• The Korean Journal of Financial Management
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• v.20 no.1
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• pp.93-124
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• 2003

This paper studies the financial analyst's forecasting activities on the firm's operating performance during the period from 1999 to 2003. In this study, financial analyst's forecasting activities are focused on the sales, operating income and net income and financial analyst's forecasting accuracy, forecasting revising patterns and forecasting activities to the unexpected firm's operating performance are studied. Some empirical findings in this study are as follows. First, standard estimate error on the sales, operating income and net income are all significantly negative value and so financial analyst's forecast on the firm's operating performance are upwardly biased. Second, domestic financial analyst's forecasting activities is relatively more accuracy than foreign financial analyst's forecasting activities. Third, forecasting time is more close to the end of the operating performance announcement day, forecasting activities are more accuracy. Fourth, comparing with individual financial analyst's forecast, consensus forecast is more accuracy. Fifth, in the comparative forecasting activities study according to the prior firm's operating performance, financial analyst's forecasting revision activities are found to be upward or downward. Sixth, financial analysts overreact in the sales forecast and underreact in the operating income and net income forecast. Seventh, in the empirical analysis on the Easterwood-Nutt's test model(1999) which the firm's performance change are divided into the expected performance change and the unexpected performance change, it is found that financial analyst's forecasting activities on the firm's operating performance are systematically optimistic.

• Journal of KIISE:Computer Systems and Theory
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• v.26 no.9
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• pp.1158-1165
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• 1999

조건부 분기 명령어(conditional branch instruction)의 잘못된 분기 예측(branch misprediction)은 프로세서의 성능 향상에 심각한 장애 요인이 되고 있다. 특히 시분할(time-sharing) 시스템과 같이 문맥 교환(context switch)이 발생하는 멀티프로그래밍 환경(multiprogramming environment)에서는 더욱 낮은 분기 예측 정확성(branch prediction accuracy)을 보인다. 본 논문에서는 문맥 교환이 발생하는 멀티프로그래밍 환경에서 높은 분기 예측 정확성을 보이는 중첩 분기 예측표 교환(Overlapped Predictor Table Switch, OPTS) 기법을 소개한다. 분기 예측표(predictor table)를 분할하여 각각의 프로세스(process)에 할당하는 OPTS 기법은 문맥 교환의 영향을 최소화함으로써 높은 분기 예측 정확성을 유지하는 분기 예측 방법이다.Abstract There is wide agreement that one of the most important impediments to the performance of current and future pipelined superscalar processors is the presence of conditional branches in the instruction stream. Accurate branch prediction is required to overcome this performance limitation. Many branch predictors have been proposed to help to alleviate this problem, including the two-level adaptive branch predictor, and more recently, hybrid branch predictor. In a less idealized environment, such as a time-sharing system, code of interest involves context switches. Context switches, even at fairly large intervals, can seriously degrade the performance of many of the most accurate branch prediction schemes. In this study, we measure the effect of context switch on the branch prediction accuracy in various situation and show the feasibility of our new mechanism, OPTS(Overlapped Predictor Table Switch), which save and restore branch history table at every context switch.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.381-381
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• 2020

자료동화(Data Assimilation) 기법은 실시간 수문학적 예측에 있어 정확도 향상을 위해 필수적인 과정이다. 가장 대중적으로 사용되는 기법들 중 하나가 모델 상태변수와 매개변수를 동시에 업데이트할 수 있는 이중 앙상블 칼만 필터(Dual Ensemble Kalman Filter)이다. 이 방법은 정확도 개선 및 적용의 용이성 때문에 많은 연구 분야에서 사용되어져 왔지만, 앙상블을 생성하는 과정에서 상당시간이 소요되는 단점이 존재한다. 본 연구에서는 상태변수와 매개변수를 동시에 업데이트 하면서 홍수 예측의 정확성을 보장할 뿐만 아니라, 앙상블 생성에 있어 계산 효율을 크게 향상시킬 수 있는 기법을 제안한다. Polynomial Chaos Expansion(PCE) 기법을 사용하여 앙상블 칼만 필터를 모방(mimic)할 수 있는 새로운 대체필터(Surrogate Filter)를 개발하는 것을 목표로 한다. 구체적으로 대체필터를 구성하기 위한 다양한 필터를 설계하였다. 첫째 시간에 대해서 PCE가 변화하지 않는 '불변 필터'(즉, 전체 예측기간에 대해 하나의 필터를 사용하여 자료동화할 수 있는 대체필터)와, 매 시간마다 PCE가 변화하는 '시변 필터'(즉, 예측하는 매 시간마다 새로운 필터를 생성해야 하는 대체필터)를 설계하여 적용성, 정확성, 예측성 등을 비교하였다. 또한, PCE의 하이퍼 매개변수를 최적화하기 위한 최적의 프레임 워크가 제안되어, 대체필터를 구축하는 데 효율을 높이고 PCE의 과적합(overfitting) 현상을 피할 수 있도록 하였다. 본 연구에서 제안된 기법은 기존 단일 및 이중 앙상블 칼만 필터(EnKF)의 결과와 비교 검증하였으며, 그 결과는 다음과 같다. (1) 대체필터의 대부분은 원래 EnKF와 비슷한 정도의 불확실성을 설명할 수 있음; (2) 모든 대체 필터는 선행시간이 짧은 경우의 예측에 있어 우수한 결과를 제공하며, 시변 필터가 불변 필터보다 더 정확한 예측 결과를 제공함; (3) 대체필터는 원래 앙상블 칼만필터보다 최대 500배 빠른 속도로 성능을 향상시킬 수 있음. 제안된 대체필터는 자료동화를 수행하는 기존필터와 비슷한 정도의 정확성, 매우 향상된 효율성을 보장함을 확인할 수 있었다.

• Journal of Animal Environmental Science
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• v.18 no.3
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• pp.257-266
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• 2012

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid, accurate method of evaluating some chemical constituents in cereal and dired animal forages. Analysis of forage quality by NIRS usually involves dry grinding samples. Costs might be reduced if samples could be analyzed without drying or grinding. The objective of this study was to investigate effect of sample preparations on prediction ability of chemical composition and fermentation parameter for Italian ryegrass silages by NIRS. A population of 147 Italian ryegrass silages representing a wide range in chemical parameters were used in this investigation. Samples were scanned at 1nm intervals over the wavelength range 680-2500 nm and the optical data recorded as log 1/Reflectance (log 1/R) and scanned in oven-dried grinding and fresh ungrinding condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with four spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV) and maximizing the correlation coefficient of cross validation (${R^2}_{CV}$). The results of this study show that NIRS predicted the chemical parameters with high degree of accuracy in oven-dried grinding treatment except for moisture contents. Prediction accuracy of the moisture contents was better for fresh ungrinding treatment (SECV 1.37%, $R^2$ 0.96) than for oven-dried grinding treatments (SECV 4.31%, $R^2$ 0.68). Although the statistical indexes for accuracy of the prediction were the lower in fresh ungrinding treatment, fresh treatment may be acceptable when processing is costly or when some changes in component due to the processing are expected. Results of this experiment showed the possibility of NIRS method to predict the chemical composition and fermentation parameter of Italian ryegrass silages as routine analysis method in feeding value evaluation and for farmer advice.

• Journal of Energy Engineering
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• v.4 no.3
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• pp.387-393
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• 1995

본 연구에서는 단기 전력수요예측에 있어서 필요한 데이터의 수와 계산시간을 경감하면서 보다 정확성을 기할 수 있는 앨고리즘의 개발을 위하여 이에 적합한 칼만필터링 앨고리즘을 고찰하였다. 또한 칼만필터 앨고리즘을 토대로 필터의 모형화를 통하여 단기 전력수요를 예측할 수 있는 실시간 동적예측 모형을 구축하고 그 적용 가능성을 시험하였다.

• Journal of the Korea Society of Computer and Information
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• v.8 no.3
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• pp.113-123
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• 2003

Many researches on the application of neural networks for making financial index prediction have proven their advantages over statistical and other methods. In this paper, a neural network model is proposed for the Buying, Holding or Selling timing prediction in stocks by the price index of stocks by inputting the closing price and volume of dealing in stocks and the technical indexes(MACD, Psychological Line). This model has an equalized multi-layer arithmetic function as well as the time series prediction function of backpropagation neural network algorithm. In the case that the numbers of learning data are unbalanced among the three categories (Buying, Holding or Selling), the neural network with conventional method has the problem that it tries to improve only the prediction accuracy of the most dominant category. Therefore, this paper, after describing the structure, working and learning algorithm of the neural network, shows the equalized multi-layer arithmetic method controlling the numbers of learning data by using information about the importance of each category for improving prediction accuracy of other category. Experimental results show that the financial index prediction using the equalized multi-layer arithmetic neural network has much higher correctness rate than the other conventional models.

• Phonetics and Speech Sciences
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• v.15 no.4
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• pp.1-10
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• 2023

This study investigated the relative effectiveness of letter-based versus syllable-based measures of speech rate and articulation rate in predicting the articulation score, prosody fluency, and rating sum using "English speech data of Koreans for education" from AI Hub. We extracted and analyzed 900 utterances from the training data, including three balanced age groups (13, 19, and 26 years old). The study built three models that best predicted the pronunciation assessment scores using linear mixed-effects regression and compared the predicted scores with the actual scores from the validation data (n=180). The correlation coefficients between them were also calculated. The findings revealed that syllable-based measures of speech and articulation rates were more effective than letter-based measures in all three pronunciation assessment categories. The correlation coefficients between the predicted and actual scores ranged from .65 to .68, indicating the models' good predictive power. However, it remains inconclusive whether speech rate or articulation rate is more effective.

• The korean journal of orthodontics
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• v.37 no.1 s.120
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• pp.16-28
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• 2007

Objective: Evaluation of individual growth is important in orthodontics. The aim of this study was to develop a convenient software that can evaluate current growth status and predict further growth. Methods: Stature data of 2 to 20 year-old Koreans (4893 boys and 4987 girls) were extracted from a nationwide data. Age-sex-specific continuous functions describing percentile growth curves were constructed using natural cubic spline function (NCSF). Then, final stature prediction algorithm was developed and its validity was tested using longitudinal series of stature measurements on randomly selected 200 samples. Various accuracy measurements and analyses of errors between observed and predicted stature using NCSF growth curves were performed. Results: NCSF growth curves were shown to be excellent models in describing reference percentile stature growth curie over age. The prediction accuracy compared favorably with previous prediction models, even more accurate. The current prediction models gave more accurate results in girls than boys. Although the prediction accuracy was high, the error pattern of the validation data showed that in most cases, there were a lot of residuals with the same sign, suggestive of autocorrelation among them. Conclusion: More sophisticated growth prediction algorithm is warranted to enhance a more appropriate goodness of model fit for individual growth.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2213-2214
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• 2006

전력계통 수요예측은 주로 과거의 부하실적을 바탕으로 미래의 수요를 예측한다. 그러나 전력수요는 사회, 기상, 환경 등 다양한 분야의 영향을 받으므로, 예측의 정확성을 향상시키기 위해서는 전력수요에 영향을 미치는 요인에 대한 분석이 선행되어야 할 것이다. 본 논문은 전력총수요와 기상 상태와의 상관관계를 분석함으로써 기상이 전력총수요에 미치는 영향에 대하여 고찰한다. 기상 상태를 태풍, 장마 등 형태에 따라 분류하고 각각의 기상 형태가 전력총수요에 미치는 상관관계를 분석한다. 분석된 상관관계는 전력계통 수요특성에 관한 기본 자료로 활용될 수 있을 것이며, 기존 수요예측의 정확성 향상에 기여할 수 있을 것이다.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.581-582
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• 2006

전력계통 수요예측은 주로 과거의 부하실적을 바탕으로 미래의 수요를 예측한다. 그러나 전력수요는 사회, 기상, 환경 등 다양한 분야의 영향을 받으므로, 예측의 정확성을 향상시키기 위해서는 전력수요에 영향을 미치는 요인에 대한 분석이 선행되어야 한 것이다. 본 논문은 전력총수요와 기상 상태와의 상관관계를 분석함으로써 기상이 전력총수요에 미치는 영향에 대하여 고찰한다. 기상 상대론 태풍, 장마 등 형태에 따라 분류하고 각각의 기상 형태가 전력총수요에 미치는 상관관계를 분석한다. 분석된 상관관계는 전력계통 수요특성에 관한 기본 자료로 활용될 수 있을 것이며, 기존 수요예측의 정확성 향상에 기여할 수 있을 것이다.