• Journal of Korea Water Resources Association
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• v.47 no.12
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• pp.1145-1154
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• 2014

In recent years, as frequency and intensity of severe weather disasters such as flash flood have been increasing, providing accurate and prompt information to the public is very important and needs of user-friendly monitoring/warning system are growing. This paper introduces a method that re-produces radar observations as multimedia contents and applies reproduced data to mesh-up services. In addition, a accurate GIS matching technique to help to track the exact location going on serious atmospheric phenomena is presented. The proposed method create multimedia contents having structures such as two dimensional images, vector graphics or three dimensional volume data by re-producing various radar variables obtained from a weather radar. After then, the multimedia formatted weather radar data are matched with various detailed raster or vector GIS map platform. Results of simulation test with various scenarios indicate that the display system based on the proposed method can support for users to figure out easily and intuitively routes and degrees of risk of severe weather. We expect that this technique can also help for emergency manager to interpret radar observations properly and to forecast meteorological disasters more effectively.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.6
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• pp.65-71
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• 2017

Digital images and videos are subject to various types of noise during storage and transmission. Among these noises, Salt & Pepper noise degrades the compression efficiency of the original data and causing deterioration of performance in edge detection or segmentation used in an image processing method. In order to mitigate this noise, there are many filters such as Median Filter, Weighted Median Filter, Center Weighted Median Filter, Switching Weighted Median Filter and Adaptive Median Filter. However these methods are inferior in performance at high noise density. In this paper we propose a new type of filter for noise mitigation in wireless communication environment where Salt & Pepper noise occurs. The proposed filter detects the location of the damaged pixel by Salt & Pepper noise detection and mitigates the noise by using adjacent pixel values which are not damaged in a certain area. Among the proposed filters, the performance of the filter using the $3{\times}3$ error mask is compared with that of the conventional methods and it is confirmed that when density of noise in the image is 95%, their performances are improved as 13.24 dB compared to MF and 13.09 dB compared to AMF.

• Journal of Science Education
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• v.33 no.2
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• pp.336-345
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• 2009

The purpose of this study is to analysis ERF components patterns of causal questions generated during the observation of biological phenomenon. First, the system that shows pictures causing causal questions based on biological phenomenon (evoked picture system) was developed in a way of cognitive psychology. The ERF patterns of causal questions based on time-series brain processing was observed using MEG. The evoked picture system was developed by R&D method consisting of scientific education experts and researchers. Tasks were classified into animal (A), microbe (M), and plant (P) tasks according to biological species and into interaction (I), all (A), and part (P) based on the interaction between different species. According to the collaboration with MEG team in the hospital of Seoul National University, the paradigm of MEG task was developed. MEG data about the generation of scientific questions in 5 female graduate student were collected. For examining the unique characteristic of causal question, MEG ERF components were analyzed. As a result, total 100 pictures were produced by evoked picture and 4 ERF components, M1(100~130ms), M2(220~280ms), M3(320~390ms), M4(460~520ms). The present study could guide personalized teaching-learning method through the application and development of scientific question learning program.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.169-178
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• 2020

The reductions of troops/human resources, and improvement in combat power have made Korean Department of Defense actively adapt 4th Industrial Revolution technology (Artificial Intelligence, Big Data). The defense information system has been developed in various ways according to the task and the uniqueness of each military. In order to take full advantage of the 4th Industrial Revolution technology, it is necessary to improve the closed defense datamanagement system.However, the establishment and usage of data standards in all information systems for the utilization of defense big data and artificial intelligence has limitations due to security issues, business characteristics of each military, anddifficulty in standardizing large-scale systems. Based on the interworking requirements of each system, data sharing is limited through direct linkage through interoperability agreement between systems. In order to implement smart defense using the 4th Industrial Revolution technology, it is urgent to prepare a system that can share defense data and make good use of it. To technically support the defense, it is critical to develop Multi Repository Meta-Data Management (MRMM) that supports systematic standard management of defense data that manages enterprise standard and standard mapping for each system and promotes data interoperability through linkage between standards which obeys the Defense Interoperability Management Development Guidelines. We introduced MRMM, and implemented by using vocabulary similarity using machine learning and statistical approach. Based on MRMM, We expect to simplify the standardization integration of all military databases using artificial intelligence and bigdata. This will lead to huge reduction of defense budget while increasing combat power for implementing smart defense.