• Maxillofacial Plastic and Reconstructive Surgery
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• v.42
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• pp.18.1-18.9
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• 2020

Background: Bone grafting has been considered the gold standard for hard tissue reconstructive surgery and is widely used for large mandibular defect reconstruction. However, the midface encompasses delicate structures that are surrounded by a complex bone architecture, which makes bone grafting using traditional methods very challenging. Three-dimensional (3D) bioprinting is a developing technology that is derived from the evolution of additive manufacturing. It enables precise development of a scaffold from different available biomaterials that mimic the shape, size, and dimension of a defect without relying only on the surgeon's skills and capabilities, and subsequently, may enhance surgical outcomes and, in turn, patient satisfaction and quality of life. Review: This review summarizes different biomaterial classes that can be used in 3D bioprinters as bioinks to fabricate bone scaffolds, including polymers, bioceramics, and composites. It also describes the advantages and limitations of the three currently used 3D bioprinting technologies: inkjet bioprinting, micro-extrusion, and laserassisted bioprinting. Conclusions: Although 3D bioprinting technology is still in its infancy and requires further development and optimization both in biomaterials and techniques, it offers great promise and potential for facial reconstruction with improved outcome.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.27 no.2
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• pp.1-8
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• 2019

Optimal path planning refers to find the safe route to the destination at a low cost, is a major problem with regard to autonomous navigation. Sampling Based Planning(SBP) approaches, such as Rapidly-exploring Random Tree Star($RRT^*$), are the most influential algorithm in path planning due to their relatively small calculations and scalability to high-dimensional problems. $RRT^*$-Smart introduced path optimization and biased sampling techniques into $RRT^*$ to increase convergent rate. This paper presents an improvement plan that has changed the biased sampling method to increase the initial convergent rate of the $RRT^*$-Smart, which is specified as m$RRT^*$-Smart. With comparison among $RRT^*$, $RRT^*$-Smart and m$RRT^*$-Smart in 2 & 3-D environments, m$RRT^*$-Smart showed similar or increased initial convergent rate than $RRT^*$ and $RRT^*$-Smart.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.477-484
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• 2016

Small industrial appliances such as fan, audio, electric rice cooker mostly consist of ABS, PP, PS materials. In colored plastics, it is possible to classify by near infrared(NIR) spectroscopy, while in black plastics, it is very difficult to classify black plastic because of the characteristic of black material that absorbs the light. So the RBFNNs pattern classifier is introduced for sorting electrical and electronic waste plastics through LIBS(Laser Induced Breakdown Spectroscopy) spectrometer. At the preprocessing part, PCA(Principle Component Analysis), as a kind of dimension reduction algorithms, is used to improve processing speed as well as to extract the effective data characteristics. In the condition part, FCM(Fuzzy C-Means) clustering is exploited. In the conclusion part, the coefficients of linear function of being polynomial type are used as connection weights. PSO and 5-fold cross validation are used to improve the reliability of performance as well as to enhance classification rate. The performance of the proposed classifier is described based on both optimization and no optimization.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.6
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• pp.649-656
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• 2011

A model for the numerical simulation of lithium-ion batteries (LIBs) is developed for use in battery cell design, with a view to improving the performances of such batteries. The model uses Newman-type electrochemical and transfer $theories^{(1,2)}$ to describe the behavior of the lithium-ion cell, together with the Levenberg-Marquardt optimization scheme to estimate the performance or design parameters in nonlinear problems. The mathematical model can provide an insight into the mechanism of LIB behavior during the charging/discharging process, and can therefore help to predict cell performance. Furthermore, by means of least-squares fitting to experimental discharge curves measured at room temperature, we were able to obtain the values of transport and kinetic parameters that are usually difficult to measure. By comparing the calculated data with the life-test discharge curves (SB LiMotive cell), we found that the capacity fade is strongly dependent on the decrease in the reaction area of active materials in the anode and cathode, as well as on the electrolyte diffusivity.

• Journal of the Korean Geophysical Society
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• v.3 no.3
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• pp.161-174
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• 2000

Bayesian inversion is a stable approach to infer the subsurface structure with the limited data from geophysical explorations. In geophysical inverse process, due to the finite and discrete characteristics of field data and modeling process, some uncertainties are inherent and therefore probabilistic approach to the geophysical inversion is required. Bayesian framework provides theoretical base for the confidency and uncertainty analysis for the inference. However, most of the Bayesian inversion require the integration process of high dimension, so massive calculations like a Monte Carlo integration is demanded to solve it. This method, though, seemed suitable to apply to the geophysical problems which have the characteristics of highly non-linearity, we are faced to meet the promptness and convenience in field process. In this study, by the Gaussian approximation for the observed data and a priori information, fast Bayesian inversion scheme is developed and applied to the model problem with electric well logging and dipole-dipole resistivity data. Each covariance matrices are induced by geostatistical method and optimization technique resulted in maximum a posteriori information. Especially a priori information is evaluated by the cross-validation technique. And the uncertainty analysis was performed to interpret the resistivity structure by simulation of a posteriori covariance matrix.

• Journal of Korean Ophthalmic Optics Society
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• v.8 no.1
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• pp.35-39
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• 2003

The display technology on the retina is the key role in inspecting the condition of the patients. 2-dimensional retina image is widely used in the eye examination as of today. Recently, 3-dimensional retina image ones have been introduced to this area, but the quality of the image is not fully satisfied to the operator. For the purpose of developing 3-D retina imaging instrument, the optimization of a 3-D retina imaging system using Code-V tool was investigated in this thesis. He-Ne laser having the wavelength 632.8 nm was used to make a power source to detect the retina. Several lenses and mirrors installed on sledge which were developed to perform focus control on 3-D device were designed to make a beam focusing and direct line. Polygon scanner having 24 mirror facets and galvanometer making tilting movement were utilized to make a 2-D laser plane. Also, design of eye ball had been fulfilled to see the focus of the 2-D plane. Reflected ray from retina detected on the sensor array with the same path. All cognitive components were optimized for aberration correction in order to focus on retina. Results of optimization were compared to those of initial designed optics system. On the basis of above results, the result of third aberration has been corrected to stable values to the optical system. MTF evaluating the resolution of an image has been closely correlated to the diffraction limit and PSF indicating the strength distribution of an image has shown the SR value as 0.9998 having high performance. The possibility of new and powerful 3-D retina image instrument was verified by simulating each component of the instrument by Code-V.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.6
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• pp.1726-1732
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• 2008

Injection molding is one of plastic forming technology which can easily mass-produce plastic parts with various and even complex shape. The technology has lots of difficulties in making a good part due to phase change of material, high applied pressure, and fast melt flow speed in the cavity. To overcome the problems, they had to make trial and error method until the CAE(Computer Aided Engineering) could be a tool for concurrent engineering. In this paper, we investigate the optimal design for a plastic DVD tray part by systematic approach of the commercial CAE program. In design, we should consider two objectives which are both dimensional stability and cost-down. The dimension of the part is crucial because the tray should carry a DVD correctly, but the part is too thin to injection-mold easily. In order to improve the moldability, the mold is designed in the form of stack mold which is a kind of 4 hot runner system. In first, we changed the stack-mold system with one hot-runner to cost down, and decided the optimal position of the gate. After that, we investigate the effect of both the layout of cooling channels and the cooling temperature on the shrinkage of the DVD tray. A optimal simulation approach, the gate design is 2Gate#3 and the layout is Case2 cooling line as the optimal temperature of $70^{\circ}C$. The Moldflow and PC+ABS are used for the CAE program and material respectively.

• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.152-152
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• 2016

We studied the effect of the silver grid size on graphene transparent conducting films for flexible organic solar cells (OSCs). The silver grid was used an assistant layer of the graphene to reduce the sheet resistance of substrates. Silver grid with various graphene sizes for optimizing transmittance and sheet resistance of substrates were fabricated on polyethylene terephthalate (PET) substrates to form the hybrid films. The optimized grid geometry on the single layer graphene (SLG) was the grid dimension $200{\mu}m{\times}200{\mu}m{\times}50nm{\times}2{\mu}m$ (length ${\times}$ width ${\times}$ height ${\times}$ linewidth), where the sheet resistance was $55.73{\Omega}/square$ with the average transmittance of ~ 92.83 % at 550 nm. The properties of the OSCs fabricated using SLG with optimized silver grids on PET substrates show a short circuit current of $10.9mA/cm^2$, an open circuit voltage of 0.58 V, a fill factor of 60.8 %, and a power conversion efficiency (PCE) of 3.9 %. The PCE was improved about 91% than that of the OSCs using the SLG without the silver grid. These results demonstrate that the optimized grid geometry to the based on the graphene transparent electrodes contribute to improving the performance of OSCs.

• Journal of Digital Contents Society
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• v.16 no.4
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• pp.605-613
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• 2015

This paper addresses the problem to make sparse the projection matrix in pattern recognition method. Recently, the size of computer program is often restricted in embedded systems. It is very often that developed programs include some constant data. For example, many pattern recognition programs use the projection matrix for dimension reduction. To improve the recognition performance, very high dimensional feature vectors are often extracted. In this case, the projection matrix can be very big. Recently, RSR(roated sparse regression) method[1] was proposed. This method has been proved one of the best algorithm that obtains the sparse matrix. We propose three methods to improve the RSR; outlier removal, sampling and elastic net RSR(E-RSR) in which the penalty term in RSR optimization function is replaced by that of the elastic net regression. The experimental results show that the proposed methods are very effective and improve the sparsity rate dramatically without sacrificing the recognition rate compared to the original RSR method.