• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.179-190
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• 2022

Data imbalance refers to a phenomenon in which the number of data in one category is too large or too small compared to another category. Due to this, it has been raised as a major factor that deteriorates performance in machine learning that utilizes classification algorithms. In order to solve the data imbalance problem, various ovrsampling methods for amplifying prime number distribution data have been proposed. Among them, SMOTE is the most representative method. In order to maximize the amplification effect of minority distribution data, various methods have emerged that remove noise included in data (SMOTE-IPF) or enhance only border lines (Borderline SMOTE). This paper proposes a method to ultimately improve classification performance by improving the processing method for anomaly data in the traditional SMOTE method that amplifies minority classification data. The proposed method consistently presented relatively high classification performance compared to the existing methods through experiments.

• The Journal of the Acoustical Society of Korea
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• v.25 no.6
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• pp.282-290
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• 2006

This paper proposes a new Digital Reverberator that models Analog Helical Coil Spring Reverberator for guitar amplifiers. While the conventional digital reverberators are proposed to provide better sound field mainly based on room acoustics, no algorithm or analysis of digital reverberators those model Helical Coil Spring Reverberator was proposed. Considering the fact that approximately $70{\sim}80$ percent of guitar amplifiers are still with Helical Coil Spring Reverberator, research was performed based not on Room Acoustics but on Helical Coil Spring Reverberator itself as an effector. After performing simulations with proposed algorithm, it was confirmed that the Digital Reverberator by proposed algorithm provides perceptually equivalent response to the conventional Analog Helical Coil Spring Reverberators.

• The Journal of Bigdata
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• v.8 no.1
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• pp.157-165
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• 2023

Seafood is a major source of protein in many countries and its consumption is increasing. In Korea, consumption of seafood is increasing, but self-sufficiency rate is decreasing, and the importance of safety management is increasing as the amount of imported seafood increases. There are hundreds of species of aquatic products imported into Korea from over 110 countries, and there is a limit to relying only on the experience of inspectors for safety management of imported aquatic products. Based on the data, a model that can predict the customs inspection results of imported aquatic products is developed, and a machine learning classification model that determines the non-conformity of aquatic products when an import declaration is submitted is created. As a result of customs inspection of imported marine products, the nonconformity rate is less than 1%, which is very low imbalanced data. Therefore, a sampling method that can complement these characteristics was comparatively studied, and a preprocessing method that can interpret the classification result was applied. Among various machine learning-based classification models, Random Forest and XGBoost showed good performance. The model that predicts both compliance and non-conformance well as a result of the clearance inspection is the basic random forest model to which ADASYN and one-hot encoding are applied, and has an accuracy of 99.88%, precision of 99.87%, recall of 99.89%, and AUC of 99.88%. XGBoost is the most stable model with all indicators exceeding 90% regardless of oversampling and encoding type.

• Journal of Broadcast Engineering
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• v.18 no.1
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• pp.10-20
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• 2013

Nowadays, video technology has been successfully improved creating tremendous results. As video technology improve, multimedia devices and demands from users are diversified. Therefore, a video codec used in these devices should support various displays with different resolutions. The technology to generate a higher resolution image from the associated low-resolution image is called interpolation. Interpolation is generally performed in either the spatial domain or the DCT domain. To use the advantages of both domains, we have proposed the new adaptive interpolation algorithm based on edge direction, which adaptively exploits the advantages of both domains. The experimental results demonstrate that our algorithm performs well in terms of PSNR and reduces the blocking artifacts.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.2
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• pp.130-134
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• 2008

This paper presents a clock and data recovery(CDR) using a quarter-rate technique. The proposed CDR helps reduce the VCO frequency and is thus advantageous for high speed application. It can achieve a low jitter operation and extend the pull-in range without a reference clock. The CDR consists of a quarter-rate bang-bang type phase detector(PD) quarter-rate frequency detector(QRFD), two charge pumps circuits(CPs), low pass filter(LPF) and a ring voltage controlled oscillator(VCO). The Proposed CDR has been fabricated in a standard $0.18{\mu}m$ 1P6M CMOS technology. It occupies an active area $1{\times}1mm^2$ and consumes 98 mW from a single 1.8 V supply.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.99-110
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• 2023

This study proposed a classification of malicious network traffic using the cyber threat framework(Mitre ATT&CK) and machine learning to solve the real-time traffic detection problems faced by current security monitoring systems. We applied a network traffic dataset called UNSW-NB15 to the Mitre ATT&CK framework to transform the label and generate the final dataset through rare class processing. After learning several boosting-based ensemble models using the generated final dataset, we demonstrated how these ensemble models classify network traffic using various performance metrics. Based on the F-1 score, we showed that XGBoost with no rare class processing is the best in the multi-class traffic environment. We recognized that machine learning ensemble models through Mitre ATT&CK label conversion and oversampling processing have differences over existing studies, but have limitations due to (1) the inability to match perfectly when converting between existing datasets and Mitre ATT&CK labels and (2) the presence of excessive sparse classes. Nevertheless, Catboost with B-SMOTE achieved the classification accuracy of 0.9526, which is expected to be able to automatically detect normal/abnormal network traffic.