• Journal of Information Technology Applications and Management
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• v.24 no.4
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• pp.187-196
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• 2017

It is urgent to prepare countermeasures for traffic congestion problems of Korea's metropolitan area where central functions such as economic, social, cultural, and education are excessively concentrated. Most users of public transportation in metropolitan areas including Seoul use the traffic cards. If various information is extracted from traffic big data produced by the traffic cards, they can provide basic data for transport policies, land usages, or facility plans. Therefore, in this study, we extract valuable information such as the subway passengers' frequent travel patterns from the big traffic data provided by the Seoul Metropolitan Government Big Data Campus. For this, we use a Hadoop (High-Availability Distributed Object-Oriented Platform) to preprocess the big data and store it into a Mongo database in order to analyze it by a sequential pattern data mining technique. Since we analysis the actual big data, that is, the traffic cards' data provided by the Seoul Metropolitan Government Big Data Campus, the analyzed results can be used as an important referenced data when the Seoul government makes a plan about the metropolitan traffic policies.

• Korean Journal of Optics and Photonics
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• v.32 no.4
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• pp.172-179
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• 2021

A near-normal-incidence ellipsometer (NNIE) is suggested as an optical critical dimension (OCD) measurement system that is highly sensitive to the bottom defect of a sample with high-aspect-ratio structured patterns. Incident light passes through a polarizer and a phase retarder in sequence, and the reflected light from the sample also passes through them, but in reverse order. The operating principle of this NNIE, where a single polarizer and a single phase retarder are shared by the incident and reflected light, is studied, and a method to determine the ellipsometric constants from the measured intensities at proper combinations of the azimuthal angles of polarizer and retarder is presented.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.193-200
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• 2022

Unknown cryptographic communication protocols may have advantage of guaranteeing personal and data privacy, but when used for malicious purposes, it is almost impossible to identify and respond to using existing network security equipment. In particular, there is a limit to manually analyzing a huge amount of traffic in real time. Therefore, in this paper, we attempt to identify packets of unknown cryptographic communication protocols and separate fields comprising a packet by using machine learning techniques. Using sequential patterns analysis, hierarchical clustering, and Pearson's correlation coefficient, we found that the structure of packets can be automatically analyzed even for an unknown cryptographic communication protocol.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.703-708
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• 2022

K-means clustering is a representative clustering technique. However, there is a limitation in not being able to integrate the performance evaluation scale and the method of determining the minimum number of clusters. In this paper, a method for numerically determining the minimum number of clusters is introduced. The explained variance is presented as an integrated measure. We propose that the k-means clustering method should be performed in the subspace of the PCA in order to simultaneously satisfy the minimum number of clusters and the threshold of the explained variance. It aims to present an explanation in principle why principal component analysis and k-means clustering are sequentially performed in pattern recognition and machine learning.

• The Journal of the Acoustical Society of Korea
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• v.29 no.2
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• pp.133-140
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• 2010

We improve the performance of cardiac disorder classification by adding new temporal features extracted from continuous heart sound signals. We add three kinds of novel temporal features to a conventional feature based on mel-frequency cepstral coefficients (MFCC): Heart sound envelope, murmur probabilities, and murmur amplitude variation. In cardiac disorder classification and detection experiments, we evaluate the contribution of the proposed features to classification accuracy and select proper temporal features using the sequential feature selection method. The selected features are shown to improve classification accuracy significantly and consistently for neural network-based pattern classifiers such as multi-layer perceptron (MLP), support vector machine (SVM), and extreme learning machine (ELM).

• Journal of Korea Technology Innovation Society
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• v.21 no.1
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• pp.92-129
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• 2018

In general, a technology and innovation strategy has been established first. Then, the technology and innovation activities are conducted accordingly. The literature on the technology management process points out that the technology and innovation activities exist in some sequences, nonlinear or linear pattern. However, it is also argued that a certain technology and innovation activity can be occurred or disappeared at certain timing. In this paper, it has been analyzed and clarified how the technology and innovation activities are performed and working together with the technology and innovation strategies in certain context especially when the handset market moves from feature phone to smartphone during a last decade. Empirical results show that the starting point of the technology and innovation activity changes according to the technology and innovation strategies. And, technology innovation activities exhibit multi-layer architecture types. It is confirmed that technology and innovation activities follow a specific pattern. However, if there are some changes in the technology and innovation strategy due to the external environment change, some technology and innovation activities can be skipped because the priority of the technology and innovation activities would be changed. If the firm which has the strategy type of 'innovators' fails to adapt to the fast changing external environment and has the inadequate technology and innovation activities, it is required to change the technology and innovation strategy. It will have a huge impact on the firm's survival.

• Journal of the Korean Magnetics Society
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• v.18 no.2
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• pp.49-53
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• 2008

Magnetic and crystallographic properties of patterned media fabricated by nanoimprint lithography and Co-Pt electroplating were studied. Thin films of Ru(20 nm)/Ta(5 nm)/$SiO_2$(100 nm) were deposited on Si(100) wafer and then 25 nm hole pattern was fabricated by nanoimprint lithography on substrate. The electroplated Co-Pt nano-dots have the diameter of 35 nm and the height of 27 nm. Magnetic dot patterns of Co-Pt alloy were created using electroplated Co-Pt alloy and then their properties were measured by MFM, SQUID, SEM, TEM and AFM. We observed single domain with perendicular anisotropy for each dot and achieved optimum coercivity of 2900 Oe. These results mean that patterned media fabricated by nanoimprint lithography and electroplating have good properties in view of extending superparamagnetic limit while satisfying the writability requirements with the present write heads.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.1
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• pp.133-146
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• 2015

Ever sophisticated e-finance fraud techniques have led to an increasing number of reported phishing incidents. Financial authorities, in response, have recommended that we enhance existing Fraud Detection Systems (FDS) of banks and other financial institutions. FDSs are systems designed to prevent e-finance accidents through real-time access and validity checks on client transactions. The effectiveness of an FDS depends largely on how fast it can analyze and detect abnormalities in large amounts of customer transaction data. In this study we detect fraudulent transaction patterns and establish detection rules through e-finance accident data analyses. Abnormalities are flagged by comparing individual client transaction patterns with client profiles, using the ruleset. We propose an effective flagging method that uses decision trees to normalize detection rules. In demonstration, we extracted customer usage patterns, customer profile informations and detection rules from the e-finance accident data of an actual domestic(Korean) bank. We then compared the results of our decision tree-normalized detection rules with the results of a sequential detection and confirmed the efficiency of our methods.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1656-1666
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• 1999

The more complex and large digital circuits become, the more important delay test becomes which guarantees that circuits operate in time. In this paper, the proposed algorithm is developed, which enable the fast indirect implication for efficient test pattern generation in sequential circuits of standard scan environment. Static learning algorithm enables application of a new implication value using contrapositive proposition. The static learning procedure found structurally, analyzes the gate structure in the preprocessing phase and store the information of learning occurrence so that it can be used in the test pattern generation procedure if it satisfies the implication condition. If there exists a signal line which include all paths from some particular primary inputs, it is a partitioning point. If paths passing that point have the same partial path from primary input to the signal or from the signal to primary output, they will need the same primary input values which separated by the partitioning point. In this paper test pattern generation can be more effective by using this partitioning technique. Finally, an efficient delay fault test pattern generator using indirect implication is developed and the effectiveness of these algorithms is demonstrated by experiments.

• The KIPS Transactions:PartD
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• v.13D no.4 s.107
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• pp.455-462
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• 2006

Recently, the filtering approach using vector approximation such as VA-file[1] or LPC-file[2] have been proposed to support similarity search in high dimensional data space. This approach filters out many irrelevant vectors by calculating the approximate distance from a query vector using the compact approximations of vectors in database. Accordingly, the total elapsed time for similarity search is reduced because the disk I/O time is eliminated by reading the compact approximations instead of original vectors. However, the search time of the VA-file or LPC-file is not much lessened compared to the brute-force search because it requires a lot of computations for calculating the approximate distance. This paper proposes a new bitmap index structure in order to minimize the calculating time. To improve the calculating speed, a specific value of an object is saved in a bit pattern that shows a spatial position of the feature vector on a data space, and the calculation for a distance between objects is performed by the XOR bit calculation that is much faster than the real vector calculation. According to the experiment, the method that this paper suggests has shortened the total searching time to the extent of about one fourth of the sequential searching time, and to the utmost two times of the existing methods by shortening the great deal of calculating time, although this method has a longer data reading time compared to the existing vector approximation based approach. Consequently, it can be confirmed that we can improve even more the searching performance by shortening the calculating time for filtering of the existing vector approximation methods when the database speed is fast enough.