• Journal of Information Science Theory and Practice
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• v.1 no.2
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• pp.6-15
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• 2013

Dr. Ranganathan's "Principles for Helpful Sequence" among the set of normative principles play an exclusive role in the arrangement of subject isolates. Each subject in the universe of subjects is regulated by a guiding principle of its own which analogously determines the sequence of Arrays in ordering the subject surrogates or isolates. For example, the "Principle of Later-in-Evolution" is applied for sequencing isolates of Animal and Plant Species; this concept can be applied to one of the tools of KOS viz. Taxonomies. The application of Principles for Helpful Sequence is summarily presented and in the process the paper highlights the inherent elements of knowledge organization in each one of these principles in a manner that might map the future course of research in this area with the potentiality to bring about a relation between principles for helpful sequence and KOS.

• Journal of the Korean Society for information Management
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• v.18 no.4
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• pp.201-215
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• 2001

The purpose of this study is to identify how to construct an internet shopping mall classification system used on the library classification theories. To aid in identifying classification system, this study focused on the Ranganathan’s classification canons; canons for characteristics, canons for terms. The study shows six priniciples for an internet shopping mall classification system construct: products’characteristics, inclusiveness, various access points, category sequence and term consistency, term currency and obviousness, no term duplication. For future research, product’s search patterns and relationship to interface are suggested.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.25 no.4
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• pp.296-311
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• 2021

Topological Data Analysis (TDA), a relatively new field of data analysis, has proved very useful in a variety of applications. The main persistence tool from TDA is persistent homology in which data structure is examined at many scales. Representations of persistent homology include persistence barcodes and persistence diagrams, both of which are not straightforward to reconcile with traditional machine learning algorithms as they are sets of intervals or multisets. The problem of faithfully representing barcodes and persistent diagrams has been pursued along two main avenues: kernel methods and vectorizations. One vectorization is the Betti sequence, or Betti curve, derived from the persistence barcode. While the Betti sequence has been used in classification problems in various applications, to our knowledge, the stability of the sequence has never before been discussed. In this paper we show that the Betti sequence is unstable under the 1-Wasserstein metric with regards to small perturbations in the barcode from which it is calculated. In addition, we propose a novel stabilized version of the Betti sequence based on the Gaussian smoothing seen in the Stable Persistence Bag of Words for persistent homology. We then introduce the normalized cumulative Betti sequence and provide numerical examples that support the main statement of the paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.364-370
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• 2020

This paper is related to detection and classification for micro-sized aircraft that flies at low-altitude. The deep-learning based method using sounds coming from the micro-sized aircraft is proposed to detect and identify them efficiently. We use MFCC as sound features and CNN as a detector and classifier. We've proved that each micro-drones have their own distinguishable MFCC feature and confirmed that we can apply CNN as a detector and classifier even though drone sound has time-related sequence. Typically many papers deal with RNN for time-related features, but we prove that if the number of frame in the MFCC features are enough to contain the time-related information, we can classify those features with CNN. With this approach, we've achieved high detection and classification ratio with low-computation power at the same time using the data set which consists of four different drone sounds. So, this paper presents the simple and effecive method of detection and classification method for micro-sized aircraft.

• Journal of Haehwa Medicine
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• v.10 no.1
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• pp.201-220
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• 2001

1. The frequency of source of prescriptions is dangyesimbub(丹溪心法), euihakibmun(醫學入門), dongwonsibseo(東垣十書), goguemeuibang(古今醫方) in sequence. 2. The classification of prescriptions by efficacy is chungyeulsahoayak(淸熱瀉火藥), boumyak(補陰藥), hoalhyulguayak(活血祛瘀藥), igiyak(理氣藥), chunghwayuldamyak(淸化熱痰藥), bogiyak(補氣藥), balsanpunghanyak(發散風寒藥), balsanpungyeolak(發散風熱藥), etc. in sequence. 3. The frequency of used medicines is hoangum(黃芩), danguy(當歸), jakyak(芍藥), insam(人蔘), saengjihoang(生地黃), bokryung(茯笭), hoangbaek(黃柏), jimo(知母), makmundong(麥門冬), chija(梔子) etc. in sequence. 4. The sung of used medicines is mainly hangsung(寒性), onsung(溫性), the mi(味) is gomi(苦味), sinmi(辛味), gammi(甘味) in sequence. the gwigyung(歸經) is bigyung(脾經), wigyung(胃經), simgyung(心經), etc, in sequence.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2881-2894
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• 2014

In complicated environment, context information plays an important role in image segmentation/labeling. The recently proposed auto-context algorithm is one of the effective context-based methods. However, the standard auto-context approach samples the context locations utilizing a fixed radius sequence, which is sensitive to large scale-change of objects. In this paper, we present a scale invariant auto-context (SIAC) algorithm which is an improved version of the auto-context algorithm. In order to achieve scale-invariance, we try to approximate the optimal scale for the image in an iterative way and adopt the corresponding optimal radius sequence for context location sampling, both in training and testing. In each iteration of the proposed SIAC algorithm, we use the current classification map to estimate the image scale, and the corresponding radius sequence is then used for choosing context locations. The algorithm iteratively updates the classification maps, as well as the image scales, until convergence. We demonstrate the SIAC algorithm on several image segmentation/labeling tasks. The results demonstrate improvement over the standard auto-context algorithm when large scale-change of objects exists.

• Korean Journal of Mathematics
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• v.23 no.1
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• pp.115-128
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• 2015

Plumbing surfaces of links were introduced to study the geometry of the complement of the links. A basket surface is one of these plumbing surfaces and it can be presented by two sequential presentations, the first sequence is the flat plumbing basket code found by Furihata, Hirasawa and Kobayashi and the second sequence presents the number of the full twists for each of annuli. The minimum number of plumbings to obtain a basket surface of a knot is defined to be the basket number of the given knot. In present article, we first find a classification theorem about the basket number of knots. We use these sequential presentations and the classification theorem to find the basket number of all prime knots whose crossing number is 7 or less except two knots $7_1$ and $7_5$.

• Biomedical Science Letters
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• v.24 no.3
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• pp.221-229
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• 2018

Trichophyton rubrum is one of the well-known pathogenic fungi and causes dermatophytosis and cutaneous mycosis in human world widely. However, there are not an available sequence type (ST) classification methods and previous studies for T. rubrum until now. Therefore, currently, molecular biological tools using their DNA sequences are used for genotype identification and classification. In the present study, in order to characterize the genetic diversity and the phylogenetic relation of T. rubrum clinical isolates, five different housekeeping genes, such as actin (ACT), calmodulin (CAL), RNA polymerase II (RPB2), superoxide dismutase 2 (SOD2), and ${\beta}$-tubulin (BT2) were analyzed using by multilocus sequence typing (MLST). Also, DNA sequence analysis was performed to examine the differences between the sequences of Trichophyton strains and the identified genetic variations sequence. As a result, most of the sequences were shown to have highly matched rates in their housekeeping genes. However, genetic variations were found on three different positions of ${\beta}$-tubulin gene and were shown to have changed from $C{\rightarrow}G$ (1766), $G{\rightarrow}T$ (1876), and $C{\rightarrow}A$ (1886). To confirm the association with T. rubrum inheritance, a phylogenetic tree analysis was performed. It was classified as four clusters, but there was little significant correlation. Even so, MLST analysis is believed to be helpful for determining the genetic variations of T. rubrum in cases where there is more large-scale data accumulation. In conclusion, the present study demonstrated the first MLST analysis of T. rubrum in Korea and explored the possibility that MLST could be a useful tool for studying the epidemiology and evolution of T. rubrum through further studies.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.7
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• pp.531-535
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• 2004

A new method is presented to construct a classifier. This was accomplished by combining a generalized regression neural network (GRNN) and a genetic algorithm (GA). The classifier constructed in this way is referred to as a GA-GRNN. The GA played a role of controlling training factors simultaneously. The GA-GRNN was applied to classify 4 different Promoter sequences. The training and test data were composed of 115 and 58 sequence patterns, respectively. The classifier performance was investigated in terms of the classification sensitivity and prediction accuracy. Compared to conventional GRNN, GA-GRNN significantly improved the total classification sensitivity as well as the total prediction accuracy. As a result, the proposed GA-GRNN demonstrated improved classification sensitivity and prediction accuracy over the convention GRNN.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.551-556
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• 2004

According to revealing the DNA sequence of human and living things, it increases that a demand on a new computational processing method which utilizes DNA sequence information. In this paper we propose a classification algorithm based on negative selection of the immune system to classify DNA patterns. Negative selection is the process to determine an antigenic receptor that recognize antigens, nonself cells. The immune cells use this antigen receptor to judge whether a self or not. If one composes n group of antigenic receptor for n different patterns, they can classify into n patterns. In this paper we propose a pattern classification algorithm based on negative selection in nucleotide base level and amino acid level.