• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.179-184
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• 2023

We propose the modeling methodology of CMOS inverter made of LTPO TFT using a machine learning. LTPO can achieve advantages of LTPS TFT with high electron mobility as a driving TFT and IGZO TFT with low off-current as a switching TFT. However, since the unified model of both LTPS and IGZO TFTs is still lacking, it is necessary to develop a SPICE-compatible compact model to simulate the LTPO current-voltage characteristics. In this work, a generic framework for combining the existing formula of I-V characteristics with artificial neural network is presented. The weight and bias values of ANN for LTPS and IGZO TFTs is obtained and implemented into PSPICE circuit simulator to predict CMOS inverter. This methodology enables efficient modeling for predicting LTPO TFT circuit characteristics.

• Journal of the Korean Institute of Gas
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• v.17 no.2
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• pp.59-69
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• 2013

This study presents development of the ANN simulator for well placement of infill drilling in gas fields. The input data of the ANN simulator includes the production time, well location, all inter well distances, boundary inter well distance, infill well position, productivity potential, functional links, reservoir pressure. The output data includes the bottomhole pressure in addition to the production rate. Thus, it is possible to calculate the productivity and bottomhole pressure during production period simultaneously, and it is expected that this model could replace conventional simulators. Training for the 20 well placement scenarios was conducted. As a result, it was found that accuracy of ANN simulator was high as the coefficient of correlation for production rate was 0.99 and the bottomhole pressure 0.98 respectively. From the resultes, the validity of the ANN simulator has been verified. The term, which could produce Maximum Daily Quantity (MDQ) at the gas field and the productivity according to the well location was analyzed. As a result, the MDQ could be maintained for a short time in scenario C-1, which has the three infill wells nearby aquifer boundary, and a long time in scenario A-1. In conclusion, it was found that scenario A maintained the MDQ up to 21% more than those of scenarios B and C which include parameters that might affect the productivity. Thus, the production rate can be maximized by selecting the location of production wells in comprehensive consideration of parameters that may affect the productivity. Also, because the developed ANN simulator could calculate both production rate and bottomhole pressure, respectively, it could be used as the forward simulator in a various inverse model.

• The Journal of the Acoustical Society of Korea
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• v.24 no.2
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• pp.103-110
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• 2005

Providing adequate amount of spatial impression for spaciousness) has been known to be one of the most important design considerations for the good acoustics of rooms for music. and the measurement, of room acoustics using parameters. such as LEF and IACC, forms an essential part of such evaluation. However. it is unavoidable to use different transducers (figure of eight microphones. head and torso) for the measurement of each parameter and it tends to make the measurement procedure complicated. The Present work tried to provide a simpler way to measure these binaural room acoustic parameters including monaural ones with a single measurement system using both spatial information collected through a 5-channel microphone and a trained neural network. A computer simulation program, CATT-Acoustic V7.2. which allowed us to obtain exactly the same spatial information as a 5-channel microphone was used. since it requires quite a large amount of data for practical training of a neural network. Since each reflection has different energy. delay and direction, energy should be integrated properly. the concept of ray tracing method was applied inversely in this work. Also applying weightings according to the delay times was considered in this work. Finally, predicted results were compared with the measured data md their correlations were analyzed and discussed.

• Journal of Korea Society of Industrial Information Systems
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• v.5 no.3
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• pp.117-124
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• 2000

A systolic array is a parallel processing system which consists of processing elements of basic computation capabilities, connected with regular and local communication lines. It has been known that a systolic array is on of effective systems to solve complicated communication problems occurred between densely connected neurons on ANN(Artificial Neural Network). In this paper, a systolic array simulator for the back-propagation ANN, which automatically constructs the proper systolic array for a given number of neurons of the ANN, is designed and constructed. With animation techniques of the simulators, it is easy for users to be able to examine the execution of the back-propagation algorithm on the designed systolic array step by step. Moreover the simulator can perform forward and backward operations of the back-propagation algorithm either in sequence or in parallel on the designed systolic array. Parallel execution can be performed by feeding continuous input patterns and by executing bidirectional propagations on all of processing elements of a systolic array at the same time.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.523-527
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• 2005

현재 기업에서는 생산 효율을 극대화시키기 위하여 많은 비용을 투자하여 Package형태의 솔루션(S/W)을 도입하고 있다. 그러나 솔루션들은 그 특성상 기업특성을 생산 일정계획 수립에 충분히 반영하지 못하고 있어 실제 도입 후에 사용에 어려움이 겪고 있으며 일부 기업에서는 생산일정 전문가를 통하여 재생산계획을 수립하고 있다. 본 연구는 상용화 되고 있는 솔루션에서 제시된 생산일정을 생산일정 전문가가 회사의 특성을 고려하여 재생산일정을 수립하는 단계에 대한 사용자의 패턴을 추출 후 신경망을 통하여 패턴을 학습하여 재생산일정 수립 시 소요되는 시간을 최소화 시키며 기업의 특성을 반영하는 적응적 일정계획 지원 시뮬레이터의Framework을 제시하고자 한다.

• Nuclear Engineering and Technology
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• v.29 no.2
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• pp.167-174
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• 1997

The method that the operating condition of Control Rod Drive Mechanism (CRDM) can be monitored without mounting sensors within CRDM housing was developed, and by using this developed method the closed-loop controller for the CRDM was designed which can optimize the performance and maximize the reliability of CRDM operation. Neural network is utilized as pattern recognition engine in detecting CRDM actuation. In this paper, most problems in previous open loop system are resolved. The control algorithms for closed-loop system ore developed and implemented within the hardware of timing controller based on microprocessor. All functions in the timing controller ore verified by means of real time CRDM simulator. The results show that the timing controller performs its intended functions properly.

• Electronics and Telecommunications Trends
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• v.35 no.4
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• pp.81-90
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• 2020

The use of machine learning technologies such as deep and reinforcement learning has proliferated in various domains with the advancement of deep neural network studies. To make the learning successful, both big data acquisition and fast processing are required. However, for some physical world applications such as autonomous drone flight, it is difficult to achieve efficient learning because learning with a premature A.I. is dangerous, cost-ineffective, and time-consuming. To solve these problems, simulation-based approaches can be considered. In this study, we analyze recent trends in drone simulation technologies and compare their features. Subsequently, we introduce Octopus, which is a highly precise and scalable drone simulator being developed by ETRI.

• Journal of Electrical Engineering and Technology
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• v.5 no.3
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• pp.493-496
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• 2010

At present, Metal Oxide gas Sensors (MOXs) are widely used in gas detection because of its advantages, including high sensitivity and low cost. However, MOX presents well-known problems, including lack of selectivity and environment effect, which has motivated studies on different measurement strategies and signal-processing algorithms. In this paper, we present an artificial neural network (ANN) that models an MOX sensor (TGS822) used in a dynamic environment. This model takes into account dependence in relative humidity and in gas nature. Using MATLAB interface in the design phase and optimization, the proposed model is implemented as a component in an electronic simulator library and accurately expressed the nonlinear character of the response and that its dependence on temperature and relative humidity were higher than gas nature.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.839-841
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• 2017

일반적으로 자율 주행에 딥러닝을 적용하기 위해서 실제 차량에 관련 장비를 설치하고 테스트 한다. 본 논문에서는 The Open Racing Car Simulator(TORCS)에서 다양한 신경망 구조를 적용하도록 Convolutional Neural Network(CNN)을 통하여 학습 및 테스트할 수 있는 시스템을 제안한다. 가상 환경에서 테스트함으로써 하드웨어를 구매하거나 제작하지 않아도 되며 신경망 구조를 선택후 학습함으로써 다양한 데이터에 적합한 신경망 구조를 적용할 수 있다.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.78.3-78.3
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• 2019

We develop a novel technique that can constrain the evolutionary track of the epoch of reionization (EoR) by applying the convolutional neural network (CNN) to the 21-cm differential brightness temperature. We use 21cmFAST, a fast semi-numerical cosmological 21-cm signal simulator, to produce mock 21-cm map between z=6-13. We design a CNN architecture that predicts the volume-averaged neutral hydrogen fraction from the given 21-cm map. The estimated neutral fraction has a good agreement with its truth value even after smoothing the 21-cm map with somewhat realistic choices of beam size and the frequency bandwidth of the Square Kilometre Array (SKA). Our technique could be further utilized to denoise the 21-cm map or constrain the properties of the radiation sources.