• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.2936-2951
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• 2016

An emerging satellite technology, Operationally Responsive Space (ORS) is expected to provide a fast and flexible solution for emergency response, such as target tracking, dense earth observation, communicate relaying and so on. To realize large distance transmission, we propose the use of available relay satellites as relay nodes. Accordingly, we apply software defined network (SDN) technology to ORS networks. We additionally propose a satellite network architecture refered to as the SDN-based ORS-Satellite (Sat) networking scheme (SDOS). To overcome the issures of node failures and dynamic topology changes of satellite networks, we combine centralized and distributed routing mechanisms and propose a fast recovery routing algorithm (FRA) for SDOS. In this routing method, we use centralized routing as the base mode.The distributed opportunistic routing starts when node failures or congestion occur. The performance of the proposed routing method was validated through extensive computer simulations.The results demonstrate that the method is effective in terms of resoving low end-to-end delay, jitter and packet drops.

• Journal of Korea Spatial Information System Society
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• v.10 no.3
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• pp.19-30
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• 2008

Recently, the query processing algorithm in the field of spatial network database(SNDB) has been attracted by many Interests. But, there is little research on route-based queries. Since the moving objects move only in spatial networks, the efficient route-based query processing algorithms, like in-route nearest neighbor(IRNN), are essential for Location-based Service(LBS) and Telematics application. However, the existing IRNN query processing algorithm has a problem that it does not consider traffic jams in the road network. In this thesis, we propose an IRNN query processing algorithm which considers space restriction. Finally, we show that space-constrained IRNN query processing algorithm is efficient compared with the existing IRNN algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.465-468
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• 2008

Clustering techniques in wireless sensor networks is developed to minimize the energy consumption of node, show the effect that increases the network lifetime. Existing clustering techniques proposed the method that increases the network lifetime equalizing each node's the energy consumption by rotating the role of CH(Cluster Head), but these algorithm did not present the resolution that minimizes the energy consumption of neighboring nodes with sink. In this paper, we propose the clustering algorithm that prolongs the network lifetime by not including a part of nodes in POS(Personal Operating Space) of the sink in a cluster and communicating with sink directly to reduce the energy consumption of CH closed to sink.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.6
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• pp.1107-1112
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• 2008

Clustering techniques, which are algorithm to increase the network lifetime in wireless sensor networks, is developed to minimize the energy consumption of nodes. Existing clustering techniques by to increase the network lifetime with equalizing each node's the energy consumption by rotating the role of CH(Cluster Head), but these algorithms did not present the solution that minimizes the energy consumption of neighboring nodes with sink. In this paper, we propose the clustering algorithm that prolongs the network lifetime by not including a part of nodes in POS(Personal Operating Space) of the sink in a cluster and communicating with sink directly to reduce the energy consumption of CH closed to sink.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2805-2826
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• 2021

In most cases, the routing policy of networks shows a preference for a static one-to-one mapping of communication pairs to routing paths, which offers adversaries a great advantage to conduct thorough reconnaissance and organize an effective attack in a stress-free manner. With the evolution of network intelligence, some flexible and adaptive routing policies have already proposed to intensify the network defender to turn the situation. Routing mutation is an effective strategy that can invalidate the unvarying nature of routing information that attackers have collected from exploiting the static configuration of the network. However, three constraints execute press on routing mutation deployment in practical: insufficient route mutation space, expensive control costs, and incompatibility. To enhance the availability of route mutation, we propose an OpenFlow-based route mutation technique called Polymorphic Path Transferring (PPT), which adopts a physical and virtual path segment mixed construction technique to enlarge the routing path space for elevating the security of communication. Based on the Markov Decision Process, with considering flows distribution in the network, the PPT adopts an evolution routing path scheduling algorithm with a segment path update strategy, which relieves the press on the overhead of control and incompatibility. Our analysis demonstrates that PPT can secure data delivery in the worst network environment while countering sophisticated attacks in an evasion-free manner (e.g., advanced persistent threat). Case study and experiment results show its effectiveness in proactively defending against targeted attacks and its advantage compared with previous route mutation methods.

• Journal of Information Processing Systems
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• v.19 no.3
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• pp.310-322
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• 2023

Although the network environment is gradually improving, the virtual nature of the network is still the same fact, which has brought a great influence on the supervision of Weibo network public opinion dissemination. In order to reduce this influence, the user information of Weibo network public opinion dissemination is studied by using Python technology. Specifically, the 2019 "Ethiopian air crash" event was taken as the research subject, the relevant data were collected by using Python technology, and the data from March 10, 2019 to June 20, 2019 were constructed by using the implicit Dirichlet distribution topic model and the naive Bayes classifier. The Weibo network public opinion user identity graph model under the "Ethiopian air crash" on June 20 found that the public opinion users of ordinary netizens accounted for the highest proportion and were easily influenced by media public opinion users. This influence is not limited to ordinary netizens. Public opinion users have an influence on other types of public opinion users. That is to say, in the network public opinion space of the "Ethiopian air crash," media public opinion users play an important role in the dissemination of network public opinion information. This research can lay a foundation for the classification and identification of user identity information types under different public opinion life cycles. Future research can start from the supervision of public opinion and the type of user identity to improve the scientific management and control of user information dissemination through Weibo network public opinion.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.6
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• pp.1230-1237
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• 2009

In this paper, we develop a design methodology of Granular-based Radial Basis Function Neural Networks(GRBFNN) by context-based clustering. In contrast with the plethora of existing approaches, here we promote a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The output space is granulated making use of the K-Means clustering while the input space is clustered with the aid of a so-called context-based fuzzy clustering. The number of information granules produced for each context is adjusted so that we satisfy a certain reconstructability criterion that helps us minimize an error between the original data and the ones resulting from their reconstruction involving prototypes of the clusters and the corresponding membership values. In contrast to "standard" Radial Basis Function neural networks, the output neuron of the network exhibits a certain functional nature as its connections are realized as local linear whose location is determined by the values of the context and the prototypes in the input space. The other parameters of these local functions are subject to further parametric optimization. Numeric examples involve some low dimensional synthetic data and selected data coming from the Machine Learning repository.

• Spatial Information Research
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• v.23 no.3
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• pp.113-122
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• 2015

Recently, according to developing advanced construction technologies, buildings has been enlarged such as high-rise buildings or complex buildings associated with underground facilities. The number of indoor activity population has increased very quickly. Because of that, technical requirements for Indoor location based service (Indoor LBS) also have been increased. Although indoor networks have to be constructed for efficient LBSs in indoor space based on OGC IndoorGML, it is not suitable for large and complex space to apply the simple network structure to constructing indoor navigation networks. The indoor navigation network has to be constructed according to logical, physical, and functional constraints for indoor space. In order to do that, subspacing methods are required to partition large and complex indoor space into proper size of subspace. In this paper, we proposed the basic requirements of subspacing in indoor space for creating efficient indoor network, as well the work process of subspacing in indoor space.

• Journal of Platform Technology
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• v.6 no.1
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• pp.3-8
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• 2018

This paper proposes a technique for recognizing online handwritten cursive data obtained by tracing a motion trajectory while a user is in the 3D space based on a convolution neural network (CNN) algorithm. There is a difficulty in recognizing the virtual character input by the user in the 3D space because it includes both the character stroke and the movement stroke. In this paper, we divide syllable into consonant and vowel units by using labeling technique in addition to the result of localizing letter stroke and movement stroke in the previous study. The coordinate information of the separated consonants and vowels are converted into image data, and Korean handwriting recognition was performed using a convolutional neural network. After learning the neural network using 1,680 syllables written by five hand writers, the accuracy is calculated by using the new hand writers who did not participate in the writing of training data. The accuracy of phoneme-based recognition is 98.9% based on convolutional neural network. The proposed method has the advantage of drastically reducing learning data compared to syllable-based learning.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.45-55
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• 2021

This paper has proposed an algorithm that detecting for dense small vehicle in large image efficiently. It is consisted of two Ensemble Deep-Learning Network algorithms based on Coarse to Fine method. The system can detect vehicle exactly on selected sub image. In the Coarse step, it can make Voting Space using the result of various Deep-Learning Network individually. To select sub-region, it makes Voting Map by to combine each Voting Space. In the Fine step, the sub-region selected in the Coarse step is transferred to final Deep-Learning Network. The sub-region can be defined by using dynamic windows. In this paper, pre-defined mapping table has used to define dynamic windows for perspective road image. Identity judgment of vehicle moving on each sub-region is determined by closest center point of bottom of the detected vehicle's box information. And it is tracked by vehicle's box information on the continuous images. The proposed algorithm has evaluated for performance of detection and cost in real time using day and night images captured by CCTV on the road.