• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.189-200
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• 2022

Ground subsidence on urban roads is a social issue that can lead to human and property damages. Therefore, it is crucial to detect underground cavities in advance and repair them. Underground cavity detection is mainly performed using ground penetrating radar (GPR) surveys. This process is time-consuming, as a massive amount of GPR data needs to be interpreted, and the results vary depending on the skills and subjectivity of experts. To address these problems, researchers have studied automation and quantification techniques for GPR data interpretation, and recent studies have focused on deep learning-based interpretation techniques. In this study, we described a hyperbolic event detection process based on deep learning for GPR data interpretation. To demonstrate this process, we implemented a series of algorithms introduced in the preexisting research step by step. First, a deep learning-based YOLOv3 object detection model was applied to automatically detect hyperbolic signals. Subsequently, only hyperbolic signals were extracted using the column-connection clustering (C3) algorithm. Finally, the horizontal locations of the underground cavities were determined using regression analysis. The hyperbolic event detection using the YOLOv3 object detection technique achieved 84% precision and a recall score of 92% based on AP50. The predicted horizontal locations of the four underground cavities were approximately 0.12 ~ 0.36 m away from their actual locations. Thus, we confirmed that the existing deep learning-based interpretation technique is reliable with regard to detecting the hyperbolic patterns indicating underground cavities.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.71-78
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• 2020

In the case of power generation using renewable energy, power production may not be smooth due to the influence of the weather. The energy storage system (ESS) is used to increase the efficiency of solar and wind power generation. ESS has been continuously fired due to a lack of battery protection systems, operation management, and control system, or careless installation, leading to very big casualties and economic losses. ESS stability and battery protection system operation management technology is indispensable. In this paper, we present a battery level calculation algorithm and a failure prediction algorithm for ESS optimization and stable operation. The proposed algorithm calculates the correct battery level by accumulating the current amount in real-time when the battery is charged and discharged, and calculates the battery failure by using the voltage imbalance between battery cells. The proposed algorithms can predict the exact battery level and failure required to operate the ESS optimally. Therefore, accurate status information on ESS battery can be measured and reliably monitored to prevent large accidents.

• Korean Business Review
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• v.12
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• pp.255-274
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• 1999

Electronic Commerce (EC) is a relatively new and has been attracting a considerable amount of attention. Even though it is still in an introductory stage, growth rates are impressively high and its economic importance will continue to grow. Electronic Commerce is born to be global because its connectivity using the Internet is universal. As Electronic Commerce accelerates the globalization, it will also increase both the interdependence of national economies in different countries and the need for international cooperation and coordination. Electronic Commerce is really a global challenge that requires global solutions. For this reason, Electronic Commerce becomes an important agenda in major international cooperative organizations including APEC, OECD, and G7 (G8). However, current international discussions on Global Electronic Commerce are initiated mostly by the United States and European Union (EU). The objective of this paper is to raise awareness creation activities in which the international cooperation is needed for promoting the global electronic marketplace in Asia. For this purpose, this paper reviews the approaches of U.S. and European countries (or European Union), and suggests an development framework on the basis of the reviews. The framework is used to analyze current issues around Electronic Commerce, and identify some fields which require coherent work among researchers in different countries.

• Journal of the Korean earth science society
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• v.39 no.1
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• pp.89-102
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• 2018

We analyzed the meteorological and radiation characteristics of Seoul metropolitan area using data from energy flux towers that were installed and operated by the Weather Information Service Engine (WISE). The meteorological and radiation variables included temperature, pressure, wind speed, wind direction, relative humidity, surface temperature, rainfall amount, upward and downward solar radiation, upward and downward longwave radiation, albedo and emissivity from 14 energy flux stations located in the Seoul metropolitan area from July 2016 to July 2017. According to the monthly data during the period, the albedo is low and emissivity is high at the Jungnang station in the urban and opposite at Bucheon station in the suburban area. For a station in natural state, the albedo was higher than urban stations because solar radiation reflects effectively. Relatively high temperatures were shown at stations located in urban area with low albedo and high emissivity, in general. However, temperature was high at Gajwa and Ttukseom stations, the albedo was relatively high due to the station environment surrounded by glass wall buildings and the Han river. In the station located in suburban area, both emissivity and temperature were low. Among these stations, Bucheon station had the highest emissivity values because the surface temperature was relatively lower than that of the suburban area. As a result, the albedo decreased and the emissivity increased at stations in urban areas. Additionally, Seoul metropolitan area had less than $100Wm^{-2}$ of net radiation, which implied that radiation energy could be absorbed in the atmosphere.

• KIISE Transactions on Computing Practices
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• v.22 no.3
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• pp.133-138
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• 2016

Motion detection algorithms based on difference image are classified into background subtraction and previous frame subtraction. 1) Background subtraction is a convenient and effective method for detecting foreground objects in a stationary background. However in real world scenarios, especially outdoors, this restriction, (i.e., stationary background) often turns out to be impractical since the background may not be stable. 2) Previous frame subtraction is a simple technique for detecting motion in an image. The difference between two frames depends upon the amount of motion that occurs from one frame to the next. Both these straightforward methods fail when the object moves very "slightly and slowly". In order to efficiently deal with the problem, in this paper we present an algorithm for motion detection that incorporates "reflected light area" and "difference image". This reflected light area is generated during the frame production process. It processes multiplex difference image and AND-arithmetic of bitwise. This process incorporates the accuracy of background subtraction and environmental adaptability of previous frame subtraction and reduces noise generation. Also, the performance of the proposed method is demonstrated by the performance assessment of each method using Gait database sample of CASIA.

• The Journal of the Korea Contents Association
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• v.10 no.8
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• pp.95-103
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• 2010

Raw files record luminance values corresponds to each pixel of a digital camera sensor. In digital imaging, controlling exposure to capture the first highlight stop is important on linear-distribution of raw file characteristic. This study sought to verify the efficiency of ETTR method and found the optimum over-exposure amount to maintain the first highlight stop to be the largest number of levels. This was achieved by over-exposing a scene with a raw file and converting it to under-exposure in a raw file converting software. Our paper verified the efficiency of ETTR by controlling the exposure range and ISOs. Throughout the results, if exposure increases gradually 6 steps, dynamic range is also increased. And it shows that the optimized exposure value is around + $1\frac{2}{3}$ stop over compared to the normal exposure with the high ISOs simultaneously. We compared visual noise value at $1\frac{2}{3}$ stop to the normal exposure visual noise. Based on the normal exposure's visual noise, we can confirm that visual noise decrement is increased by increasing ISOs. In this experimental result, we confirm that overexposure about + $1\frac{2}{3}$ stop is the optimum value to make the widest dynamic range and lower visual noise in high ISOs. Based on the study results, we can provide the effective ETTR information to consumers and manufacturers. This method will contribute to the optimum image performance in maximizing dynamic range and minimizing noise in a digital imaging.

• Journal of KIISE:Software and Applications
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• v.35 no.3
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• pp.197-206
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• 2008

In the mobile Internet environment, frequent communications between a mobile device and a content server are required for searching or downloading learning materials. In this paper, we propose an efficient prefetching technique to reduce the network cost and to improve the communication efficiency in the mobile dictionary. Our prefetching-based approach can be explained as follows. First, we propose an overall framework for the prefetching-based mobile dictionary. Second, we present a systematic way of determining the amount of prefetching data for each of packet-based and flat-rate billing cases. Third, by focusing on the English-Korean mobile dictionary for middle or high school students, we propose an intuitive method of determining the words to be prefetched in advance. Fourth, based on these determination methods, we propose an efficient prefetching algorithm. Fifth, through experiments, we show the superiority of our prefetching-based method. From this approach, we can summarize major contributions as follows. First, to our best knowledge, this is the first attempt to exploit prefetching techniques in mobile applications. Second, we propose a systematic way of applying prefetching techniques to a mobile dictionary. Third, using prefetching techniques we improve the overall performance of a network-based mobile dictionary. Experimental results show that, compared with the traditional on-demand approach, our prefetching based approach improves the average performance by $9.8%{\sim}33.2%$. These results indicate that our framework can be widely used not only in the mobile dictionary but also in other mobile Internet applications that require the prefetching technique.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.6
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• pp.367-378
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• 2006

A mobile phone has became as a payment tool in e-commerce and on-line banking areas. This trend of a payment system using various types of mobile devices is rapidly growing, especially in the Internet transaction and small-money payment. Hence, there will be a need to define its standard for secure and safe payment technology. In this thesis, we consider the service types of the current mobile payments and the authentication method, investigate the disadvantages, problems and their solutions for smart and secure payment. Also, we propose a novel authentication method which is easily adopted without modification and addition of the existed mobile hardware platform. Also, we present a simple implementation as a demonstration version. Based on virtual machine (VM) approach, the proposed model is to use a pseudo-random number which is confirmed by the VM in a user's mobile phone and then is sent to the authentication site. This is more secure and safe rather than use of a random number received by the previous SMS. For this payment operation, a user should register the serial number at the first step after downloading the VM software, by which can prevent the illegal payment use by a mobile copy-phone. Compared with the previous SMS approach, the proposed method can reduce the amount of packet size to 30% as well as the time. Therefore, the VM-based method is superior to the previous approaches in the viewpoint of security, packet size and transaction time.

• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.1-8
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• 2020

The need for storage, management, and retrieval techniques for alternative data has emerged as technologies based on data generated from business activities conducted by enterprises have emerged as the key to business success in recent years. Existing big data platform systems must load a large amount of data generated in real time without delay to process unstructured data, which is an alternative data, and efficiently manage storage space by utilizing a deduplication system of different storages when redundant data occurs. In this paper, we propose a multi-layer distributed data deduplication process system using the similarity of the Cuckoo hashing filter technique considering the characteristics of big data. Similarity between virtual machines is applied as Cuckoo hash, individual storage nodes can improve performance with deduplication efficiency, and multi-layer Cuckoo filter is applied to reduce processing time. Experimental results show that the proposed method shortens the processing time by 8.9% and increases the deduplication rate by 10.3%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.163-170
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• 2020

With the development of information and communication technologies, the growing volume of data is increasing exponentially, raising interest in big data. As technologies related to big data have developed, big data is being collected, stored, processed, analyzed, and utilized in many fields. Big data analytics in the health care sector, in particular, is receiving much attention because they can also have a huge social and economic impact. It is predicted that it will be able to use Big Data technology to analyze patients' diagnostic data and reduce the amount of money that is spent on simple hospital care. Therefore, in this thesis, patient data is analyzed to present to patients who are unable to go to the hospital or caregivers who do not have medical expertise with close care guidelines. First, the collected patient data is stored in HDFS and the data is processed and classified using R, a big data processing and analysis tool, in the Hadoop environment. Visualize to a web server using R Shiny, which is used to implement various functions of R on the web.