• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.6
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• pp.1139-1145
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• 2014

User recognition technology, which identifies or verifies a certain individual is absolutely essential under robotic environments for intelligent services. The conventional face recognition algorithm using single distance face image as training images has a problem that face recognition rate decreases as distance increases. The face recognition algorithm using face images by actual distance as training images shows good performance but this has a problem that it requires user cooperation. This paper proposes the LDA-based long distance face recognition method which uses multiple distance face images from a zoom camera for training face images. The proposed face recognition technique generated better performance by average 7.8% than the technique using the existing single distance face image as training. Compared with the technique that used face images by distance as training, the performance fell average 8.0%. However, the proposed method has a strength that it spends less time and requires less cooperation to users when taking face images.

• Journal of Korean society of Dental Hygiene
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• v.7 no.4
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• pp.365-379
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• 2007

The objective of the current study is to provide basic data necessary for the development of systematic program that is required for the systematic occupational work training of dental hygienists who newly employed at dental hospitals or clinics. The results of the surveys were listed as in below. The survey was conducted for 175 experienced dental hygienists who are in charge of occupational work training in 6 regions(Seoul, Kyunggi, Busan, Ulsan, Kwangju, Chungnam, Kyungnam) of the country where the occupational work training for new dental hygienists is systematically operated. 1. The recognition of experienced dental hygienists for the importance of occupational work training revealed that image training was the most importantly recognized by dental hygienists in Seoul Kyunggi regions(pE.01). In case of Busan region, periodontic training and conservative dentistry training were the most importantly recognized, and customer service training was mostly highly recognized in Ulsan region(pE.01). In case of Kwangjuregion, dental health insurance claim training was recognized as most important subject, and Patient consultation training was the most importantly recognized in Chungnam region. In case of Kyungnam region. Oral surgery was recognized as the most important training subject. 2. Regard on the importance of the range of occupational work training, the experienced dental hygienists with less than 2 years of experience were found to recognize the training of greeting and naming most importantly, the dental hygienists with 2~3 years of experience most importantly recognized oral surgery, and the dental hygienists with 4~5 year of experience were found to recognize conservative training most importantly. In case of dental hygienists having 6~9 year of experience recognized periodontic and conservation trainings as the most important subjects, and the dental hygienist having more than 10 years of experience were found to recognize conservative and image trainings mostly importantly.

• Journal of Institute of Convergence Technology
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• v.11 no.1
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• pp.1-5
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• 2021

Deep learning based methods achieve state-of-the-art accuracy, however, they typically rely on supervised training with large labeled datasets. It is known in many medical applications that labeling medical images requires significant expertise and much time, and typical hand-tuned approaches for data augmentation fail to capture the complex variations in such images. This paper proposes a 3D image augmentation method to overcome these difficulties. It allows us to enrich diversity of training data samples that is essential in medical image segmentation tasks, thus reducing the data overfitting problem caused by the fact the scale of medical image dataset is typically smaller. Our numerical experiments demonstrate that the proposed approach provides significant improvements over state-of-the-art methods for 3D medical image segmentation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.111-116
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• 2021

The hyper-spectral image is data obtained by dividing the electromagnetic wave band in the infrared region into hundreds of wavelengths. It is used to find or classify objects in various fields. Recently, deep learning classification method has been attracting attention. In order to use hyper-spectral image data as deep learning training data, a processing technique is required compared to conventional visible light image data. To solve this problem, we developed a software that selects specific wavelength images from the hyper-spectral data cube and performs the ground truth task. We also developed software to manage data including environmental information. This paper describes the configuration and function of the software.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.1
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• pp.49-59
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• 2019

Generative adversarial networks(GANs) have received great attention in the machine learning field for their capacity to model high-dimensional and complex data distribution implicitly and generate new data samples from the model distribution. This paper investigates the model training methodology, architecture, and various applications of generative adversarial networks. Experimental evaluation is also conducted for generating synthetic image dataset for defense using two types of GANs. The first one is for military image generation utilizing the deep convolutional generative adversarial networks(DCGAN). The other is for visible-to-infrared image translation utilizing the cycle-consistent generative adversarial networks(CycleGAN). Each model can yield a great diversity of high-fidelity synthetic images compared to training ones. This result opens up the possibility of using inexpensive synthetic images for training neural networks while avoiding the enormous expense of collecting large amounts of hand-annotated real dataset.

• Journal of Biomedical Engineering Research
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• v.44 no.4
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• pp.293-301
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• 2023

Scoliosis is a three-dimensional deformation of the spine that is a deformity induced by physical or disease-related causes as the spine is rotated abnormally. Early detection has a significant influence on the possibility of nonsurgical treatment. To train a deep learning model with preprocessed images and to evaluate the results with and without data augmentation to enable the diagnosis of scoliosis based only on a chest X-ray image. The preprocessed images in which only the spine, rib contours, and some hard tissues were left from the original chest image, were used for learning along with the original images, and three CNN(Convolutional Neural Networks) models (VGG16, ResNet152, and EfficientNet) were selected to proceed with training. The results obtained by training with the preprocessed images showed a superior accuracy to those obtained by training with the original image. When the scoliosis image was added through data augmentation, the accuracy was further improved, ultimately achieving a classification accuracy of 93.56% with the ResNet152 model using test data. Through supplementation with future research, the method proposed herein is expected to allow the early diagnosis of scoliosis as well as cost reduction by reducing the burden of additional radiographic imaging for disease detection.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.25-33
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• 2023

The purpose of this study is to evaluate the impact of intermediate features on FER performance. To achieve this objective, intermediate features were extracted from the input images at specific layers (FM1~FM4) of the pre-trained network (Resnet-18). These extracted intermediate features and original images were used as inputs to the vision transformer (ViT), and the FER performance was compared. As a result, when using a single image as input, using intermediate features extracted from FM2 yielded the best performance (training accuracy: 94.35%, testing accuracy: 75.51%). When using the original image as input, the training accuracy was 91.32% and the testing accuracy was 74.68%. However, when combining the original image with intermediate features as input, the best FER performance was achieved by combining the original image with FM2, FM3, and FM4 (training accuracy: 97.88%, testing accuracy: 79.21%). These results imply that incorporating intermediate features alongside the original image can lead to superior performance. The findings can be referenced and utilized when designing the preprocessing stages of a deep learning model in FER. By considering the effectiveness of using intermediate features, practitioners can make informed decisions to enhance the performance of FER systems.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.103-110
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• 2022

Since satellite images generally include clouds in the atmosphere, it is essential to detect or mask clouds before satellite image processing. Clouds were detected using physical characteristics of clouds in previous research. Cloud detection methods using deep learning techniques such as CNN or the modified U-Net in image segmentation field have been studied recently. Since image segmentation is the process of assigning a label to every pixel in an image, precise pixel-based dataset is required for cloud detection. Obtaining accurate training datasets is more important than a network configuration in image segmentation for cloud detection. Existing deep learning techniques used different training datasets. And test datasets were extracted from intra-dataset which were acquired by same sensor and procedure as training dataset. Different datasets make it difficult to determine which network shows a better overall performance. To verify the effectiveness of the cloud detection network such as Cloud-Net, two types of networks were trained using the cloud dataset from KOMPSAT-3 images provided by the AIHUB site and the L8-Cloud dataset from Landsat8 images which was publicly opened by a Cloud-Net author. Test data from intra-dataset of KOMPSAT-3 cloud dataset were used for validating the network. The simulation results show that the network trained with KOMPSAT-3 cloud dataset shows good performance on the network trained with L8-Cloud dataset. Because Landsat8 and KOMPSAT-3 satellite images have different GSDs, making it difficult to achieve good results from cross-sensor validation. The network could be superior for intra-dataset, but it could be inferior for cross-sensor data. It is necessary to study techniques that show good results in cross-senor validation dataset in the future.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.380-386
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• 2022

This paper proposes a method for structuring the preprocessing process of a training image when color is applied using Pix2Pix, one of the adversarial generative neural network techniques. This paper concentrate on the prediction result can be damaged according to the degree of light reflection of the training image. Therefore, image preprocesisng and parameters for model optimization were configured before model application. In order to increase the image resolution of training and prediction results, it is necessary to modify the of the model so this part is designed to be tuned with parameters. In addition, in this paper, the logic that processes only the part where the prediction result is damaged by light reflection is configured together, and the pre-processing logic that does not distort the prediction result is also configured.Therefore, in order to improve the usability, the accuracy was improved through experiments on the part that applies the light reflection tuning filter to the training image of the Pix2Pix model and the parameter configuration.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.10 no.3
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• pp.92-97
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• 2011

In this paper, a controller recognizing human gestures using EMG signal is shown. The tiny and band-type controller is developed for image training to excercise the specific area in the body, and uses a dry-type silver fiber electrode easy to be attached or detached itself to a skin. The captured EMG signals are converted to 10-bit digital values via amplification and frequency filtering processes within the controller, and are transmitted to the server by wireless. As the gesture recognition ratio using the proposed controller on biceps is up to 80%, we expect the practical potential of the controller is very promising.