• Journal of Digital Convergence
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• v.17 no.8
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• pp.205-219
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• 2019

As the education and training is such a virtual environment is applied to various fields, its usability is endless. However, there is an underestimation of the depth of perception in the training environment. In order to solve this problem, we tried to solve the problem by applying the top-down correction method. However, it is difficult to classify the result as a learning effect or perception change. In this study, it was confirmed that the proportion of spatial components of urine had a significant effect on the depth perception, and it was confirmed that the size perception were corrected together. In this study, we propose a correction method using spatial component and depth perception to improve the accuracy of depth perception.

• Korean Journal of Cognitive Science
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• v.4 no.1
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• pp.177-212
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• 1993

My research is based on the parser for ill-formed input by Mellish in a paper in ACL 27th meeting Proceedings. 1989. My system is composed of two parsers:WFCP and IFCP. When WFCP fails to give the parse tree for the input sentence, the sentence is identified as ill-formed and is parsed by IFCP for error detection and recovery at the syntactic level. My system is indendent of grammatical rules. It does not take into account semantic ill-formedness. My system uses a grammar composed of 25 context-free rules. My system consistes of two major parsing strategies:top-down expection and bottem-up satisfaction. With top-down expectation. rules are retrieved under the inference condition and expaned by inactive arcs. When doing bottom-up parsing. my parser used two modes:Left-Right parsing and Right-to-Left parsing. My system repairs errors sucessfully when the input contains an omitted word or an unknown word substitued for a valid word. Left- corner and right-corner errors are more easily detected and repaired than ill-formed senteces where the error is in teh middle. The deviance note. with repair details, is kept in new inactive arcs which are generated by the error correction procedure. The implementation of my system is quite different from Mellish's. When rules are invoked. my system invokes all rules with minimal inference. My bottom up parsing strategy uses Left-to-Right mode and Right-to-Left mode. My system is bottom-up-parsing-oriented like the chart parser. Errors are repaired in two ways:using top-down hypothesis, and using Need-Chart which keeps the information of expectation and complection of expanded goals by rules. To reduce the number of top-down cycles. all rules are invoked simultaneously and this invocation information is kept in Need-Chart. This idea will be extended for the implementation of multiple error recovery system.

• JSTS:Journal of Semiconductor Technology and Science
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• v.6 no.1
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• pp.1-9
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• 2006

We introduce our in-house program, NANOCAD, for the modeling and simulation of carrier transport in nanoscale MOSFET devices including quantum-mechanical effects, which implements two kinds of modeling approaches: the top-down approach based on the macroscopic quantum correction model and the bottom-up approach based on the microscopic non-equilibrium Green’s function formalism. We briefly review these two approaches and show their applications to the nanoscale bulk MOSFET device and silicon nanowire transistor, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10C
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• pp.1019-1024
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• 2007

In this paper, the 10bit 20MHz pipelined analog-to-digital converter that is able to apply to WLAN system was modeled for ADC design. Each blocks in converter such as sample and hold amplifier(SHA), comparator, multiplyng DAC(MDAC), and digital correction logic(DCL) was modeled. The pipelined ADC with these modeled blocks takes 1/50 less time than the one of simulation using HSPICE.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.3120-3122
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• 1999

In this paper we have designed the signal processing system for driving the Poly-Si TFT LCD of EWS. The signal processing system consist of timing controller, ramp signal generator and video signal processing system. Timing controller includes the top-down inversion. left right inversion, left-right shifting and control signal generator according to multi-source signal. The video signal processing system generates sawtooth-shaped waveform by using PROM and DAC for multi-gray scales and implements gamma correction function for compensating the TFT-LCD nonlinear charcteristic of the TFT-LCD. Finally we have discussed the experiment results and its application according to the designed TFT-LCD driving system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.465-468
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• 2022

The incidence of traffic accidents is increasing every year, and Korea is among the top OECD countries. In order to improve this, various road traffic laws are being implemented, and various traffic control methods using equipment such as unmanned speed cameras and traffic control cameras are being applied. However, as drivers avoid crackdowns by detecting the location of traffic control cameras in advance through navigation, a mobile crackdown system that can be cracked down is needed, and research is needed to increase the recognition rate of vehicle license plates on the side of the road for accurate crackdown. This paper proposes a method to improve the vehicle number recognition rate on the road side by applying a gradient correction algorithm using image processing. In addition, custom data learning was conducted using a CNN-based YOLO algorithm to improve character recognition accuracy. It is expected that the algorithm can be used for mobile traffic control cameras without restrictions on the installation location.