• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.4
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• pp.36-42
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• 2008

40Gb/s CMOS Clock and Data Recovery circuit design for optical serial link is proposed. The circuit generates 8 multiphase clock using LC tank PLL and controls the phase between the clock and the data using the $2{\times}$ oversampling Bang-Bang PD. 40Gb/s input data is 1:4 demultiplexed and recovered to 4 channel 10Gb/s outputs. The design was progressed to separate the analog power and the digital power. The area of the chip is $2.8{\times}2.4mm^2$ for the inductors and the power dissipation is about 200mW. The chip has been fabricated using 0.18um CMOS process. The measured results show that the chip recovers the data up to 9.5Gb/s per channel(Equivalent to serial input rate of up to 38Gb/s).

• Proceedings of the KIPE Conference
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• 2013.11a
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• pp.19-20
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• 2013

3상 250레벨의 MMC형 HVDC시스템의 구현에서는 한 개의 상마다 500여개의 SM(sub module)이 필요하게 되고 MM(master module)에서 10Khz의 샘플링을 처리한다고 하면 100us동안에 500여개의 SM에서 각종 데이터를 받아서 처리한 후에 다시 SM에 처리된 정보를 전송해 줘야한다. 따라서 SM에서 데이터를 받고 전송하는데 따른 100us 동안의 필요한 처리양과 네트워크의 속도 및 오버헤드(overhead)등의 구성과 토포로지에 관해 알아보았고, 이를 적용하여 멀티레벨의 HVDC에 필요한 데이터 네트워크에의 전송량 계산에 대한 소프트웨어를 제작하여 손쉽게 설계할 수 있도록 하였다.

• Proceedings of the KIPE Conference
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• 2002.11a
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• pp.140-143
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• 2002

위상제어 정류기 시스템에서 예측전류제어는 전류 응답속도가 매우 빠르고 오버슈트가 없는 것으로 알려져 있다. 하지만 전원과 부하의 전압 전류방정식에 의존하는 예측전류제어는 부하 파라메터 값이 틀릴 경우 전류지령 값과 피드백 사이에 정상상태 오차를 보이게 된다. 본 논문에서는 디지털 순시치 샘플링과 최소자승법을 이용하여 온라인으로 부하의 파라메터를 추정하는 알고리즘을 제안하였고, 이를 이용하여 예측전류제어를 수행함으로써 빠르고 정밀한 전류제어응답을 보였다.

• Journal of the Korean earth science society
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• v.44 no.2
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• pp.105-118
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• 2023

In this study, we predicted the presence of fog with a one-hour delay using the XGBoost DART machine learning algorithm for Andong, which had the highest occurrence of fog among inland stations from 2016 to 2020. We used six datasets: meteorological data, agricultural observation data, additional derived data, and their expanded data. The weather phenomenon numbers obtained through naked-eye observations and the visibility distances measured by visibility meters were classified as fog [1] or no-fog [0]. We set up twelve machine learning modeling experiments and used data from 2021 for model validation. We mainly evaluated model performance using recall and AUC-ROC, considering the harmful effects of fog on society and local communities. The combination of oversampled meteorological data features and the target induced by weather phenomenon numbers showed the best performance. This result highlights the importance of naked-eye observations in predicting fog using machine learning algorithms.

• The Journal of the Korea Contents Association
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• v.18 no.8
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• pp.525-535
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• 2018

If the frequency of a particular class is excessively higher than the frequency of other classes in the classification problem, data imbalance problems occur, which make machine learning distorted. Corporate bankruptcy prediction often suffers from data imbalance problems since the ratio of insolvent companies is generally very low, whereas the ratio of solvent companies is very high. To mitigate these problems, it is required to apply a proper sampling technique. Until now, oversampling techniques which adjust the class distribution of a data set by sampling minor class with replacement have popularly been used. However, they are a risk of overfitting. Under this background, this study proposes ROSE(Random Over Sampling Examples) technique which is proposed by Menardi and Torelli in 2014 for the effective corporate bankruptcy prediction. The ROSE technique creates new learning samples by synthesizing the samples for learning, so it leads to better prediction accuracy of the classifiers while avoiding the risk of overfitting. Specifically, our study proposes to combine the ROSE method with SVM(support vector machine), which is known as the best binary classifier. We applied the proposed method to a real-world bankruptcy prediction case of a Korean major bank, and compared its performance with other sampling techniques. Experimental results showed that ROSE contributed to the improvement of the prediction accuracy of SVM in bankruptcy prediction compared to other techniques, with statistical significance. These results shed a light on the fact that ROSE can be a good alternative for resolving data imbalance problems of the prediction problems in social science area other than bankruptcy prediction.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.2
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• pp.143-159
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• 2017

A probabilistic fragility assessment procedure is developed in this paper to predict risks of damage arising from seismic loading to the two-cell RC box tunnel. Especially, the paper focuses on establishing a simplified methodology to derive fragility curves which are an indispensable ingredient of seismic fragility assessment. In consideration of soil-structure interaction (SSI) effect, the ground response acceleration method for buried structure (GRAMBS) is used in the proposed approach to estimate the dynamic response behavior of the structures. In addition, the damage states of tunnels are identified by conducting the pushover analyses and Latin Hypercube sampling (LHS) technique is employed to consider the uncertainties associated with design variables. To illustrate the concepts described, a numerical analysis is conducted and fragility curves are developed for a large set of artificially generated ground motions satisfying a design spectrum. The seismic fragility curves are represented by two-parameter lognormal distribution function and its two parameters, namely the median and log-standard deviation, are estimated using the maximum likelihood estimates (MLE) method.

• Korean Journal of Construction Engineering and Management
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• v.21 no.6
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• pp.46-55
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• 2020

The maintenance cost for highway pavement is gradually increasing due to the continuous increase in road extension as well as increase in the number of old routes that have passed the public period. As a result, there is a need for a method of minimizing costs through preventative grievance Preventive maintenance requires the establishment of a strategic plan through accurate prediction old Highway pavement. herefore, in this study, the XGBoost among machine learning classification-based models was used to develop a highway pavement damage prediction model. First, we solved the imbalanced data issue through data sampling, then developed a predictive model using the XGBoost. This predictive model was evaluated through performance indicators such as accuracy and F1 score. As a result, the over-sampling method showed the best performance result. On the other hand, the main variables affecting road damage were calculated in the order of the number of years of service, ESAL, and the number of days below the minimum temperature -2 degrees Celsius. If the performance of the prediction model is improved through more data accumulation and detailed data pre-processing in the future, it is expected that more accurate prediction of maintenance-required sections will be possible. In addition, it is expected to be used as important basic information for estimating the highway pavement maintenance budget in the future.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.2
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• pp.108-119
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• 1999

In this paper, a three phase PWM AC to DC boost converter that operates with unity power factor and sinusodial input currents si presented. The current control of the converter is based on the space vector PWM strategy with fixed switching frequency and the imput current tracks the reference current within one sampling time interval. Space vector PWM strategy for current control was materialized as a digital control method by using DSP. By using this control strategy low ripples in the output voltage, low harmonics in the input current and fast dynamic responses are achieved with a small capacitance in the dc link.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.1C
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• pp.59-66
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• 2007

This paper proposes a windowing method to reduce noise effect and bit synchronization error caused by tolerance of a tag-response signal for ISO/IEC 18000-6 type-B readers. A tag response signal has very weak power because of a back-scattering modulation scheme and thus may be sensitive to noise. In addition, bit tolerance admitted in a tag response signal requires robust timing synchronization because it affects readers' detection performance. To reduce the two undesirable effects in a tag signal, we acquire bit transition position by using variable windows from over-sampled data, and average whole data in one bit duration. With a hardware system adopting the proposed method, we tested and verified its performance.

• The Transactions of the Korean Institute of Power Electronics
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• v.12 no.2
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• pp.183-190
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• 2007

In this paper, a robust controller for PWM converter is proposed. The proposde converter controller consists of a PI controller for DC output voltage and a current controller using error-space approach for maintaining the sinusoidal current waveform and unity power factor. Conventionally, the try and error method has been used to design the current controller considering the switching frequency of the devices and sampling frequency of the digital controller. But this proposed method is based on characteristic ratio assignment(CRA) method which has the advantage to design the optimal gain to meet the referenced response and overshoot within the limit range. First, the CRA based current controller algorithm is explained. Then the validity of proposed controller is verified through the PSiM simulation and experience results.