• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.575-583
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• 2000

Consider the problem of estimating regression function from a set of data which is contaminated by a long-tailed error distribution. The linear smoother is a kind of a local weighted average of response, so it is not robust against outliers. The kernel M-smoother and the lowess attain robustness against outliers by down-weighting outliers. However, the kernel M-smoother and the lowess requires the iteration for computing the robustness weights, and as Wang and Scott(1994) pointed out, the requirement of iteration is not a desirable property. In this article, we propose the robust nonparametic regression method which does not require the iteration. Robustness can be achieved not only by down-weighting outliers but also by transforming outliers. The rank transformation is a simple procedure where the data are replaced by their corresponding ranks. Iman and Conover(1979) showed the fact that the rank transformation is a robust and powerful procedure in the linear regression. In this paper, we show that we can also use the rank transformation to nonparametric regression to achieve the robustness.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.05a
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• pp.62-63
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• 2018

Oyster shell is produce by shucking process in oyster farming in southern coast of Korea. In average, about 6.7kg of oyster shell is produced as an industrial waste for 1kg of oyster flesh, and even only in last year, it is estimated that about 150,000 ton of oyster shell is produced. Oyster shell is light weighted and the strength characteristic of it is similar to send. So we produced mortar test piece using grounded oyster shell according to aggregate and reviewed strength characteristic. Therefore, in this study, the strength characteristics of the test specimen are evaluated by artificially altering fineness modulus and curing method by processing oyster shells.

• The Journal of Asian Finance, Economics and Business
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• v.2 no.4
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• pp.5-11
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• 2015

This study is the first to analyze performance of Taiwanese domestic equity funds between January 2009 and October 2014, the period during which quantitative redirected capital flows toward developing economies and the Taiwanese Stock Exchange Weighted Index compounded at approximately 12.9% annually. Adopting methods endorsed by earlier research, we evaluated 15 Taiwanese equity funds' performance relative to market averages using the Sharpe (1966) and Treynor (1965) ratios and Jensen's alpha method (1968). To test market timing proficiency, we applied the Treynor and Mazuy (1966) and Henriksson and Merton (1981) regression analysis methods. Jensen's alpha method (1968) was used to measure fund managers' stock selection skills. Results revealed that funds significantly under-performed Taiwan's average annual market return and demonstrated no exceptional stock-selection skills and market timing proficiency during the era of quantitative easing.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.500-509
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• 2022

With the rapid development of educational informatization, teaching methods become diversified characteristics, but a large number of information data restrict the evaluation on teaching subject and object in terms of the effect of English education. Therefore, this study adopts the concept of incremental learning and eigenvalue interval algorithm to improve the weighted decision tree, and builds an English education effect evaluation model based on association rules. According to the results, the average accuracy of information classification of the improved decision tree algorithm is 96.18%, the classification error rate can be as low as 0.02%, and the anti-fitting performance is good. The classification error rate between the improved decision tree algorithm and the original decision tree does not exceed 1%. The proposed educational evaluation method can effectively provide early warning of academic situation analysis, and improve the teachers' professional skills in an accelerated manner and perfect the education system.

• Annual Conference of KIPS
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• 2010.11a
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• pp.668-671
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• 2010

In this paper, we present a novel automatic algorithm for scalp and skull segmentation in T1-weighted head MR images. First, the scalp and skull part are constructed by using intensity threshold. Second, the scalp outer surface is extracted based on an active level set method. Third, the skull inner surface is extracted using a canny edge detection algorithm. Finally, the fast sweeping, tagging and level set methods are applied to reconstruct surfaces from the detected points in three-dimensional space. The results of the new segmentation algorithm on MRI data acquired from eight persons were compared with manual segmented data. The average similarity indices for the scalp and skull segmented regions were equal to 84.42% for the test data.

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.15-22
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• 2020

In this study, we propose an improvement method that can create U-Net model which detect fine concrete cracks by applying a weighted loss function. Because cracks in concrete are a factor that threatens safety, it is important to periodically check the condition and take prompt initial measures. However, currently, the visual inspection is mainly used in which the inspector directly inspects and evaluates with naked eyes. This has limitations not only in terms of accuracy, but also in terms of cost, time and safety. Accordingly, technologies using deep learning is being researched so that minute cracks generated in concrete structures can be detected quickly and accurately. As a result of attempting crack detection using U-Net in this study, it was confirmed that it could not detect minute cracks. Accordingly, as a result of verifying the performance of the model trained by applying the suggested weighted loss function, a highly reliable value (Accuracy) of 99% or higher and a harmonic average (F1_Score) of 89% to 92% was derived. The performance of the learning improvement plan was verified through the results of accurately and clearly detecting cracks.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.39 no.2
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• pp.92-98
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• 2003

The quantification of fish school abundance was carried out by using luminance of pixel on scanning sonar image, and compared with the indices of fish school abundance e.g. school number, school area and weighted school area. The survey was carried out in Funka Bay off southern Hokkaido, Japan using research vessel Ushio-Maru during December 1999. A 180-degree scanning sonar with a frequency of 164kHz was used. The school number was counted both left and right 40-degree radial lines from the center of own vessel mark on a scanning image. The school area was measured approximately as an ellipse from the school length and width. The weighted school area was calculated by multiplying school area and average value of inner pixel luminance. A quantification of pixel luminance was also measured to integrate squared pixel luminance value on these lines. Fish school and school bottom were discriminated by the produced sonar echogram using pixel luminance value on these lines. The relationships between the quantified luminance value and other abundance indices such as school area and weighted school area revealed a good correlation. Therefore, the quantified luminance is a useful method in estimating fish school abundance in the acoustic survey using sonar.

• Journal of Korea Water Resources Association
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• v.50 no.12
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• pp.815-825
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• 2017

Groundwater management vulnerability was assessed using TOPSIS (Techniques for Order Performance by Similarity to Ideal Solution) for 21 administrative districts in Nakdong river basin. Ten indicators were selected for 21 administrative districts in the Nakdong river basin by collecting natural, human, and social data sets. The selected indicators were standardized using rescale method, and each indicator was weighted by considering the questionnaire of expert group. The results of the weights determination survey showed that the annual average groundwater level index was 0.157 and this is the highest value. The annual average precipitation index was 0.154 and the annual groundwater recharge index was 0.152. The lowest weighted index was 0.043 for population density. Finally, the result of assessment of groundwater management vulnerability showed that Sangju-si was the most vulnerable to groundwater management among 21 administrative districts in Nakdong river basin because the annual average precipitation, annual average groundwater recharge, and annual average groundwater use indicators were highly vulnerable. The second and the third vulnerable regions were Yecheon-gun and Haman-gun respectively. The assessment of groundwater management vulnerability for the five major river basins in Korea can be a essential basis for the establishment of groundwater management policy.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.6
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• pp.105-111
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• 2017

In this paper, the analysis results are shown about several weights of Weighted K-Nearest Neighbor method, Recently, it is employed for the indoor positioning technologies using WiFi fingerprint which has been actively studied. In spite of the simplest feature, the W-KNN method shows comparable performance to another methods using WiFi fingerprint technology. So W-KNN method has employed in the existing indoor positioning system. It shows positioning error performance according to data preprocessing and weight factor, and the analysis on the weight is very important. In this paper, based on the real measured WiFi fingerprint data, the estimation error is analyzed and the performances are compared, for the case of data processing methods, of the weight of average, variance, and distance, and of the averaging several position of number K. These results could be practically useful to construct the real indoor positioning system.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.3
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• pp.193-199
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• 2000

In many studies, considerable attention has been focussed upon choosing a model which represents underlying process of time series and forecasting the future. In the real world, however, there may be some cases that one model can not reflect all the characteristics of original time series. Under such circumstances, we may get better performance by combining the forecasts from several models. The most popular methods for combining forecasts involve taking a weighted average of multiple forecasts. But the weights are usually unstable. In cases the assumptions of normality and unbiasedness for forecast errors are satisfied, a Bayesian method can be used for updating the weights. In the real world, however, there are many circumstances the Bayesian method is not appropriate. This paper proposes a PNN(Probabilistic Neural Net) approach as a method for combining forecasts that can be applied when the assumption of normality or unbiasedness for forecast errors is not satisfied. In this paper, PNN method, which is similar to Bayesian approach, is suggested as an updating method of the unstable weights in the combination of the forecasts. The PNN method has been usually used in the field of pattern recognition. Unlike the Bayesian approach, it requires no assumption of a specific prior distribution because it gets probabilities by using the distribution estimated from given data. Empirical results reveal that the PNN method offers superior predictive capabilities.