• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.698-701
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• 2005

This paper examines that is extracted certain information in forest areas within high resolution imagery based on wavelet transformation. First of all, study areas are selected one more species distributed spots refer to forest type map. Next, study area is cut 256 x 256 pixels size because of image processing problem in large volume data. Prior to wavelet transformation, five texture parameters (contrast, dissimilarity, entropy, homogeneity, Angular Second Moment (ASM≫ calculated by using Gray Level Co-occurrence Matrix (GLCM). Five texture images are set that shifting window size is 3x3, distance .is 1 pixel, and angle is 45 degrees used. Wavelet function is selected Daubechies 4 wavelet basis functions. Result is summarized 3 points; First, Wavelet transformation images derived from contrast, dissimilarity (texture parameters) have on effect on edge elements detection and will have probability used forest road detection. Second, Wavelet fusion images derived from texture parameters and original image can apply to forest area classification because of clustering in Homogeneous forest type structure. Third, for grading evaluation in forest fire damaged area, if data fusion of established classification method, GLCM texture extraction concept and wavelet transformation technique effectively applied forest areas (also other areas), will obtain high accuracy result.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.5
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• pp.29-37
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• 2002

This paper presents the diagnostic method for fault prevention in metal clad switchgear(MCS) through comparison of signals before and after detecting the partial discharge using electromagnetic detection technique. Electromagnetic waves detected by antennas of the inside and outside of MCS are analyzed and compared by frequency spectrum analysis method which can estimate an insulation abnormality and normality of MCS. As a result of the experiment by the proposed method, we can detect the insulation abnormality as partial discharge in MCS and these results can be applied to preventive diagnosis of MCS.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.1
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• pp.61-67
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• 2018

The effects of subsurface inhomogeneities on the detection of buried metal pipes in ground-penetrating radar(GPR) signals are investigated numerically. To model the electrical properties of the subsurface inhomogeneities, the continuous random media(CRM) generation technique is introduced. For the electromagnetic simulation of GPR signals, the finite-difference time-domain(FDTD) method is implemented. As a function of the standard deviation and the correlation length of the relative permittivity distribution for a randomly inhomogeneous ground, the GPR signals of the buried metal pipes are compared using numerical simulations. As the subsurface inhomogeneities increase, the GPR signals of the buried pipes are distorted because of the effect of the subsurface clutter.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.4
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• pp.200-205
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• 2017

In this paper, we propose a technique to determine the normal and arc of an alternating current using a mixed filter composed of an average filter and a band-pass filter and a frequency analysis. The proposed method uses the moving average filter of the FIR filter structure for noise removal and the band-pass filter of the IIR filter structure for detecting only specific frequency components after normalizing the measured current signal based on the maximum value. After performing Fast Fourier Transform (FFT) using the band-pass filtered signal, the total energy is calculated using the magnitude component of the frequency, and the arc is detected using the magnitude of the calculated energy. In order to show the validity of the proposed method, we experimented with various data and found that arc and steady state can be easily discriminated by calculating spectral energy. Therefore, it is considered that the proposed method can be applied to arc diagnosis of low voltage electric wire.

• Journal of KIISE
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• v.43 no.3
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• pp.346-352
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• 2016

The fishing industry requires many workers to manually carry out the jobs of sorting and cutting fishes. There are therefore many dangerous situations in their working environment and the throughput is inefficiently low. This paper introduces an automatic fin cutting system based on RANSAC that is able to increase the throughput of fish processing jobs. The system proposed in this paper first detects the edges of a fish using a high-pass filter. The boundary lines between fin and body are then detected by adjusting parameters and the threshold of the noise filters. Finally, the optimal cutting lines are detected using RANSAC. Through an experiment with a sample of 50 fishes, this paper shows that the proposed system detects the cutting lines with about 90% accuracy.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.1
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• pp.43-50
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• 2014

This paper introduces a robot platform which can do weeding while traveling between rice seedlings stably against irregular land surface of a paddy field. Also, an autonomous navigation technique that can track on stable state without any damage of the seedlings in the working area is proposed. Detection of the rice seedlings and avoidance knocking down by the robot platform is achieved by the sensor fusion of a laser range finder (LRF) and an inertial measurement unit (IMU). These sensors are also used to control navigating direction of the robot to keep going along the column of rice seedling consistently. Deviation of the robot direction from the rice column that is sensed by the LRF is fed back to a proportional and derivative controller to obtain stable adjustment of navigating direction and get proper returning speed of the robot to the rice column.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2315-2325
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• 2015

Induction motors are widely used in industrial processes since they offer a very high degree of reliability. But like any other machine, they are vulnerable to faults, which if left unmonitored, might lead to an unexpected interruption at the industrial plant. Therefore, the condition monitoring of the induction motors have been a challenging topic for many electrical machine researchers. Indeed, the effectiveness of the fault diagnosis and prognosis techniques depends very much on the quality of the fault features selection. However, in induction-motor drives, rotor defects are the most complex in terms of detection since they interact with the supply frequency within a restricted band around this frequency, especially in the no-loaded case. To overcome this drawback, this paper deals with an efficient and new method to diagnose the induction-motor rotor fault based on the digital implementation of the monitoring algorithm based on the association of the Time Synchronous Averaging technique and Discrete Wavelet Transform. Experimental results are presented in order to show the effectiveness of the proposed method. The obtained results are largely satisfactory, indicating a promising industrial application of the combined “Time Synchronous Averaging – Discrete Wavelet Transform” approach.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.02a
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• pp.85-85
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• 2013

Label-free, sensitive and selective detection methods with high spatial resolution are critically required for future applications in chemical sensor, biological sensor, and nanospectroscopic imaging. Here I describe the development of Plasmon Resonance Energy Transfer (PRET)-based molecular imaging in living cells as the first demonstration of intracellular imaging with PRET-based nanospectroscopy. In-vivo PRET imaging relied on the overlap between plasmon resonance frequency of gold nanoplasmonic probe (GNP) and absorption peak frequencies of conjugated molecules, which leads to create 'quantized quenching dips' in Rayleigh scattering spectrum of GNP. The position of these dips exactly matched with the absorption peaks of target molecules. As another innovative application of PRET, I present a highly selective and sensitive detection of metal ions by creating conjugated metal-ligand complexes on a single GNP. In addition to conferring high spatial resolution due to the small size of the metal ion probes (50 nm in diameter), this method is 100 to 1,000 folds more sensitive than organic reporter-based methods. Moreover, this technique achieves high selectivity due to the selective formation of Cu2+complexes and selective resonant quenching of GNP by the conjugated complexes. Since many metal ion ligand complexes generate new absorption peak due to the d-d transition in the metal ligand complex when a specific metal ion is inserted into the complex, we can match with the scattering frequency of nanoplasmonic metal ligand systems and the new absorption peak.

• Textile Coloration and Finishing
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• v.30 no.2
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• pp.63-69
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• 2018

In this study, we report on successful attempt towards the synthesis of sulfur self-doped $g-C_3N_4$ by directly heating thiourea in air. The synthesized materials were characterized using UV-vis spectral technique, FT-IR, XRD and TEM analysis. Further, the obtained material shows an excellent detection of carcinogenic TNP(Tri nitro phenol) in the presence of 10-fold excess of various other common interferences. The strong inner filter effect and molecular interactions(electrostatic, ${\pi}-{\pi}$, and hydrogen bonding interactions) between TNP and the $S-g-C_3N_4$ Nano sheets led to the fluorescence quenching of the $S-g-C_3N_4$ Nano sheets with an excellent selectivity and sensitivity towards TNP compared to that of other nitro aromatics under optimal conditions and the detection limit calculated was found to be 6.324 nM for TNP. The synthesized nanocomposite provides a promising platform for the development of sensors with improved reproducibility and stability for ultra-sensitive and selective sensing of TNP.

• Environmental Analysis Health and Toxicology
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• v.29
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• pp.16.1-16.9
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• 2014

Objectives Rapid increase in engineered nanoparticles (ENPs) in many goods has raised significant concern about their environmental safety. Proper methodologies are therefore needed to conduct toxicity and exposure assessment of nanoparticles in the environment. This study reviews several analytical techniques for nanoparticles and summarizes their principles, advantages and disadvantages, reviews the state of the art, and offers the perspectives of nanometrology in relation to ENP studies. Methods Nanometrology is divided into five techniques with regard to the instrumental principle: microscopy, light scattering, spectroscopy, separation, and single particle inductively coupled plasma-mass spectrometry. Results Each analytical method has its own drawbacks, such as detection limit, ability to quantify or qualify ENPs, and matrix effects. More than two different analytical methods should be used to better characterize ENPs. Conclusions In characterizing ENPs, the researchers should understand the nanometrology and its demerits, as well as its merits, to properly interpret their experimental results. Challenges lie in the nanometrology and pretreatment of ENPs from various matrices; in the extraction without dissolution or aggregation, and concentration of ENPs to satisfy the instrumental detection limit.