• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.72-75
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• 2008

Image segmentation techniques becoming increasingly important in the field of remote sensing image analysis in areas such as object oriented image classification. This paper presents a new method for image segmentation in High Resolution Remote Sensing Image based on Seeded Region Growing (SRG) and Edge Information. Firstly, multi-spectral edge detection was done using an entropy operator in pan-sharpened QuickBird imagery. Then, the initial seeds were automatically selected from the obtained edge map. After automatic selection of significant seeds, an initial segmentation was achieved by applying SRG. Finally the region merging process, using region adjacency graph (RAG), was carried out to get the final segmentation result. Experimental results demonstrated that the proposed method has good potential for application in the segmentation of high resolution satellite images.

• Journal of Electrical Engineering and Technology
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• v.1 no.1
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• pp.52-57
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• 2006

In this paper, a new 'impedance sensing' method is described. This method overcomes the shortcomings of the impedance sensing method. According to the new method, sensing voltage pulse is applied to the idle phase in the minimum inductance region and the beginning of the increasing inductance region to detect rotor position. The negative torque produced by the sensing voltage pulse can be neglected in the minimum inductance region and the efficiency of SRM is improved. In the minimum inductance region the back electromotive force (EMF) can be neglected. And in the increasing inductance region the EMF opposes the rise of current in the phase, so the position estimation scheme is reliable. Therefore the new 'impedance sensing' method is sufficiently precise even under the high back EMF effect. The adjustment of turn-on angle and turn-off angle is also easy to be realized. The technique is very useful in applications where cost or size is primary concerns, such as electric bicycle drives. Experimental results are presented to verify the proposed method.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.5
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• pp.57-65
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• 2022

This study investigated the electromechanical response of smart ultra-high performance fiber reinforced concretes (S-UHPFRCs) under flexural loading to evaluate the self-sensing capacity of S-UHPFRCs in both tension and compression region. The electrical resistivity of S-UHPFRCs under flexural continuously changed even after first cracking due to the deflection-hardening behavior of S-UHPFRCs with the appearance of multiple microcracks. As the equivalent bending stress increased, the electrical resistivity of S-UHPFRCs decreased from 976.57 to 514.05 kΩ(47.0%) as the equivalent bending stress increased in compression region, and that did from 979.61 to 682.28 kΩ(30.4%) in tension region. The stress sensitivity coefficient of S-UHPFRCs in compression and tension region was 1.709 and 1.098 %/MPa, respectively. And, the deflection sensitivity coefficient of S-UHPFRCs in compression region(30.06 %/mm) was higher than that in tension region(19.72 %/mm). The initial deflection sensing capacity of S-UHPFRCs was almost 50% of each deflection sensitivity coefficient, and it was confirmed that it has an excellent sensing capacity for the initial deflection. Although both stress- and deflection-sensing capacity of S-UHPFRCs under flexural were higher in compression region than in tension region, S-UHPFRCs are sufficient as a self-sensing material to be applied to the construction field.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.731-740
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• 2010

A region merging technique is suggested in this paper for the segmentation of high-spatial resolution imagery. It employs a region growing scheme based on the region adjacency graph (RAG). The proposed algorithm uses directional neighbor-line average feature vectors to improve the quality of segmentation. The feature vector consists of 9 components which includes an observation and 8 directional averages. Each directional average is the average of the pixel values along the neighbor line for a given neighbor line length at each direction. The merging coefficients of the segmentation process use a part of the feature components according to a given merging coefficient order. This study performed the extensive experiments using simulation data and a real high-spatial resolution data of IKONOS. The experimental results show that the new approach proposed in this study is quite effective to provide segments of high quality for the object-based analysis of high-spatial resolution images.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.65-68
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• 2007

FORMOSAT-3 was launched in April of 2006. It consists of six low earth orbit (LEO) satellites that will be eventually deployed to an orbit at 800 km height. Its scientific goal is to utilize the radio occultation (RO) signals to measure the bending angles when the GPS signals transect the atmosphere. The bending angle is then used to infer atmospheric parameters, including refractivity, temperature, pressure, and relative humidity fields of global distributions through inversion schemes and auxiliary information. The expected number of RO events is around 2500 per day, of which 200 events or so fall into the polar region. Consequently, the FORMOSAT-3 observations are expected to play a key role to improve our knowledge in the weather forecasting and space physics research in the polar region. In this paper, we use temperature profiles retrieved from FORMOSAT-3 RO observations to study the climatology of gravity wave activity in the polar region. FORMOSAT-3 can provide about 200 RO observations a day in the polar region, much more than previous GPS RO missions, and, hence, more detailed climatology of gravity wave activity can be obtained.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.790-792
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• 2003

The remote sensing image server provides advanced image serving capabilities for geospatial image. Wide seamless image mosaics of Landsat 5 over GMS countries, which exceed a 15 GB or more in size per image, can integrate with other GIS map servers. The approach of two improvement algorithms leads to speed up the response time while preserving the data quality. This system does not only provide images on the web, but also provide GIS layers to WMS client map servers. The advantage of this approach is its efficiency lower cost in terms of cost, time and updating to obtain and utilize remote sensing image.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.9
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• pp.1733-1740
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• 2011

In this research, a MEMS vibratory gyroscope with dual-mass system in the sensing mode has been proposed to increase the stability of the device using wide bandwidth. A wide flat region between the two resonance peaks of the dual-mass system removes the need for a frequency matching typically required for single mass vibratory gyroscopes. Bandwidth, mass ratio, spring constant, and frequency response of the dual-mass system have been analyzed with MATLAB and ANSYS simulation. Designed first and second peaks of sensing mode are 5,917 and 8,210Hz, respectively. Driving mode resonance frequency of 7,180Hz was located in the flat region between the two resonance peaks of the sensing mode. The device is fabricated with anodically bonded silicon-on-glass substrate. The chip size is 6mm x 6mm and the thickness of the silicon device layer is $50{\mu}m$. Despite the driving mode resonance frequency decrease of 2.8kHz and frequency shift of 176Hz from the sensing mode due to fabrication imperfections, measured driving frequency was located within the bandwidth of sensing part, which validates the utilized dual-mass concept. Measured bandwidth was 768Hz. Sensitivity calculated with measured displacement of driving and sensing parts was 22.4aF/deg/sec. Measured slope of the sensing point was 0.008dB/Hz.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.8
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• pp.1843-1859
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• 2013

The continuous increase of the spatial resolution of remote sensing images brings great challenge to image analysis and processing. Traditional prior knowledge-based region detection and target recognition algorithms for processing high resolution remote sensing images generally employ a global searching solution, which results in prohibitive computational complexity. In this paper, a more efficient region of interest (ROI) detection algorithm based on visual attention and threshold segmentation (VA-TS) is proposed, wherein a visual attention mechanism is used to eliminate image segmentation and feature detection to the entire image. The input image is subsampled to decrease the amount of data and the discrete moment transform (DMT) feature is extracted to provide a finer description of the edges. The feature maps are combined with weights according to the amount of the "strong points" and the "salient points". A threshold segmentation strategy is employed to obtain more accurate region of interest shape information with the very low computational complexity. Experimental statistics have shown that the proposed algorithm is computational efficient and provide more visually accurate detection results. The calculation time is only about 0.7% of the traditional Itti's model.

• Korean Journal of Remote Sensing
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• v.21 no.1
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• pp.83-89
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• 2005

This study utilized a spatial region growing segmentation and a classification using fuzzy membership vectors to detect the changes in the images observed at different dates. Consider two co-registered images of the same scene, and one image is supposed to have the class map of the scene at the observation time. The method performs the unsupervised segmentation and the fuzzy classification for the other image, and then detects the changes in the scene by examining the changes in the fuzzy membership vectors of the segmented regions in the classification procedure. The algorithm was evaluated with simulated images and then applied to a real scene of the Korean Peninsula using the KOMPSAT-l EOC images. In the expertments, the proposed method showed a great performance for detecting changes in land-cover.

• Korean Journal of Remote Sensing
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• v.20 no.3
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• pp.215-225
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• 2004

A multistage hierarchical clustering technique, which is an unsupervised technique, was suggested in this paper for classifying large remotely-sensed imagery. The multistage algorithm consists of two stages. The 'local' segmentor of the first stage performs region-growing segmentation by employing the hierarchical clustering procedure of CN-chain with the restriction that pixels in a cluster must be spatially contiguous. The 'global' segmentor of the second stage, which has not spatial constraints for merging, clusters the segments resulting from the previous stage, using the conventional agglomerative approach. Using simulation data, the proposed method was compared with another hierarchical clustering technique based on 'mutual closest neighbor.' The experimental results show that the new approach proposed in this study considerably increases in computational efficiency for larger images with a low number of bands. The technique was then applied to classify the land-cover types using the remotely-sensed data acquired from the Korean peninsula.