• Journal of radiological science and technology
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• v.32 no.2
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• pp.183-194
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• 2009

Purpose : This study was performed not only to compare the brain activation regions associated with sexual arousal induced by visual stimulation and SP6 acupuncture, but also to evaluate its differential neuro-anatomical mechanism in healthy women using functional magnetic resonance imaging (fMRI) at 3 Tesla (T). Subjects and methods : A total of 21 healthy right-handed female volunteers (mean age 22 years, range 19 to 32) underwent fMRI on a 3T MR scanner. The stimulation paradigm for sexual arousal consisted of two alternating periods of rest and activation. It began with a 1-minute rest period, 3 minutes of stimulation with either of an erotic video film or SP6 acupuncture, followed by 1-minute rest. In addition, a comparative study on the brain activation patterns between an acupoint and a shampoint nearby GB37 was performed. The fMRI data were obtained from 20 slices parallel to the AC-PC line on an axial plane, giving a total of 2,000 images. The mean activation maps were constructed and analyzed by using the statistical parametric mapping (SPM99) software. Results : As comparison with the shampoint, the acupoint showed 5 times and 2 times higher activities in the neocortex and limbic system, respectively. Note that brain activation in response to stimulation with the shampoint was not observed in the regions including the HTHL in the diencephalon, GLO and AMYG in the basal ganglia, and SMG in the parietal lobe. In the comparative study of visual stimulation vs. SP6 acupuncture, the mean activation ratio of stimulus was not significantly different to each other in both the neocortex and the limbic system (p < 0.05). The mean activities induced by both stimuli were not significantly different in the neocortex, whereas the acupunctural stimulation showed higher activity in the limbic system (p < 0.05). Conclusions : This study compared the differential brain activation patterns and the neural mechanisms for sexual arousal, which were induced by visual stimulation and SP6 acupuncture by using 3T fMRI. These findings will be useful to understand the theory of traditional acupuncture and acupoint channel in scientific point of view.

• The Journal of Korean Academy of Prosthodontics
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• v.55 no.4
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• pp.381-388
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• 2017

Purpose: Unpredictable shrinkage of zirconia during sintering process causes discrepancy. Therefore, there have been attempts to reduce discrepancy by milling zirconia after sintering. However, due to the hardness of sintered zirconia, milling takes longer time, causes damage to the machine and causes chip formation. With customized zirconia block using the mean dimension of prepared natural dentition, it is expected to overcome these shortcomings. Materials and methods: The mean dimension of prepared natural dentition was analyzed as STL file after scanning of prepared teeth treated at SNUDH. The transverse, frontal and sagittal planes were set using Mimics and Photoshop. 3D volume was projected on each plane, and the outer line was measured through external tangent line, and the inner line was measured through inflection point of tangent line. Results: The mean height of prepared incisal (N = 57) is $6.60{\pm}1.05mm$, mesiodistal length is $2.98{\pm}0.73mm$, buccolingual length is $2.04{\pm}0.73mm$. The mean height of prepared premolar (N = 15) is $5.37{\pm}1.49mm$, mesiodistal length is $4.10{\pm}1.78mm$, buccolingual length is $5.86{\pm}1.55mm$. And the mean height of prepared molar (N = 13) is $5.11{\pm}1.29mm$, mesiodistal length is $6.80{\pm}1.18mm$, buccolingual length is $7.34{\pm}1.40mm$. Conclusion: Using the mean dimension of prepared natural dentition, it is expected to be able to fabricate customized zirconia block.

• Journal of Radiation Protection and Research
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• v.33 no.2
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• pp.47-51
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• 2008

Objective: This study analyzed errors due to rotation or tilt of the magnetic resonance (MR) imaging indicator during image acquisition for a stereotactic radiosurgery. The error correction procedure of a commercially available stereotactic neurosurgery treatment planning program has been verified. Materials and Methods: Software virtual phantoms were built with stereotactic images generated by a commercial programming language, Interactive Data Language (version 5.5). The thickness of an image slice was 0.5 mm, pixel size was $0.5{\times}0.5mm$, field of view was 256 mm, and image resolution was $512{\times}512$. The images were generated under the DICOM 3.0 standard in order to be used with Leksell GammaPlan$^{(R)}$. For the verification of the rotation error correction function of Leksell GammaPlan$^{(R)}$, 45 measurement points were arranged in five axial planes. On each axial plane, there were nine measurement points along a square of length 100 mm. The center of the square was located on the z-axis and a measurement point was on the z-axis, too. Five axial planes were placed at z=-50.0, -30.0, 0.0, 30.0, 50.0 mm, respectively. The virtual phantom was rotated by $3^{\circ}$ around one of x, y, and z-axis. It was also rotated by $3^{\circ}$ around two axes of x, y, and z-axis, and rotated by $3^{\circ}$ along all three axes. The errors in the position of rotated measurement points were measured with Leksell GammaPlan$^{(R)}$ and the correction function was verified. Results: The image registration errors of the virtual phantom images was $0.1{\pm}0.1mm$ and it was within the requirement of stereotactic images. The maximum theoretical errors in position of measurement points were 2.6 mm for a rotation around one axis, 3.7 mm for a rotation around two axes, and 4.5 mm for a rotation around three axes. The measured errors in position was $0.1{\pm}0.1mm$ for a rotation around single axis, $0.2{\pm}0.2mm$ for double and triple axes. These small errors verified that the rotation error correction function of Leksell GammaPlan$^{(R)}$ is working fine. Conclusion: A virtual phantom was built to verify software functions of stereotactic neurosurgery treatment planning program. The error correction function of a commercial treatment planning program worked within nominal error range. The virtual phantom of this study can be applied in many other fields to verify various functions of treatment planning programs.

• Journal of the Korean Vacuum Society
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• v.17 no.6
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• pp.528-537
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• 2008

60 nm and 20 nm thick hydrogenated amorphous silicon(a-Si:H) layers were deposited on 200 nm $SiO_2$/single-Si substrates by inductively coupled plasma chemical vapor deposition(ICP-CVD). Subsequently, 30 nm-Ni layers were deposited by an e-beam evaporator. Finally, 30 nm-Ni/(60 nm and 20 nm) a-Si:H/200 nm-$SiO_2$/single-Si structures were prepared. The prepared samples were annealed by rapid thermal annealing(RTA) from $200^{\circ}C$ to $500^{\circ}C$ in $50^{\circ}C$ increments for 40 sec. A four-point tester, high resolution X-ray diffraction(HRXRD), field emission scanning electron microscopy(FE-SEM), transmission electron microscopy(TEM), and scanning probe microscopy(SPM) were used to examine the sheet resistance, phase transformation, in-plane microstructure, cross-sectional microstructure, and surface roughness, respectively. The nickel silicide from the 60 nm a-Si:H substrate showed low sheet resistance from $400^{\circ}C$ which is compatible for low temperature processing. The nickel silicide from 20 nm a-Si:H substrate showed low resistance from $300^{\circ}C$. Through HRXRD analysis, the phase transformation occurred with silicidation temperature without a-Si:H layer thickness dependence. With the result of FE-SEM and TEM, the nickel silicides from 60 nm a-Si:H substrate showed the microstructure of 60 nm-thick silicide layers with the residual silicon regime, while the ones from 20 nm a-Si:H formed 20 nm-thick uniform silicide layers. In case of SPM, the RMS value of nickel silicide layers increased as the silicidation temperature increased. Especially, the nickel silicide from 20 nm a-Si:H substrate showed the lowest RMS value of 0.75 at $300^{\circ}C$.

• Journal of Sensor Science and Technology
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• v.12 no.6
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• pp.273-281
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• 2003

A stoichiometric mixture for $CdIn_2Te_4$ single crystal was prepared from horizontal electric furnace. The $CdIn_2Te_4$ single crystal was grown in the three-stage vertical electric furnace by using Bridgeman method. The quality of the grown crystal has been investigated by the x-ray diffraction and the photoluminescence measurements. The (001) growth plane of oriented $CdIn_2Te_4$ single crystal was confirmed from back-reflection Laue patterns. The carrier density and mobility of $CdIn_2Te_4$ single crystal measured with Hall effect by van der Pauw method are $8.61{\times}10^{16}\;cm^{-3}$ and $242\;cm^2/V{\cdot}s$ at 293 K, respectively. The temperature dependence of the energy band gap of the $CdIn_2Te_4$ single crystal obtained from the absorption spectra was well described by the Varshni's relation, $E_g(T)=1.4750\;eV-(7.69{\times}10^{-3}\;eV)T^2/(T+2147)$. After the as-grown $CdIn_2Te_4$ single crystal was annealed in Cd-, In-, and Te-atmospheres, the origin of point defects of $CdIn_2Te_4$ single crystal has been investigated by the photoluminescence(PL) at 10 K. The native defects of $V_{Te}$, $Cd_{int}$, and $V_{Cd}$, $Te_{int}$ obtained by PL measurements were classified as a donors or acceptors type. And we concluded that the heat-treatment in the Cd-atmosphere converted $CdIn_2Te_4$ single crystal to an optical n-type. Also, we confirmed that In in $CdIn_2Te_4$ did not form the native defects because In in $CdIn_2Te_4$ single crystal existed in the form of stable bonds.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.