• International Journal of Safety
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• v.1 no.1
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• pp.28-35
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• 2002

Kullback Discrimination Information (KDI) is one of the pattern recognition methods. KDI defined as a measure of the mutual dissimilarity computed between two time series was studied for detection and classification of bearing flaking on outer-race and inner-races. To model the damages, the bearings in normal condition, outer-race flaking condition and inner-races flaking condition were provided. The vibration sensor was attached by the bearing housing. This produced the total 25 pieces of data each condition, and we chose the standard data and measure of distance between standard and tested data. It is difficult to detect the flaking because similar pulses come out when balls pass the defection point. The detection and classification method for inner and outer races are defected by KDI and nearest neighbor classification rule is proposed and its high performance is also shown.

• Journal of Power System Engineering
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• v.2 no.3
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• pp.60-67
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• 1998

Edge detection is the first step and very important step in image analysis. We cast edge detection as a problem in cost minimization. This is achieved by the formulation of a cost function that evaluates the quality of edge configurations. The cost function can be used as a basis for comparing the performances of different detectors. This cost function is made of desirable characteristics of edges such as thickness, continuity, length, region dissimilarity. And we use a simulated annealing algorithm for minimum of cost function. Simulated annealing are a class of adaptive search techniques that have been intensively studied in recent years. We present five strategies for generating candidate states. Experimental results(building image and test image) which verify the usefulness of our simulated annealing approach to edge detection are better than other operator.

• The Journal of the Korean Society for Microbiology
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• v.22 no.1
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• pp.95-99
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• 1987

When tetracycline resistancy were cured by ethidium bromide treatment, some of the cured strains lost the tetracycline resistance plasmid while other strains kept the plasmids. Both strains of lost and remained plasmids were digested with restriction endonuclease Hind III and these cleaved plasmids were compared with that of parent strains, two plasmid remained strains showed same cleavage patterns between parent and cured strains, however, one plasmid lost strain showed dissimilarity with parent strain, but in the other one strain, among 4 plasmid lost colonies, 2 showed same but other 2 showed different patterns compared to parent strain. Tetracycline was transfered by conjugation in on set(Staphylococcus aureus donor versus Staphylococcus epidermidis, recipient) with relative high frequency but the other 2 sets showed a low degree of frequency and the other 2 sets exhibited no transfer.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.3
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• pp.281-288
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• 2001

We consider in this article a multi-objective part-machine grouping problem in which parts have alternative process plans and expected annual demand of each part is known. This problem is characterized as optimally determining part sets and corresponding machine cells such that total sum of distance (or dissimilarity) between parts and total sum of load differences between machines are simultaneously minimized. Two heuristic algorithms are proposed, and examples are given to compare the performance of the algorithms.

• The Transactions of the Korean Institute of Power Electronics
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• v.8 no.2
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• pp.137-142
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• 2003

Two existing continuous-time models for the current-mode control have presented noticeable differences in their small-signal predictions. As an attempt to clarify the origin of these disparities, this paper presents an alternative way of deriving a continuous-time model for the current-model control. The results of this paper would provide insights to comprehend the dissimilarity in the modeling method and final results of the earlier models of current-mode control models.

• International Journal of Ocean System Engineering
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• v.2 no.1
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• pp.16-23
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• 2012

In this paper we discussed a new methodology for Urdu Character Recognition system using Dempster-Shafer theory which can powerfully estimate the similarity ratings between a recognized character and sampling characters in the character database. Recognition of character is done by five probability calculation methods such as (similarity, hamming, linear correlation, cross-correlation, nearest neighbor) with Dempster-Shafer theory of belief functions. The main objective of this paper is to Recognition of Urdu letters and numerals through five similarity and dissimilarity algorithms to find the similarity between the given image and the standard template in the character recognition system. In this paper we develop a method to combine the results of the different distance measurement methods using the Dempster-Shafer theory. This idea enables us to obtain a single precision result. It was observed that the combination of these results ultimately enhanced the success rate.

• Journal of Information Display
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• v.10 no.1
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• pp.1-5
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• 2009

A novel computational integral imaging technique with enhanced depth sensitivity is proposed. For each lateral position at a given depth plane, the dissimilarity between corresponding pixels of the elemental images is measured and used as a suppressing factor for that position. The intensity values are aggregated to determine the correct depth plane of each plane object. The experimental and simulation results show that the reconstructed depth image on the incorrect depth plane is effectively suppressed, and that the depth image on the correct depth plane is reconstructed clearly without any noise. The correct depth plane is also exactly determined.

• 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.

• Molecules and Cells
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• v.25 no.2
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• pp.279-288
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• 2008

The analysis of microarray data is essential for large amounts of gene expression data. In this review we focus on clustering techniques. The biological rationale for this approach is the fact that many co-expressed genes are co-regulated, and identifying co-expressed genes could aid in functional annotation of novel genes, de novo identification of transcription factor binding sites and elucidation of complex biological pathways. Co-expressed genes are usually identified in microarray experiments by clustering techniques. There are many such methods, and the results obtained even for the same datasets may vary considerably depending on the algorithms and metrics for dissimilarity measures used, as well as on user-selectable parameters such as desired number of clusters and initial values. Therefore, biologists who want to interpret microarray data should be aware of the weakness and strengths of the clustering methods used. In this review, we survey the basic principles of clustering of DNA microarray data from crisp clustering algorithms such as hierarchical clustering, K-means and self-organizing maps, to complex clustering algorithms like fuzzy clustering.

• Korean Journal of Plant Resources
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• v.22 no.3
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• pp.242-247
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• 2009

Randomly Amplified Polymorphic DNA (RAPD) was performed to define the genetic variation and relationships of Artemisia capillaris. Fifteen populations by the distributions and habitat were collected to conduct RAPD analysis. RAPD markers were observed mainly between 300bp and 1600bp. Total 72 scorable markers from 7 primers were applied to generate the genetic matrix, and 69 bands were polymorphic and only 3 bands were monomorphic. The genetic dissimilarity matrix by Nei's genetic distance (1972) and UPGMA phenogram were produced from the data matrix. Populations of Artemisia capillaris were clustered with high genetic affinities and cluster patterns were correlated with distributional patterns. Two big groups were clustered as southern area group and middle area group. The closest OTUs were GW2 and GG1 in middle area group, and GB1 from southern area group was clustered with OTUs in middle area group. RAPD data was useful to define the genetic variations and relationships of A. capillaris.