• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3238-3257
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• 2019

Closed-circuit television (CCTV) technology continuously captures and stores video streams. Users are typically required by policy to store all the captured video for a certain period. Accordingly, increasing the number of CCTV operation cycles and photographing positions expands the amount of data to be stored. However, expanding the available storage space for video data incurs increased costs. In recent years, this problem has been addressed with cloud storage solutions, which enable multiple users and devices to access and store data simultaneously. However, because of the large amount of data to be stored, a vast storage space is required. Consequently, cloud storage administrators need a way to store data more efficiently. To save storage space, deduplication technology has been proposed to prevent duplicate storage of the same data. However, because cloud storage is hosted on remote servers, data encryption technology must be applied to address data exposure issues. Although deduplication techniques for encrypted data have been studied, there have been various security vulnerabilities. We attempted to solve this problem by addressing various issues such as poison attacks, property forgery, and ownership management while removing the redundant data and handling the data more securely.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.14 no.2
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• pp.32-40
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• 2018

Since 2000, the frequency of earthquakes beyond the 5.0 magnitude quake has been increasing in the Korean peninsula. For instance, the 5.0-magnitude earthquake in Baekryong-do in 2003 has occurred, and recent earthquake with Gyeongju(2016) and Pohang(2017) measured respectively magnitude of 5.2 and 5.8 on the Richter scale. As results, the public concern and anxiety about earthquakes are increasing, and therefore it is necessarily required for social infrastructure to reinforce seismic design and energy production facilities directly related to the national economy and security. This study represents the analysis of seismic performance evaluation methodology such as Seismic Margin Assessment (SMA), Seismic Probabilistic Risk Assessment (SPRA), High Confidence Low Probability Failure (HCLPF) in nuclear power plants in order to develop optimal seismic performance improvement. Current methodologies to evaluate nuclear power plants are also addressed. Through review of the nuclear structure evaluation past and current trend, it contributes to be the basis for the improvement of evaluation techniques on the next generation of nuclear power plants.

• Journal of the Korean Society of Clothing and Textiles
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• v.43 no.2
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• pp.255-273
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• 2019

Perceptions of beauty change and are shared with others in the media of emotional words. In the modern age, a mix of traditional and modern make-up cultures has changed the standards of beauty. Therefore, an analysis using emotional words (an image that consumers have for certain subjects) and an image scale that intuitively shows them can be an important means for understanding changes in the "beauty of the time". This paper considered changes in typical aesthetic characteristics that women pursued through make-up by analyzing emotional words in white make-up powder advertisement texts from the 1920s through the 1930s. Imported modern technologies changed cosmetic manufacturing techniques and advertising methods to create a momentum that changed women's make-up culture from light to heavy make-up or from white to color make-up. Such changes have led to changes in the perception of beauty and were expressed through sensitive vocabularies such as pure, new, fresh, lofty, lively, healthy, and cheery. Such changes reflect social aspects such as women's aspirations for high status, pursuit of security, or women's roles under the wartime regime to show a change from beauty with an attribute of [+cool] to that of [+soft].

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.564-565
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• 2019

The Internet of Things technology is regarded as the next major technology that will be the driving force behind the fourth industrial revolution. The characteristics of entities for Internet of Things application are changing more actively and actively, requiring a more detailed approach, but existing access control technologies are designed around users, requiring access control techniques that maintain efficiency and security with less system load to apply complex and variable content. Therefore, research on role-based access controls that are appropriate for Internet of Things entities is essential. In this study, the relevant research for the study of access control of the Internet of Things entities and the RBAC and AC methods that can define the properties of the various entities within the Internet of Things.

• International journal of advanced smart convergence
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• v.10 no.3
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• pp.122-130
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• 2021

This study is about an Artificial Intelligence-based fake news identification system and its methods to determine the authenticity of content distributed over the Internet. Among the news we encounter is news that an individual or organization intentionally writes something that is not true to achieve a particular purpose, so-called fake news. In this study, we intend to design a system that uses Artificial Intelligence techniques to identify fake content that exists within the news. The proposed identification model will propose a method of extracting multiple unit factors from the target content. Through this, attempts will be made to classify unit factors into different types. In addition, the design of the preprocessing process will be carried out to parse only the necessary information by analyzing the unit factor. Based on these results, we will design the part where the unit fact is analyzed using the deep learning prediction model as a predetermined unit. The model will also include a design for a database that determines the degree of fake news in the target content and stores the information in the identified unit factor through the analyzed unit factor.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.96-98
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• 2014

Wireless Sensor Network(WSN) is a wireless real-time information(Acquired from the sensor nodes that have the computing power and wireless communication capabilities.) collected, and to take advantage of processing techniques. Currently it is very diverse, such as environmental monitoring, health care, security, smart home, smart grid applications is that. Thus it is required in the wireless sensor network, the algorithm for the efficient use of the limited energy capacity. Suggested by the algorithm for selecting a cluster head node for a hybrid type and clustered, by comparing the amount of energy remaining and a connection between the nodes In this paper, we aim to increase efficiency and accuracy of the wireless sensor network.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.3
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• pp.208-214
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• 2021

Recently, much of the intelligent security scenario and criminal investigation demands for matching photo and non-photo. Existing face recognition system can not sufficiently guarantee these needs. In this paper, we propose an algorithm to improve the performance of heterogeneous face recognition systems by reducing the different modality between sketches and photos of the same person. The proposed algorithm extracts each image's texture features through texture descriptors (gray level co-occurrence matrix, multiscale local binary pattern), and based on this, generates a transformation matrix through eigenfeature regularization and extraction techniques. The score value calculated between the vectors generated in this way finally recognizes the identity of the sketch image through the score normalization methods.

• Food Science and Industry
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• v.49 no.2
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• pp.51-55
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• 2016

The needs to develop high value-added foods are expanding due to the trends of food market, such as importance of food security by enlarged international free trades and expanded market size for processed foods. However, our home country exhibits limit transport to development of new products and high-value added area compared to other advanced countries. For this reason Ministry of Agriculture, Food and Rural Affairs and Institute of Planning and Evaluation for Technology have launched "High Value-Added Food Technology Development Program" for improvement of value of food resources for practical use since 2013. The program contains research to increase the values of agricultural and animal resources and the construction of database. Main research topics are studies in pretreatment techniques to improve the values of agricultural and animal resources, improvement of acceptability, and processing qualities that would be engrafted on materializaton and commercialization technologies. In addition the construction of a consolidated database regarding the research achievements is included in the program. Currently, studies in processing qualities for about 30 food materials re under progress, being suitable for materialization, for examples drying, fermentation, grinding, heat-treatment, and so on. The research results are provided in public through the consolidated database website after reconstruction in the form of united database format. To date the database containing the about 300 of research contents for process qualities has been constructed.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.387-397
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• 2021

The object of this article is to assess the factors affecting the behavioral intention of young retail customers to use digital banking services in Vietnam. In this article, multivariate data analysis techniques including Cronbach's Alpha, Exploratory factor analysis (EFA), Confirmatory factor analysis (CFA), Structure equation model (SEM), and Bootstrapping are used to analyze the data collected from 525 young respondents under the age of 35 who are using or having opportunities to experience digital banking services. The people taking part in the survey were mainly University students with incomes of most of them under VND 5 million. The result from the analysis illustrates that (1) perceived ease of use positively affects intention to use, (2) social influence positively impacts intention to use, and (3) customer support has a positive impact on the intention of young people to use digital banking services. While technology self-efficacy, convenience, and perceived security were found to have an impact on intention to use services in former studies, the influences of these factors on intention to use digital banking services are found insignificant in this research. From these results, the author provides implications for commercial banks to increase the intention to use digital banking of young people in Vietnam.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.169-176
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• 2021

Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation. We first generate gridded trajectory images by mapping the raw vessel trajectories into two dimensional matrix. Based on the gridded image input, we test the proposed model along with the other deep autoencoder-based models for the abnormal trajectory data generated through rotation and speed variation from normal trajectories. We show that the proposed model improves detection accuracy for the generated abnormal trajectories compared to the other models.