• Bulletin of the Korean Chemical Society
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• v.29 no.3
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• pp.647-655
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• 2008

Microsomal prostaglandin E2 synthase (mPGES-1) is a potent target for pain and inflammation. Various QSAR (quantitative structure activity relationship) analyses used to understand the factors affecting inhibitory potency for a series of MK886 analogues. We derived four QSAR models utilizing various quantum mechanical (QM) descriptors. These QM models indicate that steric, electrostatic and hydrophobic interaction can be important factors. Common pharmacophore hypotheses (CPHs) also have studied. The QSAR model derived by best-fitted CPHs considering hydrophobic, negative group and ring effect gave a reasonable result (q2 = 0.77, r2 = 0.97 and Rtestset = 0.90). The pharmacophore-derived molecular alignment subsequently used for 3D-QSAR. The CoMFA (Comparative Molecular Field Analysis) and CoMSIA (Comparative Molecular Similarity Indices Analysis) techniques employed on same series of mPGES-1 inhibitors which gives a statistically reasonable result (CoMFA; q2 = 0.90, r2 = 0.99. CoMSIA; q2 = 0.93, r2 = 1.00). All modeling results (QM-based QSAR, pharmacophore modeling and 3D-QSAR) imply steric, electrostatic and hydrophobic contribution to the inhibitory activity. CoMFA and CoMSIA models suggest the introduction of bulky group around ring B may enhance the inhibitory activity.

• Journal of KIISE
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• v.43 no.9
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• pp.1034-1042
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• 2016

Together with machine-learning frameworks, area-based imagery registration techniques can be easily applied to diverse types of image pairs without predefined features and feature descriptors. However, feature detectors are often used to quickly identify candidate image patch pairs, limiting the applicability of these registration techniques. In this paper, we propose a ConvNet (Convolutional Network) "Dart" that provides not only the matching metric between patches, but also information about their distance, which are helpful in reducing the search space of the corresponding patch pairs. In addition, we propose a ConvNet "Fad" to identify the patches that are difficult for Dart to improve the accuracy of registration. These two networks were successfully implemented using Deep Learning with the help of a number of training instances generated from a few registered image pairs, and were successfully applied to solve a simple image registration problem, suggesting that this line of research is promising.

• Nuclear Engineering and Technology
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• v.20 no.4
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• pp.253-264
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• 1988

Our experience of neutron noise analysis in French-type 900 MWe pressurized water reactor (PWR) is presented. Neutron noise analysis is based on the technique of interpreting the signal fluctuations of ex-core detectors caused by core reactivity changes and neutron attenuation due to lateral core motion. It also provides advantages over deterministic dynamic-testing techniques because existing plant instrumentation can be utilized and normal operation of the plant is not disturbed. The data of this paper were obtained in the ULJIN unit 1 reactor during the start-up test period and the statistical descriptors, useful for our purpose, are power spectral density (PSD), coherence function (CF), and phase difference between detectors. It is found that core support barrel (CSB) motions induced by coolant flow forces and pressure pulsations in a reactor vessel were indentified around 8 Hz of frequency.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.5 s.311
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• pp.38-47
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• 2006

Conventional classification method uses a single classifier based on shape or motion feature. However this method exhibits a weakness if naively used since the classification performance is highly sensitive to the accuracy of moving region to be detected. The detection accuracy, in turn, depends on the condition of the image background. In this paper, we propose to resolve the drawback and thus strengthen the classification reliability by employing a Bayesian decision fusion and by optimally combining the decisions of three classifiers. The first classifier is based on shape information obtained from Fourier descriptors while the second is based on the shape information obtained from image gradients. The third classifier uses motion information. Our experimental results on the classification Performance of human and vehicle with a static camera in various directions confirm a significant improvement and indicate the superiority of the proposed decision fusion method compared to the conventional Majority Voting and Weight Average Score approaches.

• Journal of the East Asian Society of Dietary Life
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• v.24 no.5
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• pp.529-543
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• 2014

The objectives of this study were to perform a descriptive analysis to characterize and compare the sensory properties of commercial Makgeolli products as well as a cross-cultural comparison of the sensory descriptions of these products between two sensory panels (Korean and Japanese). The samples used in this study were seven different types of Makgeolli, which were commercial products. A total of 10 Korean and 7 Japanese panelists were selected in Korea. Each group was trained, and they identified product attributes and performed descriptive analyses independently. The Korean and Japanese panelists generated 34 and 28 sensory attributes, respectively, to describe appearance, odor/aroma, taste/flavor, texture, and after flavor of the products. There were significant differences among the samples for 24 attributes by Korean and 23 attributes by Japanese panelists. Although there was not a large difference in the number of descriptors between Korean and Japanese panels, the Korean panelists generated more various attributes associated with flavor than the Japanese panelists, and the attributes of Japanese panelists included references to non-food products such as rotten grass. Multiple factor analysis (MFA) was applied to the descriptive analysis data from the Korean and Japanese panels to delineate the associations between Makgeolli samples and their sensory characteristics. Both the Korean and Japanese panels clustered the JRM, JSM, KRM and KSM samples into one group and the CRM and BSM samples into another group. The ESM sample was distinguished from the other six samples. These results of the cross-cultural comparison suggest that comparative analyses of sensory profiles between cultures should be conducted regularly in future studies, and further research such as consumer acceptance tests should be conducted to determine the sensory characteristics that drive consumer acceptance of Makgeolli products in the context of increasing food product exports to other countries.

• The Transactions of the Korea Information Processing Society
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• v.1 no.1
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• pp.23-37
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• 1994

In this paper, we design and implement the prototype system, and propose the Extended Faceted Classification. Scheme and the Hybrid Retrieval Method that support classifying the software components, storing in library, and efficient retrieval according to user's request. In order to designs the classification scheme, we identify several necessary items by analyzing basic classes of software components that are to be classified. Then, we classify the items by their characteristics, decide the facets, and compose the component descriptors. According to their basic characteristics, we store software components in the library by clustering their application domains and are assign weights to the facets and its items to describe the component characteristics. In order to retrieve the software components, we use the retrieval-by-query model, and the weights and similarity for easy retrieval of similar software components. As the result of applying proposed classification scheme and retrieval model, we can easily identify similar components and the process of classification become simple. Also, the construction of queries becomes simple, the control of the size and order of the components to be retrieved possible, and the retrieval effectiveness is improved.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.5
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• pp.48-55
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• 2009

Prior research works in indoor-outdoor classification have been conducted based on a simple combination of low-level features. However, since there are many challenging problems due to the extreme variability of the scene contents, most methods proposed recently tend to combine the low-level features with high-level information such as the presence of trees and sky. To extract these regions from videos, we need to conduct additional tasks, which may yield the increasing number of feature dimensions or computational burden. Therefore, an efficient indoor-outdoor scene classification method is proposed in this paper. First, the video is divided into the five same-sized blocks. Then we define and use the edge and color orientation histogram (ECOH) descriptors to represent each sub-block efficiently. Finally, all ECOH values are simply concatenated to generated the feature vector. To justify the efficiency and robustness of the proposed method, a diverse database of over 1200 videos is evaluated. Moreover, we improve the classification performance by using different weight values determined through the learning process.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.3
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• pp.30-36
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• 2008

The multimedia that is characterized by multi-media, multi-features, multi-representations, huge volume, and varieties, is rapidly spreading out due to the increasing of application domains. Thus, it is urgently needed to develop a multimedia information system that can retrieve the needed information rapidly and accurately from the huge amount of multimedia data. For the content-based retrieval of moving picture, picture information is generally used. It is generally used when video is segmented. Through that, it can be a structural video browsing. The tasking that divides video to shot is called video segmentation, and detecting the cut for video segmentation is called cut detection. The goal of this paper is to divide moving picture using HMMD(Hue-Mar-Min-Diff) color model and edge histogram descriptor among the MPEG-7 visual descriptors. HMMD color model is more familiar to human's perception than the other color spaces. Finally, the proposed retrieval system is implemented as hardware.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.670-678
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• 2014

This paper presents a method to extract objects in radiographic images where all the allowable combinations of segmented regions are compared to a target object using Fourier descriptor. In the object extraction for usual images, a main problem is occlusion. In radiographic images, there is an advantage that the shape of an object is not occluded by other objects. It is because radiographic images represent the amount of radiation penetrated through objects. Considering the property of no occlusion in radiographic images, the shape based descriptors can be very effective to find objects. After all, the proposed object extraction method consists of three steps of segmenting regions, finding all the combinations of the segmented regions, and matching the combinations to the shape of the target object. In finding the combinations, we reduce a lot of computations to remove unnecessary combinations before matching. In matching, we employ Fourier descriptor so that the proposed method is rotation and shift invariant. Additionally, shape normalization is adopted to be scale invariant. By experiments, we verify that the proposed method works well in extracting objects.

• School Mathematics
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• v.19 no.3
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• pp.461-480
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• 2017

The aim of this study was to analyze characteristics of teachers' knowledge about correlation with data presented in $2{\times}2$ tables. In order to achieve the aim, this study conducted didactical analysis about two-way tables through examining previous researches and developed a questionnaire with reference to the results of the analysis. The questionnaire was given to 53 middle and high school teachers and qualitative methods were used to analyze the data obtained from the written responses by the participants. This study also elaborated the framework descriptors for interpreting the teachers' responses in the light of the didactical analysis and the data was elucidated in terms of this framework. The specific features of teachers' knowledge about correlation with data presented in $2{\times}2$ tables were categorized into three types as a result. This study raised several implications for teachers' professional development for effective mathematics instruction about correlation and related concepts dealt with in probability and statistics.