• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.12B
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• pp.1081-1090
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• 2003

NGI(Next Generation Internet) is characterized by QoS(Quality of Service) and high speed transmission. Recently, DiffServ and MPLS become most influential NGI architecture. To guarantee end-to-end QoS, it is essential for NGI to interwork MPLS with DiffServ. Here, MPLS WG(Working Group) in IETF(Internet Engineering Task Force) proposed the method of E-LSP(EXP inferred-PSC LSPs) and L-LSP(Label-Only-Inferred PSC LSPs), but both of them have serious problems to satisfy perfect interworking. In this paper, we proposed an extended E-LSP architecture that supports Perfect DiffServ class and experimental function in MPLS such as ECN(Explicit Congestion Notification) capability. We verify that the proposed E-LSP architecture improves QoS in NGI by using ns2 simulator.

• The Transactions of the Korea Information Processing Society
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• v.6 no.9
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• pp.2493-2503
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• 1999

The TMN that appears to operate the various communication networks generally and efficiently is developed under the different platform environment such as the different hardware and the different operating system. One of the main problems is that all the agents of the TMN system must be duplicated and maintain the software and the data blocks that perform the identical function. Therefore, the multi-platform cannot be supported in the development of the TMN agent. In order to overcome these problems, the Farming methodology that is based on the Farmer model has been suggested. With the Farming methodology, the software and the data components which are duplicated and stored in each distributed object are saved in the platform independent class repository (PICR) by converting into the format of the independent componentware in the platform, so that the componentwares that are essential for the execution can be loaded and used statically or dynamically from PICR as described in the framework of each distributed object. The distributed TMN agent of the personal communication network is designed and developed by using the Farmer model.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.142-146
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• 1996

In this paper, we present the main research results on the 2 Tesla class - superconducting MRI magnet which we have developed. Multi section type superconducting MRI main coil and various superconducting shims were designed and fabricated for obtaining the high field homogeneity, which is requested in the MR imaging. After assembling the magnet with room temperature bore cryostat field homogenity has been measured and analyzed by NMR field mapping system. According to this, field homogeneity of 22 ppm / 30 cm dsv was confirmed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.9
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• pp.4447-4454
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• 2013

In October 2011, a commercialized 100kW class floating photovoltaic system positive plant was installed at Hapcheon dam a multi-purpose reservoir the first time ever in the nation. Floating photovoltaic system differs in water float, mooring device and underwater cable process from land photovoltaic system. As for land and building photovoltaic power generation equipments, many installation cases and skilled experiences are available, and thus installation is not difficult. However, commercial power generation floating photovoltaic system, which is attempted for the first time in the nation, requires to be designed and installed through a series of processes like technical review and verification of data by process in comparison with similar cases. The structure of floating photovoltaic system, an equipment for float photovoltaic module and other electrical equipment, is required to withstand weather environments like wind or typhoon etc and yet not affect water quality negatively, and for implementation of this system, construction efficiency and economy etc should be considered comprehensively. In this paper, the techniques of installing floating photovoltaic structure, mooring device, underwater cable, electrical equipment and remote monitoring control system are explained. The 100kW floating PV system is operating with 15% average capacity factor.

• Journal of Information Science Theory and Practice
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• v.7 no.4
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• pp.56-64
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• 2019

Planting intercropping in rubber plantations is another alternative for generating more income for farmers. However, farmers still lack the knowledge of choosing plants. In addition, information for decision making comes from many sources and is knowledge accumulated by the expert. Therefore, this research aims to create a decision support system for growing rubber trees for individual farmers. It aims to get the highest income and the lowest cost by using semantic web technology so that farmers can access knowledge at all times and reduce the risk of growing crops, and also support the decision supporting system (DSS) to be more intelligent. The integrated intercropping ontology and rule are a part of the decision-making process for selecting plants that is suitable for individual rubber plots. A list of suitable plants is important for decision variables in the allocation of planting areas for each type of plant for multiple purposes. This article presents designing and developing the intercropping ontology for DSS which defines a class based on the principle of intercropping in rubber plantations. It is grouped according to the characteristics and condition of the area of the farmer as a concept of the rubber plantation. It consists of the age of rubber tree, spacing between rows of rubber trees, and water sources for use in agriculture and soil group, including slope, drainage, depth of soil, etc. The use of ontology for recommended plants suitable for individual farmers makes a contribution to the knowledge management field. Besides being useful in DSS by offering options with accuracy, it also reduces the complexity of the problem by reducing decision variables and condition variables in the multi-objective optimization model of DSS.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.2
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• pp.315-320
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• 2008

Nowadays, a lot of related data obtained from these research could be given a new present meaning to accomplish the original purpose of the whole research as a human genome project. In such a thread, construction of gene expression analysis system and a basis rank analysis system is being watched newly. Recently, being identified fact that particular sub-class of tumor be related with particular chromosome, microarray started to be used in diagnosis field by doing cancer classification and predication based on gene expression information. In this thesis, we used cDNA microarrays of 3840 genes obtained from neuronal differentiation experiment of cortical stem cells on white mouse with cancer, created system that can extract informative gene list through normalization separately and proposed combination method for selecting more significant genes. And possibility of proposed system and method is verified through experiment. That result is that PC-ED combination represent 98.74% accurate and 0.04% MSE, which show that it improve classification performance than case to experiment after generating gene list using single similarity scale.

• Korean Journal of Remote Sensing
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• v.20 no.6
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• pp.383-396
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• 2004

The application of spatial statistics to obtain the spatial uncertainty distributions in classification of remote sensing images is investigated in this paper. Two quantitative methods are presented for describing two kinds of uncertainty; one related to class assignment and the other related to the connection of reference samples. Three quantitative indices are addressed for the first category of uncertainty. Geostatistical simulation is applied both to integrate the exhaustive classification results with the sparse reference samples and to obtain the spatial uncertainty or accuracy distributions connected to those reference samples. To illustrate the proposed methods and to discuss the operational issues, the experiment was done on a multi-sensor remote sensing data set for supervised land-cover classification. As an experimental result, the two quantitative methods presented in this paper could provide additional information for interpreting and evaluating the classification results and more experiments should be carried out for verifying the presented methods.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.123-132
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• 2021

Malicious activities of Botnets are responsible for huge financial losses to Internet Service Providers, companies, governments and even home users. In this paper, we try to confirm the possibility of detecting botnet traffic by applying the deep learning model Convolutional Neural Network (CNN) using the CTU-13 botnet traffic dataset. In particular, we classify three classes, such as the C&C traffic between bots and C&C servers to detect C&C servers, traffic generated by bots other than C&C communication to detect bots, and normal traffic. Performance metrics were presented by accuracy, precision, recall, and F1 score on classifying both known and unknown botnet traffic. Moreover, we propose a stackable botnet detection system that can load modules for each botnet type considering scalability and operability on the real field.

• Journal of the Korean Society for information Management
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• v.36 no.2
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• pp.57-77
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• 2019

Random Forest (RF), a representative ensemble technique, was applied to automatic classification of journal articles in the field of library and information science. Especially, I performed various experiments on the main factors such as tree number, feature selection, and learning set size in terms of classification performance that automatically assigns class labels to domestic journals. Through this, I explored ways to optimize the performance of random forests (RF) for imbalanced datasets in real environments. Consequently, for the automatic classification of domestic journal articles, Random Forest (RF) can be expected to have the best classification performance when using tree number interval 100~1000(C), small feature set (10%) based on chi-square statistic (CHI), and most learning sets (9-10 years).

• The Journal of the Korea Contents Association
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• v.19 no.4
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• pp.682-689
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• 2019

This study aims to spatialize the gap between obesity levels through the body mass index, an objective indicator of the level of health among vulnerable people. Thus, areas where the BMI showed cluster patterns with spatial high and low values were extracted and the characteristics of the region were analyzed. The analysis showed that the I statistics for the obesity rate were 0.07 and the z-score was 4.39, confirming spatial autocorrelation. For z-score, it was much larger than the maximum threshold of 2.57. This means that the rate of obesity among the socially vulnerable is regional, and this gap is spatially significant. The results of comparing and analyzing the local environment for these areas of obesity and health care were found to be areas with poor public transportation, less readily available parks, and a concentration of single and multi-generation housing. The analysis results of this study are meaningful in that they provide spatial implications for the health of the socially vulnerable class that previous studies have neglected.