• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.11
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• pp.2269-2274
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• 2009

Customer clustering method is used to make a TDLP (typical daily load profile) to estimate the quater hourly load profile of non-AMR (Automatic Meter Reading) customer. In this paper, repeated small-sized clustering method is supposed to improve the classifying ability of TDLP. K-means algorithm is well-known clustering technology of data mining. To reduce the local maxima of k-means algorithm, proposed method clusters average load profiles to small-sized clusters and selects the highest error rated cluster and clusters this to small-sized clusters repeatedly to minimize the local maxima.

• Asian review of World Histories
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• v.1 no.1
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• pp.45-84
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• 2013

Empirical research into Đại Việt before the $14^{th}$ century has made little progress since the 1990s. To improve this situation, I here examine how the L$\acute{y}$ dynasty (1009-1226), the first long-lasting dynasty of Đại Việt, established stable local ruleafter the "Tang-Song Transition" in China that changed the entire picture of East Asia (including both Southeast and Northeast Asia). This paper focuses on two issues. First are the local administrative units and their governors. The nature of both higher units like lộ(circuits), phủ and ch$\hat{a}$u (provinces), and basic units like hươg and gi$\acute{a}$p (districts?) will be examined. Second, I examine non-institutional channels of local rule by the imperial family. By combining such administrative and non-administrative means, the L$\acute{y}$ central court enforced a considerably stable local rule for two centuries. Finally, I attempt some preliminary comparisons with the local rule of Goryeo (918-1392) in the Korean peninsula, a polity that shared many features with Đại Việt in the process of localization of the Tang and Song models. I hope this approach of viewing small empires from the standpoint not of their "goal" (modern states) but of their "start" (charter polities), will enrich the discussion of East Asian small empires.

• Korean Journal of Mathematics
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• v.28 no.1
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• pp.65-73
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• 2020

This article concerns a property of local rings and domains. A ring R is called weakly local if for every a ∈ R, a is regular or 1-a is regular, where a regular element means a non-zero-divisor. We study the structure of weakly local rings in relation to several kinds of factor rings and ring extensions that play roles in ring theory. We prove that the characteristic of a weakly local ring is either zero or a power of a prime number. It is also shown that the weakly local property can go up to polynomial (power series) rings and a kind of Abelian matrix rings.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.07a
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• pp.75-77
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• 2015

디스플레이 해상도가 지속적으로 고해상화가 되면서, 기존 저해상도 영상을 고해상도 디스플레이 크기에 맞춰 해상도를 키우는 기법인 초해상화(super-resolution, SR) 기법에 많은 관심이 쏟아지고 있으며 이에 대한 많은 초해상화 논문들이 게재되었다. 이 중 현재 최상 품질의 고해상도 영상을 복원하는 한 초해상화 기법은, 입력 받은 저해상도 영상을 자가 예제(self-examples)로 사용하여 선형 매핑(linear mapping)을 통해 점진적으로 여러 레벨(level)를 거쳐 조금씩 키우는 방법이다. 이때 각 레벨마다 기존 저해상도 영상 크기로 반복적으로 줄여 오차를 줄이는 역투영법(back-projection)을 사용하는데, 이 방법은 처리된 영상에 시각적 품질을 낮추는 링 아티팩트(ringing artifacts)를 생산하며, 이는 매 레벨마다 계속 누적이 되어 고해상도 결과 이미지 품질에 악영향을 미치는 단점이 있다. 이를 보완하기 위해, 본 논문에서는 저해상도 정지 영상을 고해상도 정지 영상으로 점진적으로 키울 때 일반적인 역투영법 대신 비국소적 평균법(non-local means, NLM) 기반 역투영법을 사용하는 초해상화 기법을 제안한다. 제안하는 기법은 매 레벨마다 생기는 링 아티팩트를 효과적으로 제거하여 높은 시각적 품질의 고해상도 영상을 복원할 수 있게 한다. 실험을 통해 제안된 초해상화 기법을 사용 시 기존 초해상화 기법보다 향상된 고품질 고해상도 영상 복원이 가능한 것을 확인하였다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.92-98
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• 2017

Data clustering determines a group of patterns using similarity measure in a dataset and is one of the most important and difficult technique in data mining. Clustering can be formally considered as a particular kind of NP-hard grouping problem. K-means algorithm which is popular and efficient, is sensitive for initialization and has the possibility to be stuck in local optimum because of hill climbing clustering method. This method is also not computationally feasible in practice, especially for large datasets and large number of clusters. Therefore, we need a robust and efficient clustering algorithm to find the global optimum (not local optimum) especially when much data is collected from many IoT (Internet of Things) devices in these days. The objective of this paper is to propose new Hybrid Simulated Annealing (HSA) which is combined simulated annealing with K-means for non-hierarchical clustering of big data. Simulated annealing (SA) is useful for diversified search in large search space and K-means is useful for converged search in predetermined search space. Our proposed method can balance the intensification and diversification to find the global optimal solution in big data clustering. The performance of HSA is validated using Iris, Wine, Glass, and Vowel UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KSAK (K-means+SA+K-means) and SAK (SA+K-means) are better than KSA(K-means+SA), SA, and K-means in our simulations. Our method has significantly improved accuracy and efficiency to find the global optimal data clustering solution for complex, real time, and costly data mining process.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.4
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• pp.61-67
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• 2017

Low-dose X-ray fluoroscopic image sequences to avoid radiation exposure risk are contaminated by quantum noise. To restore these noisy sequences, we propose a 3D nonlocal means (NLM) filter based on stochastic distancesed can be applied to the denoising of X-ray fluoroscopic image sequences. The stochastic distance is obtained within motion-compensated noise filtering support to remove the Poisson noise. In this paper, motion-adaptive weight which reflected the frame similarity is proposed to restore the noisy sequences without motion artifact. Experimental results including comparisons with conventional algorithms for real X-ray fluoroscopic image sequences show the proposed algorithm has a good performance in both visual and quantitative criteria.

• Spatial Information Research
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• v.11 no.4
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• pp.409-420
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• 2003

Geographic information systems are the crucial means in local governments to improve the diverse internal operations or service to citizens. A eat number of local governments have introduced GIS. However in spite of the rapid spread of GIS projects in local governments there are severe level differences of Geographic Information Infrastructure between medium or large cities and small cities or counties(Gun), especially in non-capital area. The differences are one of the main reasons of regional inequalities in local governments'administrative services. Thus this article suggested that the central government should place policy emphasis on improving the levels of geographic information infrastructure in small cities, counties(Gun) and especially non-capital areas.

• Computers and Concrete
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• v.20 no.6
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• pp.689-704
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• 2017

The paper presents experimental and numerical investigations of prefabricated composite structural building reinforced concrete slabs with the insulating material for a residential building construction. The building slabs were composed of concrete and expanded polystyrene. In experiments, the slabs in the full-scale 1:1 were subjected to vertical concentrated loads and failed along a diagonal shear crack. The experiments were numerically evaluated using the finite element method based on two different constitutive continuum models for concrete. First, an elasto-plastic model with the Drucker-Prager criterion defined in compression and with the Rankine criterion defined in tension was used. Second, a coupled elasto-plastic-damage formulation based on the strain equivalence hypothesis was used. In order to describe strain localization in concrete, both models were enhanced in the softening regime by a characteristic length of micro-structure by means of a non-local theory. Attention was paid to the formation of critical diagonal shear crack which was a failure precursor.

• KIISE Transactions on Computing Practices
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• v.20 no.11
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• pp.573-578
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• 2014

In this letter, we propose a noise reduction method for use in magnetic resonance images that is based on non-local mean and guided image filters. Our method consists of two phases. In the first phase, the guidance image is obtained from a noisy image by using an adaptive non-local mean filter. The spread of the kernel is adaptively by controlled by implementing the concept of edgeness. In the second phase, the noisy images and the guidance images are provided to the guided image filter as input in order to produce a noise-free image. The improved performance of the proposed method is investigated by conducting experiments on standard datasets that contain magnetic resonance images. The results show that the proposed scheme is superior over the existing approaches.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.729-733
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• 2003

The presence of local features within clusters incurred by multi-modal nature of data prohibits many conventional clustering techniques from working properly. Especially, the clustering of datasets with non-Gaussian distributions within a cluster can be problematic when the technique with implicit assumption of Gaussian distribution is used. Current study proposes a simple tandem clustering method composed of k-means type algorithm and hierarchical method to solve such problems. The multi-modal dataset is first divided into many small pre-clusters by k-means or fuzzy k-means algorithm. The pre-clusters found from the first step are to be clustered again using agglomerative hierarchical clustering method with Kullback- Leibler divergence as the measure of dissimilarity. This method is not only effective at extracting the multi-modal clusters but also fast and easy in terms of computation complexity and relatively robust at the presence of outliers. The performance of the proposed method was evaluated on three generated datasets and six sets of publicly known real world data.