• Radiation Oncology Journal
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• v.19 no.1
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• pp.53-65
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• 2001

Purpose : To improve the local control of patients with nasopharyngeal cancer, we have implemented 3-D conformal radiotherapy and forward intensity modulated radiation therapy (IMRT) to used of compensating filters. Three dimension conformal radiotherapy with intensity modulation is a new modality for cancer treatments. We designed 3-D treatment planning with 3-D RTP (radiation treatment planning system) and evaluation dose distribution with tumor control probability (TCP) and normal tissue complication probability (NTCP). Material and Methods : We have developed a treatment plan consisting four intensity modulated photon fields that are delivered through the compensating tilters and block transmission for critical organs. We get a full size CT imaging including head and neck as 3 mm slices, and delineating PTV (planning target volume) and surrounding critical organs, and reconstructed 3D imaging on the computer windows. In the planning stage, the planner specifies the number of beams and their directions including non-coplanar, and the prescribed doses for the target volume and the permissible dose of normal organs and the overlap regions. We designed compensating filter according to tissue deficit and PTV volume shape also dose weighting for each field to obtain adequate dose distribution, and shielding blocks weighting for transmission. Therapeutic gains were evaluated by numerical equation of tumor control probability and normal tissue complication probability. The TCP and NTCP by DVH (dose volume histogram) were compared with the 3-D conformal radiotherapy and forward intensity modulated conformal radiotherapy by compensator and blocks weighting. Optimization for the weight distribution was peformed iteration with initial guess weight or the even weight distribution. The TCP and NTCP by DVH were compared with the 3-D conformal radiotherapy and intensitiy modulated conformal radiotherapy by compensator and blocks weighting. Results : Using a four field IMRT plan, we have customized dose distribution to conform and deliver sufficient dose to the PTV. In addition, in the overlap regions between the PTV and the normal organs (spinal cord, salivary grand, pituitary, optic nerves), the dose is kept within the tolerance of the respective organs. We evaluated to obtain sufficient TCP value and acceptable NTCP using compensating filters. Quality assurance checks show acceptable agreement between the planned and the implemented MLC(multi-leaf collimator). Conclusion : IMRT provides a powerful and efficient solution for complex planning problems where the surrounding normal tissues place severe constraints on the prescription dose. The intensity modulated fields can be efficaciously and accurately delivered using compensating filters.

• Korean Journal of Heritage: History & Science
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• v.56 no.4
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• pp.230-246
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• 2023

The wooden fence(木柵), which began to appear in the Bronze Age and is presumed to be the oldest defense facility in human history, was used as a fortress for the purpose of further strengthening military defense functions until after the Japanese Invasion of Korea in 1592 in the Joseon Dynasty(壬辰倭亂). As it was established as the concept of a fortress or a fence installed outside a fence castle(城柵) or barracks fence(營柵), its importance as an essential facility for defense was further highlighted. This study is the result of exploring wooden fence that were used as official facilities during the Joseon Dynasty, focusing on literature surveys such as 『Annals of the Joseon Dynasty』 and 『New Jeungdonggukyeojiseungram』 In this study, in particular, the conclusion of this study is as follows, focusing on the use and function of Nokgakseong(鹿角城), underwater wooden fence, installation methods, and materials of wooden fences, is as follows. The conclusions of this study, which focused on the materials of the wooden fence, are as follows. First, as invasions by foreign enemies became more frequent in the late Goryeo and early Joseon Dynasty, wooden fences played a major role as a major out-of-castle defense facility((防禦施設). In addition, wooden fences were modified and installed into various types such as wooden fences(木柵城), Nokgakseong, a fence made up of large branches in the shape of a deer antler, and underwater wooden fences(水中木柵) according to the circumstances of the times, government policy, and location environment. Second, wooden fences were installed in strategic locations in defense facilities for military purposes, such as mountain fortress(山城), fortresses(營), camps(鎭), forts(堡), and castles(邑城) in strategic locations, and were used for defense in case of emergency. According to the urgency of farming, it was installed in accordance with the non-farming season, when it is easy to mobilize manpower to avoid the busy farming season. The size of the wooden fence of the Joseon Dynasty, which are confirmed through literature records, was converted into Pobaekchuk(布帛尺), and the circumference was very diverse from 4,428chuk(2,066m) to 55chuk(25m). Third, Nokgakseong is an efficient combat support facility that is more aggressive than a general wooden fence, and the records of Nokgakseong in the Annals of the Joseon Dynasty appeared during the King Sejong period the record was 20 times, the most. By region, it was found that it was mainly installed in coastal rugged areas such as Pyeongan and Hamgildo(12), which are the 6-jin areas of the 4th Army. Fourth, in the early 15th century, as the royal court established a maritime defense strategy for the coastal area of the southern coast, after the Sampo Invasion(三浦倭亂), riots by Japanese settlers in Sampo in 1510, major military posts including eupseong(邑城), camps, and forts were established. The installation of underwater barriers around various government facilities rapidly increased as a defense facility to block the warships of Japanese pirates around various government facilities. Fifth, between the 15th and 17th centuries before and after the Japanese Invasion of Korea in Sampo, underwater fences were installed in the Southern coast and Ganghwa Island. In particular, in the 15th century, underwater fences were intensively installed in coastal areas of Gyeongsangnam-do, such as Jepo. Pine trees and Oaks are the main materials used for underwater fences, but other materials such as Oldham's meliosma, Loose-flower hornbeam and The vines of arrowroots were also used as materials for wooden fences.

• 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.