• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.74.2-74.2
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• 2019

We present a possible bias in the estimation of velocity dispersions for galaxy groups due to the contribution of subgroups which are infalling into the groups. We execute a systematic search for flux-limited galaxy groups and subgroups based on the spectroscopic galaxies with r < 17.77 mag of SDSS data release 12, by using DBSCAN (Density-Based Spatial Clustering of Application with Noise) and Hierarchical Clustering Method which are well known unsupervised machine learning algorithm. A total of 2042 groups with at least 10 members are found and ~20% of groups have subgroups. We found that the estimation of velocity dispersions of groups using total galaxies including those in subgroups are underestimated by ~10% compared to the case of using only galaxies in main groups. This result suggests that the subgroups should be properly considered for mass measurement of galaxy groups based on the velocity dispersion.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.527-539
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• 2023

In the positioning algorithm of two-dimensional planar sensor array, the estimation error of time difference-ofarrival (TDOA) algorithm is difficult to avoid. Thus, how to achieve accurate positioning is a key problem of the positioning technology based on planar array. In this paper, a method of sensor reliability discrimination is proposed, which is the foundation for selecting positioning sensors with small error and excellent performance, simplifying algorithm, and improving positioning accuracy. Then, a positioning model is established. The estimation characteristics of the least square method are fully utilized to calculate and fuse the positioning results, and the non-uniform weighting method is used to correct the weighting factors. It effectively handles the decreased positioning accuracy due to measurement errors, and ensures that the algorithm performance is improved significantly. Finally, the characteristics of the improved algorithm are compared with those of other algorithms. The experiment data demonstrate that the algorithm is better than the standard least square method and can improve the positioning accuracy effectively, which is suitable for vibration detection with large noise interference.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.62-66
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• 2023

With the advancement in technology and rapid increase in the demand for microelectromechanical systems (MEMS) based inertial measurement units (IMUs), high-volume production and test system remain a major challenge for the MEMS industry. To compete with the challenging market of Industry 4.0, here we developed an automatic test system to evaluate the performance of the ovenized IMU sensors as well as analyze the data. The automatic test system was developed by interfacing a commercial MEMS IMU (BMI 088) using LabVIEW. The BMI 088 was tested experimentally for long-term bias stability, ON/OFF bias repeatability, and root mean square (rms) noise. Furthermore, the data was analyzed through the developed test system. The results show that the automatic test system has improved the test time and reduced human effort. The developed automatic test system is a significant approach to MEMS research and development (R&D) to increase and improve the mass production of IMUs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2082-2102
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• 2024

Accurate segmentation of magnetic resonance (MR) images is crucial for providing doctors with effective quantitative information for diagnosis. However, the presence of weak boundaries, intensity inhomogeneity, and noise in the images poses challenges for segmentation models to achieve optimal results. While deep learning models can offer relatively accurate results, the scarcity of labeled medical imaging data increases the risk of overfitting. To tackle this issue, this paper proposes a novel fuzzy c-means (FCM) model that integrates a deep learning approach. To address the limited accuracy of traditional FCM models, which employ Euclidean distance as a distance measure, we introduce a measurement function based on the skewed normal distribution. This function enables us to capture more precise information about the distribution of the image. Additionally, we construct a regularization term based on the Kullback-Leibler (KL) divergence of high-confidence deep learning results. This regularization term helps enhance the final segmentation accuracy of the model. Moreover, we incorporate orthogonal basis functions to estimate the bias field and integrate it into the improved FCM method. This integration allows our method to simultaneously segment the image and estimate the bias field. The experimental results on both simulated and real brain MR images demonstrate the robustness of our method, highlighting its superiority over other advanced segmentation algorithms.

• Korean Journal of Optics and Photonics
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• v.7 no.3
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• pp.213-218
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• 1996

An improved crystal rotation method with increased accuracy and range is proposed and experimentally verified for simultaneous measurement of molecular tilt angle and thickness of LC (liquid crystal) layer of an LC cell. The improvement is brought about by direct determination of difference between phases instead of intensities of two components of orthogonal linear polarization of the light passing through an LC cell filled with uniformly oriented molecules. By comparing the experimental data with theoretical result the thickness and pretilt angle are determined more precisely. Further improvement is brought about by use of a liquid gate filled with an index matching liquid in which the LC cell is immersed. Because of the index matching liquid reflection of light at the surfaces of an LC cell almost completely disappears and the range of angle of refraction in the LC layer increases significantly, which gives rise to increased signal to noise ration as well as decreased statistical error. With this improvement precise measurement for either very thin (<10 ${\mu}{\textrm}{m}$) and/or higher pretilt angle($\geq$10$^{\circ}$) LC cells become possible.

• Transactions on Electrical and Electronic Materials
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• v.14 no.4
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• pp.211-215
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• 2013

Partial discharge diagnosis techniques using ultra high frequencies do not affect load movement, because there is no interruption of power. Consequently, these techniques are popular among the prevention diagnosis methods. For the first time, this measurement technique has been applied to the GIS, and has been tested by applying an extra high voltage switchboard. This particular technique makes it easy to measure in the live state, and is not affected by the noise generated by analyzing the causes of faults ? thereby making risk analysis possible. It is reported that the analysis data and the evaluation of the risk level are improved, especially for poor location, and that the measurement of Ultra high frequency (UHF) partial discharge of the real live wire in industrial switchgear is spectacular. Partial discharge diagnosis techniques by using the Ultra High Frequency sensor have been recently highlighted, and it is verified by applying them to the GIS. This has become one of the new and various power equipment techniques. Diagnosis using a UHF sensor is easy to measure, and waveform analysis is already standardized, due to numerous past case experiments. This technique is currently active in research and development, and commercialization is becoming a reality. Another aspect of this technique is that it can determine the occurrences and types of partial discharge, by the application diagnosis for live wire of ultra high voltage switchgear. Measured data by using the UHF partial discharge techniques for ultra high voltage switchgear was obtained from 200 places in Gumi, Yeosu, Taiwan and China's semiconductor plants, and also the partial discharge signals at 15 other places were found. It was confirmed that the partial discharge signal was destroyed by improving the work of junction bolt tightening check, and the cable head reinforcement insulation at 8 places with a possibility for preventing the interruption of service. Also, it was confirmed that the UHF partial discharge measurement techniques are also a prevention diagnosis method in actual industrial sites. The measured field data and the usage of the research for risk assessment techniques of the live wire status of power equipment make a valuable database for future improvements.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.8
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• pp.1075-1081
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• 2010

Recently, the results of many studies have clarified the successful performance of magnetostrictive transducers in which a ferromagnetic patch is used for the transduction of guided shear waves; this is because a thin ferromagnetic patch with strong magnetostriction is very useful for generating and detecting shear wave. This investigation deals with bulk shear wave transduction by means of magnetostriction; on the other hand, the existing studies have been focused on guided shear waves. A modular transducer was developed; this transducer comprised a coil, magnets, and a thin ferromagnetic patch that was made of Fe-Co alloy. Some experiments were conducted to verify the performance of the developed transducer. Radiation directivity pattern of the developed transducer was obtained, and a test to detect the damage on a side drill hole of a steel block specimen was carried out. From the results of these tests, the good performance of the transducer for nondestructive testing was verified on the basis of the signal-to-noise ratio and narrow beam directivity.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.1
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• pp.6-12
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• 2010

Fiber optic sensor using fiber Bragg grating(FBG) probes is used for monitoring strain and temperature distributed on the wide surfaces of large structures. In this paper, in order to use many FBG probes in one optical fiber line, we propose a complex multiplexing technology which is composed of two techniques, one is time division multiplexing and another is wavelength division multiplexing. However, we only investigate the characteristics of time division multiplexing because FBG sensors basically can be operated by wavelength division multiplexing. We calculate the optimal reflectivities and the lengthwise location of five FBG probes in serial connection in order to obtain the unique reflected intensities from the FBG probes. We fabricate five FBG probes with the reflectivities of 13%, 16%, 25%, 40% and 80%, which are determined by the theoretical calculation, and observe the signal reflected from each FBG in the time domain from the experiment. There are differences between experimental and theoretical results caused by the signal noise and the differences of reflectivities of FBG probes. But the experimental results shows the reflected signals of five FBG probes which prove the availability of complex multiplexing.

• Journal of the Korean Magnetics Society
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• v.24 no.4
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• pp.123-127
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• 2014

We developed a digital 3-axis flux-gate magnetometer for magnetic field signal measurement from warship during demagnetizing and degaussing processes. For the magnetometer design, we considered following points; the distance between magnetic field measurement station and magnetometer located under sea is about several 100 m, the magnetometer is exposed to magnetic field of ${\pm}1mT$ during demagnetizing process, and magnetometer is located under the sea about 30 m depth. To overcome long distance problem, magnetometer could be operated on wide input supply voltage range of 16~36 V using DC/DC converter, and for the data communication between the magnetometer and measurement station a RS422 serial interface was employed. To improve perming effect due to the ${\pm}1mT$ during demagnetizing process, magnetometer could be compensated external magnetic field up to ${\pm}1mT$ but magnetic field measuring rang is only ${\pm}100{\mu}T$. The perming effect was about ${\pm}2nT$ under ${\pm}1mT$ external magnetic field. The magnetometer was tested water vessel with air pressure up to 6 bar for the sea water pressure problems. Linearity of the magnetometer was better than 0.01 % in the measuring range of ${\pm}0.1mT$ and noise level was $30pT/\sqrt{Hz}$ at 1 Hz.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.2
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• pp.413-418
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• 2011

The thermodynamic properties of steam table are obtained by measurement or approximate calculation under appropriate assumptions. Therefore they are supposed to have basic measurement errors. And thermodynamic properties should be modeled through function approximation for using in numerical analysis. In order to make noised thermodynamic properties corresponding to measurement errors, random numbers are generated, adjusted to appropriate magnitudes and added to original thermodynamic properties. Both neural networks and quadratic spline interpolation method are introduced for function approximation of these modified thermodynamic properties in the saturated water based on pressure and temperature. In analysis spline interpolation method gives much less relative errors than neural networks at both ends of data. Excluding the both ends of data, the relative errors of neural networks is generally within ${\pm}0.2%$ and those of spline interpolation method within ${\pm}0.5$~1.5%. This means that the neural networks give smaller relative errors compared with quadratic spline interpolation method within range of use. From this fact it was confirmed that the neural networks trace the original values better than the quadratic interpolation method and neural networks are more appropriate method in modelling the saturated steam table.