• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.460-465
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• 2003

High-speed machining is one of the most effective technology to improve productivity. Because of the high speed and high feed rate, high-speed machining can give great advantages fur the machining of dies and molds. This paper describes on the improvement of machining accuracy in high-speed machining and an estimate about machining accuracy of high-speed machining.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.3
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• pp.327-334
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• 1988

In this paper, the performance of PRA (pel recurdive algorithm) and BMA(block matching algorithm), which are the most well-known motion estimation techniques, is compared and the effects of the motion estimation accuracy on the motion compensated coding are described. Results of computer simulation on the real images indicate that the TSS (three step search), which is one of the BMA,is slightly better than the PRA in terms of the accuracy however, the required bit rate is 6.6-8.2 Kbps higher that of the PRA because the TSS requires a transmission of motion estimation vectors.

• Journal of electromagnetic engineering and science
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• v.8 no.2
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• pp.84-89
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• 2008

In a wireless channel above microwave frequency, rain attenuation is very important. In order to predict rain attenuation, 1-min. rain rate distribution is required. This paper discusses appropriate conversion methods to estimate 1-minute rain rate from that of other integration time. Based on the measurement data filed in ITU-R WP3J including ETRI data for 6 consecutive years, distributions of rain rate with 1-, 5-, 10-, 20-, 30-minute integration time were analyzed, both on the global and regional basis, and the parametric relationship between the statistical characteristics of 1-minute and other measurement data were investigated to deduce the conversion methods. It is shown that the global model works good with good accuracy for 5-, 10-, 20-min integration time, and the global model is also applicable globally with good accuracy for 5-, 10-, 20-min integration time. The global conversion model was adopted last year as an ITU-R document for new recommendation. The regional conversion model would also be very useful for locations of similar climatic zone.

• The KSFM Journal of Fluid Machinery
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• v.2 no.2 s.3
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• pp.19-26
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• 1999

Thermal output in a nuclear power plant is verified with calorimetric heat balance on the secondary plant. The calorimetry involves the precise measurement of the feedwater flow rate. However, the correct indication of feedwater flow rate obtained by a pressure-difference measurement across a venturi can be affected by instrument errors, fouling or a poorly developed velocity profile. This can result in an inaccurate mass flow rate and consequently an inaccurate estimate of power. The purpose of this study is to develop verification methods with accuracy better than $0.5\%$ for high precision flow measurement to be used for measuring feedwater flow rate. This chemical tracer method is a testing process that uses tracers which can be applied to quantify losses in electrical output due to the incorrect measurements of feedwater flow rate. And this system has good response to the variation of the flow rate. Accuracy of better than 0.5 percent can be expected for feedwater flow measurement, providing that the system can be stabilized during the test. This methodology is applicable to other flow systems well.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.2
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• pp.51-58
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• 2023

This paper proposes a deep learning method for estimating the heart rate from facial videos. Our proposed method estimates remote photoplethysmography (rPPG) signals to predict the heart rate. Although there have been proposed several methods for estimating rPPG signals, most previous methods can not be utilized in low-power single board computers due to their computational complexity. To address this problem, we construct a lightweight student model and employ a knowledge distillation technique to reduce the performance degradation of a deeper network model. The teacher model consists of 795k parameters, whereas the student model only contains 24k parameters, and therefore, the inference time was reduced with the factor of 10. By distilling the knowledge of the intermediate feature maps of the teacher model, we improved the accuracy of the student model for estimating the heart rate. Experiments were conducted on the UBFC-rPPG dataset to demonstrate the effectiveness of the proposed method. Moreover, we collected our own dataset to verify the accuracy and processing time of the proposed method on a real-world dataset. Experimental results on a NVIDIA Jetson Nano board demonstrate that our proposed method can infer the heart rate in real time with the mean absolute error of 2.5183 bpm.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.447-450
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• 2007

Positioning technology for moving object is an important and essential component of ubiquitous. Also RFID(Radio Frequency IDentification) is a core technology of ubiquitous wireless communication. In this study we adapted kalman-filter theory to RFID-based Positioning System in order to trace a time-variant moving object and verify the positioning accuracy using RMSE (Roong technology for moving object is an important and essential component of ubiquitous Mean Square Error). The purpose of this study is to verify an effect of kalman-filter on the positioning accuracy and to analyze what does each design factor have an effect on the positioning accuracy by means of simulations and to suggest a standard of optimal design factor of a RFID-based Positioning System. From the results of simulations, Kalman-filer improved the positioning accuracy remarkably; the detection range of RFID tag is not a determining factor. The smaller standard deviation of detection range improves the positioning accuracy. However it accompanies a smaller fluctuation of the positioning accuracy. The larger detection rate of RFID tag yields the smaller fluctuation in the positioning accuracy and has more stable system and improves the positioning accuracy;

• Journal of Physiology & Pathology in Korean Medicine
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• v.25 no.6
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• pp.1113-1118
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• 2011

In this paper, when a physician make a diagnosis of the pattern identification (PI) in Korean stroke patients, the development methods of the PI classification function is considered by diagnostic questionnaire of the PI for stroke patients. Clinical data collected from 1,502 stroke patients who was identically diagnosed for the PI subtypes diagnosed by two physicians with more than 3 years experiences in 13 oriental medical hospitals. In order to develop the classification function into PI using Korean Stroke Syndrome Differentiation Standard was consist of the 44 items (Fire heat(19), Qi deficiency(11), Yin deficiency(7), Dampness-phlegm(7)). Using the 44 items, we took diagnostic and prediction accuracy rate through of discriminant model. The overall diagnostic and prediction accuracy rate of the PI subtypes for discriminant model was 74.37%, 70.88% respectively.

• ETRI Journal
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• v.40 no.1
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• pp.122-132
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• 2018

This paper proposes a hierarchical dual filtering (HDF) algorithm to estimate the spatial region between a Cloud of Things (CoT) gateway and an Internet of Things (IoT) device. The accuracy of the spatial region estimation is important for autonomous CoT clustering. We conduct spatial region estimation using a hidden Markov model (HMM) with a raw Bluetooth received signal strength indicator (RSSI). However, the accuracy of the region estimation using the validation data is only 53.8%. To increase the accuracy of the spatial region estimation, the HDF algorithm removes the high-frequency signals hierarchically, and alters the parameters according to whether the IoT device moves. The accuracy of spatial region estimation using a raw RSSI, Kalman filter, and HDF are compared to evaluate the effectiveness of the HDF algorithm. The success rate and root mean square error (RMSE) of all regions are 0.538, 0.622, and 0.75, and 0.997, 0.812, and 0.5 when raw RSSI, a Kalman filter, and HDF are used, respectively. The HDF algorithm attains the best results in terms of the success rate and RMSE of spatial region estimation using HMM.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.7
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• pp.31-39
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• 2014

Recently, as Intelligent Transportation System (ITS) and self-driving system become influential in the ground transportation system, automotive radar systems have been actively studied among the various radar systems to implement the vehicle collision detection system and distance measurement system between vehicles. Most of the automotive radars are Frequency Modulated Continuous Wave (FMCW) radar type which can calculate distance and velocity of target by estimating the frequency difference between the transmitted signal and received signal. Therefore, accurate frequency estimation is very important in the FMCW radar system. For this reason, to improve the measurement accuracy of the FMCW radar, Reverse Directional FRI (RD-FRI) Super-Resolution technique which has high frequency estimation accuracy is applied to the FMCW radar system. The feasibility of the proposed technique is evaluated with simulation results and compared with FFT and conventional Super-Resolution techniques. The simulation results show that the proposed technique estimates the frequency with high accuracy and the distance with centimeter accuracy.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.6
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• pp.289-296
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• 2018

Drone detection in FMCW radar system needs complex techniques because a drone beat frequency is highly dynamic and unpredictable. Therefore, the current static signal processing algorithms cannot show appropriate detection accuracy. With dynamic signal fluctuation and environmental clutters, it can fail to detect a drone or make false detection. It affects to the radar system integrity and safety. Constant false alarm rate (CFAR), one of famous static signal process algorithm is effective for static environment. But for drone detection, it shows low detection accuracy. In this paper, we suggest neural network based FMCW radar system for detecting a drone. We use recurrent neural network (RNN) because it is the effective neural network for signal processing. In our FMCW radar system, one transmitter emits FMCW signal and four-way fixed receivers detect reflected drone beat frequency. The coordinate of the drone can be calculated with four receivers information by triangulation. Therefore, RNN only learns and inferences reflected drone beat frequency. It helps higher learning and detection accuracy. With several drone flight experiments, RNN shows false detection rate and detection accuracy as 21.1% and 96.4%, respectively.