• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.1
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• pp.205-214
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• 2008

To transfer large amount of packets fast in sensor network, it is necessary that the delay between successive packet transmissions should be minimized as possible. In Sensor network, since the Operating Systems are worked on the event driven, the Timer Event is used to transfer packets successively. However, since the transferring time of packet completely is varies very much, it is very hard to set appropriate interval. If interval is too long, delay also becomes too long but if interval is too short, the fail of transfer request would increase. In this paper, we propose ESTEO which reduces the delay between successive packet transmissions by using SendDone Event which informs that a packet transmission has been completed. In ESTEO, the delay between successive packet transmissions is shortened very much since the transmission of next Packet starts at the time when the transmission of previous packet has completed, irrespective of the transmission time. Therefore ESTEO could provide high packet transmission rate given large amount of packets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3567-3588
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• 2018

In the Internet of Things (IoT) concept, devices communicate autonomously with applications in the Internet. A significant aspect of IoT that makes it stand apart from present-day networked devices and applications is a) the very large number of devices, produced by diverse makers and used by an even more diverse group of users; b) the applications residing and functioning in what were very private sanctums of life e.g. the car, home, and the people themselves. Since these diverse devices require high-level security, an operational model for an IoT system is required, which has built-in security. We have proposed the societal model as a simple operational model. The basic concept of the model is borrowed from human society - there will be infants, the weak and the handicapped who need to be protected by guardians. This natural security mechanism works very well for IoT networks which seem to have inherently weak security mechanisms. In this paper, we discuss the requirements of the societal model and examine its feasibility by doing a proof-of-concept implementation.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.26-51
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• 2003

Large scale protein interaction maps provide a new, global perspective with which to analyse protein function. PSIMAP, the Protein Structural Interactome Map, is a database of all the structurally observed interactions between superfamilies of protein domains with known three-dimensional structure in thePDB. PSIMAP incorporates both functional and evolutionary information into a single network. It makes it possible to age protein domains in terms of taxonomic diversity, interaction and function. One consequence of it is to predict the most important protein domain structure in evolution. We present a global analysis of PSIMAP using several distinct network measures relating to centrality, interactivity, fault-tolerance, and taxonomic diversity. We found the following results: ${\bullet}$ Centrality: we show that the center and barycenter of PSIMAP do not coincide, and that the superfamilies forming the barycenter relate to very general functions, while those constituting the center relate to enzymatic activity. ${\bullet}$ Interactivity: we identify the P-loop and immunoglobulin superfamilies as the most highly interactive. We successfully use connectivity and cluster index, which characterise the connectivity of a superfamily's neighbourhood, to discover superfamilies of complex I and II. This is particularly significant as the structure of complex I is not yet solved. ${\bullet}$ Taxonomic diversity: we found that highly interactive superfamilies are in general taxonomically very diverse and are thus amongst the oldest. This led to the prediction of the oldest and most important protein domain in evolution of lift. ${\bullet}$ Fault-tolerance: we found that the network is very robust as for the majority of superfamilies removal from the network will not break up the network. Overall, we can single out the P-loop containing nucleotide triphosphate hydrolases superfamily as it is the most highly connected and has the highest taxonomic diversity. In addition, this superfamily has the highest interaction rank, is the barycenter of the network (it has the shortest average path to every other superfamily in the network), and is an articulation vertex, whose removal will disconnect the network. More generally, we conclude that the graph-theoretic and taxonomic analysis of PSIMAP is an important step towards the understanding of protein function and could be an important tool for tracing the evolution of life at the molecular level.

• Imaging Science in Dentistry
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• v.50 no.4
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• pp.331-337
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• 2020

Purpose: As cone-beam computed tomography (CBCT) has become the most widely used 3-dimensional (3D) imaging modality in the dental field, storage space and costs for large-capacity data have become an important issue. Therefore, if 3D data can be stored at a clinically acceptable compression rate, the burden in terms of storage space and cost can be reduced and data can be managed more efficiently. In this study, a deep learning network for super-resolution was tested to restore compressed virtual CBCT images. Materials and Methods: Virtual CBCT image data were created with a publicly available online dataset (CQ500) of multidetector computed tomography images using CBCT reconstruction software (TIGRE). A very deep super-resolution (VDSR) network was trained to restore high-resolution virtual CBCT images from the low-resolution virtual CBCT images. Results: The images reconstructed by VDSR showed better image quality than bicubic interpolation in restored images at various scale ratios. The highest scale ratio with clinically acceptable reconstruction accuracy using VDSR was 2.1. Conclusion: VDSR showed promising restoration accuracy in this study. In the future, it will be necessary to experiment with new deep learning algorithms and large-scale data for clinical application of this technology.

• Journal of The Geomorphological Association of Korea
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• v.23 no.3
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• pp.57-66
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• 2016

The objective of this study is to find out geomorphological characteristics of historical ruins where people produced and consumed large jar coffins excavated in the Yeongsan river basin using the map of old drainage network to restore distribution network. For this purpose, we chose the 21 consumption sites. The results are as follows. First of all, large jar coffins(relics, 47.6% of total) in the Yeongsan River basin were located in Sampo stream basin, almost all of them were located within the Yeongsan River main stream basin and Sampo stream basin. Also, distance from all consumption site to river was within about 2km. Therefore, it is thought that the all consumption sites are located at the place of the gift of nature that was very favorable to water transport of jar coffins. The results of this study may be used as basic data for research of cultural relics in the Yeongsan river basin.

• Journal of Mechanical Science and Technology
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• v.16 no.8
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• pp.1033-1038
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• 2002

Genetic alsorithm (GA) , compared to the gradient-based optimization, has advantages of convergence to a global optimized solution. The genetic algorithm requires so many number of analyses that may cause high computational cost for genetic search. This paper proposes a personal computer network programming based on TCP/IP protocol and client-server model using socket, to improve processing speed of the genetic algorithm for optimization of composite laminated structures. By distributed processing for the generated population, improvement in processing speed has been obtained. Consequently, usage of network-based genetic algorithm with the faster network communication speed will be a very valuable tool for the discrete optimization of large scale and complex structures requiring high computational cost.

• Tribology and Lubricants
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• v.11 no.3
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• pp.24-30
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• 1995

This paper was undertaken to recognize the pattern of the wear debris by neural network as a link for the development of diagnosis system for movable condition of the lubricated machine surface. The wear test was carried out under different experimental conditions using the wear test device was made in laboratory and wear testing specimen of the pin-on-disk type were rubbed in paraffine series base oil, by varying applied load, sliding distance and mating material. The neural network has been used to pattern recognition of four parameter (diameter, elongation, complex and contrast) of the wear debris and learned the friction condition of five values (material 3, applied load 1, sliding distance 1). The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by the neural network. The characteristic parameter of the large wear debris over a few micron size enlarged recognition ability.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3B
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• pp.225-234
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• 2009

Recently IPTV service is penetrating to the ordinary home users very swiftly. One of the first phase of IPTV service is considered to be VoD, and a nationwide availability of the VoD service imposes tremendous pressure to the network resource due to its requirements for the broad bandwidth, the inherent nature of unicast technology, and the large scalability, etc. This work suggests a novel and practical method to the design of network resource for the VoD service. Especially, we explore the distributed content storage problem that takes into account the popularity of the video contents and its corresponding link dimensioning problem that takes into account the grade of service for the flow level quality of service about the VoD service. By assuming a realistic topology for the nationwide IP backbone network of Korea, which is a typical tree topology, we suggest an analytic method for the design of VoD service.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.22 no.2
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• pp.115-123
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• 2018

This paper discusses extended Kalman filter method for solving learning problems of multilayered neural networks. A lot of learning algorithms for deep layered network are sincerely suffered from complex computation and slow convergence because of a very large number of free parameters. We consider an efficient learning algorithm for deep neural network. Extended Kalman filter method is applied to parameter estimation of neural network to improve convergence and computation complexity. We discuss how an efficient algorithm should be developed for neural network learning by using Extended Kalman filter.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.11
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• pp.5885-5889
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• 2013

In general, the network survivability means that The user do not know the network faults and the recovery of those. For this, we use the dual(multi) routes between each two nodes. It is important that the each dual routes have efficient costs(or minimum). Even if one route is the minimum cost in case of no fault, another route of dual may be very large cost in case of fault case. Therefore we need the dual routes of each two nodes having the efficient(or minimum) costs. In this paper we find the network design method for the dual routes of each two node having the efficient costs. Although the design method is very simple and heuristic and it may be not useful for some networks, we will use it in various network environment.. Because this design method can be used very easy. A sample design will proof this usefulness.