• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.1001-1007
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• 2023

Dose limits are not applied to medical radiation exposure therefore justification and optimization should be essential for protecting radiation. This study explores methods to reduce exposure dose undergoing general radiation exam by bolus(tissue equivalent material) with keeping image quality. Hand PA projection with 50 kVp, 5 mAs, SID 100 cm, and 8×10 inch is referred by covered bolus of thickness 0, 3, 5, 8, and 10 mm for evaluation entrance dose and SNR. The entrance dose (μGy) to the hand by bolus thickness was 125.41±0.288, 106.85±0.255, 104.97±0.221, 91.68±0.299, and 90.94±0.106 showing a significant reduction in radiation exposure depending on if the bolus was used and bolus thickness. The SNR of the image was 13.997, 13.906, 12.240, 12.538, and 12.548 at each bolus thickness, showing no significant difference. It was confirmed that if appropriate thickness and size of bolus is used depending on the type of radiological imaging exam and the body site, a significant radiation dose reduction effect can be achieved without deteriorating image quality.

• Journal of the Microelectronics and Packaging Society
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• v.30 no.4
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• pp.69-78
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• 2023

With the rapid growth of artificial intelligence, the demand for semiconductors is enormously increasing everywhere. To ensure the manufacturing quality and quantity simultaneously, the importance of automatic defect detection during the packaging process has been re-visited by adapting various deep learning-based methodologies into automatic packaging defect inspection. Deep learning (DL) models require a large amount of data for training, but due to the nature of the semiconductor industry where security is important, sharing and labeling of relevant data is challenging, making it difficult for model training. In this study, we propose a new framework for securing sufficient data for DL models with fewer computing resources through a divide-and-conquer approach. The proposed method divides high-resolution images into pre-defined sub-regions and assigns conditional labels to each region, then trains individual sub-regions and boundaries with boundary loss inducing the globally coherent and seamless images. Afterwards, full-size image is reconstructed by combining divided sub-regions. The experimental results show that the images obtained through this research have high efficiency, consistency, quality, and generality.

• The Journal of the Acoustical Society of Korea
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• v.25 no.2
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• pp.61-71
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• 2006

An MP3 encoder chip has been designed and fabricated with the hardware and software co-design concepts. In the aspect of the software. the calculation cycles of the distortion control loop. which requires most of the calculation cycles in MP3 encoding procedure. have been reduced to $67\%$ of the original algorithm through the 'scale factor Pre-calculation'. By using a floating Point 32 bit DSP core and designing the FFT block with the hardware. we can get the additional reduction of the calculation cycles in addition to the software optimization. The designed chip has been verified using HW emulation and fabricated via 0.25um CMOS technology The fabricated chip has the size of $6.2{\time}6.2mm^2$ and operates normally on the test board in the qualitative and quantitative aspect.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.6
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• pp.85-96
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• 2023

This study proposes a method for forest vegetation monitoring using high-resolution aerial imagery captured by unmanned aerial vehicles(UAV) and deep learning technology. The research site was selected in the forested area of Mountain Dogo, Asan City, Chungcheongnam-do, and the target species for monitoring included Pinus densiflora, Quercus mongolica, and Quercus acutissima. To classify vegetation species at the pixel level in UAV imagery based on characteristics such as leaf shape, size, and color, the study employed the semantic segmentation method using the prominent U-net deep learning model. The research results indicated that it was possible to visually distinguish Pinus densiflora Siebold & Zucc, Quercus mongolica Fisch. ex Ledeb, and Quercus acutissima Carruth in 135 aerial images captured by UAV. Out of these, 104 images were used as training data for the deep learning model, while 31 images were used for inference. The optimization of the deep learning model resulted in an overall average pixel accuracy of 92.60, with mIoU at 0.80 and FIoU at 0.82, demonstrating the successful construction of a reliable deep learning model. This study is significant as a pilot case for the application of UAV and deep learning to monitor and manage representative species among climate-vulnerable vegetation, including Pinus densiflora, Quercus mongolica, and Quercus acutissima. It is expected that in the future, UAV and deep learning models can be applied to a variety of vegetation species to better address forest management.

• Resources Recycling
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• v.33 no.2
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• pp.37-45
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• 2024

Molybdenum is used in various industries because of its high heat and corrosion resistance. It was selected as a critical mineral in Korea. However, there have been recent challenges in production because of the increased depth and decreased grade of molybdenum veins. Consequently, it is necessary to enhance the effectiveness of the molybdenum beneficiation process. In this study, a basic evaluation of beneficiation characteristics was conducted to enhance the effectiveness of the domestic molybdenum ore beneficiation process. The properties of the beneficiation process were assessed using mineralogical analysis, work index, and flotation kinetics. The results revealed that the allowable particle size of the molybdenum ore for liberation was ~100 ㎛. In addition, the work index was calculated to be 14.57 kWh/t. The operating conditions in the flotation units were achieved by determining the optimal flotation time for each process based on flotation kinetics. Finally, the characteristics of molybdenum ore beneficiation provided in this study can be utilized to diagnose the grinding and flotation processes of large-scale molybdenum beneficiation plants.

• The Journal of Korean Society for Radiation Therapy
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• v.31 no.1
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• pp.7-15
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• 2019

Purpose: The purpose of this study is to evaluate beam delivery accuracy for small sized lung SBRT through experiment. In order to assess the accuracy, Eclipse TPS(Treatment planning system) equipped Acuros XB and radiochromic film were used for the dose distribution. Comparing calculated and measured dose distribution, evaluated the margin for PTV(Planning target volume) in lung tissue. Materials and Methods : Acquiring CT images for Rando phantom, planned virtual target volume by size(diameter 2, 3, 4, 5 cm) in right lung. All plans were normalized to the target Volume=prescribed 95 % with 6MV FFF VMAT 2 Arc. To compare with calculated and measured dose distribution, film was inserted in rando phantom and irradiated in axial direction. The indexes of evaluation are percentage difference(%Diff) for absolute dose, RMSE(Root-mean-square-error) value for relative dose, coverage ratio and average dose in PTV. Results: The maximum difference at center point was -4.65 % in diameter 2 cm size. And the RMSE value between the calculated and measured off-axis dose distribution indicated that the measured dose distribution in diameter 2 cm was different from calculated and inaccurate compare to diameter 5 cm. In addition, Distance prescribed 95 % dose($D_{95}$) in diameter 2 cm was not covered in PTV and average dose value was lowest in all sizes. Conclusion: This study demonstrated that small sized PTV was not enough covered with prescribed dose in low density lung tissue. All indexes of experimental results in diameter 2 cm were much different from other sizes. It is showed that minimized PTV is not accurate and affects the results of radiation therapy. It is considered that extended margin at small PTV in low density lung tissue for enhancing target center dose is necessary and don't need to constraint Maximum dose in optimization.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.4
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• pp.16-21
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• 2017

In this paper, a Small-sized and planer resonator design of Magnetic Resonance - Wireless Power Transfer(MR-WPT) were proposed for practical applications of mobile devices, such as a laptop, a smart-phone and a tablet pc. The proposed MR-WPT system were based on four coil MR-WPT and designed as a transmitter part (Tx) and a receiver part (Rx) both are the same shape with the same loop and resonator. There are four different spiral coil type of resonators with variable of line length, width, gap and turns in $50mm{\times}50mm$ size. The both of top and bottom side of substrate(acrylic; ${\varepsilon}_r=2.56$, tan ${\delta}=0.008$) ere used to generate high inductance and capacitance in limited small volume. Loops were designed on the same plane of resonator to reduce their volume, and there are three different size. The proposed MR-WPT system were fabricated with two acrylic substrate plane of Tx and Rx each, the Rx and Tx loops and resonators were fabricated of copper sheets. There are 12 combinations of 3 loops and 4 resonators, each combination were measured to calculate transfer efficiency and resonance frequency in transfer distance from 1cm to 5cm. The measured results, the highest transfer efficiency was about 70%, and average transfer efficiency was 40%, on the resonance frequency was about 6.78 MHz, which is standard band by A4WP. We proposed small-sized and planer resonator of MR-WPT and showed possibility of mobile applications for small devices.

• Analytical Science and Technology
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• v.28 no.6
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• pp.453-459
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• 2015

Split-flow thin cell fractionation (SPLITT fractionation, SF) is a particle separation technique that allows continuous (and thus a preparative scale) separation into two subpopulations based on the particle size or the density. In SF, there are two basic performance parameters. One is the throughput (TP), which was defined as the amount of sample that can be processed in a unit time period. Another is the fractionation efficiency (FE), which was defined as the number % of particles that have the size predicted by theory. Full-feed depletion mode (FFD-SF) have only one inlet for the sample feed, and the channel is equipped with a flow stream splitter only at the outlet in SF mode. In conventional FFD-mode, it was difficult to extend channel due to splitter in channel. So, we use large scale splitter-less FFD-SF to increase TP from increase channel scale. In this study, a FFD-SF channel was developed for a large-scale fractionation, which has no flow stream splitters (‘splitter less’), and then was tested for optimum TP and FE by varying the sample concentration and the flow rates at the inlet and outlet of the channel. Polyurethane (PU) latex beads having two different size distribution (about 3~7 µm, and about 2~30 µm) were used for the test. The sample concentration was varied from 0.2 to 0.8% (wt/vol). The channel flow rate was varied from 70, 100, 120 and 160 mL/min. The fractionated particles were monitored by optical microscopy (OM). The sample recovery was determined by collecting the particles on a 0.1 µm membrane filter. Accumulation of relatively large micron sized particles in channel could be prevented by feeding carrier liquid. It was found that, in order to achieve effective TP, the concentration of sample should be at higher than 0.4%.

• Food Engineering Progress
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• v.21 no.4
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• pp.360-366
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• 2017

Fish skin peptide-loaded liposomes were prepared in 100 mL and 1 L solution as lab scales, and 10 L solution as a prototype scale. The particle size and zeta potential were measured to determine the optimal conditions for the production of fish skin peptide-loaded liposome. The liposome was manufactured by the following conditions: (1) primary homogenization at 4,000 rpm, 8,000 rpm, and 12,000 rpm for 3 minutes; (2) secondary homogenization at 40 watt (W), 60 W, and 80 W for 3 minutes. From this experimental design, the optimal conditions of homogenization were selected as 4,000 rpm and 60 W. For the next step, fish peptides were prepared as the concentrations of 3, 6, and 12% at the optimum manufacturing conditions of liposome and stored at $4^{\circ}C$. Particle size, polydispersion index (pdI), and zeta potential of peptide-loaded liposome were measured for its stability. Particle size increased significantly as manufacture scale and peptide concentration increased, and decreased over storage time. The zeta potential results increased as storage time increased at 10 L scale. In addition, 12% peptide showed the formation of a sediment layer after 3 weeks, and 6% peptide was considered to be the most suitable for industrial application.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.