• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.55-62
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• 2019

Convolutional neural networks (CNNs) show high performance in the computer vision, but they require an enormous amount of operations, making them unsuitable for some resource- or energy-starving environments like the embedded environments. To overcome this problem, there have been much research on accelerators or pruning of CNNs. The previous pruning schemes have not considered the architecture of CNN accelerators, so the accelerators for the pruned CNNs have some inefficiency. This paper proposes a new pruning scheme, Conv-XP, which considers the architecture of CNN accelerators. In Conv-XP, the pruning is performed following the 'X' or '+' shape. The Conv-XP scheme induces a simple architecture of the CNN accelerators. The experimental results show that the Conv-XP scheme does not degrade the accuracy of CNNs, and that the accelerator area can be reduced by 12.8%.

• Journal of Forest and Environmental Science
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• v.24 no.1
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• pp.47-51
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• 2008

The first pruning works of planted trees on forest area carry out when tree height reached at 6 meters. And the second works carry out when it grow to 12~13 meters of tree height. Pruning works are necessary for producing straight log without knar by tool or machine. Generally, the mechanized pruning works Self-propelled pruning machine, chain pruning saw and other tools are used in mechanized pruning works. However, manual pruning saw which is usually using pruning tool was for this study. To investigate the pruning works efficiency, Pinus densiflora, Pinus koraiensis and Pinus rigida which were distributed in Kangwon-Do was surveyed. Height of surveyed the trees were 10~16 meters and its pruning works range were 6.2~6.7 meters of tree height. As results, pruning works efficiency of Pinus densiflora, Pinus koraiensis and Pinus rigida were 3.14 min/tree, 5.06 min/tree and 4.44 min/tree, respectively. Also, possible pruning works of man-day for Pinus densiflora, Pinus koraiensis and Pinus rigida was 104, 64, and 81 trees, respectively.

• 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 the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1371-1383
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• 2014

Classification ensemble technique is a method to combine diverse classifiers to enhance the accuracy of the classification. It is known that an ensemble method is successful when the classifiers that participate in the ensemble are accurate and diverse. However, it is common that an ensemble includes less accurate and similar classifiers as well as accurate and diverse ones. Ensemble pruning method is developed to construct an ensemble of classifiers by choosing accurate and diverse classifiers only. In this article, we proposed an ensemble pruning method called WAVE-bagging. We also compared the results of WAVE-bagging with that of the existing pruning method called Lasso-bagging. We showed that WAVE-bagging method performed better than Lasso-bagging by the extensive empirical comparison using 26 real dataset.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10a
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• pp.80-82
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• 2000

공간 데이터베이스 관리 시스템에서 제공하는 공간 질의는 많은 디스크 참조와 CPU 처리시간을 필요로 한다. 이 중에서 {{{{k}}}}-최근접 질의는 많은 디스크 참조를 요구하는 질의로써 지금까지 많은 연구가 이루어져 왔다. 트리 구조의 색인을 사용하는 {{{{k}}}}=최근접질의 처리방법은, 조건을 만족하지 않는 노드를 가지 치기 기법을 사용하여 노드 방문 횟수를 줄인다. 그러나, 이 방법은 가지치기 과정에서 불필요한 디스크 참고가 발생하여 성능을 저하시키는 단점을 가지고 있다. 본 논문에서는 가지치기 기법 대신 주어진 {{{{k}}}} 개의 최근접객체가 존재할 영역을 미리 예측함으로써 디스크 참조 횟수를 줄이는 방법을 제시한다. 이 영역을 예측하기 위해서 본 논문에서는 데이터 분포에 대한 밀도를 이용하였다. 실험에 의하면 이러한 방법은 기존의 가지치기 기법을 이용한 방법에 비해서 최고 22%, 평균 7%정도의 디스크 참조 횟수의 감소 효과가 있음을 알 수 있다.

• 산림경영
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• s.128
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• pp.4-5
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• 1997

• 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%.

• Journal of KIISE:Databases
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• v.35 no.2
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• pp.182-191
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• 2008

Most of previous works for skyline queries have focused only on static attributes of target objects. With the advance in mobile applications, however, the need of continuous skyline queries for moving objects has been increasing. Even though several techniques to process continuous skyline queries have been proposed recently, they cannot process subspace queries, which use only the subset of attribute dimensions. Therefore it is not feasible to utilize those methods for mobile applications which must consider moving objects and subspaces simultaneously. In this paper, we propose a dominant object-based pruning method to compute subspace skyline of moving objects efficiently at query time and present the experimental results to show the effectiveness of the proposed method.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10c
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• pp.22-27
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• 2006

4단계 스카이라인 영역 결정 기법[2]은 영역 결정 시간이 객체의 개수에 비례해서 현저히 증가하기 때문에 다수의 객체를 포함하는 도메인들에 적용하기 어렵다. 이러한 문제점은 스카이라인 영역이 지배 객체 집합의 부분 집합으로 이루어지는 특성을 고려하지 않았기 때문에 발생한다. 이 논문에서는 스카이라인 영역 결정에 불필요한 객체들을 제거할 수 있는 거리 기반 가지치기 기법과 영역 결정 선분의 범위 축소 기법을 제안한다. 제안한 기법들을 R*-트리와 INN(Incremental Nearest Neighbor) 알고리즘에 적용함으로써 점진적으로 스카이라인 영역을 결정할 수 있으며 영역 결정 시간을 현저하게 감소시킬 수 있다. 제안한 기법의 성능 향상을 증명하기 위해 4단계 영역 결정 기법과의 비교 실험을 수행한다.

• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.58-64
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• 2007

In pattern recognition field, there are many optimization problems having exponential search spaces. To solve of sequential search algorithms seeking sub-optimal solutions have been used. The algorithms have limitations of stopping at local optimums. Recently lots of researches attempt to solve the problems using genetic algorithms. This paper explains the huge search spaces of typical problems such as feature selection, classifier ensemble selection, neural network pruning, and clustering, and it reviews the genetic algorithms for solving them. Additionally we present several subjects worthy of noting as future researches.