• Journal of Biomedical Engineering Research
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• v.23 no.4
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• pp.269-279
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• 2002

When we inject a current into an electrically conducting subject such as a human body, voltage and current density distributions are formed inside the subject. The current density within the subject and injection current in the lead wires generate a magnetic field. This magnetic flux density within the subject distorts phase of spin-echo magnetic resonance images. In Magnetic Resonance Current Density Imaging (MRCDI) technique, we obtain internal magnetic flux density images and produce current density images from $\bigtriangledown{\times}B/\mu_\theta$. This internal information is used in Magnetic Resonance Electrical Impedance Tomography (MREIT) where we try to reconstruct a cross-sectional resistivity image of a subject. This paper describes numerical techniques of computing voltage. current density, and magnetic flux density within a subject due to an injection current. We use the Finite Element Method (FEM) and Biot-Savart law to calculate these variables from three-dimensional models with different internal resistivity distributions. The numerical analysis techniques described in this paper are used in the design of MRCDI experiments and also image reconstruction a1gorithms for MREIT.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.19 no.8
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• pp.599-606
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• 2007

An experimental study was performed to measure a ice concentration of ice slurry flowing in a pipe in a real time. In the present paper, we suggested a measuring method by a refractive index and compared it to other methods by a freezing point and a density. To measure the refractive index of the solution, ice particles in the ice slurry should be completely removed and a hydro-cyclone was introduced instead of a mesh. The measuring method through the refractive index coincided with the density method using the real-time solution density within ${\pm}5%$ error range, having the error range less than the other two methods. In the other hand, the measuring method through the density has a good resolution, but the result using the initial density of the solution was different more than 10% error from that using the real-time density. And it has an error range 1.5 times greater than the method through the refractive index.

• Journal of Korea Multimedia Society
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• v.19 no.1
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• pp.1-9
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• 2016

In this paper, we propose a method of measuring bone mineral density in a peripheral-type clinical X-ray CT using a phantom, and we propose a method of classifying osteoporosis using bone mineral density and bone structure parameters together. It segments the trabecular bone region and cortical bone region for the six sections of the phantom and calculates the average HU value of the segmented regions. By using these values, it derives an expression converting HU value to bone mineral density. It segments trabecular bone of 1 cm region in the end part of distal radius and extracts the bone mineral density and structural parameters for the trabecular bone region. We extracted bone mineral density and structural parameters for the 18 subjects each of normal and osteoporotic group. We carried out classification experiments using three classification methods; SAD, SVM, ANN. The sensitivity, specificity, accuracy, positive predictive value, negative predictive value, likelihood ratio of the classification was improved in the order of ANN, SVM and SAD. Also, The sensitivity, specificity, accuracy, positive predictive value, negative predictive value, likelihood ratio of the classification was improved when we use the bone mineral density and structural parameters together.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2005.07a
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• pp.570-571
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• 2005

In this paper, we introduced a LIF measurement method and summarized the theoretical side. When an altered wavelength of laser and electric power, lamp applied electric power, we measured the relative density of the metastable state in mercury after observing a laser induced fluorescence signal of 404.8nm and 546.2nm, and confirmed the horizontal distribution of plasma density in the discharge lamp. The results confirmed the resonance phenomenon regarding the energy level of atoms along a wavelength change, and also confirmed that the largest fluorescent signal in 436nm, and that the density of atoms in 546.2nm ($6^3S_1\rightarrow6^3P_2$) were larger than 404.8nm ($6^3S_1\rightarrow6^3P_1$). According to the increase of lamp applied electric power, plasma density increased, too. When increased with laser electric power, the L1F signal reached a saturation state in more than 2.6mJ. When partial plasma density distribution along a horizontal axis was measured using the laser induced fluorescence method, the density decreased by recombination away from the center.

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

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.255-262
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• 1998

In this paper, we study the saddlepoint approximations for the ratio of independent random variables. In Section 2, we derive the saddlepoint approximation to the probability density function. In Section 3, we represent a numerical example which shows that the errors are small even for small sample size.

• Journal for History of Mathematics
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• v.16 no.3
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• pp.95-100
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• 2003

The recent surge of interest in the more technical aspects of nonparametric density estimation and nonparametric regression estimation has brought the subject into public view. In this paper, we investigate the general concept of the nonparametric density estimation, the nonparametric regression estimation and its performance criteria.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.1
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• pp.171-181
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• 2012

This paper introduces the double-density discrete wavelet transform(DWT) using quincunx sampling, which is a DWT that combines the double-density DWT and quincunx sampling method, each of which has its own characteristics and advantages. The double-density DWT is an improvement upon the critically sampled DWT with important additional properties: Firstly, It employs one scaling function and two distinct wavelets, which are designed to be offset from one another by one half. Secondly, the double-density DWT is overcomplete by a factor of two, and Finally, it is nearly shift-invariant. In two dimensions, this transform outperforms the standard DWT in terms of denoising; however, there is room for improvement because not all of the wavelets are directional. That is, although the double-density DWT utilizes more wavelets, some lack a dominant spatial orientation, which prevents them from being able to isolate those directions. A solution to this problem is a quincunx sampling method. The quincunx lattice is a sampling method in image processing. It treats the different directions more homogeneously than the separable two dimensional schemes. Proposed wavelet transformation can generate sub-images of multiple degrees rotated versions. Therefore, This method services good performance in image processing fields.

• Journal of the Korean Society of Safety
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• v.6 no.2
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• pp.15-20
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• 1991

In this paper, we have investigated the effect of organic additives and prestressed D C. voltage on the impulse tree initiation in low density polyethylene. The five klnds of organic compounds was selected for the purpose of inhibiting tree initiation and 10 wt % of each additive was mixed in low density polyethylene. The positive or negative impulse voltage was applied after prestressed D.C. voltage was applied in order to investigate the effect of the space charge influenced on tree initiation. The lengths of tree initiation in case of belng same polarity between prestressed D.C. voltage and impulse voltage were longer than those in case of being different polarity between prestressed D.C. voltage and impulse voltage. When the polarity prestressed D.C. voltage was the different plarity of impulse voltage, the length of tree initiation increased with increasing the prestressed D.C. voltage and decreasing the rest time Among the organic additives used in this paper, the m-cresol can be shown to be the most effective inhibiter to tree initiation.

• The Journal of the Acoustical Society of Korea
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• v.17 no.4E
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• pp.3-10
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• 1998

This paper concerns the dynamical behavior, in probabilistic sense, of a feedforward neural network performing auto association for novelty. Networks of retinotopic topology having a one-to-one correspondence between and output units can be readily trained using back-propagation algorithm, to perform autoassociative mappings. A novelty filter is obtained by subtracting the network output from the input vector. Then the presentation of a "familiar" pattern tends to evoke a null response ; but any anomalous component is enhanced. Such a behavior exhibits a promising feature for enhancement of weak signals in additive noise. As an analysis of the novelty filtering, this paper shows that the probability density function of the weigh converges to Gaussian when the input time series is statistically characterized by nonsymmetrical probability density functions. After output units are locally linearized, the recursive relation for updating the weight of the neural network is converted into a first-order random differential equation. Based on this equation it is shown that the probability density function of the weight satisfies the Fokker-Planck equation. By solving the Fokker-Planck equation, it is found that the weight is Gaussian distributed with time dependent mean and variance.