• The Journal of Internal Korean Medicine
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• v.37 no.4
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• pp.661-668
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• 2016

Objectives: To describe the effect of traditional Korean medicine and treatment on Miller Fisher syndrome (MFS).Methods: A 54-year-old woman diagnosed with MFS presented with eyeball dysfunction, diplopia, vertigo, right facial palsy, and back dysesthesia. The patient had been treated with immunoglobulin for 21 d, but her symptoms failed to improve. Thus, herbal medicine, acupuncture, electropuncture, pharmacopuncture, and moxibustion were added. Length of eyeball movement, distance that the patient recognize double images in the eyes and Visual Analogue Scale (VAS) are measures for the syndrome.Results: The symptoms of the patient considerably improved, with the return of eyeball movement to normal and disappearance of diplopia.Conclusions: The results suggest that Korean medicine may be an effective therapy for MFS.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.748-753
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• 2012

In this paper, Face recognition algorithm is designed based on optimized pRBFNNs pattern classifier using three-dimensional scanner. Generally two-dimensional image-based face recognition system enables us to extract the facial features using gray-level of images. The environmental variation parameters such as natural sunlight, artificial light and face pose lead to the deterioration of the performance of the system. In this paper, the proposed face recognition algorithm is designed by using three-dimensional scanner to overcome the drawback of two-dimensional face recognition system. First face shape is scanned using three-dimensional scanner and then the pose of scanned face is converted to front image through pose compensation process. Secondly, data with face depth is extracted using point signature method. Finally, the recognition performance is confirmed by using the optimized pRBFNNs for solving high-dimensional pattern recognition problems.

• Journal of the Korea Society of Computer and Information
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• v.18 no.11
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• pp.167-174
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• 2013

TIn this paper, we find the face in color images and the ability to track the face was implemented. Face tracking is the work to find face regions in the image using the functions of the computer system and this function is a necessary for the robot. But such as extracting skin color in the image face tracking can not be performed. Because face in image varies according to the condition such as light conditions, facial expressions condition. In this paper, we use the skin color pixel extraction function added lighting compensation function and the entire processing system was implemented, include performing finding the features of eyes, nose, mouth are confirmed as face. Lighting compensation function is a adjusted sine function and although the result is not suitable for human vision, the function showed about 4% improvement. Face features are detected by amplifying, reducing the value and make a comparison between the represented image. The eye and nose position, lips are detected. Face tracking efficiency was good.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.5
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• pp.430-437
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• 2021

In this paper, we propose a robust face recognition method against soft errors with a deep convolutional generative adversarial network(DCGAN) based compensation method by a cross-layer approach. When soft-errors occur in block data of JPEG files, these blocks can be decoded inappropriately. In previous results, these blocks have been replaced using a mean face, thereby improving recognition ratio to a certain degree. This paper uses a DCGAN-based compensation approach to extend the previous results. When soft errors are detected in an embedded system layer using parity bit checkers, they are compensated in the application layer using compensated block data by a DCGAN-based compensation method. Regarding soft errors and block data loss in facial images, a DCGAN architecture is redesigned to compensate for the block data loss. Simulation results show that the proposed method effectively compensates for performance degradation due to soft errors.

• The Journal of the Korea Contents Association
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• v.18 no.12
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• pp.108-117
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• 2018

Xiao Quan is a leading documentary portrait photographer in China. He tried to shoot portrait photographs of celebrities in the literary and artistic world. By doing this, he represented their time period. We explored the way Xiao Quan implemented the times they lived in by analyzing their portrait photographs included in . Our research showed that Xiao Quan used images of their living environments, clothes and facial expressions and composition of portraits. Such various methods of creation are a means for the symbolic expressions of their times. This research not only finds the way Chinese documentary portraits are created but also provides an opportunity to increase the value of documentary portraits as historic documents.

• The Journal of Korean Institute of Information Technology
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• v.16 no.11
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• pp.105-113
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• 2018

In this paper, we proposed a method for effective classification of eye, nose, and mouth of human face. Most recent image classification uses Convolutional Neural Network(CNN). However, the features extracted by CNN are not sufficient and the classification effect is not too high. We proposed a new algorithm to improve the classification effect. The proposed method can be roughly divided into three parts. First, the Haar feature extraction algorithm is used to construct the eye, nose, and mouth dataset of face. The second, the model extracts CNN features of image using AlexNet. Finally, Haar-CNN features are extracted by performing convolution after Haar feature extraction. After that, CNN features and Haar-CNN features are fused and classify images using softmax. Recognition rate using mixed features could be increased about 4% than CNN feature. Experiments have demonstrated the performance of the proposed algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.4
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• pp.381-387
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• 2019

The limitations of applying a variety of artificial intelligence to the medical community are, first, subjective views, extensive interpreters and physical fatigue in interpreting the image of an interpreter's illness. And there are questions about how long it takes to collect annotated data sets for each illness and whether to get sufficient training data without compromising the performance of the developed deep learning algorithm. In this paper, when collecting basic images based on acne data sets, the selection criteria and collection procedures are described, and a model is proposed to classify data into small loss rates (5.46%) and high accuracy (96.26%) in the sequential structure. The performance of the proposed model is compared and verified through a comparative experiment with the model provided by Keras. Similar phenomena are expected to be applied to the field of medical and skin care by applying them to the acne classification model proposed in this paper in the future.

• Archives of Craniofacial Surgery
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• v.22 no.4
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• pp.193-198
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• 2021

Background: Nasal bone fractures are frequently encountered in clinical practice. Although fracture reduction is simple and correction requires a short operative time, low patient satisfaction and relatively high complication rates remain issues for many surgeons. These challenges may result from inaccuracies in fracture recognition and assessment or inappropriate surgical planning. Findings from immediate postoperative computed tomography (CT) scans and those performed at 4 to 6 weeks postoperatively were compared to evaluate the accuracy and outcomes of nasal fracture reduction. Methods: This retrospective study included patients diagnosed with nasal bone fractures at our department who underwent closed reduction surgery. Patients who did not undergo additional CT scans were excluded from the study. Clinical examinations, patient records, and radiographic images were evaluated in 20 patients with nasal bone fractures. Results: CT findings from immediately after surgery and a 1month follow-up were compared in 20 patients. Satisfactory nasal projection and aesthetically acceptable results were observed in patients with accurate correction or mild overcorrection, while undercorrection was associated with unfavorable results. Conclusion: Closed reduction surgery for correcting nasal bone fractures usually provides acceptable outcomes with relatively few complications. If available, immediate postoperative CT scans are recommended to guide surgeons in the choice of whether to perform secondary adjustments if the initial results are unsatisfactory. Based on photogrammetric data, nasal bone reduction with accurate correction or mild overcorrection achieved acceptable and stable outcomes at 1 month postoperatively. Therefore, when upward dislocation is observed on postoperative CT, one can simply observe without a subsequent intervention.

• The korean journal of orthodontics
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• v.51 no.4
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• pp.250-259
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• 2021

Objective: The aim of this study was to compare the differences in mandibular posterior anatomic limit (MPAL) distances stratified by vertical patterns in patients with skeletal Class III malocclusion by using cone-beam computed tomography (CBCT). Methods: CBCT images of 48 patients with skeletal Class III malocclusion (mean age, 22.8 ± 3.1 years) categorized according to the vertical patterns (hypodivergent, normodivergent, and hyperdivergent; n = 16 per group) were analyzed. While parallel to the posterior occlusal line, the shortest linear distances from the distal root of the mandibular second molar to the inner cortex of the mandibular body were measured at depths of 4, 6, and 8 mm from the cementoenamel junction. MPAL distances were compared between the three groups, and their correlations were analyzed. Results: The mean ages, sex distribution, asymmetry, and crowding in the three groups showed no significant differences. MPAL distance was significantly longer in male (3.8 ± 2.6 mm) than in female (1.8 ± 1.2 mm) at the 8-mm root level. At all root levels, MPAL distances were significantly different in the hypodivergent and hyperdivergent groups (p < 0.001) and between the normodivergent and hyperdivergent groups (p < 0.01). MPAL distances were the shortest in the hyperdivergent group. The mandibular plane angle highly correlated with MPAL distances at all root levels (p < 0.01). Conclusions: MPAL distances were the shortest in patients with hyperdivergent patterns and showed a decreasing tendency as the mandibular plane angle increased. MPAL distances were significantly shorter (~3.16 mm) at the 8-mm root level.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.115-121
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• 2020

With the spread of IoT technology, various IoT applications using facial recognition are emerging. This paper describes the design and implementation of a remote control system using deep learning-based face recognition and hand gesture recognition. In general, an application system using face recognition consists of a part that takes an image in real time from a camera, a part that recognizes a face from the image, and a part that utilizes the recognized result. Raspberry PI, a single board computer that can be mounted anywhere, has been used to shoot images in real time, and face recognition software has been developed using tensorflow's FaceNet model for server computers and hand gesture recognition software using OpenCV. We classified users into three groups: Known users, Danger users, and Unknown users, and designed and implemented an application that opens automatic door locks only for Known users who have passed both face recognition and hand gestures.