• Journal of Sensor Science and Technology
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• v.25 no.1
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• pp.20-26
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• 2016

The bioelectrical impedance (BI) for the young and the elderly was measured using bioelectrical impedance spectroscopy (BIS). First, while applying a current of $600{\mu}A$ to the foot and hand, BI was measured at 50 frequencies ranging from 5 to 1000 kHz. The BI for young subjects was considerably lower than that for old subjects since young subjects have more lean mass (hydration). The prediction marker was 0.74 for young subjects and 0.78 for old subjects. Second, a Cole-Cole diagram was obtained for young subjects and old subjects, indicating the different characteristic frequencies. At 50 kHz, the average phase angle was $7.8^{\circ}$ for young subjects whereas that was $6.1^{\circ}$ for old subjects. Third, BIVA was analyzed for young subjects and old subjects. The vector length was 210.89 [${\Omega}/m$] for young subjects and 326.12 [${\Omega}/m$] for old subjects. At 50 kHz, the resistance (R/H) and the reactance ($X_C/H$) divided by height were 208.94 [${\Omega}/m$] and 28.68 [${\Omega}/m$] for young subject, and 324.33 [${\Omega}/m$] and 34.09 [${\Omega}/m$] for old subjects.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.3
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• pp.53-60
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• 2009

The vision-based driver fatigue detection is one of the most prospective commercial applications of facial expression recognition technology. The facial feature tracking is the primary technique issue in it. Current facial tracking technology faces three challenges: (1) detection failure of some or all of features due to a variety of lighting conditions and head motions; (2) multiple and non-rigid object tracking; and (3) features occlusion when the head is in oblique angles. In this paper, we propose a new active approach. First, the active IR sensor is used to robustly detect pupils under variable lighting conditions. The detected pupils are then used to predict the head motion. Furthermore, face movement is assumed to be locally smooth so that a facial feature can be tracked with a Kalman filter. The simultaneous use of the pupil constraint and the Kalman filtering greatly increases the prediction accuracy for each feature position. Feature detection is accomplished in the Gabor space with respect to the vicinity of predicted location. Local graphs consisting of identified features are extracted and used to capture the spatial relationship among detected features. Finally, a graph-based reliability propagation is proposed to tackle the occlusion problem and verify the tracking results. The experimental results show validity of our active approach to real-life facial tracking under variable lighting conditions, head orientations, and facial expressions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.603-607
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• 2004

The vision-based driver fatigue detection is one of the most prospective commercial applications of facial expression recognition technology. The facial feature tracking is the primary technique issue in it. Current facial tracking technology faces three challenges: (1) detection failure of some or all of features due to a variety of lighting conditions and head motions; (2) multiple and non-rigid object tracking and (3) features occlusion when the head is in oblique angles. In this paper, we propose a new active approach. First, the active IR sensor is used to robustly detect pupils under variable lighting conditions. The detected pupils are then used to predict the head motion. Furthermore, face movement is assumed to be locally smooth so that a facial feature can be tracked with a Kalman filter. The simultaneous use of the pupil constraint and the Kalman filtering greatly increases the prediction accuracy for each feature position. Feature detection is accomplished in the Gabor space with respect to the vicinity of predicted location. Local graphs consisting of identified features are extracted and used to capture the spatial relationship among detected features. Finally, a graph-based reliability propagation is proposed to tackle the occlusion problem and verify the tracking results. The experimental results show validity of our active approach to real-life facial tracking under variable lighting conditions, head orientations, and facial expressions.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.4
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• pp.545-552
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• 2013

This paper is a part of developing a computer code that can be used to generate IR images of a naval ship by considering the emitted and reflected infrared signals. The spectral radiance received by an IR sensor is consisted of the self-emitted component from the ship surface, the reflected component of the solar/sky irradiance at the ship surface, the emitted radiance from the ship surface and the exhaust plume gas, and the scattered radiance by the atmosphere. The plume gas radiance occupies a large part of the emitted radiance from a naval ship in operation. Therefore plume gas radiance must be taken into account when calculating the radiance from a naval ship for reliable IR images. In this paper, IR images of a naval ship with the exhaust gas effect in various environmental conditions are generated by using an exhaust gas prediction model called the JPL model. The contrast radiance (CR) values of the IR images are calculated to analyze the effect of the exhaust gas radiance quantitatively. The results obtained by quantitative analysis show that the IR signatures with the exhaust plume gas are 2.26 times larger than those neglecting the plume gas effect. The effect of the exhaust plume gas is shown to be more eminent in winter than in summer in the daytime.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.464-464
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• 2010

A few years ago, for maintaining high stability and production yield of production equipment in a semiconductor fab, on-line monitoring of wafers is required, so that semiconductor manufacturers are investigating a software based process controlling scheme known as virtual metrology (VM). As semiconductor technology develops, the cost of fabrication tool/facility has reached its budget limit, and reducing metrology cost can obviously help to keep semiconductor manufacturing cost. By virtue of prediction, VM enables wafer-level control (or even down to site level), reduces within-lot variability, and increases process capability, $C_{pk}$. In this research, we have practiced VM on $SiO_2$ etch rate with optical emission spectroscopy(OES) data acquired in-situ while the process parameters are simultaneously correlated. To build process model of $SiO_2$ via, we first performed a series of etch runs according to the statistically designed experiment, called design of experiments (DOE). OES data are automatically logged with etch rate, and some OES spectra that correlated with $SiO_2$ etch rate is selected. Once the feature of OES data is selected, the preprocessed OES spectra is then used for in-situ sensor based VM modeling. ICP-RIE using 葰.56MHz, manufactured by Plasmart, Ltd. is employed in this experiment, and single fiber-optic attached for in-situ OES data acquisition. Before applying statistical feature selection, empirical feature selection of OES data is initially performed in order not to fall in a statistical misleading, which causes from random noise or large variation of insignificantly correlated responses with process itself. The accuracy of the proposed VM is still need to be developed in order to successfully replace the existing metrology, but it is no doubt that VM can support engineering decision of "go or not go" in the consecutive processing step.

• Journal of Korean Society on Water Environment
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• v.23 no.2
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• pp.236-243
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• 2007

Fluorescence measurements of dissolved organic matter (DOM) have the superior advantages over other analysis tools for the applications to water quality management due to their rapid analysis. It is known that protein-like fluorescence characteristics are well corelated with microbial activities and biodegradable organic matter. In this study, potential biochemical oxygen demand (BOD) predictor were explored using the fluorescence peak intensities and/or the integrated fluorescence intensities derived from synchronous fluorescence spectra and the first derivative spectra of river samples. A preliminary study was conducted using a mixture of a river and a treated sewage to test the feasibility of the approach. It was demonstrated that the better BOD predictor can be derived from synchronous fluorescence spectra and the derivatives when the difference between the emission and the excitation wavelengths (${\Delta}{\gamma}$) was large. The efficacy of several selected fluorescence parameters was rivers in Seoul. The fluorescence parameters exhibited relatively good correlation coefficients with the BOD values, ranging from 0.59 to 0.90. Two parameters were suggested to be the optimum BOD predictors, which were a fluorescence peak at a wavelength of 283 nm from the synchronous spectrum at the ${\Delta}{\gamma}$ value of 75 nm, and the integrated fluorescence intensity of the first derivatives of the spectra at the wavelength range between 245 nm and 280 nm. Each BOD predictor showed the correlation coefficients of 0.89 and 0.90, respectively. It is expected that the results of this study will provide important information to develop a real-time efficient sensor for river BOD in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2315-2322
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• 2008

The requesting a consultation of the farm manager is about the diagnosis and prevention of the breeding and extermination for diseases and insects in greenhouse, the managing problem for diseases and insects turn up a main issue. To solve these problems, this paper proposes a PDA based greenhouse, diseases and insects management system for mobile(GDIMS) uses as keeping up with ubiquitous time, which makes prediction and management for diseases and insects more efficiently checked at any time and anywhere you want to, and go well with the motto of ubiquitous. This system is using the environmental data from the greenhouse attached sensors provide the accurate diagnosis and recipes, which supports to product clean crops. There are no need to visit the greenhouse because our system is based on mobile devices that obtain the information in the greenhouse, which makes management in efficient with little number of people. This wort builds simply virtual greenhouse model that assembles system component of environmental sensor for performance analysis and offers a PDA view of the greenhouse status.

• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.33-39
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• 2019

In this paper, we propose a parametric model for analyzing the motion information obtained from the acceleration sensors to measure the activity of the human body. The motion of the upper body and the lower body does not occur at the same time, and the motion analysis method using a single motion sensor involves a lot of errors. In this study, the 3-axis accelerometer is attached to the arms and legs, the body's activity data are measured, the momentum of the arms and legs are calculated for each channel, and the linear predictive coefficient is obtained for each channel. The periodicity of the upper body and the lower body is determined by analyzing the correlation between the channels. The linear predictive coefficient and the periodic value are used as data to measure the type of exercise and the amount of exercise. In the proposed method, we measured four types of movements such as walking, stair climbing, slow hill climbing, and fast hill descending. In order to verify the usefulness of the parameters, the recognition results are presented using the linear predictive coefficient and the periodic value for each motion as the neural network input.

• Physical Therapy Rehabilitation Science
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• v.7 no.2
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• pp.54-60
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• 2018

Objective: Persons with chronic stroke fall more often than healthy elderly individuals. The Timed Up and Go test (TUG) is used as a fall prediction tool, but only provides a result for the total measurement time. This study aimed to determine the optimal cut-off values for each of the 6 components of the TUG. Design: Retrospective study. Methods: Thirty persons with chronic stroke participated in the study. TUG evaluation was performed using a wearable miniaturized inertial sensor. Sensitivity, specificity, and predictive values were calculated using the Receiver Operating Characteristic (ROC) curve analysis for the measured values in each section. Optimal values for fall risk classification were determined. Logistic regression analysis was used to investigate the risk of future falls based on TUG. Results: The cut-off values of the 6 sections of the TUG were determined, as follows: sit-to-stand >2.00 seconds (p<0.05), forward gait >4.68 seconds (p<0.05), mid-turn >3.82 seconds (p<0.05), return gait >4.81 seconds (p<0.05), end-turn >2.95 seconds (p<0.05), and stand-to-sit >2.13 seconds (p<0.05). The risk of falling increased by 2.278 times when the mid-turn value was >3.82 seconds (p<0.05). Conclusions: The risk of falls increased by 2.28 times when the value of the mid-turn interval exceeded 3.82 seconds. Therefore, when interpreting TUG results, the predictive accuracy for falls will be higher when the measurement time for each section is analyzed, together with the total time for TUG.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.139-145
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• 2021

In this paper, by using the cattle feed intake, rumination, and in heat monitoring technology, RFI (Residual Feed Intake) monitoring and wearable devices and PCs for predicting abnormalities in budding target web and smart A monitoring system using a phone application was designed and implemented. With the development of this system, the farmer is expected to increase economic efficiency. By analyzing the feed intake, it is possible to identify the difference between the recommended feed amount based on the cow's weight and the feed amount consumed by the cow, and it is expected that early detection of metabolic disorders (abnormality of metabolism) is possible. Farmers using the results of this thesis can distinguish the cows with the most efficient performance, and the 6-axis motion sensor signals input from the wearable device attached to the cow's skin (neck) and the microphone attached to the wearable device. It is possible to measure the cow's rumination and feed intake through the sound of the cow's throat. In the future, improvements will be made to measure additional vital signs such as heart rate and respiration.