• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1490-1499
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• 2022

Arrhythmia is a condition in which the heart has an irregular rhythm or abnormal heart rate, early diagnosis and management is very important because it can cause stroke, cardiac arrest, or even death. In this paper, we propose arrhythmia classification using hybrid combination model of CNN-BLSTM. For this purpose, the QRS features are detected from noise removed signal through pre-processing and a single bit segment was extracted. In this case, the GAN oversampling technique is applied to solve the data imbalance problem. It consisted of CNN layers to extract the patterns of the arrhythmia precisely, used them as the input of the BLSTM. The weights were learned through deep learning and the learning model was evaluated by the validation data. To evaluate the performance of the proposed method, classification accuracy, precision, recall, and F1-score were compared by using the MIT-BIH arrhythmia database. The achieved scores indicate 99.30%, 98.70%, 97.50%, 98.06% in terms of the accuracy, precision, recall, F1 score, respectively.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.75-86
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• 2022

This study aims to solve the problem of class imbalance in numerical data by using a deep learning-based Variational AutoEncoder and to improve the performance of the learning model by augmenting the learning data. We propose 'D-VAE' to artificially increase the number of records for a given table data. The main features of the proposed technique go through discretization and feature selection in the preprocessing process to optimize the data. In the discretization process, K-means are applied and grouped, and then converted into one-hot vectors by one-hot encoding technique. Subsequently, for memory efficiency, sample data are generated with Variational AutoEncoder using only features that help predict with RFECV among feature selection techniques. To verify the performance of the proposed model, we demonstrate its validity by conducting experiments by data augmentation ratio.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.861-869
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• 2022

In patients with Tourette's syndrome, a tic occurs when Mu wave synchronization is broken. Conversely, when Mu wave synchronization is achieved, a tick does not occur. When the tic is suppressed, the cognitive control response process is changed, and if the neurofeedback training that adjusts the EEG frequency power is performed with the changed, the patient will be treated autonomously without artificially suppressing the tic. The results of the research test suggest that if the tic patient does not artificially synchronize mu waves in the premotor cortex (Frontal Cortical 3 site), and if EEG control is performed autonomously like neurofeedback training, as a result, tics do not occur. Cognitive control response processes are altered when a subject is inhibited from tics. By training the altered cognitive control with neurofeedback that modulates EEG frequency power, the patient can be treated autonomously without artificially suppressing the tic.Mu-wave synchronizationcan now be added to existing neurofeedback treatment protocols such as SMR reinforcement, theta-beta-wave imbalance correction, and alpha-wave reinforcement. This study will be used in follow-up studies and clinical trials to more scientifically verify the neurofeedback treatment protocol, a treatment for patients with Tourette's syndrome.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1608-1614
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• 2022

As competition between universities intensifies due to the recent decrease in the number of students, it is recognized as an essential task of universities to predict students who are underperforming at an early stage and to make various efforts to prevent dropouts. For this, a high-performance model that accurately predicts student performance is essential. This paper proposes a method to improve prediction performance by removing or amplifying abnormal data in a classification prediction model for identifying underperforming students. Existing anomaly data processing methods have mainly focused on deleting or ignoring data, but this paper presents a criterion to distinguish noise from change indicators, and contributes to improving the performance of predictive models by deleting or amplifying data. In an experiment using open learning performance data for verification of the proposed method, we found a number of cases in which the proposed method can improve classification performance compared to the existing method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.10-16
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• 2021

This paper proposes a model that considers the parity-time symmetric structure as a state detection circuit for sensor applications using a stretchable inductor. In particular, to obtain a more practical computer simulation result, the stretchable inductor model was applied to this study model by referring to previously reported experimental results. The resistance component and phase component were controlled through the negative differential resistance circuit used in this study. In addition, the imbalance of the circuit caused by a change in the characteristics of the stretchable inductor could be compensated for using a negative differential resistance circuit. In particular, an analysis of the frequency characteristics of the sensor driving circuit of the parity-time symmetric structure proposed in this study confirmed that the Q-factor could be increased up to 20 times compared to the conventional resonant circuit.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.816-827
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• 2021

The purpose of this study is to understand the relationship between fear and anger emotions in the discourse produced by the media during the period of major emerging infectious diseases (SARS, Swine Flu, MERS, and COVID-19) that occurred since 2000 in Korea. The researcher collected editorial articles of the major daily newspaper after a significant epidemic of new infectious diseases and analyzed them using the Extended Parallel Processing Model (EPPM) and text mining techniques. In all epidemic times, fear appears stronger than anger, but the smaller the fear, the greater the risk control message is produced. In detail, fear emerges strongly in the discourse of the risk of infectious diseases or the economic crisis. Anger appears strong when the government's quarantine failures, groups where group infections occurred, and concealing information about infectious diseases. In this process, anger is strongly expressed against the factors that threaten the safety of society. Anger is also an emotion that can justify strong quarantine, but it can be the basis for discourse on minority hate. In this respect, anger is a two-sided emotion, so it must be handled carefully in the media.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.641-648
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• 2022

This paper is a study to find out the factors affecting the defects that occur during the use of Electron Beam Melting (EBM), one of the 3D printer output methods, through data analysis. By referring to factors identified as major causes of defects in previous studies, log files occurring between processes were analyzed and related variables were extracted. In addition, focusing on the fact that the data is time series data, the concept of a window was introduced to compose variables including data from all three layers. The dependent variable is a binary classification problem with the presence or absence of defects, and due to the problem that the proportion of defect layers is low (about 4%), balanced training data were created through the SMOTE technique. For the analysis, I use XGBoost using Gridsearch CV, and evaluate the classification performance based on the confusion matrix. I conclude results of the stuy by analyzing the importance of variables through SHAP values.

• Journal of Digital Policy
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• v.1 no.2
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• pp.17-24
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• 2022

In China, the continuous promotion and coverage of scanning code payment has caused an imbalance in the coin market. Coin changers can not only alleviate this problem, but also affect the business development of commercial banks. Therefore, it is meaningful to study the coin changer. The purpose of this paper is to study the impact of coin changers on the business of commercial banks in China. Through on-the-spot visits and based on the manually collected customer data of Chinese commercial banks as the object, combined with the calculation method of financial indicators to conduct case analysis. The results of the study show that the coin changer has a positive impact on the business development of Chinese commercial banks. This paper provides feasible suggestions and new ideas for business development to Chinese commercial banks. At present, there are few related studies on coin exchange machines. This study combines the calculation of financial indicators to verify the policy results, which is the innovation of this study.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1175-1180
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• 2022

STI has been studied a lot as a process technology for wide area planarization according to miniaturization and high integration of semiconductor devices. In this study, as methods for improving the STI profile, wet etching of pad oxide using hydrofluorine solution and dry etching of O₂+CF₄ after STI dry etching were proposed. This process technology showed improvement in profile imbalance and leakage current between patterns according to device density compared to the conventional method. In addition, as a result of measuring the HLD thickness after CMP for a device having the same STI depth and HLD deposition, the measured value was different depending on the device density. It was confirmed that this was due to the difference in the thickness of the nitride film according to the device density after CMP and the selectivity of the slurry.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.885-896
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• 2022

Motor failure in manufacturing plays an important role in future A/S and reliability. Motor failure is detected by measuring sound, current, and vibration. For the data used in this paper, the sound of the car's side mirror motor gear box was used. Motor sound consists of three classes. Sound data is input to the network model through a conversion process through MelSpectrogram. In this paper, various methods were applied, such as data augmentation to improve the performance of classifying fault motors and various methods according to class imbalance were applied resampling, reweighting adjustment, change of loss function and representation learning and classification into two stages. In addition, the curriculum learning method and self-space learning method were compared through a total of five network models such as Bidirectional LSTM Attention, Convolutional Recurrent Neural Network, Multi-Head Attention, Bidirectional Temporal Convolution Network, and Convolution Neural Network, and the optimal configuration was found for motor sound classification.