• Journal of the Korean Society for Marine Environment & Energy
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• v.5 no.1
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• pp.30-37
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• 2002

Recently, there have been frequent environmental threats associated with the red tide. The discharge of bilge water and waste lubricating oil from fishing vessels is known to be the major cause of such problems. In oder to treat those wastes, post processing units at harbours are being considered, and an accurate prediction of the amount of such wastes is required. In this paper we have proposed a method to estimate the amount of bilge water and waste lubricating oil from fishing vessels. We identified the key parameters affecting the quantity of oily bilge water and waste lubricating oil, and proposed a prediction model. We applied the present model to estimate the quantity of oily bilge water and waste lubricating oil in the sample fishing port.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.3
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• pp.661-669
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• 2004

As a large number of distributed systems becoming more popular, interoperability, portability and security are becoming major concerns of modern computing. CORBA and object-oriented database which provide transparency of network and database are increasingly being used as the basis for distributed system to solve these problems. The two methods can help accomplish assurance of security by using a method-based access control technique or an attribute-based access control technique. These methods also enhance the unavailability or inefficiency caused by the delay of access process and bottleneck of the network due to the complex instance-based access control. We propose a security model on the type information based access control system that can enhance both security and availability by separating the functions delivered from CORBA and object-oriented databases. We apply the access control model specifically to enhancement of security system and also perform a test to verify the security and availability of our model.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.485-492
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• 2016

A role of the binarization is very important in separating the foreground and the background in the field of the computer vision. In this study, an adaptive fuzzy binarization is proposed. An ${\alpha}$-cut control ratio is obtained by the distribution of grey level of pixels in a sliding window, and binarization is performed using the value. To obtain the ${\alpha}$-cut, existing thresholding methods which execution speed is fast are used. The threshold values are set as the center of each membership function and the fuzzy intervals of the functions are specified with the distribution of grey level of the pixel. Then ${\alpha}$-control ratio is calculated using the specified function and binarization is performed according to the membership degree of the pixels. The experimental results show the proposed method can segment the foreground and the background well than existing binarization methods and decrease loss of the foreground.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.195-200
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• 2019

The purpose of this paper is to explore security guard's status and roles in society through media reports. Research method is to anlyze security Guard's 'Keyword Trend' and 'Keyword Frequency Analysis' by 'Big Kind' which enables 'News Big Data' analysis. The result came out by the analysis in sectional private security guard's history of settling down, growing up (quantity), and growing up (quality) by separating generations is that there are lots of attention and exposure from media about crime, security guard job, minimum wage, and 'Gabjil', but the images of security guard are recognized as victim of crime and 'Gabjil', and working in poor environment with minimum waged and ambiguous job, instead of people preventing crimes. In the future, stabilizing security guard's social status and work responsibility, and developing job professionalism are necessary to improve the images of security guard.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.127-136
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• 2019

In the social media, information that users share with service providers can be divided into personal identification information such as gender and age and privacy-related information such as photos and comments. However, previous works on IS and service management have shed relatively little light on the difference of information-sharing decisions depending on the type of information. This study examines information-sharing decisions by separating the two types of information. A structural equation modeling method is used to test the research model based on a sample of 350 Facebook in South Korea. Analysis results show that self-expression, trust, and perceived security had a significant positive effect on both user's intentions to give personal identification information and their intentions to give privacy-related information. However, privacy concerns negatively affected their intentions to give personal identification and intention to give privacy-related information. The analysis results confirm that there was no difference between decision-making processes about sharing personal identification information and ones about sharing privacy-related information.

• Journal of the Korean Society for Railway
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• v.11 no.5
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• pp.513-518
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• 2008

Currently, urban areas face congestion problems that diminish personal mobility and freight-transport productivity. An increasing number of experts suggest that separating freight traffic from passenger traffic makes sense in terms of economics, the environment, and safety. Some experts suggest that tube transportation is the solution. The objective of this paper is to evaluate the potential benefits and limitations of tube transportation system as a viable mode of transport that can alleviate congestion on roadways. Specifically, this research employs theoretical and practical method identifying, evaluating, and selecting appropriate freight pipeline systems; evaluating the technical and economic feasibility of tube transportation; and estimating environmental, energy, and safety benefits.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.644-650
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• 2021

This study characterized the reforming of methane with carbon dioxide, which is a major cause of global warming. The methane decomposition reaction with carbon dioxide was carried out using transition metal catalysts. The reactivity of tin was lower than that of a transition metal, such as nickel and iron. Most of the decomposition reaction occurred in the solid state. The melting point of tin is 505.03 K. Tin reacts in a liquid phase at the reaction temperature and has the advantage of separating carbon produced by the decomposition of methane from the liquid tin catalyst. Therefore, deactivation due to the deposition of carbon in the liquid tin can be prevented. Methane decomposition with carbon dioxide produced carbon monoxide and hydrogen. Ni was used to promote the catalyst performance and enhance the activity of the catalyst and lifetime. In this study, catalysts were synthesized using the excess wet impregnation method. The effect of the reaction temperature, space velocity was measured to calculate the activity of catalysts, such as the activation energy and regeneration of catalysts. The carbon-deposited tin catalyst regeneration temperature was 1023 K. The reactivity was improved using a nickel co-catalyst and a water supply.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2086-2097
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• 2021

The current cultural assets are being restored depending on the opinions of experts (craftsmen). We intend to introduce digitalized artificial intelligence techniques, excluding the personal opinions of experts on reconstruction of such cultural properties. The first step toward restoring digitized cultural properties is separation. The restoration of cultural properties should be reorganized based on recorded documents, period historical backgrounds and regional characteristics. The cultural properties in the form of photographs or images should be collected by separating the background. In addition, when restoring cultural properties most of them depend a lot on the tendency of the restoring person workers. As a result, it often occurs when there is a problem in the accuracy and reliability of restoration of cultural properties. In this study, we propose a search method for learning stored digital cultural assets using AI technology. Pagoda was selected for restoration of Cultural Properties. Pagoda data collection was collected through the Internet and various historical records. The pagoda data was classified by period and region, and grouped into similar buildings. The collected data was learned by applying the well-known CNN algorithm for artificial intelligence learning. The pagoda search used Yolo Marker to mark the tower shape. The tower was used a total of about 100-10,000 pagoda data. In conclusion, it was confirmed that the probability of searching for a tower differs according to the number of pagoda pictures and the number of learning iterations. Finally, it was confirmed that the number of 500 towers and the epochs in training of 8000 times were good. If the test result exceeds 8,000 times, it becomes overfitting. All so, I found a phenomenon that the recognition rate drops when the enemy repeatedly learns more than 8,000 times. As a result of this study, it is believed that it will be helpful in data gathering to increase the accuracy of tower restoration.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1351-1356
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• 2019

The purpose of this study is to investigate the effect of personal factors and community factors on the quality of life based on the presence of chronic patients based on the Big Data Platform. As a method of study, second data of 2017 community health survey and Statistics Korea by City·Gun·Gu public office were used and a multi-level analysis was conducted after separating EQ-5D index, individual factor and community factor. As a result, men, age, education level, monthly household income, having economic activity, the number of sports infrastructure were positively associated with the quality of life, and subjective health not good, extremely perceived stress were negatively associated with the quality of life. Research will continue to provide a platform independent of hardware that can utilize the cloud and open source for medical big data analysis in the future.

• Smart Media Journal
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• v.11 no.8
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• pp.65-72
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• 2022

Due to COVID-19, the importance of non-face-to-face counseling is increasing as the face-to-face counseling method has progressed to non-face-to-face counseling. The advantage of non-face-to-face counseling is that it can be consulted online anytime, anywhere and is safe from COVID-19. However, it is difficult to understand the client's mind because it is difficult to communicate with non-verbal expressions. Therefore, it is important to recognize emotions by accurately analyzing text and voice in order to understand the client's mind well during non-face-to-face counseling. Therefore, in this paper, text data is vectorized using FastText after separating consonants, and voice data is vectorized by extracting features using Log Mel Spectrogram and MFCC respectively. We propose a multi-emotion recognition model that recognizes five emotions using vectorized data using an LSTM model. Multi-emotion recognition is calculated using RMSE. As a result of the experiment, the RMSE of the proposed model was 0.2174, which was the lowest error compared to the model using text and voice data, respectively.