• Current Optics and Photonics
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• v.8 no.2
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• pp.127-137
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• 2024

The distribution of tags is an important factor that affects the performance of radio-frequency identification (RFID). To study RFID performance, it is necessary to obtain RFID tags' coordinates. However, the positioning method of RFID technology has large errors, and is easily affected by the environment. Therefore, a new method using optical measurement is proposed to achieve RFID performance analysis. First, due to the possibility of blurring during image acquisition, the paper derives a new image prior to removing blurring. A nonlocal means-based method for image deconvolution is proposed. Experimental results show that the PSNR and SSIM indicators of our algorithm are better than those of a learning deep convolutional neural network and fast total variation. Second, an RFID dynamic testing system based on photoelectric sensing technology is designed. The reading distance of RFID and the three-dimensional coordinates of the tags are obtained. Finally, deep learning is used to model the RFID reading distance and tag distribution. The error is 3.02%, which is better than other algorithms such as a particle-swarm optimization back-propagation neural network, an extreme learning machine, and a deep neural network. The paper proposes the use of optical methods to measure and collect RFID data, and to analyze and predict RFID performance. This provides a new method for testing RFID performance.

• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4607-4616
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• 2023

We implement machine learning regression models to predict peak pressures of primary and secondary systems, a major safety concern in Loss Of Condenser Vacuum (LOCV) accident. We selected the Multi-dimensional Analysis of Reactor Safety-KINS standard (MARS-KS) code to analyze the LOCV accident, and the reference plant is the Korean Optimized Power Reactor 1000MWe (OPR1000). eXtreme Gradient Boosting (XGBoost) is selected as a machine learning tool. The MARS-KS code is used to generate LOCV accident data and the data is applied to train the machine learning model. Hyperparameter optimization is performed using a simulated annealing. The randomly generated combination of initial conditions within the operating range is put into the input of the XGBoost model to predict the peak pressure. These initial conditions that cause peak pressure with MARS-KS generate the results. After such a process, the error between the predicted value and the code output is calculated. Uncertainty about the machine learning model is also calculated to verify the model accuracy. The machine learning model presented in this paper successfully identifies a combination of initial conditions that produce a more conservative peak pressure than the values calculated with existing methodologies.

• Investigative Magnetic Resonance Imaging
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• v.20 no.2
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• pp.81-87
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• 2016

Purpose: To analyze the feasibility of three-dimensional (3D) diffusion-weighted (DW) PSIF (reversed FISP [fast imaging with steady-state free precession]) sequence in order to evaluate peripheral nerves in the elbow. Materials and Methods: Ten normal, asymptomatic volunteers were enrolled (6 men, 4 women, mean age 27.9 years). The following sequences of magnetic resonance images (MRI) of the elbow were obtained using a 3.0-T machine: 3D DW PSIF, 3D T2 SPACE (sampling perfection with application optimized contrasts using different flip angle evolution) with SPAIR (spectral adiabatic inversion recovery) and 2D T2 TSE (turbo spin echo) with modified Dixon (m-Dixon) sequence. Two observers used a 5-point grading system to analyze the image quality of the ulnar, median, and radial nerves. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of each nerve were measured. We compared 3D DW PSIF images with other sequences using the Wilcoxon-signed rank test and Friedman test. Inter-observer agreement was measured using intraclass correlation coefficient (ICC) analysis. Results: The mean 5-point scores of radial, median, and ulnar nerves in 3D DW PSIF (3.9/4.2/4.5, respectively) were higher than those in 3D T2 SPACE SPAIR (1.9/2.8/2.8) and 2D T2 TSE m-Dixon (1.7/2.8/2.9) sequences (P < 0.05). The mean SNR in 3D DW PSIF was lower than 3D T2 SPACE SPAIR, but there was no difference between 3D DW PSIF and 2D T2 TSE m-Dixon in all of the three nerves. The mean CNR in 3D DW PSIF was lower than 3D T2 SPACE SPAIR and 2D T2 TSE m-Dixon in the median and ulnar nerves, but no difference among the three sequences in the radial nerve. Conclusion: The three-dimensional DW PSIF sequence may be feasible to evaluate the peripheral nerves around the elbow in MR imaging. However, further optimization of the image quality (SNR, CNR) is required.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.11a
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• pp.14-17
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• 2010

Following the proliferation of three-dimensional video contents and displays, many terrestrial broadcasting companies prepare for starting stereoscopic 3DTV service. In terrestrial stereoscopic broadcast, it is a difficult task to code and transmit two video sequences while sustaining as high quality as 2DTV broadcast attains due to the limited bandwidth defined by the existing digital TV standards such as ATSC. Thus, a terrestrial 3DTV broadcasting system with heterogeneous video coding systems is considered for terrestrial 3DTV broadcast where the left image and right images are based on MPEG-2 and H.264/AVC, respectively, in order to achieve both high quality broadcasting service and compatibility for the existing 2DTV viewers. Without significant change in the current terrestrial broadcasting systems, we propose a joint rate control scheme for stereoscopic 3DTV service. The proposed joint rate control scheme applies to the MPEG-2 encoder a quadratic rate-quantization model which is adopted in the H.264/AVC. Then the controller is designed for the sum of two bit streams to meet the bandwidth requirement of broadcasting standards while the sum of image distortions is minimized by adjusting quantization parameter computed from the proposed optimization scheme. Besides, we also consider a condition on quality difference between the left and right images in the optimization. Experimental results demonstrate that the proposed bit rate control scheme outperforms the rate control method where each video coding standard uses its own bit rate control algorithm in terms of minimizing the mean image distortion as well as the mean value and the variation of absolute image quality differences.

• Clean Technology
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• v.25 no.4
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• pp.324-330
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• 2019

The selective catalytic reduction system is a highly effective technique for the denitrification of the flue gases emitted from the industrial facilities. The distribution of mixing ratio between ammonia and nitrogen oxide at the inlet of the catalyst layers is important to the efficiency of the de-NOx process. In this study, computational analysis tools have been applied to improve the uniformity of NH₃/NO molar ratio by controlling the flow rate of the ammonia injection nozzles according to the distribution pattern of the nitrogen oxide in the inlet flue gas. The root mean square of NH₃/NO molar ratio was chosen as the optimization parameter while the design of experiment was used as the base of the optimization algorithm. As the inlet conditions, four (4) types of flow pattern were simulated; i.e. uniform, parabolic, upper-skewed, and random. The flow rate of the eight nozzles installed in the ammonia injection grid was adjusted to the inlet conditions. In order to solve the two-dimensional, steady, incompressible, and viscous flow fields, the commercial software ANSYS-FLUENT was used with the k-𝜖 turbulence model. The results showed that the improvement of the uniformity ranged between 9.58% and 80.0% according to the inlet flow pattern of the flue gas.

• Korean Journal of Applied Biomechanics
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• v.25 no.2
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• pp.157-165
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• 2015

Object : The goals of this research were to make Performance Enhanced Model(PE) taken the largest performance index (PI) through artificial variation of principle components calculated by principle component analysis for trial data, and to verify the effect through comparing kinematic factors between trial data (Raw) and PE. Method : Ten subjects (5 men, 5 women) were recruited and 80% of their maximal record was considered. The PI is a regression equation. In order to develop PE, we extracted Principle components from trial position data (by Principle Components Analysis (PCA)). Before PCA, we made 17 position data to 3 row matrix according to components. We calculated 3 eigen value (principle components) through PCA. And except Y (medial-lateral direction) component (because motion of Y component is small), principle components of X (anterior-posterior direction) and Z (vertical direction) components were changed as following. Changed principle components = principle components + principle components ${\times}$ k. After changing the each principle component, we reconstructed position data using the changed principle components and calculated performance index (PI). A Paired t-test was used to compare Raw data and Performance Enhanced Model data. The level of statistical significance was set at $p{\leq}0.05$. Result : The PI was significantly increased about 12.9kg at PE ($101.92{\pm}6.25$) when compared to the Raw data ($91.29{\pm}7.10$). It means that performance can be increased by optimizing 3D positions. The difference of kinematic factors as follows : the movement distance of the bar from start to lock out was significantly larger (about 1cm) for PE, the width of anterior-posterior bar position in full phase was significantly wider (about 1.3cm) for PE and the horizontal displacement toward the weightlifter after beginning of descent from maximal height was significantly greater (about 0.4cm) for PE. Additionally, the minimum knee angle in the 2-pull phase was significantly smaller (approximately 2.7cm) for the PE compared to that of the Raw. PE was decided at proximal position from the Raw (origin point (0,0)) of PC variation). Conclusion : PI was decided at proximal position from the Raw (origin point (0,0)) of PC variation). This means that Performance Enhanced Model was decided by similar motion to the Raw without a great change. Therefore, weightlifters could be accept Performance Enhanced Model easily, comfortably and without large stress. The Performance Enhance Model can provide training direction for athletes to improve their weightlifting records.

• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.1
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• pp.1-21
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• 2020

The domestic abandoned mines are generating subsidence and it is difficult to predict this subsidence and evaluate the risk. The study of the subsidence risk evaluation using the existing numerical analysis only applies the integrative property to the geological structure and ground condition, and analyzes the goaf peripheral plastic domain. Also, there is a realistic limit that only restricted materials can be apprehended in securing the input information, which leads to the low reliability of the numerical analysis result. In this study, 2-dimensional modeling was performed by applying the field geological structure and ground information targeting abandoned mine where the subsidence occurred. Also, the analysis model was revised by repeating the numerical analysis for the difference between the real subsidence ground information and the analysis result to be minimized by modifying the ground property. This revision was automated by applying the optimization technique and the gradational optimal design method dividing multiple ground properties was developed.

• Korean Journal of Food Science and Technology
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• v.30 no.3
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• pp.523-528
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• 1998

Response surface methodology (RSM) was used to monitor extraction characteristics of electron donating ability and organoleptic properties for ethanol extracts from Chrysanthemum petals, thereby determining optimum extraction conditions. A central composite design was applied to investigate effects of solvent per sample $(X_1)$, ethanol concentration $(X_2)$ and extraction time $(X_3)\;at\;60^{\circ}C$ on dependent variables such as electron donating ability $(Y_1)$, organoleptic color $(Y_2)$ and organoleptic aroma $(Y_3)$ of the extracts. Second-order models were employed to generate 4-dimensional response surfaces for qualitative and quantitative aspects of ethanol extracts. Coefficients of determination $(R_2)$ of the models for dependent variables were ranged from 0.8180 to 0.9696. Optimum extraction conditions for each variable were 50 mL/g, 61% and 16 hrs in electron donating ability, 88 mL/g, 21% and 16 hrs in organoleptic color, 55 mL/g, 73% and 19 hrs in organoleptic aroma, respectively. The optimum condition ranges for maximized characteristics of ethanol extracts were $65{\sim}78\;mL/g,\;90{\sim}100%\;and\;15{\sim}25\;hrs$. Predicted values at the optimum conditions were in good agreement with experimental values.

• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.3
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• pp.449-454
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• 2010

Extraction characteristics of pumpkin (Cucurbita moschata Duch.) and the functional properties of the corresponding extracts were monitored by the response surface methodology (RSM). The maximum electron donating ability was found as 24.57% at the conditions of 33.13 watt microwave power, 53.67% ethanol concentration and 3.76 min extraction time. The maximum tyrosinase inhibition was 96.59% at 56.21 watt, 68.02% and 7.97 min. The SOD-like activity was 45.57% at 36.00 watt, 71.51% and 5.28 min. The total polyphenol content was 85.02% at 127.39 watt, 76.18% and 2.68 min. Based on superimposition of four dimensional RSM with respect to electron donating ability, tyrosinase inhibition, SOD-like activity and total polyphenol contents obtained under the various extraction conditions, the optimum ranges of extraction conditions were found to be microwave power of 72~144 watt, ethanol concentration of 0~38% and extraction time of 6~9 min.

• Journal of the Korean Magnetics Society
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• v.23 no.1
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• pp.1-6
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• 2013

Spin-orbit coupling (SOC) effect is known to be the physical origin for various exotic magnetic phenomena in the low-dimensional systems. Recently, SOC also draws lots of attention in the study on magnetically doped thermoelectric alloys to determine their properties as the thermoelectric application as well as the topological insulator via the exact electronic structures determination near the Fermi level. In this research, aiming to investigate the spin-orbit coupling effect on the structural properties such as the lattice constants and the bulk modulus of the most widely investigated thermoelectric host material, $Bi_2Te_3$, we carried out the first-principles electronic structure calculation using the all-electron FLAPW (full-potential linearized augmented plane-wave) method. Employing both the local density approximation (LDA) and the generalized gradient approximation (GGA), the structural optimization is achieved by varying the in-plane lattice constant fixing the perpendicular lattice constant and vice versa, to find that the SOC effect increases the equilibrium lattices slightly in both directions while it markedly reduces the bulk modulus value implying the strong orientational dependence, which are attributed to the material's intrinsic structural anisotropy.