• Proceedings of the Korea Concrete Institute Conference
• /
• 2005.05b
• /
• pp.525-528
• /
• 2005

This study determines the best composite grade to minimize the void of aggregate mixture based on the maximum density theory in an attempt to suggest a mix proportion method design for asphalt mixtures. Study results show that the grading curve with the maximum mass per unit capacity of each aggregate mixture satisfied the KS standards and the optimum AP content to meet the optimal asphalt mixture void rate of 4$\%$ was 5.7$\%$, less than the optimum AP content of 6.5$\%$ suggested in the Marshal mix proportion method design. At the same time, the asphalt mixture produced based upon the suggested mix proportion method had a flow value 17$\%$ lower than that of asphalt mixture produced according to the Marshal method, while its density was greater by 0.06$\～$0.09. This suggests that the introduced mix proportion method design helps to improve the shape flexibility and crack-resistance of asphalt concrete.

• Journal of Korean Society for Quality Management
• /
• v.49 no.2
• /
• pp.201-211
• /
• 2021

Purpose: This paper proposes a density adaptive grid algorithm for the k-NN regression model to reduce the computation time for large datasets without significant prediction accuracy loss. Methods: The proposed method utilizes the concept of the grid with centroid to reduce the number of reference data points so that the required computation time is much reduced. Since the grid generation process in this paper is based on quantiles of original variables, the proposed method can fully reflect the density information of the original reference data set. Results: Using five real-life datasets, the proposed k-NN regression model is compared with the original k-NN regression model. The results show that the proposed density adaptive grid-based k-NN regression model is superior to the original k-NN regression in terms of data reduction ratio and time efficiency ratio, and provides a similar prediction error if the appropriate number of grids is selected. Conclusion: The proposed density adaptive grid algorithm for the k-NN regression model is a simple and effective model which can help avoid a large loss of prediction accuracy with faster execution speed and fewer memory requirements during the testing phase.

• Communications for Statistical Applications and Methods
• /
• v.19 no.4
• /
• pp.517-526
• /
• 2012

In this paper a support vector method is proposed for use when the sample observations are contaminated by a normally distributed measurement error. The performance of deconvolution density estimators based on the support vector method is explored and compared with kernel density estimators by means of a simulation study. An interesting result was that for the estimation of kurtotic density, the support vector deconvolution estimator with a Gaussian kernel showed a better performance than the classical deconvolution kernel estimator.

• Journal of the Semiconductor & Display Technology
• /
• v.6 no.2 s.19
• /
• pp.45-48
• /
• 2007

The proposed method offers an improved DC/DC converter scheme to increase power density. It is based on half-bridge topology with newly introduced pulse-grouping control method, which helps to reduce the transformer size and the volume of semiconductor devices maintaining high efficiency. Test results with 85W(18.5V/4.6A) design shows that the measured efficiency is 93.5% with power density of $36W/in^3$.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.56 no.1
• /
• pp.197-205
• /
• 2007

We have proposed a novel line detection method based on the estimated probability density function of gradient directions of edges. By estimating peaks of the density function, we determine groups of edges that have the same gradient direction. For edges in the same groups, we detect lines that correspond to peaks of the connectivity weighted distribution of the distances from the origin. In the experiments using the Data Matrix barcode images and LCD images, the proposed method showed better performance than conventional Methods in terms of the processing speed and accuracy.

• Communications for Statistical Applications and Methods
• /
• v.23 no.1
• /
• pp.71-83
• /
• 2016

In this paper we discuss a deconvolution density estimator obtained using the support vector machines (SVM) and Tikhonov's regularization method solving ill-posed problems in reproducing kernel Hilbert space (RKHS). A remarkable property of SVM is that the SVM leads to sparse solutions, but the support vector deconvolution density estimator does not preserve sparsity as well as we expected. Thus, in section 3, we propose another support vector deconvolution estimator (method II) which leads to a very sparse solution. The performance of the deconvolution density estimators based on the support vector method is compared with the classical kernel deconvolution density estimator for important cases of Gaussian and Laplacian measurement error by means of a simulation study. In the case of Gaussian error, the proposed support vector deconvolution estimator shows the same performance as the classical kernel deconvolution density estimator.

• Journal of Powder Materials
• /
• v.4 no.3
• /
• pp.188-195
• /
• 1997

In most of sintered metal powder compacts, the sintered density distribution is controlled to be as high and uniform as possible to ensure the required mechanical properties. In general, the density distribution in the compacts is not uniform and not easy to measure. In the present study, a method for measuring the density distribution was developed, based on the indentation force equation by which the hardness and the relative density were related. The indentation force equation, expressed as a function of strength constant, workhardening coefficient and relative density, was obtained by finite element analysis of rigid-ball indentation on sintered powder metal compacts. The present method was verified by comparing the predicted density distribution in the sintered Fe-0.5%C-2%Cu compacts with that obtained by experiments, in which the density distribution was directly measured by machining the compacts from the outer surface progressively.

• Proceedings of the KIPE Conference
• /
• 2007.07a
• /
• pp.286-288
• /
• 2007

The proposed method offers an improved control method for high power density AC/DC adapter by using more energy efficient electrical equipments. Power factor corrector (PFC) topology is based on boost topology with boundary conduction mode (BCM). DC/DC topology is based on half-bridge topology with newly introduced off-time control method, which helps to reduce size of the semiconductor and the magnetic devices. Test results with 85W AC/DC adapter (18.5V/4.6A) design shows that the measured efficiency is 90% with power density of $36W/in^3$. It also show low no load power consumption of about 0.5W.

• International Journal of Contents
• /
• v.6 no.4
• /
• pp.39-42
• /
• 2010

In this paper we developed density measurement equipment for Asphalt Pavement based on the electromagnetic capacitance. This kind of Non-Nuclear Density Gauges technology and products is used or studied in USA, Finland, Sweden as standardization of authorized method for pavement density measurement. Effective permitivity of pavement asphalt is characterized in electromagnetic capacitance by the asphalt material, mixed ratio, and harden grade of pavement asphalt. We can get a density value of asphalt by replacing value of electromagnetic capacitance with standard density value and characteristic transformation curve. We are conformed that measurement data according to temperature, humidity, and real field asphalt of our density measurement equipment can be a precise value.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.9
• /
• pp.2170-2190
• /
• 2012

There are nowadays strong demands for intelligent surveillance systems, which can infer or understand more complex behavior. The application of crowd density estimation methods could lead to a better understanding of crowd behavior, improved design of the built environment, and increased pedestrian safety. In this paper, we propose a new crowd density estimation method, which aims at estimating not only a moving crowd, but also a stationary crowd, using images captured from surveillance cameras situated in various public locations. The crowd density of the moving people is measured, based on the moving area during a specified time period. The moving area is defined as the area where the magnitude of the accumulated optical flow exceeds a predefined threshold. In contrast, the stationary crowd density is estimated from the coarseness of textures, under the assumption that each person can be regarded as a textural unit. A multilayer neural network is designed, to classify crowd density levels into 5 classes. Finally, the proposed method is experimented with PETS 2009 and the platform of Gangnam subway station image sequences.