• Asia pacific journal of information systems
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• v.23 no.4
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• pp.29-51
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• 2013

In line with network technology development and smart device penetration, the social network service (SNS) has expanded its influence. The SNS which is a service based on communication and sharing among people, has grown based on users' voluntary engagement and participation and its influence has appeared beyond the cyberspace into the overall areas of domestic and foreign culture and society. In particular, SNS-based real-time communication during diverse disasters, can help prevent further damage. By sharing information on social donation activities and environmental campaigns, people have used SNS as a tool to change the society in a more positive way. Such series of activities functioning as a power to change the society have been made much faster and wider through the help of a new media called SNS. To better understand such trends, we are required to study about the SNS and its user relationships first. In this context, this study sought to identify the effects of people's desire to build friendships through SNS on the voluntary and society-friendly activities of people. This study considers online pro-social behavior and proposes online citizenship behavior. Citizenship behavior has been examined in organization context. That is, organizational citizenship behavior explains an employee's pro-social behavior in an organization context. Organizational citizenship behavior is characterized by the individual's helping others and promoting the functioning of the organization. By applying organizational citizenship behavior to an online context, we propose online citizenship behavior, an individual's pro-social behavior in an online context. An individual's pro-social behavior, i.e., online citizenship behavior, could be considered as a way for the better management of online community and society. It also needs to examine the development of online citizenship behavior. This study examined online citizenship behavior from the friendship desire. Because online society or community is characterized by online relationships between members, the friendship between members would lead to pro-social behavior, i.e., helping others and promoting the functioning of the online society, in such online context. This study further examines the antecedents of friendship desire in terms of SNS interactivity with its four factors. The findings based on the survey from real SNS users explain that the three factors of SNS interactivity (connectivity, enjoyment, and synchronicity) increases online friendship desire which then increases online citizenship behavior significantly. This study contributes to the literature by examining the key role of online friendship desire in leading to online citizenship behavior and identifying its antecedents in terms of SNS characteristics. The findings in this study also provide guidance on how to manage online society and how to promote the effective functioning of SNS.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.4
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• pp.1-11
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• 2014

Traffic congestion cost is more likely to occur in the inner city than interregional road, and it accounts for about 63.39% of the whole. Therefore, it is important to mitigate traffic congestion of the inner city. Traffic congestion in the urban could be divided into Recurrent congestion and Non-recurrent congestion. Quick and accurate detection of Non-recurrent congestion is also important in order to relieve traffic congestion. The existing studies about incident detection have been variously conducted, however it was limited to Uninterrupted Traffic Flow Facilities such as freeway. Moreover study of incident detection on the interrupted Traffic Flow Facilities is still inadequate due to complex geometric structure such as traffic signals and intersections. Therefore, in this study, incident detection model was constructed using by Artificial Neural Network to aim at urban arterial road that is interrupted traffic flow facility. In the result of the reliability assessment, the detection rate were 46.15% and false alarm rate were 25.00%. These results have a meaning as a result of the initial study aimed at interrupted traffic flow. Furthermore, it demonstrates the possibility that Non-recurrent congestion can be detected by using car navigation data such as car navigator system device.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.5
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• pp.513-522
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• 2021

A smartphone (SMP) includes a MEMS sensor that can record 3-components motions and has a wireless network device to transmit data in live. These features and relatively low maintenance costs are the advantage of using SMPs as an auxiliary seismic observation network. Currently, 279 SMPs are monitoring seismic motions. In this study, we compare the SMP records with the seismic station (SS) records to validate SMP records. The data used for comparison are records for five earthquakes that occurred in 2019, which are 321 SS data recorded by the Korea Meteorological Administration and the Korea Institute of Geoscience and Mineral Resources and 145 recorded by SMPs. The analysis shows that the event-term corrected average residual of the SMP MEMS sensor records is 0.59 which indicating that the peak horizontal acceleration by SMP is 1.8 factor bigger than the peak ground acceleration by SS. In addition, the residuals tend to decrease as the installation floor of the smartphone MEMS sensor increases, which is the similar trend with response spectra from SS.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.731-751
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• 2021

The Internet of Things is a huge group of devices communicating each other and the interconnection of objects in the network is a basic requirement. Choosing a reliable device is critical because malicious devices can compromise networks and services. However, it is difficult to create a trust management model due to the mobility and resource constraints of IoT devices. For the centralized approach, there are issues of single point of failure and resource expansion and for the distributed approach, it allows to expand network without additional equipment by interconnecting each other, but it has limitations in data exchange and storage with limited resources and is difficult to ensure consistency. Recently, trust management models using fog nodes and blockchain have been proposed. However, blockchain has problems of low throughput and delay. Therefore, in this paper, a trust management model for selecting reliable devices in a fog-based IoT environment is proposed by applying IOTA, a blockchain technology for the Internet of Things. In this model, Directed Acyclic Graph-based ledger structure manages trust data without falsification and improves the low throughput and scalability problems of blockchain.

• The Journal of Society for e-Business Studies
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• v.24 no.3
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• pp.73-86
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• 2019

After the 4th industrial revolution, the healthcare industry is striving to find new business models through new technologies. Among them, blockchain technology is one of the technologies that have great interest in the healthcare industry. Most providers of personal health record systems have difficulty in securing marketability due to various problems. Therefore, they try to integrate blockchain technology to develop new systems and gain marketability. However, blockchain has limitations in solving the problems of the personal health record system. In this study, we have designed a personalized health data management framework that enables information subjects to acquire full ownership rights of individual's health data, based on distributed ledger technology. For the framework design, we refer to the structure of R3 Corda. It was designed with a different network structure than the existing blockchain systems so that the node can be operated on the personal user's mobile device. This allows information subjects to directly store and manage their own data and share data with authorized network members. Through the proposed system, the information utilization of the healthcare industry can be improved and the public health promotion and medical technology development can be realized.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.573-581
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• 2020

Using CCTV images and weather parameters, a method for estimating PM (Particulate Matter) index was proposed, and an experiment was conducted. For CCTV images, we proposed a method of estimating the PM index by applying a deep learning technique based on a CNN (Convolutional Neural Network) with ROI(Region Of Interest) image including a specific spot and an full area image. In addition, after combining the predicted result values by deep learning with the two weather parameters of humidity and wind speed, a post-processing experiment was also conducted to calculate the modified PM index using the learned regression model. As a result of the experiment, the estimated value of the PM index from the CCTV image was R2(R-Squared) 0.58~0.89, and the result of learning the ROI image and the full area image with the measuring device was the best. The result of post-processing using weather parameters did not always show improvement in accuracy in all cases in the experimental area.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1087-1101
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• 2020

Various security devices are used to protect internal networks and valuable information from rapidly evolving cyber attacks. Firewall, which is the most commonly used security device, tries to prevent malicious attacks based on a text-based filtering rule (i.e., access control policy), by allowing or blocking access to communicate between inside and outside environments. However, in order to protect a valuable internal network from large networks, it has no choice but to increase the number of access control policy. Moreover, the text-based policy requires time-consuming and labor cost to analyze various types of vulnerabilities in firewall. To solve these problems, this paper proposes a 3D-based hierarchical visualization method, for intuitive analysis and management of access control policy. In particular, by providing a drill-down user interface through hierarchical architecture, Can support the access policy analysis for not only comprehensive understanding of large-scale networks, but also sophisticated investigation of anomalies. Finally, we implement the proposed system architecture's to verify the practicality and validity of the hierarchical visualization methodology, and then attempt to identify the applicability of firewall data analysis in the real-world network environment.

• Knowledge Management Research
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• v.23 no.2
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• pp.123-142
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• 2022

The non-face-to-face environment accelerated by COVID-19 has speeded up the dissemination of digital virtual ecosystems and metaverse. In order for the metaverse to be sustainable, digital twins that are compatible with the real world are key, and critical technology for that is AR (Augmented Reality). In this study, we examined research trends about AR, and will propose the directions for future AR research. We conducted LDA based topic modeling on 11,049 abstracts of published domestic and foreign AR related papers from 2009 to Mar 2022, and then looked into AR that was comprehensive research trends, comparison of domestic and foreign research trends, and research trends before and after the popularity of metaverse concepts. As a result, the topics of AR related research were deduced from 11 topics such as device, network communication, surgery, digital twin, education, serious game, camera/vision, color application, therapy, location accuracy, and interface design. After popularity of metaverse, 6 topics were deduced such as camera/vision, training, digital twin, surgical/surgical, interaction performance, and network communication. We will expect, through this study, to encourage active research on metaverse AR with convergent characteristics in multidisciplinary fields and contribute to giving useful implications to practitioners.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.107-119
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• 2023

The technology that recognizes a soldier's motion and movement status has recently attracted large attention as a combination of wearable technology and artificial intelligence, which is expected to upend the paradigm of troop management. The accuracy of state determination should be maintained at a high-end level to make sure of the expected vital functions both in a training situation; an evaluation and solution provision for each individual's motion, and in a combat situation; overall enhancement in managing troops. However, when input data is given as a timer series or sequence, existing feedforward networks would show overt limitations in maximizing classification performance. Since human behavior data (3-axis accelerations and 3-axis angular velocities) handled for military motion recognition requires the process of analyzing its time-dependent characteristics, this study proposes a high-performance data-driven classifier which utilizes the long-short term memory to identify the order dependence of acquired data, learning to classify eight representative military operations (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). Since the accuracy is highly dependent on a network's learning conditions and variables, manual adjustment may neither be cost-effective nor guarantee optimal results during learning. Therefore, in this study, we optimized hyperparameters using Bayesian optimization for maximized generalization performance. As a result, the final architecture could reduce the error rate by 62.56% compared to the existing network with a similar number of learnable parameters, with the final accuracy of 98.39% for various military operations.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.251-258
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• 2023

In this paper, we propose DLP(Dual Linear Polarization) stack antenna for private network. The proposed antenna has general stack structure and design airgap between two substrate to obtain the maximum gain. Also, to improve cross polarization isolation, two feeding port is designed to separate for each substrate. The size of each patch antenna is 17.80 mm(W1)×16.70 mm(L1) for lower patch and 18.56 mm(W2)×18.73 mm(L2) for upper patch, which is designed on the FR-4 substrate which thickness (h) is 1.6 mm, and the dielectric constant is 4.3, and which is 40.0 mm(W)×40.0 mm(L) for total size of substrate. From the fabrication and measurement results, bandwidths of 100 MHz (4.74 to 4.84 GHz) for feeding port 1, and 150 MHz (4.67 to 4.82 GHz) for feeding port 2 are obtained on the basis of -10 dB return loss and transmission coefficient S21 is got under the -20 dB. Also, cross polarization isolation between each feeding port obtained