• Current Research on Agriculture and Life Sciences
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• v.26
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• pp.45-54
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• 2008

The purpose of this research is to develope street trees management program for more an effective street trees management. The principal point of this program is to relate spatial data and attribute data that is the main concept in GIS(Geographic Information System). To do this function, MapObjects which is ESRI's mapping and GIS components was used to process spatial data and Access which had been developed by MS was used to manipulate attribute data in this program. Visual Basic also was used to design and develop user interfaces and procedures, relate two sort of data, and lastly complete Application. Relational data model was adopted to design tables and their relation, Antenucci's GIS development model was selected to design and complete this program. The configuration of this application is composed of management data and reference data. The management data includes the location of street tree, a growth condition, a surrounding environment, the characters of tree, an equipments, a management records and etc. The reference data include general information about tree, blight and insects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2801-2815
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• 2022

With the advent and realization of Software Defined Network (SDN) architecture, many organizations are now shifting towards this paradigm. SDN brings more control, higher scalability, and serene elasticity. The SDN spontaneously changes the network configuration according to the dynamic network requirements inside the constrained environments. Therefore, a monitoring system that can monitor the physical and virtual entities is needed to operate this type of network technology with high efficiency and proficiency. In this manuscript, we propose a real-time monitoring system for data collection and visualization that includes the Prometheus, node exporter, and Grafana. A node exporter is configured on the physical devices to collect the physical and virtual entities resources utilization logs. A real-time Prometheus database is configured to collect and store the data from all the exporters. Furthermore, the Grafana is affixed with Prometheus to visualize the current network status and device provisioning. A monitoring system is deployed on the physical infrastructure of the KOREN topology. Data collected by the monitoring system is further pre-processed and restructured into a dataset. A monitoring system is further enhanced by including machine learning techniques applied on the formatted datasets to identify the elephant flows. Additionally, a Random Forest is trained on our generated labeled datasets, and the classification models' performance are verified using accuracy metrics.

• Progress in Superconductivity and Cryogenics
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• v.12 no.3
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• pp.12-15
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• 2010

Important key technologies of high-$T_c$ superconducting (HTS) transformer may include the HTS wire technology, bushing technology, cooling technology, AC loss, reduction technology, large current technology, and cryogenic temperature insulation technology. From among others, the cryogenic temperature insulation technology may be specifically a core technology for ensuring reliability for the smaller size, stability, economic efficiency, and power supply of a transformer. Therefore, the electric insulation technology of a superconducting transformer should be prerequisite. Such relevant studies are ongoing, but still, they are very insufficient for establishing the cryogenic insulation technology as of yet. Therefore, this paper simulated HTS transformer applied with continuous transposed conductor (CTC), which has been studied as a way of reducing AC loss. Also, the paper analyzed the insulation configuration of HTS transformer and bushing, and, accordingly, reviewed various characteristics of insulation breakdown out of liquid nitrogen. Thus, the paper constituted insulation database, and it is going to design the insulation of a transmission class HTS transformer and bushing.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.38-47
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• 2021

This study is about a blockchain-based collective quarantine management system and its commercialization model. The configuration of this system includes a biometric information transmission unit that generates biometric information based on measured values generated from wearable devices, a biometric information transmission unit that transmits biometric information generated here from a quarantine management platform, and action information transmitted from the community server. is a system including an action information receiving unit for receiving from the quarantine management platform. In addition, a biometric information receiving unit that collects biometric information from the terminal, an encryption unit that encodes biometric information generated through the biometric information receiving unit based on blockchain encryption technology, and a database of symptoms of infectious diseases to store symptom information and an infection diagnosis database. The generated database includes a location information check unit that receives from the terminal of the user identified as a symptomatic person and determines whether the user has arrived in the community based on the location information confirmation unit and the location of the user after the location is confirmed. It includes a community arrival judgment unit that judges. And, the community server helps the interaction between the generated information. Such a blockchain based collective quarantine management system can help to advance the existing quarantine management system and realize a safer and healthier society.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.15 no.4
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• pp.321-332
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• 2017

In order to build a general model of a high-throughput uranium electrorefining process according to the electrode configuration, numerical analysis was conducted using the COMSOL Multiphysics V5.3 electrodeposition module with Ordinary Differential Equation (ODE) interfaces. The generated model was validated by comparing a current density-potential curve according to the distance between the anode and cathode and the electrode array, using a lab-scale (1kg U/day) multi-electrode electrorefiner made by the Korea Atomic Energy Research Institute (KAERI). The operating temperature was $500^{\circ}C$ and LiCl-KCl eutectic with 3.5wt% $UCl_3$ was used for molten salt. The efficiency of the uranium electrorefining apparatus was improved by lowering the cell potential as the distance between the electrodes decreased and the anode/cathode area ratio increased. This approach will be useful for constructing database for safety design of high throughput spent nuclear fuel electrorefiners.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.726-734
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• 2015

Relational databases used by structuralizing data are the most widely used in data management at present. However, in relational databases, service becomes slower as the amount of data increases because of constraints in the reading and writing operations to save or query data. Furthermore, when a new task is added, the database grows and, consequently, requires additional infrastructure, such as parallel configuration of hardware, CPU, memory, and network, to support smooth operation. In this paper, in order to improve the web information services that are slowing down due to increase of data in the relational databases, we implemented a model to extract a large amount of data quickly and safely for users by processing Hadoop Distributed File System (HDFS) files after sending data to HDFSs and unifying and reconstructing the data. We implemented our model in a Web-based civil affairs system that stores image files, which is irregular data processing. Our proposed system's data processing was found to be 0.4 sec faster than that of a relational database system. Thus, we found that it is possible to support Web information services with a Hadoop-based big data processing technique in order to process a large amount of data, as in conventional relational databases. Furthermore, since Hadoop is open source, our model has the advantage of reducing software costs. The proposed system is expected to be used as a model for Web services that provide fast information processing for organizations that require efficient processing of big data because of the increase in the size of conventional relational databases.

• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.130.1-130.1
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• 2010

미국과 유럽에서는 이미 10여 년 전부터 250MW급 이상의 대용량 석탄IGCC 플랜트를 상업운전 하고 있으며, 일본과 중국을 비롯한 아시아에서도 대용량 플랜트를 시운전하고 있거나 건설 중에 있다. 한국에서는 제4차 전력수급계획에 의거 태안화력 부지 내에 300MW급 IGCC 플랜트 건설을 추진 중이며, 두산중공업은 '10년 상반기에 IGCC 가스화 플랜트에 대한 FEED 설계 (Front-Eng Engineering Design)를 완료하였다. 그 과정 중 설계조건에 의한 기본 엔지니어링 사항과 석탄 가스화 플랜트에 대한 성능예측 결과를 본 연구에서 소개한다. 가스화 플랜트의 엔지니어링은 가스화 블록과 가스정제 블록으로 구분하여 수행하였다. Process Data를 이용하여 PFD Development, P&ID Generation, Equipment Specification 개발, HAZOP 수행, Architecture Engineering 등의 순으로 FEED 설계를 진행하였다. BOD (Basis of Design)를 기준으로 운전조건별 Heat & Mass Balance와 Process Flow를 재검토하고 각 기기별 운전개념을 반영하여 P&ID를 개발하였다. 그리고 배관, 전기 및 제어에 대한 각종 Diagram 개발과 HSE (Health, Safety and Environment) 관련 설계를 수행하였다. IGCC 1호기의 엔지니어링 수행과 함께 Next 호기 자체설계 역량 확보를 위해 두산중공업은 'DIGITs'로 명명된 개념기본설계 Tool을 개발하고 있다. DIGITs는 공정모델링, 단위기기 개념설계, 공정구성 (Process Configuration) 및 종합 Database Package 형태로 구성된다. DIGITs에 의한 계산 결과 공정사 Process Data 기준시 가스화 블록 출구에서 Syngas HHV와 Syngas 현열은 각각 약 $636MW_{th}$와 약 $18MW_{th}$로, Plant 설계조건 $630MW_{th}$를 만족하는 것으로 예측되었다. 향후 DIGITs는 가스정제 블록 및 주변 BOP 설비 등과 연계한 종합 개념기본설계 Tool로써 개발 진행 중이다.

• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.89-95
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• 2016

In a software testing domain, test case prioritization techniques improve the performance of regression testing, and arrange test cases in such a way that maximum available faults be detected in a shorter time. User-sessions and cookies are unique features of web applications that are useful in regression testing because they have precious information about the application state before and after making changes to software code. This approach is in fact a user-session based technique. The user session will collect from the database on the server side, and test cases are released by the small change configuration of a user session data. The main challenges are the effectiveness of Average Percentage Fault Detection rate (APFD) and time constraint in the existing techniques, so in this paper developed an intelligent framework which has three new techniques use to manage and put test cases in group by applying useful criteria for test case prioritization in web application regression testing. In dynamic weighting approach the hybrid criteria which set the initial weight to each criterion determines optimal weight of combination criteria by evolutionary algorithms. The weight of each criterion is based on the effectiveness of finding faults in the application. In this research the priority is given to test cases that are performed based on most common http requests in pages, the length of http request chains, and the dependency of http requests. To verify the new technique some fault has been seeded in subject application, then applying the prioritization criteria on test cases for comparing the effectiveness of APFD rate with existing techniques.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.2
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• pp.108-113
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• 2003

Research results have demonstrated that conventional client-server databases have scalability problem in the presence of many concurrent clients. The multi-tier architecture that exploits similarities in clients' object access behavior partitions clients into logical clusters according to their object request pattern. As a result, object requests that are served inside the clusters, server load and request response time can be optimized. Management of clustering by utilizing clients' access pattern-based is an important component for the system's goal. Off-line methods optimizes the quality of the global clustering, the necessary cost and clustering schedule needs to be considered and planned carefully in respect of stable system's performance. In this paper, we propose methods that detect changes in access behavior and optimize system configuration in real time. Finally this paper demonstrates the effectiveness of on-line change detection and results of experimental investigation concerning reconfiguration.