• The Journal of Engineering Geology
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• v.33 no.4
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• pp.657-671
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• 2023

As large-scale earthquakes have occurred in Korea and their aftermath continues to be felt, laws and regulations on seismic design have been emphasized, and in Korea, the seismic design standards have been newly revised after the Gyeongju earthquake. In the revised seismic design standards, a stability review for the destruction of the support activity of the breakwater was newly added. Therefore, in this study, we conducted a stability analysis on the seismic reinforcement method for the study site, and checked the ground acceleration of the subgrade and the displacement of the structure over time. As a result of the stability analysis, the safety factor increased by at least 0.5 and up to 1.7. As a result of the time history analysis, the displacement of the superstructure decreased by up to 290 mm and down to 12 mm in both the shallow and deep sections before and after reinforcement, and the ground acceleration decreased by up to 5.33 m/s and down to 0.31 m/s after reinforcement.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.97-102
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• 2023

This study is to provide improvement of tester for IoT system, which has recently become diversified and large-scale and It is about a method to improve the packet processing performance of the tester and securing flexibility in traffic protocol creation and operation. The purpose of this study is to design a OSS DPDK-based high-speed IoT network performance test system, which pre-verifies and measures the performance of data traffic transmission in an increasingly sophisticated high-capacity IoT network system. The basic structure of the high-speed IoT performance tester was designed using a DPDK-based traffic generator, the expected effect was suggested to traffic modeling and packet generation capability when the system was applied through experiments

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.34 no.1
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• pp.16-21
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• 2024

Using electrochromic devices (ECD), smart window films that can change the colors from tinted state into transparent state by applying an external voltage were manufactured. Polyethylene terephthalate (PET) film was used as a substrate instead of conventional glass, and ECD modules having a total thickness of about 50 ㎛ were manufactured by sequentially introducing an ITO/Ag/ITO electrode layer, a WO3/TIC2 organic discoloration layer, and a Nafion fluorine electrolyte layer. Through a series of sputtering, bar coating, and thermal compression processes, a large scale smart window with a horizontal and vertical length of more than 80 mm was manufactured. When DC 3.5 V was applied, the transmittance decreased from 54 % to 24 % and moreover the color change could be confirmed even with the naked eye. Reversible color change capability at low external voltage implies that external sunlight can be selectively blocked which is effective in terms of energy saving.

• Nuclear Engineering and Technology
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• v.56 no.1
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• pp.147-159
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• 2024

Plutonium-238 has always been considered as the one of the promising radioisotopes for space nuclear power supply, which has long half-life, low radiation protection level, high power density, and stable fuel form at high temperatures. The industrial-scale production of ²³⁸Pu mainly depends on irradiating solid ²³⁷NpO₂ target in high flux reactors, however the production process faces problems such as large fission loss and high requirements for product quality control. In this paper, a conceptual design study of producing ²³⁸Pu in a multi-purpose high flux reactor was evaluated and analyzed, which includes a sensitivity analysis on ²³⁸Pu production and a further study on the irradiation scheme. It demonstrated that the target structure and its location in the reactor, as well as the operation scheme has an impact on ²³⁸Pu amount and product quality. Furthermore, the production efficiency could be improved by optimizing target material concentration, target locations in the core and reflector. This work provides technical support for irradiation production of ²³⁸Pu in high flux reactors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1500-1517
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• 2024

With the rapid development of the tourism economy, large-scale construction of tourist attractions to achieve resource utilization and ensure the healthy development of the tourism industry has become a hot topic. However, there are still issues with resource utilization and coordinated management in the economic development of the tourism industry, which in turn affects the coordinated development of the tourism industry economy. Therefore, this study utilizes self-organization theory to explore the structure, organizational mechanism, conditional driving force of evolution, and evolutionary operation mechanism of the tourism system, analyze the current tourism situation in Hunan Province and related regions, and construct a self-organization evolution model of the tourism system. The result shows that the cumulative variance contribution rate of tourism areas in Hunan Province is 78.8%, with Zhangjiajie having the highest industrial management factors and tourism resource levels in the province, with 1.6 and 3.2 respectively. Hunan Province has abundant tourism resources but overall uneven development, with a comprehensive score of -1.03. Therefore, it is necessary to leverage the coordination advantages of various departments and industries to promote sustainable and healthy development of tourism areas. The self-organizing evolution of the tourism system not only discovers the current problems of the tourism industry, but also provides theoretical support and mechanism suggestions for the tourism system.

• Korean Journal of Construction Engineering and Management
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• v.25 no.2
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• pp.36-44
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• 2024

This paper presents the application of Convolutional Neural Networks (CNNs) and Region of Interest (ROI) techniques for concrete crack analysis. Surfaces of concrete structures, such as beams, etc., are exposed to fatigue stress and cyclic loads, typically resulting in the initiation of cracks at a microscopic level on the structure's surface. Early detection enables preventative measures to mitigate potential damage and failures. Conventional manual inspections often yield subpar results, especially for large-scale infrastructure where access is challenging and detecting cracks can be difficult. This paper presents data collection, edge segmentation and ROI techniques application, and analysis of concrete cracks using Convolutional Neural Networks. This paper aims to achieve the following objectives: Firstly, achieving improved accuracy in crack detection using image-based technology compared to traditional manual inspection methods. Secondly, developing an algorithm that utilizes enhanced Sobel edge segmentation and ROI techniques. The algorithm provides automated crack detection capabilities for non-destructive testing.

• Publications of The Korean Astronomical Society
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• v.30 no.2
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• pp.625-628
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• 2015

POLARBEAR is a ground-based experiment located in the Atacama desert of northern Chile. The experiment is designed to measure the Cosmic Microwave Background B-mode polarization at several arcminute resolution. The CMB B-mode polarization on degree angular scales is a unique signature of primordial gravitational waves from cosmic inflation and B-mode signal on sub-degree scales is induced by the gravitational lensing from large-scale structure. Science observations began in early 2012 with an array of 1.274 polarization sensitive antenna-couple Transition Edge Sensor (TES) bolometers at 150 GHz. We published the first CMB-only measurement of the B-mode polarization on sub-degree scales induced by gravitational lensing in December 2013 followed by the first measurement of the B-mode power spectrum on those scales in March 2014. In this proceedings, we review the physics of CMB B-modes and then describe the Polarbear experiment, observations, and recent results.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Journal of Korean Society of Forest Science
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• v.84 no.4
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• pp.525-535
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• 1995

The western-style industrial forest management practice involving large scale clearcutting, silviculture with industrially desirable species, and inadequate consideration on ecosystem preservation, has faced severe criticisms from environmentalists, ecologists and conservationists. With an increasing concern about environmental degradation the general public has also been becoming vocal in demanding ecologically sound alternative forest management. An age-old practice of sustainable ecosystem management variously defined as community forestry, social forestry or homestead forestry, has received increased attention in recent days. This type of traditional, and often not very organized method of natural resource management has been practised in many countries from the prehistoric times. It is believed that with a clear understanding of the functioning of ecosystem and community needs, the existing landuse method can be developed into a more productive one. The nature of community forestry management will vary depending on the scale, geographical location, social/community structure and expectations. This article argues that although the rate of economic growth may be lower with community forestry than with industrial forestry, the former fosters the principle of ecosystem sustainability. Industrial forestry may have an initial high growth rate but often it is associated with unsustainable harvesting leading to ecosystem degradation. A review of the traditional methods of economic analyses shows that they do not take into account the many social and environmental costs associated with forestry. It is argued that a well managed community forestry can maintain the critical balance between economic and ecosystem sustainability. An integrated model of community/homestead forestry development is proposed by coordinating the extension services of the departments of agriculture, forestry and environment.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.