• Journal of the Korean Geophysical Society
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• v.6 no.1
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• pp.39-46
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• 2003

Twin stone pagodas of the ruins of Kamunsa temple at Kyongju city, Kyungsangbukdo were believed to be built in 682 during the Unified Shilla Kingdom. The 13.4-m-high granodiolite pagodas with the base of 6.78 m x 4.4 m are the largest three-story stone pagoda in Korea. The western pagoda which was re-organized in 1959 is observed to be on the process of severe weathering. Also, some stone contacts are represented by the shape of sharp chevron, which is probably caused by the uneven loading due to the structural unbalance. For the structure-safety diagnosis of the western pagoda, it is necessary to understand its site characteristics and surrounding subsurface environment. Combined geophysical survey such as seismic and resistivity methods was carried out around the western pagoda. The range of 55∼350 Ωm is shown around the pagoda from the electrical resistivity mapping by the Wenner method. The higher resistivities occur the southwestern area, while the lower (<100 Ωm) values indicating the weaker subsurface appear to be on the northeastern area. This result coincides with the measurement of a leaning angle of the pagoda. Along 6 seismic lines, about 3-m-thick uppermost section around the pagoda shows the P-wave velocity of 200∼700 m/s from the refraction survey. Based on the integrated geophysical survey, the foundation of the pagoda is estimated to be in the form of 11-m-side square down to the depth of 3 m.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.2
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• pp.231-254
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• 2022

Industrial heritage resources produced by industrial activities have historical and conservative values and value as cultural properties as they serve as a medium connecting the past, present, and future. In other words, Industrial heritage resources should be used, managed, and preserved simultaneously as important records and evidence for the future. Metadata is well known as a necessary and important factor for the use, management, and preservation of information resources. In particular, in order to describe various industrial heritage resources that have historical, cultural, and conservative values, metadata elements from various perspectives are needed to relate to the story, type etc. of data. Therefore, this study attempted to design metadata elements that can comprehensively describe different types of industrial heritage resources. And we designed metadata that suited for the purpose of using, managing, and preserving industrial heritage resources, through mapping and combination between industrial heritage-related metadata. As a result, metadata was grouped into five types(administrative, descriptive, preservation, technical, use) and prepared into a total of 25 higher & 86 lower elements.

• Journal of KIISE
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• v.43 no.7
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• pp.781-785
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• 2016

Learning from human behaviors in the real world is essential for human-aware intelligent systems such as smart assistants and autonomous robots. Most of research focuses on correlations between sensory patterns and a label for each activity. However, human activity is a combination of several event contexts and is a narrative story in and of itself. We propose a novel approach of human activity prediction based on event cognition. Egocentric multi-sensor data are collected from an individual's daily life by using a wearable device and smartphone. Event contexts about location, scene and activities are then recognized, and finally the users" daily activities are predicted from a decision rule based on the event contexts. The proposed method has been evaluated on a wearable sensor data collected from the real world over 2 weeks by 2 people. Experimental results showed improved recognition accuracies when using the proposed method comparing to results directly using sensory features.

• Journal of KIISE:Software and Applications
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• v.36 no.2
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• pp.169-177
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• 2009

In this paper, we propose a new shot boundary detection method which is optimized for news video story parsing. This new news shot boundary detection method was designed to satisfy all the following requirements: 1) minimizing the incorrect data in data set for anchor shot detection by improving the recall ratio 2) detecting abrupt cuts and gradual transitions with one single algorithm so as to divide news video into shots with one scan of data set; 3) classifying shots into static or dynamic, therefore, reducing the search space for the subsequent stage of anchor shot detection. The proposed method, based on singular value decomposition with incremental clustering and mercer kernel, has additional desirable features. Applying singular value decomposition, the noise or trivial variations in the video sequence are removed. Therefore, the separability is improved. Mercer kernel improves the possibility of detection of shots which is not separable in input space by mapping data to high dimensional feature space. The experimental results illustrated the superiority of the proposed method with respect to recall criteria and search space reduction for anchor shot detection.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.6
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• pp.514-528
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• 2004

An emotion-based video scene retrieval algorithm is proposed in this paper. First, abrupt/gradual shot boundaries are detected in the video clip representing a specific story Then, five video features such as 'average color histogram' 'average brightness', 'average edge histogram', 'average shot duration', and 'gradual change rate' are extracted from each of the videos and mapping between these features and the emotional space that user has in mind is achieved by an interactive genetic algorithm. Once the proposed algorithm has selected videos that contain the corresponding emotion from initial population of videos, feature vectors from the selected videos are regarded as chromosomes and a genetic crossover is applied over them. Next, new chromosomes after crossover and feature vectors in the database videos are compared based on the similarity function to obtain the most similar videos as solutions of the next generation. By iterating above procedures, new population of videos that user has in mind are retrieved. In order to show the validity of the proposed method, six example categories such as 'action', 'excitement', 'suspense', 'quietness', 'relaxation', 'happiness' are used as emotions for experiments. Over 300 commercial videos, retrieval results show 70% effectiveness in average.