• Journal of IKEEE
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• v.23 no.2
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• pp.739-742
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• 2019

In this paper, we propose enhanced DAP(Dose Area Product). The development of enhanced DAP proposed in this paper has optimized the area dose meter that was developed previously. The development of enhanced DAP performed Optimized design of charge integrator and ADC circuit, optimization of line transceiver for RS-485 communication, optimization of display circuit, and optimization of PC-based control program for interlocking and aging. As a result of evaluating the performance of the proposed system in an accredited testing laboratory, Radiation dose dependence and Radiation quality dependence were measured to be 4.2%, which is below ${\pm}15%$ of international standard. Energy range/Tube voltage was confirmed in the range of 30~150kV. The sensitivity difference between sensor field and sensor field area dose sensitivity was measured to be 4.3%, and it was confirmed that it operates normally under ${\pm}15%$ of international standard. In order to measure the reproducibility of the area dosimeter, it was confirmed that it was 0% and it was operated normally at less than 2% of IEC60580 recommendation. Digital resolution was confirmed to be a minimum unit of $0.01{\mu}Gy{\cdot}m^2$ within the error range for the reference dose per hour.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.207-213
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• 2019

The fuel-cut coast-down driving mode is activated when the acceleration pedal is released with transmission gear engaged, and it's a default function for electronic-controlled engine of vehicles. The fuel economy becomes better because fuel injection stops during fuel-cut driving mode. A fuel-cut detection method is suggested in the study and it's based on the speed, acceleration and road gradient data from GPS sensor. It detects fuel-cut driving mode by comparing calculated acceleration and realtime acceleration value. The one is estimated with driving resistance in the condition of fuel-cut driving and the other is from GPS sensor. The detection accuracy is about 80% when the method is verified with road driving data. The result is estimated with 9,600 data set of vehicle speed, acceleration, fuel consumption and road gradient from test driving on the road of 12km during 16 minutes, and the road slope is rather high. It's easy to detect fuel-cut without injector signal obtained by connecting wire. The detection error is from the fact that the variation range of speed, acceleration and road gradient data, used for road resistance force, is larger than the value of fuel consumption data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.101-107
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• 2020

Road surface measurement is an essential process for quantifying the degree and displacement of roughness in road surface management. For safer road surface management and quick maintenance, it is important to accurately measure the road surface while mounted on a vehicle. In this paper, we propose a sophisticated road surface measurement system that can be measured on a moving vehicle. The proposed road surface measurement system supports more accurate measurement of the road surface by using a high-performance line laser sensor. It is also possible to measure the transverse and longitudinal profile by matching the position information acquired from the RTK, and the velocity adaptive update algorithm allows a manager to monitor in a real-time manner. In order to evaluate the proposed system, the Gocator laser sensor, MRP module, and NVIDIA Xavier processor were mounted on a test mobile and tested on the road surface. Our evaluation results demonstrate that our system measures accurate profile base on the MSE. Our proposed system can be used not only for evaluating the condition of roads but also for evaluating the impact of adjacent excavation.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.57-65
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• 2018

Conventional RF signal-based indoor localization techniques such as BLE or Wi-Fi based fingerprinting method show considerable localization errors even in small-scale indoor environments due to unstable received signal strength(RSS) of RF signals. Therefore, it is difficult to apply the existing RF-based fingerprinting techniques to large-scale indoor environments such as airports and department stores. In this paper, instead of RF signal we use the geomagnetic sensor signal for indoor localization, whose signal strength is more stable than RF RSS. Although similar geomagnetic field values exist in indoor space, an object movement would experience a unique sequence of the geomagnetic field signals as the movement continues. We use a deep neural network model called the recurrent neural network (RNN), which is effective in recognizing time-varying sequences of sensor data, to track the user's location and movement path. To evaluate the performance of the proposed geomagnetic field based indoor positioning system (IPS), we constructed a magnetic field map for a campus testbed of about $94m{\times}26$ dimension and trained RNN using various potential movement paths and their location data extracted from the magnetic field map. By adjusting various hyperparameters, we could achieve an average localization error of 1.20 meters in the testbed.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.397-418
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• 2024

The Land Surface Temperature (LST) is a crucial parameter in identifying drought. It is essential to identify how LST can increase its accuracy, particularly in mountainous and hill areas. Increasing the LST accuracy can be achieved by applying early data processing in the correction phase, specifically in the context of topographic correction on the Lambertian model. Empirical evidence has demonstrated that this particular stage effectively enhances the process of identifying objects, especially within areas that lack direct illumination. Therefore, this research aims to examine the application of the Lambertian model in estimating LST using the Multi-Channel Method (MCM) across various physiographic regions. Lambertian model is a method that utilizes Lambertian reflectance and specifically addresses the radiance value obtained from Sun-Canopy-Sensor(SCS) and Cosine Correction measurements. Applying topographical adjustment to the LST outcome results in a notable augmentation in the dispersion of LST values. Nevertheless, the area physiography is also significant as the plains terrain tends to have an extreme LST value of ≥ 350 K. In mountainous and hilly terrains, the LST value often falls within the range of 310-325 K. The absence of topographic correction in LST results in varying values: 22 K for the plains area, 12-21 K for hilly and mountainous terrain, and 7-9 K for both plains and mountainous terrains. Furthermore, validation results indicate that employing the Lambertian model with SCS and Cosine Correction methods yields superior outcomes compared to processing without the Lambertian model, particularly in hilly and mountainous terrain. Conversely, in plain areas, the Lambertian model's application proves suboptimal. Additionally, the relationship between physiography and LST derived using the Lambertian model shows a high average R² value of 0.99. The lowest errors(K) and root mean square error values, approximately ±2 K and 0.54, respectively, were achieved using the Lambertian model with the SCS method. Based on the findings, this research concluded that the Lambertian model could increase LST values. These corrected values are often higher than the LST values obtained without the Lambertian model.

• Korean Journal of Optics and Photonics
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• v.19 no.6
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• pp.416-421
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• 2008

A scheme for predicting the concentration of a glucose solution based on its relative optical absorbance at multiple probe wavelengths was proposed and verified. The relative absorbance at each of the probe wavelength was obtained with respect to the absorbance at a reference wavelength. The single reference wavelength (1310 nm) and a group of four different probe wavelengths (1064, 1550, 1685, 1798 nm) were selected to exhibit the glucose absorbance with opposite signs, thereby enhancing the accuracy of the prediction. The final glucose concentration was estimated by taking the average of the predicted values provided by the four probe wavelengths. The absorbance of the glucose solution for the path length of 5 mm was $-1.42{\times}10^{-6}\;AU$/(mg/dL) at the reference wavelength of 1310 nm and peaked at $+8.12{\times}10^{-6}\;AU$/(mg/dL) at 1685 nm. The concentration of the glucose solution was decently predicted by means of the proposed scheme with the standard error of prediction of ${\sim}28\;mg/dL$. In addition, the influence of the ambient temperature and the fat thickness upon the prediction of the glucose concentration was examined. The absorption change with the temperature was $-9.1{\times}10^{-5}\;AU/^{\circ}C$ in the temperature range of $26{\sim}40^{\circ}C$ at the reference wavelength, and $-2.08{\times}10^{-2}\;AU/^{\circ}C$ at 1550 nm. And the absorption change with respect to the fat thickness was +1.093 AU/mm at the probe wavelength of 1685 nm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37C no.9
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• pp.802-810
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• 2012

This paper describes RFID(Radio Frequency Identification) based target acquisition and docking system. RFID is non-contact identification system, which can send relatively large amount of information using RF signal. Robot employing RFID reader can identify neighboring tag attached objects without any other sensing or supporting systems such as vision sensor. However, the current RFID does not provide spatial information of the identified object, the target docking problem remains in order to execute a task in a real environment. For the problem, the direction sensing RFID reader is developed using a dual-directional antenna. The dual-directional antenna is an antenna set, which is composed of perpendicularly positioned two identical directional antennas. By comparing the received signal strength in each antenna, the robot can know the DOA (Direction of Arrival) of transmitted RF signal. In practice, the DOA estimation poses a significant technical challenge, since the RF signal is easily distorted by the surrounded environmental conditions. Therefore, the robot loses its way to the target in an electromagnetically disturbed environment. For the problem, the g-filter based error correction algorithm is developed in this paper. The algorithm reduces the error using the difference of variances between current estimated and the previously filtered directions. The simulation and experiment results clearly demonstrate that the robot equipped with the developed system can successfully dock to a target tag in obstacles-cluttered environment.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.11
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• pp.32-40
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• 2016

IEEE 802.15.4 is the one of the protocols for radio communication in a personal area network. Because of low cost and low power communication for IoT communication, it requires the highest optimization level in the implementation. Recently, the studies of location aware algorithm based on IEEE802.15.4 standard has been achieved. Location estimation is performed basically in equal consideration of reference node information and blind node information. However, an error is not calculated in this algorithm despite the fact that the coordinates of the estimated location of the blind node include an error. In this paper, we enhanced a conventual maximum likelihood estimation using weighted coefficient and implement the hardwired location aware engine for small code size and low power consumption. On the field test using test-beds, the suggested hardware based location awareness method results better accuracy by 10 percents and reduces both calculation and memory access by 30 percents, which improves the systems power consumption.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.30-39
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• 2006

Iris recognition is that identifies a user based on the unique iris texture patterns which has the functionalities of dilating or contracting pupil region. Iris recognition systems extract the iris pattern in iris image captured by iris recognition camera. Therefore performance of iris recognition is affected by the quality of iris image which includes iris pattern. If iris image is blurred, iris pattern is transformed. It causes FRR(False Rejection Error) to be increased. Optical defocusing is the main factor to make blurred iris images. In conventional iris recognition camera, they use two kinds of focusing methods such as lilted and auto-focusing method. In case of fixed focusing method, the users should repeatedly align their eyes in DOF(Depth of Field), while the iris recognition system acquires good focused is image. Therefore it can give much inconvenience to the users. In case of auto-focusing method, the iris recognition camera moves focus lens with auto-focusing algorithm for capturing the best focused image. However, that needs additional H/W equipment such as distance measuring sensor between users and camera lens, and motor to move focus lens. Therefore the size and cost of iris recognition camera are increased and this kind of camera cannot be used for small sized mobile device. To overcome those problems, we propose method to increase DOF by iris image restoration algorithm based on focus value of iris image. When we tested our proposed algorithm with BM-ET100 made by Panasonic, we could increase operation range from 48-53cm to 46-56cm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.3
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• pp.199-207
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• 2013

In hydrographic survey, data surveyed with multibeam system includes various errors due to multiple factors. These are corrected by a calibration called patch test, and if existing method is used, the test needs to be conducted for about 8 times for precise system calibration. For more prompt and precise multibeam system calibration, the exact offset of a ship was determined using terrestrial laser scanning and TS surveying, which was used as the initial input for the patch test. In the result, the error of closure was 0.001 m or less for TS surveying and backsight error was 0.005 m or less for scanning. All the surveying data based on the same local coordinate was converted into vessel reference coordinate during which R-square for all rotation angles was 0.99 or higher and standard deviation was 0.008 m or less. Finally, in a patch test using calculated offset of sensors and motion sensor offset, the offset of MBES transducer satisfied manual on hydrography only with 1-time calibration. With these results, it is thought that terrestrial laser scanning and TS surveying can fully be utilized for multibeam system calibration.