• ETRI Journal
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• v.35 no.5
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• pp.900-910
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• 2013

Small-screen mobile terminals have difficulty accessing existing Web resources designed for large-screen devices. This paper presents an adaptive transformation method based on webpage semantic features to solve this problem. According to the text density and link density features of the webpages, the webpages are divided into two types: index and content. Our method uses an index-based webpage transformation algorithm and a content-based webpage transformation algorithm. Experiment results demonstrate that our adaptive transformation method is not dependent on specific software and webpage templates, and it is capable of enhancing Web content adaptation on small-screen terminals.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.241-248
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• 2007

In this paper, we propose a Transformation-Based Learning (TBL) method on generating the Korean standard pronunciation. Previous studies on the phonological processing have been focused on the phonological rule applications and the finite state automata (Johnson 1984; Kaplan and Kay 1994; Koskenniemi 1983; Bird 1995). In case of Korean computational phonology, some former researches have approached the phonological rule based pronunciation generation system (Lee et al. 2005; Lee 1998). This study suggests a corpus-based and data-oriented rule learning method on generating Korean standard pronunciation. In order to substituting rule-based generation with corpus-based one, an aligned corpus between an input and its pronunciation counterpart has been devised. We conducted an experiment on generating the standard pronunciation with the TBL algorithm, based on this aligned corpus.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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• pp.41-49
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• 2022

This paper proposes a novel lane detection algorithm based on inverse perspective transformation and machine learning in lightweight embedded system. The inverse perspective transformation method is presented for obtaining a bird's-eye view of the scene from a perspective image to remove perspective effects. This method requires only the internal and external parameters of the camera without a homography matrix with 8 degrees of freedom (DoF) that maps the points in one image to the corresponding points in the other image. To improve the accuracy and speed of lane detection in complex road environments, machine learning algorithm that has passed the first classifier is used. Before using machine learning, we apply a meaningful first classifier to the lane detection to improve the detection speed. The first classifier is applied in the bird's-eye view image to determine lane regions. A lane region passed the first classifier is detected more accurately through machine learning. The system has been tested through the driving video of the vehicle in embedded system. The experimental results show that the proposed method works well in various road environments and meet the real-time requirements. As a result, its lane detection speed is about 3.85 times faster than edge-based lane detection, and its detection accuracy is better than edge-based lane detection.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.765-777
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• 2022

The purpose of this study is to present a digital transformation-based learning model that can be used in universities based on learning digital transformation in order f to be competitive in a rapidly changing environment. Literature review, case study, and focus group interview were conducted and the implications for the learning model from these are as follows. Universities that stand out in related fields are actively using learning analysis to implement dashboards, develop predictive models, and support adaptive learning based on big data, They also have actively introduced advanced edutech to classes. In addition, problems and difficulties faced by other universities and K University when implementing digital transformation were also confirmed. Based on these findings, a digital transformation-based learning model of K University was developed. This model consists of four dimensions: diagnosis, recommendation, learning, and success. It allows students to proceed with learning by diagnosing and recommending various learning processes necessary for individual success, and systematically managing learning outcomes. Finally, academic and practical implications about the research results were discussed.

• Journal of KIISE:Software and Applications
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• v.37 no.2
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• pp.155-160
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• 2010

This paper proposes a method for Korean comparative sentence classification which is a part of comparison mining. Comparison mining, one area of text mining, analyzes comparative relations from the enormous amount of text documents. Three-step process is needed for comparison mining - 1) identifying comparative sentences in the text documents, 2) classifying those sentences into several classes, 3) analyzing comparative relations per each comparative class. This paper aims at the second task. In this paper, we use transformation-based learning (TBL) technique which is a well-known learning method in the natural language processing. In our experiment, we classify comparative sentences into seven classes using TBL and achieve an accuracy of 80.01%.

• Geomechanics and Engineering
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• v.38 no.1
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• pp.29-44
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• 2024

This research investigates the effectiveness of data augmentation techniques in the automated analysis of B-scan images from ground-penetrating radar (GPR) using deep learning. In spite of the growing interest in automating GPR data analysis and advancements in deep learning for image classification and object detection, many deep learning-based GPR data analysis studies have been limited by the availability of large, diverse GPR datasets. Data augmentation techniques are widely used in deep learning to improve model performance. In this study, we applied four data augmentation techniques (geometric transformation, color-space transformation, noise injection, and applying kernel filter) to the GPR datasets obtained from a testbed. A deep learning model for GPR data analysis was developed using three models (Faster R-CNN ResNet, SSD ResNet, and EfficientDet) based on transfer learning. It was found that data augmentation significantly enhances model performance across all cases, with the mAP and AR for the Faster R-CNN ResNet model increasing by approximately 4%, achieving a maximum mAP (Intersection over Union = 0.5:1.0) of 87.5% and maximum AR of 90.5%. These results highlight the importance of data augmentation in improving the robustness and accuracy of deep learning models for GPR B-scan analysis. The enhanced detection capabilities achieved through these techniques contribute to more reliable subsurface investigations in geotechnical engineering.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.219-224
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• 2020

Binary classification has been broadly investigated in machine learning. In addition, binary classification can be easily extended to multi class problems. To successfully utilize machine learning methods for classification tasks, preprocessing and feature extraction steps are essential. These are important steps to improve their classification performances. In this paper, we propose a new learning method based on weighted least squares. In the weighted least squares, designing weights has a significant role. Due to this necessity, we also propose a new technique to obtain weights that can achieve feature transformation. Based on this weighting technique, we also propose a method to combine the learning and feature extraction processes together to perform both processes simultaneously in one step. The proposed method shows the promising performance on five UCI machine learning data sets.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.1
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• pp.57-65
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• 2023

A vibration response-based detection system was used to investigate the adhesive areas of single-lap joints using a nonlinear transformation approach for deep learning. In industry or engineering fields, it is difficult to know the condition of an invisible part within a structure that cannot easily be disassembled and the conditions of adhesive areas of adhesively bonded structures. To address these issues, a detection method was devised that uses nonlinear transformation to determine the adhesive areas of various single-lap-jointed specimens from the vibration response of the reference specimen. In this study, a frequency response function with nonlinear transformation was employed to identify the vibration characteristics, and a virtual spectrogram was used for classification in convolutional neural network based deep learning. Moreover, a vibration experiment, an analytical solution, and a finite-element analysis were performed to verify the developed method with aluminum, carbon fiber composite, and ultra-high-molecular-weight polyethylene specimens.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.179-188
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• 2022

There have been many successful researches on a bearing fault diagnosis based on Deep Learning, but there is still a critical issue of the data distribution difference between training data and test data from their different working conditions causing performance degradation in applying those methods to the machines in the field. As a solution, a data adaptation method has been proposed and showed a good result, but each and every approach is strictly limited to a specific applying scenario or presupposition, which makes it still difficult to be used as a real-world application. Therefore, in this study, we have proposed a method that, using a data transformation with MFCCs and a simple CNN architecture, can perform a robust diagnosis on a target domain data without an additional learning or tuning on the model generated from a source domain data and conducted an experiment and analysis on the proposed method with the CWRU bearing dataset, which is one of the representative datasests for bearing fault diagnosis. The experimental results showed that our method achieved an equal performance to those of transfer learning based methods and a better performance by at least 15% compared to that of an input transformation based baseline method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3981-4004
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• 2019

This paper proposes a novel method for locating objects in real space from a single remote image and measuring actual distances between them by automatic detection and perspective transformation. The dimensions of the real space are known in advance. First, the corner points of the interested region are detected from an image using deep learning. Then, based on the corner points, the region of interest (ROI) is extracted and made proportional to real space by applying warp-perspective transformation. Finally, the objects are detected and mapped to the real-world location. Removing distortion from the image using camera calibration improves the accuracy in most of the cases. The deep learning framework Darknet is used for detection, and necessary modifications are made to integrate perspective transformation, camera calibration, un-distortion, etc. Experiments are performed with two types of cameras, one with barrel and the other with pincushion distortions. The results show that the difference between calculated distances and measured on real space with measurement tapes are very small; approximately 1 cm on an average. Furthermore, automatic corner detection allows the system to be used with any type of camera that has a fixed pose or in motion; using more points significantly enhances the accuracy of real-world mapping even without camera calibration. Perspective transformation also increases the object detection efficiency by making unified sizes of all objects.