• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4028-4042
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• 2021

Aiming at the problem of software defect prediction difficulty caused by insufficient software defect marker samples and unbalanced classification, a semi-supervised software defect prediction model based on a tri-training algorithm was proposed by combining feature normalization, over-sampling technology, and a Tri-training algorithm. First, the feature normalization method is used to smooth the feature data to eliminate the influence of too large or too small feature values on the model's classification performance. Secondly, the oversampling method is used to expand and sample the data, which solves the unbalanced classification of labelled samples. Finally, the Tri-training algorithm performs machine learning on the training samples and establishes a defect prediction model. The novelty of this model is that it can effectively combine feature normalization, oversampling techniques, and the Tri-training algorithm to solve both the under-labelled sample and class imbalance problems. Simulation experiments using the NASA software defect prediction dataset show that the proposed method outperforms four existing supervised and semi-supervised learning in terms of Precision, Recall, and F-Measure values.

• Geomechanics and Engineering
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• v.30 no.3
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• pp.247-258
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• 2022

A field velocity resistivity probe (FVRP) can measure compressional waves, shear waves and electrical resistivity in boreholes. The objective of this study is to perform the soil classification through a machine learning technique through elastic wave velocity and electrical resistivity measured by FVRP. Field and laboratory tests are performed, and the measured values are used as input variables to classify silt sand, sand, silty clay, and clay-sand mixture layers. The accuracy of k-nearest neighbors (KNN), naive Bayes (NB), random forest (RF), and support vector machine (SVM), selected to perform classification and optimize the hyperparameters, is evaluated. The accuracies are calculated as 0.76, 0.91, 0.94, and 0.88 for KNN, NB, RF, and SVM algorithms, respectively. To increase the amount of data at each soil layer, the synthetic minority oversampling technique (SMOTE) and conditional tabular generative adversarial network (CTGAN) are applied to overcome imbalance in the dataset. The CTGAN provides improved accuracy in the KNN, NB, RF and SVM algorithms. The results demonstrate that the measured values by FVRP can classify soil layers through three kinds of data with machine learning algorithms.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.53-63
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• 2023

Many researchers are trying hard to minimize the incidence of cancers, mainly Gastric Cancer (GC). For GC, the five-year survival rate is generally 5-25%, but for Early Gastric Cancer (EGC), it is almost 90%. Predicting the onset of stomach cancer based on risk factors will allow for an early diagnosis and more effective treatment. Although there are several models for predicting stomach cancer, most of these models are based on unbalanced datasets, which favours the majority class. However, it is imperative to correctly identify cancer patients who are in the minority class. This research aims to apply three class-balancing approaches to the NHS dataset before developing supervised learning strategies: Oversampling (Synthetic Minority Oversampling Technique or SMOTE), Undersampling (SpreadSubsample), and Hybrid System (SMOTE + SpreadSubsample). This study uses Naive Bayes, Bayesian Network, Random Forest, and Decision Tree (C4.5) methods. We measured these classifiers' efficacy using their Receiver Operating Characteristics (ROC) curves, sensitivity, and specificity. The validation data was used to test several ways of balancing the classifiers. The final prediction model was built on the one that did the best overall.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.5
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• pp.808-815
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• 1990

This paper treats with the design method for the single loop oversampled Sigma-Delta A/D converter with one delay and the digital integrator. Such an integrator was kgenerated by means of the bilinear transform of the analog integrator. The frequency spectrums of the quantizer and the decimator output signal are evaluated by FFT respectively. With the performance evaluation system, the values of SNR are obtained versus the input sinusoidal signal amplitude, frequency, the oversampling ratio, the DC-input level, the loop gain and the limitting value of the integrator. As compared with existing results, values of SNR versus the input signal amplitude and the oversampling ratio for the suggested system are about 6dB higher then previously reported results respectively. Furthermore, this approach achieves an about 60dB input dynamic range.

• Proceedings of the IEEK Conference
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• 2000.11b
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• pp.152-155
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• 2000

Oversampling modulators based on high-order sigma-delta modulation provide an effective means of achieving high-resolution A/D conversion in a VLSI technology. Because high-order noise shaping great]y reduces the quantization noise in the signal band. This paper introduces a third-order cascaded sigma-delta modulator that is stable for large input level. Modulator was simulated 3.3V single power supply voltage in 0.65$\mu\textrm{m}$ CMOS technology. It achieves 80㏈ SNR for a 20㎑ input signal bandwidth. A lock frequency is 3㎒ that is 80 oversampling ratio.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.6
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• pp.817-824
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• 2016

A simultaneous bidirectional transceiver over a single wire has been developed in a 65 nm CMOS technology for a command and control bus. The echo signals of the simultaneous bidirectional link are cancelled by controlling the decision level of receiver comparators without power-hungry operational amplifier (op-amp) based circuits. With the clock information embedded in the rising edges of the signals sent from the source side to the sink side, the data is recovered by an open-loop digital circuit with 20 times blind oversampling. The data rate of the simultaneous bidirectional transceiver in each direction is 75 Mbps and therefore the overall signaling bandwidth is 150 Mbps. The measured energy efficiency of the transceiver is 56.7 pJ/b and the bit-error-rate (BER) is less than $10^{-12}$ with $2^7-1$ pseudo-random binary sequence (PRBS) pattern for both signaling directions.

• Journal of Sensor Science and Technology
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• v.26 no.5
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• pp.348-352
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• 2017

In this paper, we propose a high resolution A/D converter for a sensor interface that processes low frequency AC signals. A 6b SAR ADC with low power consumption and a 11b incremental ADC with high resolution are combined together to perform 15b resolution. Conventional hybrid ADC has a disadvantage that it can convert t only DC signal, but in this paper, it is possible to convert data to AC signal by increasing input range of incremental ADC. The decimation filter is implemented on-chip. The designed Hybrid ADC operates at supply voltage of 1.8V and consumes the current of 6.98uA. The OSR (oversampling ratio) is 90. And SFDR, SNDR, ENOB and FoMs are 96.59dB, 88.47dB, 14.4-bit and 139.5dB, respectively.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.12
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• pp.98-107
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• 1993

This paper describes a design of a multi-bit oversampling noise-shaping D/A converter which achieves a resolution of 12 bits using oversampling technique. In the architecture the essential block which determines the whole accuracy is the analog internal D/A converter, and the designed charge-integration internal D/A converter adopts a differential structure in order to minimize the reduction of the resolution due to process variation. As the proposed circuit is driven by signal clocks which contains the information of the data variation from the noise-shaping coder, it minimizes the disadvantage of a charge-integration circuit in the time axis. In order to verify the circuit, it was integrated with the active area of 950$\times$650${\mu}m^{2}$ in a double metal 1.5-$\mu$m CMOS process, and testified that it can achieve a S/N ratio of 75 dB and a S/(N+D) ratio of 60 dB for the signal bandwidth of 9.6 kHz by the measurement with a spectrum analyzer.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2411-2413
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• 1998

In this paper, using tile concept of block digital filtering, and the design procedure of time-interleaved oversampling converter are presented. it is shown that arbitrary sigma-delta A/D converter can be converted into corresponding time-interleaved structure. The TIM structure of this paper is designed by the bilinear transform. To verify the simulation results, a second-order TIM structure A/D converter has been implemented and the design process as well as experimented results are presented.

• ETRI Journal
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• v.31 no.2
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• pp.162-172
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• 2009

A linear-prediction-based blind equalization algorithm for single-input single-output (SISO) finite impulse response/infinite impulse response (FIR/IIR) channels is proposed. The new algorithm is based on second-order statistics, and it does not require channel order estimation. By oversampling the channel output, the SISO channel model is converted to a special single-input multiple-output (SIMO) model. Two forward linear predictors with consecutive prediction delays are applied to the subchannel outputs of the SIMO model. It is demonstrated that the partial parameters of the SIMO model can be estimated from the difference between the prediction errors when the length of the predictors is sufficiently large. The sufficient filter length for achieving the optimal prediction is also derived. Based on the estimated parameters, both batch and adaptive minimum-mean-square-error equalizers are developed. The performance of the proposed equalizers is evaluated by computer simulations and compared with existing algorithms.