• The KIPS Transactions:PartB
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• v.10B no.4
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• pp.397-402
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• 2003

This paper proposes an efficient fixed-point (FP) algorithm for improving performances of the independent component analysis (ICA) based on neural networks. The proposed algorithm is the FP algorithm based on Newton method for ICA using the adaptive learning parameters. The purpose of this algorithm is to improve the separation speed and performance by using the learning parameters in Newton method, which is based on the first order differential computation of entropy optimization function. The learning rate and the moment are adaptively adjusted according to an updating state of inverse mixing matrix. The proposed algorithm has been applied to the fingerprints and the images generated by random mixing matrix in the 8 fingerprints of 256${\times}$256-pixel and the 10 images of 512$\times$512-pixel, respectively. The simulation results show that the proposed algorithm has the separation speed and performance better than those using the conventional FP algorithm based on Newton method. Especially, the proposed algorithm gives relatively larger improvement degree as the problem size increases.

• Journal of the Korean Society of Radiology
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• v.10 no.8
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• pp.645-652
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• 2016

In this study, based on the analysis of texture feature values of statistical properties. And we examined the normal and the applicability of the computer-aided diagnosis of cerebral infarction in the brain computed tomography images. The experiment was analyzed to evaluate the ROC curve recognition rate of disease using six parameters representing the feature values of the texture. As a result, it showed average mean 88%, variance 92%, relative smoothness 94%, uniformity of 88%, a high disease recognition rate of entropy 84%. However, it showed a slightly lower disease recognition rate and 58% for skewness. In the analysis using ROC curve, the area under the curve for each parameter indicates 0.886 (p = 0.0001) or more, resulted in a meaningful recognition of the disease. Further, to determine the cut-off values for each parameter are determined to be the prediction of disease through the computer-aided diagnosis.

• Analytical Science and Technology
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• v.14 no.3
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• pp.197-202
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• 2001

The characteristics of the electrochemically Li intercalated synthetic graphite were determined from the studies with XRD method, DSC and solid $^7Li-NMR$ spectrophotometric analysis. From the results of X-ray diffraction method, it was found that the compounds in the stage 1 structure were predominantly formed. The enthalpy and entropy changes of the compounds can be obtained from the differential scanning calorimetric analysis results. From these results, it was found that exothermic and endothermic reactions of lithium intercalated into synthetic graphite are related to thermal stability of lithium ion between carbon graphene layers. From the $^7Li-NMR$ data, scientific observation found that bands are shift toward higher frequencies with increasing lithium concentration because non-occupied electron shells of Li increased in charge carrier density. Line widths of the Li intercalated synthetic graphite compounds decreased slowly because of non-homogeneous local magnetic order and the random electron spin direction for substituted Li.

• Bulletin of the Korean Chemical Society
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• v.28 no.5
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• pp.730-736
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• 2007

The interaction of a polyoxometal (POM), K6SiW11Co(H2O)O39.10H2O (K6) as a Keggin, with human serum albumin (HSA) was studied by different methods and techniques. Binding studies show two sets of binding sites for interaction of POM to HSA. Binding analysis and isothermal calorimetery revealed that, the first set of binding site has lower number of bound ligand per mole of protein (ν), lower Hill constant (n), higher binding constant (K), more negative entropy (ΔS) and more electrostatic interaction in comparison to the second set of binding site. In addition, differential scanning calorimetery (DSC) and spectrophotometery data showed that, there are two energetic domains. The first domain is less stable (lower Tm and Cp) which corresponds to the tail segment of HSA and another with more stability is related to the head segment of HSA. Polyoxometal also decreases the stability of protein as Tm, secondary and tertiary structure as well as quenching of the fluorescence decrease. On other hand, perturbations in tertiary structure are more than secondary structure.

• Analytical Science and Technology
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• v.10 no.3
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• pp.209-215
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• 1997

Li-AGICs as a anode of secondary battery were synthesized by high-pressure method as a function of the Li-contents. The characteristics of these prepared compounds were determined from the studies with X-ray diffraction method, UV/VIS spectrophotometric and differential scanning calorimeter(DSC) analysis. From the results of X-ray diffraction, it was found that the lower stage intercalation compounds were formed with increase of Li-contents. The mixed stages in these compounds were also observed. In the case of the $Li_{30wt%}$-AGIC, the compounds in the stage 1 structure were formed predominantly, but the structure of only pure stage 1 for structural defect of artificial graphite is not observed. According to UV/VIS spectrophotometric analysis, $Li_{30wt%}$-AGIC shows distinguishable energy state spectrum with the position of $R(%)_{min}$ values, but the characteristic spectra of almost all Li-AGICs are not observed. The enthalpy and entropy changes of the compounds can be obtained from the differential scanning calorimetric analysis results. From the results, it was found that exothermic and endothermic reactions of Li-AGICs are related to thermal stability of lithium between artificial graphite layers.

• Journal of the Korean Society of Radiology
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• v.10 no.6
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• pp.443-450
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• 2016

In this study, we are using a computer tomography image of the abdomen, as an experimental linear research for the image of the fatty liver patients texture features analysis and computer-aided diagnosis system of implementation using the ROC curve analysis, from the computer tomography image. We tried to provide an objective and reliable diagnostic information of fatty liver to the doctor. Experiments are usually a fatty liver, via the wavelet transform of the abdominal computed tomography images are configured with the experimental image section, shows the results of statistical analysis on six parameters indicating a feature value of the texture. As a result, the entropy, average luminance, strain rate is shown a relatively high recognition rate of 90% or more, the control also, flatness, uniformity showed relatively low recognition rate of about 70%. ROC curve analysis of six parameters are all shown to 0.900 (p = 0.0001) or more, showed meaningful results in the recognition of the disease. Also, to determine the cut-off value for the prediction of disease six parameters. These results are applicable from future abdominal computed tomography images as a preliminary diagnostic article of diseases automatic detection and eventual diagnosis.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.83-93
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• 2013

By comparing Wiener's MMSE on stochastic signal transmission with Shannon's mutual information first proved by C.E. Shannon in terms of information theory, connections between two approaches were investigated. What Wiener wanted to see in signal transmission in noisy channel is to try to capture fundamental limits for signal quality in signal estimation. On the other hands, Shannon was interested in finding fundamental limits of signal quantity that maximize the uncertainty in mutual information using the entropy concept in noisy channel. First concern of this paper is to show that in deriving limits of Shannon's point to point fundamental channel capacity, Shannon's mutual information obtained by exploiting MMSE combiner and Wiener filter's MMSE are interelated by integro-differential equantion. Then, At the meeting point of Wiener's MMSE and Shannon's mutual information the upper bound of spectral efficiency and the lower bound of energy efficiency were computed. Choosing a proper lattice-type code of a mod-${\Lambda}$AWGN channel model and MMSE estimation of ${\alpha}$ confirmed to lead to the fundamental Shannon capacity limits.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.165-172
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• 2010

In clinical data minig, choosing the optimal subset of features is such important, not only to reduce the computational complexity but also to improve the usefulness of the model constructed from the given data. Moreover the threshold values (i.e., cut-off points) of selected features are used in a clinical decision criteria of experts for differential diagnosis of diseases. In this paper, we propose a fuzzy discretization approach, which is evaluated by measuring the degree of separation of redundant attribute values in overlapping region, based on spatial distribution of data with continuous attributes. The weighted average of the redundant attribute values is then used to determine the threshold value for each feature and rough set theory is utilized to select a subset of relevant features from the overall features. To verify the validity of the proposed method, we compared experimental results, which applied to classification problem using 668 patients with a chief complaint of dyspnea, based on three discretization methods (i.e., equal-width, equal-frequency, and entropy-based) and proposed discretization method. From the experimental results, we confirm that the discretization methods with fuzzy partition give better results in two evaluation measures, average classification accuracy and G-mean, than those with hard partition.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.210-218
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• 2002

This paper proposes an efficient separation method of the composite images by using independent component analysis(ICA) based on neural networks of the approximate learning algorithm. The Proposed learning algorithm is the fixed point(FP) algorithm based on Secant method which can be approximately computed by only the values of function for estimating the root of objective function for optimizing entropy. The secant method is an alternative of the Newton method which is essential to differentiate the function for estimating the root. It can achieve a superior property of the FP algorithm for ICA due to simplify the composite computation of differential process. The proposed algorithm has been applied to the composite signals and image generated by random mixing matrix in the 4 signal of 500-sample and the 10 images of $512{\times}512-pixel$, respectively The simulation results show that the proposed algorithm has better performance of the learning speed and the separation than those using the conventional algorithm based method. It also solved the training performances depending on initial points setting and the nonrealistic learning time for separating the large size image by using the conventional algorithm.