• Journal of Bio-Environment Control
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• v.11 no.2
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• pp.93-100
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• 2002

This study was carried out not only to develop CFD model for numerically simulating fog cooling system but also to verify the validity of the developed model by data measured in fag cooling greenhouse. In addition the developed model was applied to investigate the effects of spraying water temperature, spraying water amount, spraying interval and evaporation percentage on the performance of the fog cooling system. According to the simulation results, the temperature differences between the measured and predicted temperatures at each measurement point were $0.1~1.4^{\circ}C$ in case of no shading and $0.2~2.3^{\circ}C$ in close of shading. The humidity differences were 0.3~6.0% and 0.7~10.6%, respectively in the cases of no shading and shading. Because the predicted data showed a good agreement with the measured ones, the developed model is supposed to be able to predict the cooling effect of the fog cooling system. The performance of fog cooling system was greatly influenced by spraying water amount, spraying interval and evaporation percentage, but it was not influenced by spraying water temperature.

• Journal of agriculture & life science
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• v.53 no.3
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• pp.147-157
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• 2019

In this study, we tried to find out the most appropriate pre-processing method and to verify the feasibility of developing a low-price sensing system for predicting the hardy kiwis sugar content based on VNIRS and subsequent spectral analysis. A total of 495 hardy kiwi samples were collected from three farms in Muju, Jeollabukdo, South Korea. The samples were scanned with a spectrophotometer in the range of 730-2300 nm with 1 nm spectral sampling interval. The measured data were arbitrarily separated into calibration and validation data for sugar content prediction. Partial least squares (PLS) regression was performed using various combinations of pre-processing methods. When the latent variable (LV) was 8 with the pre-processing combination of standard normal variate (SNV) and orthogonal signal correction (OSC), the highest R2 values of calibration and validation were 0.78 and 0.84, respectively. The possibility of predicting the sugar content of hardy kiwi was also examined at spectral sampling intervals of 6 and 10 nm in the narrower spectral range from 730 nm to 1200 nm for a low-price optical sensing system. The prediction performance had promising results with R2 values of 0.84 and 0.80 for 6 and 10 nm, respectively. Future studies will aim to develop a low-price optical sensing system with a combination of optical components such as photodiodes, light-emitting diodes (LEDs) and/or lamps, and to locate a more reliable prediction model by including meteorological data, soil data, and different varieties of hardy kiwi plants.

• Journal of Biosystems Engineering
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• v.37 no.2
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• pp.122-129
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• 2012

Purpose: Recent mechatronics technology is the most appropriate high technology in agricultural applications to save repetitious labor. Method: Cow's body parameters were measured by several traditional measurers. Image processing technology was used to measure automatically their parameters to reduce labor and time. The parameters were measured form a small model cow which is easily measured, instead to a real cow. The image processing system designed and built for this project was composed of a PC, grabber card, and two cameras, which are located on the side and the top of the model cow. Tests of verification had measured 10 dairy cows. Result: Nine parameters of the model cow's body were measured, and the difference between the real data and the data by image processing was less than 16.7%. Based on the results of the research, the parameters of a real cow had measured of chest depth, withers height, Pelvic arch height, body length, slope body length, chest width, hip width, thurl width, and pin bone width were compared with image processing data. Conclusions: In the Demonstration test, Result had obtained similar data of cow model experiments, and the most of errors were shown less than 5% relatively good result.

• Journal of Biosystems Engineering
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• v.38 no.1
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• pp.64-71
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• 2013

Purpose: The purpose of this study was to select standing posture parameters that have a significant difference according to the severity of spinal deformity, and to develop a novel Cobb angle prediction model for adolescent girls with idiopathic scoliosis. Methods: Five normal adolescents girls with no history of musculoskeletal disorders, 13 mild scoliosis patients (Cobb angle: $10^{\circ}-25^{\circ}$), and 14 severe scoliosis patients (Cobb angle: $25^{\circ}-50^{\circ}$) participated in this study. Six infrared cameras (VICON) were used to acquire data and 35 standing parameters of scoliosis patients were extracted from previous studies. Using the ANOVA and post-hoc test, parameters that had significant differences were extracted. In addition, these standing posture parameters were utilized to develop a Cobb-angle prediction model through multiple regression analysis. Results: Twenty two of the parameters showed differences between at least two of the three groups and these parameters were used to develop the multi-linear regression model. This model showed a good agreement ($R^2$ = 0.92) between the predicted and the measured Cobb angle. Also, a blind study was performed using 5 random datasets that had not been used in the model and the errors were approximately $3.2{\pm}1.8$. Conclusions: In this study, we demonstrated the possibility of clinically predicting the Cobb angle using a non-invasive technique. Also, monitoring changes in patients with a progressive disease, such as scoliosis, will make possible to have determine the appropriate treatment and rehabilitation strategies without the need for radiation exposure.

• Smart Structures and Systems
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• v.8 no.1
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• pp.119-137
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• 2011

A bio-inspired two-mode structural health monitoring (SHM) system based on the Na$\ddot{i}$ve Bayes (NB) classification method is discussed in this paper. To implement the molecular biology based Deoxyribonucleic acid (DNA) array concept in structural health monitoring, which has been demonstrated to be superior in disease detection, two types of array expression data have been proposed for the development of the SHM algorithm. For the micro-vibration mode, a two-tier auto-regression with exogenous (AR-ARX) process is used to extract the expression array from the recorded structural time history while an ARX process is applied for the analysis of the earthquake mode. The health condition of the structure is then determined using the NB classification method. In addition, the union concept in probability is used to improve the accuracy of the system. To verify the performance and reliability of the SHM algorithm, a downscaled eight-storey steel building located at the shaking table of the National Center for Research on Earthquake Engineering (NCREE) was used as the benchmark structure. The structural response from different damage levels and locations was collected and incorporated in the database to aid the structural health monitoring process. Preliminary verification has demonstrated that the structure health condition can be precisely detected by the proposed algorithm. To implement the developed SHM system in a practical application, a SHM prototype consisting of the input sensing module, the transmission module, and the SHM platform was developed. The vibration data were first measured by the deployed sensor, and subsequently the SHM mode corresponding to the desired excitation is chosen automatically to quickly evaluate the health condition of the structure. Test results from the ambient vibration and shaking table test showed that the condition and location of the benchmark structure damage can be successfully detected by the proposed SHM prototype system, and the information is instantaneously transmitted to a remote server to facilitate real-time monitoring. Implementing the bio-inspired two-mode SHM practically has been successfully demonstrated.

• Journal of Biosystems Engineering
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• v.33 no.6
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• pp.438-445
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• 2008

The purpose of this study was to develop a measurement model based on PLS (Partial least square) method for blood pressures. Measurement system for blood pressure signals consisted of pressure sensor, va interface and embedded module. A mercury sphygmomanometer was connected with the measurement system through 3-way stopcock and used as reference of blood pressures. The blood pressure signals of 20 subjects were measured and tests were repeated 5 times per each subject. Total of 100 data were divided into a calibration set and a prediction set. The PLS models were developed to determine the systolic and the diastolic blood pressures. The PLS models were evaluated by the standard methods of the British Hypertension Society (BHS) protocol and the American Association for the Advancement of Medical Instrumentation (AAMI). The results of the PLS models were compared with those of MAA (maximum amplitude algorithm). The measured blood pressures with PLS method were highly correlated to those with a mercury sphygmomanometer in the systolic ($R^2=0.85$) and the diastolic blood pressure ($R^2=0.84$). The results showed that the PLS models were the effective tools for blood pressure measurements with high accuracy, and satisfied the standards of the BHS protocol and the AAMI.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.146-152
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• 2010

In this paper, we implemented the intelligent healthcare system self-diagnosis that can achieve self-diagnosis by measured bio-signal(blood pressure, blood sugar, body fat monitor) after the recognize a user to access using RFID. The implemented healthcare self-diagnosis intelligent system is consist of kiosk structure that is RFID reader, bio-signal measuring instrument(hemadynamometer, glucometer, body fat monitor), computer for a part of database server and printer for print the result of self-diagnosis. It can achieve self-diagnosis of a user after compare and analyze the measured data and information of a user from database. The implemented system can make simple self-diagnosis even if not take a physical examination at hospital and apply to company, school, etc.

• Journal of Physiology & Pathology in Korean Medicine
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• v.28 no.1
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• pp.94-101
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• 2014

We studied to investigate the erythema reduction effects generated by Gagam-Jawoonaek(GJ) application(appl.) after UV exposure. Twenty women in their twenties to fifties with no skin diseases were recruited. We exposed UV as a 6 subsites on the left upper arm of subjects using multi-port solar simulator. After setting Gagam-Jawoonaek(GJ) application(appl.) subsites and non-appl. subsites, we measured erythema degrees($a^*$ values) of the subsites using spectrophotometer. We measured $a^*$ values four times(before UV exposure, before application of GJ, twenty-four and forty-eight hours after first application of GJ). We analyzed data using student's t-test. After UV exposure, $a^*$ values on the left upper arm increased. Twenty-four hours after first GJ treat., the changes of $a^*$ value on GJ treat. subsites($1.22{\pm}0.13AU$) were bigger than GJ non-treat. subsites($1.04{\pm}0.12AU$), but there was no statistically significance. Forty-eight hours after first GJ treat., the changes of $a^*$ value on GJ treat. subsites($1.95{\pm}0.11AU$) were bigger than GJ non-treat. subsites($1.58{\pm}0.13AU$), a statistically significance. Gagam-Jawoonaek could decrease erythema by UV exposure.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.548-551
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• 2003

The adaptive filter is proposed for removing EOG from measured EEG on the frontal lobe. The proposed adaptive filter has been implemented and the feedback output control algorithm has been employed to control the alpha wave ratio on the basis of TMS320C31 DSP board with the on-line and real time performance. The feedback algorithm controls the input voltage of stimulating devices on the portable bio-feedback system. The EEG data are acquired at the $F_{p1}$ and $F_{p2}$ localization and are processed by the proposed adaptive filter. We demonstrated that the proposed adaptive filter could effectively remove EOG from the measured EEG on the frontal lobe and the feedback algorithm is proper to control the output voltage of DSP board using the ratio of the alpha wave.

• The Journal of Korean Physical Therapy
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• v.32 no.6
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• pp.365-371
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• 2020

Objective: To investigate the action observation effects of functional electrical stimulation (FES) on the communication between motor cortex and muscle through corticomuscular coherence (CMC) analysis. Methods: Electroencephalogram (EEG) and electromyogram (EMG) of 27 healthy, nonathlete subjects were measured during action observation, FES, and action observation with FES, which lasted for 7sper session for 10 times. All trials were repeated for 30 times. Simultaneously measured EEG raw data and rectified EMG signals were used to calculate CMC. Only confidence limit values above 0.0306 were used for analysis. CMC was divided into three frequency domains, andthe grand average coherence and peak coherence were computed. Repeated ANOVA was performed to analyze the coherence value difference for each condition's frequency band. Results: CMC showed significant differences in peak coherence and average coherence between the conditions (p<0.05). Action observation application with FES in all frequency band showed the highest peak and average coherence value. Conclusions: The results of this study are assumed to be the combination of increased eccentric information transfer from the sensorymotor cortex by action observation and an increased in concentric sensory input from the peripheral by the FES, suggesting that these are reflecting the sensorimotor integration process.