• Proceedings of the KIEE Conference
• /
• 2004.11c
• /
• pp.153-155
• /
• 2004

This paper proposes a real-time approach on the rotational motion estimation and correction for the roll stabilization of the sight system. This method first estimates a rotation center by the least-mean square algorithm based on the motion vectors of some feature points. And, then, a rotation angle is searched for a best matching block between a reference block image and seccessive input images using MPC(maximum pixel count) matching criterion. Finally, motion correction is performed by the bilinear interpolation technique. Various computer simulations show that the estimation performance is good and the proposed algorithm is a real-time implementable one to the TMS320C6415(500MHz) DSP.

• Proceedings of the Korea Multimedia Society Conference
• /
• 2003.11b
• /
• pp.1006-1009
• /
• 2003

In a motion estimation method for use in encoding a moving picture, a full-pixel motion vector is estimated by stochastically sampling a pixel to be processed in a predetermined-sized block of a previous frame or a next frame as a reference frame for each of a plurality of equal-sized blocks in a current frame. Then, a half-pixel motion vector is estimated based on the full-pixel motion vector. Accordingly, both the calculation amount and the calculation time required for the motion estimation are effectively reduced. Further, it can be prevented that the hardware becomes complicated. .

• Nuclear Engineering and Technology
• /
• v.51 no.3
• /
• pp.702-708
• /
• 2019

This paper deals with break size estimation of loss of coolant accidents (LOCA) using a nonlinear autoregressive with exogenous inputs (NARX) neural network. Previous studies used static approaches, requiring time-integrated parameters and independent firing algorithms. NARX neural network is able to directly deal with time-dependent signals for dynamic estimation of break sizes in real-time. The case studied is a LOCA in the primary system of Bushehr nuclear power plant (NPP). In this study, number of hidden layers, neurons, feedbacks, inputs, and training duration of transients are selected by performing parametric studies to determine the network architecture with minimum error. The developed NARX neural network is trained by error back propagation algorithm with different break sizes, covering 5% -100% of main coolant pipeline area. This database of LOCA scenarios is developed using RELAP5 thermal-hydraulic code. The results are satisfactory and indicate feasibility of implementing NARX neural network for break size estimation in NPPs. It is able to find a general solution for break size estimation problem in real-time, using a limited number of training data sets. This study has been performed in the framework of a research project, aiming to develop an appropriate accident management support tool for Bushehr NPP.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.1225-1230
• /
• 2003

In this study, Real-time estimation and compensation procedure are developed for the laser interferometer. This system is designed with homodyne quadrature-phase detection method using the Laser interferometer. The errors in this system are due to noise, disturbance and undefined model dynamics. DSP(Digital Signal Processor) is applied for real time compensation of these errors. This estimator and compensation is verified with measurement test.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2002.10a
• /
• pp.101-104
• /
• 2002

Leading companies have embraced the new economy with new and innovative BTO models. Instead of conventional company-oriented manufacturing scheduling, customer-oriented scheduling method attracts more and more attention. To evaluate the delivery of customer order in advance, the real production capacity as well as procurement lead time should be taken into account. This paper describes a quasi-real-time order delivery estimation system using TOC(Theory of Constraints) based scheduling method.

• Proceedings of the KSME Conference
• /
• 2001.06b
• /
• pp.724-729
• /
• 2001

This paper discusses the composition of the theory of reinforcement learning, which is applied in real-time learning, and evolutionary strategy, which proves its the superiority in the finding of the optimal solution at the off-line learning method. The individuals are reduced in order to learn the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because the learning process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes. In the future, studies are needed on the proof of the theory through experiments and the characteristic considerations of the robustness against the outside disturbances.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.141.4-141
• /
• 2001

In this paper the Local Weighted Regression LWR technique is applied to the estimation of aircrcraft parameters. The method consists In improving the Local Weighted Regression LWR technique by adding a data Retention-and-Deletion RD strategy. The improvement comes with reduced computational effort since the two techniques can share their main computational procedures. The purpose of the study was to establish if the proposed algorithm could provide fast and reliable real-time estimations, with accuracy comparable to other well-known off-line identification schemes. The algorithm was tested using specific parameter estimation maneuvers and flight data of the NASA F/A-18 HARV. The results were compared with both the estimation obtained from ...

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.7
• /
• pp.510-514
• /
• 2009

If the existing real-time systems are integrated based on SOA, real-time SOA should be developed. Generally, in real-time SOA a service can be divided into several small services and their estimated execution time is given by provider systems. However, an estimation, which analyzes time elements related to transmit and receive messages among requesters and providers, is needed. In order to enhance QoS of Web service, this paper proposes enhanced worst-case execution time estimation by considering WS-transaction and common use of multi-processors system.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.3 no.3
• /
• pp.143-150
• /
• 2008

In this paper, we investigate a real-time environment recognition system based on stereo vision for moving object. This system consists of stereo matching, obstacle detection and distance estimation. In stereo matching part, depth maps can be obtained real road images captured adjustable baseline stereo vision system using belief propagation(BP) algorithm. In detection part, various obstacles are detected using only depth map in case of both v-disparity and column detection method under the real road environment. Finally in estimation part, asymmetric parabola fitting with NCC method improves estimation of obstacle detection. This stereo vision system can be applied to many applications such as unmanned vehicle and robot.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.3
• /
• pp.1058-1070
• /
• 2014

There are two major ways to implement depth estimation, multiple image depth estimation and single image depth estimation, respectively. The former has a high hardware cost because it uses multiple cameras but it has a simple software algorithm. Conversely, the latter has a low hardware cost but the software algorithm is complex. One of the recent trends in this field is to make a system compact, or even portable, and to simplify the optical elements to be attached to the conventional camera. In this paper, we present an implementation of depth estimation with a single image using a graphics processing unit (GPU) in a desktop PC, and achieve real-time application via our evolutional algorithm and parallel processing technique, employing a compute shader. The methods greatly accelerate the compute-intensive implementation of depth estimation with a single view image from 0.003 frames per second (fps) (implemented in MATLAB) to 53 fps, which is almost twice the real-time standard of 30 fps. In the previous literature, to the best of our knowledge, no paper discusses the optimization of depth estimation using a single image, and the frame rate of our final result is better than that of previous studies using multiple images, whose frame rate is about 20fps.