• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.3
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• pp.815-834
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• 2012

Recently, along with the innovative development of wireless communication techniques and mobile devices, mobile P2P services in mobile wireless networks have gained a lot of attention. In this paper, we propose a resource discovery scheme with data dissemination over mobile P2P networks. In the proposed scheme, each peer manages a local information table, a resource index table, and a routing table in a local database to enhance the accuracy and cost of resource discovery. The local information table stores the status of a mobile peer, and the resource index table stores the resource information of the neighbor peers via the ranking function. The routing table is used to communicate with the neighbor peers. We use a timestamp message to determine whether or not the resource index table will be changed before the resource information is exchanged. Our ranking function considers the interest and mobility of the mobile peer and prioritizes the resource information transmitted from the neighbor peers. It is shown via various experiments that the proposed scheme outperforms the existing scheme.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.715-717
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• 2003

Some previous works for nearest neighbor (NN) query processing technique can treat a case that query/data are both moving objects. However, they cannot find exact result owing to vagueness of criterion. In order to escape their limitations and get exact result, we propose new NN query techniques, exact CTNN (continuous trajectory NN) query, approximate CTNN query, and dynamic CTNN query. These are all superior to pervious works, by reducing of number of calculation, considering of trajectory information, and using of continuous query concept. Using these techniques, we can solve any situations and types of NN query in location-aware environment.

• Annual Conference of KIPS
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• 2010.04a
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• pp.377-380
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• 2010

This paper proposes texture descriptor for texture classification called Local Neighbor Differences (LND). LND is a high discriminating texture descriptor and also robust to illumination changes. The proposed descriptor utilizes the sign of differences between surrounding pixels in a local neighborhood. The differences of those pixels are thresholded to form an 8-bit binary codeword. The decimal values of these 8-bit code words are computed and they are called LND values. A histogram of the resulting LND values is created and used as feature to describe the texture information of an image. Experimental results, with respect to texture classification accuracies using OUTEX_TC_00001 test suite has been performed. The results show that LND outperforms LBP method, with average classification accuracies of 92.3% whereas that of local binary patterns (LBP) is 90.7%.

• Annual Conference of KIPS
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• 2011.11a
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• pp.101-103
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• 2011

E-Health is one of the most promising applications of wireless sensor networks. This paper describes a prototype for e-Health systems. Based on the system, we propose the energy-balanced location-aided routing protocol. The location and energy information of the neighbor Coordinators is collected and stored in the neighbor discovery procedure. And then the Coordinator selects the most suitable neighbor to forward the data. The simulation results show that the proposed protocol has better performance than the three other routing protocols.

• ETRI Journal
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• v.46 no.3
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• pp.473-484
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• 2024

We introduce a machine learning-based web application to help travel agents plan a package tour schedule. K-nearest neighbor (KNN) classification predicts the optimal tourists' dwelling time based on a variety of information to automatically generate a convenient tour schedule. A database collected in collaboration with an established travel agency is fed into the KNN algorithm implemented in the Python language, and the predicted dwelling times are sent to the web application via a RESTful application programming interface provided by the Flask framework. The web application displays a page in which the agents can configure the initial data and predict the optimal dwelling time and automatically update the tour schedule. After conducting a performance evaluation by simulating a scenario on a computer running the Windows operating system, the average response time was 1.762 s, and the prediction consistency was 100% over 100 iterations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.9
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• pp.1873-1882
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• 2009

In the ad-hoc mode of IEEE 802.11 standard, beacon is used for power control and network alarming between each node, and also it is for the synchronization in controlling network as PCF. However beacon is also used to inform the neighbor nodes of itself except for original purpose in the environment of IEEE 802.11s. For this, the existing beacon's transmission mechanism can't perform a function in full. In this paper, we suggest much faster neighbor discovery reducing network congestion caused by beacon through modification of beacon transmission mechanism. And we will show that suggesting algorithm more faster full neighbor discovery than traditional neighbor discovery using for IEEE 802.11 through simulation and test in real ad-hoc network.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.1
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• pp.29-36
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• 2019

Unmanned Autonomous Vehicle's driving road in the national defense includes not only paved roads, but also unpaved roads which have rough and unexpected changes. This Unmanned Autonomous Vehicles monitor and recon rugged or remote areas, and defend own position, they frequently encounter environments roads of various and unpredictable. Thus, they need additional learning to drive in this environment, we propose a Additional Learning Nearest Neighbor (ALNN) which is modified from Approximate Nearest Neighbor to allow for quick learning while avoiding the 'Forgetting' problem. In addition, since the Execution speed of the ALNN algorithm decreases as the learning data accumulates, we also propose a solution to this problem using GPU parallel processing based on OpenGL Shader Language. The ALNN based on GPU algorithm can be used in the field of national defense and other similar fields, which require frequent and quick application of additional learning in real-time without affecting the existing learning data.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.37-52
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• 2015

The recent global trends display expansion and growing solidity in both cooperative collaboration between industry, education, and research and R&D network systems. A greater support for the network and cooperative research sector would open greater possibilities for the evolution of new scholar and industrial fields and the development of new theories evoked from synergized educational research. Similarly, the national need for a strategy that can most efficiently and effectively support R&D network that are established through the government's R&D project research is on the rise. Despite the growing urgency, due to the habitual dependency on simple individual personal information data regarding R&D industry participants and generalized statistical data references, the policies concerning network system are disappointing and inadequate. Accordingly, analyses of the relationships involved for each subject who is participating in the R&D industry was conducted and on the foundation of an educational-industrial-research network system, possible changes within and of the network that may arise were predicted. To predict the R&D network transitions, Common Neighbor and Jaccard's Coefficient models were designated as the basic foundational models, upon which a new prediction model was proposed to address the limitations of the two aforementioned former models and to increase the accuracy of Link Prediction, with which a comparative analysis was made between the two models. Through the effective predictions regarding R&D network changes and transitions, such study result serves as a stepping-stone for an establishment of a prospective strategy that supports a desirable educational-industrial-research network and proposes a measure to promote the national policy to one that can effectively and efficiently sponsor integrated R&D industries. Though both weighted applications of Common Neighbor and Jaccard's Coefficient models provided positive outcomes, improved accuracy was comparatively more prevalent in the weighted Common Neighbor. An un-weighted Common Neighbor model predicted 650 out of 4,136 whereas a weighted Common Neighbor model predicted 50 more results at a total of 700 predictions. While the Jaccard's model demonstrated slight performance improvements in numeric terms, the differences were found to be insignificant.

• Journal of Information Technology Services
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• v.15 no.4
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• pp.97-109
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• 2016

The rapid deployment of millions of mobile sensors and smart devices has resulted in high demand for opportunistic encounter-based networking. For the cooperative video surveillance of dashboard cameras in nearby vehicles, a fast and energy-efficient asynchronous neighbor discovery protocol is indispensable because a dashboard camera is an energy-hungry device after the vehicle's engine has turned off. In the existing asynchronous neighbor discovery protocols, all nodes always try to discover all neighbors. However, a dashboard camera needs to discover nearby dashboard cameras when an event is detected. In this paper, we propose a fast and energy-efficient asynchronous neighbor discovery protocol, which enables nodes : 1) to have different roles in neighbor discovery, 2) to discover neighbors within a search range, and 3) to report promptly the exact discovery result. The proposed protocol has two modes: periodic wake-up mode and active discovery mode. A node begins with the periodic wake-up mode to be discovered by other nodes, switches to the active discovery mode on receiving a neighbor discovery request, and returns to the periodic wake-up mode when the active discovery mode finishes. In the periodic wake-up mode, a node wakes up at multiples of number ${\alpha}$, where ${\alpha}$ is determined by the node's remaining battery power. In the active discovery mode, a node wakes up for consecutive ${\gamma}$ slots. Then, the node operating in the active discovery mode can discover all neighbors waking up at multiples of ${\beta}$ for ${\beta}{\leq}{\gamma}$ within ${\gamma}$ time slots. Since the proposed protocol assigns one half of the duty cycle to each mode, it consumes equal to or less energy than the existing protocols. A performance comparison shows that the proposed protocol outperforms the existing protocols in terms of discovery latency and energy consumption, where the frequency of neighbor discovery requests by car accidents is not constantly high.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.6A
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• pp.85-96
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• 2008

Neighbor Discovery(ND) protocol is used to exchange an information of the neighboring nodes on the same link in the IPv6 protocol environment. For protecting the ND protocol, firstly utilizing Authentication Header(AH) of the IPsec protocol was proposed. But the method has some problems-uses of key exchange protocol is not available and it is hard to distribute manual keys. And then secondly the SEcure Neighbor Discovery(SEND) protocol which protects all of the ND message with digital signature was proposed. However, the digital signature technology on the basis of public key cryptography system is commonly known as requiring high cost, therefore it is expected that there is performance degradation in terms of the availability. In the paper, to improve performance of the SEND protocol, we proposed a modified CGA(Cryptographically Generated Address) which is made by additionally adding MAC(Media Access Control) address to the input of the hash function. Also, we proposed cache mechanism. We compared performance of the methods by experimentation.