• Journal of the Korea Society of Computer and Information
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• v.10 no.1 s.33
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• pp.85-92
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• 2005

Recently, query processing techniques for the multi-dimensional data like images have been widely used to perform content-based retrieval of the data . Range query and Nearest neighbor query are widely used multi dimensional queries . This paper Proposes the efficient pruning strategies for k-nearest neighbor query in R-tree variants indexing structures. Pruning strategy is important for the multi-dimensional indexing query processing so that search space can be reduced. We analyzed the Pruning strategies and perform experiments to show overhead and the profit of the strategies. Finally, we propose best use of the strategies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.415-418
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• 2009

최근 서비스 지향 아키텍처 (Service Oriented Architecture SOA) 기반의 애플리케이션 개발에 맞게 비즈니스 프로세스의 유연성을 확보하고 재사용을 증진시키기 위하여 비즈니스 프로세스 패밀리 모델(Business Process Family Model: BPFM)이 제시되었다. BPFM은 소프트웨어 프로덕트 라인 방법의 가변성 분석 기법을 사용하여 비즈니스 프로세스 군 (family)에서 나타날 수 있는 가변성을 분석하여 이를 명시적으로 표현하고 있는 모델이다. BPFM으로부터 여러 개의 BPM (Business Process Model)을 개발하기 위해서는 가변성 결정 및 가지치기 (Decision and Pruning) 과정을 거쳐야 한다. 이 때 가변성 사이에는 서로 협력적 또는 배타적인 관계를 가질 수 있고 이는 가변성 결정과 가지치기에 영향을 미치게 되는데, 현재 제시된 BPFM에는 이러한 바인딩 정보에 대해서 아직 고려하지 않고 있다. 본 논문에서는 비즈니스 프로세스 군에서 식별될 수 있는 가변성들 사이의 의존관계의 유형을 분석하고 이 정보를 모델의 형태로 나타낼 수 있는 방법을 제시한다. 먼저 BPFM에 포함된 가변성 정보를 독립된 의존관계 분석모델로 추출해 내고 각 가변성 결정유형에 따라 표현방법을 제시한다. 추출된 모델에서 의존관계를 표현하고 가변성 결정이 영향을 미치는 범위의 가변성들을 클러스터할 수 있는 방법을 제공한다. 본 방법을 이용함으로써 가변성 결정회수를 현저히 줄일 수 있음을 사례연구를 통해 보여준다. 또한 잘못된 가변성 결정으로 인한 BPM의 기능 불일치를 해소할 수 있음을 보여준다.

• The KIPS Transactions:PartD
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• v.14D no.6
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• pp.605-614
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• 2007

As a standard query language to search XML data, XQuery and XPath were proposed by W3C. By widely using XQuery and XPath languages, recent researches focus on the development of query processing algorithm and data structure for efficiently processing XML query with the enormous XML database system. Recently, when processing XML path expressions, the concept of the structural join which may determine the structural relationship between XML elements, e.g., ancestor-descendant or parent-child, has been one of the dominant XPath processing mechanisms. However, structural joins which frequently occur in XPath query processing require high cost. In this paper, we propose a new structural join algorithm, called SISJ, based on our structured index, called SI, in order to process XPath queries efficiently. Experimental results show that our algorithm performs marginally better than previous ones. However, in the case of high recursive documents, it performed more than 30% by the pruning feature of the proposed method.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.3
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• pp.99-103
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• 2021

Recently, convolutional neural networks (CNNs) have been widely utilized due to their outstanding performance in various computer vision fields. However, due to their computational-intensive and high memory requirements, it is difficult to deploy CNNs on hardware platforms that have limited resources, such as mobile devices and IoT devices. To address these limitations, a neural network compression research is underway to reduce the size of neural networks while maintaining their performance. This paper proposes a CNN compression technique that dynamically adjusts the thresholds of pruning, one of the neural network compression techniques. Unlike the conventional pruning that experimentally or heuristically sets the thresholds that determine the weights to be pruned, the proposed technique can dynamically find the optimal thresholds that prevent accuracy degradation and output the light-weight neural network in less time. To validate the performance of the proposed technique, the LeNet was trained using the MNIST dataset and the light-weight LeNet could be automatically obtained 1.3 to 3 times faster without loss of accuracy.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.151-161
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• 2009

In Location-Based Services (LBSs), users requesting a location-based query send their exact location to a database server and thus the location information of the users can be misused by adversaries. Therefore, a privacy protection method is required for using LBS in a safe way. In this paper, we propose a new cloaking region generation algorithm using grids for privacy protection in LBSs. The proposed algorithm creates a m inimum cloaking region by finding L buildings and then performs K-anonymity to search K users. For this, we make use of not only a grid-based index structure, but also an efficient pruning techniques. Finally, we show from a performance analysis that our cloaking region generation algorithm outperforms the existing algorithm in term of the size of cloaking region.

• 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.

• The KIPS Transactions:PartD
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• v.15D no.5
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• pp.621-628
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• 2008

Many of today's businesses need IT system's flexibility for on-demand business which can be rapidly adapted to environment changes. Service oriented architecture (SOA) provides the infrastructure which makes business flexibility possible under the on-demand operating environment. Therefore, to satisfy these requirements, new approach for assuring business flexibility and enhancing reuse is needed. In this paper, we propose an approach for developing a business process family model (BPFM) in which the variabilities in business process family can be explicitly represented by using the variability analysis method of software product line. In addition, we describe the supporting tool for this approach. It can model the BPFM and generate automatically BPMs through decision and pruning process from BPFM. By using our approach, the business and its IT system can correspond to business environment changes rapidly and efficiently.

• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.200-207
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• 2020

Recently, deep neural networks such as CNN are showing excellent performance in various fields such as image classification, object recognition, visual quality enhancement, etc. However, as the model size and computational complexity of deep learning models for most applications increases, it is hard to apply neural networks to IoT and mobile environments. Therefore, neural network compression algorithms for reducing the model size while keeping the performance have been being studied. In this paper, we apply few compression methods to CNN models and evaluate their performances in the embedded environment. For evaluate the performance, the classification performance and inference time of the original CNN models and the compressed CNN models on the image inputted by the camera are evaluated in the embedded board equipped with QCS605, which is a customized AI chip. In this paper, a few CNN models of MobileNetV2, ResNet50, and VGG-16 are compressed by applying the methods of pruning and matrix decomposition. The experimental results show that the compressed models give not only the model size reduction of 1.3~11.2 times at a classification performance loss of less than 2% compared to the original model, but also the inference time reduction of 1.2~2.21 times, and the memory reduction of 1.2~3.8 times in the embedded board.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.1
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• pp.37-47
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• 2008

Exploitation of address generation units which are typically provided in DSPs plays an important role in DSP code generation since that perform fast address computation in parallel to the central data path. Offset assignment is optimization of memory layout for program variables by taking advantage of the capabilities of address generation units, consists of memory layout generation and address pointer assignment steps. In this paper, we propose an effective address pointer assignment method to minimize the number of address calculation instructions in DSP code generation. The proposed approach reduces the time complexity of a conventional address pointer assignment algorithm with fixed memory layouts by using minimum cost-nodes breaking. In order to contract memory size and processing time, we employ a powerful pruning technique. Moreover our proposed approach improves the initial solution iteratively by changing the memory layout for each iteration because the memory layout affects the result of the address pointer assignment algorithm. We applied the proposed approach to about 3,000 sequences of the OffsetStone benchmarks to demonstrate the effectiveness of the our approach. Experimental results with benchmarks show an average improvement of 25.9% in the address codes over previous works.

• Journal of KIISE:Databases
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• v.34 no.5
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• pp.409-419
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• 2007

Database community focuses on the similar music retrieval systems for music database when a humming query is given. One of the approaches is converting the midi data to time series, building their indices and performing the similarity search on them. Queries based on humming can be transformed to time series by using the known pitch detection algorithms. The recently suggested algorithm, scaled and warped matching, is based on dynamic time warping and uniform scaling. This paper proposes Humming BIRD(Humming Based sImilaR mini music retrieval system) using sliding window and center-aligned scaled and warped matching. Center-aligned scaled and warped matching is a mixed distance measure of center-aligned uniform scaling and time warping. The newly proposed measure gives tighter lower bound than previous ones which results in reduced search space. The empirical results show the superiority of this algorithm comparing the pruning power while it returns the same results.