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Dynamic Unit State Data-Driven False Alarm Filtering for Regression Unit Testing
Youngseok Choi, Ahcheong Lee, Hyoju Nam, Insub Lee, Namhoom Jung, Kyutae Cho, Moonzoo Kim
http://doi.org/10.5626/JOK.2024.51.11.996
Regression testing focuses on testing changed parts of software to quickly find errors caused by changes. Unit testing individually tests each unit (i.e., a small component of software) to identify a bug quickly. We propose a new regression testing technique using unit testing with a dynamic unit state-based false alarm reduction model. Experimental results showed that when the proposed technique was applied to 10 C programs, acc@10 performance increased by 40%p compared to the state-of-the-art technique foridentifying a buggy function. For 7 programs, target regression bugs were ranked within the top 20% of the bugs reported by the proposed technique.
Data-Driven Computer-Aided Diagnosis of Ventricular Fibrillation Based on Ensemble Empirical Mode Decomposition of ECG
http://doi.org/10.5626/JOK.2020.47.4.387
In this paper, we propose a novel computer-aided diagnosis method to detect VF(ventricular fibrillation), one of the hazardous cardiac symptoms of arrhythmia by applying the EEMD(Ensemble Empirical Mode Decomposition) to the ECG signals. Using the EEMD to the ECG signals, it is shown that VF in the EMD region has a higher correlation with the IMFs (intrinsic mode functions) than the NSR (normal sinus rhythm) and other types of arrhythmia. To quantify this characteristic, we calculate the angle between the ECG signal and the specific IMFs, and classify the pathology by differentiating the angles. To verify the effectiveness of the proposed algorithm, we measured the accuracy of diagnosis using arrhythmia data from the PhysioNet database and confirm capacity of the proposed method.
Emotion Recognition based on Multivariate Multiscale Fuzzy Entropy Analysis of EEG Recordings
Dae-Young Lee, Young-Seok Choi
http://doi.org/10.5626/JOK.2020.47.3.227
An Electroencephalogram (EEG) signal is an immediate and continuous signal that records brain activity, and it is mainly used for emotional analysis since it can directly reflect the changes of human emotional states. Among the methods of analyzing the EEG signals, entropy analysis is one of the measures for quantifying the complexity of time series. This quantitative analysis of complexity is promising for investigating non-stationary and nonlinear physiological signals. In this paper, we propose a multivariate multiscale fuzzy entropy (MMFE) analysis method that quantifies the complexity of multivariate time series over various time scales to analyze emotional states using EEG signals recorded from multiple electrodes as input. A public database, DEAP, is used as input data in this analysis, and the results show the possibility that emotional states can be distinguished through the binary classification of high/low arousal and high/low valence.
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