• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.89-99
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• 2018

Spatial downscaling is often applied to coarse scale satellite products with high temporal resolution for environmental monitoring at a finer scale. An area-to-point regression kriging (ATPRK) algorithm is regarded as effective in that it combines regression modeling and residual correction with area-to-point kriging. However, an open source tool or package for ATPRK has not yet been developed. This paper describes the development and code organization of an R-based spatial downscaling tool, named R4ATPRK, for the implementation of ATPRK. R4ATPRK was developed using the R language and several R packages. A look-up table search and batch processing for computation of ATP kriging weights are employed to improve computational efficiency. An experiment on spatial downscaling of coarse scale land surface temperature products demonstrated that this tool could generate downscaling results in which overall variations in input coarse scale data were preserved and local details were also well captured. If computational efficiency can be further improved, and the tool is extended to include certain advanced procedures, R4ATPRK would be an effective tool for spatial downscaling of coarse scale satellite products.

• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.847-859
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• 2019

Purpose: The companies must be located in the area, scale up, create jobs, and return to the local economy. This paper attempted to analyze empirically the relationship between regional context and entrepreneurial orientation(EO) in the region of Korea. Methods: This paper analyzed survey data and regional statistics. We measured EO by region and then examined which regional context affect EO. Regional contexts were measured by population, economic size, budget size, firm size, innovation capacity, and education level. EO was measured by innovativeness, risk taking, proactiveness, autonomy, competitive aggressiveness, and need for achievement. Results: EO was high in the region where the budget size per thousand population, the number of manufacturers per thousand population, the number of new corporations per thousand population, the number of R&D personnel per thousand population, and the number of students of higher education institutions per thousand population were high. Conclusion: The implications of this paper are that regional context affect EO, and there are differences in budget scale, firm size, innovation capacity, and education level. In regions with many investment resources for innovation and startups and manufacturers, the number of R&D personnel and students of higher education institutions (future R&D personnel), in particular, determines EO.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4731-4742
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• 2022

RBMK fuel assemblies differ from other LWR FA due to a specific arrangement of the fuel rods, the low enrichment, and the used burnable absorber - erbium. Therefore, there is a challenge to adapt modeling tools, developed for other LWR types, to solve RBMK problems. A set of 10 different depletion simulation schemes were tested to estimate the impact on reactivity and spent fuel composition of possible SCALE code options for the neutron transport modelling and the use of different nuclear data libraries. The simulations were performed using cross-section libraries based on both, VII.0 and VII.1, versions of ENDF/B nuclear data, and assuming continuous energy and multigroup simulation modes, standard and user-defined Dancoff factor values, and employing deterministic and Monte Carlo methods. The criticality analysis with burn-up credit was performed for the SFP loaded with RBMK-1500 FA. Spent fuel compositions were taken from each of 10 performed depletion simulations. The criticality of SFP is found to be overestimated by up to 0.08% in simulation cases using user-defined Dancoff factors comparing the results obtained using the continuous energy library (VII.1 version of ENDF/B nuclear data). It was shown that such discrepancy is determined by the higher U-235 and Pu-239 isotopes concentrations calculated.

• KSBB Journal
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• v.20 no.3
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• pp.149-163
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• 2005

Recently preparative liquid chromatography (PLC) has been used more frequently to separate drugs and natural substances. This modern separation methodologies require reliable tools that perform on a high level in terms of efficiency and reproducibility. However, large-scale PLC easily tends to reduce the yield and purity of the product. To promote the separation efficiency of PLC, we need to properly understand the controlling effects of the process, which may enable to predict the process and to improve the design and operation of PLC. Progress in computer technology allows the use of sophisticated models, provided their parameters can be measured. Some hardwares as well as softwares for PLC were already commercially available. In this work, the separation characteristic of PLC will be reviewed and compared on both the software and the hardware.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2407-2409
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• 2005

As the cumulative amount of spent fuel increases, the reliable and effective management of the spent fuel has become a world-wide mission. For this mission, KAERI is developing the Advanced Spent Fuel Conditioning Process (ACP) as a pre-disposal treatment process for spent fuel. Conventional approach to the development of the process and the remote operation technology is to fabricate the process equipment on the same scale as the real environment and demonstrate the remote handling operation using simulated fuel called a mock-up test. But this mock-up test is expensive and time consuming, since the design may need to be modified and the equipment fabricated again to account for the problems found during a testing. To deal with this problem, we developed a digital mockup for the ACP. Also, for an effective utilization of the digital mockup, we developed user interface modules such as the data acquisition and display module and the external input device interface module. The result of this implementation shows that a continuous motion of the manipulator using the external device interface can be represented easily and the information display screens responded well to the simulation situation.

• Asian Journal of Innovation and Policy
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• v.7 no.3
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• pp.564-580
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• 2018

The creation of academic start-up firms is an important and practical issue in the management of technology in Japan. The present study designs a model for creating academic start-up firms that fits into the social context. It focuses on the case of FIRST Program, an initiative that consists of 30 projects in innovative arenas, analyses the presence of large-scale public funding, and investigates the role of venture capitalists as support personnel in each project. As a result, the presence and significance of 'long-term escort' by an 'entrepreneurial venture capitalist (EP-VCist)' were confirmed as common features across the cases observed. EP-VCist refers to a person who can maintain and fulfil dual roles at a university and a venture capital firm, and who can take the lead throughout the venturing process as a risk taker. 'Long-term escort' is a form of support that reduces risks in the venturing process by supporting university researchers in the pre-entrepreneurial stage and by exerting a robust bridging role between a university and an industry.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4795-4815
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• 2020

Drone detection can be considered as a specific sort of small object detection, which has always been a challenge because of its small size and few features. For improving the detection rate of drones, we design a Deeper SSD network, which uses large-scale input image and deeper convolutional network to obtain more features that benefit small object classification. At the same time, in order to improve object classification performance, we implemented the up-sampling modules to increase the number of features for the low-level feature map. In addition, in order to improve object location performance, we adopted the down-sampling modules so that the context information can be used by the high-level feature map directly. Our proposed Deeper SSD and its variants are successfully applied to the self-designed drone datasets. Our experiments demonstrate the effectiveness of the Deeper SSD and its variants, which are useful to small drone's detection and recognition. These proposed methods can also detect small and large objects simultaneously.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.378-380
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• 2005

Remote sensing of atmospheric aerosol using MODIS satellite data has been proven to be very useful in global/regional scale aerosol monitoring. Due to their large spatial resolution of $10km^2$ MODIS aerosol optical thickness (AOT) data have limitations for local/urban scale aerosol monitoring applications. Modified Bremen Aerosol Retrieval (BAER) algorithm developed by von Hoyningen-Huene et al. (2003) and Lee et al. (2005) has been applied in this study to retrieve AOT in fe resolutions of $500m^2$ over Korea. Look up tables (LUTs) were constructed from the aerosol properties based on sun-photometer observation and radiation transfer model calculations. It was found that relative error between the satellite products and the ground observations was within about $15\%$. Resulting AOT products were correlated with surface PMIO concentration data. There was good correlation between MODIS AOT and surface PM concentration under certain atmospheric conditions, which supports the feasibility of using the high-resolution MODIS AOT for local and urban scale air quality monitoring

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1744-1760
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• 2021

Since correlation filter appeared in the field of object tracking, it plays an increasingly vital role due to its excellent performance. Although many sophisticated trackers have been successfully applied to track the object accurately, very few of them attaches importance to the scale and rotation estimation. In order to address the above limitation, we propose a novel method combined with Fourier-Mellin transform and confidence evaluation strategy for robust object tracking. In the first place, we construct a correlation filter to locate the target object precisely. Then, a log-polar technique is used in the Fourier-Mellin transform to cope with the rotation and scale changes. In order to achieve subpixel accuracy, we come up with an efficient surface fitting mechanism to obtain the optimal calculation result. In addition, we introduce a confidence evaluation strategy modeled on the output response, which can decrease the impact of image noise and perform as a criterion to evaluate the target model stability. Experimental experiments on OTB100 demonstrate that the proposed algorithm achieves superior capability in success plots and precision plots of OPE, which is 10.8% points and 8.6% points than those of KCF. Besides, our method performs favorably against the others in terms of SRE and TRE validation schemes, which shows the superiority of our proposed algorithm in scale and rotation evaluation.

• Journal of KIISE
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• v.41 no.9
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• pp.686-698
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• 2014

Recently, large-scale streaming sensor data have emerged due to explosive supply of smart phones, diffusion of IoT and Cloud computing technology, and generalization of IoT devices. Also, researches on combination of semantic web technology are being actively pushed forward by increasing of requirements for creating new value of data through data sharing and mash-up in large-scale environments. However, we are faced with big issues due to large-scale and streaming data in the inference field for creating a new knowledge. For this reason, we propose the RDFS rule based parallel reasoning scheme to service by processing large-scale streaming sensor data with the semantic web technology. In the proposed scheme, we run in parallel each job of Rete network algorithm, the existing rule inference algorithm and sharing data using the HBase, a hadoop database, as a public storage. To achieve this, we implement our system and evaluate performance through the AWS data of the weather center as large-scale streaming sensor data.