• Proceedings of the KOSOMBE Conference
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• v.1992 no.05
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• pp.92-97
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• 1992

The pulsatile nature of blood flow makes artefacts in 2D Fourier transform image. Spatial presaturation is known to be effective in eliminating flow artefacts when the spin echo acquisition is employed. However. this method requires additional RF pulse and spoiling gradient for presaturation. In this paper a new flow saturation technique which does not require additional saturation-RF and gradient is proposed. The proposed technique is equivalent to the existing saturation technique but the elimination of the flow component is achieved by a pair of tailored $90^{\circ}-180^{\circ}$ RF pulses in tile spin echo sequence. By use of two tailored RF pulses with opposite phase polarity, a linear phase gradient is generated for those moving materials and consequently all the spins of moving materials become dephased thereby no signal is observable. Computer simulations and experimental results obtained using both a phantom and a human volunteer with a 2.0 T whole body system are also presented.

• Korean Journal of Optics and Photonics
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• v.8 no.5
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• pp.431-437
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• 1997

A gradient-index (GRIN) substrate is proposed as a novel signal propagation medium of planar optics. The GRIN substrate provides planar-optics designers not only a 3-dimensional signal propagation space, but also an additional smart optical functioning component like as a diffraction-limited imaging lens. The novel and smart functioning of the GRIN substrate was confirmed by experiment on imaging of an input signal to multiple destinations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.6
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• pp.825-832
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• 2013

In this study, Independent Component Analysis (ICA) algorithms are suggested to extract the original ECG part from the mixed signal contaminated with the unwanted frequency components and especially 60Hz power line disturbances. With this aim, we implement a novel method to suppress the baseline-wandering disturbances and power line artefacts contained in patch-electrodes sensory ECG data by separating the unmixed signal with finding the optimal weight W based on Kurtosis value. With applying brutal force and gradient ascent searching algorithm to find W, we can conclude that the unwanted frequency components especially in the ambulatory ECG data can be eliminated by Independent Component Analysis.

• Smart Structures and Systems
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• v.22 no.3
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• pp.359-368
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• 2018

A method is proposed for the identification of instantaneous frequencies (IFs) in time-varying structures. The proposed method combines a maximum gradient algorithm and a smoothing operation. The maximum gradient algorithm is designed to extract the wavelet ridges of response signals. The smoothing operation, based on a polynomial curve fitting algorithm and a threshold method, is employed to reduce the effects of random noises. To verify the effectiveness and accuracy of the proposed method, a numerical example of a signal with two frequency modulated components is investigated and an experimental test on a steel cable with time-varying tensions is also conducted. The results demonstrate that the proposed method can extract IFs from the noisy multi-component signals and practical response signals successfully. In addition, the proposed method can provide a better IF identification results than the standard synchrosqueezing wavelet transform.

• Advances in Energy Research
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• v.6 no.2
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• pp.91-101
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• 2019

This paper proposes an efficient data-driven approach to build models for predicting energy consumption in buildings. Data used in this research is collected by installing humidity and temperature sensors at different locations in a building. In addition to this, weather data from nearby weather station is also included in the dataset to study the impact of weather conditions on energy consumption. One of the main emphasize of this research is to make feature selection independent of domain knowledge. Therefore, to extract useful features from data, two different approaches are tested: one is feature selection through principal component analysis and second is relative importance-based feature selection in original domain. The regression model used in this research is gradient boosting regression and its optimal parameters are chosen through a two staged coarse-fine search approach. In order to evaluate the performance of model, different performance evaluation metrics like r2-score and root mean squared error are used. Results have shown that best performance is achieved, when relative importance-based feature selection is used with gradient boosting regressor. Results of proposed technique has also outperformed the results of support vector machines and neural network-based approaches tested on the same dataset.

• Journal of the Korean Society of Propulsion Engineers
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• v.20 no.4
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• pp.87-93
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• 2016

In a Direct Connect(DC) mode altitude engine test, a labyrinth seal is set up between an air intake duct and an engine. The labyrinth seal plays a key role in mechanically isolating them, which contributes to the accurate measurement of thrust and the other component forces. However, when high vertical temperature gradient is generated in the supplied air in the duct, the isolation breaks down. In this paper, a labyrinth seal control device is designed and installed in an effort to eliminate the issue. Test result shows the device successfully gets rid of the contact problem even when high vertical temperature gradient is produced.

• Journal of IKEEE
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• v.23 no.3
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• pp.793-799
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• 2019

In this study, the diffusion process and the thermal energy distribution gradient of the sensor were confirmed by using the finite element analysis program (COMSOL) of the mesh method to analyze the thermal diffusion in the wearable fabric (Nylon) + MWCNT gas sensor. To analyze the diffusion process of thermal energy, the structure of the gas sensor was modeled in a two dimension plane. The proposed modeling was presented with the characteristic value for the component of the sensor, and the gas sensor designed using the mesh finite element method (FEM) was proposed and analyzed by suggesting the one-way partial differential equation in the governing equation to know the degree of thermal energy diffusion and the thermal energy gradient. In addition, the temperature gradient 10[K/mm] of the anode-cathode electrode layer and the gas detection unit was investigated by suggesting the heat velocity transfer equation.

• Geophysics and Geophysical Exploration
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• v.24 no.1
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• pp.1-5
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• 2021

The closed-form expressions of the vector gravity and gravity gradient tensor due to a circular disk are derived. The gravity potential due to a circular disk with a constant density is defined for a cylindrical system. Then, the vector gravity is derived by differentiating the gravity potential with respect to cylindrical coordinates. The radial component of the vector gravity in the cylindrical system is converted into horizontal gravity components in the Cartesian system. Finally, the gravity gradient tensor due to a circular disk is obtained by differentiating the vector gravity with respect to the Cartesian coordinates.

• Publications of The Korean Astronomical Society
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• v.7 no.1
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• pp.231-253
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• 1992

A model for the distribution of stars in the disk and the spheroid of our Galaxy is reexamined from an edge-on view of the Galaxy obtained by selecting infrared sources from the IRAS Point Source Catalog. The sources are counted as a function of galactic latitude. longitude and $12{\mu}m$ apparent magnitude. The source counts are reasonably separated into the disk component and the spheroid component contributions and each of the contributions is further interpreted as a convolution of a spatial density distribution and a luminosity function based on the least-square fit method. The spatial density of the disk component has an exponential radial scale length of $h_R{\sim}2.6\;kpc$ and the vertical distribution follows a canonical $sech^2$ law with a scale height $h_z{\sim}240\;pc$. The distribution of the spheroid component can be represented by an oblate spheriod with an axis ratio $k{\sim}0.61$ and a de Vaucouleurs' $r^{1/4}$ law with an effective radius of $R_e{\sim}120\;pc$. The steep density gradient of the spheroid component is consistent with that of late M giants in the central bulge. The luminosity functions of the disk and the spheroid component stars resemble respectively those of the K luminosity function of disk M giants (Garwood and Jones 1986) and the bolometric luminosity function of M giants in bulge fields (Frogel et al, 1990).

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.3
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• pp.186-199
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• 2013

We pose pattern classification as a density estimation problem where we consider mixtures of generative models under partially labeled data setups. Unlike traditional approaches that estimate density everywhere in data space, we focus on the density along the decision boundary that can yield more discriminative models with superior classification performance. We extend our earlier work on the recursive estimation method for discriminative mixture models to semi-supervised learning setups where some of the data points lack class labels. Our model exploits the mixture structure in the functional gradient framework: it searches for the base mixture component model in a greedy fashion, maximizing the conditional class likelihoods for the labeled data and at the same time minimizing the uncertainty of class label prediction for unlabeled data points. The objective can be effectively imposed as individual mixture component learning on weighted data, hence our mixture learning typically becomes highly efficient for popular base generative models like Gaussians or hidden Markov models. Moreover, apart from the expectation-maximization algorithm, the proposed recursive estimation has several advantages including the lack of need for a pre-determined mixture order and robustness to the choice of initial parameters. We demonstrate the benefits of the proposed approach on a comprehensive set of evaluations consisting of diverse time-series classification problems in semi-supervised scenarios.