• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1358-1366
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• 2015

This paper proposes an automatic composition method using the training capability of artificial neural networks and chord progression rules that are widely used by human composers. After training a given song, the new melody is generated by the trained artificial neural networks through applying a different initial melody to the neural networks. The generated melody should be modified to fit the rhythm and chord progression rules for generating natural melody. In order to achieve this object, we devised a post-processing method such as chord candidate generation, chord progression, and melody correction. From some tests we could find that the melody after the post-processing was very improved from the melody generated by artificial neural networks. This enables our composition system to generate a melody which is similar to those generated by human composers.

• Journal of Dental Rehabilitation and Applied Science
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• v.24 no.2
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• pp.193-201
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• 2008

In edentulous mandible, implant supported overdenture was considered as a first treatment option. In case of a implant supported telescopic overdenture, sufficient inter-arch space needs for arrangement of artificial teeth and attachment. Passive fit of the implant prosthesis is important factor for preventing mechanical failure. Gold Electroforming System is particularly useful to achieve a passive fit of telescopic attachment and results in precision marginal fit and the small thickness of the coping provides optimal space for narrow inter-arch space. This article presents that application of Gold Electroforming System can provide excellent esthetics and function on four-implant supported telescopic overdenture.

• Journal of Dental Rehabilitation and Applied Science
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• v.18 no.4
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• pp.313-320
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• 2002

The purpose of the present study was to evaluate the marginal discrepancy and topography of artificial crown on teeth extracted due to severe periodontal disease. Twenty specimens were invested into metamethylacrylate resin and cutted into vertical slices along with the long axis of tooth. The selected marginal discrepancy between the outer edge of the crown and the finishing line of abutment was examined by stereo- microscope(Olympus, PM-VSP-3, Japan) at magnification of up to 10, and the topography of finishing margin on crown was observed by stereomicroscopeat magnification of up to $70{\times}$. The results were as follows. (1) The mean marginal discrepancy between extracted tooth and artificial crown were $50.82{\mu}m$. (2) There was a considerable difference in the microstructure of finishing margins among specimens. Microscopic Structure on finishing margin showed indefinite line, poor fit (open, underextended and overextended), distorted margin, and surface roughness. This study suggested that there could be necessary to consider the response of periodontium to the emergence profile of natural tooth and precision of marginal geometry while establishing treatment planning for the reconsruction of the artificial crown.

• Smart Structures and Systems
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• v.33 no.1
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• pp.65-91
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• 2024

The use of five optimization techniques for the prediction of a strength-based concrete mixture's best-fit model is examined in this work. Five optimization techniques are utilized for this purpose: Slime Mold Algorithm (SMA), Black Hole Algorithm (BHA), Multi-Verse Optimizer (MVO), Vortex Search (VS), and Whale Optimization Algorithm (WOA). MATLAB employs a hybrid learning strategy to train an artificial neural network that combines least square estimation with backpropagation. Thus, 72 samples are utilized as training datasets and 31 as testing datasets, totaling 103. The multi-layer perceptron (MLP) is used to analyze all data, and results are verified by comparison. For training datasets in the best-fit models of SMA-MLP, BHA-MLP, MVO-MLP, VS-MLP, and WOA-MLP, the statistical indices of coefficient of determination (R²) in training phase are 0.9603, 0.9679, 0.9827, 0.9841 and 0.9770, and in testing phase are 0.9567, 0.9552, 0.9594, 0.9888 and 0.9695 respectively. In addition, the best-fit structures for training for SMA, BHA, MVO, VS, and WOA (all combined with multilayer perceptron, MLP) are achieved when the term population size was modified to 450, 500, 250, 150, and 500, respectively. Among all the suggested options, VS could offer a stronger prediction network for training MLP.

• Journal of Biomedical Engineering Research
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• v.19 no.2
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• pp.163-170
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• 1998

The ultimate goal of total artificial heart is permanent substitute for a failed heart in a patient without any other therapeutic modality. Until now, infection has been the main problem related to the mechanical circulatory support system. The best way to solve this catastrophic complication and to improve the quality of life of TAH patients in terms of tethering must be implantation of TAH totally. The EH-TAH has been developed in NCVC from 1987 for this purpose. The system consists of an energy converter and pumps, which are designed to be placed in abdomen and pericardial space separately for a good anatomical fit. To evaluate the anatomical fit and hemodynamic performance of the EH-TAH, in vivo test was done. General condition of the animal and hemodynamic status had been stable until the TAH stopped on the 11th pumping day. The estimated cardiac output was about 7.7L/min. The values of mean aortic pressure, left and right atrial pressure were 93$\pm$10, 19$\pm$3 and 15$\pm$4 mmHg, respectively. The correlation coefficient between left and right atrial pressure was 0.96, which represents the dynamic function of the interatrial shunt in controlling left-right imbalance of cardiac output. During pumping days, the temperature on the surface of actuator had been maintained at 39.7$\pm$0.4$^{\circ}C$, less than 1$^{\circ}C$ higher than the rectal temperature. The TAH stopped on the 11th day due to mechanical problems. We concluded that the EH-TAH possessed satisfactory basic performance including anatomic fit and hemodynamic adequacy, although there were several mechanical problems to be solved yet.

• Journal of Digital Contents Society
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• v.17 no.6
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• pp.449-459
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• 2016

This paper proposes a new method to improve the three problems of existing automatic composition method using artificial neural networks. The first problem is that the existing beat post-processing to fit into music theories could not handle all the cases of occurring. The second one is that the pitch space generated by artificial neural networks is distorted because the rest is trained with the pitch on the same neural network with large values. The last problem is caused by the difference between the initial melody and beats given by user and those generated by an artificial neural network in the process of new composition. In order to treat these problems, we propose an enhanced post-processing of beats, initial melody processing, and adoption of artificial neural network for rest. It was found from experiments that the proposed methods totally resolved the three problems.

• BMB Reports
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• v.28 no.2
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• pp.184-190
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• 1995

Functional study of the gap junction channel has been hindered by its inaccessibility in situ. Identification of forms of this channel in artificial membrane has been elusive because of the lack of identifying channel physiology. Connexin32 forms gap junction channels between neighboring cells in rat liver. Connexin32 was affinity-purified using a monoclonal antibody and reconstituted into artificial phospholipid vesicles. The reconstituted connexin32 formed channels through the vesicle membrane that were permeable to sucrose (Stokes radius: $5{\AA}$). The permeability to sucrose was reversibly reduced by acidic pH. In addition, the pH effect on the permeability to sucrose fit well with by the Hill's equation (where, n=2.7 and pK=6.7).

• The Journal of Information Systems
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• v.25 no.4
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• pp.23-36
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• 2016

Purpose Recently, many researchers have paid much attention to the Artificial Intelligence fields of GVGP, PCG. The paper suggests that the improved MCTS algorithm to apply for the framework can generate better AI agent. Design/methodology/approach As noted, the MCTS generate magnificent performance without an advanced training and in turn, fit applying to the field of GVGP which does not need prior knowledge. The improved and modified MCTS shows that the survival rate is increased interestingly and the search can be done in a significant way. The study was done with 2 different sets. Findings The results showed that the 10 training set which was not given any prior knowledge and the other training set which played a role as validation set generated better performance than the existed MCTS algorithm. Besed upon the results, the further study was suggested.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.271-272
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• 2020

Data mining is known useful in medical area when no availability of evidence favoring a particular treatment option is found. Huge volume of structured/unstructured data is collected by the healthcare field in order to find unknown information or knowledge for effective diagnosis and clinical decision making. The data of 5,179 records considered for analysis has been collected from Korean National Health and Nutrition Examination Survey(KHANES) during 2-years. Data splitting, referred as the training and test sets, was applied to predict to fit the model. We analyzed to predict chronic kidney disease (CKD) using data mining method such as naive Bayes, logistic regression, CART and artificial neural network(ANN). This result present to select significant features and data mining techniques for the lifestyle factors related CKD.

• Transactions of Materials Processing
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• v.29 no.2
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• pp.76-88
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• 2020

The need for advanced high strength steel (AHSS) forming technology is increasing as interest in light weight and safe automobiles increases. Multipoint dieless forming (MDF) is a novel sheet metal forming technology that can create any desired longitudinal and transverse curvature in sheet metal. However, since the springback phenomenon becomes larger with high strength metal such as AHSS, predicting the required MDF to produce the exact desired curvature in two directions is more difficult. In this study, a prediction model using artificial neural network (ANN) was developed to predict the springback that occurs during AHSS forming through MDF. In order to verify the validity of model, a fit test was performed and the results were compared with the conventional regression model. The data required for training was obtained through simulation, then further random sample data was created to verify the prediction performance. The predicted results were compared with the simulation results. As a result of this comparison, it was found that the prediction of our ANN based model was more accurate than regression analysis. If a sufficient amount of data is used in training, the ANN model can play a major role in reducing the forming cost of high-strength steels.