• Journal of the Korean BIBLIA Society for library and Information Science
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• v.16 no.1
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• pp.285-314
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• 2005

The purpose of this study is to review and analyze the overlapping functions and activities of the nationwide scientific and technical information network system This study is helpful for users to gather information and for librarians and nations to reduce wastage of budgets through discarding the duplicated expenses of performing activities of the network systems. The research targets are KISTI and KERIS, which are functioning as the information network systems in the fields of science and technology. The research methods are literary reviews for theory background and analysis of their activities from internet homepages. As a result of the research, it is found and confirmed that there are some overlapping and duplicating functions and activities in the network systems.

• The Korea Journal of Herbology
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• v.38 no.1
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• pp.45-53
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• 2023

Objectives : Network pharmacology-based research is one of useful tool to predict the possible efficacy and molecular mechanisms of natural materials with multi compounds-multi targeting effects. In this study, we investigated the functional underlying mechanisms of Astragalus membranaceus Bunge (AM) on its anti-obesity effects using a network pharmacology analysis. Methods : The constituents of AM were collected from public databases and its target genes were gathered from PubChem database. The target genes of AM were compared with the gene set of obesity to find the correlation. Then, the network was constructed by Cytoscape 3.9.1. and functional enrichment analysis was conducted to predict the most relevant pathway of AM. Results : The result showed that AM network contained the 707 nodes and 6867 edges, and 525 intersecting genes were exhibited between AM and obesity gene set, indicating that high correlation with the effects of AM on obesity. Based on GO biological process and KEGG Pathway, 'Response to lipid', 'Cellular response to lipid', 'Lipid metabolic process', 'Regulation of chemokine production', 'Regulation of lipase activity', 'Chemokine signaling pathway', 'Regulation of lipolysis in adipocytes' and 'PPAR signaling pathway' were predicted as functional pathways of AM on obesity. Conclusions : AM showed high relevance with the lipid metabolism related with the chemokine production and lipolysis pathways. This study could be a basis that AM has promising effects on obesity via network pharmacology analysis.

• The Journal of the Korea Contents Association
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• v.17 no.2
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• pp.404-414
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• 2017

With the development of smartphones and online social networks, users produce a lot of contents and share them with each other. Therefore, users spend time by viewing or receiving the contents they do not want. In order to solve such problems, schemes for recommending useful contents have been actively studied. In this paper, we propose a contents recommendation scheme using collaborative filtering for users on online social networks. The proposed scheme consider a user trust in order to remove user data that lower the accuracy of recommendation. The user trust is derived by analyzing the user activity of online social network. For evaluating the user trust from various points of view, we collect user activities that have not been used in conventional techniques. It is shown through performance evaluation that the proposed scheme outperforms the existing scheme.

• Journal of Internet Computing and Services
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• v.12 no.2
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• pp.9-17
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• 2011

This paper proposes an algorithm that predicts the state of user's next actions, exploiting the HMM (Hidden Markov Model) on user profile data stored in the ubiquitous home network. The HMM, recognizes patterns of sequential data, adequately represents the temporal property implicated in the data, and is a typical model that can infer information from the sequential data. The proposed algorithm uses the number of the user's action performed, the location and duration of the actions saved by "Activity Recognition System" as training data. An objective formulation for the user's interest in his action is proposed by giving weight on his action, and change on the state of his next action is predicted by obtaining the change on the weight according to the flow of time using the HMM. The proposed algorithm, helps constructing realistic ubiquitous home networks.

• Korean Journal of Exercise Nutrition
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• v.25 no.3
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• pp.28-35
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• 2021

[Purpose] As Panax ginseng C. A. Meyer (ginseng) exhibits various physiological activities and is associated with exercise, we investigated the potential active components of ginseng and related target genes through network pharmacological analysis. Additionally, we analyzed the association between ginseng-related genes, such as the G-protein-coupled receptors (GPCRs), and improved exercise capacity. [Methods] Active compounds in ginseng and the related target genes were searched in the Traditional Chinese Medicine Database and Analysis Platform (TCMSP). Gene ontology functional analysis was performed to identify biological processes related to the collected genes, and a compound-target network was visualized using Cytoscape 3.7.2. [Results] A total of 21 ginseng active compounds were detected, and 110 targets regulated by 17 active substances were identified. We found that the active compound protein was involved in the biological process of adrenergic receptor activity in 80%, G-protein-coupled neurotransmitter in 10%, and leucocyte adhesion to arteries in 10%. Additionally, the biological response centered on adrenergic receptor activity showed a close relationship with G protein through the beta-1 adrenergic receptor gene reactivity. [Conclusion] According to bioavailability analysis, ginseng comprises 21 active compounds. Furthermore, we investigated the ginseng-stimulated gene activation using ontology analysis. GPCR, a gene upregulated by ginseng, is positively correlated to exercise. Therefore, if a study on this factor is conducted, it will provide useful basic data for improving exercise performance and health.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3836-3854
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• 2022

The student group division is benefit for universities to do the student management based on the group profile. With the widespread use of student smart cards on campus, especially where students living in campus residence halls, students' daily activities on campus are recorded with information such as smart card swiping time and location. Therefore, it is feasible to depict the students with the daily activity data and accordingly group students based on objective measuring from their campus behavior with some regular student attributions collected in the management system. However, it is challenge in feature representation due to diverse forms of the student data. To effectively and comprehensively represent students' behaviors for further student group division, we proposed to adopt activity data from student smart cards and student attributes as input data with taking account of activity and attribution relationship types from different perspective. Specially, we propose a novel student group division method based on a multi-view student attribute heterogeneous information network (MSA-HIN). The network nodes in our proposed MSA-HIN represent students with their multi-dimensional attribute information. Meanwhile, the edges are constructed to characterize student different relationships, such as co-major, co-occurrence, and co-borrowing books. Based on the MSA-HIN, embedded representations of students are learned and a deep graph cluster algorithm is applied to divide students into groups. Comparative experiments have been done on a real-life campus dataset collected from a university. The experimental results demonstrate that our method can effectively reveal the variability of student attributes and relationships and accordingly achieves the best clustering results for group division.

• Journal of the Korea Society of Computer and Information
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• v.14 no.7
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• pp.49-56
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• 2009

In this paper, we present a daily activity monitoring system by using a wireless sensor network. The proposed system is installed in glove for activity monitoring. The RFID reader, to send data by using sensor network platform and RFID tag are small size, the shape of quadrangle, and operate in the frequency of 13.56 MHz. The sensor node can read RFID tags on the various objects used in daily living such as furniture, medicines, and kitchenwares. The sensor node reads the data of RFID tags, it transmits wireless packets to the sink node. The sink node sends the received packet immediately to a server system. The data from each RFID system is collected into a database, and then the data are processed to visualize the measurement of daily living activities of users. We provide a web-based monitoring system, and can see the number of RFID tag readings per day as bar charts. The result of experiments demonstrates that the way we propose can help to check the situation of life for people who live alone.

• The Journal of the Korea Contents Association
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• v.18 no.4
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• pp.48-59
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• 2018

Recently, network technology has been used in various fields due to development of network technology. However, there has been an increase in the number of attacks targeting public institutions and companies by exploiting the evolving network technology. Meanwhile, the existing network intrusion detection system takes much time to process logs as the amount of network log increases. Therefore, in this paper, we propose a Spark-based network log analysis system that detects unstructured network attack pattern. by using Snort. The proposed system extracts and analyzes the elements required for network attack pattern detection from large amount of network log data. For the analysis, we propose a rule to detect network attack patterns for Port Scanning, Host Scanning, DDoS, and worm activity, and can detect real attack pattern well by applying it to real log data. Finally, we show from our performance evaluation that the proposed Spark-based log analysis system is more than two times better on log data processing performance than the Hadoop-based system.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04a
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• pp.130-132
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• 2002

In this work, we like to present the result of study obtained from actual experiment. We instrument the effect of smoothing on packet loss behavior in mobile terminal on broadband wireless network under various different system settings. This activity requires comprehensive streaming software suite including streaming server, CODEC, and streaming client.

• Journal of the Korean Operations Research and Management Science Society
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• v.6 no.1
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• pp.25-32
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• 1981

The optimum design problem of a proposed oil pipeline network has been formulated as a zero-one programming model to determine the optimum sizes of pipe and pump which minimize the sum of material costs and operating costs during the 20 years of life span. Applying to a real situation, the problem constitutes an assignment type zero-one programming with 372 zero-one variables and 13 constraints. A heuristic algorithm has been developed based on the modified Petersen algorithm utilizing the special form of the activity matrix. The results showed impressive cost savings of 37 percent of the total cost from the original proposal.