• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.309-319
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• 2016

This paper presents a new recognition and classification method for power quality (PQ) disturbances on the basis of pattern linguistic values. This method solves the difficulty of recognizing disturbances rapidly and accurately by using fuzzy logic. This method uses classification disturbance patterns to define the linguistic values of fuzzy input variables and used the input variables of corresponding disturbance pattern to set membership functions. This method also sets the fuzzy rules by analyzing the distribution regularities of the input variable values. One characteristic of this method is that the linguistic values of fuzzy input variables and the setting of membership functions are not only related to the input variables but also to the character of classification disturbance and the classification results. Furthermore, the number of fuzzy rules is equal to the number of disturbance patterns. By using this method for disturbance classification, the membership function and design of fuzzy rules are directly related to the objective of classification, thus effectively reducing the complexity of the design process and yielding accurate classification results. The classification results of the simulation and measured data verify the feasibility and effectiveness of this method.

• ALGAE
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• v.21 no.1
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• pp.91-101
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• 2006

Despite continued studies on red algal flora in Korea, the taxonomy of the tiny ceramiaceous algae has received little attention. We report for the first time Griffithsia okiensis from Korea on the basis of morphology and molecular data. The species is small in thalli height (0.3-1.5 cm), and in diameter of vegetative cells (50-500 μm), and the ratio of cell length/breadth is 2-3 times. It has two carpogonial branches from the supporting cell of procarp. We generated psbA and rbcL sequences from ten specimens of G. okiensis isolated from Korea and Japan and from one G. japonica species isolated Japan. Eight specimens of G. okiensis from Korea were almost identical in both psbA and rbcL regions, nevertheless they differed from Japanese specimens by 4 ucleotides in psbA and 7 in rbcL. In all analyses of psbA, rbcL, and psbA + rbcL data sets, G. okiensis was determined to be a different species from G. japonica isolated from Japan, although both species showed a sister relationship. For all that extensive collection trips, we found no evidence for the occurrence of G. japonica in Korea.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.59-60
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• 2000

The past few years have seen a dramatic increase in gene expression data on the basis of DNA microarrays or DNA chips. Going beyond a generic view on the genome, microarray data are able to distinguish between gene populations in different tissues of the same organism and in different states of cells belonging to the same tissue. This affords a cell-wide view of the metabolic and regulatory processes under different conditions, building an effective basis for new diagnoses and therapies of diseases. In this talk we present machine learning techniques for effective mining of DNA microarray data. A brief introduction to the research field of machine learning from the computer science and artificial intelligence point of view is followed by a review of recently-developed learning algorithms applied to the analysis of DNA chip gene expression data. Emphasis is put on graphical models, such as Bayesian networks, latent variable models, and generative topographic mapping. Finally, we report on our own results of applying these learning methods to two important problems: the identification of cell cycle-regulated genes and the discovery of cancer classes by gene expression monitoring. The data sets are provided by the competition CAMDA-2000, the Critical Assessment of Techniques for Microarray Data Mining.

• Journal of the Korean Data and Information Science Society
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• v.12 no.2
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• pp.11-25
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• 2001

There are several algorithms for classification in modeling relations, patterns, and rules which exist in data. We learn to classify objects on the basis of instances presented to us, not by being given a set of classification rules. The Bayesian learning uses the probability distribution to express our knowledge about unknown parameters and update our knowledge by the law of probability as the evidence gathered from data. Also, the neural network models are designed for predicting an unknown category or quantity on the basis of known attributes by training. In this paper, we compare the misclassification error rates of Bayesian Neural Network method with those of other classification algorithms, CHAID, CART, and QUBST using several data sets.

• Bulletin of the Korean Chemical Society
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• v.28 no.9
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• pp.1539-1544
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• 2007

1-Phenyl-3-p-fluorophenyl-5-p-chlorophenyl-2-pyrazoline has been synthesized and characterized by elemental analysis, IR, UV-Vis and X-ray single crystal diffraction. Density functional calculations show that B3LYP/6-311G** method can reproduce the structural parameters. The electronic absorption spectra have been predicted based on the optimized structure by using 6-311G** and 6-311++G** basis sets and compared with the experimental values. The results indicate that TD-DFT method can only predict the electronic absorption spectra of the system studied here approximately. On the basis of vibrational analyses, the thermodynamic properties of the title compound at different temperatures have been calculated, revealing the correlations between ,C0p,m,S0m,H0m and temperature.

• Journal of Integrative Natural Science
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• v.3 no.4
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• pp.231-236
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• 2010

Partial charge is an important and fundamental concept which can explain many aspects of chemistry. Since a molecule can be regarded as neclei surrounded by electron cloud, there is no way to define a partial charge accurately. Nevertheless, there have been many attempts to define these seemingly impossible parameters, since they would facilitate the understanding of molecular properties such as molecular dipole moment, solvation, hydrogen bonding, molecular spectroscopy, chemical reaction, etc. Common methods are based on the charge equalization, orbital occupancy, charge density, and electric multipole moments, and electrostatic potential fitting. Methods based on the charge equalization using electronegativity are very fast, and therefore they have been used to study many compounds. Methods to subdivide orbital occupancy using basis set conversion, relies on the notion that molecular orbitals are composed of atomic orbitals. The main idea is to reduce overlap integral between two nuclei using converted orthogonal basis sets. Using some quantum mechanical observables like electrostatic potential or charge multipole moments. Using potential grids obtained from wavefunction, partial charges can be fitted. these charges are most useful to describe intermolecular electrostatic interactions. Methods to using dipole moment and its derivatives, seems to be sensitive the level of theory, Dividing electron density using density gradient being the most rigorous theoretically among various schemes, bears best potential to describe the charge the most adequately in the future.

• The Transactions of the Korea Information Processing Society
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• v.3 no.6
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• pp.1636-1645
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• 1996

In this paper we proposed a new scheme for conditional two dimensional (2-D)moment-based classification of patterns on the basis of Gibbs random fields which are will suited for representing spatial continuity that is the characteristic of the most images. This implementation contains two parts: feature extraction and pattern classification. First of all, we extract feature vector which consists of conditional 2-D moments on the basis of estimated Gibbs parameter. Note that the extracted feature vectors are invariant under translation, rotation, size of patterns the corresponding template pattern. In order to evaluate the performance of the proposed scheme, classification experiments with training document sets of characters have been carried out on 486 66Mhz PC. Experiments reveal that the proposed scheme has high classification rate over 94%.

• Bulletin of the Korean Chemical Society
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• v.30 no.12
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• pp.2973-2978
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• 2009

The decomposition reaction mechanism of $CH_3CF_2O$ radical formed from hydroflurocarbon, $CH_3CHF_2$ (HFC-152a) in the atmosphere has been investigated using ab-initio quantum mechanical methods. The geometries of the reactant, products and transition states involved in the decomposition pathways have been optimized and characterized at DFT-B3LYP and MP2 levels of theories using 6-311++G(d,p) basis set. Calculations have been carried out to observe the effect of basis sets on the optimized geometries of species involved. Single point energy calculations have been performed at QCISD(T) and CCSD(T) level of theories. Out of the two prominent decomposition channels considered viz., C-C bond scission and F-elimination, C-C bond scission is found to be the dominant path involving a barrier height of 12.3 kcal/mol whereas the F-elimination path involves that of a 28.0 kcal/mol. Using transition-state theory, rate constant for the most dominant decomposition pathway viz., C-C bond scission is calculated at 298 K and found to be 1.3 ${\times}$ 10$^4s{-1}$. Transition states are searched on the potential energy surfaces involving both decomposition channels and each of the transition states are characterized. The existence of transition states on the corresponding potential energy surface are ascertained by performing Intrinsic Reaction Coordinate (IRC) calculation.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.285-288
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• 1998

A least-squares identification method is studied that estimates a finite number of coefficients in the series expansion of a transfer function, where the expansion is in terms of recently introduced generalized basis functions, We will expand and generalize the orthogonal functions as basis functions for dynamical system representations. To this end, use is made of balanced realizations as inner transfer functions. The orthogonal functions can be considered as generalizations of, for example, the pulse functions, Laguerre functions, and Kautz functions, and give rise to an alternative series expansion of rational transfer functions. We show that the Laplace transform of the expansion for some sets$\Psi_{\kappa}(Z)$ is equivalent to a series expansion . Techniques based on this result are presented for obtaining the coefficients $c_{n}$ as those of a series. One of their important properties is that, if chosen properly, they can substantially increase the speed of convergence of the series expansion. This leads to accurate approximate models with only a few coefficients to be estimated. The set of Kautz functions is discussed in detail and, using the power-series equivalence, the truncation error is obtained.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1201-1208
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• 2010

In this paper, we propose an image encryption method using two linear MLCA(Maximum Length Cellular Automata). The encryption method first sets arbitrary 8 bit initial values. Next, we create high quality PN(pseudo noise) sequences by converting rows and columns with the set initial values. hen we generate a basis image using the set PN sequences. Lastly, the final image with high encryption level is produced by XOR operation of the basis image and the original image. In order to verify that the proposed method has the high encryption level, we performed histogram and stability analysis.