• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.552-552
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• 2000

In recent years, a number of successful nesting approaches have been developed by using the various heuristic algorithms, and due to their application potential several commercial CAD/CAM packages include a nesting module for solving the layout problem. Since a large portion of the complexity of the part nesting problem results from the overlapping computation, the geometric representation is one of the most important factors to reduce the complexity of the problem. The proposed part representation method can easily handle parts and raw materials with widely varying geometrical shape by using the redesigning modules. This considerably reduces the amount of processed data and consequently the run time of the computer. The aim of this research is to develop parametric macro for two-dimensional cutting stock on the Auto-CAD system. Therefore, this research can be called "pre-nesting"

• 대한전자공학회논문지SP
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• 제40권1호
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• pp.87-95
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• 2003

This paper proposes an object detection algorithm using the Multiresolution Motion Estimation(MRME) in wavelet d야main. A existing motion estimation method has characteristics of motion estimation but it requires having computation. Motion estimation in higher resolution used the motion vector of the lower resolution with the MRME that has parent-child relationship on wavelet coefficients. This method reduces the search area of motion estimation in higher resolution and computational complexity. The computational complexity of the proposed method is about 40% of the existing method using 3-level Set Partitioning in Hierarchical Trees(SPIHT) wavelet transform. The experimental results with the proposed method showed about 11% decrease of Mean Absolute Difference(MAD) and gains able to precise tracking of object.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2000년도 추계학술대회 논문집
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• pp.253-260
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• 2000

In recent years, a number of successful nesting approaches have been developed by using the various heuristic algorithms, and due to their application potential several commercial CAD/CAM packages include a nesting module for solving the layout problem. Since a large portion of the complexity of the part nesting problem results from the overlapping computation, the geometric representation is one of the most important factors to reduce the complexity of the problem. The proposed part representation method can easily handle parts and raw materials with widely varying geometrical shape by using the redesigning modules. This considerably reduces the amount of processed data and consequently the run time of the computer. The aim of this research is to develop parametric macro for two-dimensional layout on the Auto-CAD system. Therefore, this research can be called "pre-nesting".

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.304-306
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• 2007

Methods of Object recognition from camera image are to compare features of color. edge or pattern with model in a general way. SIFT(scale-invariant feature transform) has good performance but that has high complexity of computation. Using simple color histogram has low complexity. but low performance. In this paper we represent a model as a color cooccurrence histogram. and we improve performance using pyramid matching. The color cooccurrence histogram keeps track of the number of pairs of certain colored pixels that occur at certain separation distances in image space. The color cooccurrence histogram adds geometric information to the normal color histogram. We suggest object recognition by pyramid matching of color cooccurrence histogram.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1994년도 FIFTH WESTERN PACIFIC REGIONAL ACOUSTICS CONFERENCE SEOUL KOREA
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• pp.1104-1111
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• 1994

Code Excited Linear Prediction(CELP) as a speech coder exhibits good performance at data rates below 4.8 kbps. The major drawback to CELP type coders is their large amount of computation. In this paper, we propose a new pitch search method that preserves the quality of the CELP vocoder with reduced complexity. The basic idea is to restrict the pitch searching range by estimating the preliminary pitches. Applying the proposed method to the CELP vocoder, we can get approximately 87% complexity reduction in the pitch search.

• Journal of Information Processing Systems
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• 제3권1호
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• pp.21-25
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• 2007

We propose several practical SMC protocols for privacy-preserving cooperative scientific computations. We consider two important scientific computations which involve linear equations: the linear systems of equations problem and the linear least-square problem. The protocols proposed in this paper achieve acceptable security in the sense of Du-Zhan's paradigm and t-wise collusion-resistance, and their communication complexity is O(tm), where t is a security parameter and m is the total number of participants. The complexity of our protocol is significantly better than the previous result O($m^2/{\mu}$) of [4], in which the oblivious transfer protocol is used as an important building block.

• 한국국방경영분석학회지
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• 제17권2호
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• pp.113-122
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• 1991

This paper presents the Allocation Priority Scheme (APS) for multiprocessor system. The objective of APS is to reduce the time-complexity on a Physical Mapping Scheme(PMS). The PMS is to allocate the nodes of the Data Dependency Graph (DDG) to the multprocessors efficiently and effectively. The APS provides the priority to each node (vertex) in the DDG. In other words, the goal of the APS is to find a request resource mapping such that the total cost (time-complexity) is minimized. The special case in which all requests have equal priorities and all resoruces have equal precedences, and the comparisons between our APS and other schems are discussed in the paper. The APS provides the heuristic rules which are based on maximum height (MH), number of children nodes ($N_c$), number of father nodes ($N_f$), and computation time ($T_c$). The estimation moth of the computaion time is in the paper.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1994년도 FIFTH WESTERN PACIFIC REGIONAL ACOUSTICS CONFERENCE SEOUL KOREA
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• pp.904-911
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• 1994

Code Excited Linear Prediction (CELP) speech coders exhibit good performance at data rates below 4.8 kbps. The major drawback to CELP type coders is their many computation. In this paper, we propose a new pitch search method that preserves the quality of the CELP vocoder with reducing complexity. The basic idea is to apply the preprocessing technique beforehand grasping the autocorrelation property of speech waveform. By using the proposed method, we can get approximately 77% complexity reduction in the pitch search.

• 반도체디스플레이기술학회지
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• 제21권1호
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• pp.75-78
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• 2022

Deep Neural Networks (DNN) has become an essential data processing architecture for the implementation of multiple computer vision tasks. Recently, DNN-based algorithms achieve much higher recognition accuracy than traditional algorithms based on shallow learning. However, training and inference DNNs require huge computational capabilities than daily usage purposes of computers. Moreover, with increased size and depth of DNNs, CPUs may be unsatisfactory since they use serial processing by default. GPUs are the solution that come up with greater speed compared to CPUs because of their Parallel Processing/Computation nature. In this paper, we analyze the inference time complexity of DNNs using well-known computer vision library, OpenCV. We measure and analyze inference time complexity for three cases, CPU, GPU-Float32, and GPU-Float16.

• ETRI Journal
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• 제35권6호
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• pp.949-959
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• 2013

Since the recent launch of Microsoft Xbox Kinect, research on 3D human pose estimation has attracted a lot of attention in the computer vision community. Kinect shows impressive estimation accuracy and real-time performance on massive graphics processing unit hardware. In this paper, we focus on further reducing the computation complexity of the existing state-of-the-art method to make the real-time 3D human pose estimation functionality applicable to devices with lower computing power. As a result, we propose two simple approaches to speed up the random-forest-based human pose estimation method. In the original algorithm, the random forest classifier is applied to all pixels of the segmented human depth image. We first use a multi-scale approach to reduce the number of such calculations. Second, the complexity of the random forest classification itself is decreased by the proposed cascade approach. Experiment results for real data show that our method is effective and works in real time (30 fps) without any parallelization efforts.