• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.56.3-56.3
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• 2019

We are currently living in the era of the wide field cosmological surveys, either spectroscopic such as Dark Energy Spectrograph Instrument or photometric such as the Dark Energy Survey or the Large Synoptic Survey Telescope. By analyzing the distribution of matter clustering, we can use the growth of structure, in combination with measurements of the expansion of the Universe, to understand dark energy or to test different models of gravity. But we also live in the era of multi-tracer or multi-messenger astrophysics. In particular, during the next decades radio surveys will map the matter distribution at higher redshifts. Like in optical surveys, there are radio imaging surveys such as continuum radio surveys such as the ongoing EMU or spectroscopic by measuring the hydrogen 21cm line. However, we can also use intensity mapping as a low resolution spectroscopic technique in which we use the intensity given by the emission from neutral hydrogen from patches of the sky, at different redshifts. By cross-correlating this maps with galaxy catalogues we can improve our constraints on cosmological parameters and to understand better how neutral hydrogen populates different types of galaxies and haloes. Creating realistic mock intensity mapping catalogues is necessary to optimize the future analysis of data. I will present the mock neutral hydrogen catalogues that we are developing, using the Horizon run 4 simulations, to cross-correlate with mock galaxy catalogues from low redshift surveys and I will show the preliminary results from the first mock catalogues.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.70-76
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• 2021

The appearance of COVID-19 virus has affected many aspects of our life. These include and not limited to social, financial and economic changes. One of the most important impacts is the economic effects. Many countries have taken actions to continue the teaching process through online teaching platforms. The students are expected to graduate during the next few semesters with certificates that include some online-completed courses and their graduation certificates are called mixed certificates. This paper considers graduation mixed certificates with some online courses and its impact on graduates seeking jobs. First, we study how well the mixed certificates are accepted by job market. In other words, how different companies, organizations and even governmental entities would accept such certificates when hiring. We study the perception of job market for such certificates for different learning fields. Secondly, we study how well the online courses are accepted by the students keeping in mind that these students are used to traditional face to face teaching. Finally, we paper our results and recommendations according to the collected data from the surveys. Some of the results show that about 60% of companies don't have policies to encourage hiring graduates with mixed certificates. Also, colleges are almost divided evenly between preferring face to face and preferring online teaching.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.89-100
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• 2021

Smart Grid Network (SGN) is a next generation electrical power network which digitizes the power distribution grid and achieves smart, efficient, safe and secure operations of the electricity. The backbone of the SGN is information communication technology that enables the SGN to get full control of network station monitoring and analysis. In any network where communication is involved security is essential. It has been observed from several recent incidents that an adversary causes an interruption to the operation of the networks which lead to the electricity theft. In order to reduce the number of electricity theft cases, companies need to develop preventive and protective methods to minimize the losses from this issue. In this paper, we have introduced a machine learning based SVM method that detects malicious nodes in a smart grid network. The algorithm collects data (electricity consumption/electric bill) from the nodes and compares it with previously obtained data. Support Vector Machine (SVM) classifies nodes into Normal or malicious nodes giving the statues of 1 for normal nodes and status of -1 for malicious -abnormal-nodes. Once the malicious nodes have been detected, we have done a trust evaluation based on the nodes history and recorded data. In the simulation, we have observed that our detection rate is almost 98% where the false alarm rate is only 2%. Moreover, a Trust value of 50 was achieved. As a future work, countermeasures based on the trust value will be developed to solve the problem remotely.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.33-39
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• 2020

The paper is to build recommendation systems leveraging Deep Learning and Big Data platform, Spark to predict item ratings of the Amazon e-commerce site. Recommendation system in e-commerce has become extremely popular in recent years and it is very important for both customers and sellers in daily life. It means providing the users with products and services they are interested in. Therecommendation systems need users' previous shopping activities and digital footprints to make best recommendation purpose for next item shopping. We developed the recommendation models in Amazon AWS Cloud services to predict the users' ratings for the items with the massive data set of Amazon customer reviews. We also present Big Data architecture to afford the large scale data set for storing and computation. And, we adopted deep learning for machine learning community as it is known that it has higher accuracy for the massive data set. In the end, a comparative conclusion in terms of the accuracy as well as the performance is illustrated with the Deep Learning architecture with Spark ML and the traditional Big Data architecture, Spark ML alone.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.139-146
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• 2021

Ahead of the commercialization of autonomous vehicles, it's application should be considered into the current transportation infrastructure. Under limited traffic circumstances, effective set of lane change rules alone could bring benefits to the autonomous driving system. In this study, a cooperative movement (local platooning) plan with limited vehicles associated as pocket driving, aiming at effective movement between vehicles in urban environment was proposed. Under congested roadway condition, the gaussian gap between vehicles was introduced to secure gap acceptance for safe lane change maneuver. Proposed lane change method showed 86.6% delay reduction along with traffic volume improvement. This result could be considered as a crucial factor in designing a next-generation roadway infrastructure with autonomous driving.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.183-188
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• 2021

Collaborative filtering based recommender systems are currently indispensable function of commercial systems in various fields, being a useful service by providing customized products that users will prefer. However, there is a high possibility that the prediction of preferrable products is inaccurate, when the user's rating data are insufficient. In order to overcome this drawback, this study suggests a stepwise method for prediction of product ratings. If the application conditions of the prediction method corresponding to each step are not satisfied, the method of the next step is applied. To evaluate the performance of the proposed method, experiments using a public dataset are conducted. As a result, our method significantly improves prediction and precision performance of collaborative filtering systems employing various conventional similarity measures and outperforms performance of the previous methods for solving rating data sparsity.

• Educational Technology International
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• v.21 no.2
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• pp.193-216
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• 2020

This study investigated university students' perspectives on good class and instructional practices through social network analysis. The subjects were 321 students in the third and fourth academic years in a Korean university. The subjects completed four open-ended questions, asking about experience of good class, good instructors' teaching practice, and their feelings and attitudes when participating in good class. As social network analysis, KrKwic (Korea Key Words in Context) was used to compute word frequencies and analyze semantic network structures and Ucinet Netdraw to assess centrality in the social network, consisting of degree centrality, closeness centrality, and between centrality. The results are as follows. First, students showed 5 keywords to depict what good class is, including 'understanding', 'example', 'video', 'interest', and 'communication'. Second, the characteristics of teaching methods by professors who practice good class indicate 'assignments', 'questions', 'understanding', 'example', and 'feedback'. Third, the top 5 keywords of students' attitudes as participating in good class are 'active', 'participation', 'focus', 'listening', and 'asking'. Last, keywords depicting desirable class that students most wanted to take next time are 'assignments', 'rewards', 'understanding', 'difficulty', and 'interest'. The findings from this study include the meanings of the semantic network structures of words in the text making up messages. Also this study can provide empirical evidence for educators and educational practitioners in higher education to create effective learning environments.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.3
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• pp.49-55
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• 2022

The design of a battery package and a BMS module for applications using seawater rechargeable batteries, which are known as next-generation energy storage devices, is proposed herein. Seawater rechargeable batteries, which are currently in the initial stage of research, comprise primarily components such as anode and cathode materials. Their application is challenging owing to their low charge capacity and limited charge/discharge voltage and current. Therefore, we design a method for packaging multiple cells and a BMS module for the safe charging and discharging of seawater rechargeable batteries. In addition, a prototype seawater rechargeable battery package and BMS module are manufactured, and their performances are verified by evaluating the prevention of overcharge, overdischarge, overcurrent, and short circuit during charging and discharging.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.757-763
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• 2022

In the NATM tunnel construction method for urban subway and underpass construction, various causes of disaster exist. In this study, in order to analyze the importance of disaster influencing factors during NATM tunnel excavation, the possible risk factors were analyzed through prior research such as drilling, charging and blasting, It was divided into the work items of wrinkle treatment, pumice cleanup, and support materials. Next, the final 21 detailed measurement indicators were selected through the FGI survey of related experts, and AHP (Analytic Hierarchy Process) analysis were conducted. As a result, it was found that the workers involved in the tunnel construction were the most influential disaster influencing factor.

• Journal of Digital Convergence
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• v.21 no.1
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• pp.9-13
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• 2023

Although low-carbon vehicles are recognized as leading the automobile market in the future, there are still many difficulties in expanding supply due to obstacles such as higher prices than conventional internal combustion engine vehicles and lack of related infrastructure. This study compares the current status of low-carbon vehicle sales and policy responses in Korea and Japan, which are ahead of the maturity of advanced Asian countries and automobile markets. The results of this study are intended to provide implications for academia and related industries and policies for the future direction of next-generation low-carbon vehicles.