• Journal of KIISE:Computer Systems and Theory
• /
• v.28 no.9
• /
• pp.479-490
• /
• 2001

In this paper, we consider the method of partitioning a sphere into faces with a set of spherical convex polygons $\Gamma$=$｛P_1...P_n｝$ for determining the maximum of minimum intersection. This problem is commonly related with five geometric problems that fin the densest hemisphere containing the maximum subset of $\Gamma$, a great circle separating $\Gamma$, a great circle bisecting $\Gamma$ and a great circle intersecting the minimum or maximum subset of $\Gamma$. In order to efficiently compute the minimum or maximum intersection of spherical polygons. we take the approach of edge-based partition, in which the ownerships of edges rather than faces are manipulated as the sphere is incrementally partitioned by each of the polygons. Finally, by gathering the unordered split edges with the maximum number of ownerships. we approximately obtain the centroids of the solution faces without constructing their boundaries. Our algorithm for finding the maximum intersection is analyzed to have an efficient time complexity O(nv) where n and v respectively, are the numbers of polygons and all vertices. Furthermore, it is practical from the view of implementation, since it computes numerical values. robustly and deals with all the degenerate cases, Using the similar approach, the boundary of a general intersection can be constructed in O(nv+LlogL) time, where : is the output-senstive number of solution edges.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.45 no.2
• /
• pp.97-102
• /
• 2008

In this paper, we address the OFDM based on DFT or DCT in Cognitive Radio system. An adaptive OFDM based on DFT or DCT in Cognitive Radio system has the capacity to nullify individual carriers to avoid interference to the licensed users. Therefore, there could be a considerably large number of zero-valued inputs/outputs for the IDFT/DFT or IDCT/DCT on the OFDM transceiver. Hence, the standard methods of DFT and DCT are no longer efficient due to the wasted operations on zero. Based on this observation, we present a transform decomposition on two dimensional(2-D) systolic array for IDFT/DFT and IDCT/DCT, this algorithm can achieve an efficient computation for OFDM in Cognitive Radio system

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.6
• /
• pp.253-260
• /
• 2004

This paper proposes a novel algorithm for hybrid lossless audio coding, which employs an integer wavelet transform and a linear prediction model. The proposed algorithm divides the input signal into flames of a proper length, decorrelates the framed data using the integer wavelet transform and linear prediction and finally entropy-codes the frame data. In particular, the adaptive Golomb-Rice coding method used for the entropy coding selects an optimal option which gives the best compression efficiency. Since the proposed algorithm uses integer operations, it significantly improves the computation speed in comparison with an algorithm using real or floating-point operations. When the coding algorithm is implemented in hardware, the system complexity as well as the power consumption is remarkably reduced. Finally, because each frame is independently coded and is byte-aligned with respect to the frame header, it is convenient to move, search, and edit the coded, compressed data.

• Journal of Broadcast Engineering
• /
• v.11 no.3 s.32
• /
• pp.320-325
• /
• 2006

An image is partitioned into non-overlapping $8{\times}8$ blocks fer JPEG compression. A scan order converter is placed before the JPEG encoder to provide $8{\times}8$ blocks from the pixels in raster scan order. In general, its architecture requires two line memories for storing eight lines separately to allow the concurrent memory access by both the camera and JPEG processors. Although such architecture is simple to be implemented, it can be inefficient due to too excessive memory requirement as the image resolution increases. However, no deterministic addressing equation has been developed for scan conversion. In this paper, an effective memory addressing algorithm is proposed that can be devised only by adders and subtracters to implement a scan converter based on the single line memory.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.16 no.12
• /
• pp.8455-8461
• /
• 2015

In this paper, we propose new search algorithms using SVD(Singular Value Decomposition) for fast search of Raman spectrum. In the proposed algorithms, small number of the eigen vectors obtained by SVD are chosen in accordance with their respective significance to achieve computation reduction. By introducing pilot test, we exclude large number of data from search and then, we apply partial distance search(PDS) for further computation reduction. We prepared 14,032 kinds of chemical Raman spectrum as the library for comparisons. Experiments were carried out with 7 methods, that is Full Search, PDS, 1DMPS modified MPS for applying to 1-dimensional space data with PDS(1DMPS+PDS), 1DMPS with PDS by using descending sorted variance of data(1DMPS Sort with Variance+PDS), 250-dimensional components of the SVD with PDS(250SVD+PDS) and proposed algorithms, PSP and PSSP. For exact comparison of computations, we compared the number of multiplications and additions required for each method. According to the experiments, PSSP algorithm shows 64.8% computation reduction when compared with 250SVD+PDS while PSP shows 157% computation reduction.

• The Journal of the Korea Contents Association
• /
• v.4 no.4
• /
• pp.189-196
• /
• 2004

Most of the recent researches for ATM switches have been based on multistage interconnection network known as regularity and self-routing property. These networks can switch packets simultaneously and in parallel. However, they are blocking networks in the sense that packet is capable of collision with each other Mainly Banyan network have been used for structure. There are several ways to reduce the blocking or to increase the throughput of banyan-type switches: increasing the internal link speeds, placing buffers in each switching node, using multiple path, distributing the load evenly in front of the banyan network and so on. Therefore, this paper proposes the use of recirculating shuffle-exchange network to reduce the blocking and to improve hardware complexity. This structures are recirculating shuffle-exchange network as simplified in hardware complexity and Rank network with tree structure which send only a packet with highest priority to the next network, and recirculate the others to the previous network. after it decides priority number on the Packets transferred to the same destination, The transferred Packets into banyan network use the function of self routing through decomposition and composition algorithm and all they arrive at final destinations. To analyze throughput, waiting time and packet loss ratio according to the size of buffer, the probabilities are modeled by a binomial distribution of packet arrival. If it is 50 percentage of load, the size of buffer is more than 15. It means the acceptable packet loss ratio. Therefore, this paper simplify the hardware complexity as use of recirculating shuffle-exchange network instead of bitonic sorter.

• The KIPS Transactions:PartB
• /
• v.14B no.4
• /
• pp.287-294
• /
• 2007

Many classification algorithms for real world data suffer from a data class imbalance problem. To solve this problem, various methods have been proposed such as altering the training balance and designing better sampling strategies. The previous methods are not satisfy in the distribution of the input data and the constraint. In this paper, we propose a focused sampling method which is more superior than previous methods. To solve the problem, we must select some useful data set from all training sets. To get useful data set, the proposed method devide the region according to scores which are computed based on the distribution of SOM over the input data. The scores are sorted in ascending order. They represent the distribution or the input data, which may in turn represent the characteristics or the whole data. A new training dataset is obtained by eliminating unuseful data which are located in the region between an upper bound and a lower bound. The proposed method gives a better or at least similar performance compare to classification accuracy of previous approaches. Besides, it also gives several benefits : ratio reduction of class imbalance; size reduction of training sets; prevention of over-fitting. The proposed method has been tested with kNN classifier. An experimental result in ecoli data set shows that this method achieves the precision up to 2.27 times than the other methods.

• Journal of KIISE:Databases
• /
• v.37 no.3
• /
• pp.146-164
• /
• 2010

Due to an increasing volume of XML data, it is considered prudent to store XML data on an industry-strength database system instead of relying on a domain specific application or a file system. For shredded XML data stored in relational tables, however, it may not be straightforward to apply existing algorithms for twig query processing, since most of the algorithms require XML data to be accessed in a form of streams of elements grouped by their tags and sorted in a particular order. In order to support XML query processing within the common framework of relational database systems, we first propose several bitmap indexes and their strategies for supporting holistic twig joining on XML data stored in relational tables. Since bitmap indexes are well supported in most of the commercial and open-source database systems, the proposed bitmapped indexes and twig query processing strategies can be incorporated into relational query processing framework with more ease. The proposed query processing strategies are efficient in terms of both time and space, because the compressed bitmap indexes stay compressed during data access. In addition, we propose a hybrid index which computes twig query solutions with only bit-vectors, without accessing labeled XML elements stored in the relational tables.

• Journal of Internet Computing and Services
• /
• v.14 no.6
• /
• pp.1-8
• /
• 2013

Data mining techniques are used to find important and meaningful information from huge databases, and pattern mining is one of the significant data mining techniques. Pattern mining is a method of discovering useful patterns from the huge databases. Frequent pattern mining which is one of the pattern mining extracts patterns having higher frequencies than a minimum support threshold from databases, and the patterns are called frequent patterns. Traditional frequent pattern mining is based on a single minimum support threshold for the whole database to perform mining frequent patterns. This single support model implicitly supposes that all of the items in the database have the same nature. In real world applications, however, each item in databases can have relative characteristics, and thus an appropriate pattern mining technique which reflects the characteristics is required. In the framework of frequent pattern mining, where the natures of items are not considered, it needs to set the single minimum support threshold to a too low value for mining patterns containing rare items. It leads to too many patterns including meaningless items though. In contrast, we cannot mine any pattern if a too high threshold is used. This dilemma is called the rare item problem. To solve this problem, the initial researches proposed approximate approaches which split data into several groups according to item frequencies or group related rare items. However, these methods cannot find all of the frequent patterns including rare frequent patterns due to being based on approximate techniques. Hence, pattern mining model with multiple minimum supports is proposed in order to solve the rare item problem. In the model, each item has a corresponding minimum support threshold, called MIS (Minimum Item Support), and it is calculated based on item frequencies in databases. The multiple minimum supports model finds all of the rare frequent patterns without generating meaningless patterns and losing significant patterns by applying the MIS. Meanwhile, candidate patterns are extracted during a process of mining frequent patterns, and the only single minimum support is compared with frequencies of the candidate patterns in the single minimum support model. Therefore, the characteristics of items consist of the candidate patterns are not reflected. In addition, the rare item problem occurs in the model. In order to address this issue in the multiple minimum supports model, the minimum MIS value among all of the values of items in a candidate pattern is used as a minimum support threshold with respect to the candidate pattern for considering its characteristics. For efficiently mining frequent patterns including rare frequent patterns by adopting the above concept, tree based algorithms of the multiple minimum supports model sort items in a tree according to MIS descending order in contrast to those of the single minimum support model, where the items are ordered in frequency descending order. In this paper, we study the characteristics of the frequent pattern mining based on multiple minimum supports and conduct performance evaluation with a general frequent pattern mining algorithm in terms of runtime, memory usage, and scalability. Experimental results show that the multiple minimum supports based algorithm outperforms the single minimum support based one and demands more memory usage for MIS information. Moreover, the compared algorithms have a good scalability in the results.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.127-148
• /
• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.