• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.6
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• pp.182-190
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• 2004

This paper presents reconstruction analysis of vehicle trajectory using records of a developed black box, and results of validation tests. For reconstruction of vehicle trajectory, the black box records the longitudinal and lateral accelerations and yaw-rate of vehicle during a pre-defined time period before and after the accident. One 2-axis accelerometer is used for measuring accelerations, and one vibrating structure type gyroscope is used for measuring yaw-rate of vehicle. The vehicle's planar trajectory can be reconstructed by integrating twice accelerations along longitudinal and lateral directions with yaw-rate values. However, there may be many kinds of errors in sensor measurements. The causes of errors are as follows: mis-alignment, low frequency offset drift, high frequency noise, and projecting 3-dimensional motion into 2-dimensional motion. Therefore, some procedures are taken for error compensation. In order to evaluate the reliability and the accuracy of trajectory reconstruction results, the black box was mounted on a passenger car. The vehicle was driven and tested along various specified lanes. Through the tests, the accuracy and usefulness of the reconstruction analysis have been validated.

• Journal of Climate Change Research
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• v.5 no.4
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• pp.323-329
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• 2014

Since the terrain of Korea is complex, micro- as well as meso-climate variability is extreme by locations in Korea. In particular, air temperature of agricultural fields is influenced by topographic features of the surroundings making accurate interpolation of regional meteorological data from point-measured data. This study was carried out to compare spatial interpolation methods to estimate air temperature in agricultural fields surrounded by rugged terrains in South Korea. Four spatial interpolation methods including Inverse Distance Weighting (IDW), Spline, Ordinary Kriging (with the temperature lapse rate) and Cokriging were tested to estimate monthly air temperature of unobserved stations. Monthly measured data sets (minimum and maximum air temperature) from 588 automatic weather system(AWS) locations in South Korea were used to generate the gridded air temperature surface. As the result, temperature lapse rate improved accuracy of all of interpolation methods, especially, spline showed the lowest RMSE of spatial interpolation methods in both maximum and minimum air temperature estimation.

• Nuclear Engineering and Technology
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• v.54 no.9
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• pp.3398-3402
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• 2022

We fabricated a 15 m long position-sensitive plastic scintillation optical fiber (PSOF) detector consisting of a PSOF, two photomultiplier tubes, four fast amplifiers, and a digitizer. A single PSOF was used as a sensing part to estimate the gamma-ray source position, and ¹³⁷Cs, an uncollimated solid-disk-type radioactive isotope, was used as a gamma-ray emitter. To improve the sensitivity, accuracy, and measurement time of a PSOF detector compared to those of previous studies, the performance of the amplifier was optimized, and the digital signal processing (DSP) was newly designed in this study. Moreover, we could measure very low dose rates of gamma-rays with high sensitivity and accuracy in a very short time using our proposed PSOF detector. The results of this study indicate that it is possible to accurately and quickly locate the position of a very low dose rate gamma-ray source in a wide range of contaminated areas using the proposed position-sensitive PSOF detector.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.61-66
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• 2022

Object classification is one of the main fields in neural networks and has attracted the interest of many researchers. Although there have been vast advancements in this area, still there are many challenges that are faced even in the current era due to its inefficiency in handling large data, linguistic and dimensional complexities. Powerful hardware and software approaches in Neural Networks such as Deep Neural Networks present efficient mechanisms and contribute a lot to the field of object recognition as well as to handle time series classification. Due to the high rate of accuracy in terms of prediction rate, a neural network is often preferred in applications that require identification, segmentation, and detection based on features. Neural networks self-learning ability has revolutionized computing power and has its application in numerous fields such as powering unmanned self-driving vehicles, speech recognition, etc. In this paper, the experiment is conducted to implement a neural approach to identify numbers in different formats without human intervention. Measures are taken to improve the efficiency of the machines to classify and identify numbers. Experimental results show the importance of having training sets to achieve better recognition accuracy.

• Journal of the Korean Society of School Health
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• v.27 no.3
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• pp.130-139
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• 2014

Purpose: The object of this study was to compare the educational effect about self-efficacy and the quality of chest compressions of Hands-only CPR and Basic CPR. Methods: It's a nonequivalent control group pre-post repeated quasi-experiment study conducted with entire fifth grade students belong to one school in H city. The study participants are 68 persons, and data were collected from December 2, 2013 to February 7, 2014. Self-efficacy was measured by 10 items, and the quality of chest compressions was measured by 5 variables which are average compression depth(mm), average rate (n/min), average count per minutes (n), abnormal placement (n), compression accuracy (%). Results: Self-efficacy of the experimental group and control group showed no significant difference but showed significant difference over time and was the highest at posttest 1 (immediately after education), the lowest at pretest (before education), middle at posttest 2 (8weeks after education) (p<.001). Experimental group was significantly higher than control group in average rate per minute. At posttest 1, experimental group was $130.0{\pm}9.38$ times, control group was $95.1{\pm}11.82$ times. At posttest2, experimental group was $124.0{\pm}14.89$ times, control group was $90.8{\pm}14.89$ times.(p<.001). Average rate (n/min) was significantly declined at control group in the quality of chest compressions over time (t=-2.400, p=.022). Average count per minute and compression accuracy were declined significantly so it were not maintained to posttest2. Conclusion: We need continuous CPR education because self-efficacy of CPR getting lower significantly over time. Hands-only CPR can't be seen as a way to increase the CPR ability of elementary school students having difficulty to perform artificial breathing. And, because the effect of education is not maintained 8wks after training, the technique centered repeated training is needed and a method which can increase compression accuracy is also needed.

• Journal of Advanced Navigation Technology
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• v.13 no.4
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• pp.482-489
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• 2009

This paper proposes a CW(Continuous-Wave) bio-radar applying a new adaptive noise canceller based on ALE(Adaptive Line Enhancer) which can remove the Gaussian noise and system noise. Recently the research works on this CW bio-radar which can be used to detect heartbeat and respiration are advanced by the university and research facility. Although the researches describe CW bio-radar not only is vulnerable for the Gaussian noise but also has a disadvantage of decreasing the heart-rate accuracy due to the noise, the researches do not demonstrate the effective method for removing the noise component in a baseband signal. In this paper, a CW bio-radar applying the new adaptive noise canceller based on ALE which can remove the noise component is proposed. This paper compares and analyzes the performance for increasing the heart-rate accuracy according to removing the Gaussian noise and system noise in the baseband signal through the quadrature receiver which can alleviate the demodulation sensitivity to target position.

• Journal of IKEEE
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• v.18 no.4
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• pp.478-484
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• 2014

In this paper, we propose new algorithm for positioning of mouse cursor using fingertip direction on kinect depth camera. The proposed algorithm uses center of parm points from distance transform when fingertip point toward screen. Otherwise, algorithm use fingertip points. After image preprocessing, the center of parm points is calculated from distance transform results. If the direction of the finger towards the camera becomes close to the distance between the fingertip point and center of parm point, it is possible to improve the accuracy of positioning by using the center of parm point. After remove arm on image, the fingertip points is obtained by using a pixel on the long distance from the center of the image. To calculate accuracy of mouse positioning, we selected any 5 points. Also, we calculated error rate between reference points and mouse points by performed 500 times. The error rate results could be confirmed the accuracy of our algorithm indicated an average error rate of less than 11%.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.11-23
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• 2020

Sampling design should provide statistics to meet a given accuracy while saving cost and time. However, a large number of non-responses are occurring due to the deterioration of survey circumstances, which significantly reduces the accuracy of the survey results. Non-responses occur for a variety of reasons. Chung and Shin (2017, 2019) and Min and Shin (2018) found that the accuracy of estimation is improved by removing the bias caused by non-response when the response rate is an exponential or linear function of variable of interests. For that case they assumed that the error of the super population model follows normal distribution. In this study, we proposed a non-response bias adjusted estimator in the case where the error of a super population model follows the gamma distribution or the log-normal distribution in a business survey. We confirmed the superiority of the proposed estimator through simulation studies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.478-485
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• 2021

An injection locking technology of a semiconductor laser is a promising technology to generate optical complex signals by adjusting optical injection parameters. The extraction of the precise injection parameters plays a key role in the generation of the optical complex signal. Rate equations of semiconductor lasers under optical injection are commonly used to map the injection parameters and the corresponding optical complex signal. The accuracy of the generated optical complex signal on the injection parameters is limited since the rate equations require a locking map-based interpolation method. We propose a novel analytic method, namely rate equation-based direct extraction method, to directly calculate the injection parameters without relying on the locking map-based interpolation method. We achieved 103-times improvement of the signal accuracy by using the proposed method compared to locking-map based interpolation method.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.111-118
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• 2022

In computer vision research, the two-dimensional human pose is a very extensive research direction, especially in pose tracking and behavior recognition, which has very important research significance. The acquisition of human pose targets, which is essentially the study of how to accurately identify human targets from pictures, is of great research significance and has been a hot research topic of great interest in recent years. Human pose recognition is used in artificial intelligence on the one hand and in daily life on the other. The excellent effect of pose recognition is mainly determined by the success rate and the accuracy of the recognition process, so it reflects the importance of human pose recognition in terms of recognition rate. In this human body gesture recognition, the human body is divided into 17 key points for labeling. Not only that but also the key points are segmented to ensure the accuracy of the labeling information. In the recognition design, use the comprehensive data set MS COCO for deep learning to design a neural network model to train a large number of samples, from simple step-by-step to efficient training, so that a good accuracy rate can be obtained.