• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.599-609
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• 2021

Data with a large difference in the number of objects between clusters are called unbalanced data. In discriminant analysis of unbalanced data, it is more important to classify objects in minority categories than to classify objects in majority categories well. However, objects in minority categories are often misclassified into majority categories. In this study, we propose a method that combined hierarchical DBSCAN (HDBSCAN) and SMOTE to solve this problem. Using HDBSCAN, it removes noise in minority categories and majority categories. Then it applies SMOTE to create new data. Area under the roc curve (AUC) and F1 scores were used to compare performance with existing methods. As a result, in most cases, the method combining HDBSCAN and synthetic minority oversampling technique (SMOTE) showed a high performance index, and it was found to be an excellent method for classifying unbalanced data.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.12
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• pp.95-102
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• 2018

The purpose of this study is to investigate the causes of conflict that arise from public pedestrian passages installed in apartment complexes through a survey of residents' perceptions and to investigate the effects on the satisfaction with and necessity of the public pedestrian passage. This has significance as a preliminary research into determining solutions to conflict related to public pedestrian passages that are open spaces, accessible 24 hours a day to pedestrians including people who live outside of the apartment complex. The result of the residents' perception survey showed that there is conflict due to the public pedestrian passage. The main problem was the noise-related variables. While management and safety variables were also perceived as problems, privacy and ownership infringement variables were not. These problems were reduced to four factors through a factor analysis: unfavorable incidents, environment management, ownership infringement, and safety crimes. Analyzing the effects of the above factors on the satisfaction with the apartment complex, satisfaction with the public pedestrian passage and the necessity of the public pedestrian passage, demonstrated that the unfavorable incidents factor influenced all the variables. The safety crimes factor, which is an extended concept of the unfavorable incidents, affected both the satisfaction and necessity of the public pedestrian passage. The ownership infringement factor was found to affect the satisfaction of the public pedestrian passage only, and the environment management factor did not affect all the variables. In planning and managing public pedestrian passages, avoiding incidents and crimes should be considered as a priority to increase the satisfaction of residents and solve conflicts.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1542-1550
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• 2019

Legacy studies for classifying arrhythmia have been studied to improve the accuracy of classification, Neural Network, Fuzzy, etc. Deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose optimal parameter extraction method based on a deep learning. For this purpose, R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 97.84% in PVC classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1341-1347
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• 2020

Legacy studies for classifying arrhythmia have been studied in order to improve the accuracy of classification, Neural Network, Fuzzy, Machine Learning, etc. In particular, deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose parameter extraction based on AR and arrhythmia classification through a deep learning. For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The classification rate of PVC is evaluated through MIT-BIH arrhythmia database. The achieved scores indicate arrhythmia classification rate of over 97%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.1-7
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• 2020

Fuzzy clustering, represented by FCM(Fuzzy C-Means), is a simple and efficient clustering method. However, the object function in FCM makes clusters affect clustering results proportional to the density of clusters, which can distort clustering results due to density difference between clusters. One method to alleviate this density problem is EDI-FCM(Extended Density-Independent FCM), which adds additional terms to the objective function of FCM to compensate for the density difference. In this paper, proposed is an enhanced EDI-FCM using regularization, Regularized EDI-FCM. Regularization is commonly used to make a solution space smooth and an algorithm noise insensitive. In clustering, regularization can reduce the effect of a high-density cluster on clustering results. The proposed method converges quickly and accurately to real centers when compared with FCM and EDI-FCM, which can be verified with experimental results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.685-690
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• 2021

In this paper, we propose a deep learning-based transmit power control scheme to maximize the sum-rates while satisfying the minimum data-rate in downlink non-orthogonal multiple access (NOMA) systems. In downlink NOMA, we consider the co-channel interference that occurs from a base station other than the cell where the user is located, and the user feeds back the signal-to-interference plus noise power ratio (SINR) information instead of channel state information to reduce system feedback overhead. Therefore, the base station controls transmit power using only SINR information. The use of implicit SINR information has the advantage of decreasing the information dimension, but has disadvantage of reducing the data-rate. In this paper, we resolve this problem with deep learning-based training methods and show that the performance of training can be improved if the dimension of deep learning inputs is effectively reduced. Through simulation, we verify that the proposed deep learning-based power control scheme improves the sum-rate while satisfying the minimum data-rate.

• Journal of Digital Convergence
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• v.19 no.7
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• pp.209-215
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• 2021

This study is a system development for voice information protection equipment in major meetings and places requiring security. Security performance and stability were secured with information leakage prevention technology through generation of false noise and ultrasonic waves. The cutoff frequency band for blocking the leakage of voice information, which has strong straightness due to the nature of the radio wave to the recording prevention module, blocks the wideband frequency of 20~20,000Hz, and the deception jamming technology is applied to block the leakage of voice information, greatly improving the security. To solve this problem, we developed a system that blocks the recording of a portable smartphone using a battery, and made the installation of a separate device smaller and lighter so that customers do not recognize it. In addition, it is necessary to continuously study measures and countermeasures for efficiently using the output of the anti-recording speaker for long-distance recording prevention.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.600-608
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• 2020

Machine learning constructs an objective function from learning data, and predicts the result of the data generated by checking the objective function through test data. In machine learning, input data is subjected to a normalisation process through a preprocessing. In the case of numerical data, normalization is standardized by using the average and standard deviation of the input data. In the case of nominal data, which is non-numerical data, it is converted into a one-hot code form. However, this preprocessing alone cannot solve the problem. For this reason, we propose a method that uses ontology to normalize input data in this paper. The test data for this uses the received signal strength indicator (RSSI) value of the Wi-Fi device collected from the mobile device. These data are solved through ontology because they includes noise and heterogeneous problems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1188-1194
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• 2019

Among the many ways to measure the flow of fluid the hot air wind speed sensor is a device for measuring the speed or temperature by heat transfer of a fluid. However, the hot wire wind speed sensor is sensitive to external environmental factors, and has a disadvantage of inaccuracy due to ambient temperature, humidity, and signal noise. In order to compensate for this disadvantage, advanced technology has been introduced by adding temperature compensation circuits, but it is expensive. In order to solve this problem, this paper studies the wind speed sensor that does not need temperature compensation. Heated wind speed sensors are very vulnerable to the ambient temperature, which is generated by electronic circuits, even among external environmental factors. in order to improve this, the auxiliary heating element is additionally installed in the heating element to control a constant temperature difference between the auxiliary heating element and the heating element.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.123-130
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• 2022

During the past decades, detection of gear defects remains as a major problem, especially when the gears are subject to non-stationary phenomena. The idea of this paper is to mixture a multilevel wavelet transform with a fast EMD decomposition in order to early detect gear defects. The sensitivity of a kurtosis is used as an indicator of gears defect burn. When the gear is damaged, the appearance of a crack on the gear tooth disrupts the signal. This is due to the presence of periodic pulses. Nevertheless, the existence of background noise induced by the random excitation can have an impact on the values of these temporal indicators. The denoising of these signals by multilevel wavelet transform improves the sensitivity of these indicators and increases the reliability of the investigation. Finally, a defect diagnosis result can be obtained after the fast transformation of the EMD. The proposed approach consists in applying a multi-resolution wavelet analysis with variable decomposition levels related to the severity of gear faults, then a fast EMD is used to early detect faults. The proposed mixed methods are evaluated on vibratory signals from the test bench, CETIM. The obtained results have shown the occurrence of a teeth defect on gear on the 5th and 8th day. This result agrees with the report of the appraisal made on this gear system.