• Transactions of the Korean Society of Automotive Engineers
• /
• v.22 no.2
• /
• pp.91-99
• /
• 2014

The common-rail injectors are the most critical component of the CRDI diesel engines that dominantly affect engine performances through high pressure injection with exact control. Thus, from now on the advanced combustion technologies for common-rail diesel injection engine require high performance fuel injectors. Accordingly, the previous studies on the numerical and experimental analysis of the diesel injector have focused on a optimum geometry to induce proper injection rate. In this study, computational predictions of performance of the diesel injector have been performed to evaluate internal flow characteristics for various needle lift and the spray pattern at the nozzle exit. To our knowledge, three-dimensional computational fluid dynamics (CFD) model of the internal flow passage of an entire injector duct including injection and return routes has never been studied. In this study, major design parameters concerning internal routes in the injector are optimized by using a CFD analysis and Response Surface Method (RSM). The computational prediction of the internal flow characteristics of the common-rail diesel injector was carried out by using STAR-CCM+7.06 code. In this work, computations were carried out under the assumption that the internal flow passage is a steady-state condition at the maximum needle lift. The design parameters are optimized by using the L16 orthogonal array and polynomial regression, local-approximation characteristics of RSM. Meanwhile, the optimum values are confirmed to be valid in 95% confidence and 5% significance level through analysis of variance (ANOVA). In addition, optimal design and prototype design were confirmed by calculating the injection quantities, resulting in the improvement of the injection performance by more than 54%.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.49 no.1
• /
• pp.29-38
• /
• 2012

The noise generated in the process of obtaining the medical image acts as the element obstructing the image interpretation and diagnosis. To restore the true image from the image polluted from the noise, the total variation optimization algorithm was proposed by the R.O. F (L.Rudin, S Osher, E. Fatemi). This method removes the noise by fitting the balance of the regularity and fidelity. However, the blurring phenomenon of the border area generated in the process of performing the iterative operation cannot be avoided. In this paper, we propose the adaptive total variation method by mapping the control parameter to the proposed transfer function for minimizing boundary error. The proposed transfer function is determined by the noise variance and the local property of the image. The proposed method was applied to 464 tooth images. To evaluate proposed method performance, PSNR which is a indicator of signal and noise's signal power ratio was used. The experimental results show that the proposed method has better performance than other methods.

• Journal of Pharmacopuncture
• /
• v.18 no.2
• /
• pp.7-18
• /
• 2015

Objectives: Lawsone (1,4 naphthoquinone) is a non redox cycling compound that can be catalyzed by DT diaphorase (DTD) into 1,2,4-trihydroxynaphthalene (THN), which can generate reactive oxygen species by auto oxidation. The purpose of this study was to evaluate the toxicity of the phytomarker 1,4 naphthoquinone and its metabolite THN by using the molecular docking program AutoDock 4. Methods: The 3D structure of ligands such as hydrogen peroxide ($H_2O_2$), nitric oxide synthase (NOS), catalase (CAT), glutathione (GSH), glutathione reductase (GR), glucose 6-phosphate dehydrogenase (G6PDH) and nicotinamide adenine dinucleotide phosphate hydrogen (NADPH) were drawn using hyperchem drawing tools and minimizing the energy of all pdb files with the help of hyperchem by $MM^+$ followed by a semi-empirical (PM3) method. The docking process was studied with ligand molecules to identify suitable dockings at protein binding sites through annealing and genetic simulation algorithms. The program auto dock tools (ADT) was released as an extension suite to the python molecular viewer used to prepare proteins and ligands. Grids centered on active sites were obtained with spacings of $54{\times}55{\times}56$, and a grid spacing of 0.503 was calculated. Comparisons of Global and Local Search Methods in Drug Docking were adopted to determine parameters; a maximum number of 250,000 energy evaluations, a maximum number of generations of 27,000, and mutation and crossover rates of 0.02 and 0.8 were used. The number of docking runs was set to 10. Results: Lawsone and THN can be considered to efficiently bind with NOS, CAT, GSH, GR, G6PDH and NADPH, which has been confirmed through hydrogen bond affinity with the respective amino acids. Conclusion: Naphthoquinone derivatives of lawsone, which can be metabolized into THN by a catalyst DTD, were examined. Lawsone and THN were found to be identically potent molecules for their affinities for selected proteins.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.23 no.1
• /
• pp.97-104
• /
• 2015

This study is a part of the high pressure injection system development on the Turbo GDI engine in order to reduce smoke emission in case of using the low volatile(high DI) fuel which is used as normal gasoline fuel in the US market. Firstly, theoretical approach was done regarding gasoline fuel property, performance, definition of particle matters and its creation as well as problems of the high DI fuel. In this experimental study, 2L Turbo GDI engine was selected and optimized system parameter was inspected by changing fuel, fuel injection mode (single/multiple), fuel pressure, distance between injector tip and combustion chamber, start of injection, intake valve timing in engine dyno at all engine speed range with full load. In case of normal gasoline fuel, opacity was contained within 2% in all conditions. On the other hands, in case of low volatile fuel (high DI fuel), it was confirmed that the opacity was rapidly increased above 5,000 rpm at 14.5 ~ 20 MPa of fuel pressure and there were almost no differences on the opacity(smoke) between 17 MPa and 20 MPa fuel pressure. According to the SOI retard, smoke decrease tendency was observed but intake valve close timing change has almost no impact on the smoke level in this area. Consequently, smoke decrease was observed and 16% at 6000rpm respectively with injector washer ring installed. By removing injector washer to make injector tip closer to the combustion chamber, smoke decrease was observed by 46% at 5,500 rpm, 42% at 6,000 rpm. It is assumed that the fuel injection interaction with cylinder head, piston head, intake and exhaust valve is reduced so that impingement is reduced in local area.

• Journal of KIISE:Software and Applications
• /
• v.29 no.1_2
• /
• pp.80-90
• /
• 2002

Clustering is a useful technique for grouping data points such that points within a single group/cluster have similar characteristics. Many clustering algorithms have been developed and used in engineering applications including pattern recognition and image processing etc. Recently, it has drawn increasing attention as one of important techniques in data mining. However, clustering algorithms such as K-means and Fuzzy C-means suffer from difficulties. Those are the needs to determine the number of clusters apriori and the clustering results depending on the initial set of clusters which fails to gain desirable results. In this paper, we propose a new clustering algorithm, which solves mentioned problems. In our method we use evolutionary algorithm to solve the local optima problem that clustering converges to an undesirable state starting with an inappropriate set of clusters. We also adopt a new measure that represents how well data are clustered. The measure is determined in terms of both intra-cluster dispersion and inter-cluster separability. Using the measure, in our method the number of clusters is automatically determined as the result of optimization process. And also, we combine heuristic that is problem-specific knowledge with a evolutionary algorithm to speed evolutionary algorithm search. We have experimented our algorithm with several sets of multi-dimensional data and it has been shown that one algorithm outperforms the existing algorithms.

• Journal of Korea Multimedia Society
• /
• v.5 no.4
• /
• pp.458-467
• /
• 2002

There has been proposed many simplification algorithms for effectively decreasing large-volumed polygonal surface data. These algorithms apply their own cost function for collapse to one of fundamental simplification unit, such as vertex, edge and triangle, and minimize the simplification error occurred in each simplification steps. Most of cost functions adopted in existing works use the error estimation method based on distance optimization. Unfortunately, it is hard to define the local characteristics of surface data using distance factor alone, which is basically scalar component. Therefore, the algorithms cannot preserve the characteristic features in surface areas with high curvature and, consequently, loss the detailed shape of original mesh in high simplification ratio. In this paper, we consider the vector component, such as surface orientation, as one of factors for cost function. The surface orientation is independent upon scalar component, distance value. This means that we can reconsider whether or not to preserve them as the amount of vector component, although they are elements with low scalar values. In addition, we develop a simplification algorithm based on half-edge collapse manner, which use the proposed cost function as the criterion for removing elements. In half-edge collapse, using one of endpoints in the edge represents a new vertex after collapse operation. The approach is memory efficient and effectively applicable to the rendering system requiring real-time transmission of large-volumed surface data.

• The Journal of Korean Institute of Next Generation Computing
• /
• v.15 no.5
• /
• pp.64-74
• /
• 2019

Although a non-rigid registration has high demands in clinical practice, it has a high computational complexity and it is very difficult for ensuring the accuracy and robustness of registration. This study proposes a method of applying a non-rigid registration to 3D magnetic resonance images of brain in an unsupervised learning environment by using a deep-learning network. A feature vector between two images is produced through the network by receiving both images from two different patients as inputs and it transforms the target image to match the source image by creating a displacement vector field. The network is designed based on a U-Net shape so that feature vectors that consider all global and local differences between two images can be constructed when performing the registration. As a regularization term is added to a loss function, a transformation result similar to that of a real brain movement can be obtained after the application of trilinear interpolation. This method enables a non-rigid registration with a single-pass deformation by only receiving two arbitrary images as inputs through an unsupervised learning. Therefore, it can perform faster than other non-learning-based registration methods that require iterative optimization processes. Our experiment was performed with 3D magnetic resonance images of 50 human brains, and the measurement result of the dice similarity coefficient confirmed an approximately 16% similarity improvement by using our method after the registration. It also showed a similar performance compared with the non-learning-based method, with about 10,000 times speed increase. The proposed method can be used for non-rigid registration of various kinds of medical image data.

• 한국노년학
• /
• v.40 no.2
• /
• pp.239-255
• /
• 2020

The purpose of this study is to examine whether family relationships of elderly single-person households affect community participation and whether these relationships differ by gender. Based on Baltes and Baltes (1990) 's selection, optimization, and compensation theory (SOC) and the argument that family members are a social capital by Prandini (2014), we test whether family relationships can affect community participation in old age. In order to verify this, single-person households were extracted from the 2017 National Survey of Living Conditions and Welfare Needs conducted by the Korea Institute for Health and Social Affairs (Male sample=370, Female sample=1770), multiple regression analysis were conducted with the dependent variables of friends·neighbors and the participation at Kyungrodang·welfare centers for the elderly. The results are as follows. In the case of men, family relations showed no significant effect on their participation in friends·neighbors, or Kyungrodang·welfare centers. However, in the case of women, the frequency of contact with family had a positive effect on the frequency of meeting friends·neighbors. Family contact frequency and child relationship satisfaction had a positive (+) effect on Kyungrodang·welfare center participation, while family meeting frequency had a negative effect on participation in Kyungrodang·welfare centers. For women, although Prandini's (2014) claim that family members are a social capital seems to be supported, it was found that the impact could vary depending on the type of community participation. In addition, practical discussions and suggestions were presented.

• Economic and Environmental Geology
• /
• v.56 no.3
• /
• pp.331-341
• /
• 2023

When creating a soil contamination map using geostatistical techniques, there are various sources that can affect prediction errors. In this study, a grid-based soil contamination map was created from the sampling data of heavy metal concentrations in soil in abandoned mine areas using Ordinary Kriging. Five factors that were judged to affect the prediction error of the soil contamination map were selected, and the variation of the root mean squared error (RMSE) between the predicted value and the actual value was analyzed based on the Leave-one-out technique. Then, using a machine learning algorithm, derived the top three factors affecting the RMSE. As a result, it was analyzed that Variogram Model, Minimum Neighbors, and Anisotropy factors have the largest impact on RMSE in the Standard interpolation. For the variogram models, the Spherical model showed the lowest RMSE, while the Minimum Neighbors had the lowest value at 3 and then increased as the value increased. In the case of Anisotropy, it was found to be more appropriate not to consider anisotropy. In this study, through the combined use of geostatistics and machine learning, it was possible to create a highly reliable soil contamination map at the local scale, and to identify which factors have a significant impact when interpolating a small amount of soil heavy metal data.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.11 no.4
• /
• pp.395-401
• /
• 2009

One of the key design factors for the ventilation and safety system at extra long tunnel is the airflow velocity induced by the natural ventilation force. Despite of the importance, it has not been widely studied due to the complicated influencing variables and the relationship among them is difficult to quantify. At this moment none of the countries in the world defines its specific value on verified ground. It is also the case in Korea. The recent worldwide disasters by tunnel fires and demands for better air quality inside tunnel by users require the optimization of the tunnel ventilation system. This indicates why the natural ventilation force is necessary to be thoroughly studied. This paper aims at predicting the natural ventilation force at a 11 km-long tunnel which is in the stage of detailed design and will be the longest vehicle tunnel in Korea. The concept of barometric barrier which can provide the maximum possible natural ventilation force generated by the topographic effect on the external wind is applied to estimate the effect of wind pressure and the chimney effect caused by the in and outside temperature difference is also analyzed.