• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.49.4-49.4
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• 2021

We Present a B and V band time-series CCD photometry of the Delta scuti stars, BV Cnc, BN Cnc, BU Cnc, BS Cnc, in the open cluster M44. The observation was carried out for 36 nights between February, 2020 and February 2021 with a 0.6m telescope equipped 2K CCD camera at Gyeonggi Science High School for the Gifted(GSHS). To detect pulsational frequencies, we wuse Discret Fourier Transformation(DFT) method. We have detected resonable pulsational frequencis compare to previous study.

• Journal of Broadcast Engineering
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• v.16 no.6
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• pp.1047-1057
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• 2011

In this paper, we propose a novel tone mapping method that implements histogram modification framework on two local regions that are classified using K-means clustering algorithm. In addition, we propose automatic parameter tuning method for histogram modification. The proposed method enhances local details better than the global histogram method. Moreover, the proposed method is fully automatic in the sense that it does not require intervention from human to tune parameters that are involved for computing tone mapping functions. In simulations and experimental studies, the proposed method showed better performance than existing histogram modification method.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.594-597
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• 2007

We present in this paper a novel mid and long term power load prediction method using temporal pattern mining from AMR (Automatic Meter Reading) data. Since the power load patterns have time-varying characteristic and very different patterns according to the hour, time, day and week and so on, it gives rise to the uninformative results if only traditional data mining is used. Also, research on data mining for analyzing electric load patterns focused on cluster analysis and classification methods. However despite the usefulness of rules that include temporal dimension and the fact that the AMR data has temporal attribute, the above methods were limited in static pattern extraction and did not consider temporal attributes. Therefore, we propose a new classification method for predicting power load patterns. The main tasks include clustering method and temporal classification method. Cluster analysis is used to create load pattern classes and the representative load profiles for each class. Next, the classification method uses representative load profiles to build a classifier able to assign different load patterns to the existing classes. The proposed classification method is the Calendar-based temporal mining and it discovers electric load patterns in multiple time granularities. Lastly, we show that the proposed method used AMR data and discovered more interest patterns.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.467-477
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• 2004

While recommender systems were used by a few E-commerce sites former days, they are now becoming serious business tools that are re-shaping the world of I-commerce. And collaborative filtering has been a very successful recommendation technique in both research and practice. But there are two problems in personalized recommender systems, it is First-Rating problem and Sparsity problem. In this paper, we solve these problems using the associative relation clustering and “Lift” of association rules. We produce “Lift” between items using user's rating data. And we apply Threshold by -cut to the association between items. To make an efficiency of associative relation cluster higher, we use not only the existing Hypergraph Clique Clustering algorithm but also the suggested Split Cluster method. If the cluster is completed, we calculate a similarity iten in each inner cluster. And the index is saved in the database for the fast access. We apply the creating index to predict the preference for new items. To estimate the Performance, the suggested method is compared with existing collaborative filtering techniques. As a result, the proposed method is efficient for improving the accuracy of prediction through solving problems of existing collaborative filtering techniques.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.111-125
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• 2011

Clustering is a process of grouping similar or relevant documents into a cluster and assigning a meaningful concept to the cluster. By this process, clustering facilitates fast and correct search for the relevant documents by narrowing down the range of searching only to the collection of documents belonging to related clusters. For effective clustering, techniques are required for identifying similar documents and grouping them into a cluster, and discovering a concept that is most relevant to the cluster. One of the problems often appearing in this context is the detection of a complex concept that overlaps with several simple concepts at the same hierarchical level. Previous clustering methods were unable to identify and represent a complex concept that belongs to several different clusters at the same level in the concept hierarchy, and also could not validate the semantic hierarchical relationship between a complex concept and each of simple concepts. In order to solve these problems, this paper proposes a new clustering method that identifies and represents complex concepts efficiently. We developed the Hierarchical Overlapping Clustering (HOC) algorithm that modified the traditional Agglomerative Hierarchical Clustering algorithm to allow overlapped clusters at the same level in the concept hierarchy. The HOC algorithm represents the clustering result not by a tree but by a lattice to detect complex concepts. We developed a system that employs the HOC algorithm to carry out the goal of complex concept detection. This system operates in three phases; 1) the preprocessing of documents, 2) the clustering using the HOC algorithm, and 3) the validation of semantic hierarchical relationships among the concepts in the lattice obtained as a result of clustering. The preprocessing phase represents the documents as x-y coordinate values in a 2-dimensional space by considering the weights of terms appearing in the documents. First, it goes through some refinement process by applying stopwords removal and stemming to extract index terms. Then, each index term is assigned a TF-IDF weight value and the x-y coordinate value for each document is determined by combining the TF-IDF values of the terms in it. The clustering phase uses the HOC algorithm in which the similarity between the documents is calculated by applying the Euclidean distance method. Initially, a cluster is generated for each document by grouping those documents that are closest to it. Then, the distance between any two clusters is measured, grouping the closest clusters as a new cluster. This process is repeated until the root cluster is generated. In the validation phase, the feature selection method is applied to validate the appropriateness of the cluster concepts built by the HOC algorithm to see if they have meaningful hierarchical relationships. Feature selection is a method of extracting key features from a document by identifying and assigning weight values to important and representative terms in the document. In order to correctly select key features, a method is needed to determine how each term contributes to the class of the document. Among several methods achieving this goal, this paper adopted the $x^2$�� statistics, which measures the dependency degree of a term t to a class c, and represents the relationship between t and c by a numerical value. To demonstrate the effectiveness of the HOC algorithm, a series of performance evaluation is carried out by using a well-known Reuter-21578 news collection. The result of performance evaluation showed that the HOC algorithm greatly contributes to detecting and producing complex concepts by generating the concept hierarchy in a lattice structure.

• Journal of the Ergonomics Society of Korea
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• v.32 no.2
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• pp.189-198
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• 2013

Objective: This research was carried out to study the menu type design of instrument cluster IVIS(In Vehicle Information System) for efficient navigation under deconcentrated situations. Background: A driver controls the IVIS menu using the rest of cognitive resources while driving a car. Although a driver controls the IVIS using extra cognition resources, his or her distraction can still cause some safety problems while driving. Menu type design of instrument cluster is absolutely important for safe and efficient navigation. Method: Four menu types including paging, flow, icon, and list type were identified through reviewing the existing IVIS of vehicle and the menu structure of cellular phone. Four menu types were evaluated through experiment. The experiment consisted of primary and secondary task, which the primary task was to simulate a driving and the secondary task was to control an IVIS menu prototype. Task performances, menu type preferences, and eye-movement patterns were measured in this experiment. Results: The result shows that icon type was the best design in aspect of task performance and preference. A clue for next menu item provided a positive effect for efficient menu navigation. It was identified that most of subjects gazed the middle-top area of IVIS screen from eye-movement pattern. Conclusion: A basic design of Instrument Cluster IVIS was proposed considering the result of this study in the final. Application: The results of this study can be effectively used in the design of Instrument Cluster IVIS.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.393-400
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• 2011

In a large scale survey, cluster sampling design in which a set of observation units called clusters are selected is often used to satisfy practical restrictions on time and cost. Especially, a two stage cluster sampling design is preferred when a strong intra-class correlation exists among observation units. The sample Primary Sampling Unit(PSU) and Secondary Sampling Unit(SSU) size for a two stage cluster sample is determined by the survey cost and precision of the estimator calculated. For this study, we derive the optimal sample PSU and SSU size when the population SSU size across the PSU are di erent by extending the result obtained under the assumption that all PSU have the same number of SSU. The results on the sample size are then applied to the $4^{th}$ Korea Hospital Discharge results and is compared to the conventional method. We also propose the optimal sample SSU (discharged patients) size for the $7^{th}$ Korea Hospital Discharge Survey.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.5
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• pp.155-162
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• 2014

We proposed a 2 level tree based cluster based routing protocol for mobile ad hoc networks. it is our crucial goal to establish improved clustering's structure in order to extend average node life-time and elevate the average packet delivery ratio. Because of insufficient wireless resources and energy, the method to form and manage clusters is useful for increasing network stability. but cluster-head fulfills roles as a host and a router in clustering protocol of Ad hoc networks environment. Therefore energy exhaustion of cluster-head causes communication interruption phenomenon. Effective management of cluster-head is key-point which determines the entire network performance. The scheme focuses on improving the performance the life time of the network and throughput through the management of cluster-heads and its neighbor nodes. In simulation, we demonstrated that it would obtain averagely better 17% performance than LS2RP.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.35-49
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• 2001

Over the years, cluster analysis has become a popular tool for marketing and segmentation researchers. There are various methods for cluster analysis. Among them, K-means partitioning cluster analysis is the most popular segmentation method. However, because the cluster analysis is very sensitive to the initial configurations of the data set at hand, it becomes an important issue to select an appropriate starting configuration that is comparable with the clustering of the whole data so as to improve the reliability of the clustering results. Many programs for K-mean cluster analysis employ various methods to choose the initial seeds and compute the centroids of clusters. In this paper, we suggest a methodology to evaluate various clustering programs. Furthermore, to explore the usability of the methodology, we evaluate four clustering programs by using the methodology.

• Reproductive and Developmental Biology
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• v.38 no.2
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• pp.79-84
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• 2014

MicroRNAs (miRNAs) are approximately 22 nucleotides of small noncoding RNAs that control gene expression at the posttranscriptional level through translational inhibition and destabilization of their target mRNAs. The miRNAs are phylogenetically conserved and have been shown to be instrumental in a wide variety of key biological processes including cell cycle regulation, apoptosis, metabolism, imprinting, and differentiation. Recently, a paper has shown that expression of the miRNA-302/367 cluster expressed abundantly in mouse and human embryonic stem cells (ESCs) can directly reprogram mouse and human somatic cells to induced pluripotent stem cells (iPSCs) efficiently in the absence of any of the four factors, Oct4, Sox2, c-Myc, and Klf4. To apply this efficient method to porcine, we analyzed porcine genomic sequence containing predicted porcine miRNA-302/367 cluster through ENSEMBL database, generated a non-replicative episomal vector system including miRNA-302/367 cluster originated from porcine embryonic fibroblasts (PEF), and tried to make porcine iPSCs by transfection of the miRNA-302/367 cluster. Colonies expressing EGFP and forming compact shape were found, but they were not established as iPSC lines. Our data in this study show that pig miRNA-302/367 cluster could not satisfy requirement of PEF reprogramming conditions for pluripotency. To make pig iPSC lines by miRNA, further studies on the role of miRNAs in pluripotency and new trials of transfection with conventional reprogramming factors are needed.