• The KIPS Transactions:PartB
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• v.10B no.5
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• pp.567-574
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• 2003

Application field of third dimension graphic is becoming diversification by the fast development of hardware recently. Various theory of details technology necessary to design game such as 3D MMORPG (Massive Multi-play Online Role Flaying Game) that do with third dimension. Cyber city should be absorbed. It is the detection speed that this treatise is necessary in game engine design. 3D MMORPG game engine has much factor that influence to speed as well as rendering processing because it express huge third dimension city´s grate many building and individual fast effectively by real time. This treatise nay get concept about the collision in 3D MMORPG and detection speed elevation of game engine through improved detection method. Space division is need to process fast dynamically wide outside that is 3D MMORPG´s main detection target. 3D is constructed with tree construct individual that need collision using processing geometry dataset that is given through new graph. We may search individual that need in collision detection and improve the collision detection speed as using hierarchical bounding box that use it with detection volume. Octree that will use by division octree is used mainly to express rightly static object but this paper use limited OSP by limited space division structure to use this in dynamic environment. Limited OSP space use limited space with method that divide square to classify typically complicated 3D space´s object. Through this detection, this paper propose follow contents, first, this detection may judge collision detection at early time without doing all polygon´s collision examination. Second, this paper may improve detection efficiency of game engine through and then reduce detection time because detection time of bounding box´s collision detection.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.101-107
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• 2014

With the development of online service, recent forms of databases have been changed from static database structures to dynamic stream database structures. Previous data mining techniques have been used as tools of decision making such as establishment of marketing strategies and DNA analyses. However, the capability to analyze real-time data more quickly is necessary in the recent interesting areas such as sensor network, robotics, and artificial intelligence. Landmark window-based frequent pattern mining, one of the stream mining approaches, performs mining operations with respect to parts of databases or each transaction of them, instead of all the data. In this paper, we analyze and evaluate the techniques of the well-known landmark window-based frequent pattern mining algorithms, called Lossy counting and hMiner. When Lossy counting mines frequent patterns from a set of new transactions, it performs union operations between the previous and current mining results. hMiner, which is a state-of-the-art algorithm based on the landmark window model, conducts mining operations whenever a new transaction occurs. Since hMiner extracts frequent patterns as soon as a new transaction is entered, we can obtain the latest mining results reflecting real-time information. For this reason, such algorithms are also called online mining approaches. We evaluate and compare the performance of the primitive algorithm, Lossy counting and the latest one, hMiner. As the criteria of our performance analysis, we first consider algorithms' total runtime and average processing time per transaction. In addition, to compare the efficiency of storage structures between them, their maximum memory usage is also evaluated. Lastly, we show how stably the two algorithms conduct their mining works with respect to the databases that feature gradually increasing items. With respect to the evaluation results of mining time and transaction processing, hMiner has higher speed than that of Lossy counting. Since hMiner stores candidate frequent patterns in a hash method, it can directly access candidate frequent patterns. Meanwhile, Lossy counting stores them in a lattice manner; thus, it has to search for multiple nodes in order to access the candidate frequent patterns. On the other hand, hMiner shows worse performance than that of Lossy counting in terms of maximum memory usage. hMiner should have all of the information for candidate frequent patterns to store them to hash's buckets, while Lossy counting stores them, reducing their information by using the lattice method. Since the storage of Lossy counting can share items concurrently included in multiple patterns, its memory usage is more efficient than that of hMiner. However, hMiner presents better efficiency than that of Lossy counting with respect to scalability evaluation due to the following reasons. If the number of items is increased, shared items are decreased in contrast; thereby, Lossy counting's memory efficiency is weakened. Furthermore, if the number of transactions becomes higher, its pruning effect becomes worse. From the experimental results, we can determine that the landmark window-based frequent pattern mining algorithms are suitable for real-time systems although they require a significant amount of memory. Hence, we need to improve their data structures more efficiently in order to utilize them additionally in resource-constrained environments such as WSN(Wireless sensor network).

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.11-26
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• 2013

In the wireless Internet, it is so restrictive to use the IPSec. The MIPv4 IPSec's path cannot include wireless links. That is, the IPSec of the wireless Internet cannot protect an entire path of Host-to-Host connection. Also wireless circumstance keeps a path static during the shorter time, nevertheless, the IKE for IPSec SA agreement requires relatively long delay. The certificate management of IPSec PKI security needs too much burden. This means that IPSec of the wireless Internet is so disadvantageous. Our paper is to construct the Mobile IPSec proper to the wireless Internet which provides the host-to-host transport mode service to protect even wireless links as applying excellent WP-IBE scheme. For this, Mobile IPSec requires a dynamic routing over a path with wireless links. FA Forwarding is a routing method for FA to extend the path to a newly formed wireless link. The FA IPSec SA for FA Forwarding is updated to comply the dynamically extended path using Source Routing based Bind Update. To improve the performance of IPSec, we apply efficient and strong future Identity based Weil Pairing Bilinear Elliptic Curve Cryptography called as WP-IBE scheme. Our paper proposes the modified protocols to apply 6 security-related algorithms of WP-IBE into the Mobile IPSec. Particularly we focus on the protocols to be applied to construct ESP Datagram.

• Journal of the Korean Electrochemical Society
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• v.13 no.2
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• pp.116-122
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• 2010

A $Cu_3Si$ film electrode is obtained by Si deposition on a Cu foil using DC magnetron sputtering, which is followed by annealing at $800^{\circ}C$ for 10 h. The Si component in $Cu_3Si$ is inactive for lithiation at ambient temperature. The linear sweep thermammetry (LSTA) and galvano-static charge/discharge cycling, however, consistently illustrate that $Cu_3Si$ becomes active for the conversion-type lithiation reaction at elevated temperatures (> $85^{\circ}C$). The $Cu_3Si$ electrode that is short-circuited with Li metal for one week is converted to a mixture of $Li_{21}Si_5$ and metallic Cu, implying that the Li-Si alloy phase generated at 0.0 V (vs. Li/$Li^+$) at the quasi-equilibrium condition is the most Li-rich $Li_{21}Si_5$. However, the lithiation is not extended to this phase in the constant-current charging (transient or dynamic condition). Upon de-lithiation, the metallic Cu and Si react to be restored back to $Cu_3Si$. The $Cu_3Si$ electrode shows a better cycle performance than an amorphous Si electrode at $120^{\circ}C$, which can be ascribed to the favorable roles provided by the Cu component in $Cu_3Si$. The inactive element (Cu) plays as a buffer against the volume change of Si component, which can minimize the electrode failure by suppressing the detachment of Si from the Cu substrate.

• The KIPS Transactions:PartA
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• v.11A no.4
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• pp.243-250
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• 2004

Monitoring is used to see if a real-time system provides a service on time. Generally, monitoring for real-time focuses on investigating the current status of a real-time system. To support a stable performance of a real-time system, it should have not only a function to see the current status of real-time process but also a function to predict executions of real-time processes, however. The legacy prediction model has some limitation to apply it to a real-time monitoring. First, it performs a static prediction after a real-time process finished. Second, it needs a statistical pre-analysis before a prediction. Third, transition probability and data about clustering is not based on the current data. We propose the execution prediction model based on learning algorithm to solve these problems and apply it to real-time monitoring. This model gets rid of unnecessary pre-processing and supports a precise prediction based on current data. In addition, this supports multi-level prediction by a trend analysis of past execution data. Most of all, We designed the model to support dynamic prediction which is performed within a real-time process' execution. The results from some experiments show that the judgment accuracy is greater than 80% if the size of a training set is set to over 10, and, in the case of the multi-level prediction, that the prediction difference of the multi-level prediction is minimized if the number of execution is bigger than the size of a training set. The execution prediction model proposed in this model has some limitation that the model used the most simplest learning algorithm and that it didn't consider the multi-regional space model managing CPU, memory and I/O data. The execution prediction model based on a learning algorithm proposed in this paper is used in some areas related to real-time monitoring and control.