• Science of Emotion and Sensibility
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• v.20 no.2
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• pp.149-160
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• 2017

In hypothesis testing, the interpretation of a statistic obtained from the data analysis relies on a probabilistic distribution of the statistic constructed according to several statistical theories. For instance, the statistical significance of a mean difference between experimental conditions is determined according to a probabilistic distribution of the mean differences (e.g., Student's t) constructed under several theoretical assumptions for population characteristics. The present study explored the logic and advantages of random-resampling approach for analyzing event-related potentials (ERPs) where a hypothesis is tested according to the distribution of empirical statistics that is constructed based on randomly resampled dataset of real measures rather than a theoretical distribution of the statistics. To motivate ERP researchers' understanding of the random-resampling approach, the present study further introduced a specific example of data analyses where a random-permutation procedure was applied according to the random-resampling principle, as well as discussing several cautions ahead of its practical application to ERP data analyses.

• The KIPS Transactions:PartB
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• v.16B no.3
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• pp.247-256
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• 2009

Typical pseudo-relevance feedback methods assume the top-retrieved documents are relevant and use these pseudo-relevant documents to expand terms. The initial retrieval set can, however, contain a great deal of noise. In this paper, we present a cluster-based resampling method to select better pseudo-relevant documents based on the relevance model. The main idea is to use document clusters to find dominant documents for the initial retrieval set, and to repeatedly feed the documents to emphasize the core topics of a query. Experimental results on large-scale web TREC collections show significant improvements over the relevance model. For justification of the resampling approach, we examine relevance density of feedback documents. The resampling approach shows higher relevance density than the baseline relevance model on all collections, resulting in better retrieval accuracy in pseudo-relevance feedback. This result indicates that the proposed method is effective for pseudo-relevance feedback.

• Journal of Broadcast Engineering
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• v.8 no.1
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• pp.37-44
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• 2003

The standard approach to signal resampling is to fit the original image to a continuous model and resample the function at a desired rate. We used the compact B-spline function as the continuous model which produces less oscillatory behavior than other tails functions. In the case of nonuniform resampling based on a B-spline model, the digital signal is fitted to a spline model, and then the fitted signal is resampled at a space varying rate determined by the transformation function. It is simple to implement but may suffer from artifacts due to data loss. The main purpose of this paper is the derivation of optimal nonuniform resampling algorithm. For the optimal nonuniform formulation, the resampled signal is represented by a combination of shift varying splines determined by the transformation function. This optimal nonuniform resampling algorithm can be verified from the experiments that It produces less errors.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.657-666
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• 2022

An informative predictor subspace is useful to estimate the central subspace, when conditions required in usual suffcient dimension reduction methods fail. Recently, for multivariate regression, Ko and Yoo (2022) newly defined a projective-resampling informative predictor subspace, instead of the informative predictor subspace, by the adopting projective-resampling method (Li et al. 2008). The new space is contained in the informative predictor subspace but contains the central subspace. In this paper, a method directly to estimate the informative predictor subspace is proposed, and it is compapred with the method by Ko and Yoo (2022) through theoretical aspects and numerical studies. The numerical studies confirm that the Ko-Yoo method is better in the estimation of the central subspace than the proposed method and is more efficient in sense that the former has less variation in the estimation.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.172-173
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• 2008

LDA를 이용한 얼굴 인식에서 발생하는 small sample sire 문제를 해결하기 위해서 regularization method를 주로 사용한다. 이 방법을 사용하게 되면 클래스 내 분산행렬의 특이성을 없앨 수 있지만, 클래스 내 분산행렬과 단위행렬 $\alpha$를 곱한 값을 더하는 과정에서 $\alpha$의 값을 임의적으로 정해주어야 되고 이 값에 따라 인식률이 개선되지 않을 수 있다는 문제점이 있다. Resampling 개념을 이용하여 학습 데이터의 수를 늘리게 되면 regularization method보다 개선된 인식률을 얻을 수 있다. 또한 경험적으로 $\alpha$값을 정해 주어야 하고, $\alpha$값에 따라 인식률의 변통이 생길 수 있는 단점이 개선되는 효과를 얻을 수 있다.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.4
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• pp.755-767
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• 1997

Analyzing autocorrelated data set is still an open problem. Developing on easy and efficient method for severe positive correlated data set, which is common in simulation output, is vital for the simulation society. Bootstrap is on easy and powerful tool for constructing non-parametric inferential procedures in modern statistical data analysis. Conventional bootstrap algorithm requires iid assumption in the original data set. Proper choice of resampling units for generating replicates has much to do with the structure of the original data set, iid data or autocorrelated. In this paper, a new bootstrap resampling scheme is proposed to analyze the autocorrelated data set : the Threshold Bootstrap. A thorough literature search of bootstrap method focusing on the case of autocorrelated data set is also provided. Theoretical foundations of Threshold Bootstrap is studied and compared with other leading bootstrap sampling techniques for autocorrelated data sets. The performance of TB is reported using M/M/1 queueing model, else the comparison of other resampling techniques of ARMA data set is also reported.

• Journal of Industrial Technology
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• v.24 no.A
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• pp.67-73
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• 2004

The purpose of this study is to analyze the vibration signal of the revolution machine using the STFT(Short Time Fourier Transform). It is common to analyze the frequency of signal through FFT algorithm with the fixed sampling rate. However, in this situation the order spectrum information useful rather than the general frequency information with the fixed sampling rate. In this paper, the resampling technique was used for getting the information of order spectrum. In resampling process, the arithmetic amount and MSE(Mean Square Error) for various kinds of the signal interpolation was compared and presented the propriety of the interpolation method while developing analysis equipment. Order tracking was implemented using signal interpolation method which it has selected. Then the analyzed results were obtained through simulation using the STFT technique.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.2
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• pp.50-56
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• 2002

In this study, we establish bootstrap control limits for EWMA chart by applying the bootstrap method, called resampling, which could not demand assumptions about pre-distribution when the process is skewed and/or the normality assumption is doubt. The results obtained in this study are summarized as follows : bootstrap EWMA control chart is developed for applying bootstrap method to EWMA chart, which is more sensitive to small shifts of process. With the purpose of eliminating a skewness of the resampling distribution, the bootstrap control limits are established by using a modified residual, and its performance is analyzed by ARL. It is shown that the bootstrap EWMA control chart developed in this study includes the properties of standard EWMA control chart that is sensitive to a small shift, and detects process in out of control more quickly than standard EWMA chart.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.59-65
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• 2023

A GNSS receiver must perform signal acquisition to estimate the code phase and Doppler frequency of the incoming satellite signals, which are essential information for baseband signal processing. Modernized GNSS signals have different modulation schemes and long PRN code lengths from legacy signals, which makes it difficult to acquire the signals and increases the computational complexity and time. This paper proposes a novel FFT/Inverse-FFT with baseband resampling to resolve the aforementioned challenges. The suggested algorithm uses a single block only for the FFT and thereby requires less hardware resources than conventional structures such as Double Block Zero Padding (DBZP). Experimental results based on a MATLAB simulation show this algorithm can successfully acquire GPS L1C/A, GPS L2C, Galileo E1OS, and GPS L5.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.89-95
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• 2023

Analyzing breast cancer patient files is becoming an exciting area of medical information analysis, especially with the increasing number of patient files. In this paper, breast cancer data is collected from Khartoum state hospital, and the dataset is classified into recurrence and no recurrence. The data is imbalanced, meaning that one of the two classes have more sample than the other. Many pre-processing techniques are applied to classify this imbalanced data, resampling, attribute selection, and handling missing values, and then different classifiers models are built. In the first experiment, five classifiers (ANN, REP TREE, SVM, and J48) are used, and in the second experiment, meta-learning algorithms (Bagging, Boosting, and Random subspace). Finally, the ensemble model is used. The best result was obtained from the ensemble model (Boosting with J48) with the highest accuracy 95.2797% among all the algorithms, followed by Bagging with J48(90.559%) and random subspace with J48(84.2657%). The breast cancer imbalanced dataset was classified into recurrence, and no recurrence with different classified algorithms and the best result was obtained from the ensemble model.