• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1029-1032
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• 2000

A neuro-fuzzy based circuit to recognize circuit pat-terns is proposed in this paper. The simple algorithm and exemption from the use of template patterns as well as multipliers enable the proposed circuit to implement on the hardware of an economical scale. Furthermore, thanks to the circuit design by using current-mode techniques, the proposed circuit call achieve easy extendability of tile circuit and efficient pattern recognition with high-speed. The validity of the proposed algorithm and tile circuit design is confirmed by computer simulations. The proposed pattern recognition circuit is integrable by a standard CMOS technology.

• Journal of Information Technology Applications and Management
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• v.17 no.1
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• pp.127-139
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• 2010

Customer behavior pattern discovery is the fundament for conducting customer oriented services and the services management. But, the composition, need, interest and experience of customers may be continuously changing, thereof lead to the difficulty in refining a stable description of their consistent behavior pattern. This paper presented a new method for the behavior pattern discovery from a changing collection of customers. It was originally inspired from the swarm intelligence of ant colony. By the adaptive clustering, some typical behavior patterns which reflect the characteristics of related customer clusters can extracted dynamically and adaptively.

• Proceedings of the IEEK Conference
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• 2003.07a
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• pp.254-257
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• 2003

본 논문에서는 휴대폰에 사용되는 Multi-band 용 helical antenna 의 radiation pattern 을 체적화하기 위한 방법을 제안하고자 하였다. 일반적으로 GSM 휴대폰에 사용되는 안테나는 GSM, DCS 의 dual band 인 경우가 다수이며 추세에 따라 요즘은 PCS 등을 포함한 triple band 의 안테나가 많이 선호되고 있다. 이러한 multi band antenna 의 radiation pattern 에 영향을 미치는 변수로 본 논문에서는 ground 의 length 와 width 그리고 폴더쪽에 사용되는 LCD 의 ground 와 폴더쪽 기구물에 사용된 EMI spray 등의 변화를 통해 pattern 의 변화를 알아보고 이러한 변수를 조절하여 radiation pattern을 최적화하는 방법에 관하여 논하였다.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.211-214
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• 2003

In this paper, we intend to find user's TV program choosing pattern and, recommend programs that he/she wants. So we suggest item dependency map which express relation between chosen program. Using an algorithm that we suggest, we can recommend an program, which a user has not saw yet but maybe is likely to interested in. Item dependency map is used as patterns for association in hopfield network so we can extract users global program choosing pattern only using users partial information. Hopfield network can extract global information from sub-information. Our algorithm can predict user's inclination and recommend an user necessary information.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.12
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• pp.954-961
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• 2013

In this paper, we propose a new learning method using a variable learning to improve pattern recognition in the FCSR(Fast Commit Slow Recode) learning method of the Fuzzy ART. Traditional learning methods have used a fixed learning rate in updating weight vector(representative pattern). In the traditional method, the weight vector will be updated with a fixed learning rate regardless of the degree of similarity of the input pattern and the representative pattern in the category. In this case, the updated weight vector is greatly influenced from the input pattern where it is on the boundary of the category. Thus, in noisy environments, this method has a problem in increasing unnecessary categories and reducing pattern recognition capacity. In the proposed method, the lower similarity between the representative pattern and input pattern is, the lower input pattern contributes for updating weight vector. As a result, this results in suppressing the unnecessary category proliferation and improving pattern recognition capacity of the Fuzzy ART in noisy environments.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.94-102
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• 2013

We propose the mirror active element pattern (AEP) method for the radiation pattern computation of linear array antennas versus scan angles. The computation time for the radiation pattern of linear array antennas using the mirror AEP method is reduced by almost half compared to that using the AEP method because the number of AEPs of elements obtained by the full-wave simulation necessary for the radiation pattern computation of linear array antennas is reduced by almost half. The difference between the radiation patterns of linear array antennas obtained by the full-wave simulation and mirror AEP method is very small for wide scan angle range when the radiation pattern of an antenna element is symmetric.

• Journal of KIISE:Databases
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• v.34 no.3
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• pp.244-260
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• 2007

The operator LIKE of the database language SQL is a string pattern search operator. By providing the string pattern, the operator can identify column values that match with the string pattern. As a phonetic symbol, each Korean syllable is composed either of a leading sound and a medial sound or of a leading sound, a medial sound, and a trailing sound. As a search pattern of Korean syllables of the operator LIKE, in addition to the traditional Korean search pattern, this paper proposes a new search pattern that is based on leading sounds and medial sounds of Korean. With the new Korean search pattern, Korean syllables having specific leading sounds, specific medial sounds, or both specific leading sounds and medial sounds can be found. Formulating predicates equivalent with the new Korean search pattern by way of existing SQL operators is cumbersome and might cause the portability problem of applications depending on the underlying character set of the DBMS. This paper presents algorithms for the execution of the operator LIKE considering the new Korean search pattern based on the characters that are represented in KS X 1001, which is a Korean standard code for information interchange of Korean and Chinese.

• Journal of the Korea Society of Computer and Information
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• v.16 no.11
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• pp.17-24
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• 2011

Collaborative filtering system is the clustering about user is built and then based on that clustering results will recommend the preferred item to the user. However, building user clustering is time consuming and also once the users evaluate and give feedback about the film then rebuilding the system is not simple. In this paper, genre pattern of movie recommendation systems is being used and in order to simplify and reduce time of rebuilding user clustering. A Frequent pattern networks is used and then extracts user preference genre patterns and through that extracted patterns user clustering will be built. Through built the clustering for all neighboring users to collaborative filtering is applied and then recommends movies to the user. When receiving user information feedback, traditional collaborative filtering is to rebuild the clustering for all neighbouring users to research and do the clustering. However by using frequent pattern Networks, through user clustering based on genre pattern, collaborative filtering is applied and when rebuilding user clustering inquiry limited by search time can be reduced. After receiving user information feedback through proposed user clustering based on genre pattern, the time that need to spent on re-establishing user clustering can be reduced and also enable the possibility of traditional collaborative filtering systems and recommendation of a similar performance.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.1-8
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• 1994

In this paper, we propose an efficient two-step pattern matching method which promises shape distortion-tolerant recognition of handwritten of handwritten Hangul syllables. In the first step, nonlinear shape normalization is carried out to compensate for global shape distortions in handwritten characters, then a preliminary classification based on simple pattern matching is performed. In the next step, nonlinear pattern matching which achieves best matching between input and reference pattern is carried out to compensate for local shape distortions, then detailed classification which determines the final result of classification is performed. As the performance of recognition systems based on pattern matching methods is greatly effected by the quality of reference patterns. we construct reference patterns by combining the proposed nonlinear pattern matching method with a well-known averaging techniques. Experimental results reveal that recognition performance is greatly improved by the proposed two-step pattern matching method and the reference pattern construction scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5023-5038
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• 2017

Big data is an emerging technology which deals with wide range of data sets with sizes beyond the ability to work with software tools which is commonly used for processing of data. When we consider a huge network, we have to process a large amount of network information generated, which consists of both normal and abnormal activity logs in large volume of multi-dimensional data. Intrusion Detection System (IDS) is required to monitor the network and to detect the malicious nodes and activities in the network. Massive amount of data makes it difficult to detect threats and attacks. Sequential Pattern mining may be used to identify the patterns of malicious activities which have been an emerging popular trend due to the consideration of quantities, profits and time orders of item. Here we propose a sequential pattern mining algorithm with fuzzy logic feature selection and fuzzy weighted support for huge volumes of network logs to be implemented in Apache Hadoop YARN, which solves the problem of speed and time constraints. Fuzzy logic feature selection selects important features from the feature set. Fuzzy weighted supports provide weights to the inputs and avoid multiple scans. In our simulation we use the attack log from NS-2 MANET environment and compare the proposed algorithm with the state-of-the-art sequential Pattern Mining algorithm, SPADE and Support Vector Machine with Hadoop environment.