• Journal of the korean Society of Automotive Engineers
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• v.17 no.2
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• pp.7-13
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• 1995

기관의 저연비화는 비단 경제적인 면뿐 아니라 환경 등 사회적인 면에서 보았을때에도 차량이 갖추어야 할 필수적인 요건이라 할 수 있다. 지금의 추세로는 차량에 희박연소기관을 탑재하는 것은 앞으로 더욱 증가될 것으로 예측된다. 이 저연비에 효과적으로 대응될 수 있는 희박연소는 일반적인 3원촉매를 사용할 경우 지금까지는 배기량 2l를 넘는 대배기량기관에서는 실용화가 어려운 것으로 되어 있어서 희박연소기관 대부분은 1.8l급까지로 되어 있다. 그런데 최근 일본의 미쓰미시사는 별개의 희박연소용 촉매를 사용하지 않으면서 2.5l급의 희박연소화한 대배기량기관을 차량에 탑재하는데 성공한 것으로 발표하고 있다. 더구나 지금까지 대부분의 희박연소기관에 채용되고 있는 전역 공연비센서가 아닌 보통의 $O_{2}$센서이고 이와 같이 큰 배기량기관의 희박연소화를 실현한 것을 세계에서 최초의 것으로 보고하고 있다.

• Transactions of the Korean hydrogen and new energy society
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• v.22 no.5
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• pp.735-740
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• 2011

본 연구에서는 직접분사식 CNG기관의 희박한계를 보다 확장하여 고효율 및 저배기 공해를 실현시키고자 실린더 내에 고압의 천연가스를 직접분사함과 동시에 흡입과정 중 흡기관 내에 소량의 저압천연가스를 보조분사하는 경우의 희박한계 확장 및 제반특성에 대해 검토하였다. 그 결과, 흡기보조분사가 없을 경우 희박한계가 ${\lambda}$ = 1.4 까지였으나, 흡기보조분사율이 5~15% 정도에서는 희박한계가 ${\lambda}$ = 1.5 까지 확장되었다. 이는 흡기보조분사에 따른 혼합기의 혼합율 향상에 기인한 것으로 해석하였다. 연소기간은 줄어들었지만, 흡기보조분사의 효과는 주연소기간에서 조기연소기간보다 강하게 나타났다.

• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.295-302
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• 2022

Sparse-view computed tomography (CT) imaging technique is able to reduce radiation dose, ensure the uniformity of image characteristics among projections and suppress noise. However, the reconstructed images obtained by the sparse-view CT imaging technique suffer from severe artifacts, resulting in the distortion of image quality and internal structures. In this study, we proposed a convolutional neural network (CNN) with wavelet transformation and residual learning for reducing artifacts in sparse-view CT image, and the performance of the trained model was quantitatively analyzed. The CNN consisted of wavelet transformation, convolutional and inverse wavelet transformation layers, and input and output images were configured as sparse-view CT images and residual images, respectively. For training the CNN, the loss function was calculated by using mean squared error (MSE), and the Adam function was used as an optimizer. Result images were obtained by subtracting the residual images, which were predicted by the trained model, from sparse-view CT images. The quantitative accuracy of the result images were measured in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The results showed that the trained model is able to improve the spatial resolution of the result images as well as reduce artifacts in sparse-view CT images effectively. Also, the trained model increased the PSNR and SSIM by 8.18% and 19.71% in comparison to the imaging model trained without wavelet transformation and residual learning, respectively. Therefore, the imaging model proposed in this study can restore the image quality of sparse-view CT image by reducing artifacts, improving spatial resolution and quantitative accuracy.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.34 no.3
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• pp.283-290
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• 2010

The main goal of this study is to examine the use of a hybrid -fuel lean reburning system with air staging for $NO_X$ reduction. The experimental variables include the reburn fuel fraction, sizes of reburn- fuel-injection nozzles, oxygen enrichment ratio, and location of reburn- fuel- injection. The effect of the flow field induced by air- staging combustion on $NO_X$ reduction is considered, and then, the $NO_X$ reduction rate is compared with only fuel lean reburning system. On the basis of the effectiveness of each De-$NO_X$ process, the advantage of using the hybrid reburning system with air staging is determined and discussed.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.871-883
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• 2015

Vector autoregressive (VAR) models in high dimension suffer from noisy estimates, unstable predictions and hard interpretation. Consequently, the sparse vector autoregressive (sVAR) model, which forces many small coefficients in VAR to exactly zero, has been suggested and proven effective for the modeling of high dimensional time series data. This paper studies coupling measures to select non-zero coefficients in sVAR. The basic idea based on the simulation study reveals that removing the effect of other variables greatly improves the performance of coupling measures. sVAR model coefficients are asymmetric; therefore, asymmetric coupling measures such as Granger causality improve computational costs. We propose two asymmetric coupling measures, filtered-cross-correlation and filtered-Granger-causality, based on the filtered residuals series. Our proposed coupling measures are proven adequate for heavy-tailed and high order sVAR models in the simulation study.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.5
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• pp.32-40
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• 2002

The study of nonlinear gas transport in rarefied condition or associated with the microscale length of the geometry has emerged as an interesting topic in recent years. Along with the DSMC method, several fluid dynamic models that come under the general category of the moment method or the Chapman-Enskog method have been used for this type of problem. In the present study, on the basis of Eu's generalized hydrodynamics, computational models for diatomic gases are developed. The rotational nonequilibrium effect is included by introducing excess normal stress associated with the bulk viscosity of the gas. The new models are applied to study the one-dimensional shock structure and the multi-dimensional rarefied hypersonic flow about a blunt body. The results indicate that the bulk viscosity plays a considerable role in fundamental flow problems such as the shock structure and shear flow. An excellent agreement with experiment is observed for the inverse shock density thickness.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.99-114
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• 2011

Collaborative Filtering (CF) suffers from two major problems:sparsity and cold-start recommendation. This paper focuses on the cold-start problem for new customers with no purchase records and the sparsity problem for the customers with very few purchase records. For the purpose, we propose a method for the new customer recommendation by using a combined measure based on three well-used centrality measures to identify the customers who are most likely to become neighbors of the new customer. To alleviate the sparsity problem, we also propose a hybrid approach that applies our method to customers with very few purchase records and CF to the other customers with sufficient purchases. To evaluate the effectiveness of our method, we have conducted several experiments using a data set from a department store in Korea. The experiment results show that the combination of two measures makes better recommendations than not only a single measure but also the best-seller-based method and that the performance is improved when applying the hybrid approach.

• Journal of the Korea Society of Computer and Information
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• v.19 no.5
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• pp.61-69
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• 2014

Collaborative filtering recommendation creates similar item subset or similar user subset based on user preference about items and predict user preference to particular item by using them. Thus, if preference matrix has low density, reliability of recommendation will be sharply decreased. To solve these problems we suggest Hybrid Preference Prediction Technique Using Weighting based Data Reliability. Preference prediction is carried out by creating similar item subset and similar user subset and predicting user preference by each subset and merging each predictive value by weighting point applying model condition. According to this technique, we can increase accuracy of user preference prediction and implement recommendation system which can provide highly reliable recommendation when density of preference matrix is low. Efficiency of this system is verified by Mean Absolute Error. Proposed technique shows average 21.7% improvement than Hao Ji's technique when preference matrix sparsity is more than 84% through experiment.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.26 no.10
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• pp.1394-1405
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• 2002

The performance of micro-actuators utilizing radiometric forces are studied numerically. The Knudsen number based on gas density and characteristic dimension is varied from near-continuum to highly rarefied conditions. Direct simulation Monte Carlo(DSMC) calculations have been performed to estimate the performance of the micro-actuators. In the present DSMC method, the variable hard sphere molecular model and no time counter technique are used to simulate the molecular collision kinetics. For simulation of diatomic gas flows, the Borgnakke-Larsen phenomenological model is adopted to redistribute the translational and internal energies.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.2
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• pp.1-9
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• 2003

In the present study, a 3-D Parallel DSMC method in developed on unstructured meshes for the efficient simulation of rarefied gas flows. Particle tracing between cells in achieved based on a linear shape function extended to three dimensions. For high parallel efficiency, successive domain decomposition is applied to achieve load balancing between processors by accounting for the number of particles. A particle weighting technique is also adopted to handle flows containing gases of significantly dirrerent number densities in the same flow domain. Application is made for flow past a 3-D delta wing and the result is compared with that from experiment and other calculation. Flow around a rocket payload at 100km altitude is also solved and the effect of plume back flow from the nozzle in studied.