• Journal of Korean Neurosurgical Society
• /
• v.52 no.6
• /
• pp.541-546
• /
• 2012

Objective : The prefabrication of customized cranioplastic implants has been introduced to overcome the difficulties of intra-operative implant molding. The authors present a new technique, which consists of the prefabrication of implant molds using three-dimensional (3D) printers and polymethyl-methacrylate (PMMA) casting. Methods : A total of 16 patients with large skull defects (>100 $cm^2$) underwent cranioplasty between November 2009 and April 2011. For unilateral cranial defects, 3D images of the skull were obtained from preoperative axial 1-mm spiral computed tomography (CT) scans. The image of the implant was generated by a digital subtraction mirror-imaging process using the normal side of the cranium as a model. For bilateral cranial defects, precraniectomy routine spiral CT scan data were merged with postcraniectomy 3D CT images following a smoothing process. Prefabrication of the mold was performed by the 3D printer. Intraoperatively, the PMMA implant was created with the prefabricated mold, and fit into the cranial defect. Results : The median operation time was $184.36{\pm}26.07$ minutes. Postoperative CT scans showed excellent restoration of the symmetrical contours and curvature of the cranium in all cases. The median follow-up period was 23 months (range, 14-28 months). Postoperative infection was developed in one case (6.2%) who had an open wound defect previously. Conclusion : Customized cranioplasty PMMA implants using 3D printer may be a useful technique for the reconstruction of various cranial defects.

• Journal of Venture Innovation
• /
• v.6 no.3
• /
• pp.165-184
• /
• 2023

Since municipal welfare institutions operate for different purposes from general companies or public enterprises, ESG practice items and model construction should be conducted through various and comprehensive social welfare studies. Since there are not many studies available in domestic welfare institutions yet and there are no suitable ESG management utilization indicators, the Cheonan Welfare Foundation's strategy and management strategy system were established to spread the model to other welfare institutions and become a leading foundation through education and training. The foundation and front-line welfare institutions selected issues identification and key issues through the foundation's empirical analysis and criticality analysis, focusing on understanding ESG management and ways to establish a practice model that positively affects institutional image and business performance. Based on this, the promotion system was examined by establishing a performance management plan after deriving appropriate strategies and establishing a strategic system for social welfare institutions. Environmental and social responsibility, transparent management, safety management system establishment, emergency and prevention, user (customer) satisfaction system establishment, anti-corruption prevention and integrity ethics monitoring and evaluation, responsible supply chains, and community contribution programs. This study attempted to specifically present efforts to settle ESG management through the consideration of the Cheonan Welfare Foundation. Therefore, it is considered to be useful data for developing ESG management by referring to the systematic development process of the Cheonan City Restoration Foundation to develop ESG measurement indicators.

• Journal of Platform Technology
• /
• v.11 no.6
• /
• pp.68-78
• /
• 2023

In this paper, the color distortion in images with haze would be analyzed. When haze is included in the scene, the color signal reflected in the scene is accompanied by color distortion due to the influence of transmittance according to the haze component. When the influence of haze is excluded by a conventional de-hazing method, the distortion of color tends to not be sufficiently resolved. Khoury et al. used the dark channel priority technique, a haze model mentioned in many studies, to determine the degree of color distortion. However, only the tendency of distortion such as color error values was confirmed, and specific color distortion analysis was not performed. This paper analyzes the characteristic of color distortion and proposes a restoration method that can reduce color distortion. Input images of databases used by Khoury et al. include Macbeth color checker, a standard color tool. Using Macbeth color checker's color values, color distortion according to changes in haze concentration was analyzed, and a new color distortion model was proposed through modeling. The proposed method is to obtain a mapping function using the change in chromaticity by step according to the change in haze concentration and the color of the ground truth. Since the form of color distortion varies from step to step in proportion to the haze concentration, it is necessary to obtain an integrated thought function that operates stably at all stages. In this paper, the improvement of color distortion through the proposed method was estimated based on the value of angular error, and it was verified that there was an improvement effect of about 15% compared to the conventional method.

• Journal of Biomedical Engineering Research
• /
• v.25 no.3
• /
• pp.171-182
• /
• 2004

This study investigates the biomechanical efficacies of vertebroplasty which is used to treat vertebral body fracture with bone cement augmentation for osteoporotic patients using image and finite element analysis. Simulated models were divided into two groups: (a) a vertebral body, (b) a functional spinal unit(FSU). For a vertebral body model, the maximum axial displacement was investigated under axial compression to evaluate the effect of structural integrity. The stiffness of each FE model simulated was normalized by the stiffness of intact model. In the case of FSU model, 3 types of compression fractures were formulated to assess the influence on spinal curvature changes. The FSU models were loaded under compressive pressure to calculate the change of spinal curvature. The results according to the various factors suggest that vertebroplasty has the biomechanical efficacy of the increment of structural reinforcement in a patient who has relatively high level of BMD and a patient with the amount of 15％, PMMA injection of the cancellous bone volume. The spinal curvatures after compression fracture simulation vary from 9$^{\circ}$ to 17$^{\circ}$ of kyphosis compared to that the spinal curvature of normal model was -2.8$^{\circ}$ of lordosis. These spinal curvature changes cause the severe spinal deformity under the same loading. As the degree of compressive fracture increases the spinal deformity also increases. The results indicate that vertebroplasty has the increasing effect of the structural integrity regardless of the amount of PMMA or BMD and the restoration of decreased vertebral body height may be an important factor when the compressive fracture caused the significant height loss of vertebral body.

• The Journal of Korean Academy of Prosthodontics
• /
• v.28 no.2
• /
• pp.53-76
• /
• 1990

The purpose of this study was to evaluate the tensile bond strength of adhesive resins to successively recast Rexillium III and Degudent-U. Recasting was done 4times successively. Specimen $A_1$, were cast by new metal, and $A_2$ by surpus of $A_1$, $A_3$ by surplus of, $A_2$ $A_4$ by surplus of $A_3$, $A_5$ by surplus of $A_4$ plus 50% new metal. The types of surface treatment for resinbonded restoration in this experiment were electrolytic etching by OXY-ETCH(Oxy dental products, Inc., Hillside, New Jersey, U.S,A.), aluminum oxide blasting, anodic oxidation by EZ-OXISOR( Towagiken Co., Kyoto, Japan), electrotinplating by Kura Ace(Kuralay Co., Kyoto, Japan). Three kinds of cementing resin used in this study were Comspan(K.P. Cauil Co, Milford Delaware, U.S.A.), Super Bond C&B(Sun-Medical Co. Ltd., Kyoto,Japan), Panavia EX(Kuralay Co., Ltd., Osaka, Japan). Tensile bond strength was measured by Instron Universal testing machineModel 1125) and all the specimen were observed with SEM(JEOL, JSM-T2000) and mode of bond failure were recorded. The obtained results were as follows : 1. In electrolytic etched group, tensile bond strength was decreassed when recast alloy was used, and tensile bond strength of Compan and panavia EX were not significantly different(P>0.05). 2. In remaining group treated by aluminum oxide blasting, EZ-OXIOR, Kura Ace, tensile bond strength were not changed when recast alloy were used, and tensile bond strength of SuperBond(C&B and Panavia EX were not significantly different(P>0.05). 3. IN SEM evaluation, electrolytic etched group and electrotinplated group exhibited different image when recast alloy was used, and remaining groups treated by aluminum oxide blasting, EZ-OXISOR exhibited the same. 4. IN observation of bond failure, electrolytic etched group exhibited adhesive failure and remaing groups treated by aluminium oxid blasting, EZ-OXISOR, Kura Ace exhibited adhesive and cohesive failure.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.175-197
• /
• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.