• Proceedings of the IEEK Conference
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• 2003.07b
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• pp.1193-1196
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• 2003

This paper proposes a 2x oversampling method with a smart sampling for a clock and data recovery(CDR) circuit in a 2.5Gbps serial data link. In the conventional 2x oversampling method, the "bang-bang" operation of the phase detection produces a systematic jitter in CDR. The smart sampling in phase detection helps the CDR to remove the "bang-bang" operation and to improve the jitter performance. The CDR with the proposed 2x oversampling method is designed using Samsung 0.25${\mu}{\textrm}{m}$ process parameters and verified by simulation. Simulation result shows the proposed 2x oversampling method removes the systematic jitter.e systematic jitter.

• Information Systems Review
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• v.24 no.4
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• pp.97-118
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• 2022

As generative adversarial network (GAN) based oversampling techniques have achieved impressive results in class imbalance of unstructured dataset such as image, many studies have begun to apply it to solving the problem of imbalance in structured dataset. However, these studies have failed to reflect the characteristics of structured data due to changing the data structure into an unstructured data format. In order to overcome the limitation, this study adapted CycleGAN to reflect the characteristics of structured data, and proposed hybridization of synthetic minority oversampling technique (SMOTE) and the adapted CycleGAN. In particular, this study tried to overcome the limitations of existing studies by using a one-dimensional convolutional neural network unlike previous studies that used two-dimensional convolutional neural network. Oversampling based on the method proposed have been experimented using various datasets and compared the performance of the method with existing oversampling methods such as SMOTE and adaptive synthetic sampling (ADASYN). The results indicated the proposed hybrid oversampling method showed superior performance compared to the existing methods when data have more dimensions or higher degree of imbalance. This study implied that the classification performance of oversampling structured data can be improved using the proposed hybrid oversampling method that considers the characteristic of structured data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1872-1879
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• 2022

Recently, with the development of artificial intelligence technology, research to use artificial intelligence to detect hacking attacks is being actively conducted. However, the fact that security data is a representative imbalanced data is recognized as a major obstacle in composing the learning data, which is the key to the development of artificial intelligence models. Therefore, in this paper, we propose a W-VAE oversampling technique that applies VAE, a deep learning generation model, to data extraction for oversampling, and sets the number of oversampling for each class through weight calculation using K-NN for sampling. In this paper, a total of five oversampling techniques such as ROS, SMOTE, and ADASYN were applied through NSL-KDD, an open network security dataset. The oversampling method proposed in this paper proved to be the most effective sampling method compared to the existing oversampling method through the F1-Score evaluation index.

• Journal of Digital Contents Society
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• v.10 no.2
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• pp.189-198
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• 2009

In this paper, we propose a method to improve the performance of orthogonal frequency division multiplexing (OFDM) receivers by using oversampling the received signals. Demodulation of the received OFDM signals is to detect the amplitude and phase components of the subcarriers. From the oversampled OFDM signals, we can get redundant informations in frequency domain for the data, which are different in phase but the same in amplitude. By using these properties, we can obtain signal to noise ratio (SNR) gain by the oversampling ratio compared to the receivers which sampled with symbol rate. In this paper, we propose oversampled receivers whose oversampling ratio is expanded from integer to general rational number. Through computer simulations, we show the validity of the proposed methods by comparing the performance of the receivers with nonideal band-limiting filters.

• Progress in Medical Physics
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• v.27 no.2
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• pp.55-63
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• 2016

We studied the method to gain a clear LSF using a thick aluminum sheet and to acquire the spatial resolution value with a high accuracy for a low spatial resolution imaging modality. In this study, aluminum sheets with thicknesses varying from 0.3 mm to 1.2 mm were tested to derive a modulation transfer function (MTF) for the oversampling and non-oversampling methods. The results were evaluated to verify the feasibility of the use of thick sheets for periodic quality assurance. Oversampling was more accurate than non-oversampling, and an aluminum sheet with a correction factor less than 2 at the cut-off frequency, which was less than 0.8 mm in this case, was confirmed to be suitable for MTF measurements. Therefore, MTF derivation from a thick aluminum sheet with thickness correction is plausible for a medical imaging modality.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.49-55
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• 2021

Recently, with the development of database, it is possible to store a lot of data generated in finance, security, and networks. These data are being analyzed through classifiers based on machine learning. The main problem at this time is data imbalance. When we train imbalanced data, it may happen that classification accuracy is degraded due to over-fitting with majority class data. To overcome the problem of data imbalance, oversampling strategy that increases the quantity of data of minority class data is widely used. It requires to tuning process about suitable method and parameters for data distribution. To improve the process, In this study, we propose a strategy to explore and optimize oversampling combinations and ratio based on various methods such as synthetic minority oversampling technique and generative adversarial networks through genetic algorithms. After sampling credit card fraud detection which is a representative case of data imbalance, with the proposed strategy and single oversampling strategies, we compare the performance of trained classifiers with each data. As a result, a strategy that is optimized by exploring for ratio of each method with genetic algorithms was superior to previous strategies.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.233-239
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• 2022

The injection molding process is a process in which thermoplastic resin is heated and made into a fluid state, injected under pressure into the cavity of a mold, and then cooled in the mold to produce a product identical to the shape of the cavity of the mold. It is a process that enables mass production and complex shapes, and various factors such as resin temperature, mold temperature, injection speed, and pressure affect product quality. In the data collected at the manufacturing site, there is a lot of data related to good products, but there is little data related to defective products, resulting in serious data imbalance. In order to efficiently solve this data imbalance, undersampling, oversampling, and composite sampling are usally applied. In this study, oversampling techniques such as random oversampling (ROS), minority class oversampling (SMOTE), ADASYN(Adaptive Synthetic Sampling), etc., which amplify data of the minority class by the majority class, and complex sampling using both undersampling and oversampling, are applied. For composite sampling, SMOTE+ENN and SMOTE+Tomek were used. Artificial neural network techniques is used to predict product quality. Especially, MLP and RNN are applied as artificial neural network techniques, and optimization of various parameters for MLP and RNN is required. In this study, we proposed an SA technique that optimizes the choice of the sampling method, the ratio of minority classes for sampling method, the batch size and the number of hidden layer units for parameters of MLP and RNN. The existing sampling methods and the proposed SA method were compared using accuracy, precision, recall, and F1 Score to prove the superiority of the proposed method.

• Journal of IKEEE
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• v.10 no.1 s.18
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• pp.10-15
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• 2006

In this paper, a clock and data recovery (CDR) circuit for a serial link with a half rate 4x oversampling phase and frequency detector structure without a reference clock is described. The phase detector (PD) and frequency detector (FD)are designed by 4X oversampling method. The PD, which uses bang-bang method, finds the phase error by generating four up/down signal and the FD, which uses the rotational method, finds the frequency error by generating up/down signal made by the PD output. And the six signals of the PD and the FD control an amount of current that flows through the charge pump. The VCO composed of four differential buffer stages generates eight differential clocks. Proposed circuit is designed using the 0.18um CMOS technology and operating voltage is 1.8V. With a 4X oversampling PD and FD technique, tracking range of 24% at 3.125Gbps is achieved.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.177-188
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• 2022

The support vector machine (SVM) has been successfully applied to various classification areas with a high level of classification accuracy. However, it is infeasible to use the SVM in analyzing massive data because of its significant computational problems. When analyzing imbalanced data with different class sizes, furthermore, the classification accuracy of SVM in minority class may drop significantly because its classifier could be biased toward the majority class. To overcome such a problem, we propose the DOC-SVM method, which uses divide-oversampling and conquers techniques. The proposed DOC-SVM divides the majority class into a few subsets and applies an oversampling technique to the minority class in order to produce the balanced subsets. And then the DOC-SVM obtains the final classifier by aggregating all SVM classifiers obtained from the balanced subsets. Simulation studies are presented to demonstrate the satisfactory performance of the proposed method.

• Imaging Science in Dentistry
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• v.49 no.2
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• pp.131-137
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• 2019

Purpose: This study was designed to investigate the effects of voxel size, the oversampling technique, and the direction and area of measurement on modulation transfer function (MTF) values to identify the optimal method of MTF measurement. Materials and Methods: Images of the wire inserts of the SedentexCT IQ phantom were acquired, and MTF values were calculated under different conditions(voxel size of 0.1, 0.2, and 0.3 mm; 5 oversampling techniques; simulated pixel location errors; and different directions and areas of measurement). The differences in the MTF values across various conditions were evaluated. Results: The MTF 10 values showed smaller standard deviations than the MTF 50 values. Stable and accurate MTF values were obtained in the 0.1-mm voxel images. In the 0.3-mm voxel images, oversampling techniques of 11 lines or more did not show significant differences in MTF values depending on the presence of simulated location errors. MTF 10 values showed significant differences according to the direction and area of the measurement. Conclusion: To measure more accurate and stable MTF values, it is better to measure MTF 10 values in small-voxel images. In large-voxel images, the proper oversampling technique is required. MTF values from the radial and tangential directions may be different, and MTF values vary depending on the measured area.