• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2479-2487
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• 2009

Earlier researches on Sensor Networks preferred symmetric key-based authentication schemes in consideration of limitations in network resources. However, recent advancements in cryptographic algorithms and sensor-node manufacturing techniques have opened suggestion to public key-based solutions such as Merkle tree-based schemes. This paper proposes a new concept of public key-based authentication using Polynomial Secret Sharing that can be effectively applied to sensor networks and a detection of malicious node using the hash function. This scheme is based on exponential distributed data concept, a derivative from Shamir's (t,n) threshold scheme, in which the authentication of neighbouring nodes are done simultaneously while minimising resources of sensor nodes and providing network scalability.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.3B
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• pp.143-152
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• 2007

In order to provide new value-added services such as a policy-based routing and the quality of services in next generation network, the Internet routers need to classify packets into flows for different treatments, and it is called a packet classification. Since the packet classification should be performed in wire-speed for every packet incoming in several hundred giga-bits per second, the packet classification becomes a bottleneck in the Internet routers. Therefore, high speed packet classification algorithms are required. In this paper, we propose an efficient packet classification architecture based on a hierarchical binary search fee. The proposed architecture hierarchically connects the binary search tree which does not have empty nodes, and hence the proposed architecture reduces the memory requirement and improves the search performance.

• Journal of Korea Multimedia Society
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• v.13 no.10
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• pp.1534-1546
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• 2010

In sensor network systems, various hierarchical clustering schemes have been proposed in order to efficiently maintain the energy consumption of sensor nodes. Most of these schemes, however, are hardly applicable in practice since these schemes might produce unbalanced clusters or randomly distributed clusters without taking into account of the distribution of sensor nodes. To overcome the limitations of such hierarchical clustering schemes, we propose a novel scheme called CSM(Clustering using Split & Merge algorithm), which exploits node split and merge algorithm of tree-based indexing structures to efficiently construct clusters. Our extensive performance studies show that the CSM constructs highly balanced clustering in a energy efficient way and achieves higher performance up to 1.6 times than the previous clustering schemes, under various operational conditions.

• ETRI Journal
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• v.38 no.1
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• pp.18-29
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• 2016

In this paper, we discuss the issues of providing protection for point-to-multipoint connections in both Ethernet and MPLS-TP-based packet transport networks. We introduce two types of per-leaf protection-linear and ring. Neither of the two types requires that modifications to existing standards be made. Their performances can be improved by a collective signal fail mechanism proposed in this paper. In addition, two schemes - tree protection and hybrid protection - are newly proposed to reduce the service recovery time when a single failure leads to multiple signal fail events, which in turn places a significant amount of processing burden upon a root node. The behavior of the tree protection protocol is designed with minimal modifications to existing standards. The hybrid protection scheme is devised to maximize the benefits of per-leaf protection and tree protection. To observe how well each scheme achieves an efficient traffic recovery, we evaluate their performances using a test bed as well as computer simulation based on the formulae found in this paper.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.1-9
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• 2021

In this study, we evaluated the accuracy of deep learning-based tree species classification model trained by using high-resolution images. We selected five species classed, i.e., pine, birch, larch, korean pine, mongolian oak for classification. We created 5,000 datasets using high-resolution orthophoto and forest type map. CNN deep learning model is used to tree species classification. We divided training data, verification data, and test data by a 5:3:2 ratio of the datasets and used it for the learning and evaluation of the model. The overall accuracy of the model was 89%. The accuracy of each species were pine 95%, birch 89%, larch 80%, korean pine 86% and mongolian oak 98%.

• Journal of Korea Water Resources Association
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• v.41 no.8
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• pp.797-806
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• 2008

The geometric properties of the drainage structures are analyzed through depicting the drainage network which is composed of the whole drainage paths in the natural basin defined at the specific scale. The theoretical consideration is performed on the general structures of networks organized by ramification process based on Fractal tree and Horton's law. The drainage network is generated via ArcGIS, ordered by Strahler's ordering scheme and investigated with Strahler's order. As a results of the Richardson's method it is shown that there may exist the distinct behavioral characteristics between overland-flow and channel flow and the natural stream networks would be space-filling Fractals. As a result, it is shown that the values estimated by considering the overland-flow on being applied to the field data give the different results from the empirical method applied until now. As expected, therefore the results obtained from this study are sure to be devoted further researches on the channel networks.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.3
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• pp.287-299
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• 2000

In this paper, we classify multicast methods into three categories, i.e., tree-based, path-based, and hybrid-based multicasts, for a multicomputer employing the bidirectional torus network and wormhole routing. We propose the dynamic partition multicast routing (DPMR) as a path-based algorithm. As a hybrid-based algorithm, we suggest the hybrid multicast routing (HMR), which employs the tree-based approach in the first phase of routing and the path-based approach in the second phase. Performance is measured in terms of the average latency for various message length to compare three multicast routing algorithms. We also compare the performance of wormhole routing having variable buffer size with virtual cut-through switching. The message latency for each switching method is compared using the DPMR algorithm to evaluate the buffer size trade-off on the performance.

• Journal of KIISE:Software and Applications
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• v.36 no.2
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• pp.144-152
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• 2009

To provide intelligent service in mobile environment, it needs to estimate user's intention or requirement, through analyzing context information of end-users such as preference or behavior patterns. In this paper, we infer context information from uncertain log stored in mobile device. And we propose the inference method of end-user's behavior to match context information with service, and the proposed method is based on context-tree. We adopt bayesian probabilistic method to infer uncertain context information effectively, and the context-tree is constructed to utilize non-numerical context which is hard to handled with mathematical method. And we verify utility of proposed method by appling the method to intelligent phone book service.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2007.04a
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• pp.101-104
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• 2007

As the proportion of underground construction increases, the impact of inappropriate selection of a underground construction method for a construction size increases. The purpose of this study is to develop an objective way of selecting an excavation method. There have been several attempts to achieve the same goal using various data mining methods such as the artificial neural network, the support vector machine, and the case-based reasoning. However, they focused only on the selection of a retaining wall construction method out of six types of retaining walls. When we categorized an underground construction work into four groups and added more number of independent variables (i.e., more number of construction methods), the predictability decreased. As an alternative, we developed a decision tree by analyzing 25 earthwork cases with detailed information. We implemented the developed decision tree as a computer-supported program called Dr. underground and are still in the process of validating and revising the decision tree. This study is still in a preliminary stage and will be improved by collecting and analyzing more cases.

• International Journal of Ocean System Engineering
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• v.3 no.3
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• pp.116-130
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• 2013

The prediction of ocean currents in real time over the warning times of a few hours or days is required in planning many operation-related activities in the ocean. Traditionally this is done through numerical models which are targeted toward producing spatially distributed information. This paper discusses a complementary method to do so when site-specific predictions are desired. It is based on the use of a recurrent type of neural network as well as the statistical tool of model tree. The measurements made at a site in Indian Ocean over a period of 4 years were used. The predictions were made over 72 time steps in advance. The models developed were found to be fairly accurate in terms of the selected error statistics. Among the two modeling techniques the model tree performed better showing the necessity of using distributed models for different sub-domains of data rather than a unique one over the entire input domain. Typically such predictions were associated with average errors of less than 2.0 cm/s. Although the prediction accuracy declined over longer intervals, it was still very satisfactory in terms of theselected error criteria. Similarly prediction of extreme values matched with that of the rest of predictions. Unlike past studies both east-west and north-south current components were predicted fairly well.