• Bulletin of the Korean Chemical Society
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• v.23 no.2
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• pp.330-336
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• 2002

A quantum-chemical investigation on the conformations and electronic properties of bis[2-{2-methoxy-4,6-di(t-butyl)phenyl}ethenyl]benzenes (MBPBs) as building block for ${\pi}$-conjugate polymer are performed in order to display the effects of t-butyl and methoxy group substitution and of kink(ortho and meta) linkage. The conjugation length of the polymers can be controlled by substituents and kink linkages of backbone. Structures for the molecules, o-, m-, and p-MBPBs as well as unsubstituted o-, m-, and p-DSBs were fully optimized by using semiempirical AM1, PM3 methods, and ab initio HF method with 3-21G(d) basis set. The potential energy curves with respect to the change of single torsion angle are obtained by using semiempirical methods and ab initio HF/3-21G(d) basis set. The curves are similar shape in the molecules with respect to the position of vinylene groups. It is shown that the conformations of the molecules are compromised between the steric repulsion interaction and the degree of the conjugation. Electronic properties of the molecules were obtained by applying the optimized structures and geometries to the ZINDO/S method. ZINDO/S analysis performed on the geometries obtained by AM1 method and HF/3-21G(d) level is reported. The absorption wavelength on the geometries obtained by AM1 method is much longer than that by HF/3-21G(d) level. The absorption wavelength of MBPBs are red shifted with comparison to that of corresponding DSBs in the same torsion angle because of electron donating substituents. The absorption wavelength of isomers with kink(orth and meta) linkage is shorter than that of para linkage.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.775-784
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• 2002

Microarray data, obtained from DNA chip technologies, is the measurement of the expression level of thousands of genes in cells or tissues. It is used for gene function prediction or cancer diagnosis based on gene expression patterns. Among diverse methods for data analysis, the Bayesian network represents the relationships among data attributes in the form of a graph structure. This property enables us to discover various relations among genes and the characteristics of the tissue (e.g., the cancer type) through microarray data analysis. However, most of the present microarray data sets are so sparse that it is difficult to apply general analysis methods, including Bayesian networks, directly. In this paper, we harness an efficient structural learning algorithm and data dimensionality reduction in order to analyze microarray data using Bayesian networks. The proposed method was applied to the analysis of real microarray data, i.e., the NC160 data set. And its usefulness was evaluated based on the accuracy of the teamed Bayesian networks on representing the known biological facts.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.6
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• pp.490-496
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• 2014

The thickness of knee joint cartilage causes most diseases of knee. Therefore, an articular cartilage segmentation of knee magnetic resonance imaging (MRI) is required to diagnose a knee diagnosis correctly. In particular, fully automatic segmentation method of knee joint cartilage enables an effective diagnosis of knee disease. In this paper, we analyze a well-known level-set based segmentation method in brain MRI, and apply that method to knee MRI with solving some problems from different image characteristics. The proposed method, a fully automatic segmentation in whole process, enables to process faster than previous semi-automatic segmentation methods. Also it can make a three-dimension visualization which provides a specialist with an assistance for the diagnosis of knee disease. In addition, the proposed method provides more accurate results than the existing methods of articular cartilage segmentation in knee MRI through experiments.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.635-651
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• 2012

To produce a level-3 monthly composite image from daily level-2 Sea-viewing Wide Field-of-view Sensor (SeaWiFS) chlorophyll-a concentration data set in the East Sea, we applied four average methods such as the simple average method, the geometric mean method, the maximum likelihood average method, and the weighted averaging method. Prior to performing each averaging method, we classified all pixels into normal pixels and abnormal speckles with anomalously high chlorophyll-a concentrations to eliminate speckles from the following procedure for composite methods. As a result, all composite maps did not contain the erratic effect of speckles. The geometric mean method tended to underestimate chlorophyll-a concentration values all the time as compared with other methods. The weighted averaging method was quite similar to the simple average method, however, it had a tendency to be overestimated at high-value range of chlorophyll-a concentration. Maximum likelihood method was almost similar to the simple average method by demonstrating small variance and high correlation (r=0.9962) of the differences between the two. However, it still had the disadvantage that it was very sensitive in the presence of speckles within a bin. The geometric mean was most significantly deviated from the remaining methods regardless of the magnitude of chlorophyll-a concentration values. Its bias error tended to be large when the standard deviation within a bin increased with less uniformity. It was more biased when data uniformity became small. All the methods exhibited large errors as chlorophyll-a concentration values dominantly scatter in terms of time and space. This study emphasizes the importance of the speckle removal process and proper selection of average methods to reduce composite errors for diverse scientific applications of satellite-derived chlorophyll-a concentration data.

• The KIPS Transactions:PartA
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• v.10A no.4
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• pp.321-330
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• 2003

This paper proposes an agent-based adaptive tutoring system that monitors learning process of learners' and provides learning materials dynamically according to the analyzed learning character. Furthermore, it uses fuzzy concept to evaluate learners' ability and to provide learning materials appropriate to the level of learners'. For this, we design a courseware knowledge structure systematically and then construct a fuzzy level set on the basis of it considering importance of learning targets, difficulty of learning materials and relation degree between learning targets and learning materials. Using agent, monitoring continually the learning process of learners 'inferencing to offer proper hints in case of incorrect answer in learning assesment, composing dynamically learning materials according to the learning feature and the evaluation of assesment, our system implements effectively adaptive instruction system. Moreover, appling the fuzzy concept to the system could naturally consider and ideal with various and uncertain items of learning environment thus could offer more flexible and effective instruction-learning methods.

• The KIPS Transactions:PartD
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• v.16D no.3
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• pp.327-338
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• 2009

A microarray dataset is 2-dimensional dataset with a set of genes and a set of conditions. A bicluster is a subset of genes that show similar behavior within a subset of conditions. Genes that show similar behavior can be considered to have same cellular functions. Thus, biclustering algorithm is a useful tool to uncover groups of genes involved in the same cellular process and groups of conditions which take place in this process. We are proposing a polynomial time algorithm to identify functionally highly correlated biclusters. Our algorithm identifies 1) the gene set that has hidden patterns even if the level of noise is high, 2) the multiple, possibly overlapped, and diverse gene sets, 3) gene sets whose functional association is strongly high, and 4) deterministic biclustering results. We validated the level of functional association of our method, and compared with current methods using GO.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.28 no.1
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• pp.51-60
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• 2018

Objectives: Currently, there is only limited knowledge regarding the hazard of low-level exposure to CMR materials in workplaces. To overcome this limitation, a reference concentration for workers($RfC_w$) from among the risk assessment tools proposed by the US EPA is widely used to set a provisional workplace exposure level(PWEL) for CMR materials for which there are no established Korea Occupational Exposure Limits(KOELs) or subjective chemicals for work environment measurements as regulated by Korea Ministry of Employment and Labor(KMOEL). A simple European calculator of derived no effect level(SECO-DNEL) as proposed by REACH can also be used in place of $RfC_w$ to set the PWEL for chemicals. This study was performed to test the acceptability of using SECO-DNEL as an alternative to $RfC_w$ when setting a PWEL for low-level exposures. Methods: The $RfC_w$ and DNEL for the five CMR materials of dinitrogen oxide, catechol, 2-phenoxy ethanol, carbitol, and carbon black were calculated using the dose-response assessments of the US EPA for $RfC_w$ and REACH guidance for SECO-DNEL, respectively. They were compared using paired t-tests to determine the statistical differences between them. Results: For the five chemicals, the $RfC_w$ were 2.53 ppm, 0.10 ppm, 1.73 ppm, 1.66 ppm, and $0.05mg/m^3$, respectively, while the SECO-DNEL were 2.01 ppm, 0.11 ppm, 1.83 ppm, 1.77 ppm, $0.14mg/m^3$, respectively. There was no statistically significant difference between $RfC_w$ and SECO-DNEL. Conclusions: This study suggests that the SECO-DNEL could be applied in place of $RfC_w$ to set a PWEL for low-level exposure to chemicals, especially CMR materials. To further ensure the reliability of SECO-DNEL as an alternative tool, more chemicals should be applied for calculation and comparison with $RfC_w$.

• The Journal of Korean Academy of Prosthodontics
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• v.46 no.6
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• pp.639-643
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• 2008

STATEMENT OF PROBLEM: The degree of conversion may influence the ultimate mechanical and physical properties of provisional crown and fixed partial denture materials. The high levels of the unreacted residual monomer may cause deleterious effect on the properties. PURPOSE: The purpose of this study was to measure the degree of conversion of bis-acrylic based provisional crown and fixed partial denture materials by using an infrared spectroscopic method. MATERIAL AND METHODS: Chemically activated three bis-acrylic based provisional crown and fixed partial denture materials, LuxaTemp [DMG, Hamburg, Germany], fast set TemPhase [Kerr, Orange, CA, USA] and Protemp 3 Garant [3M-ESPE, St Paul, MN, USA], were investigated by Fourier transform infrared spectrometry (FTIR). The FTIR spectra of the materials tested were immediately obtained after mixing. The specimens were stored under dry conditions and at $23^{\circ}C$ for 24 hours, and then the spectra of the materials were also obtained. The degree of conversion (%) was calculated from the spectrum of the absorbance between the aliphatic double bond at 1637 $cm^{-1}$ and the aromatic double bond at 1608 $cm^{-1}$ using the baseline method. The data were statistically analyzed using one-way ANOVA and the multiple comparison Scheffe test at the significance level of 0.05. RESULTS: The mean value and standard deviation of the degree of conversion were 52.5 % ${\pm}$ 1.1 %, 50.3 % ${\pm}$ 0.8 %, and 42.3 % ${\pm}$ 4.9 % for LuxaTemp, Protemp 3 Garant and fast set TemPhase, respectively. There was no significant difference between LuxaTemp and Protemp 3 Garant, whereas there was a statistically difference between Protemp 3 Garant and fast set TemPhase, and LuxaTemp and fast set TemPhase (P < .05). CONCLUSION: The degree of conversion of fast set TemPhase was significantly lower than those of the others. The degree of conversion may be correlated with the rate of polymerization.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.133-147
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• 1998

Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deterministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or ARIMA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministically chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model. This result shows that the forecasting model a, pp.opriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.9
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• pp.1-8
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• 1993

In this paper, a new approach to the minimization of two-level Reed-Muller circuits is presented. In contrast to the previous method of using Xlinking operations to join two cubes for minimization. Cube selection method tries to select cubes one at a time until they cover the ON-set of the given function. A simple heuristic for selecting appropriate cubes is presented. In this heuristic, simply all cubes from the largest to the smallest are tried and whenever they decrease the number of remaining terms they are accepted. Since cubes once selected are not considered for a new selection, our method takes less time than other methods that need repetitive optimization process. The experimental results turned out to be improved in many cases compared to the best results in the literature.