• Journal of KIISE:Databases
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• v.29 no.5
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• pp.358-366
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• 2002

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the high speed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps(SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space and generates a topological feature map. A topological feature map preserves the mutual relations (similarities) in feature spaces of input data, and clusters mutually similar feature vectors in a neighboring nodes. Therefore each node of the topological feature map holds a node vector and similar images that is closest to each node vector. We implemented a k-NN search for similar image classification as to (1) access to topological feature map, and (2) apply to pruning strategy of high speed search. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.

• Journal of Positioning, Navigation, and Timing
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• v.8 no.4
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• pp.193-200
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• 2019

The auto-encoder network which is a good candidate to handle the modeling of the signal strength attenuation is designed for denoising and compensating the distortion of the received data. It provides a non-linear mapping function by iteratively learning the encoder and the decoder. The encoder is the non-linear mapping function, and the decoder demands accurate data reconstruction from the representation generated by the encoder. In addition, the adaptive network width which supports the automatic generation of new hidden nodes and pruning of inconsequential nodes is also implemented in the proposed algorithm for increasing the efficiency of the algorithm. Simulation results show that the proposed method can improve the neural network training surface to achieve the highest possible accuracy of the signal modeling compared with the conventional modeling method.

• Journal of Communications and Networks
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• v.16 no.6
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• pp.620-626
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• 2014

In this paper, we propose a parallel collaborative sphere decoder with a scalable architecture promising quasi-maximum likelyhood performance with a relatively small amount of computational resources. This design offers a hardware-friendly algorithm using a modified node operation through fixing the variable complexity of the critical path caused by the sequential nature of the conventional sphere decoder (SD). It also reduces the computational complexity compared to the fixed-complexity sphere decoder (FSD) algorithm by tree pruning using collaboratively operated node operators. A Monte Carlo simulation shows that our proposed design can be implemented using only half the parallel operators compared to the approach using an ideal fully parallel scheme such as FSD, with only about a 7% increase of the normalized decoding time for MIMO dimensions of $16{\times}16$ with 16-QAM modulation.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.11-19
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• 2014

In this paper, a low complexity parallel sphere decoder algorithm is proposed for high-order MIMO system. It reduces the computational complexity compared to the fixed-complexity sphere decoder (FSD) algorithm by static tree-pruning and dynamic tree-pruning using scalable node operators, and offers near-maximum likelihood decoding performance. Moreover, it also offers hardware-friendly node operation algorithm through fixing the variable computational complexity caused by the sequential nature of the conventional SD algorithm. A Monte Carlo simulation shows our proposed algorithm decreases the average number of expanded nodes by 55% with only 6.3% increase of the normalized decoding time compared to a full parallelized FSD algorithm for high-order MIMO communication system with 16 QAM modulation.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.1
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• pp.16-23
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• 2002

In this paper, we propose the growing and pruning algorithm to design the optimal structure of modular wavelet neural network(MWNN) with F-projection and geometric growing criterion. Geometric growing criterion consists of estimated error criterion considering local error and angle criterion which attempts to assign wavelet function that is nearly orthogonal to all other existing wavelet functions. These criteria provide a methodology which a network designer can construct MWNN according to one's intention. The proposed growing algorithm increases in number of module or the size of modules of MWNN. Also, the pruning algorithm eliminates unnecessary node of module or module from constructed MWNN to overcome the problem due to localized characteristic of wavelet neural network which is used to modules of MWNN. We apply the proposed constructing algorithm of the optimal structure of MWNN to approximation problems of 1-D function and 2-D function, and evaluate the effectiveness of the proposed algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.782-787
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• 2001

In this paper, we propose an growing and pruning algorithm to design the adaptive structure of modular wavelet neural network(MWNN) with F-projection and geometric growing criterion. Geometric growing criterion consists of estimated error criterion considering local error and angel criterion which attempts to assign wavelet function that is nearly orthogonal to all other existing wavelet functions. There criteria provide a methodology that a network designer can constructs wavelet neural network according to one s intention. The proposed growing algorithm grows the module and the size of modules. Also, the pruning algorithm eliminates unnecessary node of module or module from constructed MWNN to overcome the problem due to localized characteristics of wavelet neural network which is used to modules of MWNN. We apply the proposed constructing algorithm of the adaptive structure of MWNN to approximation problems of 1-D function and 2-D function, and evaluate the effectiveness of the proposed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4A
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• pp.351-357
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• 2007

Numerous papers that study ad hoc networks have used a hierarchical network structure to enhance scalability. The hierarchical structure typically consists of a number of clusters, each of which has its own clusterhead that maintains information. Clusterheads often need to exchange information among themselves in order to maintain information, and for such cases, a mechanism is needed to efficiently deliver information from one clusterhead to another. Here, we proposed a new distributed algorithm in which every node independently makes the decision about whether or not it forwards a received message. We used a simulation to demonstrate that the algorithm developed for this study is a considerable improvement over the control overhead algorithm.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.48 no.1
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• pp.8-12
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• 2003

To find out the relationship between pod development and cytokinin contents during reproductive stage of peanut, the cytokinin contents, trans-zeatin ribo-side (t-ZR) and dihydrozeatin riboside (diZR), were investigated at 0, 7, 14, 21 and 28 days after flowering (DAF). The amounts of t-ZR and diZR in cotyledon and first branch among primary branches were 3, 448 pmol/g (FW) and 4, 824 p/g (FW), respectively, which were higher than those of other branches. The t-ZR and diZR contents of lower parts on the branch from cotyledon node at 7 DAF were 579 pmol/g (FW) and 2, 028 pmol/g (FW), respectively, which were higher than those of upper parts. The cytokinin contents of reproductive organs as flowering progressed were increased at 0 and 14 DAF on branch and position of node. The cytokinin contents of upper part with pruning the lower part on the branch from cotyledon node were high 112-337% at 7 DAF and 14 DAF compared with those of the control. In case of remove the upper part of the first internode on main axis, t-ZR contents was 4.7 times higher than diZR contents at 7 DAF. The pod setting rate of flower and position on the branch from cotyledon node was closely related to the cytokinin contents during floral reproductive stage.

• Horticultural Science & Technology
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• v.18 no.5
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• pp.591-593
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• 2000

This study was conducted to investigate the effect of training time on the growth and yield of 'Sinsakigake-2' pepper (Capsicum annuum L.). Plants were either left untrained as control or trained at the third node leaving four shoots per plant. Plants were traind at 20, 45, 70, or 95 days after transplanting (DAT). The earlier the training time, the more the branch number of the plant. Fruit weight was not affected by training time. However, the percentage of marketable fruits and the number of marketable fruits per plant were the highest in plants trained at 70 DAT, and the lowest in the control. Early marketable yield was the highest in the control, and the lowest in plants trained at 45 DAT.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.4
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• pp.59-70
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• 2003

Spatial data base system provides many query types and most of them are required frequent disk I/O and much CPU time. k-NN search is to find k-th closest object from the query point and up to now, several k-NN search methods have been proposed. Among these, MINMAX distance method has an aim not to access unnecessary node by adapting pruning technique. But this method accesses more disks than necessary while pruning unnecessary nodes. In this paper, we propose new k-NN search algorithm based on density of object. With this method, we predict the radius to be expected to contain k-NN objects using density of data set and search those objects within this radius and then adjust radius if failed. Experimental results show that this method outperforms the previous MINMAX distance method. This algorithm visit less disks than MINMAX method by the factor of maximum 22% and average 7%.