• Journal of Advanced Marine Engineering and Technology
• /
• v.23 no.2
• /
• pp.210-215
• /
• 1999

The proposed detector is consists of three-phase sinusoidal signal generator and peak detector. This peak detector can detect the peak voltage value at the state of variable frequency. In experi-ment three-phase sinusoidal signals are generated from D/A converter using IBM PC and deliv-ered to the peak detector. Each signals are squared by multiplier and summed up Peak value is the square root of summed value extracted by square root circuit.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.6
• /
• pp.809-817
• /
• 2020

In the design and development process of Small-Sat power distribution and transmission module, the stability of dynamic resources was evaluated by a deep learning algorithm. The requirements for the stability evaluation consisted of the power distribution function of the power distribution module and demand module to the SAR radar in Small-Sat. To verify the performance of the switching power components constituting the power module PDM, the reliability was verified using a dynamic neural network. The adoption material of deep learning for reliability verification is the power distribution function of the payload to the power supplied from the small satellite main body. Modeling targets for verifying the performance of this function are output voltage (slew rate control), voltage error, and load power characteristics. First, to this end, the Coefficient Structure area was defined by modeling, and PCB modules were fabricated to compare stability and reliability. Second, Levenberg-Marquare based Two-Way NARX neural network Sigmoid Transfer was used as a deep learning algorithm.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2010.06a
• /
• pp.76-76
• /
• 2010

유기박막트랜지스터의 특성을 개선하기 위해서는 유기반도체와의 좋은 접합과 유전상수가 주요한 요인으로 작용한다. 무기 산화물 전구체와 유기고분자를 이용하여 유기 고분자의 단정인 낮은 유전율을 개선하였다. 스핀코팅 방법이 아닌 딥코팅 방법을 이용하여 절연막 두께를 10nm정도로 낮추어 구동전압을 개선하였으며 무기 절연체의 높은 누설전류 또한 그 특성이 개선되어 우수한 절연 특성을 보였다. 유-무기 복합체를 이용한 게이트 절연막과 펜타센을 이용한 유기박막트랜지스터의 구동전압은 1V정도에서 구동가능하며, 점멸비, 이동도 모두 개선된 결과를 보였다.

• Proceedings of the KIPE Conference
• /
• 2013.07a
• /
• pp.485-486
• /
• 2013

본 논문에서는 저가형 플라이백 컨버터를 이용한 태양광 전력조절기의 연속도통모드(CCM) 제어기 설계를 제안한다. PV 전압 제어기 설계를 위하여 소신호 분석을 통해 전달함수를 유도하고 보상기를 설계하여 전압루프를 안정하게 설계하였다. 설계 결과는 MATLAB과 PSIM 시뮬레이션 비교로 검증하였다. 최종적으로 최대전력 조절제어를 위한 최대전력추종제어기를 구현하여 하드웨어로 검증하였다.

• Transactions of the Korean Society of Pressure Vessels and Piping
• /
• v.19 no.2
• /
• pp.93-101
• /
• 2023

In this study, a deep learning algorithm was used to diagnose electric potential signals obtained through CIPS and DCVG, used indirect inspection methods to confirm the soundness of buried pipes. The deep learning algorithm consisted of CNN(Convolutional Neural Network) model for diagnosing the electric potential signal and Grad CAM(Gradient-weighted Class Activation Mapping) for showing the flaw prediction point. The CNN model for diagnosing electric potential signals classifies input data as normal/abnormal according to the presence or absence of flaw in the buried pipe, and for abnormal data, Grad CAM generates a heat map that visualizes the flaw prediction part of the buried pipe. The CIPS/DCVG signal and piping layout obtained from the 3D finite element model were used as input data for learning the CNN. The trained CNN classified the normal/abnormal data with 93% accuracy, and the Grad-CAM predicted flaws point with an average error of 2m. As a result, it confirmed that the electric potential signal of buried pipe can be diagnosed using a CNN-based deep learning algorithm.

• Clean Technology
• /
• v.13 no.2
• /
• pp.115-121
• /
• 2007

In this study, we analyzed experimently the NO and $SO_2$ removal by the dielectric barrier discharge-photocatalysts hybrid process. The glass spheres were used as a dielectric material for dielectric barrier discharge and the $TiO_2$ photocatalysts were coated onto those spheres by the dip-coating method. The $TiO_2$ particles were coated in the sponge-shape, which has the larger surface area. As the voltage applied to the plasma reactor, the pulse frequency of applied voltage, or the residence time increases, the NO and $SO_2$ removal efficiencies increase. The increase in the supplied concentrations of NO and $SO_2$ leads to the higher energy for NO and $SO_2$ removal and the NO and $SO_2$ removal efficiencies decrease. These experimental results can be used as a basis to design the dielectric barrier discharge-photocatalysts hybrid process to remove NO and $SO_2$.

• Journal of Digital Convergence
• /
• v.18 no.11
• /
• pp.241-246
• /
• 2020

In this paper, we propose a neural network model to converge the marine electric propulsion system and deep learning algorithm to predict the DC/DC converter output current in the electric propulsion regeneration system and to predict the battery charge during regeneration. In order to experiment with the proposed neural network, the input voltage and current of the PCM were measured and the data set was secured on the prototype PCM board. In addition, in order to improve the learning results in the insufficient data set, the scale of the data set was increased through data fitting and its learning was executed further. After learning, the difference between the data prediction result of the neural network model and the actual measurement data was compared. The proposed neural network model effectively showed the prediction of battery charge according to changes in input voltage and current. In addition, by predicting the characteristic change of the analog circuit constituting the DC/DC converter through a neural network, it is determined that the characteristics of the analog circuit should be considered when designing the regeneration system.

• Korean Chemical Engineering Research
• /
• v.55 no.5
• /
• pp.593-599
• /
• 2017

In order to enhance ultra battery performances, the electrochemical characteristics of nano Pb/AC anode composite was investigated. Through nano Pb adsorption onto activated carbon, nano Pb/AC was synthesized and it was washed under vacuum process. The prepared anode materials was analysed by SEM, BET and EDS. The specific surface area and average pore size of nano Pb/AC composite were $1740m^2/g$ and 1.95 nm, respectively. The negative electrode of ultra battery was prepared by nano Pb/AC dip coating on lead plate. The electrochemical performances of ultra battery were studied using $PbO_2$ (the positive electrode) and prepared nano Pb/AC composite (the negative electrode) pair. Also the electrochemical behaviors of ultra battery were investigated by charge/discharge, cyclic voltammetry, impedance and rate capability tests in 5 M $H_2SO_4$ electrolyte. The initial capacity and cycling performance of the present nano Pb/AC ultra battery were improved with respect to the lead battery and the AC-coated lead battery. These experimental results indicate that the proper addition of nano Pb/AC into the negative electrode can improve the discharge capacity and the long term cycle stability and remarkably suppress the hydrogen evolution reaction on the negative electrode.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.27-30
• /
• 2022

The plasma process, which has many advantages in terms of efficiency and environment compared to conventional process methods, is widely used in semiconductor manufacturing. Plasma Sheath is a dark region observed between the plasma bulk and the chamber wall surrounding it or the electrode. The Plasma Sheath Monitoring Sensor (PSMS) measures the difference in voltage between the plasma and the electrode and the RF power applied to the electrode in real time. The PSMS data, therefore, are expected to have a high correlation with the state of plasma in the plasma chamber. In this study, a model for predicting the state of nitrogen ions in the plasma chamber is training by a deep learning machine learning techniques using PSMS data. For the data used in the study, PSMS data measured in an experiment with different power and pressure settings were used as training data, and the ratio, flux, and density of nitrogen ions measured in plasma bulk and Si substrate were used as labels. The results of this study are expected to be the basis of artificial intelligence technology for the optimization of plasma processes and real-time precise control in the future.