• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.320-323
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• 2008

This study attempts to establish a system extracting and monitoring cultural grounds of seaweeds (lavers, brown seaweeds and seaweed fulvescens) and abalone on the basis of both KOMPSAT-2 and Terrasar-X data. The study areas are located in the northwest and southwest coast of South Korea, famous for coastal cultural grounds. The northwest site is in a high tidal range area (on the average, 6.1 min Asan Bay) and has laver cultural grounds for the most. An semi-automatic detection system of laver facilities is described and assessed for spacebome optic images. On the other hand, the southwest cost is most famous for seaweeds. Aquaculture facilities, which cover extensive portions of this area, can be subdivided into three major groups: brown seaweeds, capsosiphon fulvescens and abalone farms. The study is based on interpretation of optic and SAR satellite data and a detailed image analysis procedure is described here. On May 25 and June 2, 2008 the TerraSAR-X radar satellite took some images of the area. SAR data are unique for mapping those farms. In case of abalone farms, the backscatters from surrounding dykes allows for recognition and separation of abalone ponds from all other water-covered surfaces. But identification of seaweeds such as laver, brown seaweeds and seaweed fulvescens depends on the dampening effect due to the presence of the facilities and is a complex task because objects that resemble seaweeds frequently occur, particularly in low wind or tidal conditions. Lastly, fusion of SAR and optic spatial images is tested to enhance the detection of aquaculture facilities by using the panchromatic image with spatial resolution 1 meter and the corresponding multi-spectral, with spatial resolution 4 meters and 4 spectrum bands, from KOMPSAT-2. The mapping accuracy achieved for farms will be estimated and discussed after field verification of preliminary results.

• Journal of the Korean Geotechnical Society
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• v.19 no.4
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• pp.299-310
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• 2003

In the evaluation of the subgrade stiffness structure by the SASW method, the calculation of the phase velocities is the important task controlling the reliability of the result. The interpretation of the phase spectrum should precede the phase-velocity calculation in the current practice of the SASW method. The difficulty involved in the interpretation prohibited the SASW method from being spread over to the industry. This study proposed a new method called the frequency-wave number technique, which is based on the frequency-wave number relationship of the surface wave in the multi-layered system. The frequency-wave number technique eliminates the expertise in the interpretation of the phase spectrum, automates the phase-velocity calculation and expedites the determination of the phase-velocity dispersion curve. To verify the validity of the proposed frequency-wave number method, the transfer function determined from the numerical simulation of the SASW measurements was used fir the calculation of the automatic calculation of the phase velocities and compared with the phase velocities by WinSASW employing the phase-unwrapping method. Also, the proposed method was applied to the real SASW measurements performed at$\bigcirc$$\bigcirc$area in GyeongGi-Do to see how the proposed method works with the real measurements.

• Progress in Medical Physics
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• v.29 no.2
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• pp.66-72
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• 2018

The proper position of a multi-leaf collimator (MLC) is essential for the quality of intensity-modulated radiation therapy (IMRT) and volumetric modulated arc radiotherapy (VMAT) dose delivery. Task Group (TG) 142 provides a quality assurance (QA) procedure for MLC position. Our study investigated the QA validation of the mechanical leaf gap measurement and the maintenance procedure. Two $VitalBeam^{TM}$ systems were evaluated to validate the acceptance of an MLC position. The dosimetric leaf gaps (DLGs) were measured for 6 MV, 6 MVFFF, 10 MV, and 15 MV photon beams. A solid water phantom was irradiated using $10{\times}10cm^2$ field size at source-to-surface distance (SSD) of 90 cm and depth of 10 cm. The portal dose image prediction (PDIP) calculation was implemented on a treatment planning system (TPS) called $Eclipse^{TM}$. A total of 20 VMAT plans were used to confirm the accuracy of dose distribution measured by an electronic portal imaging device (EPID) and those predicted by VMAT plans. The measured leaf gaps were 0.30 mm and 0.35 mm for VitalBeam 1 and 2, respectively. The DLG values decreased by an average of 6.9% and 5.9% after mechanical MLC adjustment. Although the passing rates increased slightly, by 1.5% (relative) and 1.2% (absolute) in arc 1, the average passing rates were still within the good dose delivery level (>95%). Our study shows the existence of a mechanical leaf gap error caused by a degenerated MLC motor. This can be recovered by reinitialization of MLC position on the machine control panel. Consequently, the QA procedure should be performed regularly to protect the MLC system.

• Smart Structures and Systems
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• v.21 no.5
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• pp.591-600
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• 2018

The probabilistic characterization of wind field characteristics is a significant task for fatigue reliability assessment of long-span railway bridges in wind-prone regions. In consideration of the effect of wind direction, the stochastic properties of wind field should be represented by a bivariate statistical model of wind speed and direction. This paper presents the construction of the bivariate model of wind speed and direction at the site of a railway arch bridge by use of the long-term structural health monitoring (SHM) data. The wind characteristics are derived by analyzing the real-time wind monitoring data, such as the mean wind speed and direction, turbulence intensity, turbulence integral scale, and power spectral density. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method is proposed to formulate the joint distribution model of wind speed and direction. For the probability density function (PDF) of wind speed, a double-parameter Weibull distribution function is utilized, and a von Mises distribution function is applied to represent the PDF of wind direction. The SQP algorithm with multi-start points is used to estimate the parameters in the bivariate model, namely Weibull-von Mises mixture model. One-year wind monitoring data are selected to validate the effectiveness of the proposed modeling method. The optimal model is jointly evaluated by the Bayesian information criterion (BIC) and coefficient of determination, $R^2$. The obtained results indicate that the proposed SQP algorithm-based finite mixture modeling method can effectively establish the bivariate model of wind speed and direction. The established bivariate model of wind speed and direction will facilitate the wind-induced fatigue reliability assessment of long-span bridges.

• Journal of Multimedia Information System
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• v.7 no.1
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• pp.1-10
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• 2020

The image analysis is an important and predominant task for classifying the different parts of the image. The analysis of complex image analysis like histopathological define a crucial factor in oncology due to its ability to help pathologists for interpretation of images and therefore various feature extraction techniques have been evolved from time to time for such analysis. Although deep reinforcement learning is a new and emerging technique but very less effort has been made to compare the deep learning and deep reinforcement learning for image analysis. The paper highlights how both techniques differ in feature extraction from complex images and discusses the potential pros and cons. The use of Convolution Neural Network (CNN) in image segmentation, detection and diagnosis of tumour, feature extraction is important but there are several challenges that need to be overcome before Deep Learning can be applied to digital pathology. The one being is the availability of sufficient training examples for medical image datasets, feature extraction from whole area of the image, ground truth localized annotations, adversarial effects of input representations and extremely large size of the digital pathological slides (in gigabytes).Even though formulating Histopathological Image Analysis (HIA) as Multi Instance Learning (MIL) problem is a remarkable step where histopathological image is divided into high resolution patches to make predictions for the patch and then combining them for overall slide predictions but it suffers from loss of contextual and spatial information. In such cases the deep reinforcement learning techniques can be used to learn feature from the limited data without losing contextual and spatial information.

• The Journal of the Acoustical Society of Korea
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• v.22 no.6
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• pp.434-442
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• 2003

Software internationalization, the process of making software easier to localize for specific languages, has deep implications when applied to speech technology, where the goal of the task lies in the very essence of the particular language. A greatdeal of work and fine-tuning has gone into language processing software based on ASCII or a single language, say English, thus making a port to different languages difficult. The inherent identity of a language manifests itself in its lexicon, where its character set, phoneme set, pronunciation rules are revealed. We propose a decomposition of the lexicon building process, into four discrete and sequential steps. For preprocessing to build a lexical model, we translate from specific language code to unicode. (step 1) Transliterating code points from Unicode. (step 2) Phonetically standardizing rules. (step 3) Implementing grapheme to phoneme rules. (step 4) Implementing phonological processes.

• Journal of Oriental Neuropsychiatry
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• v.7 no.1
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• pp.15-37
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• 1996

This thesis investigates 220 female students ranging from 19 to 26 years old attending universities in the Wonju area to determine the correlations between mental health and the density of serum iron . According to their serum iron levels, they were divided into three groups and analyzed with the GARS Scale Assessment and the THI Test Assessment. The results are as follows;1. On the comprehensive GARS Scale tests, for all groups, task, occupation and levels of university-related frustration(1) marked the highest scores.2. On the comparative assessment of each group's GARS Scale scores, Group A showed higher stress perception in terms of personal relationship(2,3) than any orther groups. 3. On the comprehensive THI test scores coverring all groups, Multiple subjective symptoms(I) marked the highest scores. 4. On the comparative assessment of each group's THI test scores, Group A showed higher Multiple subjective symptoms(I) and Irregularities of life(G) than any orther groups.5. Comparing and analyzing the GARS Scale Assessment and the THI Test, we found that Group A showed high stress perception, which may cause psychosomatic diseases such as 'yu zheng(鬱證;melancholia). With those results, we can see that the contant of serum iron is correlated to stress perception and the condition of mental health. In the future studies using the GARS Scale and the THI Test, it will be necessary to examine more subject groups in terms of multi-aspects and to investigate the standard group in more detail.

• Journal of Internet Computing and Services
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• v.7 no.5
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• pp.135-145
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• 2006

This paper presents a novel approach to real-time recognition of 3D environment and objects for intelligent robots. First. we establish the three fundamental principles that humans use for recognizing and interacting with the environment. These principles have led to the development of an integrated approach to real-time 3D recognition and modeling, as follows: 1) It starts with a rapid but approximate characterization of the geometric configuration of workspace by identifying global plane features. 2) It quickly recognizes known objects in environment and replaces them by their models in database based on 3D registration. 3) It models the geometric details on the fly adaptively to the need of the given task based on a multi-resolution octree representation. SIFT features with their 3D position data, referred to here as stereo-sis SIFT, are used extensively, together with point clouds, for fast extraction of global plane features, for fast recognition of objects, for fast registration of scenes, as well as for overcoming incomplete and noisy nature of point clouds. The experimental results show the feasibility of real-time and behavior-oriented 3D modeling of workspace for robotic manipulative tasks.

• Journal of the Korean Society of Earth Science Education
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• v.5 no.3
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• pp.235-244
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• 2012

This study examined the learning tendency & social characteristics and the science camp participation attitude of the global science talented and the science gifted c. The survey was carried out on children who are taking part in Global Bridge project group and in Science Education Institute for the gifted S National University of education. The results of this study were as follows. First, the science gifted children was more superior to the global science talented, when it comes to task commitment and fluency, creativity, enthusiasm for learning on the learning tendency. Second, the science gifted children have much more sociality than the global science talented in the sense of social characteristics. The global science talented showed lack of interpersonal relationship & confidence for human relationship. Third, both parties were positive in terms of attitude which participating science camp. It was proved that science camp made a positive affect on both groups in several senses such as improving awareness & attitude of science activity and enhancing sociality. As a result, unlike ordinary program for the science gifted children, one for the global science talented in global bridge project is highly demanded that it should be considered the characteristics of the multi-cultural students. Moreover, it might be considered that educational circumstance would be needed, under which it is able to stimulates students' scientific curiosity throughout launching science hands-on program, such as systemized science camp etc.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1963-1978
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• 2015

With the advancement of mobile web environments, identification and analysis of the user behavior play a significant role and remains a challenging task to implement with variations observed in the model. This paper presents an efficient method for mining optimized user behavior prediction model using genetic algorithm on mobile web structure. The framework of optimized user behavior prediction model integrates the temporary and permanent register information and is stored immediately in the form of integrated logs which have higher precision and minimize the time for determining user behavior. Then by applying the temporal characteristics, suitable time interval table is obtained by segmenting the logs. The suitable time interval table that split the huge data logs is obtained using genetic algorithm. Existing cluster based temporal mobile sequential arrangement provide efficiency without bringing down the accuracy but compromise precision during the prediction of user behavior. To efficiently discover the mobile users' behavior, prediction model is associated with region and requested services, a method called optimized user behavior Prediction Model using Genetic Algorithm (PM-GA) on mobile web structure is introduced. This paper also provides a technique called MAA during the increase in the number of models related to the region and requested services are observed. Based on our analysis, we content that PM-GA provides improved performance in terms of precision, number of mobile models generated, execution time and increasing the prediction accuracy. Experiments are conducted with different parameter on real dataset in mobile web environment. Analytical and empirical result offers an efficient and effective mining and prediction of user behavior prediction model on mobile web structure.