• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.952-959
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• 2018

Clustering is one of the most fundamental algorithms in machine learning. The performance of clustering is affected by the distribution of data, and when there are more data or more dimensions, the performance is degraded. For this reason, we use a stacked auto encoder, one of the deep learning algorithms, to reduce the dimension of data which generate a feature vector that best represents the input data. We use k-means, which is a famous algorithm, as a clustering. Sine the feature vector which reduced dimensions are also multi dimensional, we use the Euclidean distance as well as the cosine similarity to increase the performance which calculating the similarity between the center of the cluster and the data as a vector. A deep clustering networks combining a stacked auto encoder and k-means re-trains the networks when the k-means result changes. When re-training the networks, the loss function of the stacked auto encoder and the loss function of the k-means are combined to improve the performance and the stability of the network. Experiments of benchmark image ad document dataset empirically validated the power of the proposed algorithm.

• BMB Reports
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• v.39 no.5
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• pp.578-585
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• 2006

The cDNA of a firefly luciferase from lantern mRNA of Lampyroidea maculata has been cloned, sequenced and functionally expressed. The cDNA has an open reading frame of 1647 bp and codes for a 548-residue-long polypeptide. Noteworthy, sequence comparison as well as homology modeling showed the highest degree of similarity with H. unmunsana and L. mingrelica luciferases, suggesting a close phylogenetic relationship despite the geographical distance separation. The deduced amino acid sequence of the luciferase gene of firefly L. maculata showed 93% identity to H. unmunsana. Superposition of the three-dimensional model of L. maculata luciferase (generated by homology modeling) and three dimensional structure of Photinus pyralis luciferase revealed that the spatial arrangements of Luciferin and ATP-binding residues are very similar. Putative signature of AMP-binding domain among the various firefly species and Lampyroidea maculata was compared and a striking similarity was found. Different motifs and sites have been identified in Lampyroidea maculata by sequence analysis. Expression and purification of luciferase from Lampyroidea maculata was carried out using Ni-NTA Sepharose. Bioluminescence emission spectrum was similar to Photinus pyralis luciferase.

• Fire Science and Engineering
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• v.15 no.2
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• pp.91-95
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• 2001

Decays of the ceiling jet front velocity under a circular ceiling are investigated. To simulate the ceiling jet in fires He and $N_2$gas were injected from a nozzle to the center of the ceiling. The jet fronts in the form of turbulent eddies were traced by a high-speed camera system. The instantaneous locations of the front were obtained from visual readings of visualized front, and the radial velocity was calculated from the information of the time and the location with respect to the front. The similarity and dimensional analysis were also carried out to reveal the relationship between the velocity decay and the radial distance. It was shown that the radial velocity of the front was inversely proportional to the radial distance in the fully developed region from the experimental results and the theoretical analysis.

• Bulletin of the Korean Chemical Society
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• v.34 no.4
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• pp.1212-1220
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• 2013

The human coagulation factor XI (FXI) is a serine protease that plays a significant role in blocking of the blood coagulation cascade as an attractive antithrombotic target. Selective inhibition of FXIa (an activated form of factor XI) disrupts the intrinsic coagulation pathway without affecting the extrinsic pathway or other coagulation factors such as FXa, FIIa, FVIIa. Furthermore, targeting the FXIa might significantly reduce the bleeding side effects and improve the safety index. This paper reports on a docking-based three dimensional quantitative structure activity relationship (3D-QSAR) study of the potent FXIa inhibitors, the chloro-phenyl tetrazole scaffold series, using comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) methods. Due to the characterization of FXIa binding site, we classified the alignment of the known FXIa inhibitors into two groups according to the docked pose: S1-S2-S4 and S1-S1'-S2'. Consequently, highly predictive 3D-QSAR models of our result will provide insight for designing new potent FXIa inhibitors.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.515-522
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• 2001

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the highspeed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Map(SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space. A topological feature map preserves the mutual relations (similarity) in feature spaces of input data, and clusters mutually similar feature vectors in a neighboring nodes. Each node of the topological feature map holds a node vector and similar images that is closest to each node vector. We implemented about k-NN search for similar image classification as to (1) access to topological feature map, and (2) apply to pruning strategy of high speed search. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.

• The Journal of the Korea Contents Association
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• v.4 no.3
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• pp.81-90
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• 2004

Recently, It has been increased to use a multi-dimensional data in various applications with a rapid growth of the computing environment. In this paper, we propose the vector approximate tree for content-based retrieval of multi-dimensional data. The proposed index structure reduces the depth of tree by storing the many region information in a node because of representing region information using space partition based method and vector approximation method. Also it efficiently handles 'dimensionality curse' that causes a problem of multi-dimensional index structure by assigning the multi-dimensional data space to dynamic bit. And it provides the more correct regions by representing the child region information as the parent region information relatively. We show that our index structure outperforms the existing index structure by various experimental evaluations.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1077-1080
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• 1987

Images are 2-dimensional projection of 3-dimensional scenes and many problems of scene analysis arise due to inherent depth ambiguities in a monocular 2-D image. Therefore, depth recovery is a crucial problem in image understanding. This paper proposes modified algorithm which is focused on accurate correspondnce in stereo vision. The feature we use is zero-crossing points and the similarity measure with two property evaluation function is used to estimate initial probability. And we introduce relaxation technique for accurate and global correspondence.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1998.04a
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• pp.314-320
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• 1998

Wear debris morphology is closely related to the wear mode and mechanism occured. Image recognition of wear debris is, therefore, a powerful tool in wear monitoring. But it has usually required expert's experience and the results could be too subjective. Development of automatic tools for wear debris recognition is needed to solve this problem. In this work, an algorithm for automatic wear debris recognition was suggested and implemented by PC base software. The presented method defined a characteristic 3-dimensional feature space where typical types of wear debris were separately located by the knowledge-based system and compared the similarity of object wear debris concerned. The 3-dimensional feature space was obtained from multiple feature vectors by using a multi-dimensional scaling technique. The results showed that the presented automatic wear debris recognition was satisfactory in many cases application.

• Tribology and Lubricants
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• v.15 no.2
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• pp.206-211
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• 1999

Wear debris morphology is closely related to the wear mode and mechanism occured. Image recognition of wear debris is, therefore, a powerful tool in wear monitoring. But it has usually required expert's experience and the results could be too subjective. Development of automatic tools for wear debris recognition is needed to solve this problem. In this work, an algorithm for automatic wear debris recognition was suggested and implemented by PC base software. The presented method defined a characteristic 3-dimensional feature space where typical types of wear debris were separately located by the knowledge-based system and compared the similarity of object wear debris concerned. The 3-dimensional feature space was obtained from multiple feature vectors by using a multi-dimensional scaling technique. The results showed that the presented automatic wear debris recognition was satisfactory in many cases application.

• The Transactions of the Korea Information Processing Society
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• v.6 no.3
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• pp.821-831
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• 1999

Recognition of three-dimensional objects with convexities and concavities is a hard and challenging problem. This paper presents a feature extraction method out of three-dimensional objects for the purpose of classification. This new method not only provides invariance to scale, translation, and rotation $R^3$ but also distinguishes any three-dimensional model objects with concavities and convexities by measuring a relative similarity in the information space where a set of characteristics features of objects is mapped.