• Journal of the Korean Solar Energy Society
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• v.32 no.2
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• pp.35-41
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• 2012

Knowledge of the solar radiation components and classified wavelength data are essential for modeling many solar photovoltaic systems. This is particularly the case for applications that concentrate the incident energy to attain high photo-dynamic efficiency achievable only at the higher intensities. In order to estimate the performance of concentrating PV systems, it is necessary to know the intensity of the beam radiation, as only this components can be concentrated, and The new PV cell can generate electricity from ultraviolet and infrared light as well as visible light. The Korea Institute of Energy Research(KIER) has began collecting solar radiation components data since January, 1988, and solar radiation classified wavelength data since November, 2008. KIER's solar radiation components and classified wavelength data will be extensively used by concentrating PV system users or designers as well as by research institutes. It is essential to utilize the solar radiation data as application and development of solar energy system increase. Consider able efforts have been made constructing a standard data base system from measure data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.6
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• pp.95-100
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• 2009

Nowadays there are many studies and there's huge development about USN/RFID which have great developmental potential to many kinds of applications. More and more real time application apply USN/RFID technology to identify data collect and locate objects. Wide deployment of USN/RFID will generate an unprecedented volume of primitive data in a short time. Duplication and redundancy of primitive data will affect real time performance of application. Thus, security applications must filter primitive data and correlate them for complex pattern detection and transform them to events that provide meaningful, actionable information to end application. In this paper, we design a ECA Rule system for security monitoring of exhibition. This system will process USN/RFID primitive data and event and perform data transformation. It's had applied each now in exhibition hall through this study and efficient data transmission and management forecast that is possible.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3396-3411
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• 2015

Sensors used in Wireless Sensor Networks (WSN) have mostly limited capacity which affects the performance of their applications. One of the data-gathering methods is to use mobile sinks to visit these sensors so that they can save their limited battery energies from forwarding data packages to static sinks. The main disadvantage of employing mobile sinks is the delay of data collection due to relative low speed of mobile sinks. Since sensors have very limited memory capacities, whenever a mobile sink is too late to visit a sensor, that sensor's memory would be full, which is called a 'memory overflow', and thus, needs to be purged, which causes loss of collected data. In this work, a method is proposed to generate mobile sink tours, such that the number of overflows and the amount of lost data are minimized. Moreover, the proposed method does not need either the sensor locations or sensor memory status in advance. Hence, the overhead stemmed from the information exchange of these requirements are avoided. The proposed method is compared with a previously published heuristic. The simulation experiment results show the success of the proposed method over the rival heuristic with respect to the considered metrics under various parameters.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_1
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• pp.575-583
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• 2007

Surface segmentation aims to represent the terrain as a set of bounded and analytically defined surface patches. Many previous segmentation methods have been developed to extract planar patches from LIDAR data for building extraction. However, most of them were not fully satisfactory for more general applications in terms of the degree of automation and the quality of the segmentation results. This is mainly caused from the limited information derived from LIDAR data. The purpose of this study is thus to develop an automatic method to perform surface segmentation by combining not only LIDAR data but also images. A region-based method is proposed to generate a set of planar patches by grouping LIDAR points. The grouping criteria are based on both the coordinates of the points and the corresponding intensity values computed from the images. This method has been applied to urban data and the segmentation results are compared with the reference data acquired by manual segmentation. 76% of the test area is correctly segmented. Under-segmentation is rarely founded but over-segmentation still exists. If the over-segmentation is mitigated by merging adjacent patches with similar properties as a post-process, the proposed segmentation method can be effectively utilized for a reliable intermediate process toward automatic extraction of 3D model of the real world.

• The KIPS Transactions:PartD
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• v.14D no.2
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• pp.191-202
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• 2007

In general, test data generation techniques require the specification of an entire program path for automated test data generation. This paper presents a new way for generating test data automatically een without specifying a program path completely. For the ends, this paper presents a technique for transforming a program under test into Alloy which is the first order relational logic and then producing test data via Alloy analyzer. The proposed method reduces the burden of selecting a program path and also makes it easy to generate test data according to various test adequacy criteria. This paper illustrates the proposed method through simple, but illustrative examples.

• Spatial Information Research
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• v.19 no.1
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• pp.127-133
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• 2011

Recently, with the advances in sensor techniques and net work computing, Ubiquitous Sensor Network (USN) has been received a lot of attentions from various communities. The sensor nodes distributed in the sensor network tend to continuously generate a large amount of data, which is called stream data. Sensor stream data arrives in an online manner so that it is characterized as high-speed, real-time and unbounded and it requires fast data processing to get the up-to-date results. The data stream has many application domains such as traffic analysis, physical distribution, U-healthcare and so on. Therefore, there is an overwhelming need of a USN middleware for processing such online stream data to provide corresponding services to diverse applications. In this paper, we propose a novel USN middleware which can provide users both context-aware service and meaningful sequential patterns. Our proposed USN middleware is mainly focused on location based applications which use stream location data. We also show the implementation of our proposed USN middleware. By using the proposed USN middleware, we can save the developing cost of providing context aware services and stream sequential patterns mainly in location based applications.

• Journal of the Korean Society of Propulsion Engineers
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• v.13 no.6
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• pp.1-7
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• 2009

The gas turbine engine performance is greatly relied on its component performance characteristics. Generally, acquisition of component maps is not easy for engine purchasers because it is an expensive intellectual property of gas turbine engine supplier. In the previous work, the maps were inversely generated from engine performance deck data, but this method is limited to obtain the realistic maps due to calculated performance deck data. Therefore this work proposes newly to generate more realistic compressor map from experimental performance test data. And then a realistic compressor map can be generated form those processed data using the proposed extended scaling method at each rotational speed. Evaluation can be made through comparison between performance analysis results using the performance simulation program including the generated compressor map and on-condition monitoring performance data.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.801-803
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• 2003

In this paper, we present a method for generating and providing spatially encoded video data that can be effectively used by GIS applications. We collect the video data by a mobile mapping system called 4S-Van that is equipped by GPS, INS, CCD camera, and DVR system. The information about spatial object appearing in video, such as occupied region in each frame, attribute value, and geo-coordinate, are generated and encoded. We suggest methods that can generate such data for each frame in semi-automatic manner. We adopt standard MPEG-7 metadata format for representation of the spatially encoded video data to be generally used by GIS application. The spatial and attribute information encoded to each video frame can make visual browsing between map and video possible. The generated video data can be provided and applied to various GIS applications where location and visual data are both important.

• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3563-3579
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• 2021

Measured decay heat data of light water reactor (LWR) spent nuclear fuel (SNF) assemblies are adopted to train machine learning (ML) models. The measured data is available for fuel assemblies irradiated in commercial reactors operated in the United States and Sweden. The data comes from calorimetric measurements of discharged pressurized water reactor (PWR) and boiling water reactor (BWR) fuel assemblies. 91 and 171 measurements of PWR and BWR assembly decay heat data are used, respectively. Due to the small size of the measurement dataset, we propose: (i) to use the method of multiple runs (ii) to generate and use synthetic data, as large dataset which has similar statistical characteristics as the original dataset. Three ML models are developed based on Gaussian process (GP), support vector machines (SVM) and neural networks (NN), with four inputs including the fuel assembly averaged enrichment, assembly averaged burnup, initial heavy metal mass, and cooling time after discharge. The outcomes of this work are (i) development of ML models which predict LWR fuel assembly decay heat from the four inputs (ii) generation and application of synthetic data which improves the performance of the ML models (iii) uncertainty analysis of the ML models and their predictions.

• Journal of IKEEE
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• v.27 no.1
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• pp.38-44
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• 2023

Performance of a STAP(space-time adaptive processing) algorithm highly depends on how closely the estimated covariance matrix(CM) resembles the actual CM by the interference in CUT(cell under test). A STAP has 2 dimensional data structure determined by the number of array elements and the number of transmitting pulses and both numbers are generally not small. Thus, to meet the degree of freedom(DOF) of the CM, a huge amount of training data is required. This paper presents an algorithm to generate virtual training data from small received data, via converting them into the data in spatial frequency-Doppler plane. We theoretically derive where the clutter exist in the plane and present the procedure to implement the proposed algorithm. Finally, with the simulated scenario of small received data, we show the proposed algorithm can improve STAP performance.