• Journal of KIISE:Computing Practices and Letters
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• v.9 no.3
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• pp.322-331
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• 2003

As the complexity of a 3D game is increased by various factors of the game scenario, it has a problem for controlling the interrelation of the game objects. Therefore, a game system has a necessity of the coordination of the responses of the game objects. Also, it is necessary to control the behaviors of animations of the game objects in terms of the game scenario. To produce realistic game simulations, a system has to include a structure for designing the interactions among the game objects. This paper presents a method that designs the dynamic control mechanism for the interaction of the game objects in the game scenario. For the method, we suggest a game agent system as a framework that is based on intelligent agents who can make decisions using specific rules. Game agent systems are used in order to manage environment data, to simulate the game objects, to control interactions among game objects, and to support visual authoring interface that ran define a various interrelations of the game objects. These techniques can process the autonomy level of the game objects and the associated collision avoidance method, etc. Also, it is possible to make the coherent decision-making ability of the game objects about a change of the scene. In this paper, the rule-based behavior control was designed to guide the simulation of the game objects. The rules are pre-defined by the user using visual interface for designing their interaction. The Agent State Decision Network, which is composed of the visual elements, is able to pass the information and infers the current state of the game objects. All of such methods can monitor and check a variation of motion state between game objects in real time. Finally, we present a validation of the control method together with a simple case-study example. In this paper, we design and implement the supervised classification systems for high resolution satellite images. The systems support various interfaces and statistical data of training samples so that we can select the most effective training data. In addition, the efficient extension of new classification algorithms and satellite image formats are applied easily through the modularized systems. The classifiers are considered the characteristics of spectral bands from the selected training data. They provide various supervised classification algorithms which include Parallelepiped, Minimum distance, Mahalanobis distance, Maximum likelihood and Fuzzy theory. We used IKONOS images for the input and verified the systems for the classification of high resolution satellite images.

• The KIPS Transactions:PartC
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• v.10C no.5
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• pp.625-634
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• 2003

More than a decade after its initial proposal, deployment of IP Multicast has been limited due to the problem of traffic control in multicast routing, multicast address allocation in global internet, reliable multicast transport techniques etc. Lately, according to increase of multicast application service such as internet broadcast, real time security information service etc., overlay multicast is developed as a new internet multicast technology. In this paper, we describe an overlay multicast protocol and propose fast join mechanism that considers switching of the tree. To find a potential parent, an existing search algorithm descends the tree from the root by one level at a time, and it causes long joining latency. Also, it is try to select the nearest node as a potential parent. However, it can't select the nearest node by the degree limit of the node. As a result, the generated tree has low efficiency. To reduce long joining latency and improve the efficiency of the tree, we propose searching two levels of the tree at a time. This method forwards joining request message to own children node. So, at ordinary times, there is no overhead to keep the tree. But the joining request came, the increasing number of searching messages will reduce a long joining latency. Also searching more nodes will be helpful to construct more efficient trees. In order to evaluate the performance of our fast join mechanism, we measure the metrics such as the search latency and the number of searched node and the number of switching by the number of members and degree limit. The simulation results show that the performance of our mechanism is superior to that of the existing mechanism.

• (The)Study of the Eastern Classic
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• no.57
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• pp.467-505
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• 2014

I am working on a series of Korean linguistic studies targeting Ganchal(old typed letters in Korea) for many years and this study is for the typology of the [Safety Expression] as the part. For this purpose, [Safety Expression] were divided into a formal types and semantic types, targeting the Chinese Ganchal and Hangul Ganchal of modern Korean Language time(16th century-19th century). Formal types can be divided based on whether Normal position or not, whether Omission or not, whether the Sending letter or not, whether the relationship of the high and the low or not. Normal position form and completion were made the first type which reveal well the typicality of the [Safety Expression]. Original position while [Own Safety] omitted as the second type, while Original position while [Opposite Safety] omitted as the third type, Original position while [Safety Expression] omitted as the fourth type. Inversion type were made as the fifth type which is the most severe solecism in [Safety Expression]. The first type is refers to Original position type that [Opposite Safety] precede the [Own Safety] and the completion type that is full of semantic element. This type can be referred to most typical and normative in that it equipped all components of [Safety Expression]. A second type is that [Safety Expression] is composed of only the [Opposite Safety]. This type is inferior to the first type in terms of set pattern, it is never outdone when it comes to the appearance frequency. Because asking [Opposite Safety] faithfully, omitting [Own Safety] dose not greatly deviate politeness and easy to write Ganchal, it is utilized. The third type is the Original position type showing the configuration of the [Opposite Safety]+Own Safety], but [Opposite Safety] is omitted. The fourth type is a Original position type showing configuration of the [Opposite Safety+Own Safety], but [Safety Expression] is omitted. This type is divided into A ; [Safety Expression] is entirely omitted and B ; such as 'saving trouble', the conventional expression, replace [Safety Expression]. The fifth type is inversion type that shown to structure of the [Own Safety+Opposite Safety], unlike the Original position type. This type is the most severe solecism type and real example is very rare. It is because let leading [Own Safety] and ask later [Opposite Safety] for face save is offend against common decency. In addition, it can be divided into the direct type that [Opposite Safety] and [Own Safety] is directly connected and indirect type that separate into the [story]. The semantic types of [Safety Expression] can be classified based on whether Sending letter or not, fast or slow, whether intimate or not, and isolation or not. For Sending letter, [Safety Expression] consists [Opposite Safety(Climate+Inquiry after health+Mental state)+Own safety(status+Inquiry after health+Mental state)]. At [Opposite safety], [Climate] could be subdivided as [Season] information and [Climate(weather)] information. Also, [Mental state] is divided as receiver's [Family Safety Mental state] and [Individual Safety Mental state]. In [Own Safety], [Status] is divided as receiver's traditional situation; [Recent condition] and receiver's ongoing situation; [Present condition]. [Inquiry after health] is also subdivided as receiver's [Family Safety] and [Individual Safety], [Safety] is as [Family Safety] and [Individual Safety]. Likewise, [Inquiry after health] or [Safety] is usually used as pairs, in dimension of [Family] and [Individual]. This phenomenon seems to have occurred from a big family system, which is defined as taking care of one's parents or grand parents. As for the Written Reply, [Safety Expression] consists [Opposite Safety (Reception+Inquiry after health+Mental state)+Own safety(status+Inquiry after health+Mental state)], and only in [Opposite safety], a difference in semantic structure happens with Sending letter. In [Opposite Safety], [Reception] is divided as [Letter] which is Ganchal that is directly received and [Message], which is news that is received indirectly from people. [Safety] is as [Family Safety] and [Individual Safety], [Mental state] also as [Family Safety Mental state] and [Individual Safety Mental state].