• ETRI Journal
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• v.44 no.3
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• pp.379-388
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• 2022

This study proposes a novel low-complexity algorithm for computing inverse fast Fourier transform (IFFT)/fast Fourier transform (FFT) operations in binary phase shift keying-modulated orthogonal frequency division multiplexing (OFDM) communication systems without requiring any twiddle factor multiplications. The peak-to-average power ratio (PAPR) reduction capacity of an efficient PAPR reduction technique, that is, H-SLM method, is evaluated using the proposed IFFT algorithm without any complex multiplications, and the impact of oversampling factor for the accurate calculation of PAPR is analyzed. The power spectral density of an OFDM signal generated using the proposed multiplierless IFFT algorithm is also examined. Moreover, the bit-error-rate performance of the H-SLM technique with the proposed IFFT/FFT algorithm is compared with the classical methods. Simulation results show that the proposed IFFT/FFT algorithm used in the H-SLM method requires no complex multiplications, thereby minimizing power consumption as well as the area of IFFT/FFT processors used in OFDM communication systems.

• Journal of the Korea Society of Computer and Information
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• v.19 no.9
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• pp.125-139
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• 2014

In this paper, I propose a response modeling with a Semi-Supervised Support Vector Regression (SS-SVR) algorithm. In order to increase the accuracy and profit of response modeling, unlabeled data in the customer dataset are used with the labeled data during training. The proposed SS-SVR algorithm is designed to be a batch learning to reduce the training complexity. The label distributions of unlabeled data are estimated in order to consider the uncertainty of labeling. Then, multiple training data are generated from the unlabeled data and their estimated label distributions with oversampling to construct the training dataset with the labeled data. Finally, a data selection algorithm, Expected Margin based Pattern Selection (EMPS), is employed to reduce the training complexity. The experimental results conducted on a real-world marketing dataset showed that the proposed response modeling method trained efficiently, and improved the accuracy and the expected profit.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.870-881
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• 2020

Because SCADA monitoring data of wind turbines are large and fast changing, the unbalanced proportion of data in various working conditions makes it difficult to process fault feature data. The existing methods mainly introduce new and non-repeating instances by interpolating adjacent minority samples. In order to overcome the shortcomings of these methods which does not consider boundary conditions in balancing data, an improved over-sampling balancing algorithm SC-SMOTE (safe circle synthetic minority oversampling technology) is proposed to optimize data sets. Then, for the balanced data sets, a fault diagnosis method based on improved k-nearest neighbors (kNN) classification for wind turbine blade icing is adopted. Compared with the SMOTE algorithm, the experimental results show that the method is effective in the diagnosis of fan blade icing fault and improves the accuracy of diagnosis.

• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.69-72
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• 2001

Blind equalization of transmission channel is important in communication areas and signal processing applications because it does not need training sequence, nor does it require a priori channel information. Recently, Tong et al. proposed solutions for this problem exploit the diversity induced by antenna array or time oversampling, leading to the second order statistics techniques, fur example, subspace method, prediction error method, and so on. The linear prediction error method is perhaps the most attractive in practice due to the insensitive to blind equalizer length mismatch as well as for its simple adaptive filter implementation. Unfortunately, the previous one-step prediction error method is known to be limited in arbitrary delay. In this paper, we induce the optimal delay, and propose the adaptive blind equalizer with multi-step linear prediction using RLS-type algorithm. Simulation results are presented to demonstrate the proposed algorithm and to compare it with existing algorithms.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.7
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• pp.715-725
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• 2010

Orthgonal Frequency Division Multiplexing(OFDM) system is effective for the high data rate transmission in the frequency selective fading channel. In this paper we propose PAPR(Peak to Average Power Ratio) reduction method of problem in OFDM system used Fuzzy theory that often control machine. This thesis proposes PAPR reducing method of OFDM system using Fuzzy theory. The advantages for using Fuzzy theory to reduce PAPR are that it is easy to manage the data and embody the hardware, and required smaller amount of operation. Firstly, we proposed simple algorithm that is reconstructed at receiver with transmitted overall PAPR which is reduced PAPR of sub-block using Fuzzy. Although there are some drawbacks that the operation of the system is increased comparing conventional OFDM system and it is needed to send the information about Fuzzy indivisually, it is assured that the performance of the system is enhanced for PAPR reducing. To evaluate the perfomance, the proposed search algorithm is compared with the proposed algorithm in terms of the complementary cumulative distribution function(CCDF) of the PAPR and the computational complexity. As a result of using the QPSK and 16QAM modulation, Fuzzy theory method is more an effective method of reducing 2.3 dB and 3.1 dB PAPR than exiting OFDM system when FFT size(N)=512, and oversampling=4 in the base PR of $10^{-5}$.

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.114-120
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• 2003

Blind channel estimation of communication channels is a problem of important current theoretical concerns. Recently proposed solutions for this problem exploit the diversity induced by antenna array or time oversampling, leading to the so-called, second order statistics techniques. This paper proposes the blind adaptive channel estimation using multichannel linear prediction method. Computer simulations are presented to compare the proposed algorithm with the existing ones.

• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.317-320
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• 2001

Blind adaptive channel identification of communication channels is a problem of important current theoretical and practical concerns. Recently proposed solutions for this problem exploit the diversity induced by antenna array or time oversampling leading to the so-called, second order statistics techniques. And adaptive blind channel identification techniques based on a off-line least-squares approach have been proposed. In this paper, a new approach is proposed that is based on eigenvalue decomposition. And the eigenvector corresponding to the minimum eigenvalue of the covariance matrix of the received signals contains the channel impulse response. And we present a adaptive algorithm to solve this problem. The performance of the proposed technique is evaluated over real measured channel and is compared to existing algorithms.

• Geomechanics and Engineering
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• v.35 no.1
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• pp.67-80
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• 2023

Due to the nature of the conjunctive Cone Penetration Test(CPT), which does not verify the actual sample directly, geotechnical engineers commonly classify the underground geomaterials using CPT results with the classification diagrams proposed by various researchers. However, such classification diagrams may fail to reflect local geotechnical characteristics, potentially resulting in misclassification that does not align with the actual stratification in regions with strong local features. To address this, this paper presents an objective method for more accurate local CPT soil classification criteria, which utilizes C4.5 decision tree models trained with the CPT results from the clay-dominant southern coast of Korea and the sand-dominant region in South Carolina, USA. The results and analyses demonstrate that the C4.5 algorithm, in conjunction with oversampling, outlier removal, and pruning methods, can enhance and optimize the decision tree-based CPT soil classification model.

• 대한상한금궤의학회지
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• v.14 no.1
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• pp.41-50
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• 2022

Objective : The purpose of this study is to explore the most suitable machine learning model algorithm for Shanghanlun diagnostic system classification using natural language processing (NLP). Methods : A total of 201 data items were collected from 『Shanghanlun』 and 『Clinical Shanghanlun』, 'Taeyangbyeong-gyeolhyung' and 'Eumyangyeokchahunobokbyeong' were excluded to prevent oversampling or undersampling. Data were pretreated using a twitter Korean tokenizer and trained by logistic regression, ridge regression, lasso regression, naive bayes classifier, decision tree, and random forest algorithms. The accuracy of the models were compared. Results : As a result of machine learning, ridge regression and naive Bayes classifier showed an accuracy of 0.843, logistic regression and random forest showed an accuracy of 0.804, and decision tree showed an accuracy of 0.745, while lasso regression showed an accuracy of 0.608. Conclusions : Ridge regression and naive Bayes classifier are suitable NLP machine learning models for the Shanghanlun diagnostic system classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.790-796
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• 2014

Identification of communication channels is a problem of important current theoretical and practical concerns. Recently proposed solutions for this problem exploit the diversity induced by antenna array or time oversampling. The method resorts to an adaptive filter with a linear constraint. In this paper, an approach is proposed that is based on decomposition. Indeed, the eigenvector corresponding to the minimum eigenvalue of the covariance matrix of the received signals contains the channel impulse response. And we present an adaptive algorithm to solve this problem. Proposed technique shows the better performance than one of existing algorithms.