• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.7-10
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• 2008

We present a technique for constructing a digital elevation model (DEM) from contours. The elevation of each ground point in DEM is computed by interpolating the heights of the two adjacent contours of the point. The technique decomposes each sub-domain between adjacent contours into a set of sub-regions. The decomposition is accomplished by constructing a medial axis of the sub-domain. Each sub-region in the decomposition is classified into a variety of terrain features like hillsides, valleys, ridges, etc. The elevations of points are interpolated with different methods according to terrain features they belong to. For a given point in hillside, an approximate gradient line passing through the point is determined and the elevation of the point is interpolated from the known elevations of the two adjacent contours along the approximate gradient line. The univariate monotone rational Hermite spline is used for the interpolation. The DEM constructed by the technique is to be used to orthorectify the high-resolution KOMPSAT3 imagery.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.286-289
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• 2007

This study is to develop a cloud detection algorit1un for COMS and it is currently tested by using MODIS level 2B and MTSAT-1R satellite radiance data. Unlike many existing cloud detection schemes which use a threshold method and traditional statistical methods, in this study a feed-forward neural network method with back-propagation algorit1un is used. MODIS level 2B products are matched with feature information of five-band MTSAT 1R image data to form the training dataset. The neural network is trained over the global region for the period of January to December in 2006 with 5 km spatial resolution. The main results show that this model is capable to detect complex cloud phenomena. And when it is applied to seasonal images, it shows reliable results to reflect seasonal characteristics except for snow cover of winter. The cloud detection by the neural network method shows 90% accuracy compared to the MODIS products.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.9
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• pp.847-852
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• 2007

Design aspects of a bearing casing system of a power plant are mainly focused on the strength and weight of itself to have a more stable system. Since the rotor speed often passes through the critical speed region when the operation begins, the relation between the rotating frequency of the rotor and natural frequency of the casing is very important for a prevention of resonance. However, harmonic components above the rotating frequency have often been overlooked the design for the resonance avoidance. In this paper, it is revealed that resonance vibration is generated when the natural frequency of a bearing casing is close to the one of harmonics of basic rotating frequency(1x), and as a consequence, sensing qualify of seismoprobes attached to the bearing casing structure can be seriously damaged. In order to reduce the resonance vibration, some stiffeners are added to the casing structures. Significant reduction in the magnitude of vibration corresponding to 2x harmonic of basic rotating frequency is observed from both FE analysis and experiment.

• 한국공간정보시스템학회:학술대회논문집
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• 2002.03a
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• pp.45-52
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• 2002

Artificial neural networks (ANN) have been successfully used for classifying remotely sensed imagery. However, ANN still is not the preferable choice for classification over the conventional classification methodology such as the maximum likelihood classifier commonly used in the industry production environment. This can be attributed to the ANN characteristic built-in stochastic process that creates difficulties in dealing with unequally represented training classes, and its training performance speed. In this paper we examined some practical aspects of training classes when using a back propagation neural network model for remotely sensed imagery. During the classification process of remotely sensed imagery, representative training patterns for each class are collected by polygons or by using a region-growing methodology over the imagery. The number of collected training patterns for each class may vary from several pixels to thousands. This unequally populated training data may cause the significant problems some neural network empirical models such as back-propagation have experienced. We investigate the effects of training over- or under- represented training patterns in classes and propose the pattern repopulation algorithm, and an adaptive alpha adjustment (AAA) algorithm to handle unequally represented classes. We also show the performance improvement when input patterns are presented in random fashion during the back-propagation training.

• Journal of Korea Water Resources Association
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• v.34 no.3
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• pp.253-263
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• 2001

It is very important to select appropriate methods of collecting, predicting, and analyzing information for the development of urban water resources and the prevention of disasters. Thus, in this study an accurate data generation method is developed using Geographic Information System (GIS) and Remote Sensing (RS). The methods of development and application of an expert system are suggested to solve more efficiently the problems of water resources and quality induced by the rapid urbanization. The time-varying data in a large region, the An-Yang cheon watershed, were reasonably obtained by the application of the GIS using ARC/INFO and RS data. The ESPE (Expert System for Parameter Estimation), an expert system is developed using the CLIPS 6.0. The simulated results showed agreement with the measured data globally. These methods are expected to efficiently simulate the runoff and water quality in the rapidly varying urban area.

• Journal of Environmental Impact Assessment
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• v.6 no.1
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• pp.55-66
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• 1997

This study summarizes the use of satellite data to detect the change of southwestern coastal wetlands in Korea. The images used for this study were two Landsat Thematic Mapper(TM) images (June 12, 1984 & June 2, 1992). TM images were used to classify such different types of wetlands as aquatic bed, nonaquatic bed and other land use in the region. Then it, was possible to a) determine the status of wetlands using image classification products, and b) detect the changes of various types of wetlands influenced by both human and nature. The results from spatiotemporal analysis showed that approximately 120 lad of coastal wetlands were lost from the year of 1984 to 1992. 71 % of the lost wetlands were converted to the reclaimed land. This loss of wetlands has been causing the profound environmental impacts. It has been successfully proved that satellite data are very effective for spatiatemporal change analysis, especially for that of coastal wetlands.

• Journal of Sensor Science and Technology
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• v.21 no.5
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• pp.339-344
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• 2012

This article describes a novel method for the detection of amyloid-${\beta}$($A{\beta}$) peptide that utilizes a photo-sensitive field-effect transistor (p-FET). According to a recent study, $A{\beta}$ protein has been known to play a central role in the pathogenesis of Alzheimer's disease (AD). Accordingly, we investigated the variation of photo current generated from p-FET with and without intracellular magnetic beads conjugated with $A{\beta}$ peptides, which are placed on the p-FET sensing areas. The decrease of photo current was observed due to the presence of the magnetic beads on the channel region. Moreover, a similar characteristic was shown when the Raw 264 cells take in magnetic beads treated with $A{\beta}$ peptide. This means that it is possible to simply detect a certain protein using magnetic beads and a p-FET device. Therefore, in this paper, we suggest that our method could detect tiny amounts of $A{\beta}$ for early diagnosis of AD using the p-FET devices.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.665-672
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• 2012

Amid rapidly increasing imagery inputs and their volume in a remote sensing imagery database, Content-Based Image Retrieval (CBIR) is an effective tool to search for an image feature or image content of interest a user wants to retrieve. It seeks to capture salient features from a 'query' image, and then to locate other instances of image region having similar features elsewhere in the image database. For a CBIR approach that uses texture as a primary feature primitive, designing a texture descriptor to better represent image contents is a key to improve CBIR results. For this purpose, an extended feature vector combining the Gabor filter and co-occurrence histogram method is suggested and evaluated for quantitywise and qualitywise retrieval performance criterion. For the better CBIR performance, assessing similarity between high dimensional feature vectors is also a challenging issue. Therefore a number of distance metrics (i.e. L1 and L2 norm) is tried to measure closeness between two feature vectors, and its impact on retrieval result is analyzed. In this paper, experimental results are presented with several CBIR samples. The current results show that 1) the overall retrieval quantity and quality is improved by combining two types of feature vectors, 2) some feature is better retrieved by a specific feature vector, and 3) retrieval result quality (i.e. ranking of retrieved image tiles) is sensitive to an adopted similarity metric when the extended feature vector is employed.

• Journal of Sensor Science and Technology
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• v.26 no.4
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• pp.266-273
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• 2017

In this research a medical palpation guidance system for minimally invasive surgery (MIS) is proposed. Palpation is a useful tool for identifying a size and location of a lump during a surgery. However, conventional manual palpation is only available in open surgery, so there has been several researches about palpation assistant or guidance system for MIS. The previously developed systems are based on a pressure based or stiffness based approach. These previous approaches have some limitations in increasing complexity of the systems and lack of geometric information about the lump which is more important information for the lump removal than the stiffness information. We propose a palpation guidance system using a novel approach using contact pressure distribution. Since our approach gives the geometry information of the lump as well as the existence information, the operator can easily notice the currently identified lump region and the optimal position for the next palpation. The experiment results show that our approach can offer the geometry information of the lump correctly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5805-5825
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• 2019

Smartphones are important platforms because of their sophisticated computation, communication, and sensing capabilities, which enable a variety of applications in the Internet of Things (IoT) systems. Moreover, advancements in hardware have enabled sensors on smartphones such as environmental and chemical sensors that make sensor data collection readily accessible for a wide range of applications. However, dynamic, opportunistic, and heterogeneous mobility patterns of smartphone users that vary throughout the day, which greatly affects the efficacy of sensor data collection. Therefore, it is necessary to consider phone users mobility patterns to design data collection schedules that can reduce the loss of sensor data. In this paper, we propose a mobility-based weighted adaptive opportunistic scheduling framework that can adaptively adjust to the dynamic, opportunistic, and heterogeneous mobility patterns of smartphone users and provide prioritized scheduling based on various application scenarios, such as velocity, region of interest, and sensor type. The performance of the proposed framework is compared with other scheduling frameworks in various heterogeneous smartphone user mobility scenarios. Simulation results show that the proposed scheduling improves the transmission rate by 8 percent and can also improve the collection of higher-priority sensor data compared with other scheduling approaches.