• Composites Research
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• v.22 no.5
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• pp.15-23
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• 2009

The lubricant tribological characteristics of $Al_2O_3$ fiber and SiC particle hybrid metal matrix composites (MMCs) fabricated by squeeze casting method was investigated using a pin-on-disk wear tester. The wear tests of the MMCs were performed according to fiber/particle hybrid ratio in the planar-random (PR) and normal (N) orientations sliding against a counter steel disk at a fixed speed and $25\;kg_f$ loading under different sliding distances and temperatures. The test results showed that the wear behavior of MMCs varied with fiber orientation and hybrid ratio. At room temperature, the lubricant wear behavior of F20P0 unhybrid PR-MMCs was superior to that of N-MMCs while the hybrid composites exhibited the reverse lubricant wear behavior. It was also revealed that the wear resistance of PR-MMCs was superior to that of the N-MMCs due to the joint action of reinforcements and lubricant film between the friction surfaces at an elevated temperature of $100^{\circ}C$ for both fiber only and hybrid cases. In case of $150^{\circ}C$, although the trend of weight loss was similar to that of others, the wear resistance of PR-MMCs was better than that of N-MMCs for hybrid MMCs.

• 한국체육학회지인문사회과학편
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• v.57 no.6
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• pp.263-272
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• 2018

This study examined the association of gait variability and physical fitness with frailty status in older women. In a cross-sectional design, 168 elderly women, aged 65 years and older (75.07±5.40 years), measured body composition, gait parameters gait variability, physical fitness variables, MMSE-DS and CES-D. Subjects were classified as robust, pre-frail, and frailty based on the Fried et al.(2001) criteria for frailty weight loss, exhaustion, low hand-grip strength, low gait speed, and physical inactivity. Logistic regression analyses were used to determine the odds ratio (ORs) and 95% confidence interval (CI) of frailty status for having gait variability and physical fitness levels. Compared to the robust group (OR=1), the frailty group had significantly higher ORs of having terminal double limb stance (OR=1.48, 95% CI=0.10-2.21, p=.049), step cadence (OR=2.06, 95%CI=1.20-3.43, p=.009) variability, and significantly lower ORs of having upper-strength (OR=0.49, 95%CI=0.31-0.77, p=.002) even after adjusting for age, education, comorbidity, K-IADL, MMSE-KC and CES-D score. The finding of this study suggested that terminal double limb stance, step cadence and upper body muscular strength were independent predictors of frailty.

• Smart Media Journal
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• v.12 no.1
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• pp.9-16
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• 2023

Recently, a convolutional neural network (CNN) based system is being developed to overcome the limitations of human resources in the apple quality classification of farmhouse. However, since convolutional neural networks receive only images of the same size, preprocessing such as sampling may be required, and in the case of oversampling, information loss of the original image such as image quality degradation and blurring occurs. In this paper, in order to minimize the above problem, to generate a image patch based graph of an original image and propose a random walk-based positional encoding method to apply the graph transformer model. The above method continuously learns the position embedding information of patches which don't have a positional information based on the random walk algorithm, and finds the optimal graph structure by aggregating useful node information through the self-attention technique of graph transformer model. Therefore, it is robust and shows good performance even in a new graph structure of random node order and an arbitrary graph structure according to the location of an object in an image. As a result, when experimented with 5 apple quality datasets, the learning accuracy was higher than other GNN models by a minimum of 1.3% to a maximum of 4.7%, and the number of parameters was 3.59M, which was about 15% less than the 23.52M of the ResNet18 model. Therefore, it shows fast reasoning speed according to the reduction of the amount of computation and proves the effect.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2D
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• pp.311-318
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• 2009

Interactive terrain visualization is an important research area with applications in GIS, games, virtual reality, scientific visualization and flight simulators, besides having military use. This is a complex and challenging problem considering that some applications require precise visualizations of huge data sets at real-time rates. In general, the size of data sets makes rendering at real-time difficult since the terrain data cannot fit entirely in memory. In this paper, we suggest the effective Real-time LOD(level-of-detail) algorithm for displaying the huge terrain data and processing mass geometry. We used a hierarchy structure with $4{\times}4$ and $2{\times}2$ tiles for real-time rendering of mass volume DEM which acquired from Digital map, LiDAR, DTM and DSM. Moreover, texture mapping is performed to visualize realistically while displaying height data of normalized Giga Byte level with user oriented terrain information and creating hill shade map using height data to hierarchy tile structure of file type. Large volume of terrain data was transformed to LOD data for real time visualization. This paper show the new LOD algorithm for seamless visualization, high quality, minimize the data loss and maximize the frame speed.

• Journal of the Korean Society of Radiology
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• v.17 no.7
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• pp.1115-1122
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• 2023

This study aimed to present reasonable reception bandwidth application standards for the purpose of suppressing metal objects during MRI examinations. For this purpose, T2 contrast images were acquired using high-speed spin echo technology on a phantom made of screws for spinal surgery, and metal objects were detected. In addition, images were obtained by increasing the reception bandwidth from 100 Hz/PX to 800 Hz/PX by 100 Hz/PX. The metal artifacts were determined as the sum of the areas of the signal attenuation area and the signal accumulation area. In addition, Pearson correlation analysis was performed to analyze the pattern of metal artifacts according to imaging variables. As a result, the signal accumulation area did not change significantly as the reception bandwidth increased (p>0.05), but the signal loss area and the area of metal artifacts decreased as the reception bandwidth increased (p<0.05). Interestingly, the area of metal objects decreased to a maximum in the section where the reception bandwidth was increased from 100 Hz/PX to 200 Hz/PX, consistent with the section where the echo spacing was reduced to a maximum due to the increase in reception bandwidth. In addition, the correlation analysis results also showed that the eco spacing was more related to the signal attenuation area and the area of metal objects than to the reception bandwidth. Therefore, if the reception bandwidth is increased for the purpose of reducing metal objects, it is reasonable to set it based on a value that minimizes the echo spacing in consideration of image quality factors.

• Journal of the Korean Electrochemical Society
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• v.27 no.1
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• pp.40-46
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• 2024

Among the electrode manufacturing processes for lithium-ion batteries, the drying process is crucial for production speed and process cost. Particularly, as the loading level of the electrode increases to enhance the energy density of the battery, optimizing process conditions for electrode drying becomes more critical. In this study, we compared the drying time and electrochemical performance of the positive electrode prepared at different drying temperatures. LiNi_0.6Co_0.2Mn_0.2O₂ (NCM622) was used as the active material and manufactured under various drying temperature conditions ranging from 120 ℃ to 210 ℃ at loading levels of 2.5 and 4.5 mAh cm^-2. The physical and electrochemical properties of the electrodes were compared. As the loading level of the electrode increases, the drying time of the electrode also increases, but this time can be reduced by increasing the drying temperature. The drying temperature used in manufacturing the NCM622 positive electrode does not significantly affect the electrochemical performance but drying above 210 ℃ resulted in an increase in the volume resistivity of the electrode and a decrease in electrochemical performance. Accordingly, in the manufacture of high-loading electrodes, the drying temperature was increased to 190 ℃ to shorten the electrode manufacturing time without a loss of performance.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.407-417
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• 2024

The objective of this study is to predict and reduce potential damage caused by chlorine gas leaks, a hazardous material, when vehicles transporting it overturn due to accidents or other incidents. The goal is to forecast the anticipated damages caused by chlorine toxicity levels (ppm) and to design effective response strategies for mitigating them. To predict potential damages, we conducted quantitative assessments using the ALOHA program to calculate the toxic effects (ppm) and damage distances resulting from chlorine leaks, taking into account potential negligence of drivers during transportation. The extent of damage from toxic gas leaks is influenced by various factors, including the amount of the leaked hazardous material and the meteorological conditions at the time of the leak. Therefore, a comprehensive analysis of damage distances was conducted by examining various scenarios that involved variations in the amount of leakage and weather conditions. Under intermediate conditions (leakage quantity: 5 tons, wind speed: 3 m/s, atmospheric stability: D), the estimated distance for exceeding the AEGL-2 level of 2 ppm was calculated to be 9 km. This concentration poses a high risk of respiratory disturbance and potential human casualties, comparable to the toxicity of hydrogen chloride. In particular, leaks in urban areas can lead to significant loss of life. In the event of a leakage incident, we proposed a plan to minimize damage by implementing appropriate response strategies based on the location and amount of the leak when an accident occurs.

• Horticultural Science & Technology
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• v.32 no.3
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• pp.390-399
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• 2014

The objective of this study was to establish a processing technology for preserved leaves based on the results from the examination of the optimal period and condition for dye-absorbing treatment for Eucalyptus cinerea F. Mull. ex Benth. (silver dollar eucalyptus) being used frequently as plant material for flower design. Cut foliages of E. cinerea with uniformly matured leaves were cut into 20 cm lengths and their lower stem parts were placed in dye solution in growth chambers with different temperatures (10, 20, 30, and $40^{\circ}C$), vapor pressure deficits (VPD; 0.23, 0.70, 1.17, and 1.61 kPa), and photoperiods (0, 6, 12, 24 hours) for 3, 6, 9, and 12 days, and then dried in a room of $20^{\circ}C$ for three days. Lower temperature during preserving dye treatment reduced the changes in leaf color compared with fresh leaves and decreased ${\Delta}E$ value. Especially, high temperature increased red degree (a) and decreased yellow degree (b) due to browning. Lower VPD reduced the change in leaf color compared with fresh leaves and decreased ${\Delta}E$ value. Shorter photoperiod reduced the change in leaf color compared with fresh leaves and decreased ${\Delta}E$ value. The ${\Delta}E$ value increased with increasing absorbing duration under three environmental conditions. The flexibility of stem and leaves after dipped into preserving dye solution and dried for 3 days increased with decreasing temperature, VPD and dipping duration. Therefore, the optimal environment condition for dye treatment was 0.23-0.70 kPa VPD at $10-20^{\circ}C$ in the darkness, and the optimal and economical duration was 3 days. These conditions reduced the speed of water loss by decreasing transpiration, so yellowing or browning by rapid water loss deteriorated the quality of preserved leaves out of these ranges.

• Horticultural Science & Technology
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• v.29 no.1
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• pp.16-22
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• 2011

In developing soil wetting agent using polyoxyethylene nonylphenyl ether (PNE) and polyoxyethylene castor oil (1:1; v/v), the effect of application rates on changes in concentration of PNE, initial wetting of peatmoss + perlite (7:3) medium, and growth of hot pepper (Capsicum annuum L. 'Knockwang') plug seedlings were investigated. The elevation of application rates of wetting agent increased the amount of water retained by the root media. The treatment of 2.5 $mL{\cdot}L^{-1}$ showed similar water retention to + control ($AquaGro^L$ 3.0 $mL{\cdot}L^{-1}$). Most of the liquid wetting agent (LWA) incorporated during the medium formulation leached out in the first and second irrigation, then it decreased gradually until 10 times in irrigation. In investigation of the influence of LWA on position of water infiltrating into root media, the vertical water movements in treatments of 0.5, 1.0, and 1.5 $mL{\cdot}L^{-1}$ were much faster than those in 0.0 $mL{\cdot}L^{-1}$ (-control), but relative speed of water movement decreased by the elevation in application rate of LWA to 2.0 or 2.5 $mL{\cdot}L^{-1}$. The evaporative water loss of root media that to contained various rate of LWA and irrigated to reach container capacity was the fastest in -control among the treatments and it delayed as the application rate of LWA was elevated. The plant height of 22.2 cm in 0.5 $mL{\cdot}L^{-1}$ and stem diameter of 3.26 mm in 1.0 $mL{\cdot}L^{-1}$ were the highest among the treatments tested. The treatment of 1.0 $mL{\cdot}L^{-1}$ also had the heaviest fresh and dry weights such among treatments tested as 3.08 g and 0.861 g per plant, respectively. The elevated application rate over than 1.5 $mL{\cdot}L^{-1}$ resulted in decreased seedling growth. The results mentioned above indicate that optimum application rate of LWA is 1.0 $mL{\cdot}L^{-1}$.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.