• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.221-227
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• 2024

In autonomous navigation systems, the need for fast and accurate image processing using deep learning and advanced sensor technologies is paramount. These systems rely heavily on the ability to process and interpret visual data swiftly and precisely to ensure safe and efficient navigation. Despite the critical importance of such capabilities, there has been a noticeable lack of research specifically focused on ship image classification for maritime applications. This gap highlights the necessity for more in-depth studies in this domain. In this paper, we aim to address this gap by presenting a comprehensive comparative study of ship image classification using two distinct neural network models: the Feedforward Neural Network (FNN) and the Convolutional Neural Network (CNN). Our study involves the application of both models to the task of classifying ship images, utilizing a dataset specifically prepared for this purpose. Through our analysis, we found that the Convolutional Neural Network demonstrates significantly more effective performance in accurately classifying ship images compared to the Feedforward Neural Network. The findings from this research are significant as they can contribute to the advancement of core source technologies for maritime autonomous navigation systems. By leveraging the superior image classification capabilities of convolutional neural networks, we can enhance the accuracy and reliability of these systems. This improvement is crucial for the development of more efficient and safer autonomous maritime operations, ultimately contributing to the broader field of autonomous transportation technology.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.49-57
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• 2022

Recently, various uses of artificial intelligence have been made possible through the deep artificial neural network structure of machine learning, demonstrating human-like capabilities. Unfortunately, the deep structure of the artificial neural network has not yet been accurately interpreted. This part is acting as anxiety and rejection of artificial intelligence. Among these problems, we solve the capability part of artificial neural networks. Calculate the size of the artificial neural network structure and calculate the size of data that the artificial neural network can process. The calculation method uses the group method used in mathematics to calculate the size of data and artificial neural networks using an order that can know the structure and size of the group. Through this, it is possible to know the capabilities of artificial neural networks, and to relieve anxiety about artificial intelligence. The size of the data and the deep artificial neural network are calculated and verified through numerical experiments.

• The Journal of the Korea Contents Association
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• v.14 no.7
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• pp.178-187
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• 2014

The intention of this research lies in verifying whether the capabilities have an influence in raising their satisfaction. The results were as follows. Personal inter social skills and personal capabilities had a direct influence on the networking, and also on life satisfaction (networking acting as a mediator). However, problem solving capabilities did not have a substantial influence on the networking (which here, works as a mediating variable). Therefore, according to the above results, various measures which may advance life satisfaction by using the networking as mediator should be reviewed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4572-4586
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• 2019

Mobile Edge Computing (MEC) and Information-Centric Networking (ICN) are essential network architectures for the future Internet. The advantages of MEC and ICN such as computation and storage capabilities at the edge of the network, in-network caching and named-data communication paradigm can greatly improve the quality of video streaming applications. However, the packet loss in wireless network environments still affects the video streaming performance and the existing loss recovery approaches in ICN does not exploit the capabilities of MEC. This paper proposes a Deep Learning based Loss Recovery Mechanism (DL-LRM) for video streaming over MEC based ICN. Different with existing approaches, the Forward Error Correction (FEC) packets are generated at the edge of the network, which dramatically reduces the workload of core network and backhaul. By monitoring network states, our proposed DL-LRM controls the FEC request rate by deep reinforcement learning algorithm. Considering the characteristics of video streaming and MEC, in this paper we develop content caching detection and fast retransmission algorithm to effectively utilize resources of MEC. Experimental results demonstrate that the DL-LRM is able to adaptively adjust and control the FEC request rate and achieve better video quality than the existing approaches.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.6
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• pp.353-362
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• 2015

In order to provide smart devices with high quality multimedia streaming services, an adaptive streaming technique over HTTP has been received much attention recently and the Dynamic Adaptive Streaming over HTTP (DASH) standard has been established. In DASH, however, the technique to select an appropriate quality of multimedia based on the performance metrics measured in a smart device might have some difficulties to reflect the capabilities of other neighboring smart devices and dynamic network conditions in real time. To solve the problem, this paper proposes a novel software agent approach, called DASH agent (DA), which gathers and analyzes the device capabilities and dynamic network conditions in real time and finally determines the highest achievable quality of segment to meet the best Quality of Experience (QoE) in current situations. The simulation results show that our approach provides higher quality of multimedia segments with less frequency of quality changes to lower quality of multimedia segments.

• International Area Studies Review
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• v.16 no.3
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• pp.209-237
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• 2012

Multinational enterprises(MNEs) try to strengthen their global innovative capabilities by incorporating the foreign subsidiaries' knowledge bases. Foreign subsidiaries play an important role in MNEs' knowledge creation activities. This study develops a strategy model that strengthening the knowledge creation capabilities of Korean firms' foreign subsidiaries. Four strengthening strategy types are derived from three research areas related with center of excellence, subsidiary strategic roles, and knowledge creation capabilities. The strategies that strengthen knowledge creation capabilities are including organizational culture and autonomy reinforcing strategy, subsidiary's absorptive capacity reinforcing strategy, local environment management strategy, and network building strategy. Strategic fit conditions which support the success of each strategy type are also discussed.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.1
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• pp.259-268
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• 2020

The purpose of this study is to examine the predictive effects of intuition, business analytic, networking capabilities on innovation performance. The data was collected using a cross-sectional quantitative survey. A total of 292 useable responses were collected from Thai Processed Food Exporters (TPFE). The findings also indicated that the hypothesized relationships between the independent and dependent variables fit the empirical data. Specifically, it is revealed that strategic intuition, business analytic capabilities, network-based capabilities and dynamic capabilities had a direct effect on dynamic strategy. They also had statistically significant direct and indirect effects on dynamic performance. Based on the results of the correlation test, the researchers developed a dynamic capability model for the development of the dynamic performance of the operators, which included concepts, principles, methods, tools and guidelines. Furthermore, the impacts of intuition, business analytic, networking capabilities on dynamic strategy are also examined in this study. It makes a considerable contribution to the existing literature on dynamic strategy of TPFE, particularly in regards to explaining the performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.6
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• pp.1065-1069
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• 2006

With a home network, a device can dynamically join a home network obtain an IP address, convey its capabilities, and learn about the presence and capabilities of other devices. Devices can subsequently communicate with each other directly. Device discovery protocol defines how network services can be discovered on the network. In this paper, we propose the secure discovery method of devices that uses mutual authentication with symmetric key between devices. This method that we present distributes symmetric-key to home network devices by the home server. Using this key, mutual authentication is performed between home appliances. It enables any appliance under any middleware's control to securely communicate any other appliances.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.745-748
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• 2006

With a home network, a device can dynamically join a home network, obtain an IP address, convey its capabilities, and learn about the presence and capabilities of other devices. Devices can subsequently communicate with each other directly. Device discovery protocol defines how network services can be discovered on the network. In this paper, we propose the secure discovery method of devices that uses mutual authentication with symmetric key between devices. This method that we present distributes symmetric-key to home network devices by the home server. Using this key, mutual authentication is performed between home appliances. It enables any appliance under any middleware's control to securely communicate any other appliances.

• Korea Trade Review
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• v.47 no.6
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• pp.135-171
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• 2022

This study is to find out how psychological characteristics and management capabilities of small manufacturers influence business performance through parameters including self-efficiency, network, and collaboration strategies. The survey was implemented between December 26, 2017 and January 15, 2018. The final 439 valid questionnaires were collected and used for analysis. The results were followed. First, the relationship between psychological characteristics and self-efficiency, and the path coefficient for psychological characteristics and network were significant. Second, management capabilities was related to self-efficiency, and the path factor for managing capability and network relationships was significant. Lastly, the path coefficients for self-efficiency and collaboration strategies were not significant, and the path coefficients for network and collaboration strategies were significant.