• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.4
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• pp.161-169
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• 2011

There is a consensus in the field of vehicular network security that public key cryptography should be used to secure communications. A certificate revocation list (CRL) should be distributed quickly to all the vehicles in the network to protect them from malicious users and malfunctioning equipment as well as to increase the overall security and safety of vehicular networks. Thus, a major challenge in vehicular networks is how to efficiently distribute CRLs. This paper proposes a CRL distribution method aided by terrestrial digital multimedia broadcasting (T-DMB). By using T-DMB data broadcasting channels as alternative communication channels, the proposed method can broaden the network coverage, achieve real-time delivery, and enhance transmission reliability. Even if roadside units are not deployed or only sparsely deployed, vehicles can obtain recent CRLs from the T-DMB infrastructure. A new transport protocol expert group (TPEG) CRL application was also designed for the purpose of broadcasting CRLs over the T-DMB infrastructure.

• International Journal of Knowledge Content Development & Technology
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• v.10 no.4
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• pp.29-46
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• 2020

The paper describes the quantitative and qualitative dimensions of artificial neural networks (ANN) in India in the global context. The study is based on research publications data (8260) as covered in the Scopus database during 1999-2018. ANN research in India registered 24.52% growth, averaged 11.95 citations per paper, and contributed 9.77% share to the global ANN research. ANN research is skewed as the top 10 countries account for 75.15% of global output. India ranks as the third most productive country in the world. The distribution of research by type of ANN networks reveals that Feed Forward Neural Network type accounted for the highest share (10.18% share), followed by Adaptive Weight Neural Network (5.38% share), Feed Backward Neural Network (2.54% share), etc. ANN research applications across subjects were the largest in medical science and environmental science (11.82% and 10.84% share respectively), followed by materials science, energy, chemical engineering and water resources (from 6.36% to 9.12%), etc. The Indian Institute of Technology, Kharagpur and the Indian Institute of Technology, Roorkee lead the country as the most productive organizations (with 289 and 264 papers). Besides, the Indian Institute of Technology, Kanpur (33.04 and 2.76) and Indian Institute of Technology, Madras (24.26 and 2.03) lead the country as the most impactful organizations in terms of citation per paper and relative citation index. P. Samui and T.N. Singh have been the most productive authors and G.P.S.Raghava (86.21 and 7.21) and K.P. Sudheer (84.88 and 7.1) have been the most impactful authors. Neurocomputing, International Journal of Applied Engineering Research and Applied Soft Computing topped the list of most productive journals.

• Journal of radiological science and technology
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• v.44 no.6
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• pp.655-661
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• 2021

Positron emission tomography (PET) images is affected by acquisition time, short acquisition times results in low gamma counts leading to degradation of image quality by statistical noise. Noise2Void(N2V) is self supervised denoising model that is convolutional neural network (CNN) based deep learning. The purpose of this study is to evaluate denoising performance of N2V for PET image with a short acquisition time. The phantom was scanned as a list mode for 10 min using Biograph mCT40 of PET/CT (Siemens Healthcare, Erlangen, Germany). We compared PET images using NEMA image-quality phantom for standard acquisition time (10 min), short acquisition time (2min) and simulated PET image (S2 min). To evaluate performance of N2V, the peak signal to noise ratio (PSNR), normalized root mean square error (NRMSE), structural similarity index (SSIM) and radio-activity recovery coefficient (RC) were used. The PSNR, NRMSE and SSIM for 2 min and S2 min PET images compared to 10min PET image were 30.983, 33.936, 9.954, 7.609 and 0.916, 0.934 respectively. The RC for spheres with S2 min PET image also met European Association of Nuclear Medicine Research Ltd. (EARL) FDG PET accreditation program. We confirmed generated S2 min PET image from N2V deep learning showed improvement results compared to 2 min PET image and The PET images on visual analysis were also comparable between 10 min and S2 min PET images. In conclusion, noisy PET image by means of short acquisition time using N2V denoising network model can be improved image quality without underestimation of radioactivity.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1153-1160
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• 2021

This paper proposes a routing algorithm that determines the optimal path using deep reinforcement learning in software-defined networks. The deep reinforcement learning model for learning is based on DQN, the inputs are the current network state, source, and destination nodes, and the output returns a list of routes from source to destination. The routing task is defined as a discrete control problem, and the quality of service parameters for routing consider delay, bandwidth, and loss rate. The routing agent classifies the appropriate service class according to the user's quality of service profile, and converts the service class that can be provided for each link from the current network state collected from the SDN. Based on this converted information, it learns to select a route that satisfies the required service level from the source to the destination. The simulation results indicated that if the proposed algorithm proceeds with a certain episode, the correct path is selected and the learning is successfully performed.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.33 no.2
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• pp.5-31
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• 2022

This study aims to investigate and analyze the current status of book curation operation in elementary school libraries in Busan and the perception of librarians, and then to propose a plan to activate book curation in school libraries. A list of 304 school libraries in the Busan area was secured using the current status of school libraries in the 2021 National Library Statistical System. And then after selecting 121 school libraries with librarians, 83 schools that decided to participate in the survey were selected as final research subjects. Based on the current status of book curation operation and the results of the librarian's perception survey, the following are proposed as a way to activate the book curation of the school library: 1) expansion and provision of specialized formal education on book curation, 2) development and dissemination of materials related to the operation of school library book curation, 3) continuous efforts of librarians to strengthen the capacity for book curation, 4) between school library librarians collaboration and information sharing.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.93-100
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• 2022

In the era of information overload on the Internet, the recommendation system, which is an indispensable function, is a service that recommends products that a user may prefer, and has been successfully provided in various commercial sites. Recently, studies to reflect the rating time of items to improve the performance of collaborative filtering, a representative recommendation technique, are active. The core idea of these studies is to generate the recommendation list by giving an exponentially lower weight to the items rated in the past. However, this has a disadvantage in that a time function is uniformly applied to all items without considering changes in users' preferences according to the characteristics of the items. In this study, we propose a time-aware collaborative filtering technique from a completely different point of view by developing a new similarity measure that integrates the change in similarity values between items over time into a weighted sum. As a result of the experiment, the prediction performance and recommendation performance of the proposed method were significantly superior to the existing representative time aware methods and traditional methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.91-97
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• 2022

There is hare and tortoise racing algorithm(HTA) for single-source(SS) singly linked list(SLL) with O(n) time complexity. But the fast method is unknown for general graph with multi-source, multi-destination, and multi-branch(MSMDMB). This paper suggests linear time cycle detection algorithm for given undirected and digraph with MSMDMB. The proposed method reduced the given graph G contained with unnecessary vertices(or nodes) to cycle into reduced graph G' with only necessary vertices(or nodes) to cycle based on the condition of cycle formation. For the reduced graph G', we can be find the cycle set C and cycle length λ using linear search within linear time. As a result of experiment data, the proposed algorithm can be obtained the cycle for whole data.

• Journal of the Korean Institute of Rural Architecture
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• v.24 no.3
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• pp.39-46
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• 2022

This study aims to derive basic living service facility items in rural areas and construct related spatial data. To do this, a literature review on the laws and systems related to the residential environment and services in rural areas, rural spatial planning, and the 'Rural Convention' strategic plan reports for the Jeolla and Gyeongsang Region in 2021 was conducted. Primary data collection and review on the list of basic living service items in rural areas derived from the analysis were conducted. After data collection, 12 sectors and 44 types of rural basic living service items were derived; the data selection was carried out based on the clarity of the subject of data management, whether it was established nationwide, whether it was disclosed and provided, whether it was periodically updated, and whether it was an underlying law. Afterwards, data on the derived rural basic living service items were constructed. Afterwards, spatial data on the derived rural basic living service items were constructed. Because open data provided through various institutions were employed, data structure unification such as data attribute values and code names was needed, and abnormal data such as address errors and omissions were refined. After that, the data provided in text form was converted into spatial data through geocoding, and through comparative review of the distribution status of the converted data and the provided address, spatial data related to rural basic living services were finally constructed for about 540,000 cases. Finally, implications for data construction for diagnosing rural living areas were derived through the data collection and construction process. The derived implications include data unification, data update system establishment, the establishment of attribute values necessary for rural living area diagnosis and spatial planning, data establishment plan for facilities that provide various services, rural living area analysis method, and diagnostic index development. This study is meaningful in that it laid the foundation for data-based rural area diagnosis and rural planning, by selecting the basic rural living service items, and constructing spatial data on the selected items.

• Safety and Health at Work
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• v.13 no.3
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• pp.308-314
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• 2022

Background: Occupational cancer is a global health issue. The Korean CARcinogen EXposure (K-CAREX), a database of CARcinogen EXposure, was developed for the Korean labor force to estimate the number of workers exposed to carcinogens by industry. The present study aimed to estimate the intensity of exposure to carcinogens by industry, in order to supply complementary information about CARcinogen EXposure intensity to the K-CAREX. Methods: We used nationwide workplace monitoring data from 2014 to 2016 and selected target carcinogens based on the K-CAREX list. We computed the 95th percentile levels of measurements for each industry by carcinogens. Based on the 95th percentile level relative to the occupational exposure limit, we classified the CARcinogen EXposure intensity into five exposure ratings (1-5) for each industry. Results: The exposure ratings were estimated for 21 carcinogenic agents in each of the 228 minor industry groups. For example, 3,058 samples were measured for benzene in the manufacturing industry of basic chemicals. This industry was assigned a benzene exposure rating of 3. Conclusions: We evaluated the CARcinogen EXposure ratings across industries in Korean workers. The results will provide information on the exposure intensity to carcinogens for integration into the K-CAREX. Furthermore, it will aid in prioritizing control efforts and identifying industries of concern.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.171-178
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• 2023

As the risk of privacy invasion due to self-disclosure increases in SNS environment, many studies have tried to discover the influencing factors of self-disclosure. This study is an extension of this research stream and pays attention to the role of interaction privacy controls(friend list and privacy settings) as a new influencing factor. Specifically, the study theorizes and test the logic that the ability to effectively control interactions between individuals using IPC(called IPC usefulness) satisfies the three psychological needs(autonomy, relationship, and competency needs) suggested by the Self-Determination Theory, and in turn increase the amount of self-disclosure. As a result of data analysis, it was found that IPC usefulness has a very strong influence on the satisfaction of psychological needs and is a major factor in increasing the degree of self-disclosure by users. Based on these findings, the study discusses the theoretical and practical implications as well as future research directions.