• The Korean Journal of Physiology and Pharmacology
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• v.9 no.2
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• pp.103-108
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• 2005

To study the direct effect of somatostatin (SS) on calcium channel current ($I_{Ba}$) in guinea-pig gastric myocytes, $I_{Ba}$ was recorded by using whole-cell patch clamp technique in single smooth muscle cells. Nicardipine ($1{\mu}M$), a L-type $Ca^{2+}$ channel blocker, inhibited $I_{Ba}$ by $98{\pm}1.9$% (n=5), however $I_{Ba}$ was decreased in a reversible manner by application of SS. The peak $I_{Ba}$ at 0 mV were decreased to $95{\pm}1.5$, $92{\pm}1.9$, $82{\pm}4.0$, $66{\pm}5.8$, $10{\pm}2.9$% at $10^{-10}$, $10^{-9}$, $10^{-8}$, $10^{-7}$, $10^{-5}$ M of SS, respectively (n=3∼6; $mean{\pm}SEM$). The steady-state activation and inactivation curves of $I_{Ba}$ as a function of membrane potentials were well fitted by a Boltzmann equation. Voltage of half-activation ($V_{0.5}$) was $-12{\pm}0.5$ mV in control and $-11{\pm}1.9$ mV in SS treated groups (respectively, n=5). The same values of half-inactivation were $-35{\pm}1.4$ mV and $-35{\pm}1.9$ mV (respectively, n=5). There was no significant difference in activation and inactivation kinetics of $I_{Ba}$ by SS. Inhibitory effect of SS on $I_{Ba}$ was significantly reduced by either dialysis of intracellular solution with $GDP_{\beta}S$, a non-hydrolysable G protein inhibitor, or pretreatment with pertussis toxin (PTX). SS also decreased contraction of guinea-pig gastric antral smooth muscle. In conclusion, SS decreases voltage-dependent L-type calcium channel current ($VDCC_L$) via PTXsensitive signaling pathways in guinea-pig antral circular myocytes.

• Proceedings of the KWS Conference
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• 2002.10a
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• pp.612-619
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• 2002

The dynamic behavior of Al-Mg alloys plasma was very unstable and this instability was closely related to the unstable motion of keyhole during laser irradiation. The keyhole fluctuated both in size and shape and its fluctuation period was about 440 ${\mu}{\textrm}{m}$. This instability has been estimated to be caused by the evaporation phenomena of metals with different boiling point and latent heats of vaporization. Therefore, the authors have conducted the spectroscopic diagnostics of plasma induced in the pulsed YAG laser welding of Al-Mg alloys in air and argon atmospheres. In the air environment, the identified spectra were atomic lines of Al, Mg, Cr, Mn, Cu, Fe and Zn, and singly ionized Mg line, as well as strong molecular spectrum of AlO, MgO and AIH. It was confirmed that the resonant lines of Al and Mg were strongly self-absorbed, in particular in the vicinity of pool surface. The self-absorption of atomic Mg line was more eminent in alloys containing higher Mg. These facts showed that the laser-induced plasma was relatively a low temperature and high density metallic vapor. The intensities of molecular spectra of AlO and MgO were different each other depending on the power density of laser beam. Under the low power density irradiation condition, the MgO band spectra were predominant in intensity, while the AlO spectra became much stronger in higher power density. In argon atmosphere the band spectra of MgO and AlO completely vanished, but AlH molecular spectra was detected clearly. The hydrogen source was presumably the hydrogen solved in the base Metal, absorbed water on the surface oxide layer or H$_2$ and $H_2O$ in the shielding gas. The temporal change in spectral line intensities was quite similar to the fluctuation of keyhole. The time average plasma temperature at 1 mm high above the surface of A5083 alloy was determined by the Boltzmann plot method of atomic Cr lines of different excitation energy. The obtained electron temperature was 3, 280$\pm$150 K which was about 500 K higher than the boiling point of pure aluminum. The electron number density was determined by measuring the relative intensities of the spectra1lines of atomic and singly ionized Magnesium, and the obtained value was 1.85 x 1019 1/㎥.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.3 no.1
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• pp.18-25
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• 1978

The 1/f noise spectrum of short-circuited output drain current due to the Shockley-Read-Hal] recombination centers with a single lifetime in homogeneous nondegenerate MOS-field effcte transtors with n-type channel is calculated under the assumptions that the quasi-Fermi level for the carriers in each energy band can not be defined if we include the fluctuation for time varying quantities. and so 1/f noise is a majority carrier effect. Under these assumptions the derived 1/f noise in this paper show some essential features of the 1/f noise in MOS-field effect transistors. That is, it has no lowfrequency plateau and is proportionnal to the channel cross area A and to the driain bias voltage Vd and inversely proportional to the channel length L3 in MOS field effect transistors. This model can explain the discrepancy between the transition frequency of the noise spectrum from 1/f- response to 1/f2 and the frequency corresponding to the relaxation time related to the surface centers in p-n junction diodes. In this paper the results show that the functional form of noise spectrum is greatly influenced by the functional forms of the electron capture probability cn (E) and the relaxation time r (E) for scattering and the case of lattice scattering show to be responsible for the 4 noise in MOS fold effect transistors. So we canconclude that the source of 1/f noise is due to lattice scattering.

• Journal of the Korean Society for Nondestructive Testing
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• v.33 no.4
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• pp.336-341
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• 2013

In this paper, the design concept of a low-temperature vacuum blackbody was described, and thermophysical model of the blackbody was numerically evaluated. Also the working performance of low-temperature vacuum blackbody was evaluated using infrared camera system. The blackbody system was constructed to operate under high-vacuum conditions ($2.67{\times}10^{-2}$ Pa) to reduce temperature uncertainty, which is caused by vapor condensation at low temperatures usually below 273 K. In addition, both heat sink and heat shield including cold shield were installed around radiator to prevent heat loss from the blackbody. Simplified mathematical model of blackbody radiator was analyzed using modified Stefan-Boltzmann's rule. The infrared radiant performance of the blackbody was evaluated using infrared camera. Based on the results of measurements, and simulation, temperature stability of the low-temperature vacuum blackbody demonstrated that the blackbody system can serve as a highly stable reference source for the calibration of an infrared optical system.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.36 no.3
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• pp.204-208
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• 1991

Five models for net radiation estimation reported by Linacre(1968), Berljand(1956), Nakayama et al. (1983), Chang (1970) and Doorenbos et al. (1977) were tested for the adaptability to Korea. A new model with effective longwave radiation term parameterized by air temperature, solar radiation and vapor pressure was formulated and tested for its accuracy. Above five models with original parameter values showed large absolute mean deviations ranging from 0.86 to 1.64 MJ/$m^2$/day. The parameters of the above five models were reestimated by using net radiation and meteorological elements measured in Suwon, Korea. These five models with new parameter values showed absolute mean deviations ranging from 0.74 to 0.88 MJ/$m^2$/day. The following model was newly formulated: Rn=(1- $\alpha$) Rs- $\sigma$ $T_{k}$$^{4}$ (0.0103 Exp (0 .0731 Rs) -0.0475 (equation omitted) +0 .2478) ($R^2$=0.997, n=63) where $\alpha$ =albedo, $\sigma$=Stefan-Boltzmann constant, Rs=solar radiation in MJ/$m^2$/day, Tk =air temperature in Kelvin and $e_{a}$=vapor pressure in mb. This model revealed 0.4988 MJ/$m^2$/day in absolute mean deviation when applied to an independent set of meteorological data.a.a.

• Journal of The Korean Astronomical Society
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• v.36 no.3
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• pp.89-95
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• 2003

Observations of dark matter dominated dwarf and low surface brightness disk galaxies favor density profiles with a flat-density core, while cold dark matter (CDM) N-body simulations form halos with central cusps, instead. This apparent discrepancy has motivated a re-examination of the microscopic nature of the dark matter in order to explain the observed halo profiles, including the suggestion that CDM has a non-gravitational self-interaction. We study the formation and evolution of self-interacting dark matter (SIDM) halos. We find analytical, fully cosmological similarity solutions for their dynamics, which take proper account of the collisional interaction of SIDM particles, based on a fluid approximation derived from the Boltzmann equation. The SIDM particles scatter each other elastically, which results in an effective thermal conductivity that heats the halo core and flattens its density profile. These similarity solutions are relevant to galactic and cluster halo formation in the CDM model. We assume that the local density maximum which serves as the progenitor of the halo has an initial mass profile ${\delta}M / M {\propto} M^{-{\epsilon}$, as in the familiar secondary infall model. If $\epsilon$ = 1/6, SIDM halos will evolve self-similarly, with a cold, supersonic infall which is terminated by a strong accretion shock. Different solutions arise for different values of the dimensionless collisionality parameter, $Q {\equiv}{\sigma}p_br_s$, where $\sigma$ is the SIDM particle scattering cross section per unit mass, $p_b$ is the cosmic mean density, and $r_s$ is the shock radius. For all these solutions, a flat-density, isothermal core is present which grows in size as a fixed fraction of $r_s$. We find two different regimes for these solutions: 1) for $Q < Q_{th}({\simeq} 7.35{\times} 10^{-4}$), the core density decreases and core size increases as Q increases; 2) for $Q > Q_{th}$, the core density increases and core size decreases as Q increases. Our similarity solutions are in good agreement with previous results of N-body simulation of SIDM halos, which correspond to the low-Q regime, for which SIDM halo profiles match the observed galactic rotation curves if $Q {\~} [8.4 {\times}10^{-4} - 4.9 {\times} 10^{-2}]Q_{th}$, or ${\sigma}{\~} [0.56 - 5.6] cm^2g{-1}$. These similarity solutions also show that, as $Q {\to}{\infty}$, the central density acquires a singular profile, in agreement with some earlier simulation results which approximated the effects of SIDM collisionality by considering an ordinary fluid without conductivity, i.e. the limit of mean free path ${\lambda}_{mfp}{\to} 0$. The intermediate regime where $Q {\~} [18.6 - 231]Q_{th}$ or ${\sigma}{\~} [1.2{\times}10^4 - 2.7{\times}10^4] cm^2g{-1}$, for which we find flat-density cores comparable to those of the low-Q solutions preferred to make SIDM halos match halo observations, has not previously been identified. Further study of this regime is warranted.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.