• 한국진공학회:학술대회논문집
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• 한국진공학회 2010년도 제39회 하계학술대회 초록집
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• pp.234-234
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• 2010

The PR transitions in asymmetric dot-in-double-quantum-well (DdWELL) photodetector is identified by bias-dependent spectral behaviors. Discrete n-i-n infrared photodetectors were fabricated on a 30-period asymmetric InAs-QD/[InGaAs/GaAs]/AlGaAs DdWELL wafer that was prepared by MBE technique. A 2.0-monolayer (ML) InAs QD ensemble was embedded in upper combined well of InGaAs/GaAs and each stack is separated by a 50-nm AlGaAs barrier. Each pixel has circular aperture of 300 um in diameter, and the mesa cell ($410{\times}410\;{\mu}m^2$) was defined by shallow etching. PR measurements were performed in the spectral range of $3{\sim}13\;{\mu}m$ (~ 100-400 meV) by using a Fourier-transform infrared (FTIR) spectrometer and a low-noise preamplifier. The asymmetric photodetector exhibits unique transition behaviors that near-/far-infrared (NIR/FIR) photoresponse (PR) bands are blue/red shifted by the electric field, contrasted to mid-infrared (MIR) with no dependence. In addition, the MIR-FIR dual-band spectra change into single-band feature by the polarity. A four-level energy band model is proposed for the transition scheme, and the field dependence of FIR bands numerically calculated by a simplified DdWELL structure is in good agreement with that of the PR spectra. The wavelength shift by the field strength and the spectral change by the polarity are discussed on the basis of four-level transition.

• 대기
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• 제31권3호
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• pp.341-359
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• 2021

This technical note introduces the new Korea Meteorological Administration (KMA) Global Seasonal forecasting system version 6 (GloSea6) to provide a reference for future scientific works on GloSea6. We describe the main areas of progress and improvements to the current GloSea5 in the scientific and technical aspects of all the GloSea6 components - atmosphere, land, ocean, and sea-ice models. Also, the operational architectures of GloSea6 installed on the new KMA supercomputer are presented. It includes (1) pre-processes for atmospheric and ocean initial conditions with the quasi-real-time land surface initialization system, (2) the configurations for model runs to produce sets of forecasts and hindcasts, (3) the ensemble statistical prediction system, and (4) the verification system. The changes of operational frameworks and computing systems are also reported, including Rose/Cylc - a new framework equipped with suite configurations and workflows for operationally managing and running Glosea6. In addition, we conduct the first-ever run with GloSea6 and evaluate the potential of GloSea6 compared to GloSea5 in terms of verification against reanalysis and observations, using a one-month case of June 2020. The GloSea6 yields improvements in model performance for some variables in some regions; for example, the root mean squared error of 500 hPa geopotential height over the tropics is reduced by about 52%. These experimental results show that GloSea6 is a promising system for improved seasonal forecasts.

• 대기
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• 제29권2호
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• pp.115-129
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• 2019

Surface winds over the ocean influence not only the climate change through air-sea interactions but the coastal erosion through the changes in wave height and direction. Thus, demands on a reliable projection of future changes in surface winds have been increasing in various fields. For the future projections, climate models have been widely used and, as a priori, their simulations of surface wind are required to be evaluated. In this study, we evaluate the climatological mean surface winds over the Korean Waters simulated by five regional climate models participating in Coordinated Regional Climate Downscaling Experiment (CORDEX) for East Asia (EA), an international regional climate model inter-comparison project. Compared with the ERA-interim reanalysis data, the CORDEX-EA models, except for HadGEM3-RA, produce stronger wind both in summer and winter. The HadGEM3-RA underestimates the wind speed and inadequately simulate the spatial distribution especially in summer. This summer wind error appears to be coincident with mean sea-level pressure in the North Pacific. For wind direction, all of the CORDEX-EA models simulate the well-known seasonal reversal of surface wind similar to the ERA-interim. Our results suggest that especially in summer, large-scale atmospheric circulation, downscaled by regional models with spectral nudging, significantly affect the regional surface wind on its pattern and strength.

• 한국농공학회논문집
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• 제61권1호
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• pp.1-8
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• 2019

Developing reliable soil moisture prediction techniques at agricultural regions is a pivotal issue for sustaining stable crop productions. In this study, a physically-based SWAP(Soil-Water-Atmosphere-Plant) model was suggested to estimate soil moisture dynamics at the study sites. ROSETTA was also integrated to derive the soil hydraulic properties(${\alpha}$, n, ${\Theta}_r$, ${\Theta}_s$, $K_s$) as the input variables to SWAP based on the soil information(Sand, Silt and Clay-SSC, %). In order to predict the soil moisture dynamics in future, the mid-term TIGGIE(THORPEX Interactive Grand Global Ensemble) and long-term S2S(Subseasonal to Seasonal) weather forecasts were used, respectively. Our proposed approach was tested at the six study sites of RDA(Rural Development Administration). The estimated soil moisture values based on the SWAP model matched the measured data with the statistics of Root Mean Square Error(RMSE: 0.034~0.069) and Temporal Correlation Coefficient(TCC: 0.735~0.869) for validation. When we predicted the mid-/long-term soil moisture values using the TIGGE(0~15 days)/S2S(16~46 days) weather forecasts, the soil moisture estimates showed less variations during the TIGGE period while uncertainties were increased for the S2S period. Although uncertainties were relatively increased based on the increased leading time of S2S compared to those of TIGGE, these results supported the potential use of TIGGE/S2S forecasts in evaluating agricultural drought. Our proposed approach can be useful for efficient water resources management plans in hydrology, agriculture, etc.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권10호
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• pp.3708-3728
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• 2021

Indian herbal plants are used in agriculture and in the food, cosmetics, and pharmaceutical industries. Laboratory-based tests are routinely used to identify and classify similar herb species by analyzing their internal cell structures. In this paper, we have applied computer vision techniques to do the same. The original leaf image was preprocessed using the Chan-Vese active contour segmentation algorithm to efface the background from the image by setting the contraction bias as (v) -1 and smoothing factor (µ) as 0.5, and bringing the initial contour close to the image boundary. Thereafter the segmented grayscale image was fed to a leaky capacitance fired neuron model (LCFN), which differentiates between similar herbs by combining different groups of pixels in the leaf image. The LFCN's decay constant (f), decay constant (g) and threshold (h) parameters were empirically assigned as 0.7, 0.6 and h=18 to generate the 1D feature vector. The LCFN time sequence identified the internal leaf structure at different iterations. Our proposed framework was tested against newly collected herbal species of natural images, geometrically variant images in terms of size, orientation and position. The 1D sequence and shape features of aloe, betel, Indian borage, bittergourd, grape, insulin herb, guava, mango, nilavembu, nithiyakalyani, sweet basil and pomegranate were fed into the 5-fold Bayesian regularization neural network (BRNN), K-nearest neighbors (KNN), support vector machine (SVM), and ensemble classifier to obtain the highest classification accuracy of 91.19%.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.27-33
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• 2021

The whole world now is dealing with Coronavirus, and it has turned to be one of the most widespread and long-lived pandemics of our times. Reports reveal that the infectious disease has taken toll of the almost 80% of the world's population. Amidst a lot of research going on with regards to the prediction on growth and transmission through Symptomatic carriers of the virus, it can't be ignored that pre-symptomatic and asymptomatic carriers also play a crucial role in spreading the reach of the virus. Classification Algorithm has been widely used to classify different types of COVID-19 carriers ranging from simple feature-based classification to Convolutional Neural Networks (CNNs). This research paper aims to present a novel technique using a Random Forest Machine learning algorithm with hyper-parameter tuning to classify different types COVID-19-carriers such that these carriers can be accurately characterized and hence dealt timely to contain the spread of the virus. The main idea for selecting Random Forest is that it works on the powerful concept of "the wisdom of crowd" which produces ensemble prediction. The results are quite convincing and the model records an accuracy score of 99.72 %. The results have been compared with the same dataset being subjected to K-Nearest Neighbour, logistic regression, support vector machine (SVM), and Decision Tree algorithms where the accuracy score has been recorded as 78.58%, 70.11%, 70.385,99% respectively, thus establishing the concreteness and suitability of our approach.

• 한국결정성장학회지
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• 제31권6호
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• pp.258-263
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• 2021

ZnO 바리스터는 다결정구조를 가지는 반도체 소자로 결정립과 입계의 미세구조 제어를 통해 비선형적인 전류/전압 특성을 가지기 때문에 서지(surge)전압으로부터 회로를 보호하는 역할을 한다. 이러한 ZnO 바리스터에서 원하는 전기적 물성을 얻기 위해서는 소결 공정에서 미세구조의 제어가 중요하다. 따라서 소결 공정에서 중요한 변수들과 소결체의 전기적 물성인 유전율로 구성된 데이터셋을 정의한 후 실험계획법 기반으로 데이터를 수집했다. 수집된 실험데이터셋을 기계학습 알고리즘에 학습하여 메타모델을 개발했고, 개발된 메타모델에 수치기반 최적화 알고리즘인 HMA(Hybrid Metaheuristic Algorithm)를 적용하여 최대 유전율을 가질 수 있는 공정조건을 도출했다. 이러한 메타모델 기반의 최적화를 다변수 시스템인 세라믹공정에 적용한다면 최소한의 실험만으로 최적 공정조건 탐색이 가능할 것으로 판단된다.

• 스마트미디어저널
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• 제12권2호
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• pp.9-14
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• 2023

이 논문에서는 gradCAM를 활용한 적은 데이터로 얼굴 전체 또는 더 다양한 feature을 사용하여 얼굴인식을 할 수 있는 새로운 앙상블 방법론을 제안하였다. 인공지능 모델의 판단 근거는 gradCAM을 통하여 saliency map으로 표현될 수 있다. 따라서 본 논문에서는 학습된 얼굴인식 모델이 어느 부분에 편향적으로 관찰하여 판단했는지 gradCAM으로 시각화한다. 계산된 saliency map에서 일정 수치 이상의 돌출된 부분을 추가 모델이 학습에 사용할 수 없도록 노이즈를 추가해 데이터를 생산한다. 노이즈를 추가해서 만든 데이터로 학습할 경우 노이즈 부분을 활용하여 학습을 할 수 없으므로 새로운 얼굴 부분을 사용하여 얼굴인식 네트워크를 학습하게 된다. 기본 데이터로 학습한 네트워크와 돌출 부분에 노이즈를 추가해서 학습한 모델은 얼굴의 서로 다른 얼굴 feature을 사용할 수밖에 없고, 앙상블로 결합했을 때 얼굴의 좀 더 다양한 부분들을 사용한 임베딩 feature를 만들 수 있다. 이 논문에서 제안하는 앙상블 기법은 일반적인 앙상블 모델보다 정확도는 1.79% 상승하였고 equal error rate (EER)은 0.01788 감소하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.162-162
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• 2023

Climate change is a complex phenomenon having its impact on diverse sectors. Temperature and precipitation are two of the most fundamental variables used to characterize climate, and changes in these variables can have significant impacts on ecosystems, agriculture, and human societies. This study evaluated the historical (1981-2010) and future (2011-2100) climatic trends in the Seti-Gandaki basin of Nepal based on 5 km resolution Multi Model Ensemble (MME) of 18 Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for SSP1-2.6, SSP2-4.5 and SSP5-85 scenarios. For this study, ERA5 reanalysis dataset is used for historical reference dataset instead of observation dataset due to a lack of good observation data in the study area. Results show that the basin has experienced continuous warming and an increased precipitation pattern in the historical period, and this rising trend is projected to be more prominent in the future. The Seti basin hosts 13 operational hydropower projects of different sizes, with 10 more planned by the government. Consequently, the findings of this study could be leveraged to design adaptation measures for existing hydropower schemes and provide a framework for policymakers to formulate climate change policies in the region. Furthermore, the methodology employed in this research could be replicated in other parts of the country to generate precise climate projections and offer guidance to policymakers in devising sustainable development plans for sectors like irrigation and hydropower.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.174-174
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• 2023

The impact of global warming on the south Asian summer monsoon is of critical importance for the large population of this region. This study aims to investigate the future changes of the precipitation extremes during pre-monsoon and monsoon, across this region in a more organized regional structure. The study area is divided into six major divisions based on the Köppen-Geiger's climate structure and 10 sub-divisions considering the geographical locations. The future changes of extreme precipitation indices are analyzed for each zone separately using five indices from ETCCDI (Expert Team on Climate Change Detection and Indices); R10mm, Rx1day, Rx5day, R95pTOT and PRCPTOT. 10 global climate model (GCM) outputs from the latest CMIP6 under four combinations of SSP-RCP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) are used. The GCMs are bias corrected using nonparametric quantile transformation based on the smoothing spline method. The future period is divided into near future (2031-2065) and far future (2066-2100) and then the changes are compared based on the historical period (1980-2014). The analysis is carried out separately for pre-monsoon (March, April, May) and monsoon (June, July, August, September). The methodology used to compare the changes is probability distribution functions (PDF). Kernel density estimation is used to plot the PDFs. For this study we did not use a multi-model ensemble output and the changes in each extreme precipitation index are analyzed GCM wise. From the results it can be observed that the performance of the GCMs vary depending on the sub-zone as well as on the precipitation index. Final conclusions are made by removing the poor performing GCMs and by analyzing the overall changes in the PDFs of the remaining GCMs.