• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.578-591
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• 2016

In this paper, the fast encoding algorithm in High Efficiency Video Coding (HEVC) encoder was studied. For the encoding efficiency, the current HEVC reference software is divided the input image into Coding Tree Unit (CTU). then, it should be re-divided into CU up to maximum depth in form of quad-tree for RDO (Rate-Distortion Optimization) in encoding precess. But, it is one of the reason why complexity is high in the encoding precess. In this paper, to reduce the high complexity in the encoding process, it proposed the method by determining the maximum depth of the CU using a hierarchical clustering at the pre-processing. The hierarchical clustering results represented an average combination of motion vectors (MV) on neighboring blocks. Experimental results showed that the proposed method could achieve an average of 16% time saving with minimal BD-rate loss at 1080p video resolution. When combined the previous fast algorithm, the proposed method could achieve an average 45.13% time saving with 1.84% BD-rate loss.

• Smart Media Journal
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• v.10 no.3
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• pp.23-30
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• 2021

Although the manufacturing time series data clustering technique is an important grouping solution in the field of detecting and improving manufacturing large data-based equipment and process defects, it has a disadvantage of low accuracy when applying the existing static data target clustering technique to time series data. In this paper, an evolutionary computation-based time series cluster analysis approach is presented to improve the coherence of existing clustering techniques. To this end, first, the image shape resulting from the manufacturing process is converted into one-dimensional time series data using linear scanning, and the optimal sub-clusters for hierarchical cluster analysis and split cluster analysis are derived based on the Pearson distance metric as the target of the transformation data. Finally, by using a genetic algorithm, an optimal cluster combination with minimal similarity is derived for the two cluster analysis results. And the performance superiority of the proposed clustering is verified by comparing the performance with the existing clustering technique for the actual manufacturing process image.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.487-487
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• 2023

농업 분야에서 미생물은 영양분 가용화, 유기물 분해 등 토양 영양분 공급에 중요한 역할을 하며, 토양 건강성 증진, 식량 안보 및 식품 건강 면에서 많은 활용 가능성을 지니고 있다. 최근 유역 환경 건강성, 생물 다양성 보존, 효율적인 고품질 농산물 생산에 대한 관심이 커져, 지속 가능한 농업 중 하나인 유기농업과 관행농업 토양의 이화학적 및 생물학적 특성에 관한 비교 연구가 진행되고 있다. 미생물은 지속 가능한 농업 발전의 중요한 요소 중 하나로써, 미생물 다양성이 풍부할수록 토양 비옥도, 작물 성장 면에서 긍정적인 영향을 미친다고 알려져 있다. 본 연구는 이에 대한 기초 데이터를 제공하기 위해 논 경작지를 대상으로 유기 및 관행농업 토양의 미생물 군집조성과 Alpha diversity analysis(Chao1, Shannon, Simpson index)을 통해 비교하였다. 경기도 양평군에서 유기 및 관행 논 지역을 각각 1지점씩 선정하였으며, 8월부터 11월까지 총 4회 현장 조사를 진행하였다. 미생물 분석은 차세대염기서열분석을 실시하였으며, bacteria는 16S rRNA V3-4 영역, fungi는 ITS 3-4 영역을 sequencing 하였다. 미생물 군집조성은 문수준에서는 큰 차이가 없었으나, 속수준에서는 fungi 군집조성에 차이를 보였다. 예로 Ustilaginoidea 속은 관행 논 토양에서만 발견되었으며, 벼 이삭누룩병을 일으키는 병원균으로 과도한 질소 비료 시비가 원인으로 추정된다. 종 다양성은 bacteria diversity의 경우 관행 논 토양에서 높게 측정되는 반면, fungi diversity의 경우 유기 논 토양에서 높게 측정되었다. 결론적으로 체계적인 시비 관리 통해 미생물 군집은 조절될 수 있으며, 관행농업은 적절한 시비를 통해 토양 건강성 및 식품 건강성 면에서 유기농업과 비슷한 효과를 보여줄 가능성이 있다고 판단된다.

• Journal of IKEEE
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• v.15 no.3
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• pp.191-197
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• 2011

An image segmentation model using fuzzy c-means with intuition (FCM-I) model is proposed for the segmentation of magnetic resonance image in this paper. In FCM-I, a measurement called intuition level is adopted so that the intuition level helps to alleviate the effect of noises. A practical magnetic resonance image data set is used for image segmentation experiment and the performance is compared with those of some conventional algorithms. Results show that the segmentation method based on FCM-I compares favorably to several conventional clustering algorithms. Since FCM-I produces cluster prototypes less sensitive to noises and to the selection of involved parameters than the other algorithms, FCM-I is a good candidate for image segmentation problems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.612-614
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• 2022

This paper propose a clustering algorithm for mobile nodes that possible more efficient clustering using context-aware attribute information in adaptive learning. In typically, the data will be provided to classify interrelationships within cluster properties. If a new properties are treated as contaminated information in comparative clustering, it can be treated as contaminated properties in comparison clustering. In this paper, To solve this problems in this paper, we have new present a context-awareness learning based model that can analyzes the clustering attributed parameters from the node properties using accumulated information properties.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.01a
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• pp.227-228
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• 2013

본 논문에서는 연속적으로 입력되는 스테레오 입체 영상으로부터 2차원과 3차원의 특징을 결합하여 군집화함으로써 대상 물체를 보다 강건하게 분할하는 기법을 제안한다. 제안된 방법에서는 촬영된 장면의 좌우 영상으로부터 스테레오 정합 알고리즘을 이용해 영상의 각 화소별로 카메라와 물체 사이의 거리를 나타내는 깊이 특징을 추출한다. 그런 다음, 깊이와 색상 특징을 효과적으로 군집화하여 배경에 해당하는 영역을 제외하고, 전경에 해당하는 대상 물체를 감지한다. 실험에서는 제안된 방법을 여러가지 영상에 적용하여 테스트를 해 보았으며, 제안된 방법이 기존의 2차원 기반의 물체 분리 방법에 비해 보다 강건하게 대상물체를 분할함을 확인하였다.

• Annual Conference on Human and Language Technology
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• 2007.10a
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• pp.194-201
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• 2007

인용 매칭(Citation Matching, CM)은 동일한 논문을 지칭하는 인용레코드(Citation Record)를 군집화하는 것으로 인용 관계를 가진 사회연계망 구축시 필요한 기술의 하나이다. 인용 매칭의 전단계로써, 인용 레코드를 저자, 논문 제목, 게재지명, 발행연도 등의 필드로 구분하는 인용 필드 분해가 고려될 수 있다. 본 논문은 인용 필드 분해(Citation Field Segmentation, CFS)와 인용 매칭의 상관관계를 분석하고자 한다. 즉, 인용 필드 분해가 인용 매칭에 필수적인 단계인지를 밝히고 개별 인용 필드가 인용 매칭에 미치는 영향을 분석한다. 실험을 통해 인용 필드 분해를 한 인용 매칭(CFS-based CM)이 인용 필드 분해를 적용하지 않은 인용 매칭(CFS-free CM)에 비해 1% 내외의 성능의 차이를 보이므로, 인용매칭의 성능에 크게 영향을 미친다고 보기 어려웠다. 이는 인용 레코드의 서로 다른 필드들 사이에서 어휘 중복 비율이 크게 낮기 때문에 따로 필드를 구별하지 않아도 필드가 구별되는 특성때문이었다.

• Journal of the Korea Organic Resources Recycling Association
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• v.26 no.4
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• pp.31-43
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• 2018

This study was conducted to investigate the effects of organic farmland soil and nutrient management on soil properties depending on organic (animal manure compost and green manure [hairy vetch]) and chemical fertilization, tillage and no-tillage, and crop rotation (corn-wheat, corn-.hairy vetch). It was found that the application of organic matter such as animal manure compost and hairy vetch, increased the soil organic matter content, the soil microbial density and microbial biomass C content as compared with the chemical fertilizer treatment. It was also confirmed that the functional diversity of soil microbial community was increased. As a result of the comparison with the crop rotation and single cropping, the soil chemistry showed no significant difference between the treatments, but the corn-wheat and corn-hairy vetch rotation treatments tended to have higher microbial biomass C content and shannon's diversity index than the single cropping. Soil chemical properties of tillage and no-tillage treatments showed no significant difference between treatments. There was no statistically significant difference in substrate utilization of soil microbial community between tillage and no-tillage treatment. Correlation analysis between soil chemical properties and soil microbial activity revealed that soil organic matter content and exchangeable potassium content were positively correlated, with statistical significance, with substrate utilization, and substrate richness. To conclude, organic fertilization had positive effects on the short-term improvement of soil chemical properties and diversity of microbial communities.

• Korean Journal of Remote Sensing
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• v.22 no.2
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• pp.111-121
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• 2006

The classification using spectral angle is a new approach based on the fact that the spectra of the same type of surface objects in RS data are approximately linearly scaled variations of one another due to atmospheric and topographic effects. There are many researches on the unsupervised classification using spectral angle recently. Nevertheless, there are only a few which consider the characteristics of Hyperspectral data. On this study, we propose the ISOMUSAC(Iterative Self-Organizing Modified Unsupervised Spectral Angle Classification) which can supplement the defects of previous unsupervised spectral angle classification. ISOMUSAC uses the Angle Division for the selection of seed points and calculates the center of clusters using spectral angle. In addition, ISOMUSAC perform the iterative merging and splitting clusters. As a result, the proposed algorithm can reduce the time of processing and generate better classification result than previous unsupervised classification algorithms by visual and quantitative analysis. For the comparison with previous unsupervised spectral angle classification by quantitative analysis, we propose Validity Index using spectral angle.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.32-40
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• 2008

Fuzzy rules are very useful and efficient to describe classification rules especially when the attribute values are continuous and fuzzy in nature. However, it is generally difficult to determine membership functions for generating efficient fuzzy classification rules. In this paper, we propose a method of automatic generation of efficient fuzzy classification rules using evolutionary algorithm. In our method we generate a set of initial membership functions for evolutionary algorithm by supervised clustering the training data set and we evolve the set of initial membership functions in order to generate fuzzy classification rules taking into consideration both classification accuracy and rule comprehensibility. To reduce time to evaluate an individual we also propose an evolutionary algorithm with data partition evaluation in which the training data set is partitioned into a number of subsets and individuals are evaluated using a randomly selected subset of data at a time instead of the whole training data set. We experimented our algorithm with the UCI learning data sets, the experiment results showed that our method was more efficient at average compared with the existing algorithms. For the evolutionary algorithm with data partition evaluation, we experimented with our method over the intrusion detection data of KDD'99 Cup, and confirmed that evaluation time was reduced by about 70%. Compared with the KDD'99 Cup winner, the accuracy was increased by 1.54% while the cost was reduced by 20.8%.