• Journal of Navigation and Port Research
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• v.49 no.1
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• pp.18-35
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• 2025

This study introduced a novel methodology for enhancing Automatic Identification System (AIS) trajectory forecasting in regions characterized by significant tidal variations through feature augmentation, specifically indirect incorporation of sea level data via the nearest tidal gauge. Traditional AIS prediction models predominantly utilize features such as latitude, longitude, speed over ground (SOG), and course over ground (COG) for time series forecasting. However, these models often overlook the influence of tidal fluctuations, which can significantly impact prediction accuracy in areas with pronounced tidal changes. To address this limitation, we proposed a feature augmentation approach by incorporating the Haversine distance to the nearest tidal gauge and the real-time sea level at that gauge as additional features. Direct access to sea level data at a vessel's precise location presents practical challenges, making this indirect method an efficient and effective solution. Through comprehensive analyses across multiple deep learning models and test scenarios, our results demonstrate that this augmented feature set can substantially improve AIS forecasting performance in regions with significant tidal variation surrounding the Korean Peninsula.

• Annual Conference of KIPS
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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 the Korea Computer Graphics Society
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• v.30 no.4
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• pp.19-29
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• 2024

In this paper, we examine the effectiveness of StyleGAN-generated images for data augmentation in training deep neural networks for medical image classification. We apply StyleGAN data augmentation to train VGG-16 networks for pneumonia diagnosis from chest X-ray images and focal liver lesion classification from abdominal CT images. Through quantitative and qualitative analyses, our experiments reveal that StyleGAN data augmentation expands the outer class boundaries in the feature space. Thanks to this expansion characteristics, the StyleGAN data augmentation can enhance classification performance when properly combined with real training images.

• The Journal of Korea Robotics Society
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• v.20 no.3
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• pp.471-481
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• 2025

This paper proposes adaptive image augmentation and preprocessing techniques to improve the performance of the ORB-SLAM2 system in low-texture environments. In such environments, ORB-SLAM2 often experiences degradation in pose estimation accuracy and system stability. The proposed method analyzes the texture characteristics of images using a Laplacian mask, dynamically adjusting sharpness and contour extraction to effectively enhance structural details in low-texture images. Additionally, an adaptive ORB feature thresholding mechanism is introduced to prevent excessive feature extraction caused by image augmentation, thereby improving accuracy in both low-and high-texture regions. Experimental results show that the proposed method significantly increases the number of ORB features, the number of matched features between consecutive frames, and the proportion of frames in a stable tracking state. These improvements are particularly pronounced in low-texture environments, enhancing the overall stability and accuracy of the ORB-SLAM system.

• 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.

• International Journal of Naval Architecture and Ocean Engineering
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• v.16
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• pp.100597.1-100597.1
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• 2024

The intelligent recognition of ship geometric features is a prerequisite for enabling computers to automatically generate and deform ship hull surfaces according to requirements, thereby replacing the work of human designers to improve design efficiency. This paper aims to research the recognition of geometric features in threedimensional ship data using PointNet. To achieve this goal, we first construct two ship point cloud datasets suitable for global feature classification and feature part segmentation of three-dimensional hulls. Subsequently, we conducted recognition capability testing to determine the optimal hyperparameters for identifying ship feature networks. Finally, we employ ship models with non-standard positions to implement data augmentation, enhancing the network's robustness in recognizing the initial positions of ships and achieving rapid cognition of three-dimensional ship geometric features. The findings of this research will provide technical support for ship design based on artificial intelligence technology.