• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.455-457
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• 2020

Recent studies demonstrate that deep learning model is easily biased by trained with unbalanced datasets. For example, the deep network can be trained to make a prediction by background feature instead the real target's feature. For those problem, a measurement called leakage was introduced to digitize this tendency. In this paper, we propose augmentation strategy which are used generally in computer vision problem to remedy this bias problem and we showed a simple augmentation methods have a effect to this task with experiments.

• ETRI Journal
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• v.44 no.2
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• pp.327-338
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• 2022

Classification models for human action recognition require robust features and large training sets for good generalization. However, data augmentation methods are employed for imbalanced training sets to achieve higher accuracy. These samples generated using data augmentation only reflect existing samples within the training set, their feature representations are less diverse and hence, contribute to less precise classification. This paper presents new data augmentation and action representation approaches to grow training sets. The proposed approach is based on two fundamental concepts: virtual video generation for augmentation and representation of the action videos through robust features. Virtual videos are generated from the motion history templates of action videos, which are convolved using a convolutional neural network, to generate deep features. Furthermore, by observing an objective function of the genetic algorithm, the spatiotemporal features of different samples are combined, to generate the representations of the virtual videos and then classified through an extreme learning machine classifier on MuHAVi-Uncut, iXMAS, and IAVID-1 datasets.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.47-54
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• 2023

Infants utilize crying as a non-verbal means of communication [1]. However, deciphering infant cries presents challenges. Extensive research has been conducted to interpret infant cry audios [2,3]. This paper proposes the classification of infant cries using 3D feature vectors augmented with various audio data techniques. A total of 5 classes (belly pain, burping, discomfort, hungry, tired) are employed in the study dataset. The data is augmented using 5 techniques (Pitch, Tempo, Shift, Mixup-noise, CutMix). Tempo, Shift, and CutMix augmentation techniques demonstrated improved performance. Ultimately, applying effective data augmentation techniques simultaneously resulted in a 17.75% performance enhancement compared to models using single feature vectors and original data.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.1-5
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• 2012

Data reduction has been used widely in data mining for convenient analysis. Principal component analysis (PCA) and factor analysis (FA) methods are popular techniques. The PCA and FA reduce the number of variables to avoid the curse of dimensionality. The curse of dimensionality is to increase the computing time exponentially in proportion to the number of variables. So, many methods have been published for dimension reduction. Also, data augmentation is another approach to analyze data efficiently. Support vector machine (SVM) algorithm is a representative technique for dimension augmentation. The SVM maps original data to a feature space with high dimension to get the optimal decision plane. Both data reduction and augmentation have been used to solve diverse problems in data analysis. In this paper, we compare the strengths and weaknesses of dimension reduction and augmentation for classification and propose a classification method using data reduction for classification. We will carry out experiments for comparative studies to verify the performance of this research.

• Journal of Mechanical Science and Technology
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• v.18 no.10
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• pp.1819-1828
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• 2004

The purpose of this paper is to investigate the effects of slits and swirl vanes on the turbulence augmentation in the flow fields of a gun-type gas burner using an X-type hot-wire probe. The gun-type gas burner adopted in this study is composed of eight slits and swirl vanes located on the surface of an inclined baffle plate. Experiment was carried out at a flow rate of 450 ι/min in burner model installed in the test section of subsonic wind tunnel. Swirl vanes playa role diffusing main flow more remarkably toward the radial direction than axial one, but slits show a reverse feature. Consequently, both slits and swirl vanes remarkably increase turbulence intensity in the whole range of a gun-type gas burner with a cone-type baffle plate.

• Archives of Craniofacial Surgery
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• v.17 no.1
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• pp.20-24
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• 2016

Background: Midface concavity is a relatively common facial feature in East Asian populations. Paranasal augmentation is becoming an increasingly popular procedure for patients with mild concavity and normal occlusion. In this study, we evaluate clinical outcomes following a series of paranasal augmentation. Methods: A retrospective review was performed for patients with Class I occlusion who had undergone bilateral paranasal augmentation using custom-made silicone implants, between October 2005 and September 2013. Patient charts were reviewed for demographic information, concomitant operations, and postoperative complications. Preoperative and postoperative (1-month) photographs were used to evaluate operative outcome. Results: The review identified a total of 93 patients meeting study criteria. Overall, aesthetic outcomes were satisfactory. Five-millimeter thick silicone implant was used in 81 cases, and the mean augmentation was 4.26 mm for this thickness. Among the 93 patients, 2 patients required immediate implant removal due to discomfort. An additional 3 patients experienced implant migration without any extrusion. Nine patients complained of transient paresthesia, which had resolved by 2 weeks. There were no cases of hematoma or infection. All patients reported improvement in their lateral profile and were pleased at follow-up. Complications that arose postoperatively included 9 cases of numbness in the upper lip and 3 cases of implant migration. All cases yielded satisfactory results without persisting complications. Sensations were fully restored postoperatively after 1 to 2 weeks. Conclusion: Paranasal augmentation with custom-made silicone implants is a simple, safe, and inexpensive method that can readily improve the lateral profile of a patient with normal occlusion. When combined with other aesthetic procedures, paranasal augmentation can synergistically improve outcome and lead to greater patient satisfaction.

• 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.

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.55-62
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• 2017

The Named Entity Recognition system is a system that recognizes words or phrases with object names such as personal name (PS), place name (LC), and group name (OG) in the document as corresponding object names. Traditional approaches to named entity recognition include statistical-based models that learn models based on hand-crafted features. Recently, it has been proposed to construct the qualities expressing the sentence using models such as deep-learning based Recurrent Neural Networks (RNN) and long-short term memory (LSTM) to solve the problem of sequence labeling. In this research, to improve the performance of the Korean named entity recognition system, we used a hand-crafted feature, part-of-speech tagging information, and pre-built lexicon information to augment features for representing sentence. Experimental results show that the proposed method improves the performance of Korean named entity recognition system. The results of this study are presented through github for future collaborative research with researchers studying Korean Natural Language Processing (NLP) and named entity recognition system.

• Journal of Korea Multimedia Society
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• v.21 no.10
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• pp.1150-1161
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• 2018

Classification is widely used in medical images to categorize patients and non-patients. However, conventional classification requires a complex procedure, including some rigid steps such as pre-processing, segmentation, feature extraction, detection, and classification. In this paper, we propose a novel convolutional neural network (CNN), called LeukemiaNet, to specifically classify two different types of leukemia, including acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), and non-cancerous patients. To extend the limited dataset, a PCA color augmentation process is utilized before images are input into the LeukemiaNet. This augmentation method enhances the accuracy of our proposed CNN architecture from 96.9% to 97.2% for distinguishing ALL, AML, and normal cell images.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.1-12
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• 2023

Ensuring the security of Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS) is paramount to safeguarding the reliability and safety of critical infrastructure. This paper addresses the significant threat posed by reconnaissance attacks on SCADA/ICS networks and presents an innovative methodology for enhancing their protection. The proposed approach strategically employs imbalance dataset handling techniques, ensemble methods, and feature engineering to enhance the resilience of SCADA/ICS systems. Experimentation and analysis demonstrate the compelling efficacy of our strategy, as evidenced by excellent model performance characterized by good precision, recall, and a commendably low false negative (FN). The practical utility of our approach is underscored through the evaluation of real-world SCADA/ICS datasets, showcasing superior performance compared to existing methods in a comparative analysis. Moreover, the integration of feature augmentation is revealed to significantly enhance detection capabilities. This research contributes to advancing the security posture of SCADA/ICS environments, addressing a critical imperative in the face of evolving cyber threats.