• Proceedings of the Korea Multimedia Society Conference
• /
• 1998.04a
• /
• pp.366-370
• /
• 1998

One major limitation of the efficiency of parallel computer designs has been the prohibitively high cost of parallel communication between processors and memories. Linear order concentrators can be used to build theoretically optimal interconnection schemes. Current designs call for building superconcentrators from concentrators, then using these to recursively partition the connection streams O(log2N) times to achieve point-to-point routing. Since the superconcentrators each have O(N) hardware complexity but O(log2N) depth, the resulting networks are optimal in hardware, but they are of O(log2N) depth. This pepth is not better than the O(log2N) depth Bitonic sorting networks, which can be implemented on the O(N) shuffle-exchange network with message passing. This paper introduces a new method of constructing networks using linear order concentrators and expanders, which can be used to build interconnection networks with O(log2N) depth as well as O(Nlog2N) hardware cost. (All logarithms are in base 2 throughout paper)

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.2
• /
• pp.331-338
• /
• 2011

Objectives of shipboard integration networks are to exchange and manage integrated information. Shipboard integration networks use UDP(User Datagram Protocol) multicast for the exchange of information. However, such information can be missed or damaged because UDP can't guarantee reliability. The standard of shipboard integration networks defines error log functions for the missed or damaged information. In this paper, we analyze internal and external log functions. The internal log function records errors internally, and the external log function sends error messages to a log server and records them in a database. We also develop a log processing module and log server for the external log function.

• Journal of Communications and Networks
• /
• v.13 no.5
• /
• pp.525-535
• /
• 2011

In this paper, we study the contribution of network coding (NC) in improving the multicast capacity of random wireless ad hoc networks when nodes are endowed with multi-packet transmission (MPT) and multi-packet reception (MPR) capabilities. We show that a per session throughput capacity of ${\Theta}$(nT$^3$(n)) can be achieved as a tight bound when each session contains a constant number of sinks where n is the total number of nodes and T(n) is the transmission range. Surprisingly, an identical order capacity can be achieved when nodes have only MPR and MPT capabilities. This result proves that NC does not contribute to the order capacity of multicast traffic in wireless ad hoc networks when MPR and MPT are used in the network. The result is in sharp contrast to the general belief (conjecture) that NC improves the order capacity of multicast. Furthermore, if the communication range is selected to guarantee the connectivity in the network, i.e., ${\Omega}$($\sqrt{log\;n/n}$)=T(n) = O(log log n / log n), then the combination of MPR and MPT achieves a throughput capacity of ${\Theta}$(log$^{\frac{3}{2}}$ n/$\sqrt{n}$) which provides an order capacity gain of ${\Theta}$(log$^2$ n) compared to the point-to-point multicast capacity with the same number of destinations.

• Journal of Korea Multimedia Society
• /
• v.25 no.8
• /
• pp.1203-1211
• /
• 2022

Melanoma is a skin cancer that starts in pigment-producing cells (melanocytes). The death rates of skin cancer like melanoma can be reduced by early detection and diagnosis of diseases. It is common for doctors to spend a lot of time trying to distinguish between skin lesions and healthy cells because of their striking similarities. The detection of melanoma lesions can be made easier for doctors with the help of an automated classification system that uses deep learning. This study presents a new approach for melanoma classification based on an ensemble of deep convolution neural networks and a Log-Gabor filter. First, we create the Log-Gabor representation of the original image. Then, we input the Log-Gabor representation into a new ensemble of deep convolution neural networks. We evaluated the proposed method on the melanoma dataset collected at Yonsei University and Dongsan Clinic. Based on our numerical results, the proposed framework achieves more accuracy than other approaches.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2004.05a
• /
• pp.544-547
• /
• 2004

Current increasingly information systems log historic information in a systematic way. Not only workflow management systems, but also ERP, CRM, SCM, and B2B systems often provide a so-called 'event log'. Unfortunately, the information in these event logs is rarely used to analyze the underlying processes. Process mining aims at improving this problem by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs. This paper focuses on the mining social networks. This is possible because event logs typically record information about the users executing the activities recorded in the log. To do this we combine concepts from workflow management and social network analysis. This paper introduces the approach and presents a tool to mine social networks from event logs.

• KIISE Transactions on Computing Practices
• /
• v.21 no.10
• /
• pp.651-657
• /
• 2015

The amount of web traffic has increased as a result of the rapid growth of the use of web-based applications. In order to obtain valuable information from web logs, we need to develop systems that can support interactive, flexible, and efficient ways to analyze and handle large amounts of data. In this paper, we present CERES, a log-based, interactive web analytics system for backbone networks. Since CERES focuses on analyzing web log records generated from backbone networks, it is possible to perform a web analysis from the perspective of a network. CERES is designed for deployment in a server cluster using the Hadoop Distributed File System (HDFS) as the underlying storage. We transform and store web log records from backbone networks into relations and then allow users to use a SQL-like language to analyze web log records in a flexible and interactive manner. In particular, we use the data cube technique to enable the efficient statistical analysis of web log. The system provides users a web-based, multi-modal user interface.

• Journal of Communications and Networks
• /
• v.16 no.2
• /
• pp.227-237
• /
• 2014

Throughput scaling laws for two coexisting ad hoc networks with m primary users (PUs) and n secondary users (SUs) randomly distributed in an unit area have been widely studied. Early work showed that the secondary network performs as well as stand-alone networks, namely, the per-node throughput of the secondary networks is ${\Theta}(1/\sqrt{n{\log}n})$. In this paper, we show that by exploiting directional spectrum opportunities in secondary network, the throughput of secondary network can be improved. If the beamwidth of secondary transmitter (TX)'s main lobe is ${\delta}=o(1/{\log}n)$, SUs can achieve a per-node throughput of ${\Theta}(1/\sqrt{n{\log}n})$ for directional transmission and omni reception (DTOR), which is ${\Theta}({\log}n)$ times higher than the throughput with-out directional transmission. On the contrary, if ${\delta}={\omega}(1/{\log}n)$, the throughput gain of SUs is $2{\pi}/{\delta}$ for DTOR compared with the throughput without directional antennas. Similarly, we have derived the throughput for other cases of directional transmission. The connectivity is another critical metric to evaluate the performance of random ad hoc networks. The relation between the number of SUs n and the number of PUs m is assumed to be $n=m^{\beta}$. We show that with the HDP-VDP routing scheme, which is widely employed in the analysis of throughput scaling laws of ad hoc networks, the connectivity of a single SU can be guaranteed when ${\beta}$ > 1, and the connectivity of a single secondary path can be guaranteed when ${\beta}$ > 2. While circumventing routing can improve the connectivity of cognitive radio ad hoc network, we verify that the connectivity of a single SU as well as a single secondary path can be guaranteed when ${\beta}$ > 1. Thus, to achieve the connectivity of secondary networks, the density of SUs should be (asymptotically) bigger than that of PUs.

• Korean Journal of Cognitive Science
• /
• v.20 no.4
• /
• pp.535-554
• /
• 2009

Information collected on mobile devices might be utilized to support user's memory, but it is difficult to effectively retrieve them because of the enormous amount of information. In order to organize information as an episodic approach that mimics human memory for the effective search, it is required to detect important event like landmarks. For providing new services with users, in this paper, we propose the prediction model to find landmarks automatically from various context log information based on attributed Bayesian networks. The data are divided into daily and weekly ones, and are categorized into attributes according to the source, to learn the Bayesian networks for the improvement of landmark prediction. The experiments on the Nokia log data showed that the Bayesian method outperforms SVMs, and the proposed attributed Bayesian networks are superior to the Bayesian networks modelled daily and weekly.

• The Transactions of the Korea Information Processing Society
• /
• v.4 no.7
• /
• pp.1842-1850
• /
• 1997

This paper proposes a new rearrangeable algorithm in multistage reverse shuffle/exchange network. The best known lower bound of stages for rearrangeability in symmetric network is 2logN-1 stages. However, it has never been proved for nonsymmetric networks before. Currently, the best upper bound for the rearrangeability of a shuffle/exchange network in nonsymmetric network is 3logN-3 stages. We describe the rearrangeability of reverse shuffle/exchange multistage interconnection network on every arbitrary permutation with $N{\le}16$. This rearrangeability can be established by setting one more stages in the middle stage of the network to allow the reduced network to be topological equivalent to a class of rearrangeable networks. The results in this paper enable us to establish an upper bound, 2logN stages for rearrangeable reverse shuffle/exchange network with $N{\le}16$, and leads to the possibility of this bound when $N{\le}16$.

• Journal of Communications and Networks
• /
• v.6 no.3
• /
• pp.209-215
• /
• 2004

In this paper, a butterfly Log-MAP decoding algorithm for turbo code is proposed. Different from the conventional turbo decoder, we derived a generalized formula to calculate the log-likelihood ratio (LLR) and drew a modified butterfly states diagram in 8-states systematic turbo coded system. By comparing the complexity of conventional implementations, the proposed algorithm can efficiently reduce both the computations and work units without bit error ratio (BER) performance degradation.