• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.32-40
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• 2013

In this paper, we propose a multi-channel speech enhancement algorithm using beamforming and direction-of-arrival (DOA)-based masking. The proposed algorithm enhances noisy speech basically by the linearly constrained minimum variance (LCMV) algorithm and then a mel-scale Wiener filter designed using DOA-based masking is applied to remove still remaining noises. To improve the performance, we optimize the learning rate of the adaptive filters in LCMV and the DOA threshold to detect target speech spectrum. As performance indices, the perceptual evaluation of speech quality (PESQ) score and output SNRs are measured. Experimantal results show that the proposed algorithm outperforms the conventional LCMV beamformer by 0.09 in PESQ score and 5.75 dB in output SNR, respectively.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.63-75
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• 2022

This paper proposes the so-called MOSUM-based sparse projection method for change points detection in high-dimensional time series. Our method is inspired by Wang and Samworth (2018), however, our method improves their method in two ways. One is to find change points all at once, so it minimizes sequential error. The other is localized so that more robust to the mean changes offsetting each other. We also propose data-driven threshold selection using block wild bootstrap. A comprehensive simulation study shows that our method performs reasonably well in finite samples. We also illustrate our method to stock prices consisting of S&P 500 index, and found four change points in recent 6 years.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.411-411
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• 2023

미래 대기 이산화탄소 농도가 증가함에 따라 강수 등 기후의 변화하고, 이는 유출량을 포함한 수문 순환 뿐 아니라 지면 식생 생장에 영향을 줄 것으로 예상된다. 이에 본 연구에서는 미래 CO2 증가에 따른 식생의 변화와 이로 인한 지표 유출량의 변화에 대해 이해하고자 한다. Intergovernmental Panel on Climate Change (IPCC) 6차 평가보고서에서 제시한 표준 온실가스 경로 중 탄소 모듈이 포함된 Coupled Model Intercomparison Project phase 6 biogeochemistry (CMIP6-BGC) 모델과 탄소 모듈이 포함안된 CMIP6 모델 결과를 활용하였다. 공통 사회경제경로 시나리오(Shared Socio-economic Pathway; SSP) 중 고탄소 시나리오인 SSP585에 따른 모델 결과물을 활용하였다. 표면 유출량 자료에 과거 기간 임계수준 방법을 (Threshold Level Method) 적용하여 동아시아 지역 극한 건조 및 습윤 상태의 빈도와 강도를 CMIP6-BGC와 CMIP6에 대해 평가하였다. CMIP6-BGC 경우, 건조 및 습윤 상태의 빈도는 각각 6.17%, 5.03% , CMIP6 경우 각각 9.29%, 6.70% 으로 예측되어, CMIP6-BGC가 CMIP6 보다 극한 상태를 과소평가하는 경향을 보였다. 또한, 잎 면적 지수(Leaf Area Index; LAI), 증산량 등의 변수를 분석하여, 기 도출된 CMIP6-BGC와 CMIP6 간의 극한 건조 및 습윤 상태 예측의 차이가 발생한 메카니즘을 이해하고자 하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.95-95
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• 2021

전 세계 담수 자원의 예측 및 관리에 있어서 유출량 변화의 예측은 중요하다. 하지만 강수량 대비 유출량의 비율인 유출율이 수자원 시스템에 중요한 영향을 미치는지에 대한 여부는 여전히 불분명하다. 본 연구에서는 전 세계의 1,636개 관측소에서 관측된 유출량과 강수량 자료를 이용하여 최근 60년 (1956-2015) 동안 유출량, 강수량, 증발산량의 관계에 있어서 유출율의 역할에 관해 연구하였다. 수문 기후학적으로 습한 지역과 건조한 지역을 구분할 수 있는 임계값으로 유출율을 사용할 수 있다는 점을 Budyko 공간에서 적용하여 건조 지수와의 비교를 통해 발견할 수 있었다. 유출량 변화에 대한 상세한 분석을 위해 강수량 및 증발산량 변화율에 따라 6가지의 범주를 설정하였고 그 결과 대부분의 관측소에서는 강수량의 변화와 일관된 방향으로 유출량이 변화하는 것으로 밝혀져 유출량의 변화는 강수 변화에 더 민감하다는 결론을 얻었으며 모든 범주에 있어서 유출율은 강수량과 증발산량의 변화에 의한 유출량 변화의 크기에 상당한 영향이 있음이 밝혀졌다. 본 연구의 결과들을 통해 우리는 기후 변화에 따른 유출량 변화에 대한 예측성 향상에 있어서 유출율에 대한 물리적인 이해가 잠재적인 주요한 요소라고 제안한다. 토지 피복 및 토지 이용과 같은 인위적 영향은 유출율을 직접 감소 또는 증가시켜 유출량의 민감도를 변화시킬 수 있다. 본 연구에서 제안된 수치적인 접근 방식은 수자원 가용성에 대한 기후 변화 및 인위적 영향을 완화하기 위해 실행 가능한 정보를 제공할 것으로 보인다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.128-128
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• 2022

최근 호우의 빈도와 규모는 증가하는 추세이며 이에 따른 홍수 피해는 많은 피해를 야기하고 있다. 이러한 관점에서 홍수 피해에 대한 선제적 대응을 위한 요소로써 초단시간 강우예측 정보의 중요성은 매우 높다. 특히, 레이더 자료 기반의 강우예측은 수치예보모델과 비교하여 3시간 이내의 짧은 선행시간 이내의 높은 정확도를 갖고 있어 홍수예보에 다수 활용되고 있다. 최근에는 강우자료의 복잡한 관계와 특징을 고려하기 위해 딥러닝 기반의 강우예측 활용 사례가 증가하고 있으나 국내 적용 사례는 적어 관련 연구가 요구되는 실정이다. 본 연구에서는 레이더 강우를 활용한 딥러닝 기반의 강우예측 기법을 제안하고 이에 대한 적용성을 평가하고자 한다. 2차원 레이더 강우자료의 특징과 시계열 특성을 고려하기 위한 심층신경망 구조를 제안하였으며 기존 딥러닝 모형과의 비교를 통해 활용 가능성을 제시하고자 하였다. 적용 대상지역은 한강 유역으로 선정하였다. 정성적 평가를 위해 임계성공지수(CSI)를 활용하여 예측 강우에 대한 정확도를 평가하였으며 정량적 평가를 위해 예측 강우와 관측 강우의 상관관계를 분석하였다. 평가 결과, 제안하는 방법이 기존 모형과 비교하여 예측오차의 범위가 적고 강우의 위치 변화를 잘 반영하는 것으로 나타났다. 본 연구결과는 초단기간 강우예측 자료를 활용하는 홍수예보의 정확도 향상에 기여할 것으로 기대된다.

• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.507-519
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• 2017

The applications of NDVI (Normalized Difference Vegetation Index) as a vegetation index has been widely used to understand vegetation biomass and physiological activities. However, NDVI is not suitable way for monitoring vegetation stress because it is less sensitive to change in physiological state than biomass. PRI (Photochemical Reflectance Index) is well developed to present physiological activities of vegetation, particularly high-light-stress condition, and it has been adopted in several satellites to be launched in the future. Thus, the understanding of PRI performance and the development of analysis method will be necessary. This study aims to interpret the characteristics of light-stress-sensitive PRI in shadow areas and to evaluate the PRI calculated by other wavelengths (i.e., 488.9 nm, 553.6 nm, 646.9 nm, and 668.4 nm) instead of 570 nm that used in original PRI. Using airborne-based hyperspectral image, we found that PRI values were increased in shadow detection due to the reduction of high light induced physiological stress. However, the qualities of both PRI and NDVI data were dramatically decreased when the shadow index (SI) exceeded the threshold (SI<25). In addition, the PRI calculated using by 553.6 nm had best correlation with original PRI. This relationship was improved by multiple regression analysis including reflectances of RED and NIR. These results will be helpful to the understanding of physiological meaning on the application of PRI.

• Korean Journal of Materials Research
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• v.4 no.3
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• pp.268-279
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• 1994

The x-ray powtler pattern of single phase $Bi_2S_2CaCu_2O_{8+x}$ has been identified and fullyindexed using a pseudotetragonal subcell with a= 5.408, c = 30.83 $\AA$ and an incommensurate supercellwith reciprocal lattice vector, X$q^*$, given by $q^*=0.211b^*-c^*$. The x -ray powder pattern of the Pb-free110K superconductor phase "$Bi_2S_2CaCu_2O_{10+x}$" has many lines which belong t.o an incommensuratesupercell. Using elect.ron d~ffraction pImt.ographs as a indexing guide, an indexing scheme for the powderpattern has been obtained. The unit cell has a geometrically orthorhombic subcell a=5.411, b= 5.420, c=37.29(2) $\AA$. Supercell reflections have indices that are derived from the subcell k, 1 indices by addition uf$\pm q^*$, where $\pm q^*=0.211b^*-0.78c^*$The incommensurate con~ponent In the b dwection, $\delta$, is the same for both phases but on going from2212 to 2223 phase, the superlattic component in the c direction changes from commensurate($\varepsilon$=1) toincommensurate($\varepsilon$=0.78).X>$\varepsilon$=0.78).

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.57-62
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• 2023

This research is a comparative analysis of the U.S. S&P 500 index using the volatility breakout strategy against the Buy and Hold approach. The volatility breakout strategy is a trading method that exploits price movements after periods of relative market stability or concentration. Specifically, it is observed that large price movements tend to occur more frequently after periods of low volatility. When a stock moves within a narrow price range for a while and then suddenly rises or falls, it is expected to continue moving in that direction. To capitalize on these movements, traders adopt the volatility breakout strategy. The 'k' value is used as a multiplier applied to a measure of recent market volatility. One method of measuring volatility is the Average True Range (ATR), which represents the difference between the highest and lowest prices of recent trading days. The 'k' value plays a crucial role for traders in setting their trade threshold. This study calculated the 'k' value at a general level and compared its returns with the Buy and Hold strategy, finding that algorithmic trading using the volatility breakout strategy achieved slightly higher returns. In the future, we plan to present simulation results for maximizing returns by determining the optimal 'k' value for automated trading of the S&P 500 index using artificial intelligence deep learning techniques.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• Journal of Korea Water Resources Association
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• v.52 no.9
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• pp.615-626
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• 2019

Soil moisture and runoff have very close relationship. Especially the water retention capacity and drainage characteristics of the soil are determined by various factors of the soil. In this study, a total of 40 rainfall events were identified from the entire rainfall events of Sulma basin in 2016 and 2017. For each selected events, the constant-K method was used to separate direct runoff and baseflow from total flow and calculate the runoff coefficient which shows positive exponential curve with Antecedent Soil Moisture (ASM). In addition to that, the threshold of soil moisture was determined at the point where the runoff coefficient starts increasing dramatically. The threshold of soil moisture shows great correlation with runoff and depth to water table. It was founded that not only ASM but also various factors, such as Initial Soil Moisture (ISM), storage capacity of soil and precipitation, affect the results of runoff response. Furthermore, wet condition and dry condition are separated by ASM threshold and the start and peak response are analyzed. And the results show that the response under wet condition occurred more quickly than that of dry condition. In most events occurred in dry condition, factors reached peak in order of soil moisture, depth to water table and runoff. However, in wet condition, they reached peak in order of depth to water table, runoff and soil moisture. These results will help identify the interaction among factors which affect the runoff, and it will help establish the relationship between various soil conditions and runoff.