• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.1
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• pp.1-8
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• 2014

This paper proposes an emotion classifier from EEG signals based on Bayes' theorem and a machine learning using a perceptron convergence algorithm. The emotions are represented on the valence and arousal dimensions. The fast Fourier transform spectrum analysis is used to extract features from the EEG signals. To verify the proposed method, we use an open database for emotion analysis using physiological signal (DEAP) and compare it with C-SVC which is one of the support vector machines. An emotion is defined as two-level class and three-level class in both valence and arousal dimensions. For the two-level class case, the accuracy of the valence and arousal estimation is 67% and 66%, respectively. For the three-level class case, the accuracy is 53% and 51%, respectively. Compared with the best case of the C-SVC, the proposed classifier gave 4% and 8% more accurate estimations of valence and arousal for the two-level class. In estimation of three-level class, the proposed method showed a similar performance to the best case of the C-SVC.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.521-524
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• 2002

This paper deals with the rule generation from data for control system and data mining using rough set. If the cores and reducts are searched for without consideration of the frequency of data belonging to the same equivalent class, the unnecessary attributes may not be discarded, and the resultant rules don't represent well the characteristics of the data. To improve this, we handle the inconsistent data with a probability measure defined by support, As a result the effect of uncertainty in knowledge reduction can be reduced to some extent. Also we construct the rule base in a hierarchical structure by applying core as the classification criteria at each level. If more than one core exist, the coverage degree is used to select an appropriate one among then to increase the classification rate. The proposed method gives more proper and effective rule base in compatibility and size. For some data mining example the simulations are performed to show the effectiveness of the proposed method.

• Bulletin of the Korean Mathematical Society
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• v.61 no.3
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• pp.867-873
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• 2024

A pseudo-Riemannian solvmanifold is a solvable Lie group endowed with a left invariant pseudo-Riemannian metric. In this short note, we investigate the nilpotency of the Ricci operator of pseudo-Riemannian solvmanifolds. We focus on a special class of solvable Lie groups whose Lie algebras can be expressed as a one-dimensional extension of a nilpotent Lie algebra ℝD⋉n, where D is a derivation of n whose restriction to the center of n has at least one real eigenvalue. The main result asserts that every solvable Lie group belonging to this special class admits a left invariant pseudo-Riemannian metric with nilpotent Ricci operator. As an application, we obtain a complete classification of three-dimensional solvable Lie groups which admit a left invariant pseudo-Riemannian metric with nilpotent Ricci operator.

• Journal of Sensor Science and Technology
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• v.30 no.5
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• pp.286-289
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• 2021

Image classification is one of the fundamental applications of computer vision. It enables a system to identify an object in an image. Recently, image classification applications have broadened their scope from computer applications to edge devices. The convolutional neural network (CNN) is the main class of deep learning neural networks that are widely used in computer tasks, and it delivers high accuracy. However, CNN algorithms use a large number of parameters and incur high computational costs, which hinder their implementation in edge hardware devices. To address this issue, this paper proposes a lightweight image classifier that provides good accuracy while using fewer parameters. The proposed image classifier diverts the input into three paths and utilizes different scales of receptive fields to extract more feature maps while using fewer parameters at the time of training. This results in the development of a model of small size. This model is tested on the CIFAR-10 dataset and achieves an accuracy of 90% using .26M parameters. This is better than the state-of-the-art models, and it can be implemented on edge devices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2558-2573
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• 2019

We present a deep CNN-based approach for classifying artistic media from artwork images. We aim to classify most frequently used artistic media including oilpaint brush, watercolor brush, pencil and pastel, etc. For this purpose, we extend VGGNet, one of the most widely used CNN structure, by substituting its last layer with a fully convolutional layer, which reveals class activation map (CAM), the region of classification. We build two artwork image datasets: YMSet that collects more than 4K artwork images for four most frequently used artistic media from various internet websites and WikiSet that collects almost 9K artwork images for ten most frequently used media from WikiArt. We execute a human baseline experiment to compare the classification performance. Through our experiments, we conclude that our classifier is superior in classifying artistic media to human.

• Smart Media Journal
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• v.12 no.7
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• pp.52-58
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• 2023

Alzheimer's disease is one type of dementia, the symptoms can be treated by detecting the disease at its early stages. Recently, many computer-aided diagnosis using magnetic resonance image(MRI) have shown a good results in the classification of AD. Taken these MRI images and feed to Free surfer software to extra the features. In consideration, using T1-weighted images and classifying using the convolution neural network (CNN) model are proposed. In this paper, taking the subjects from ADNI of subcortical and cortical features of 190 subjects. Consider the study to reduce the complexity of the model by using the single layer in the Res-Net, VGG, and Alex Net. Multi-class classification is used to classify four different stages, CN, EMCI, LMCI, AD. The following experiment shows for respective classification Res-Net, VGG, and Alex Net with the best accuracy with VGG at 96%, Res-Net, GoogLeNet and Alex Net at 91%, 93% and 89% respectively.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.244-247
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• 2020

Data augmentation has been helpful in improving the performance in deep learning, when we have a limited data and random erasing is one of the augmentations that have shown impressive performance in deep learning in multiple domains. But the main issue is that sometime it loses good features when randomly selected region is erased by some random values, that does not improve performance as it should. We target that problem in way that good features should not be lost and also want random erasing at the same time. For that purpose, we introduce new augmentation technique named Intra-Class Random Erasing (ICRE) that focuses on data to learn robust features of the same class samples by randomly exchanging randomly selected region. We perform multiple experiments by using different models including resnet18, VGG16 over variety of the datasets including ESC10, UrbanSound8K. Our approach has shown effectiveness over others methods including random erasing.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.22 no.1
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• pp.149-160
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• 1992

The purpose of this study was to localize the inferior alveolar canals in relation to the root apices of the mandibular third molars, according to their positions and degrees of impaction using vertical tube shift technique. One hundred three mandibular third molars, from 95 persons consisted of 57 males and 38 females, were clinically and radiographically investigated. The mandibular third molars had no pericoronitis and periapical lesions, and showed an evidence of complete root formation. The obtained results were as follows: 1. In localiztion of the inferior alveolar canals in relation to the root apices of the mandibular third molars, the inferior alveolar canal was located at the buccal side of the root apices of mandibular third molar in 77.7%, below the root apices in 16.5%, and the lingual side of root apices in 5.8%. 2. The positions of the mandibular third molars according to the Winter's Classification were as follows; 36.9% in Class Ⅰ, 21.3% in Class Ⅱ, 14.7% in Class Ⅲ, 4.8% in Class Ⅳ, 1.9% in Class Ⅴ, 17.5% in Class Ⅵ, 2.9% in Class Ⅶ. In localization of the inferior alveolar canals in relation to the root apices of the mandibular third molars according to the Winter's Classification, 92.1 % of Class Ⅰ, 86.4% of Class Ⅱ, 80.0% of Class Ⅲ, and 100.0% of Class Ⅳ and Ⅴ were located at the buccal side. In Class Ⅵ, however, 33.3% was located at the buccal side, 44.5% below the root apices, and 22.2% at the lingual side. 3. The degree of impaction was revealed to be 53.4% in Degree Ⅰ, 36.9% in Degree Ⅱ, and 9.7% in Degree Ⅲ. In localization of the inferior alveolar canals in relation to the root apices of mandibular third molars according to degree of impaction, 98.2% of Degree Ⅰ was located at the buccal side. In Degree Ⅱ, 60.5% was located at the bucal side, 31.6% below the root apices, and 7.9% at the lingual side. In Degree Ⅲ, 30.0% was located at the buccal side, 40.0% below the root apices, and 30.0% at the lingual side.

• Journal of the Korean Society for information Management
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• v.35 no.2
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• pp.37-62
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• 2018

This study examined the factors affecting the performance of automatic classification based on machine learning for domestic journal articles in the field of LIS. In particular, In view of the classification performance that assigning automatically the class labels to the articles in "Journal of the Korean Society for Information Management", I investigated the characteristics of the key factors(weighting schemes, training set size, classification algorithms, label assigning methods) through the diversified experiments. Consequently, It is effective to apply each element appropriately according to the classification environment and the characteristics of the document set, and a fairly good performance can be obtained by using a simpler model. In addition, the classification of domestic journals can be considered as a multi-label classification that assigns more than one category to a specific article. Therefore, I proposed an optimal classification model using simple and fast classification algorithm and small learning set considering this environment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.5
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• pp.435-441
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• 2020

Sentinel-2A satellite imagery provides VNIR (Visible Near InfraRed) and SWIR (ShortWave InfraRed) wavelength bands, and it is known to be effective for land cover classification, cloud detection, and environmental monitoring. Greenhouse is one of the middle classification classes for land cover map provided by the Ministry of Environment of the Republic of Korea. Since greenhouse is a class that has a lot of changes due to natural disasters such as storm and flood damage, there is a limit to updating the greenhouse at a rapid cycle in the land cover map. In the present study, we utilized Sentinel-2A satellite images that provide both VNIR and SWIR bands for the detection of greenhouse. To utilize Sentinel-2A satellite images for the detection of greenhouse, we produced high-resolution SWIR bands applying to the fusion technique performed in two stages and carried out the detection of greenhouse using SVM (Support Vector Machine) supervised classification technique. In order to analyze the applicability of SWIR bands to greenhouse detection, comparative evaluation was performed using the detection results applying only VNIR bands. As a results of quantitative and qualitative evaluation, the result of detection by additionally applying SWIR bands was found to be superior to the result of applying only VNIR bands.