• Analytical Science and Technology
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• v.18 no.2
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• pp.163-167
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• 2005

In this study, a novel analytical method has been developed for the rapid determination of L-carnitine in human plasma using electrospray ionization tandem mass spectrometry. Free carnitine (FC) was analyzed after extraction with 80% methanol and total carnitine (TC) was analyzed after hydrolysis and extraction. Acyl carnitine (AC) was subtracted FC from TC. Analytical methods used multiple reaction monitoring (MRM) scan modes. A correlation coefficient of linear regression ($r^2$) was 0.9995, recovery was 97%, reproducibility was less than 10%, and limit of detection (LOD) was $0.0016{\mu}mol/L$. This method reduced sample preparation time and showed high resolution and good reproducibility compared to that with liquid chromatographic methods. Normal control showed AC was lower than FC. Clinical management of patients with inborn error of metabolism showed FC was lower than AC. Thus, carnitine fraction level was very important to monitoring patients with metabolic disorder.

• Journal of Korea Multimedia Society
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• v.17 no.7
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• pp.818-828
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• 2014

After emerging of Microsoft Kinect, the interest in three-dimensional (3D) depth image was significantly increased. Depth image data of an object can be converted to 3D coordinates by simple arithmetic calculation and then can be reconstructed as a 3D model on computer. However, because the surface coordinates can be acquired only from the front area facing Kinect, total solid which has a closed surface cannot be reconstructed. In this paper, 3D registration method for multiple Kinects was suggested, in which surface information from each Kinect was simultaneously collected and registered in real time to build 3D total solid. To unify relative coordinate system used by each Kinect, 3D perspective transform was adopted. Also, to detect control points which are necessary to generate transformation matrix, 3D randomized Hough transform was used. Once transform matrices were generated, real time 3D reconstruction of various objects was possible. To verify the usefulness of suggested method, human arms were 3D reconstructed and the volumes of them were measured by using four Kinects. This volume measuring system was developed to monitor the level of lymphedema of patients after cancer treatment and the measurement difference with medical CT was lower than 5%, expected CT reconstruction error.

• YAKHAK HOEJI
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• v.55 no.4
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• pp.277-283
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• 2011

Simultaneous monitoring of catecholamines and serotonin with their appropriate extraction from the biological samples is required in order to understand thoroughly the regulation of the central and peripheral nervous system. In the present research the segmented gradient elution with the solid phase extraction using a C18 cartridge rather than the previous isocratic elution with alumina extraction is successfully employed to determine norepinephrine (NE), epinephrine (E), dopamine (DA), serotonin (5HT), 3,4-dihydroxyphenylacetic acid (DOPAC) and 5-hydroxyindoleacetic acid (5HIAA) simultaneously within 20 minutes using 3,4-dihydroxyhydrocinnamic aicd as the internal standard (IS). Linearities were obtained in the concentration range between $5{\times}10^{-6}M$ and $1{\times}10^{-4}M$ for all 7 compounds with detection limits of 0.6~1.9 ${\mu}M$. The present HPLC/ECD method yielded reasonable accuracy (relative error; -1.4~1.1%) and precision (relative standard deviation; 0.4~1.9%) for 9 measurements of the standard solution consisting of NE, E, DA, 5HT, DOPAC and 5HIAA compounds. Recoveries of catecholamines, serotonin and their metabolites from human serum were in the range of 57%~86%. While the concentrations of NE and 5HT in the serum of normal Sprague-Dawley rat were found as $1.4{\times}10^{-6}M$ and $2.6{\times}10^{-6}M$, respectively, the contents of NE and 5HT in the serum of the stressed rat were increased 5.6 times and 1.4 times more, respectively.

• YAKHAK HOEJI
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• v.56 no.4
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• pp.222-229
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• 2012

In order to determine acetylcholine and choline in the biological samples, the specific enzymes of acetylcholinesterase (AChE) and choline oxidase (ChO), which utilize acetylcholine and choline as substrates, were employed to convert substrates to $H_2O_2$. The produced $H_2O_2$ was coupled to 4-aminoantipyrine/phenol with peroxidase (PO) yielding quinoneimine dye which was measured at 508 nm. In the present enzymatic spectrophotometric analysis the product at the equilibrium state was measured considering accuracy, precision, time and cost of the analysis. The developed analytical method yielded good linearity (calibration curve; $A_{508}$=9534[acetylcholine]+0.009, correlation coefficient ($R^2$); 0.999) with detection limit of $1.11{\times}10^{-7}M$, reasonable precision (relative standard deviation; 0.10~1.62% at $2.5{\times}10^{-6}M{\sim}1.25{\times}10^{-4}M$) and accuracy (relative error; -0.24~0.97% at $4.13{\times}10^{-6}M{\sim}1.01{\times}10^{-4}M$) for acetylcholine chloride standard solution. The concentrations of acetylcholine and choline in human serum were found as $3.20{\times}10^{-5}M$ and $1.14{\times}10^{-4}M$, respectively. The brain tissues of Sprague-Dawley strain rat contained 9.82${\mu}g/g$ of acetylcholine and 6.53 ${\mu}g/g$ of choline in the cerebrum, while 7.37 ${\mu}g/g$ of acetylcholine and 5.34 ${\mu}g/g$ of choline in the cerebellum.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.1
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• pp.18-24
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• 2007

If an error occurs in a product that contains a source of vibration, an abnormal noise vibration will occur. Recently a system that has been modified from the previous method of noise detection-a method of appraising the quality of manufactured automobile part by using human ears-is being implemented in the industries of automobile parts. This new system distinguishes the product's vibration signals by measuring and analyzing the signals. Following the recent trend, it has been concluded that the appraisal process of Window Regulator Module needed an improvement. Thus, a vibration monitoring system using LabVIEW, which measures and analyzes vibration signals from a sector gear's connected part by using an accelerometer, has been developed. By analyzing the characteristics of vibration signals of both inferior and superior goods, now the quality of the product can be evaluated much more accurately.

• Korean Journal of Agricultural Science
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• v.49 no.3
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• pp.483-493
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• 2022

It is important to improve the efficiency of plant breeding and crop yield to fulfill increasing food demands. In plant phenotyping studies, the capability to correlate morphological traits such as plant height, stem diameter, leaf length, leaf width, leaf angle and size of panicle of the plants has an important role. However, manual phenotyping of plants is prone to human errors and is labor intensive and time-consuming. Hence, it is important to develop techniques that measure plant phenotypic traits accurately and rapidly. The aim of this study was to determine the feasibility of point cloud data based on a 3D light detection and ranging (LiDAR) system for plant phenotyping. The obtained results were then verified through manually acquired data from the sorghum samples. This study measured the plant height, plant crown diameter and the panicle height and diameter. The R² of each trait was 0.83, 0.94, 0.90, and 0.90, and the root mean square error (RMSE) was 6.8 cm, 1.82 cm, 5.7 mm, and 7.8 mm, respectively. The results showed good correlation between the point cloud data and manually acquired data for plant phenotyping. The results indicate that the 3D LiDAR system has potential to measure the phenotypes of sorghum in a rapid and accurate way.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.1
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• pp.113-119
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• 2023

In this paper, we propose an optimization of a pose estimation deep learning model for recognition of abnormal behavior in unmanned stores using radio frequencies. The radio frequency use millimeter wave in the 30 GHz to 300 GHz band. Due to the short wavelength and strong straightness, it is a frequency with less grayness and less interference due to radio absorption on the object. A millimeter wave radar is used to solve the problem of personal information infringement that may occur in conventional CCTV image-based pose estimation. Deep learning-based pose estimation models generally use convolution neural networks. The convolution neural network is a combination of convolution layers and pooling layers of different types, and there are many cases of convolution filter size, number, and convolution operations, and more cases of combining components. Therefore, it is difficult to find the structure and components of the optimal posture estimation model for input data. Compared with conventional millimeter wave-based posture estimation studies, it is possible to explore the structure and components of the optimal posture estimation model for input data using genetic algorithms, and the performance of optimizing the proposed posture estimation model is excellent. Data are collected for actual unmanned stores, and point cloud data and three-dimensional keypoint information of Kinect Azure are collected using millimeter wave radar for collapse and property damage occurring in unmanned stores. As a result of the experiment, it was confirmed that the error was moored compared to the conventional posture estimation model.

• Journal of the Korea Institute of Building Construction
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• v.23 no.3
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• pp.273-284
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• 2023

This study is centered on the combined use of LiDAR(Light Detection and Ranging) and AR(Augmented Reality) technologies during vertical and horizontal frame measurements in construction projects. The intention is to enhance the quality control procedure, elevate accuracy, and curtail manual labor along with time expenditure. Present methods for accuracy inspection in frame construction often grapple with reliability concerns due to subjective interpretation and the scope for human error. This research recommends the application of LiDAR and AR technologies to counter these issues and augment the efficiency of the inspection process, along with facilitating the dissemination of results. The suggested technique involves the collection of 3D point cloud data of the frame utilizing LiDAR and leveraging this data for checks on construction accuracy. Furthermore, the inspection outcomes are fed into a BIM (Building Information Modeling) model, and the results are visualized via AR. Upon juxtaposing this methodology with the current approach, it is evident that it offers benefits in terms of objective inspection, speed, precise result sharing, and potential enhancements to the overall quality and productivity of construction projects.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.81-81
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• 2022

Recently, many studies on big data based smart farming have been conducted. Research to quantify morphological characteristics using image data from various crops in smart farming is underway. Rice is one of the most important food crops in the world. Much research has been done to predict and model rice crop yield production. The number of productive tillers per plant is one of the important agronomic traits associated with the grain yield of rice crop. However, modeling the basic growth characteristics of rice requires accurate data measurements. The existing method of measurement by humans is not only labor intensive but also prone to human error. Therefore, conversion to digital data is necessary to obtain accurate and phenotyping quickly. In this study, we present an image-based method to predict leaf number and evaluate tiller number of individual rice crop using YOLOv5 deep learning network. We performed using various network of the YOLOv5 model and compared them to determine higher prediction accuracy. We ako performed data augmentation, a method we use to complement small datasets. Based on the number of leaves and tiller actually measured in rice crop, the number of leaves predicted by the model from the image data and the existing regression equation were used to evaluate the number of tillers using the image data.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.4
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• pp.418-425
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• 2014

This paper presents a Ku-Band Doppler Radar System to measure respiration and heart rate. It was measured by using simultaneous radar and ECG(Electrocardiogram). Arctangent demodulation without dc offset compensation can be applied to transmitted I/Q(In-phase & Quadrature-phase) signal in order to improve the RMSE(Root Mean Square Error) about 50 %. The power leaked to receiving antenna from the transmitting antenna is always generated because of continuously opening the transceiver of CW(Continuous Wave) Doppler radar. As the output power increase, leakage power has an effect on the SNR(Signal-to-Noise Ratio) of the system. Therefore, in this paper, leakage cancellation technique that adds the signal having the opposite phase of the leakage power to the leakage power was implemented in order to minimize the decline of receiver sensitivity. By applying the leakage cancellation techniques described above, it is possible to measure the heart rate and respiration of the human at a distance of up to 35 m. the heart rate of the measured data at a distance of 35 m accords with the heart rate extracted from the ECG data.