• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.709-716
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• 2004

This paper describes an intelligent real-time monitoring system of a semiconductor processing equipment, which determines normal or not for a wafer in processing, using multiple time-series pattern recognition. The proposed system consists of three phases, initialization, learning and real-time prediction. The initialization phase sets the weights and tile effective steps for all parameters of a monitoring equipment. The learning phase clusters time series patterns, which are producted and fathered for processing wafers by the equipment, using LBG algorithm. Each pattern has an ACI which is measured by a tester at the end of a process The real-time prediction phase corresponds a time series entered by real-time with the clustered patterns using Dynamic Time Warping, and finds the best matched pattern. Then it calculates a predicted ACI from a combination of the ACI, the difference and the weights. Finally it determines Spec in or out for the wafer. The proposed system is tested on the data acquired from etching device. The results show that the error between the estimated ACI and the actual measurement ACI is remarkably reduced according to the number of learning increases.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.2
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• pp.89-102
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• 2024

Land Surface Phenology (LSP) plays a crucial role in understanding vegetation dynamics. The near-infrared reflectance of vegetation (NIRv) has been increasingly adopted in LSP studies, being recognized as a robust proxy for gross primary production (GPP). However, NIR v is sensitive to the terrain effects in mountainous areas due to artifacts in NIR reflectance cannot be canceled out. Because of this, estimating phenological metrics in mountainous regions have a substantial uncertainty, especially in the end of season (EOS). The topographically corrected NIRv (TCNIRv) employs the path length correction (PLC) method, which was deduced from the simplification of the radiative transfer equation, to alleviate limitations related to the terrain effects. TCNIRv has been demonstrated to estimate phenology metrics more accurately than NIRv, especially exhibiting improved estimation of EOS. As the topographic effect is significantly influenced by terrain properties such as slope and aspect, our study compared phenology metrics estimations between south-facing slopes (SFS) and north-facing slopes (NFS) using NIRv and TCNIRv in two distinct mountainous regions: Gwangneung Forest (GF) and Odaesan National Park (ONP), representing relatively flat and rugged areas, respectively. The results indicated that TCNIR v-derived EOS at NFS occurred later than that at SFS for both study sites (GF : DOY 266.8/268.3 at SFS/NFS; ONP : DOY 262.0/264.8 at SFS/NFS), in contrast to the results obtained with NIRv (GF : DOY 270.3/265.5 at SFS/NFS; ONP : DOY 265.0/261.8 at SFS/NFS). Additionally, the gap between SFS and NFS diminished after topographic correction (GF : DOY 270.3/265.5 at SFS/NFS; ONP : DOY 265.0/261.8 at SFS/NFS). We conclude that TCNIRv exhibits discrepancy with NIR v in EOS detection considering slope orientation. Our findings underscore the necessity of topographic correction in estimating photosynthetic phenology, considering slope orientation, especially in diverse terrain conditions.

• Journal of Broadcast Engineering
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• v.16 no.3
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• pp.416-426
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• 2011

In this paper, we proposed an intra coding method with merging intra prediction mode to achieve intra coding gain. The proposed method uses signaling of prediction mode with merging prediction modes, which is different from the conventional method. If the number of blocks that has the same prediction mode compared to that to be predicted from neighboring blocks exceeds the predefined threshold, then the proposed method is used in order to reduce bits of intra prediction mode for coding efficiency. Otherwise the conventional method is used. Experimental results show the proposed method achieves the PSNR gain of about 0.05 dB in RD curve and reduces the bit rates about 1 % compared with H.264/AVC. In particular, the PSNR gain of about 0.1 dB in RD curve and reduces the bit rates about 1.7 % compared with H.264/AVC at low bit-rates. we can know that the proposed method is efficient tool at low bit-rates.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.56 no.3
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• pp.233-243
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• 2011

This experiment was conducted to investigate the effect of future climate change on growing period and temperature in different rice maturity types as global warming progressed, where Odaebyeo, Hwaseongbyeo, Ilpumbyeo were used as a representative cultivar of early, medium, and medium-late rice maturity type, respectively, and A1B scenario was applied to weather data for future climate change at 57 sites in Korea. When cropping season was not adjusted to climate change, entire growing period and growing temperature were shorten and risen, respectively, as global warming progressed. On the other side, when cropping season was adjusted to climate change, growing period and temperature after heading date were not changed in contrast to growing period and growing temperature before heading which were more seriously shortened and risen as global warming progressed than in not adjusted cropping season. It is supposed that adjusting cropping season to climate change can alleviate rice yield reduction and quality deterioration to some degree by improving growing temperature condition during grain-filling period, but also still have a limit such as seriously shortened growing period indicating that there need to develope actively new rice cultivation methods and varieties for future climate change.

• Proceedings of the Korean Vacuum Society Conference
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• 1999.07a
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• pp.232-232
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• 1999

교류형 플라즈마 방전 표시기(AC Plasma Display Panel, AC PDP)에 사용되는 플라즈마는 그 부피가 너무 작아서 플라즈마에 변화를 일으키지 않고 그 물성을 관측하기란 쉬운일이 아니다. 그래서 주로 PDP 내의 물성을 관측하는 데 시뮬레이션에 의존하게 된다. 그 물성중에 PDP내의 전계 분포에 대한 정보는 방전의 형성 및 소멸에 대한 많은 단서를 제공하고 있다. 특히 AC PDP의 경우, 유전체에 형성되는 벽적하(wall charge)가 방전의 형성 및 PDP 구동에 중요한 역할을 하는데, 이는 PDP 내의 전계 분포를 살펴봄으로써 대략 예측할 수 있다. 본 연구에서는 시뮬레이션에 의존하지 않고, 직접 레이저 유도 형광법을 이용하여 AC PDP 내의 전계를 측정하였다. 방전 가스인 헬륨(He)의 에너지 준위는 전계의 크기에 따라 에너지 준위가 변화하여, Rydberg(n$\geq$8) 준위가 여러 개의 준위로 나누어지는 현상이 일어나는데, 이를 Stack 효과라고 한다. 따라서 전계의 세기가 커짐에 따라서 각 준위와 준위 사이 값(splitting)이 커지는데, 이를 이용하면 전계를 측정할 수 있다. 즉, 헬륨 원자를 여기시키는 레이저 파장을 변화시키면서 관측되는 레이저 유도 형광 신호를 관측하면, 준위의 splitting을 관측할 수 있다. 본 연구에서는 PDP 내의 전계의 시간적 변화를 관측하였다. 50%, 40kHz의 구형파를 PDP의 두 전극에 가하였을 때, 플라즈마가 켜진 상태뿐만 아니라 플라즈마가 꺼진 후에도 전계에 의한 Splitting 신호가 관측이 되었는데, 전계로 환산하였을 때, 그 값은 대략 수 kV/cm의 값을 갖았는데, 이는 wall charge에 의한 값으로 사료된다.결과로 생각되어진다.플라즈마의 강도값을 입력하여 플라즈마의 radiation을 검출하고, 스퍼터링 공정중 실질적인 in-situ 정보로 이용하였다. PEM을 통하여 In/Sn의 플라즈마 강도변화를 조사하였다. 초기 In/Sn의 플라즈마 강도(intensity)는 강도를 100하여, 산소를 주입한 결과, plasma intensity가 35 줄어들었고, 이때 우수한 ITO 박막을 얻을 수 있었다. Pulsed DC power를 사용하여 아크 현상을 방지하였다. PET 상에 coating 된 ITO 박막의 표면저항과 광투과도는 4-point prove와 spectrophotometer를 이용하여 분석하였고, AES로 박막의 두께에 따른 성분비를 확인하였다. ITO 박막의 광투과도는 산소의 유량과 sputter 된 In/Sn ion의 plasma emission peak에 따라 72%-92%까지 변화하였으며, 저항은 37$\Omega$/$\square$ 이상을 나타내었다. 박막의 Sn/In atomic ratio는 0.12, O/In의 비율은 In2O3의 화학양론적 비율인 1.5보다 작은 1.3을 나타내었다.로 보인다.하면 수평축과 수직축의 분산 장벽의 비에 따라 cluster의 두께비가 달라지는 성장을 볼 수 있었고, 한 축 방향으로의 팔 넓이는 fcc(100) 표면의 경우 동일한 Ed+Ep값에 대응하는 팔 넓이와 거의 동일한 결과가 나타나는 것을 볼 수 있다. 따라서 이러한 비대칭적인 모양을 가지는 성장의 경우도 cluster 밀도, cluster 모양, cluster의 양 축 방향 길이 비, 양 축 방향의 평균 팔 넓이로부터 각 축 방향의 분산 장벽을 얻어낼 수 있을 것으로 보인다. 기대할 수 있는 여러

• Journal of the Korean Wood Science and Technology
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• v.37 no.1
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• pp.48-55
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• 2009

In the glulam beam deflection it is necessary to check the reliability of theory formula, because of wood anisotropy and wood qualities (knot, slop of grain). In this experiment, when bending stress occurred on glulam, practice deflection of glulam measuring with AICON DPA-Pro 3D system were compared with prediction deflection calculated as substituting MOE through non-destructive testing and static MOE through bending test in differential equation of deflection curve. MOE using ultrasonic wave tester of laminae, MOE using natural frequencies of longitudinal vibrations ($E_{cu}$, $E_{cf}$), MOE using ultrasonic wave tester of glulam ($E_{gu}$) and MOE using natural frequencies of longitudinal vibrations ($E_{gf}$) were substituted in this experiment. When practice deflection measured by 3D system was compared with prediction deflection calculated with differential equation of deflection curve, within proportional limit the ratio of practice deflection and prediction deflection was similar as 1.12 and 1.14, respectively. Deflection using ultrasonic wave tester was 0.89 and 0.95, Deflection using natural frequencies of longitudinal vibrations was 1.07 and 1.10. The results showed that prediction deflection calculated by substituting using non-destructive MOE of glulam having anisotropy in differential equation of deflection curve was agreed well with practice deflection.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.5
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• pp.505-511
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• 2023

In the present study, a deep learning model was established to predict the motion response of small fishing vessels. Hydrodynamic performances were evaluated for two small fishing vessels for the dataset of deep learning model. The deep learning model of the Long Short-Term Memory (LSTM) which is one of the recurrent neural network was utilized. The input data of LSTM model consisted of time series of six(6) degrees of freedom motions and wave height and the output label was selected as the time series data of six(6) degrees of freedom motions. The hyperparameter and input window length studies were performed to optimize LSTM model. The time series motion response according to different wave direction was predicted by establised LSTM. The predicted time series motion response showed good overall agreement with the analysis results. As the length of the time series increased, differences between the predicted values and analysis results were increased, which is due to the reduced influence of long-term data in the training process. The overall error of the predicted data indicated that more than 85% of the data showed an error within 10%. The established LSTM model is expected to be utilized in monitoring and alarm systems for small fishing vessels.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2007.12a
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• pp.31-33
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• 2007

최근에 선박이 대형화, 고속화되면서 선수 충격파 영향으로 인하여 선체 또는 화물에 잦은 손상을 초래하기도 하고, 극단적인 상황에서는 선박이 절단되기도 한다. 본 논문에서는 내항성능 평가요소 중 하나인 상하가속도 값을 선교에 설치된 선체감시장치를 이용하여 해상상태별 변화량을 계측하고, 이를 모형시험 및 이론계산 결과와 상호 비교하였다. 또한 ITTC 에서 제시하는 내항성능 기준치와도 비교함으로써 실습선 한바다호의 내항성능을 확인하였다. 이러한 결과는 앞으로 경험할 수 있는 다양한 해상상태에서의 작업가능성 및 위험성 정도의 예측을 가능하게 함으로써 선박의 안전운항에 큰 도움이 될 것으로 판단되며, 또한 다양한 실선계측 자료를 통하여 조선소에서는 보다 우수한 성능의 선박 건조가 가능하리라 본다.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.05c
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• pp.85-91
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• 1996

원전 증기발생기 시제 전열관으로 제조된 Alloy 600 및 690 에 대하여 ASTM 규정 (B163-86a)에 따라 확관실험을 실시하여 평가하였으며, 관 요소에 작용하는 응력을 해석하였다. 실험 결과 시제 전열관은 ASTM에서 요구하는 확관율 30% 및 그 이상의 35% 까지 확관할 경우에도 양호한 확관상태를 보였다. 확관에 따른 유동곡선의 축력은 Alloy 690 이 Alloy 600 에 비해 높았으며, 확관율의 증가에 따라 차이가 점진적으로 크지는 경향을 보였다. 얇은 벽 튜브의 확관에 대한 응력 해석식은 Modified Tresca's Yield Criterion를 도입하여 얻었으며, 소성변형식을 이용하여 확관율에 따른 응력을 예측하였다. 유동곡선의 이론 계산치와 실험치를 비교한 결과 Alloy 600의 경우 이론치는 실험치보다 약간 낮은 값으로 잘 일치되었으나, Alloy 690 경우는 Alloy 600에 비하여 확관율의 증가에 따라 차이가 커지는 경향을 보였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.1-18
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• 2023

With the recent developments in big data and deep learning, a variety of traffic information is collected widely and used for traffic operations. In particular, long short-term memory (LSTM) is used in the field of traffic information prediction with time series characteristics. Since trends, seasons, and cycles differ due to the nature of time series data input for an LSTM, a trial-and-error method based on characteristics of the data is essential for prediction models based on time series data in order to find hyperparameters. If a methodology is established to find suitable hyperparameters, it is possible to reduce the time spent in constructing high-accuracy models. Therefore, in this study, a traffic information prediction model is developed based on highway vehicle detection system (VDS) data and LSTM, and an impact assessment is conducted through changes in the LSTM evaluation indicators for each hyperparameter. In addition, a methodology for finding hyperparameters suitable for predicting highway traffic information in the transportation field is presented.