and 16-plaintexts. The implementation targets the A10x fusion processor. On the target processor, the existing reference PIPO code has performance of 34.6 cpb and 44.7 cpb in 64/128 and 64/256 standards. Among the proposed methods, the general implementation has a performance of 12.0 cpb and 15.6 cpb in the 8-plaintexts 64/128 and 64/256 standards, and 6.3 cpb and 8.1 cpb in the 16-plaintexts 64/128 and 64/256 standards. Compared to the existing reference code implementation, the 8-plaintexts parallel implementation for each standard has about 65.3%, 66.4%, and the 16-plaintexts parallel implementation, about 81.8%, and 82.1% better performance. The register minimum alignment implementation shows performance of 8.2 cpb and 10.2 cpb in the 8-plaintexts 64/128 and 64/256 specifications, and 3.9 cpb and 4.8 cpb in the 16-plaintexts 64/128 and 64/256 specifications. Compared to the existing reference code implementation, the 8-plaintexts parallel implementation has improved performance by about 76.3% and 77.2%, and the 16-plaintext parallel implementation is about 88.7% and 89.3% higher for each standard.

• Korean Journal of Heritage: History & Science
• /
• v.57 no.1
• /
• pp.82-108
• /
• 2024

With the transition of archaeological recording method's transition from analog to digital, the 3D scanning technology has been actively adopted within the field. Research on the digital archaeological digital data gathered from 3D scanning and photogrammetry is continuously being conducted. However, due to cost and manpower issues, most buried cultural heritage organizations are hesitating to adopt such digital technology. This paper aims to present a digital recording method of relics utilizing open-source software and photogrammetry technology, which is believed to be the most efficient method among 3D scanning methods. The digital recording process of relics consists of three stages: acquiring a 3D model, creating a joining map with the edited 3D model, and creating an digital drawing. In order to enhance the accessibility, this method only utilizes open-source software throughout the entire process. The results of this study confirms that in terms of quantitative evaluation, the deviation of numerical measurement between the actual artifact and the 3D model was minimal. In addition, the results of quantitative quality analysis from the open-source software and the commercial software showed high similarity. However, the data processing time was overwhelmingly fast for commercial software, which is believed to be a result of high computational speed from the improved algorithm. In qualitative evaluation, some differences in mesh and texture quality occurred. In the 3D model generated by opensource software, following problems occurred: noise on the mesh surface, harsh surface of the mesh, and difficulty in confirming the production marks of relics and the expression of patterns. However, some of the open source software did generate the quality comparable to that of commercial software in quantitative and qualitative evaluations. Open-source software for editing 3D models was able to not only post-process, match, and merge the 3D model, but also scale adjustment, join surface production, and render image necessary for the actual measurement of relics. The final completed drawing was tracked by the CAD program, which is also an open-source software. In archaeological research, photogrammetry is very applicable to various processes, including excavation, writing reports, and research on numerical data from 3D models. With the breakthrough development of computer vision, the types of open-source software have been diversified and the performance has significantly improved. With the high accessibility to such digital technology, the acquisition of 3D model data in archaeology will be used as basic data for preservation and active research of cultural heritage.

• Korean Journal of Soil Science and Fertilizer
• /
• v.7 no.3
• /
• pp.177-184
• /
• 1974

An attempt was made to determine simple and the most reasonable fertilizer recommendation for barley utilizing the present knowledge about the effect of soil and climatic factors on barley response to NPK fertilizer in Korea and establishing the critical contents of available nutrients in soils. The results were summarized as follows. 1. The relationships between relative yields or fertilizers rates for maximum yields from quadratic response curves and contents of organic matter, available $P_2O_5$, exchangeable K in soils were examined. The trend was more prospective with relative yields because of smaller variation than with fertilizer rates. 2. Since the relationship between N relative yields and organic matter contents in soils was almost linear over the practical range, it was difficult to determine the critical content for nitrogen response by quadrant methods. However, 2.6%, country average of organic matter content in upland soils was recommended as the critical point. 3. There showed a trend that average optimum nitrogen rater was higher in heavy texture soils, colder regions. 4. The critical $P_2O_5$ contents in soil were 96 or 118 ppm in two different years, which were very close to the country average, 114 ppm of $P_2O_5$ contents in upland soils. The critical K content in soil was 0.32 me/100g, which was exactly coincident to the country average of exchangeable K in upland soils. 5. According to the contents of avaiiable $P_2O_5$ and exchangeable K, several ranges were established for the purpose of convenience in fertilizer recommendation, that is, very low, Low, Medium, High and very High. 6. More phosphate was recommended in the northern region, clayey soils, and paddy soils, whereas less in the southern region and sandy soils. More potash was recommended in the northern region and sandy soils, whereas less in the southern region and clayey soils. 7. The lower the PH, the more fertilizers were recommended. However, liming was considered to be more effective than increas in amount of fertilizers.

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