• Journal of Internet Computing and Services
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• v.20 no.2
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• pp.1-7
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• 2019

The latest mobile applications such as augmented reality, virtual reality, and deep learning can be used efficiently in various fields such as emergency management and game. Accordingly the corresponding applications have been developed for these purposes. However modern mobile applications such as augmented reality and virtual reality increase the energy burden on mobile devices. In order for mobile devices to focus their energy on the latest mobile applications, energy consumption should be minimized for communication and networking, such as cognitive radio. In this paper, we propose a method to reduce the energy consumption of Centralized Cooperative Spectrum Sensing (CCSS) scheme in cognitive radio by devising Stop Reporting Algorithm (SRA). Simulation results show that SRA can reduce energy consumption of mobile devices using cognitive radio.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.69-77
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• 2021

The main channels of the financial industry are rapidly changing to mobile. In this environment, banks are focusing on information and communication technology to secure their competitiveness, and rapid innovation is being pursued especially in the payment settlement sector. In October 2019, Korea also introduced open banking services to accelerate the financial innovation, such as the open conversion of financial settlement networks and the expansion of the use of simple payments. In this paper, an empirical study was conducted on the effect of domestic open banking quality factors on usage intention. The service quality factors for open banking were classified into interface design, innovation, security, and data sharing, and the technology acceptance model (TAM) was used to verify whether it has a significant effect on perceived convenience, usefulness and intention to use. According to the analysis results, while innovation and security did not have a significant effect on convenience and usefulness, interface design and data sharing were found to have an effect on perceived convenience. The results would provide implications for some quality issues for companies seeking to introduce open banking services as well as for the related academic arena.

• Journal of Digital Convergence
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• v.19 no.4
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• pp.89-101
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• 2021

Scale-up of ICT startups is not easy because of limited capabilities, lack of resources, and immature networks for the business. Therefore, this research selected a representative startup succeeded in scale-up and applied the ERIS model in analyzing their scale-up process in the initial stages of scale-up. Analysis of qualitative data collected revealed that the entrepreneurs' experience, convergence of knowledge between diverse industries, participation in public-sector-led R&D, management of communication channels between customers and businesses, and utilization of project-oriented campaigns are found to be critical success factors in scaling up ICT startups. Academically, this study validates the utility of ERIS model in analyzing the scale-up process. For practitioners, this study will be used as a reference for strategic development in seucring the competitiveness of the initial market of ICT startups and scale-up.

• Journal of Radiation Protection and Research
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• v.46 no.3
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• pp.143-150
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• 2021

Since its establishment in 2018, the Young Generation Network (YGN) has been dedicated, with support of the International Radiation Protection Association (IRPA), to a variety of activities to promote communication, collaboration and professional development of students and young professionals in the area of radiation protection and its allied fields. This article reports our recent activities from the middle of 2018 to the beginning of 2021, with highlights on some important events: "Joint JHPS-SRP-KARP Workshop of Young Generation Network" (December 2019 in Japan); contribution to "Nuclear Energy Agency Workshop on Optimization: Rethinking the Art of Reasonable" (January 2020 in Portugal); survey on the impact of coronavirus disease 2019 (COVID-19) on radiation protection among IRPA YGN members (March 2020); and contribution to IRPA15 (15^th International Congress of the IRPA; January-February 2021, online). The discussion and insight obtained from each activity are also summarized. The IRPA YGN will aim to achieve its on-going activities and continue to follow the ways paved in the Strategic Agenda and despite the challenges raised by the COVID-19 pandemic. Namely, running an international survey (for example, on the usage of social media in radiation protection, and on the long-term consequences of the COVID-19 pandemic), engaging national YGNs, extending the network, finding new relationships with networks with an interest in the young generation and participation in (remote) events will be aspired for.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.914-924
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• 2020

In this study we developed artificial network generation model, which can generate on-line social network. The suggested model can represent not only scale-free and small-world properties, but also can produce networks with various values of topological characteristics through controlling two input parameters. For this purpose, two parameter K and P are introduced, K for controlling the strength of preferential attachment and P for controlling clustering coefficient. It is found out on-line social network can be generated with the combinations of K(0~10) and P(0.3~0.5) or K=0 and P=0.9. Under these combinations of P and K small-world and scale-free properties are well represented. Node degree distribution follows power-law. Clustering coefficients are between 0.130 and 0.238, and average shortest path distance between 5.641 and 5.985. It is also found that on-line social network properties are maintained as network node size increases from 5,000 to 10,000.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.842-848
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• 2020

Music markets have entered the era of streaming. In order to select and propose music that suits the taste of music consumers, there is an active demand and research on an automatic music genre classification system. We propose a method to improve the accuracy of genre unclassified songs, which was a lack of the previous system, by using a generative adversarial network (GAN) to further develop the automatic voting system for deep learning music genre using Softmax proposed in the previous paper. In the previous study, if the spectrogram of the song was ambiguous to grasp the genre of the song, it was forced to leave it as an unclassified song. In this paper, we proposed a system that increases the accuracy of genre classification of unclassified songs by converting the spectrogram of unclassified songs into an easy-to-read spectrogram using GAN. And the result of the experiment was able to derive an excellent result compared to the existing method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1604-1611
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• 2020

Light field image captured by a microlens array-based camera has many limitations in practical use due to its low spatial resolution and angular resolution. High spatial resolution images can be easily acquired with a single image super-resolution technique that has been studied a lot recently. But there is a problem in that high angular resolution images are distorted in the process of using disparity information inherent among images, and thus it is difficult to obtain a high-quality angular resolution image. In this paper, we propose light field angular super-resolution that extracts an initial feature map using an dilated convolutional neural network in order to effectively extract the view difference information inherent among images and generates target image using a residual neural network. The proposed network showed superior performance in PSNR and subjective image quality compared to existing angular super-resolution networks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1477-1483
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• 2020

The stability guarantee of wireless network based control systems is still challenging due to the lossy links and node failures. This paper proposes a hierarchical cluster-based network protocol called robust wireless sensor and actuator network (R-WSAN) by combining time, channel, and space resource diversity. R-WSAN includes a scheduling algorithm to support the network resource allocation and a control task sharing scheme to maintain the control stability of multiple plants. R-WSAN was implemented on a real test-bed using Zolertia RE-Mote embedded hardware platform running the Contiki-NG operating system. Our experimental results demonstrate that R-WSAN provides highly reliable and robust performance against lossy links and node failures. Furthermore, the proposed scheduling algorithm and the task sharing scheme meet the stability requirement of control systems, even if the controller fails to support the control task.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1588-1594
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• 2020

In this paper, we discuss a data balancing method for learning a neural network that generates digital holograms using a deep neural network (DNN). Deep neural networks are based on deep learning (DL) technology and use a generative adversarial network (GAN) series. The fringe pattern, which is the basic unit of a hologram to be created through a deep neural network, has very different data types depending on the hologram plane and the position of the object. However, because the criteria for classifying the data are not clear, an imbalance in the training data may occur. The imbalance of learning data acts as a factor of instability in learning. Therefore, it presents a method for classifying and balancing data for which the classification criteria are not clear. And it shows that learning is stabilized through this.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.37-43
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• 2020

Recently, many researches have been actively to diagnose symptoms of heart disease using ECG signal, which is an electrical signal measuring heart status. In particular, the electrocardiogram signal can be used to monitor and diagnose arrhythmias that indicates an abnormal heart status. In this paper, we proposed 1-D convolutional neural network for arrhythmias classification systems. The proposed model consists of deep 11 layers which can learn to extract features and classify 5 types of arrhythmias. The simulation results over MIT-BIH arrhythmia database show that the learned neural network has more than 99% classification accuracy. It is analyzed that the more the number of convolutional kernels the network has, the more detailed characteristics of ECG signal resulted in better performance. Moreover, we implemented a practical application based on the proposed one to classify arrythmias in real-time.