• Journal of the Korean institute of surface engineering
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• v.29 no.2
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• pp.81-92
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• 1996

The cementless fixation of bone ingrowth by porous coatings on artificial hip joint prostheses are replacing polymethylmethacrylate(PMMA) bone cement fixations. However, the major interests in the field of porous metal coating are environmental corrosivity accelerated by metal ion release, deterioration in the mechanical property of the coating, and the mechanical failure of the coatings as well as the substrate. Therefore, the selection of right materials for coatings and the development of porous coating techniques must be accomplished. Because of the existing problems in Ti and Ti alloys which are used extensively, this study is focused on the plasma spraying technique for coating on super stainless steel substrate. In order to determine the optimum conditions which satisfy the requirement for the porous coatings, under the plasma spraying, we selected the experimental parameters which extensively influenced on the characteristics of the coating through the pre-examination. Spray distance has been selected among 120, 160, and 200mm and primary gas flow rate among 70, 100, and 130 SCFH. Current and secondary gas($H_2$) flow rate was fixed at 400A, and 15 SCFH respectively. To understand the characteristics of the coatings, surface morphology, cross-sectional micro-structure, surface roughness, residual stress, and corrosion resistance were elucidated and the best conditions for the bone ingrowth improvement on artificial hip joint prostheses were found.

• Korean Journal of Artificial Intelligence
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• v.9 no.2
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• pp.35-40
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• 2021

In this paper, many game companies seek better optimization and easy-to-apply logic to prolong the game's lifespan and provide a better game environment for users. Therefore, research will be showing the game's key input response method called RoS (Response of Speed). The purpose of the method is to simultaneously perform various motions with the character showing natural motion without errors even if the character's control key is duplicated. This method is for the developers so they can reduce bugs and development time in future game development. To be used with quickly generating game environments, the new method compares with the popular motion method, so which method is faster and can adapt to diverse games. The paper suggested that the Response of Speed method is a better method for optimizing frames and reducing the number of reacting seconds by showing a faster response and speed). With the method popularity of scrollers, many 2D cross-scroll games follow the formula of Dash, Shoot, Walk, Stay, and Crouch. With the development of game engines, it is becoming easier to implement them. Therefore, although the method presented in the above paper differs from the popular method, it is expected that there will be no great difficulty in applying it to the game because transplantation is easy. In the future, we plan to study to minimize the delay of each connection of the character motion so that the game can be optimized to best.

• Imaging Science in Dentistry
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• v.52 no.2
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• pp.187-195
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• 2022

Purpose: This study developed a convolutional neural network (CNN) model to diagnose maxillary sinusitis on panoramic radiographs(PRs) and cone-beam computed tomographic (CBCT) images and evaluated its performance. Materials and Methods: A CNN model, which is an artificial intelligence method, was utilized. The model was trained and tested by applying 5-fold cross-validation to a dataset of 148 healthy and 148 inflamed sinus images. The CNN model was implemented using the PyTorch library of the Python programming language. A receiver operating characteristic curve was plotted, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive values for both imaging techniques were calculated to evaluate the model. Results: The average accuracy, sensitivity, and specificity of the model in diagnosing sinusitis from PRs were 75.7%, 75.7%, and 75.7%, respectively. The accuracy, sensitivity, and specificity of the deep-learning system in diagnosing sinusitis from CBCT images were 99.7%, 100%, and 99.3%, respectively. Conclusion: The diagnostic performance of the CNN for maxillary sinusitis from PRs was moderately high, whereas it was clearly higher with CBCT images. Three-dimensional images are accepted as the "gold standard" for diagnosis; therefore, this was not an unexpected result. Based on these results, deep-learning systems could be used as an effective guide in assisting with diagnoses, especially for less experienced practitioners.

• Korean Journal of Artificial Intelligence
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• v.9 no.1
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• pp.9-14
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• 2021

This paper predicted a model that indicates whether to buy a car based on primary health insurance customer data. Currently, automobiles are being used to land transportation and living, and the scope of use and equipment is expanding. This rapid increase in automobiles has caused automobile insurance to emerge as an essential business target for insurance companies. Therefore, if the car insurance sales are predicted and sold using the information of existing health insurance customers, it can generate continuous profits in the insurance company's operating performance. Therefore, this paper aims to analyze existing customer characteristics and implement a predictive model to activate advertisements for customers interested in such auto insurance. The goal of this study is to maximize the profits of insurance companies by devising communication strategies that can optimize business models and profits for customers. This study was conducted through the Microsoft Azure program, and an automobile insurance purchase prediction model was implemented using Health Insurance Cross-sell Prediction data. The program algorithm uses Two-Class Logistic Regression and Two-Class Boosted Decision Tree at the same time to compare two models and predict and compare the results. According to the results of this study, when the Threshold is 0.3, the AUC is 0.837, and the accuracy is 0.833, which has high accuracy. Therefore, the result was that customers with health insurance could induce a positive reaction to auto insurance purchases.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.137-144
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• 2023

The purpose of this study is to examine consumers' perceptions of domestic infertility support policies based on infertility-related keywords and the trends of their changes. To this end, Momsholic, a mom cafe which has the most active infertility-related bulletin boards on Naver, was selected as the analysis target, and 'infertility' was selected as a keyword for data search. The data was collected for three months. In addition, network analysis and visualization were performed using R for data collection and analysis, and cross-validation was attempted using the NetDraw function of 'textom 1.0' and the UCINET6 program. As a result of the analysis, the main keywords were cost, artificial insemination, in vitro fertilization, freezing, harvest, ovulation, and how much. Next, looking at the central value of the degree of connection, it was found that the degree of connection between the words cost, cost, how much, problem, public health center, and artificial insemination was high. According to the results of this study, women who visit mom cafes due to infertility in Korea are more interested in the cost. It is believed to be closely related to infertility treatment as well as in vitro fertilization and egg freezing. Therefore, by examining keywords related toinfertility, it has academic significance in that it is possible to identify major factors that end users are interested in. Furthermore, it is possible to redefine the guidelines for domestic infertility support policies by presenting infertility support policies that reflect the factors of interest of end consumers.

• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• Journal of Preventive Medicine and Public Health
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• v.56 no.4
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• pp.327-337
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• 2023

Objectives: Considering the importance of social determinants of health (SDHs) in promoting the health of residents of informal settlements and their diversity, abundance, and breadth, this study aimed to identify, measure, and rank SDHs for health promotion interventions targeting informal settlement residents in a metropolitan area in Iran. Methods: Using a hybrid method, this study was conducted in 3 phases from 2019 to 2020. SDHs were identified by reviewing studies and using the Delphi method. To examine the SDHs among informal settlement residents, a cross-sectional analysis was conducted using researcher-made questionnaires. Multilayer perceptron analysis using an artificial neural network was used to rank the SDHs by priority. Results: Of the 96 determinants identified in the first phase of the study, 43 were examined, and 15 were identified as high-priority SDHs for use in health-promotion interventions for informal settlement residents in the study area. They included individual health literacy, nutrition, occupational factors, housing-related factors, and access to public resources. Conclusions: Since identifying and addressing SDHs could improve health justice and mitigate the poor health status of settlement residents, ranking these determinants by priority using artificial intelligence will enable policymakers to improve the health of settlement residents through interventions targeting the most important SDHs.

• IMMUNE NETWORK
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• v.21 no.3
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• pp.24.1-24.13
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• 2021

Due to the inconsistent fluctuation of blood supply for transfusion, much attention has been paid to the development of artificial blood using other animals. Although mini-pigs are candidate animals, contamination of mini-pig T cells in artificial blood may cause a major safety concern. Therefore, it is important to analyze the cross-reactivity of IL-7, the major survival factor for T lymphocytes, between human, mouse, and mini-pig. Thus, we compared the protein sequences of IL-7 and found that porcine IL-7 was evolutionarily different from human IL-7. We also observed that when porcine T cells were cultured with either human or mouse IL-7, these cells did not increase the survival or proliferation compared to negative controls. These results suggest that porcine T cells do not recognize human or mouse IL-7 as their survival factor.

• Journal of Periodontal and Implant Science
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• v.50 no.4
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• pp.238-250
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• 2020

Purpose: This study aimed to evaluate the biocompatibility and the mechanical properties of ultraviolet (UV) cross-linked and biphasic calcium phosphate (BCP)-added collagen membranes and to compare the clinical results of ridge preservation to those obtained using chemically cross-linked collagen membranes. Methods: The study comprised an in vitro test and a clinical trial for membrane evaluation. BCP-added collagen membranes with UV cross-linking were prepared. In the in vitro test, scanning electron microscopy, a collagenase assay, and a tensile strength test were performed. The clinical trial involved 14 patients undergoing a ridge preservation procedure. All participants were randomly divided into the test group, which received UV cross-linked membranes (n=7), and the control group, which received chemically cross-linked membranes (n=7). BCP bone substitutes were used for both the test group and the control group. Cone-beam computed tomography (CBCT) scans were performed and alginate impressions were taken 1 week and 3 months after surgery. The casts were scanned via an optical scanner to measure the volumetric changes. The results were analyzed using the nonparametric Mann-Whitney U test. Results: The fastest degradation rate was found in the collagen membranes without the addition of BCP. The highest enzyme resistance and the highest tensile strength were found when the collagen-to-BCP ratio was 1:1. There was no significant difference in dimensional changes in the 3-dimensional modeling or CBCT scans between the test and control groups in the clinical trial (P>0.05). Conclusions: The addition of BCP and UV cross-linking improved the biocompatibility and the mechanical strength of the membranes. Within the limits of the clinical trial, the sites grafted using BCP in combination with UV cross-linked and BCP-added collagen membranes (test group) did not show any statistically significant difference in terms of dimensional change compared with the control group.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.2
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• pp.47-55
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• 2011

Hemorrhagic shock is a cause of one third of death resulting from injury in the world. Early diagnosis of hemorrhagic shock makes it possible for physician to treat successfully. The objective of this paper was to select an optimal classifier model using physiological signals from rats measured during hemorrhagic experiment. This data set was used to train and predict survival rate using artificial neural network (ANN) and support vector machine (SVM). To avoid over-fitting, we chose the best classifier according to performance measured by a 10-fold cross validation method. As a result, we selected ANN having three hidden nodes with one hidden layer and SVM with Gaussian kernel function as trained prediction model, and the ANN showed 88.9 % of sensitivity, 96.7 % of specificity, 92.0 % of accuracy and the SVM provided 97.8 % of sensitivity, 95.0 % of specificity, 96.7 % of accuracy. Therefore, SVM was better than ANN for survival prediction.