• Transactions of Materials Processing
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• v.28 no.5
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• pp.239-246
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• 2019

Various surface texturing techniques have been studied because of the effective applicability of micro or nano scale surface patterns. Particularly, the most promising types of patterns include the hierarchical patterns, which consists of micro/nano structures. Different processes such as MEMS, laser machining, micro cutting and micro grinding have been applied in the production of hierarchical patterns on various material surfaces. This study demonstrates the process of hot imprinting to induce the hierarchical patterns on the Al alloy surfaces. Wire electrical discharge machining (WEDM) process was used to imprint molds with micro scale sinusoidal pattern. In addition, the sinusoidal pattern with rough surface morphology was obtained as a result of the discharge craters. Consequently, the hierarchical patterns consisting of the sinusoidal pattern and the discharge craters were prepared on the imprinting mold surface. Hot imprinting process for the Al plates was conducted on the prepared mold, and the replication performance was analyzed. As a result, it was confirmed that the hierarchical patterns of the mold were effectively duplicated on the surface of Al plate.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.969-977
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• 2021

We cluster the electricity consumption of households in A-apartment in Seoul, Korea using Hierarchical K-means clustering algorithm. The data is recorded from the advanced metering infrastructure (AMI), and we focus on the electricity consumption during evening weekdays in summer. Compare to the conventional clustering algorithms, Hierarchical K-means clustering algorithm is recently applied to the electricity usage data, and it can identify usage patterns while reducing dimension. We apply Hierarchical K-means algorithm to the AMI data, and compare the results based on the various clustering validity indexes. The results show that the electricity usage patterns are well-identified, and it is expected to be utilized as a major basis for future applications in various fields.

• Smart Structures and Systems
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• v.8 no.1
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• pp.69-92
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• 2011

This paper presents an emergent pattern recognition approach based on the immune network theory and hierarchical clustering algorithms. The immune network allows its components to change and learn patterns by changing the strength of connections between individual components. The presented immune-network-based approach achieves emergent pattern recognition by dynamically generating an internal image for the input data patterns. The members (feature vectors for each data pattern) of the internal image are produced by an immune network model to form a network of antibody memory cells. To classify antibody memory cells to different data patterns, hierarchical clustering algorithms are used to create an antibody memory cell clustering. In addition, evaluation graphs and L method are used to determine the best number of clusters for the antibody memory cell clustering. The presented immune-network-based emergent pattern recognition (INEPR) algorithm can automatically generate an internal image mapping to the input data patterns without the need of specifying the number of patterns in advance. The INEPR algorithm has been tested using a benchmark civil structure. The test results show that the INEPR algorithm is able to recognize new structural damage patterns.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.7
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• pp.55-62
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• 1994

Interpattern association (IPA) model which the interconnection weight matrix(IWM) is constructed by the association between patterns is effective in similar pattern recognitions. But, if the number of reference patterns is increased, the ability of recognition is decreased. Using a hierarchical recognition algorithm which adopts the tree search strategy, we classified reference patterns into sub-groups by similarity. In IPA model, if input includes random noise we make it converge to reference pattern by means of input includes random noise we make it converge to reference pattern by means of increasing the number of pixels of prohibited state in IWM. In relation to reference patterns the pixel of prohibited state made partially prohibited state of no connected state using which is not included common and feature regions by each reference patterns.

• Journal of Communications and Networks
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• v.13 no.4
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• pp.319-326
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• 2011

Beamforming techniques have been successfully utilized for traffic channels in order to solve the interference problem. However, their use for control channels has not been sufficiently investigated. In this paper, a (semi-) centralized beamforming strategy that adaptively changes beam patterns and controls the total transmit power of cells is proposed for the performance enhancement of the common channel in hierarchical cell structure (HCS) networks. In addition, some examples of its practical implementation with low complexity are presented for two-tier HCS networks consisting of macro and pico cells. The performance of the proposed scheme has been evaluated through multi-cell system-level simulations under optimistic and pessimistic interference scenarios. The cumulative distribution function of user geometry or channel quality has been used as a performance metric since in the case of common control channel the number of outage users is more important than the sum rate. Simulation results confirm that the proposed scheme provides a significant gain compared to the random beamforming scheme as well as conventional systems that do not use the proposed algorithm. Finally, the proposed scheme can be applied simultaneously to several adjacent macro and pico cells even if it is designed primarily for the pico cell within macro cells.

• Journal of Industrial Technology
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• v.19
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• pp.403-408
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• 1999

An efficient method is developed for classifying fingerprint data based on 2-D discrete wavelet transform. Fingerprint data is first converted to a binary image. Then a multi-level 2-D wavelet transform is performed. Vertical and horizontal subbands of the transformed data show typical energy distribution patterns relevant to the fingerprint categories. The proposed method with moderate level of wavelet transform is successful in classifying fingerprints into 5 different types. Finer classification is possible by higher frequency subbands and closer analysis of energy distribution.

• Educational Technology International
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• v.18 no.2
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• pp.159-191
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• 2017

Academic analytics guides university decision-makers to assign limited resources more effectively. Especially, diverse academic courses clustered by the usage patterns and levels on Learning Management System(LMS) help understanding instructors' pedagogical approach and the integration level of technologies. Further, the clustering results can contribute deciding proper range and levels of financial and technical supports. However, in spite of diverse analytic methodologies, clustering analysis methods often provide different results. The purpose of this study is to present implications by using three different clustering analysis including Gaussian Mixture Model, K-Means clustering, and Hierarchical clustering. As a case, we have clustered academic courses based on the usage levels and patterns of LMS in higher education using those three clustering techniques. In this study, 2,639 courses opened during 2013 fall semester in a large private university located in South Korea were analyzed with 13 observation variables that represent the characteristics of academic courses. The results of analysis show that the strengths and weakness of each clustering analysis and suggest that academic leaders and university staff should look into the usage levels and patterns of LMS with more elaborated view and take an integrated approach with different analytic methods for their strategic decision on development of LMS.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.143-145
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• 2005

In this paper, an empirical study result on pattern estimation method is devoted to reveal underlying data patterns with a relatively reduced computational cost. Presented method performs crisp type clustering with given n number of data samples by means of the sequential agglomerative hierarchical nested model (SAHN). Conventional SAHN based clustering requires large computation time in the initial step of algorithm. To deal with this concern, we modified overall process with a partial approach. In the beginning of this method, we divide given data set to several sub groups with uniform sampling and then each divided sub data group is applied to SAHN based method. The advantage of this method reduces computation time of original process and gives similar results. Proposed is applied to several test data set and simulation result with conceptual analysis is presented.

• Journal of the Korean Society of Clothing and Textiles
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• v.40 no.2
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• pp.273-286
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• 2016

Protective coveralls are very uncomfortable to work in comparison to ordinary top and bottom separated clothing. A pattern maker has to consider the size of the human body and human motion range when designing protective coverall patterns. It is difficult to produce well-fitted coveralls because of the lack of readymade patterns despite the increased need for protective coveralls at various jobs. Patterns are decomposed by unsewing 18 products in the domestic market to provide the fundamental information on developing patterns for protective coveralls. The characteristics and differences of pattern types are compared after grouping patterns with information taken from the analysis of the shapes and measurements of patterns from the acquired patterns. The results of the analysis showed that on-market protective coveralls were less curved but much linear when compared to ordinary clothing patterns; however, the breasts and crotch circumferences were very loose and bulky, which is quite different from the other all-in one style working clothes. For the pattern shapes, patterns are classified into waistline-seamed and bustline-seamed types. The result of the hierarchical cluster analysis with 27 measurement variables were classified into four groups. Types by shape and measurements were related to each other; therefore, we expect the information of each type to be used in developing protective clothing patterns.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.766-771
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• 2006

In this paper, we focus on the building domain ontology from corpus by extracting concepts and properties relationships based on linguistic patterns. The pharmacy field is selected as an experiment domain and we present an algorithm to extract hierarchical structure for terminology based on the noun/suffix patterns of terminology in domain texts. In order to show usefulness of our domain ontology, we compare a typical keyword based retrieval method with an ontology based retrieval mettled which uses related information in an ontology for a related feedback. As a result, our method shows the improvement of precision by 4.97% without losing recall.