• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.260-262
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• 2004

Spatial database 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 visit unnecessary node by applying pruning technique. But this method access more disk than necessary while pruning unnecessary node. 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 object 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 fewer disks than MINMAX method by the factor of maximum $22\%\;and\;average\;6\%.$

• Journal of Internet Computing and Services
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• v.8 no.4
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• pp.71-79
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• 2007

The communicative behaviors in Wireless Sensor Networks(WSNs) can be characterized by two different types: routing and broadcasting. The broadcasting is used for effective route discoveries and packet delivery. However, broadcasting shorten the network lifetime due to the energy overconsumption by redundant transmissions. In this paper, we proposed a algorithm that remove redundant forward nodes based on Dominant Pruning method using 2-hop neighbors knowledge. Simulation results show that the proposed algorithm appears superior performance in respect of the number of forward nodes and the network lifetime.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.11a
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• pp.1-3
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• 2015

In this paper, we propose a sparse signal reconstruction method referred to as the matching pursuit with a pruning-based tree search (PTS-MP). Two key ingredients of PTS-MP are the pre-selection to put a restriction on columns of the sensing matrix to be investigated and the tree pruning to eliminate unpromising paths from the search tree. In our simulations, we confirm that PTS-MP is effective in recovering sparse signals and outperforms conventional sparse recovery algorithms.

• Journal of Agricultural Extension & Community Development
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• v.7 no.1
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• pp.167-173
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• 2000

This study was carried out to investigate suitable pruning methods to avoid poor berry setting of 'Kyoho' grapevine in Suwon and Anseong areas. Light pruning increased percentage of bud bursts and number of shoots per bearing mother branches(BMB), cluster weight and percentage of berry sating but it decreased shoot length, number of leaves and, leaf area and leaf weight. There was little difference in soluble solids, titratable acidity, and anthocyanin contents of berries by the treatment of either light or heavy pruning. Shoot length became longer as diameter of BMB thickened, leaf area and chlorophyll content also became larger and diameter of BMB. Total carbohydrate and N, P, K, Ca, and Mg contents showed significantly little difference, but total carbohydrate and Ca contents showed a tendency of continuous increase as diameter of BMB thinned. Number of seeded berries per cluster became fewer as BMB thinned but percentage of berry setting showed an increasing tendency. Cluster weight, berry weight, berry number per cluster, soluble solids, anthocyanin content were higher in case of around 1cm of diameters of BMB.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.2
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• pp.65-74
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• 2009

In optimizing the neural network structure, there are two methods of the pruning scheme and the constructive scheme. In this paper we use the pruning scheme to optimize neural network structure, and the genetic algorithm to find out its optimum node pruning. In the conventional researches, the input and hidden layers were optimized separately. On the contrary we attempted to optimize the two layers simultaneously by encoding two layers in a chromosome. The offspring networks inherit the weights from the parent. For teaming, we used the existing error back-propagation algorithm. In our experiment with various databases from UCI Machine Learning Repository, we could get the optimal performance when the network size was reduced by about $8{\sim}25%$. As a result of t-test the proposed method was shown better performance, compared with other pruning and construction methods through the cross-validation.

• Journal of Broadcast Engineering
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• v.7 no.2
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• pp.145-152
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• 2002

As the number of digital images available to users is exponentially growing due to the rapid development of digital technology, content-based image retrieval (CBIR) has been one of the most active research areas. A variety of image retrieval methods have been proposed, where, given an input query image, the images that are similar to the input are retrieved from an image database based on low-level features such as colors and textures. However, most of the existing retrieval methods did not consider the case when an input query image is a part of a whole image in the database due to the high complexity involved in partial matching. In this paper, we present an efficient method for partial image matching by using the histogram distribution relationships between query image and whole image. The proposed approach consists of two steps: the first step prunes the search space and the second step performs block-based retrieval using partial image matching to rank images in candidate set. The experimental results demonstrate the feasibility of the proposed algorithm after assuming that the response tune of the system is very high while retrieving only by using partial image matching without Pruning the search space.

• Research in Plant Disease
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• v.30 no.2
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• pp.194-198
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• 2024

Pathogens of fire blight can survive for a long time on pruning tools, etc., and fire blight can be spread through agricultural implements. In particular, in Korea, pruning is frequently performed to remove flowers, immature fruits, and succulent shoots, and this farm work is known to be a major factor of the spread of fire blight. Therefore, in this study, in order to completely sterilize pathogens on pruning shears, we verified the disinfection effect of disinfectants distributed domestically and used them to identify an effective disinfection method. When disinfecting by immersion after inoculating Erwinia amylovora TS3128 on the scissor blade, 70% ethanol and 1% and 0.2% sodium hypochlorite sterilized immediately 10 sec after immersion, while 30% chlorine dioxide showed little disinfection effect. When disinfecting by spraying, 70% ethanol sterilized bacteria 1 sec after spraying, but 1% and 0.2% sodium hypochlorite disinfected bacteria after 10 and 60 sec, respectively. After cutting the naturally disease occurring branches five and 100 times, the bacteria were not hardly sterilized in immersion treatment in 70% ethanol for 30 sec but perfectly disinfected over 60 sec. Considering these results, pruning shears should be disinfected by frequently immersing them in 70% ethanol for at least 60 sec during pruning work in the field.

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.136-147
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• 2003

To achieve an optimal performance for a given problem, we need an integrative process of the parameter optimization via learning and the structure optimization via model selection. In this paper, we propose an efficient optimization method for improving generalization performance by considering the property of each sub-method and by combining them with common theoretical properties. First, weight parameters are optimized by natural gradient teaming with adaptive regularization, which uses a diverse error function. Second, the network structure is optimized by eliminating unnecessary parameters with natural pruning. Through iterating these processes, candidate models are constructed and evaluated based on the Bayesian Information Criterion so that an optimal one is finally selected. Through computational experiments on benchmark problems, we confirm the weight parameter and structure optimization performance of the proposed method.

• The Journal of the Acoustical Society of Korea
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• v.22 no.6
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• pp.457-462
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• 2003

In this paper, we propose a speaker pruning method for real-time processing and improving performance of speaker identification system based on GMM(Gaussian Mixture Model). Conventional speaker identification methods, such as ML (Maximum Likelihood), WMR(weighting Model Rank), and MWMR(Modified WMR) we that frame likelihoods are calculated using the whole frames of each input speech and all of the speaker models and then a speaker having the biggest accumulated likelihood is selected. However, in these methods, calculation cost and processing time become larger as the increase of the number of input frames and speakers. To solve this problem in the proposed method, only a part of speaker models that have higher likelihood are selected using only a part of input frames, and identified speaker is decided from evaluating the selected speaker models. In this method, fm can be applied for improving the identification performance in speaker identification even the number of speakers is changed. In several experiments, the proposed method showed a reduction of 65% on calculation cost and an increase of 2% on identification rate than conventional methods. These results means that the proposed method can be applied effectively for a real-time processing and for improvement of performance in speaker identification.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.2
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• pp.77-86
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• 2004

In general, small-sized neural networks, even though they show good generalization performance, tend to fail to team the training data within a given error bound, whereas large-sized ones learn the training data easily but yield poor generalization. Therefore, a way of achieving good generalization is to find the smallest network that can learn the data, called the optimal-sized neural network. This paper proposes a new scheme for network pruning with ‘impact factor’ which is defined as a multiplication of the variance of a neuron output and the square of its outgoing weight. Simulation results of function approximation problems show that the proposed method is effective in regression.