• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.17 no.1
• /
• pp.167-172
• /
• 2017

Mobile nodes are used for prolonging the life-time of the entire wireless sensor networks and many studies that use drones to collected data have been actively conducted with the development of drone related technology. In case of associating a drone and tactical wireless sensor networks, real-time feature and efficiency are improved. The previous studies so focus on reducing drone's flight distance that the energy consumption of sensor nodes is unbalanced. This unbalanced energy consumption accelerates the network partition and increases drone's flight distance. In this paper, we proposed a new selection scheme considered drone's flight distance and nodes' life-time to solve this problem when rendezvous nodes that collect data from their cluster and directly communicate with a drone are selected.

• Proceedings of the KSR Conference
• /
• 2007.11a
• /
• pp.1731-1737
• /
• 2007

This paper considers subway routing problem. Given a schedule of train to be routed by a railway stock, the routing problem determines a sequence of trains while satisfying turnaround time and maintenance restrictions. Generally, the solution of routing problem is generated from set partition formulation solved by column generation method, a typical integer programming approach for train-set. However, we find the characteristics of metropolitan subway which has a simple rail network, a few end stations and 13 departure-arrival patterns. We reflect a turn-around constraint due to spatial limitations has no existence in conventional railroad. Our objective is to minimize the number of daily train-sets. In this paper, we develop two basic techniques that solve the subway routing problem in a reasonable time. In first stage, we formulate the routing problem as a Min-cost-flow problem. Then, in the second stage, we attempt to normalize the distance covered to each routes and reduce the travel distance using our heuristic approach. Applied to the current daily timetable, we could find the subway routings, which is an approximately 14% improvement on the number of train-sets reducing 15% of maximum traveling distance and 8% of the standard deviation.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.28 no.12B
• /
• pp.1066-1080
• /
• 2003

In this paper a scalable QoS routing scheme for distributed multimedia applications in a hierarchical wide area network is proposed. The problem of QoS routing is formulated as a multicriteria shortest path problem, known as NP-complete. The proposed hierarchical routing scheme consists of two phases. In Phase 1, every border node periodically pre-computes the QoS distance for the paths between every pair of border nodes in any level of domain hierarchy. This phase is independet of the QoS request from an application. In Phase II, distributed graph construction algorithm is performed to model the network as a graph by retrieving pre-computed QoS distances. The graph is constructed by the on-demand algorithm and contains a part of the network topology which is completely neglected or partially considered by existing routing schemes, thus maintaining more accurate topology information. By using retrieval approach rather than advertising one, no global QoS state information exchange among nodes is needed. In this Phase, distributed partition algorithm for QoS routing problem is also performed, thus eliminating virtual links on the hierarchically complete path.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.11 no.11
• /
• pp.2158-2165
• /
• 2007

In this paper, a Modified Fuzzy C-Means algorithm with Gaussian Weights(MFCM_GW) is presented for nonlinear blind channel equalization. The proposed algorithm searches the optimal channel output states of a nonlinear channel from the received symbols, based on the Bayesian likelihood fitness function and Gaussian weighted partition matrix instead of a conventional Euclidean distance measure. Next, the desired channel states of a nonlinear channel are constructed with the elements of estimated channel output states, and placed at the center of a Radial Basis Function(RBF) equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with those of a simplex genetic algorithm(GA), a hybrid genetic algorithm(GA merged with simulated annealing(SA): GASA), and a previously developed version of MFCM. It is shown that a relatively high accuracy and fast search speed has been achieved.

• Korean Journal of Weed Science
• /
• v.17 no.3
• /
• pp.303-309
• /
• 1997

Variation of morphological and physiological traits of 50 Bidens tripartita accessions were studied and the accessions were grouped through cluster analysis based on four major characters; plant type, leaf partition, achene length, days to flowering. Bidens tripartite accessions have shown significant variations in plant type, stem length, days to flowering, leaf shape, leaf partition, chlorophyll content, leaf color, stem color, achene color, achene length and achene shape. Most of Bidens tripartite accessions appeared to have strong dormancy and also photodormancy with some exceptions. Plants could be classified into 5 types from straight(I) to triangle(V), and intermediate diamond type(III) was prevalent. The plant type score has negative correlation with the stem length. None, three, and five part leaved plants were observed and most of them were three or five parted. Leaf partition had negative correlation with achene length and chlorophyll content. Average days to flowering was 108 days in the range of 94~141 days. It had positive correlation with achene length and leaf shape and negative correlation with achene color. Average achene length was 10.0mm and it had positive correlation with achene shape, stem length, days to flowering and leaf shape. It also had negative correlation with leaf color, stem color, achene color, leaf partition. Bidens tripartite accessions could be divided into identifiable six groups from the cluster analysis at the distance 0.06 using Ward's minimum-variance method.

• Journal of IKEEE
• /
• v.12 no.1
• /
• pp.65-74
• /
• 2008

One of the popular methods used for pattern classification is the MBR (Memory-Based Reasoning) algorithm. Since it simply computes distances between a test pattern and training patterns or hyperplanes stored in memory, and then assigns the class of the nearest training pattern, it is notorious for memory usage and can't learn additional information from new data. In order to overcome this problem, we propose an incremental learning algorithm (iMPA). iMPA divides the entire pattern space into fixed number partitions, and generates representatives from each partition. Also, due to the fact that it can not learn additional information from new data, we present iMPA which can learn additional information from new data and not require access to the original data, used to train. Proposed methods have been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory using benchmark data sets from UCI Machine Learning Repository.

• Journal of KIISE:Software and Applications
• /
• v.31 no.4
• /
• pp.478-487
• /
• 2004

We had proposed the RPA(Recursive Partition Averaging) method in order to improve the storage requirement and classification rate of the Memory Based Reasoning. That algorithm worked not bad in many area, however, the major drawbacks of RPA are it's partitioning condition and the way of extracting major patterns. We propose an adaptive RPA algorithm which uses the FPD(feature-based population densimeter) to stop the ARPA partitioning process and produce, instead of RPA's averaged major pattern, optimizing resulting hyperrectangles. The proposed algorithm required only approximately 40% of memory space that is needed in k-NN classifier, and showed a superior classification performance to the RPA. Also, by reducing the number of stored patterns, it showed an excellent results in terms of classification when we compare it to the k-NN.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.67 no.2
• /
• pp.285-292
• /
• 2018

In this paper, we propose a distributed search of a cluster robot using tree structure in an unknown environment. In the proposed method, the cluster robot divides the unknown environment into 4 regions by using the LRF (Laser Range Finder) sensor information and divides the maximum detection distance into 4 regions, and detects feature points of the obstacle. Also, we define the detected feature points as Voronoi Generators of the Voronoi Diagram and apply the Voronoi diagram. The Voronoi Space, the Voronoi Partition, and the Voronoi Vertex, components of Voronoi, are created. The generated Voronoi partition is the path of the robot. Voronoi vertices are defined as each node and consist of the proposed tree structure. The root of the tree is the starting point, and the node with the least significant bit and no children is the target point. Finally, we demonstrate the superiority of the proposed method through several simulations.

• Journal of Korea Multimedia Society
• /
• v.10 no.11
• /
• pp.1516-1522
• /
• 2007

We had proposed the FPA(Fixed Partition Averaging) method in order to improve the storage requirement and classification rate of the Memory Based Reasoning. The algorithm worked not bad in many area, but it lead to some overhead for memory usage and lengthy computation in the multi classes area. We propose an Repetitive FPA algorithm which repetitively partitioning pattern space in the multi classes area. Our proposed methods have been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory.

• Journal of Korea Multimedia Society
• /
• v.11 no.12
• /
• pp.1625-1634
• /
• 2008

In this study, a modified Fuzzy C-Means algorithm with Gaussian weights (MFCM_GW) is presented for the problem of nonlinear blind channel equalization. The proposed algorithm searches for the optimal channel output states of a nonlinear channel based on received symbols. In contrast to conventional Euclidean distance in Fuzzy C-Means (FCM), the use of the Bayesian likelihood fitness function and the Gaussian weighted partition matrix is exploited in this method. In the search procedure, all possible sets of desired channel states are constructed by considering the combinations of estimated channel output states. The set of desired states characterized by the maxima] value of the Bayesian fitness is selected and updated by using the Gaussian weights. After this procedure, the Bayesian equalizer with the final desired states is implemented to reconstruct transmitted symbols. The performance of the proposed method is compared with those of a simplex genetic algorithm (GA), a hybrid genetic algorithm (GA merged with simulated annealing (SA):GASA), and a previously developed version of MFCM. In particular, a relative]y high accuracy and a fast search speed have been observed.