• Journal of the Korea Computer Graphics Society
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• v.12 no.4
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• pp.23-32
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• 2006

In recent years, since the digital camera has been popularized, so users can easily collect hundreds of photos in a single usage. Thus the managing of hundreds of digital photos is not a simple job comparing to the keeping paper photos. We know that managing and classifying a number of digital photo files are burdensome and annoying sometimes. So people hope to use an automated system for managing digital photos especially for their own purposes. The previous studies, e.g. content-based image retrieval, were focused on the clustering of general images, which it is not to be applied on digital photo clustering and classification. Recently, some specialized clustering algorithms for images clustering digital camera images were proposed. These algorithms exploit mainly the statistics of time gap between sequent photos. Though they showed a quite good result in image clustering for digital cameras, still lots of improvements are remained and unsolved. For example the current tools ignore completely the image transformation with the different focal lengths. In this paper, we present a photo considering focal length information recorded in EXIF. We propose an algorithms based on MVA(Matching Vector Analysis) for classification of digital images taken in the every day activity. Our experiment shows that our algorithm gives more than 95% success rates, which is competitive among all available methods in terms of sensitivity, specificity and flexibility.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.331-348
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• 2019

Understanding the tumor heterogeneity due to differences in the growth pattern of metastatic tumors and rate of change is important for understanding the sensitivity of tumor cells to drugs and finding appropriate therapies. It is often possible to test for differences in population means using t-test or ANOVA when the group of N samples is distinct. However, these statistical methods can not be used unless the groups are distinguished as the data covered in this paper. Statistical methods have been studied to test heterogeneity between samples. The minimum combination t-test method is one of them. In this paper, we propose a maximum combinatorial t-test method that takes into account combinations that bisect data at different ratios. Also we propose a method based on the idea that examining the heterogeneity of a sample is equivalent to testing whether the number of optimal clusters is one in the cluster analysis. We verified that the proposed methods, maximum combination t-test method and gap statistic, have better type-I error and power than the previously proposed method based on simulation study and obtained the results through real data analysis.

• Journal of Korea Port Economic Association
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• v.33 no.2
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• pp.53-82
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• 2017

The purpose of this study is to show the clustering trend and compare empirical results, as well as to choose the clustering ports for 3 Korean ports (Busan, Incheon, and Gwangyang) by using meta-frontier DEA (Data Envelopment Analysis) and integer models on 38 Asian container ports over the period 2005-2014. The models consider 4 input variables (birth length, depth, total area, and number of cranes) and 1 output variable (container TEU). The main empirical results of the study are as follows. First, the meta-frontier DEA for Chinese seaports identifies as most efficient ports (in decreasing order) Shanghai, Hongkong, Ningbo, Qingdao, and Guangzhou, while efficient Korean seaports are Busan, Incheon, and Gwangyang. Second, the clustering results of the integer model show that the Busan port should cluster with Dubai, Hongkong, Shanghai, Guangzhou, Ningbo, Qingdao, Singapore, and Kaosiung, while Incheon and Gwangyang should cluster with Shahid Rajaee, Haifa, Khor Fakkan, Tanjung Perak, Osaka, Keelong, and Bangkok ports. Third, clustering through the integer model sharply increases the group efficiency of Incheon (401.84%) and Gwangyang (354.25%), but not that of the Busan port. Fourth, the efficiency ranking comparison between the two models before and after the clustering using the Wilcoxon signed-rank test is matched with the average level of group efficiency (57.88 %) and the technology gap ratio (80.93%). The policy implication of this study is that Korean port policy planners should employ meta-frontier DEA, as well as integer models when clustering is needed among Asian container ports for enhancing the efficiency. In addition Korean seaport managers and port authorities should introduce port development and management plans accounting for the reference and clustered seaports after careful analysis.

• Proceedings of the Korean Vacuum Society Conference
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• 1999.07a
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• pp.115-115
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• 1999

GaN는 직접천이형 wide band gap(3.4eV) 반도체로서 청색/자외선 발광소자 및 고출력 전자장비등에의 응용성 때문에 폭넓게 연구되고 있다. 이러한 넓은 분야의 응용을 위해서는 열 적으로 안정된 Ohmic contact을 반드시 실현되어야 한다. n-type GaN의 경우에는 GaN계면에서의 N vacancy가 n-type carrier로 작용하기 때문에 Ti, Al, 같은 금속을 접합하여 nitride를 형성함에 의해서 낮은 접촉저항을 갖는 Ohmic contact을 하기가 쉽다. 그러나 p-type의 경우에는 일 함수가 크고 n-type와 다르게 nitride가 형성되지 않는 금속이 Ohmic contact을 할 가능성이 많다. 시료는 HF(HF:H2O=1:1)에서 10분간 초음파 세척을 한 후 깨끗한 물에 충분히 헹구었다. 그런 후에 고순도 Ar 가스로 건조시켰다. Pd와 Ni은 열적 증착법(thermal evaporation)을 사용하여 p-GaN에 상온에서 증착하였다. 현 연구에서는 열처리에 의한 Pd의 clustering을 줄이기 위해서 wetting이 좋은 Ni을 Pd 증착 전과 후에 삽입하였으며, monchromatic XPS(x-ray photoelectron spectroscopy) 와 SAM(scanning Auger microscopy)을 사용하여 열처리 전과 40$0^{\circ}C$, 52$0^{\circ}C$ 그리고 695$0^{\circ}C$에서 3분간 열처리 후의 온도에 따른 morphology 변화, 계면반응(interfacial reaction) 및 벤드 휨(band bending)을 비교 연구하였다. Nls core level peak를 사용한 band bending에서 Schottky barrier height는 Pd/Ni bi-layer 접합시 2.1eV를, Ni/Pd bi-layer의 경우에 2.01eV를 얻었으며, 이는 Pd와 Ni의 이상적인 Schottky barrier height 값 2.38eV, 2.35eV와 비교해 볼 때 매우 유사한 값임을 알 수 있다. 시료를 후열처리함에 의해 52$0^{\circ}C$까지는 barrier height는 큰 변화가 없으나, $650^{\circ}C$에서 3분 열처리 후에 0.36eV, 0.28eV 만큼 band가 더 ？을 알 수 있었다. Pd/Ni 및 Ni/Pd 접합시 $650^{\circ}C$까지 후 열 처리 과정에서 계면에서 matallic Ga은 온도에 비례하여 많은 양이 형성되어 표면으로 편석(segregation)되어지나, In-situ SAM을 이용한 depth profile을 통해서 Ni/Pd, Pd/Ni는 증착시 uniform하게 성장함을 알 수 있었으며, 후열처리 함에 의해서 점차적으로 morphology 의 변화가 일어나기 시작함을 볼 수 있었다. 이는 $650^{\circ}C$에서 열처리 한후의 ex-situ AFM을 통해서 재확인 할 수 있었다. 이상의 결과로부터 GaN에 Pd를 접합 시 심한 clustering이 형성되어 Ohoic contact에 문제가 있으나 Pd/Ni 혹은 Ni/Pd bi-layer를 사용함에 의해서 clustering의 크기를 줄일 수 있었다. Clustering의 크기는 Ni/Pd bi-layer의 경우가 작았으며, $650^{\circ}C$ 열처리 후에 barrier height는 Pd/Ni bi-layer의 경우에도 Ni의 영향을 받음을 알 수 있었다.

• ETRI Journal
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• v.13 no.4
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• pp.42-51
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• 1991

MBE 성장시 기판 표면에서의 성장과정을 운동론적 지배과정과 열역학적 지배과정으로 나누어 성장모델을 제시하였으며, 화학적 평형상태에서의 열역학이 III-V compound의 성장속도와 composition 에 미치는 영향을 기존의 보고된 결과 데이터와 비교 분석하였다. 특히 miscibility gap 내에 존재하는 III-V ternary compound의 경우 박막의 성질 및 소자의 특성에 영향을 미치는 alloy clustering은 저온 성장시 surface kinetics에 의해, 고온성장시에는 열역학적 spinodal decomposition에 의해 결정됨을 알수 있었다. 열역학적 모델에서는 기판과 layer사이의 lattice mismatch와 재료의 elastic coefficient의 함수인 additive strain Gibbs free energy, 그리고 ternary solid solution의 regular behavior를 가정하여 ternary alloy의 mixing에 기인한 excess Gibbs free energy를 고려하였다.

• ETRI Journal
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• v.29 no.5
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• pp.700-702
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• 2007

We present a relevance feedback approach based on multi-class support vector machine (SVM) learning and cluster-merging which can significantly improve the retrieval performance in region-based image retrieval. Semantically relevant images may exhibit various visual characteristics and may be scattered in several classes in the feature space due to the semantic gap between low-level features and high-level semantics in the user's mind. To find the semantic classes through relevance feedback, the proposed method reduces the burden of completely re-clustering the classes at iterations and classifies multiple classes. Experimental results show that the proposed method is more effective and efficient than the two-class SVM and multi-class relevance feedback methods.

• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.857-866
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• 2006

Performance of an image retrieval system is usually very low because of the semantic gap between the low level feature and the high level concept in a query image. Semantically relevant images may exhibit very different visual characteristics, and may be scattered in several clusters. In this paper, we propose a content based image rertrieval approach which combines region based image retrieval and a new relevance feedback method using adaptive clustering together. Our main goal is finding semantically related clusters to narrow down the semantic gap. Our method consists of region based clustering processes and cluster-merging process. All segmented regions of relevant images are organized into semantically related hierarchical clusters, and clusters are merged by finding the number of the latent clusters. This method, in the cluster-merging process, applies r: using v principal components instead of classical Hotelling's $T_v^2$ [1] to find the unknown number of clusters and resolve the singularity problem in high dimensions and demonstrate that there is little difference between the performance of $T^2$ and that of $T_v^2$. Experiments have demonstrated that the proposed approach is effective in improving the performance of an image retrieval system.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.2
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• pp.151-156
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• 2002

There are two methods in evaluation student s achievement. The two evaluation methods are absolute evaluation and relative evaluation. They have much advantages respectively, but also have some limitations such as being too stereotyped or causing overcompetition among learners. This paper suggests a new evaluation method which evaluates student s achievements by considering the score distribution and the frequency The proposed method classifies the scores into several clusters considering the goodness. This approach calculates the goodness by applying the RE(relaxation error), and grades the achievement scores based on the goodness. The suggested method can avoid the problem of grading caused by the narrow gap of scores because it sets a standard for grading by the calculated goodness considering the score distribution and frequency of occurrence. The method can differentiate achievements of a school from those of others, and that it is useful for selecting advanced students and dull ones, and for evaluation of classes based on student s achievement.

• The Journal of Advanced Prosthodontics
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• v.16 no.1
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• pp.57-65
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• 2024

PURPOSE. The aim of this stuldy was to compare the clinical marginal fit of CAD-CAM inlays obtained from intraoral digital impression or addition silicone impression techniques. MATERIALS AND METHODS. The study included 31 inlays for prosthodontics purposes of 31 patients: 15 based on intraoral digital impressions (DI group); and 16 based on a conventional impression technique (CI group). Inlays included occlusal and a non-occlusal surface. Inlays were milled in ceramic. The inlay-teeth interface was replicated by placing each inlay in its corresponding uncemented clinical preparation and taking interface impressions with silicone material from occlusal and free surfaces. Interface analysis was made using white light confocal microscopy (WLCM) (scanning area: 694 × 510 ㎛²) from the impression samples. The gap size and the inlay overextension were measured from the microscopy topographies. For analytical purposes (i.e., 95-%-confidence intervals calculations and P-value calculations), the procedure REGRESS in SUDAAN was used to account for clustering (i.e., multiple measurements). For p-value calculation, the log transformation of the dependent variables was used to normalize the distributions. RESULTS. Marginal fit values for occlusal and free surfaces were affected by the type of impression. There were no differences between surfaces (occlusal vs. free). Gap obtained for DI group was 164 ± 84 ㎛ and that for CI group was 209 ± 104 ㎛, and there were statistical differences between them (p = .041). Mean overextension values were 60 ± 59 ㎛ for DI group and 67 ± 73 ㎛ for CI group, and there were no differences between then (p = .553). CONCLUSION. Digital impression achieved inlays with higher clinical marginal fit and performed better than the conventional silicone materials.