• Resources Recycling
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• v.29 no.6
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• pp.104-113
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• 2020

Bubble size in froth flotation has long been recognized as a key factor which affects the bubble residence time, the bubble surface area flux (Sb) and the carrying rate (Cr). This paper presents method of bubble size measurement, relationship between operating variables and gas dispersion properties in flotation column. Using high speed camera and image analysis system, bubble size has been directly measured as a function of operating parameters (e.g., superficial gas rate (Jg), superficial wash water rate (Jw), frother concentration) in flotation column. Relationship compared to measured and estimated bubble size was obtained within error ranges of ±15~20% and mean bubble size was 0.718mm. From this system the empirical relationship to control the bubble size and distribution has been developed under operating conditions such as Jg of 0.65~1.3cm/s, Jw of 0.13~0.52cm/s and frother concentration of 60~200ppm. Surface tension and bubble size decreased as frother concentration increased. It seemed that critical coalescence concentration (CCC) of bubbles was 200ppm so that surface tension was the lowest (49.24mN/m) at frother concentration of 200ppm. Bubble size tend to increase when superficial gas rate (Jg) decreases and superficial wash water rate Jw and frother concentration increase. Gas holdup is proportional to superficial gas rate as well as frother concentration and superficial wash water rate (at the fixed superficial gas rate).

• Therapeutic Science for Rehabilitation
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• v.10 no.1
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• pp.105-114
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• 2021

Objective : The purpose of this study was to identify and compare the sex differences in episodic memory and spatial cognition in healthy young adults. Methods : Forty-eight undergraduates (male=24, female=24) were assessed for sex differences using the visual stimuli episodic memory task and the virtual reality-based spatial cognition task. The accuracy rates (%) for the What, Where, and When conditions of the episodic memory task and the average distance error (cm) for 10 trials of the spatial cognition task were analyzed. Results : There were no significant sex differences between the three conditions. The male participants showed a significantly higher performance on the spatial cognition task than the female participants Conclusion : The results of this study indicated that the sex differences in episodic memory could be altered by the test methods. Although episodic memory and spatial cognition mainly depend on the hippocampus, the sex-related differences between the two functions were inconsistent, suggesting that these two functions are independent.

• Journal of Broadcast Engineering
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• v.26 no.3
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• pp.269-282
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• 2021

As the distribution of 3D content such as augmented reality and virtual reality increases, the importance of real-time computer animation technology is increasing. However, the computer animation process consists mostly of manual or marker-attaching motion capture, which requires a very long time for experienced professionals to obtain realistic images. To solve these problems, animation production systems and algorithms based on deep learning model and sensors have recently emerged. Thus, in this paper, we study four methods of implementing natural human movement in deep learning model and kinect camera-based animation production systems. Each method is chosen considering its environmental characteristics and accuracy. The first method uses a Kinect camera. The second method uses a Kinect camera and a calibration algorithm. The third method uses deep learning model. The fourth method uses deep learning model and kinect. Experiments with the proposed method showed that the fourth method of deep learning model and using the Kinect simultaneously showed the best results compared to other methods.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.2
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• pp.122-133
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• 2021

Cultivar parameter calibration can be affected by the reliability of the input data to a crop growth model. In South Korea, two sets of weather stations, which are included in the automated synoptic observing system (ASOS) or the automatic weather system (AWS), are available for preparation of the weather input data. The objectives of this study were to estimate the cultivar parameter using those sets of weather data and to compare the uncertainty of these parameters. The cultivar parameters of CERES-Rice model for Shindongjin cultivar was calibrated using the weather data measured at the weather stations included in either ASO S or AWS. The observation data of crop growth and management at the experiment farms were retrieved from the report of new cultivar development and research published by Rural Development Administration. The weather stations were chosen to be the nearest neighbor to the experiment farms where crop data were collected. The Generalized Likelihood Uncertainty Estimation (GLUE) method was used to calibrate the cultivar parameters for 100 times, which resulted in the distribution of parameter values. O n average, the errors of the heading date decreased by one day when the weather input data were obtained from the weather stations included in AWS compared with ASO S. In particular, reduction of the estimation error was observed even when the distance between the experiment farm and the ASOS stations was about 15 km. These results suggest that the use of the AWS stations would improve the reliability and applicability of the crop growth models for decision support as well as parameter calibration.

• Journal of Apiculture
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• v.34 no.1
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• pp.47-55
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• 2019

Honey bees are a vital part of the food chain as the most important pollinators for a broad palette of crops and wild plants. The climate change and colony collapse disorder (CCD) phenomenon make it challenging to develop ICT solutions to predict changes in beehive and alert about potential threats. In this paper, we report the test results of the bee-counting system which stands out against the previous analogues due to its comprehensive components including an improved dual infrared sensor to detect honey bees entering and leaving the hive, environmental sensors that measure ambient and interior, a wireless network with the bluetooth low energy (BLE) to transmit the sensing data in real time to the gateway, and a cloud which accumulate and analyze data. To assess the system accuracy, 3 persons manually counted the outgoing and incoming honey bees using the video record of 360-minute length. The difference between automatic and manual measurements for outgoing and incoming scores were 3.98% and 4.43% respectively. These differences are relatively lower than previous analogues, which inspires a vision that the tested system is a good candidate to use in precise apicultural industry, scientific research and education.

• Journal of the Korean Geosynthetics Society
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• v.19 no.4
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• pp.41-50
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• 2020

Since cracks in concrete structures expedite corrosion of reinforced concrete over a long period of time, regular on-site inspections are essential to ensure structural usability and prevent degradation. Most of the safety inspections of facilities rely on visual inspection with naked eye, so cost and time consuming are severe, and the reliability of results differs depending on the inspector. In this study, a portable measuring device that can be used for safety diagnosis and maintenance was developed as a device that measures the width and length of concrete cracks through image analysis of cracks photographed with a camera. This device captures the cracks found within a close distance (3 m), and accurately calculates the unit pixel size by laser distance measurement, and automatically calculates the crack length and width with the image processing algorithm developed in this study. In measurement results using the crack image applied to the experiment, the measurement of the length of a 0.3 mm crack within a distance of 3 m was possible with a range of about 10% error. The crack width showed a tendency to be overestimated by detecting surrounding pixels due to vibration and blurring effect during the binarization process, but it could be effectively corrected by applying the crack width reduction function.

• Ecology and Resilient Infrastructure
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• v.8 no.1
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• pp.1-8
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• 2021

A surface image velocimeter (SIV) measures the velocity of a particle group by calculating the intensity distribution of the particle group in two consecutive images of the water surface using a cross-correlation method. Therefore, to increase the accuracy of the flow velocity calculated by a SIV, it is important to accurately calculate the displacement of the particle group in the images. In other words, the change in the physical distance of the particle group in the two images to be analyzed must be accurately calculated. In the image of an actual river taken using a camera, camera lens distortion inevitably occurs, which affects the displacement calculation in the image. In this study, we analyzed the effect of camera lens distortion on the displacement calculation using a dense and uniformly spaced grid board. The results showed that the camera lens distortion gradually increased in the radial direction from the center of the image. The displacement calculation error reached 8.10% at the outer edge of the image and was within 5% at the center of the image. In the future, camera lens distortion correction can be applied to improve the accuracy of river surface flow rate measurements.

• Clean Technology
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• v.27 no.1
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• pp.47-54
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• 2021

The adsorption of Acid Fuchsin (AF) on granular activated carbon (GAC) was investigated for isothermal adsorption and kinetics and thermodynamic parameters by experimenting with the initial concentration, contact time, temperature, and pH of the dye as adsorption parameters. In the pH effect experiment, the adsorption of AF on activated carbon showed a bathtub type with increased adsorption at pH 3 and 11. The adsorption equilibrium data of AF fit well with the Freundlich isotherm model, and the calculated separation factor (1/n) value was found in which activated carbon can effectively remove AF. The pseudo-second-order kinetic model fits well within 7.88% of the error percent in the adsorption process. According to Weber and Morris's model plot, it was divided into two straight lines. The intraparticle diffusion rate was slow because the stage 2 (intraparticle diffusion) slope was smaller than that of stage 1 (boundary layer diffusion). Therefore, it was confirmed that the intraparticle diffusion was a rate-controlling step. The activation energy of AF (13.00 kJ mol-1) corresponded to the physical adsorption process (5 - 40 kJ mol-1). The free energy change of the AF adsorption by activated carbon showed negative values at 298-318 K. As the spontaneity increased with increasing temperature. The adsorption of AF was an endothermic reaction (ΔH = 22.65 kJ mol-1).

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.7
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• pp.892-903
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• 2020

The rapid and accurate prediction of storm-surge height during typhoon attacks is essential in responding to coastal disasters. Most methods used for predicting typhoon data are based on numerical modeling, but numerical modeling takes significant computing resources and time. Recently, various studies on the expeditious production of predictive data based on artificial intelligence have been conducted, and in this study, artificial intelligence-based storm-surge height prediction was performed. Several learning data were needed for artificial intelligence training. Because the number of previous typhoons was limited, many synthesized typhoons were created using the tropical cyclone risk model, and the storm-surge height was also generated using the storm surge model. The comparison of the storm-surge height predicted using artificial intelligence with the actual typhoon, showed that the root-mean-square error was 0.09 ~ 0.30 m, the correlation coefficient was 0.65 ~ 0.94, and the absolute relative error of the maximum height was 1.0 ~ 52.5%. Although errors appeared to be somewhat large at certain typhoons and points, future studies are expected to improve accuracy through learning-data optimization.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.547-554
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• 2021

In order to quickly and accurately diagnose pneumonia on a chest X-ray image, different batch sizes of 4, 8, 16, and 32 were applied to the same Xception deep learning model, and modeling was performed 3 times, respectively. As a result of the performance evaluation of deep learning modeling, in the case of modeling to which batch size 32 was applied, the results of accuracy, loss function value, mean square error, and learning time per epoch showed the best results. And in the accuracy evaluation of the Test Metric, the modeling applied with batch size 8 showed the best results, and the precision evaluation showed excellent results in all batch sizes. In the recall evaluation, modeling applied with batch size 16 showed the best results, and for F1-score, modeling applied with batch size 16 showed the best results. And the AUC score evaluation was the same for all batch sizes. Based on these results, deep learning modeling with batch size 32 showed high accuracy, stable artificial neural network learning, and excellent speed. It is thought that accurate and rapid lesion detection will be possible if a batch size of 32 is applied in an automatic diagnosis study for feature extraction and classification of pneumonia in chest X-ray images using deep learning in the future.