• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.209-213
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• 2023

Currently, 2.4 GHz and 5 GHz bands are used as frequencies for drone operation. In December 2019, the Ministry of Science and ICT newly allocated the 433 MHz band for the invisible long-distance operation of drones. However, since the 433 MHz band is the same as the previously allocated frequency band for amateur radio communication, interference cannot be avoided. Therefore, as a prerequisite for the development of a drone operation system based on the 433 MHz band, interference avoidance technology for this frequency band must be developed and applied. In this paper, we report the results of measurement and analysis of 433 MHz band signals necessary for the development of interference avoidance and reduction technologies for 433 MHz signals. The measurement and analysis of the 433 MHz band signal are performed through the spectrum measured at 5-minute intervals at three locations. Since the measurements and analyzes performed in this study considered spatial characteristics, temporal characteristics, and traffic characteristics, it is considered to be the basic data necessary for the development of interference avoidance technology in the 433 MHz band.

• The KIPS Transactions:PartC
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• v.11C no.7 s.96
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• pp.1033-1042
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• 2004

According to the arbitration mechanism among the peers in the network, the P2P networking environments can be classified into hybrid P2P networking environments and pure P2P networking environments. In hybrid P2P networking environments, each peer gets continual services from the servers that arc operational most of the time, and so, network isolation does not occur because every peer can always keep connection to the server. In pure P2P networking environments, however, every peer directly connects to another peer without server intervention, and so, network isolation can occur when the per mediating the connection is terminated. In this paper, we propose a scheme for each peer to keep connection information of other peers by maintaining IDs of its neighbor peers, to reconnect to another peers when the mediating peer fails to work. and, for efficiency. to balance the number of connections that should be maintained by each peer. With our mechanism, each pier in the network can continuously maintain connection to the network and get seamless services from other peers. Through the simulation, we ascer-tained that network isolation does not occur in the pure P2P network adopting our mechanism and that our mechanism distributes and balances the connections that are maintained by each peer. We also analyzed the total network traffic and the mean number of hops for the connections made by each peer according to the recommended number of connections that is established at system setup time.

• The KIPS Transactions:PartA
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• v.16A no.2
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• pp.113-124
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• 2009

With increasing multicore system, much effort has been put on the performance improvement of its application. Because multicore system has multiple processing devices in one system, its processing power increases compared to the single core system. However in many cases the advantages of multicore can not be exploited fully because the existing software and hardware were designed to be suitable for single core. When the existing software runs on multicore, its performance improvement is limited by the bottleneck of sharing resources and the inefficient use of cache memory on multicore. Therefore, according as the number of core increases, it doesn't show performance improvement and shows performance drop in the worst case. In this paper we propose a method of performance improvement of multicore system by applying Flow-Level Parallelism to the existing TCP/IP network application and operating system. The proposed method sets up the execution environment so that each core unit operates independently as much as possible in network application, TCP/IP stack on operating system, device driver, and network interface. Moreover it distributes network traffics to each core unit through L2 switch. The proposed method allows to minimize the sharing of application data, data structure, socket, device driver, and network interface between each core. Also it allows to minimize the competition among cores to take resources and increase the hit ratio of cache. We implemented the proposed methods with 8 core system and performed experiment. Experimental results show that network access speed and bandwidth increase linearly according to the number of core.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.107-129
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• 2022

It is important to preempt new technology because the technology competition is getting much tougher. Stakeholders conduct exploration activities continuously for new technology preoccupancy at the right time. Gartner's Hype Cycle has significant implications for stakeholders. The Hype Cycle is a expectation graph for new technologies which is combining the technology life cycle (S-curve) with the Hype Level. Stakeholders such as R&D investor, CTO(Chef of Technology Officer) and technical personnel are very interested in Gartner's Hype Cycle for new technologies. Because high expectation for new technologies can bring opportunities to maintain investment by securing the legitimacy of R&D investment. However, contrary to the high interest of the industry, the preceding researches faced with limitations aspect of empirical method and source data(news, academic papers, search traffic, patent etc.). In this study, we focused on two research questions. The first research question was 'Is there a difference in the characteristics of the network structure at each stage of the hype cycle?'. To confirm the first research question, the structural characteristics of each stage were confirmed through the component cohesion size. The second research question is 'Is there a pattern of diffusion at each stage of the hype cycle?'. This research question was to be solved through centralization index and network density. The centralization index is a concept of variance, and a higher centralization index means that a small number of nodes are centered in the network. Concentration of a small number of nodes means a star network structure. In the network structure, the star network structure is a centralized structure and shows better diffusion performance than a decentralized network (circle structure). Because the nodes which are the center of information transfer can judge useful information and deliver it to other nodes the fastest. So we confirmed the out-degree centralization index and in-degree centralization index for each stage. For this purpose, we confirmed the structural features of the community and the expectation diffusion patterns using Social Network Serice(SNS) data in 'Gartner Hype Cycle for Artificial Intelligence, 2021'. Twitter data for 30 technologies (excluding four technologies) listed in 'Gartner Hype Cycle for Artificial Intelligence, 2021' were analyzed. Analysis was performed using R program (4.1.1 ver) and Cyram Netminer. From October 31, 2021 to November 9, 2021, 6,766 tweets were searched through the Twitter API, and converting the relationship user's tweet(Source) and user's retweets (Target). As a result, 4,124 edgelists were analyzed. As a reult of the study, we confirmed the structural features and diffusion patterns through analyze the component cohesion size and degree centralization and density. Through this study, we confirmed that the groups of each stage increased number of components as time passed and the density decreased. Also 'Innovation Trigger' which is a group interested in new technologies as a early adopter in the innovation diffusion theory had high out-degree centralization index and the others had higher in-degree centralization index than out-degree. It can be inferred that 'Innovation Trigger' group has the biggest influence, and the diffusion will gradually slow down from the subsequent groups. In this study, network analysis was conducted using social network service data unlike methods of the precedent researches. This is significant in that it provided an idea to expand the method of analysis when analyzing Gartner's hype cycle in the future. In addition, the fact that the innovation diffusion theory was applied to the Gartner's hype cycle's stage in artificial intelligence can be evaluated positively because the Gartner hype cycle has been repeatedly discussed as a theoretical weakness. Also it is expected that this study will provide a new perspective on decision-making on technology investment to stakeholdes.