• 제목/요약/키워드: model initialization

검색결과 108건 처리시간 0.023초

겨울철 동아시아 지역 기온의 계절 예측에 눈깊이 초기화가 미치는 영향 (Impact of Snow Depth Initialization on Seasonal Prediction of Surface Air Temperature over East Asia for Winter Season)

  • 우성호;정지훈;김백민;김성중
    • 대기
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    • 제22권1호
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    • pp.117-128
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    • 2012
  • Does snow depth initialization have a quantitative impact on sub-seasonal to seasonal prediction skill? To answer this question, a snow depth initialization technique for seasonal forecast system has been implemented and the impact of the initialization on the seasonal forecast of surface air temperature during the wintertime is examined. Since the snow depth observation can not be directly used in the model simulation due to the large systematic bias and much smaller model variability, an anomaly rescaling method to the snow depth initialization is applied. Snow depth in the model is initialized by adding a rescaled snow depth observation anomaly to the model snow depth climatology. A suite of seasonal forecast is performed for each year in recent 12 years (1999-2010) with and without the snow depth initialization to evaluate the performance of the developed technique. The results show that the seasonal forecast of surface air temperature over East Asian region sensitively depends on the initial snow depth anomaly over the region. However, the sensitivity shows large differences for different timing of the initialization and forecast lead time. Especially, the snow depth anomaly initialized in the late winter (Mar. 1) is the most effective in modulating the surface air temperature anomaly after one month. The real predictability gained by the snow depth initialization is also examined from the comparison with observation. The gain of the real predictability is generally small except for the forecasting experiment in the early winter (Nov. 1), which shows some skillful forecasts. Implications of these results and future directions for further development are discussed.

전지구 계절 예측 시스템의 토양수분 초기화 방법 개선 (Improvement of Soil Moisture Initialization for a Global Seasonal Forecast System)

  • 서은교;이명인;정지훈;강현석;원덕진
    • 대기
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    • 제26권1호
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    • pp.35-45
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    • 2016
  • Initialization of the global seasonal forecast system is as much important as the quality of the embedded climate model for the climate prediction in sub-seasonal time scale. Recent studies have emphasized the important role of soil moisture initialization, suggesting a significant increase in the prediction skill particularly in the mid-latitude land area where the influence of sea surface temperature in the tropics is less crucial and the potential predictability is supplemented by land-atmosphere interaction. This study developed a new soil moisture initialization method applicable to the KMA operational seasonal forecasting system. The method includes first the long-term integration of the offline land surface model driven by observed atmospheric forcing and precipitation. This soil moisture reanalysis is given for the initial state in the ensemble seasonal forecasts through a simple anomaly initialization technique to avoid the simulation drift caused by the systematic model bias. To evaluate the impact of the soil moisture initialization, two sets of long-term, 10-member ensemble experiment runs have been conducted for 1996~2009. As a result, the soil moisture initialization improves the prediction skill of surface air temperature significantly at the zero to one month forecast lead (up to ~60 days forecast lead), although the skill increase in precipitation is less significant. This study suggests that improvements of the prediction in the sub-seasonal timescale require the improvement in the quality of initial data as well as the adequate treatment of the model systematic bias.

A Novel Parameter Initialization Technique for the Stock Price Movement Prediction Model

  • Nguyen-Thi, Thu;Yoon, Seokhoon
    • International journal of advanced smart convergence
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    • 제8권2호
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    • pp.132-139
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    • 2019
  • We address the problem about forecasting the direction of stock price movement in the Korea market. Recently, the deep neural network is popularly applied in this area of research. In deep neural network systems, proper parameter initialization reduces training time and improves the performance of the model. Therefore, in our study, we propose a novel parameter initialization technique and apply this technique for the stock price movement prediction model. Specifically, we design a framework which consists of two models: a base model and a main prediction model. The base model constructed with LSTM is trained by using the large data which is generated by a large amount of the stock data to achieve optimal parameters. The main prediction model with the same architecture as the base model uses the optimal parameter initialization. Thus, the main prediction model is trained by only using the data of the given stock. Moreover, the stock price movements can be affected by other related information in the stock market. For this reason, we conducted our research with two types of inputs. The first type is the stock features, and the second type is a combination of the stock features and the Korea Composite Stock Price Index (KOSPI) features. Empirical results conducted on the top five stocks in the KOSPI list in terms of market capitalization indicate that our approaches achieve better predictive accuracy and F1-score comparing to other baseline models.

철도여객수요예측을 위한 Holt-Winters모형의 초기값 설정방법 비교 (An Empirical Comparison of Initialization Methods for Holt-Winters Model with Railway Passenger Demand Data)

  • 김성호;홍순흠
    • 한국철도학회:학술대회논문집
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    • 한국철도학회 2001년도 추계학술대회 논문집
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    • pp.97.1-103
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    • 2001
  • Railway passenger demand forecasts may be used directly, or as inputs to other optimization model which is use the demand forecasts to produce estimates of other activities. The optimization models require demand forecasts at the most detailed level. In this environment exponential smoothing forecasting methods such as Holt-Winters are appropriate because it is simple and inexpensive in terms of computation. There are several initialization methods for Holt-Winters Model. The purpose of this paper is to compare the initialization methods for Holt-Winters model.

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The Effects of Typhoon Initialization and Dropwindsonde Data Assimilation on Direct and Indirect Heavy Rainfall Simulation in WRF model

  • Lee, Ji-Woo
    • 한국지구과학회지
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    • 제36권5호
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    • pp.460-475
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    • 2015
  • A number of heavy rainfall events on the Korean Peninsula are indirectly influenced by tropical cyclones (TCs) when they are located in southeastern China. In this study, a heavy rainfall case in the middle Korean region is selected to examine the influence of typhoon simulation performance on predictability of remote rainfall over Korea as well as direct rainfall over Taiwan. Four different numerical experiments are conducted using Weather Research and Forecasting (WRF) model, toggling on and off two different improvements on typhoon in the model initial condition (IC), which are TC bogussing initialization and dropwindsonde observation data assimilation (DA). The Geophysical Fluid Dynamics Laboratory TC initialization algorithm is implemented to generate the bogused vortex instead of the initial typhoon, while the airborne observation obtained from dropwindsonde is applied by WRF Three-dimensional variational data assimilation. Results show that use of both TC initialization and DA improves predictability of TC track as well as rainfall over Korea and Taiwan. Without any of IC improvement usage, the intensity of TC is underestimated during the simulation. Using TC initialization alone improves simulation of direct rainfall but not of indirect rainfall, while using DA alone has a negative impact on the TC track forecast. This study confirms that the well-suited TC simulation over southeastern China improves remote rainfall predictability over Korea as well as TC direct rainfall over Taiwan.

철도여객수요예측을 위한 Holt-Winters모형의 초기값 설정방법 비교 (An Empirical Comparison among Initialization Methods of Holt-Winters Model for Railway Passenger Demand Forecast)

  • 최태성;김성호
    • 한국철도학회논문집
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    • 제7권1호
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    • pp.9-13
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    • 2004
  • Railway passenger demand forecasts may be used directly, or as inputs to other optimization models use them to produce estimates of other activities. The optimization models require demand forecasts at the most detailed level. In this environment exponential smoothing forecasting methods such as Holt-Winters are appropriate because it is simple and inexpensive in terms of computation. There are several initialization methods for Holt-Winters Model. The purpose of this paper is to compare the initialization methods for Holt-Winters model.

Q-value Initialization을 이용한 Reinforcement Learning Speedup Method (Reinforcement learning Speedup method using Q-value Initialization)

  • 최정환
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2001년도 하계종합학술대회 논문집(3)
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    • pp.13-16
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    • 2001
  • In reinforcement teaming, Q-learning converges quite slowly to a good policy. Its because searching for the goal state takes very long time in a large stochastic domain. So I propose the speedup method using the Q-value initialization for model-free reinforcement learning. In the speedup method, it learns a naive model of a domain and makes boundaries around the goal state. By using these boundaries, it assigns the initial Q-values to the state-action pairs and does Q-learning with the initial Q-values. The initial Q-values guide the agent to the goal state in the early states of learning, so that Q-teaming updates Q-values efficiently. Therefore it saves exploration time to search for the goal state and has better performance than Q-learning. 1 present Speedup Q-learning algorithm to implement the speedup method. This algorithm is evaluated. in a grid-world domain and compared to Q-teaming.

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현업 기후예측시스템에서의 지면초기화 적용에 따른 예측 민감도 분석 (Application of Land Initialization and its Impact in KMA's Operational Climate Prediction System)

  • 임소민;현유경;지희숙;이조한
    • 대기
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    • 제31권3호
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    • pp.327-340
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    • 2021
  • In this study, the impact of soil moisture initialization in GloSea5, the operational climate prediction system of the Korea Meteorological Administration (KMA), has been investigated for the period of 1991~2010. To overcome the large uncertainties of soil moisture in the reanalysis, JRA55 reanalysis and CMAP precipitation were used as input of JULES land surface model and produced soil moisture initial field. Overall, both mean and variability were initialized drier and smaller than before, and the changes in the surface temperature and pressure in boreal summer and winter were examined using ensemble prediction data. More realistic soil moisture had a significant impact, especially within 2 months. The decreasing (increasing) soil moisture induced increases (decreases) of temperature and decreases (increases) of sea-level pressure in boreal summer and its impacts were maintained for 3~4 months. During the boreal winter, its effect was less significant than in boreal summer and maintained for about 2 months. On the other hand, the changes of surface temperature were more noticeable in the southern hemisphere, and the relationship between temperature and soil moisture was the same as the boreal summer. It has been noted that the impact of land initialization is more evident in the summer hemispheres, and this is expected to improve the simulation of summer heat wave in the KMA's operational climate prediction system.

Iterative LBG Clustering for SIMO Channel Identification

  • Daneshgaran, Fred;Laddomada, Massimiliano
    • Journal of Communications and Networks
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    • 제5권2호
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    • pp.157-166
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    • 2003
  • This paper deals with the problem of channel identification for Single Input Multiple Output (SIMO) slow fading channels using clustering algorithms. Due to the intrinsic memory of the discrete-time model of the channel, over short observation periods, the received data vectors of the SIMO model are spread in clusters because of the AWGN noise. Each cluster is practically centered around the ideal channel output labels without noise and the noisy received vectors are distributed according to a multivariate Gaussian distribution. Starting from the Markov SIMO channel model, simultaneous maximum ikelihood estimation of the input vector and the channel coefficients reduce to one of obtaining the values of this pair that minimizes the sum of the Euclidean norms between the received and the estimated output vectors. Viterbi algorithm can be used for this purpose provided the trellis diagram of the Markov model can be labeled with the noiseless channel outputs. The problem of identification of the ideal channel outputs, which is the focus of this paper, is then equivalent to designing a Vector Quantizer (VQ) from a training set corresponding to the observed noisy channel outputs. The Linde-Buzo-Gray (LBG)-type clustering algorithms [1] could be used to obtain the noiseless channel output labels from the noisy received vectors. One problem with the use of such algorithms for blind time-varying channel identification is the codebook initialization. This paper looks at two critical issues with regards to the use of VQ for channel identification. The first has to deal with the applicability of this technique in general; we present theoretical results for the conditions under which the technique may be applicable. The second aims at overcoming the codebook initialization problem by proposing a novel approach which attempts to make the first phase of the channel estimation faster than the classical codebook initialization methods. Sample simulation results are provided confirming the effectiveness of the proposed initialization technique.

개선된 Dual Active Contour Model을 이용한 물체 윤곽선 검출에 관한 연구 (A study on Object Contour Detection using improved Dual Active Contour Model)

  • 문창수;유봉길;이웅기
    • 한국컴퓨터정보학회논문지
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    • 제3권1호
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    • pp.81-94
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    • 1998
  • 영상에서 관심있는 물체의 윤곽선을 추출하기 위해서 Kass등은 Snakes라고 불리우는 능동적 윤곽선 모델(active contour model)을 제안하였다. Snakes 모델은 내부 에너지,영상 에너지, 외부 에너지라는 에너지 함수를 사용하여 물체의 윤곽선을 정의하는 모델로 이 에너지 함수를 최소화함으로써 물체의 윤곽선을 찾을 수 있다 이 모델은 속도가 느리며초기화에 민감하다. 이 문제를 개선하기 위해 Gunn은 두 개의 초기화를 이용하여 정확한 윤곽선을 추출하고 초기화에 덜 민감하도록 하였다. 이 방법은 기존의 다른 방법에 비해 정확한 윤곽선을 추출할 수 있었으나, 속도면 에서는 상당히 효율적이지 못하고 잡음에 민감하였다. 본 논문에서는 이 문제를 해결하기 위하여 snakes을 이루는 각 윤곽점에 8$\times$8크기의 윈도우를 적용하여 윈도우내의 화소에 대해서만 에너지 최소화 알고리즘을 적용하였다.본 논문에서 제안한 방법은 원 영상과 컵 영상의 윤곽선 추출에 적용하였다. 제안한 방법을사용하여 얼굴을 추적하므로써 가상현실등에 응용되고 물체의 움직임 추적에도 응용될 수 있다.

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