• Food Science of Animal Resources
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• v.43 no.5
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• pp.805-825
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• 2023

This experiment aims to investigate the impact of probiotic feed on growth performance, carcass traits, plasma lipid biochemical parameters, intramuscular fat and triglyceride content, fatty acid composition, mRNA expression levels of genes related to lipid metabolism, and the activity of the enzyme in Sunit sheep. In this experiment, 12 of 96 randomly selected Sunit sheep were assigned to receive the basic diet or the basic diet supplemented with probiotics. The results showed that supplementation with probiotics significantly increased the loin eye area, and decreased plasma triglycerides and free fatty acids, increasing the content of intramuscular fat and triglycerides in the muscle and improving the composition of the fatty acids. The inclusion of probiotics in the diet reduced the expression of adenosine 5'-monophosphate-activated protein kinase alpha 2 (AMPKα2) mRNA and carnitine palmitoyltransferase 1B (CPT1B) mRNA, while increasing the expression of acetyl-CoA carboxylase alpha (ACCα) mRNA, sterol regulatory element-binding protein-1c (SREBP-1c) mRNA, fatty acid synthase mRNA, and stearoyl-CoA desaturase 1 mRNA. The results of this study indicate that supplementation with probiotics can regulate fat deposition and improves the composition of fatty acids in Sunit sheep through the signaling pathways AMPK-ACC-CPT1B and AMPK-SREBP-1c. This regulatory mechanism leads to an increase in intramuscular fat content, a restructuring of muscle composition of the fatty acids, and an enhancement of the nutritional value of meat. These findings contribute to a better understanding of the food science of animal resources and provide valuable references for the production of meat of higher nutritional value.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.4
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• pp.587-594
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• 2023

In this paper, we present the design and optimization process of an on-body microstrip patch antenna with a paired T-type defect for monitoring fracture recovery of human legs. This antenna is designed to be light, thin and compact despite the improvement of return loss and bandwidth performance by adjusting the size of the T-type defect. The structure around the applied human leg is structured as a 5-layer dielectric plane, and the complex dielectric constant of each layer is calculated using the 4-pole Cole-Cole model parameters. In a normal case without bone fracture, the return loss of the on-body antenna is -66.71dB at 4.0196GHz, and the return loss difference ΔS₁₁ is 37.95dB when the gallus layer have a length of 10.0mm, width of 1.0mme, and height of 2.0mm. A 3'rd degree polynomial is presented to predict the height of the gallus layer for the change in return loss, and the polynomial has a very high prediction suitability as RSS = 1.4751, R² = 0.9988246, P-value = 0.0001841.

• Resources Recycling
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• v.31 no.6
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• pp.81-91
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• 2022

The use of lithium-ion batteries increases significantly with the rapid spread of electronic devices and electric vehicle and thereby an increase in the amount of waste batteries is expected in the near future. Therefore, studies are continuously being conducted to recover various resources of cathode active material (Ni, Co, Mn, Li) from waste battery. In order to recover the cathode active material, black mass is generally recovered from waste battery. The general process of recovering black mass is a waste battery collection - discharge - dismantling - crushing - classification process. This study focus on the crushing/classification process among the processes. Specifically, the particle size distribution of various samples at each crushing/classification step were evaluated, and the particle shape of each particle fraction was analyzed with a microscope and SEM (Scanning Electron Microscopy)-EDS(Energy Dispersive Spectrometer). As a result, among the black mass particle, fine particle less than 74 ㎛ was the mixture of cathode and anode active material which are properly liberated from the current metals. However, coarse particle larger than 100 ㎛ was present in a form in which the current metal and active material were combined. In addition, this study developed a PBM(Population Balance Model) system that can simulate two-species mixture sample with two different crushing properties. Using developed model, the breakage parameters of two species was derived and predictive performance of breakage distribution was verified.

• Journal of the Earthquake Engineering Society of Korea
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• v.27 no.1
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• pp.25-35
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• 2023

Considering the non-linear behavior of structure and soil when evaluating a nuclear power plant's seismic safety under a beyond-design basis earthquake is essential. In order to obtain the nonlinear response of a nuclear power plant structure, a time-domain SSI analysis method that considers the nonlinearity of soil and structure and the nonlinear Soil-Structure Interaction (SSI) effect is necessary. The Boundary Reaction Method (BRM) is a time-domain SSI analysis method. The BRM can be applied effectively with a Perfectly Matched Layer (PML), which is an effective energy absorbing boundary condition. The BRM has a characteristic that the magnitude of the response in far-field soil increases as the boundary interface of the effective seismic load moves outward. In addition, the PML has poor absorption performance of low-frequency waves. For this reason, the accuracy of the low-frequency response may be degraded when analyzing the combination of the BRM and the PML. In this study, the accuracy of the analysis response was improved by adjusting the PML input parameters to improve this problem. The accuracy of the response was evaluated by using the analysis response using KIESSI-3D, a frequency domain SSI analysis program, as a reference solution. As a result of the analysis applying the optimal PML parameter, the average error rate of the acceleration response spectrum for 9 degrees of freedom of the structure was 3.40%, which was highly similar to the reference result. In addition, time-domain nonlinear SSI analysis was performed with the soil's nonlinearity to show this study's applicability. As a result of nonlinear SSI analysis, plastic deformation was concentrated in the soil around the foundation. The analysis results found that the analysis method combining BRM and PML can be effectively applied to the seismic response analysis of nuclear power plant structures.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.649-654
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• 2023

This paper proposes the design of a neural network structure search model using graph convolutional neural networks. Deep learning has a problem of not being able to verify whether the designed model has a structure with optimized performance due to the nature of learning as a black box. The neural network structure search model is composed of a recurrent neural network that creates a model and a convolutional neural network that is the generated network. Conventional neural network structure search models use recurrent neural networks, but in this paper, we propose GC-NAS, which uses graph convolutional neural networks instead of recurrent neural networks to create convolutional neural network models. The proposed GC-NAS uses the Layer Extraction Block to explore depth, and the Hyper Parameter Prediction Block to explore spatial and temporal information (hyper parameters) based on depth information in parallel. Therefore, since the depth information is reflected, the search area is wider, and the purpose of the search area of the model is clear by conducting a parallel search with depth information, so it is judged to be superior in theoretical structure compared to GC-NAS. GC-NAS is expected to solve the problem of the high-dimensional time axis and the range of spatial search of recurrent neural networks in the existing neural network structure search model through the graph convolutional neural network block and graph generation algorithm. In addition, we hope that the GC-NAS proposed in this paper will serve as an opportunity for active research on the application of graph convolutional neural networks to neural network structure search.

• Journal of Korean society of Dental Hygiene
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• v.23 no.1
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• pp.25-31
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• 2023

Objectives: This study performed a comparative evaluation of the effects of oil pulling on the periodontal health of smokers. Methods: The experimental (15 subjects) and control (15 subjects) were provided coconut oil and distilled water, respectively. We evaluated the pocket depth (≥4 mm), bleeding on exploration, and Patient Hygiene Performance (PHP) index in both groups following the interventions. Clinical parameters were assessed at baseline, after 4 weeks, and after 8 weeks. Dry mouth and oral health-related quality of life were evaluated at baseline and after 8 weeks. Results: Bleeding on exploring in the control group decreased from 26.17 to 18.33 and from 26.07 to 12.53 in the experimental group (p=0.030), with significant differences in measurement time (p<0.001), and the interaction between group and measurement time (p=0.002). The PHP index in the control group decreased from 24.50 to 16.17 and from 24.00 to 9.83 in the experimental group (p=0.027), with significant differences in measurement time (p<0.001), and the interaction between group and measurement time (p=0.001). Furthermore, the experimental group showed a significant decrease in dry mouth (p<0.001) and a significant increase in oral health-related quality of life (p=0.025). Conclusions: The coconut oil intervention positively affected the periodontal health of smokers.

• Journal of the Korean Institute of Gas
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• v.27 no.1
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• pp.70-77
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• 2023

Air environment regulations have been strengthened due to increasing air pollutant emissions, the target of reducing emissions has increased and interest in gas measurement methods is also increasing. The sampling method is mainly used, but due to the spatial and temporal measurement limitations, the laser absorption spectroscopy which is a real-time and in-situ method is in the spotlight. In this study, we studied the wavelength modulation spectroscopy and described the calibration-free algorithm. The developed algorithm was modified to reflect 46 multi-absorption lines and was applied to light absorption signal analysis in visible and mid-infrared regions. In addition, the difference between the modulation parameters of laser was analyzed. As a result of reviewing the performance through O₂ and NO gas measurement experiments of various concentration conditions, the linearity was R²_O2=0.99999 and R²_NO=0.99967.

• Korean Journal of Environmental Biology
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• v.40 no.4
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• pp.651-659
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• 2022

This study was conducted to investigate the effects of different water temperatures (8, 11, 14 and 17℃) on growth, survival and hematological parameters of juvenile chum salmon(Oncorhynchus keta) for eight weeks. At the end of the experiment, at 14℃, the final body weights of the O. keta group were the highest compared to the other groups. Also, the O. keta showed a higher tendency in the 14℃ group than the 8, 11, and 17℃ groups in terms of growth performances, including specific growth rate (SGR), feed conversion ratio (FCR), feed efficiency (FE), weight gain (WG), and condition factor (CF). The survival rate (SR) was 100% at 8 and 11℃ groups, 96% at 14℃ group and 98% at 17℃ group. In the plasma components, the alanine aminotransferase (ALT) was significantly decreased at 17℃ group, whereas there was no significant change in the albumin (ALB), total protein (TP), sodium (Na⁺), potassium (K⁺) and chloride (Cl^-) levels. Among the whole-body composition of salmon, moisture, crude protein, and ash were not significantly affected by water temperature. However, crude lipid in the 8℃ group was significantly higher than in other water temperature groups. The results of this study demonstrated that the optimal temperature to stable growth performance for juvenile O. keta was 14℃.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.145-151
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• 2023

3D-CNN is one of the deep learning techniques for learning time series data. Such three-dimensional learning can generate many parameters, so that high-performance machine learning is required or can have a large impact on the learning rate. When learning dynamic hand-gestures in spatiotemporal domain, it is necessary for the improvement of the efficiency of dynamic hand-gesture learning with 3D-CNN to find the optimal conditions of input video data by analyzing the learning accuracy according to the spatiotemporal change of input video data without structural change of the 3D-CNN model. First, the time ratio between dynamic hand-gesture actions is adjusted by setting the learning interval of image frames in the dynamic hand-gesture video data. Second, through 2D cross-correlation analysis between classes, similarity between image frames of input video data is measured and normalized to obtain an average value between frames and analyze learning accuracy. Based on this analysis, this work proposed two methods to effectively select input video data for 3D-CNN deep learning of dynamic hand-gestures. Experimental results showed that the learning interval of image data frames and the similarity of image frames between classes can affect the accuracy of the learning model.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.4
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• pp.29-39
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• 2017

Various electronic warfare situations drive the need to develop an integrated electronic warfare simulator that can perform electronic warfare modeling and simulation on radar threats. In this paper, we analyze the components of a simulation system to reversely model the radar threats that emit electromagnetic signals based on the parameters of the electronic information, and propose a method to gradually maintain the reverse extrapolation model of RF threats. In the experiment, we will evaluate the effectiveness of the incremental model update and also assess the integration method of reverse extrapolation models. The individual model of RF threats are constructed by using decision tree, naive Bayesian classifier, artificial neural network, and clustering algorithms through Euclidean distance and cosine similarity measurement, respectively. Experimental results show that the accuracy of reverse extrapolation models improves, while the size of the threat sample increases. In addition, we use voting, weighted voting, and the Dempster-Shafer algorithm to integrate the results of the five different models of RF threats. As a result, the final decision of reverse extrapolation through the Dempster-Shafer algorithm shows the best performance in its accuracy.