• Journal of Digital Convergence
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• v.11 no.2
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• pp.299-307
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• 2013

The aim of this paper is the improvement of identification performance and the reduction of computational quantities in the face recognition system based on SIFT-Grid. Firstly, we propose a composition method of integrated template by removing similar SIFT keypoints and blending different keypoints in variety training images of one face class. The integrated template is made up of computation of similarity matrix and threshold-based histogram from keypoints in a same sub-region which divided by applying SIFT-Grid of training images. Secondly, we propose a computation method of similarity for identify of test image from composed integrated templates efficiently. The computation of similarity is performed that a test image to compare one-on-one with the integrated template of each face class. Then, a similarity score and a threshold-voting score calculates according to each sub-region. In the experimental results of face recognition tasks, the proposed methods is founded to be more accurate than both two other methods based on SIFT-Grid, also the computational quantities are reduce.

• KIISE Transactions on Computing Practices
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• v.22 no.8
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• pp.363-368
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• 2016

A storage-based VDI (Virtual Desktop Infrastructure) system has the disadvantage of degraded performance when IOs for the VDI system are concentrated on the storage. The performance of the VDI system decreases rapidly especially, in case of the boot storm wherein all virtual desktops boot simultaneously. In this paper, we propose a main memory-based virtual desktop system managing virtual desktop images on main memory to solve the performance degradation problem including the boot storm. Performance of the main memory-based VDI system is improved by storing the virtual desktop image on the main memory. Also, the virtual desktop images with large size can be stored in the main memory using deduplication technology. Implementation of the proposed VDI system indicated that it has 4 times performance benefit than the storage-based VDI system in case of the boot storm.

• Korean Security Journal
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• no.61
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• pp.163-179
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• 2019

The purpose of this study is to analyze the effect of service quality of security safety system users on corporate image and customer satisfaction. To confirm this through empirical analysis, a survey was conducted on about 200 users nationwide for about 40 days from May 2 to June 12, 2019. The survey was composed of "service quality, corporate image and customer satisfaction". The empirical analysis was conducted mainly on the analysis of importance, reliability, validity and correlation. This study used SPSS WIN 18.0 to calculate structural equations and exploratory factors. The research results are as follows: The users of the security safety system showed that the better the service quality, the better the corporate image. The better the corporate image, the higher the customer satisfaction. Also, the better the service quality, the higher the customer satisfaction. Therefore, each security safety system company should steadily improve the service quality to improve customer satisfaction.

• Smart Media Journal
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• v.11 no.5
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• pp.26-37
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• 2022

In this paper, images are automatically acquired to control the initial disease of strawberries among facility cultivation crops, and disease analysis is performed using the EfficientNet model to inform farmers of disease status, and disease diagnosis service is proposed by experts. It is possible to obtain an image of the strawberry growth stage and quickly receive expert feedback after transmitting the disease diagnosis analysis results to farmers applications using the learned EfficientNet model. As a data set, farmers who are actually operating facility cultivation were recruited and images were acquired using the system, and the problem of lack of data was solved by using the draft image taken with a cell phone. Experimental results show that the accuracy of EfficientNet B0 to B7 is similar, so we adopt B0 with the fastest inference speed. For performance improvement, Fine-tuning was performed using a pre-trained model with ImageNet, and rapid performance improvement was confirmed from 100 Epoch. The proposed service is expected to increase production by quickly detecting initial diseases.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.251-258
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• 2022

The purpose of this study was to investigate of resilience on nursing students in COVID-19 pandemic situation. Data were collected from 320 nursing students in B city and analyzed by t-test, ANOVA, Pearson correlation coefficient, and multiple regression using SPSS/WIN 22.0. The degree of resilience in nursing students was 3.78±0.61. There were significant differences in college life adaptation with department (F=9.932, p<.001), number of friend (F=5.681, p<.001), close professor (t=3.739, p<.001), There was positive correlation between resilience and grit (r=.469, p<.001), nursing professionalism (r=.614, p<.001), nurses' image (r=.552, p<.001). The factors affecting resilience of the study subjects were grit (β=.321, p<.001), nursing professionalism (β=.385, p<.001), nurses' image (β=.165, p=.022), with an explanatory power of 47.3%. Through the results of this study, it was confirmed that it is necessary to improve grit, nursing professionalism, and nurses' image in order to improve the resilience of nursing students in the COVID-19 pandemic.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.85-92
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• 2023

Deep learning applications are increasingly being leveraged for disease detection tasks in medical imaging modalities such as X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). Most data-centric deep learning challenges necessitate the use of supervised learning methodologies to attain high accuracy and to facilitate performance evaluation through comparison with the ground truth. Supervised learning mandates a substantial amount of image and label sets, however, procuring an adequate volume of medical imaging data for training is a formidable task. Various data augmentation strategies can mitigate the underfitting issue inherent in supervised learning-based models that are trained on limited medical image and label sets. This research investigates the enhancement of a deep learning-based rib fracture segmentation model and the efficacy of data augmentation techniques such as left-right flipping, rotation, and scaling. Augmented dataset with L/R flipping and rotations(30°, 60°) increased model performance, however, dataset with rotation(90°) and ⨯0.5 rescaling decreased model performance. This indicates the usage of appropriate data augmentation methods depending on datasets and tasks.

• The Journal of the Korea Contents Association
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• v.16 no.9
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• pp.276-285
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• 2016

In the highly competitive cosmetic industry, each cosmetic brand is striving to increase its profit by providing excellent product quality and delightful marketing campaigns. In this regard, this study investigates consumers' post-purchasing decision-making processes. It considers the two critical post-purchasing behaviors as repurchasing intention and recommendation intention. We posit customer satisfaction and brand image as the key antecedents of post-purchasing behaviors. The effects of cosmetic quality, price fairness, and event activities on consumer satisfaction and brand image are examined. We conducted a questionnaire survey of women in their twenties, which are critical groups in consumer life cycle of cometic products. Data collected from 163 female respondents were empirically tested against a theoretical framework using partial least squares. Analysis results found that both consumer satisfaction and brand image significantly influence post-purchasing behaviors, respectively. Especially, brand image has a stronger effect on post-purchasing behaviors than customer satisfaction does. Cosmetic quality and price fairness play an important role on customer satisfaction and brand image. However, event activities significantly affect consumer satisfaction, while they do not significantly influence brand image.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.6
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• pp.315-320
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• 2017

Estimating the correct pose of augmented objects on the real camera view efficiently is one of the most important questions in image tracking area. In computer vision, Homography is used for camera pose estimation in augmented reality system with markerless. To estimating Homography, several algorithm like SURF features which extracted from images are used. Based on extracted features, Homography is estimated. For this purpose, RANSAC algorithm is well used to estimate homography and DCS-RANSAC algorithm is researched which apply constraints dynamically based on Constraint Satisfaction Problem to improve performance. In DCS-RANSAC, however, the dataset is based on pattern of feature distribution of images manually, so this algorithm cannot classify the input image, pattern of feature distribution is not recognized in DCS-RANSAC algorithm, which lead to reduce it's performance. To improve this problem, we suggest the KCS-RANSAC algorithm using K-means clustering in CS-RANSAC to cluster the images automatically based on pattern of feature distribution and apply constraints to each image groups. The suggested algorithm cluster the images automatically and apply the constraints to each clustered image groups. The experiment result shows that our KCS-RANSAC algorithm outperformed the DCS-RANSAC algorithm in terms of speed, accuracy, and inlier rate.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.451-457
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• 2020

Format recognition and OCR techniques are widely used as methods for detecting and protecting personal information from electronic documents. However, due to the poor recognition rate of the OCR engine, personal information cannot be detected or false positives commonly occur. It also takes a long time to analyze a large amount of electronic documents. In this paper, we propose a method to improve the speed of image analysis of electronic documents, character recognition rate of the OCR engine, and detection rate of personal information by improving the existing method. The analysis speed was increased using the format recognition method while the analysis speed and character recognition rate of the OCR engine was improved by image correction. An algorithm for analyzing personal information from images was proposed to increase the reconnaissance rate of personal information. Through the experiments, 1755 image format recognition samples were analyzed in an average time of 0.24 seconds, which was 0.5 seconds higher than the conventional PAID system format recognition method, and the image recognition rate was 99%. The proposed method in this paper can be used in various fields such as public, telecommunications, finance, tourism, and security as a system to protect personal information in electronic documents.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.211-220
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• 2022

Recently, research on smart factories triggered by the 4th industrial revolution is being actively conducted. Accordingly, the manufacturing industry is conducting various studies to improve productivity and quality based on deep learning technology with robust performance. This paper is a study on the method of detecting tire surface defects in the visual inspection stage of the tire manufacturing process, and introduces a tire surface defect detection method using a depth image acquired through a 3D camera. The tire surface depth image dealt with in this study has the problem of low contrast caused by the shallow depth of the tire surface and the difference in the reference depth value due to the data acquisition environment. And due to the nature of the manufacturing industry, algorithms with performance that can be processed in real time along with detection performance is required. Therefore, in this paper, we studied a method to normalize the depth image through relatively simple methods so that the tire surface defect detection algorithm does not consist of a complex algorithm pipeline. and conducted a comparative experiment between the general normalization method and the normalization method suggested in this paper using YOLO V3, which could satisfy both detection performance and speed. As a result of the experiment, it is confirmed that the normalization method proposed in this paper improved performance by about 7% based on mAP 0.5, and the method proposed in this paper is effective.