• 한국생산제조학회지
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• 제8권6호
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• pp.55-63
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• 1999

The acceleration of the performance of machine tools influences the development of the semi-conductor and optical technology as the development of NC and measurement technology. Because the measurement has been done to unload condition without considering of mechanical stiffness in the case of machining center as we measure the quasi-static error of machine tools on general study people who works on the spot has many problems on the data value. Also there are no satisfiable results until now in spite of many studys about this because the deflections of the table and the shaft supporting a workpiece influence, influence the accuracy of the table and shaft supporting a workpiece influence the accuracy of the workpiece. And there is doubt about the inspection method of measured error. In this paper Therefor we will help working more accurately on the spot by measuring analyzing displaying the defoec-tion of the table and support shaft when we load on the table and the support shaft of machining center using laser interfer-ometer. Also we try to settle new conception of the measurement method and more accurate grasp of the deflection tenden-cy by verifing the tendency of the error measured through the comparison of the simulated error measured through the comparison of the simulated error using ANSYS a common finite element analysis program which is able to measure heat deformation material deformation and error resulted from this study.

• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제55권4호
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• pp.199-203
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• 2006

This paper presents an ASER (Accelerated Soft Error Rate) integral model. The model is based on the facts that the generated EHP/s(electron hole pairs) are diminished after some residual range of the incident alpha particle, where residual range is a function of the incident angle and the capping layer thickness over the semiconductor junction. The ASER is influenced by the flux of the alpha particles, the junction area ratio, the alpha particle incident angle when the critical charge is same as the collected charge, and the sizes of the alpha source and the chip. The model was examined with 8M static RAM samples. The measured ASER data showed good agreement with the calculated values using the model. The ASER decreased exponentially with respect to the operational voltage. As the capping layer thickness increases up to $16{\mu}m$, the ASER increases, and after that thickness, the ASER decreases. The ASER increased as the depth of BNW increased from $0{\mu}m\;to\;4{\mu}m$. and then saturated. The ASER decreased as the node capacitance increased from 2fF to 5fF.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1996년도 가을 학술발표회 논문집
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• pp.486-491
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• 1996

The purpose of present study is to propose a improved damage detection and assessment algorithm that has its basis on the method of system identification. This method allows the use of composite data which is constitute of static displacements and eigenmodes. In the dynamic test, thecurvature and slope of mode shape are introduced to formulate the error responses. The effectiveness of the proposed staristical system identification method is investigated through simulated and experimental studies. Real test data obtained from measurements are used to identify the actual location of damage and to revise the design variables in a concrete structure.

• 한국정보과학회논문지:정보통신
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• 제30권6호
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• pp.755-763
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• 2003

전파(propagation) 오류가 빈번한 무선 이동 네트워크에서는 전송 성능을 향상하기 위해 FEC(Forward Error Correction)알고리즘을 채택한다. 그러나 정적인 FEC방식은 연속적으로 변화하는 전파 오류율에 알맞은 정정 코드(check code)를 적용하지 못해 성능이 저하된다. 본 논문에서는 변화하는 무선 채널의 전파 오류율에 따라 FEC의 정정도를 알맞게 결정하는 링크 계층용 적응적 FEC기법인 FECA(FEC-level Adaptation)를 제안한다. FECA는 오류율이 높고, 오류율이 천천히 변화하는 무선 환경에 알맞은 알고리즘이다. 일례로 전파 간섭이 있는 환경에서 센서(sensor) 네트워크는 평균 오류율이 $10^{-6}$이상이며 오류율이 평균 수백 밀리초 이상 지속되는 것으로 관찰되었다. FECA는 분석적인 무선채널 시뮬레이션과 패킷 트레이스 기반(trace-driven) 시뮬레이션에서 정적 FEC 알고리즘에 비해 최대 15%이상 성능을 향상하였다.

• 전기학회논문지
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• 제66권1호
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• pp.171-178
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• 2017

This paper studies the state estimation problem for static neural networks with time-varying delay. Unlike other studies, the controller scheme, which involves time-varying sampling and uncertainties, is first employed to design the state estimator for delayed static neural networks. Based on Lyapunov functional approach and linear matrix inequality technique, the non-fragile sampled-data estimator is designed such that the resulting estimation error system is globally asymptotically stable with $H_{\infty}$ performance. Finally, the effectiveness of the developed results is demonstrated by a numerical example.

• 동력기계공학회지
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• 제22권1호
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• pp.18-24
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• 2018

This paper focuses on systematic design about the membership ranges of the main design factors such as control error, control error rate, and sampling time for the fuzzy logic control of the variable speed drive refrigeration cycle. The upper and the lowest limit of the membership ranges are set up from the data of static characteristics obtained by experiments. Three kinds of membership ranges on the control error and the control error rate are tested by experiments. Especially, an effect of sampling time on control performance is also investigated in the same way. Experimental data showed the control error rate and the sampling time strongly effected on the control performance of the refrigeration cycle with a variable speed drive.

• Membrane and Water Treatment
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• 제9권2호
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• pp.115-121
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• 2018

Artificial neural network (ANN) simulation is used to predict the dynamic change of permeate flux during wheat starch industry wastewater microfiltration with and without static turbulence promoter. The experimental program spans range of a sedimentation times from 2 to 4 h, for feed flow rates 50 to 150 L/h, at transmembrane pressures covering the range of $1{\times}10^5$ to $3{\times}10^5Pa$. ANN predictions of the wastewater microfiltration are compared with experimental results obtained using two different set of microfiltration experiments, with and without static turbulence promoter. The effects of the training algorithm, neural network architectures on the ANN performance are discussed. For the most of the cases considered, the ANN proved to be an adequate interpolation tool, where an excellent prediction was obtained using automated Bayesian regularization as training algorithm. The optimal ANN architecture was determined as 4-10-1 with hyperbolic tangent sigmoid transfer function transfer function for hidden and output layers. The error distributions of data revealed that experimental results are in very good agreement with computed ones with only 2% data points had absolute relative error greater than 20% for the microfiltration without static turbulence promoter whereas for the microfiltration with static turbulence promoter it was 1%. The contribution of filtration time variable to flux values provided by ANNs was determined in an important level at the range of 52-66% due to increased membrane fouling by the time. In the case of microfiltration with static turbulence promoter, relative importance of transmembrane pressure and feed flow rate increased for about 30%.

• Geomechanics and Engineering
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• 제37권3호
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• pp.253-262
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• 2024

Dynamic properties are pivotal in soil analysis, yet their experimental determination is hampered by complex methodologies and the need for costly equipment. This study aims to predict dynamic soil properties using static properties that are relatively easier to obtain, employing machine learning techniques. The static properties considered include soil cohesion, friction angle, water content, specific gravity, and compressional strength. In contrast, the dynamic properties of interest are the velocities of compressional and shear waves. Data for this study are sourced from 26 boreholes, as detailed in a geotechnical investigation report database, comprising a total of 130 data points. An importance analysis, grounded in the random forest algorithm, is conducted to evaluate the significance of each dynamic property. This analysis informs the prediction of dynamic properties, prioritizing those static properties identified as most influential. The efficacy of these predictions is quantified using the coefficient of determination, which indicated exceptionally high reliability, with values reaching 0.99 in both training and testing phases when all input properties are considered. The conventional method is used for predicting dynamic properties through Standard Penetration Test (SPT) and compared the outcomes with this technique. The error ratio has decreased by approximately 0.95, thereby validating its reliability. This research marks a significant advancement in the indirect estimation of the relationship between static and dynamic soil properties through the application of machine learning techniques.

• 지구물리와물리탐사
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• 제5권3호
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• pp.193-198
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• 2002

시추공간 파선 토모그래피에 있어서 결과물로 얻어지는 지하의 속도구조를 왜곡시키는 요인으로는 source static, 초동발췌오차 및 부적절한 초기속도 모델에 의한 국부적인 최소값으로의 수렴 등으로 요약된다. 본 논문에서는 이러한 오차생성 요인들 중에서 source static을 자동으로 보정해주는 알고리즘을 소개하고 있다. 소개된 알고리즘은 발췌된 초동자료의 송신기방향으로의 변화를 이용하여 정보정을 자동으로 계산하는 방법으로서, 실제 자료에 대해 적용해 본 결과 매우 만족스러운 결과를 보였으며, 사용자로 하여금 일관되며 자동적인 정보정 적용으로 인해서, 보다 신뢰할 수 있는 속도구조를 얻는데 도움을 줄 수 있을 것으로 예상된다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.146-151
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• 1994

An empirical comparison of static fuzzy relational models which are identified with different fuzzy implication operators and inferred by different composition operators is made in case that all the information is represented by the fuzzy discretization. Four performance measures (integral of mean squared error, maximal error, fuzzy equality index and mean lack of sharpness) are adopted to evaluate and compare the quality of the fuzzy relational models both at the numerical level and logical level. As the results, the fuzzy implication operators useful in various fuzzy modeling problems are discussed and it is empirically shown that the selection of data pairs is another important factor for identifying the fuzzy model with high quality.