• Proceedings of the Korean Geotechical Society Conference
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• 2000.03b
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• pp.599-606
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• 2000

본 연구는 피에조콘(Piezocone) 관입 시험에 의한 과잉간극수압의 소산(Dissipation)특성을 파악하기 위하여, 실측된 소산실험 결과치와 Gupta & Davidson에 의해 개발된 연속 공동확장이론(Successive Cavity Expansion Theory) 모델을 비교하였고, 그 경험적 이론의 적합성을 규명하였다. 연속 공동확장 이론이란, 콘 관입이 유발하는 관입 주변지반의 변환 메커니즘을 연속적인 공동확장의 전개과정로 파악할 때, 관입주변의 연속적 공동확장 영역에서 발생된 과잉간극수압들은 연속적으로 소산되어지고, 결국에는 관입멈춤직후 얻게 되는 소산시험의 결과도 이러한 과잉간극수압의 연속적 소산 메커니즘으로부터 그 영향을 받는다는 개념이다. 본 연구의 실험방법은 Piezocone 관입을 위한 연약모형지반 조성을 위하여 초대형 Slurry Consolidometer에 Slurry를 45일간 압밀시킨후 Calibration Chamber(Louisiana State University Calibration Chamber System)에 옮긴 후 2차 압밀시키는 Two-Stage Consolidation Method를 사용하였다. 또한 모형지반내에 8개의 Piezometers를 설치하여 Piezometers를 설치하여 Piezocone 관입시 유발되는 지반 내에서의 과잉간극수압의 변환을 측정하였다. 실험결과와 이론 예측치를 비교함으로써 연속 공동확장이론 모델은 u$_2$형식의 피에조콘 관입 소산시험 결과들과 잘 들어맞는 모습을 보여줬으나, 관입으로 인한 주변 지반의 과잉간극수압의 소산변화는 정성적으로만 모사 되는 모습을 보여줬다.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.6
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• pp.679-687
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• 2003

In this study, we investigated the analytical techniques to quantify the ambient concentration of hydrogen sulfide (H$_2$S) in air at ppt concentration level. For this purpose, an on-line GC analytical system equipped with both pulsed-flame photometric detector (PFPD) and thermal desorption unit (TDU) was investigated by collecting ambient air samples. The results of our study generally indicated that calibration conditions of GC system is highly sensitive to affect the accuracy of the analytical technique. Most importantly. we found that the use of different matrices in the the preparation stage of working standards was sensitive to control the overall performance of this technique. The calibration of our analytical system was tested by the two types of working standard (prepared by mixing either with high purity $N_2$ or with the ambient air). According to this test, the latter represented more efficiently the detecting conditions of actual air samples. The peak occurrence patterns of both air samples and standards (prepared by mixing with ambient air) were altered in a similar manner as the function of the loaded volume; however, it was not the case for the $N_2$-mixed standards. Results of our study suggest that detection of H$_2$S is highly different from other sulfides and that its quantification requires minimiaing interfering effects of non -pure substance (like water vapor) and (either sorptive or destructive) loss effects.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.71-74
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• 2012

We have manufactured a close range Dicke type radiometer which consists of two stage low noise amplifier and diode detector. Frequency range of this system is 35 GHz. And this is used for studying temperature calibration on specific objects. We have present millimeter-wave radiometer's thermal calibration method and its characteristics. From absolute temperature 299K to 309K, in proportion to increase temperature, output voltages are linearly increased. In this case, undefined objects can be measured thermal noise temperature relatively. Overall from absolute temperature 214K to 309K, we have obtained relation of temperature and output voltage;V= 0.03601K - 10.70517.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.11a
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• pp.561-568
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• 2000

본 연구의 목적은 Piezocone 관입시험을 이용한 연약지반의 OCR 평가에 있어 기존의 여러 가지 해석방법들과 최근에 새롭게 제안된 방법들을 실내 모형토조에서 실측된 피에조콘 관입 실험치에 적용하여 각 해석방법들의 차이와 장단점들을 비교 분석하는데 있다. 본 연구의 연구실험방법으로는, Piezocone 관입을 위한 연약 모형지반 조성을 위하여 초대형 Slurry Consolidometer에 Free Stress 상태의 Slurry를 45일간 압밀시킨후 Automatic Computer Control Calibration Chamber (LSU/CALCHAS; Louisiana Slate University Calibration Chamber System)에 옮긴후 다시한번 압밀시키는 Two-Stage Consolidation Method를 사용하였다. 모형지반은 여러 가지 Boundary Condition들과 Stress Condition 그리고 Stress History등을 달리하여 총 5개의 지반을 조성하였다. 관입시험은 총 25개의 Piezocone 관입이 수행되어졌고, 그중 4개는 Standard 10 cm2 Piezocone이고, 나머지 21개는 Miniature Piezocone이 사용되었다. Piezocone 실험치들에 대한 여러 가지 OCR 해석방법 적용결과, Schmertmann방법은 5개 모형지반 모두에서 과다한 OCR평가를 보였으며, $B_{q}$ 방법은 일부모형지반에서 음의 OCR값으로 계산되어졌다. 그러나, Critical-Stale Soil Mechanics 와 Cavity Expansion 이론에 근거하여 Mayne(1991), Kurup(1993), Tumay et al (1995) 들이 제안한 OCR 평가방법들은 실험치와 잘맞는 경향을 보여주었다. 이와같은 이론 모델값들의 차이는 응력조건(Stress Condition)과 경계조건(Boundary Condition)들에 대한 각 해석방법들의 고려정도에 따른 결과로 판단된다.

• Journal of Broadcast Engineering
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• v.24 no.4
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• pp.580-591
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• 2019

This paper focuses on to improving object detection performance using the camera and LiDAR on autonomous vehicle platforms by fusing detected objects from individual sensors through a late fusion approach. In the case of object detection using camera sensor, YOLOv3 model was employed as a one-stage detection process. Furthermore, the distance estimation of the detected objects is based on the formulations of Perspective matrix. On the other hand, the object detection using LiDAR is based on K-means clustering method. The camera and LiDAR calibration was carried out by PnP-Ransac in order to calculate the rotation and translation matrix between two sensors. For Sensor fusion, intersection over union(IoU) on the image plane with respective to the distance and angle on world coordinate were estimated. Additionally, all the three attributes i.e; IoU, distance and angle were fused using logistic regression. The performance evaluation in the sensor fusion scenario has shown an effective 5% improvement in object detection performance compared to the usage of single sensor.

• Journal of Biosystems Engineering
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• v.40 no.1
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• pp.89-93
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• 2015

Purpose: Machine vision-based image processing methods can be useful for estimating the fresh weight of plants. This study analyzes the ability of two different image processing methods, i.e., morphological and pixel-value analysis methods, to measure the fresh weight of lettuce grown in a closed hydroponic system. Methods: Polynomial calibration models are developed to relate the number of pixels in images of leaf areas determined by the image processing methods to actual fresh weights of lettuce measured with a digital scale. The study analyzes the ability of the machine vision- based calibration models to predict the fresh weights of lettuce. Results: The coefficients of determination (> 0.93) and standard error of prediction (SEP) values (< 5 g) generated by the two developed models imply that the image processing methods could accurately estimate the fresh weight of each lettuce plant during its growing stage. Conclusions: The results demonstrate that the growing status of a lettuce plant can be estimated using leaf images and regression equations. This shows that a machine vision system installed on a plant growing bed can potentially be used to determine optimal harvest timings for efficient plant growth management.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.9
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• pp.1847-1855
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• 2012

A 1V 1.6-GS/s 6-bit flash analog-to-digital converter (ADC) with a clock calibration circuit is proposed. A single track/hold circuit with a bootstrapped analog switch is used as an input stage with a supply voltage of 1V for the high speed operation. Two preamplifier-arrays and each comparator composed of two-stage are implemented for the reduction of analog noises and high speed operation. The clock calibration circuit in the proposed flash ADC improves the dynamic performance of the entire flash ADC by optimizing the duty cycle and phase of the clock. It adjusts the reset and evaluation time of the clock for the comparator by controlling the duty cycle of the clock. The proposed 1.6-GS/s 6-bit flash ADC is fabricated in a 1V 90nm 1-poly 9-metal CMOS process. The measured SNDR is 32.8 dB for a 800 MHz analog input signal. The measured DNL and INL are +0.38/-0.37 LSB, +0.64/-0.64 LSB, respectively. The power consumption and chip area are $800{\times}500{\mu}m2$ and 193.02mW.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.4
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• pp.161-166
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• 2014

An accurate gain-of-two amplifier, which successfully reduces the capacitor mismatch error is proposed. This amplifier has similar circuit complexity and linearity improvement to the capacitor error-averaging technique, but operates with two clock phases just like the conventional pipeline stage. This makes it suitable for high-speed, high-resolution analog-to-digital converters (ADCs). Two ADC architectures employing the proposed accurate gain-of-two amplifier are also presented. The simulation results show that the proposed ADCs can achieve 15-bit linearity with 8-bit capacitor matching.

• Journal of Korea Water Resources Association
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• v.33 no.2
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• pp.247-262
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• 2000

In this paper, the WSANN(Water Stage Analysis with Neural Network) model was presented so as to predict flood water stage at Jindong which has been the major stream gauging station in Nakdong river basin. The WSANN model used the improved backpropagation training algorithm which was complemented by the momentum method, improvement of initial condition and adaptive-learning rate and the data which were used for this study were classified into training and testing data sets. An empirical equation was derived to determine optimal hidden layer node between the hidden layer node and threshold iteration number. And, the calibration of the WSANN model was performed by the four training data sets. As a result of calibration, the WSANN22 and WSANN32 model were selected for the optimal models which would be used for model verification. The model verification was carried out so as to evaluate model fitness with the two-untrained testing data sets. And, flood water stages were reasonably predicted through the results of statistical analysis. As results of this study, further research activities are needed for the construction of a real-time warning of the impending flood and for the control of flood water stage with neural network method in river basin. basin.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1357-1369
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• 2022

Cloud removal is an essential image processing step for any task requiring time-series optical images, such as vegetation monitoring and change detection. This paper presents a two-stage cloud removal method that combines conditional generative adversarial networks (cGANs) with regression-based calibration to construct a cloud-free time-series optical image set. In the first stage, the cGANs generate initial prediction results using quantitative relationships between optical and synthetic aperture radar images. In the second stage, the relationships between the predicted results and the actual values in non-cloud areas are first quantified via random forest-based regression modeling and then used to calibrate the cGAN-based prediction results. The potential of the proposed method was evaluated from a cloud removal experiment using Sentinel-2 and COSMO-SkyMed images in the rice field cultivation area of Gimje. The cGAN model could effectively predict the reflectance values in the cloud-contaminated rice fields where severe changes in physical surface conditions happened. Moreover, the regression-based calibration in the second stage could improve the prediction accuracy, compared with a regression-based cloud removal method using a supplementary image that is temporally distant from the target image. These experimental results indicate that the proposed method can be effectively applied to restore cloud-contaminated areas when cloud-free optical images are unavailable for environmental monitoring.