The damage of natural disasters has occurred on huge scale more frequently than before. The damage caused by the disasters are summarized and are analyzed, and are managed as a very general type of documentation, not showing the position of previous damage records and related information such as weather, facilities of CCTV, hospitals, and gas station on the maps. Therefore, it needs to provide map-based searching systems considering damaged area as well as search key-words. This paper focuses on the development of a search system based on the map to manage previous disaster records and related information each disaster using spatial databases. This system consists of three modules, which are databases to store disaster data, the SQL procedure-based search module to extract needed information from the constructed databases, and the map module to express the search results on the map. This paper will contribute to provide framework of a system development for managing the disaster information according to each year and disaster based on the maps and to be utilized as the basic framework in developing damage prediction and prevention systems for disasters in future.

We extend the octree subdivision process from Cartesian coordinates to spherical coordinates to develop more efficient space-partitioning structure for surface models. As an application of the proposed structure, we apply the octree subdivision in spherical coordinates ("S-Octree") to geometry compression in progressive mesh coding. Most previous researches on geometry-driven progressive mesh compression are devoted to improve predictability of geometry information. Unlike this, we focus on the efficient information storage for the space-partitioning structure. By eliminating void space at initial stage and aligning the R axis for the important components in geometry information, the S-Octree improves the efficiency in geometry information coding. Several meshes are tested in the progressive mesh coding based on the S-Octree and the results for performance parameters are presented.

The linearized suffix tree (LST) is an array data structure supporting traversals on suffix trees. We apply this LST to two dimensional (2D) suffix trees and obtain a space-efficient substitution of 2D suffix trees. Given an $n{\times}n$ text matrix and an $m{\times}m$ pattern matrix over an alphabet ${\Sigma}$, our 2D-LST provides pattern matching in $O(m^2log{\mid}{\Sigma}{\mid})$ time and $O(n^2)$ space.

In digital TV broadcasting, such as terrestrial, cable, satellite, and IPTV, the head-end of digital TV broadcasting has a more complicated transmission structure than that of analog TV broadcasting. Furthermore, digital TV broadcasting has a feature that supports multiplex models, such as Multiple Program Transport Stream (MPTS). Therefore, the purpose of our work was to design and examine a more efficient new system of emergency alert message transmission to support the digital TV broadcasting environments. Digital TV broadcasting is the IP generation or RF transmission of 8-VSB, QAM, and QPSK modulated through a multiplexer or re-multiplexer multiplexed stream as a MPEG-2 Transport Stream after content encoding. The new system proposed in this paper transmits an emergency alert message without scrambling after replacing the PID and payload of the -packet with the message prototype in the TS stream from the multiplexer. If we need to transmit an emergency alert message under digital TV broadcasting services, then the receiver first checks the PID of each packet in the TS stream for the emergency alert message. Next, if a packet is determined to be an emergency alert message, then the set-top box displays the message on the TV screen using its function of On Screen Display, or the PC based software displays the message on the monitor screen using its function of overlay with user interface if the packet is found to be an emergency alert message. We have designed an emergency alert message protocol and a system model. By experiments and analysis of the system, we concluded that the system achieved efficiency and the ability to send and receive emergency alert messages using the system under different digital TV broadcasting service environments.

This paper proposes a new video-based method of analyzing human gait which is a highly variable dynamic process. It captures a human gait of varying directions as a trajectory in the phase space. The proposed method includes two options of a stochastic process model and a self-organizing feature map as the tool of feature space representation and analysis. Test results show that the model is highly intuitive and we believe it can contribute to our understanding of human activity as well as gait behavior.

Automatic understanding of contents in document image is a very hard problem due to involvement with mathematically challenging problems originated mainly from the over-determined system induced by document segmentation process. In both academic and industrial areas, there have been incessant and various efforts to improve core parts of content retrieval technologies by the means of separating out segmentation related issues using semi-structured document, e.g., invoice,. In this paper we proposed classification models for text lines on invoice document in which text lines were clustered into the five categories in accordance with their contents: purchase order header, invoice header, summary header, surcharge header, purchase items. Our investigation was concentrated on the performance of machine learning based models in aspect of linear-discriminant-analysis (LDA) and non-LDA (logic based). In the group of LDA, na$\"{\i}$ve baysian, k-nearest neighbor, and SVM were used, in the group of non LDA, decision tree, random forest, and boost were used. We described the details of feature vector construction and the selection processes of the model and the parameter including training and validation. We also presented the experimental results of comparison on training/classification error levels for the models employed.

TCP with AIMD mechanism, one of the most popular protocols in internet, can solve congestion control in wired networks. This protocol, however, is not efficient in wireless networks. This paper proposes a new mechanism namely General AIMD with Congestion Window Upper Bound in which congestion window is limited by an upper bound. By applying optimization theory, we find an optimal policy for congestion window upper bound to maximize network throughput.

In this paper, we address the issue of how to improve performance of integrated optical path and optical packet. For supporting ultra-high-speed traffic, integration of optical paths and packets in a switch is one of key techniques in New Generation Networks. However, the wavelength allocation for optical packets and optical paths has not been efficiently resolved yet because there lacks of a systematic model for evaluating performance of the integrated switch. This paper models the operation of the integrated switch as a system of two servers, one for optical paths and the other for optical packets. From the model, we utilize Newton method to find an optimal policy for sharing of wavelength resources. Afterwards, we propose an algorithm to dynamically allocate wavelength resources in an integrated switch. Finally, we evaluate performance of that algorithm.

Since 1987, when statistical analyzing guide for thermal life test of Accelerated Life Test(ALT) was proposed as ANSI/IEEE Std 101, this guide has been used widely for many experiment data. Shim(2004) had done Monte Carlo simulation to compare life of two different systems or materials, based on statistic values obtained from ANSI/IEEE Std 101 data. In this study, a profile analysis is proposed for comparing life of two different systems or materials, and some examples using pre-existing data are given.

When interacting with unknown environments, an autonomous agent needs to decide which action or action order can result in a good state and determine the transition probability based on the current state and the action taken. The traditional multiple sequential learning model requires predefined probability of the states' transition. This paper proposes a multiple sequential learning and prediction system with definition of autonomous states to enhance the automatic performance of existing AI algorithms. In sequence learning process, the sensed states are classified into several group by a set of proposed motivation filters to reduce the learning computation. In prediction process, the learning agent makes a decision based on the estimation of each state's cost to get a high payoff from the given environment. The proposed learning and prediction algorithms heightens the automatic planning of the autonomous agent for interacting with the dynamic unknown environment. This model was tested in a virtual library.

This paper introduces a new automatic processing system: "Contents Factory" for the mass production of contents. Through the contents factory, we provide an authoring environment to improve the usability and the efficiency in producing contents. The contents factory integrates recycling techniques for contents resources, contents development engines, authoring tools, and interfaces into a total processing system. Since it is multi-platform based including mobile devices as well as PCs, one can easily produce complete PC and mobile contents from raw resources. We produced an example, "Sejong square" via the contents factory in order to demonstrate its effectiveness and usability.

The utilization of underground space has recently increased with the complication of road, the rise of the land price, and the development of green technology. Underground space ranges from classical excavations to subway, underground cities, and shopping malls where there are crowds of people. At this time, government has spent a lot of money in installing various types of safety facilities for preparations of increasing potential disasters. Therefore, an effective facility management system is required. In this paper, we propose an information retrieval process to effectively extract the facilities' information based on the ontology and spatial analysis in underground space. The ontology-based searching supports hierarchical and associated results as well as knowledge sharing with hierarchy concepts. The spatial analysis based searching has "Buffer" and "Near" functions to operate on a map without understanding any property of the facility information.

In virtual reality, there are increasing qualitative development in service technologies for realtime interaction system development, 3- dimensional contents, 3D TV and augment reality services. These services experience difficulties to generate depth value that is essential to recover 3D space to form solidity on existing contents. Hence, research for the generation of effective depth-map using 2D is necessary. This thesis will describe a shortcoming of an existing depth-map generation for the recovery of 3D space using 2D image and will propose an enhanced depth-map generation algorithm that complements a shortcoming of existing algorithms and utilizes the definition of depth direction based on the vanishing point within image.