• Journal of Electrical Engineering and Technology
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• v.5 no.2
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• pp.209-219
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• 2010

In this paper, a novel dynamic available transfer capability (DATC) has been computed for real time applications using three different intelligent techniques viz. i) back propagation algorithm (BPA), ii) radial basis function (RBF), and iii) adaptive neuro fuzzy inference system (ANFIS) for the first time. The conventional method of DATC is tedious and time consuming. DATC is concerned with calculating the maximum increase in point to point transfer such that the transient response remains stable and viable. The ATC information is to be continuously updated in real time and made available to market participants through an internet based Open Access Same time Information System (OASIS). The independent system operator (ISO) evaluates the transaction in real time on the basis of DATC information. The dynamic contingency screening method [1] has been utilized and critical contingencies are selected for the computation of DATC using the energy function based potential energy boundary surface (PEBS) method. The PEBS based DATC has been utilized to generate patterns for the intelligent techniques. The three different intelligent methods are tested on New England 68-bus 16 machine and 39-bus 10 machine systems and results are compared with the conventional PEBS method.

• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.44-51
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• 2007

For real-time recognizing of model objects in remote position a new Neural Networks algorithm is proposed. The proposed neural networks technique is the real time computation methods through the inter-node diffusion. In the networks, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. The most reliable algorithm derived for real time recognition of objects, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed. 1-D LIPN hardware has been composed and various experiments with static and dynamic signals have been implemented.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.1
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• pp.44-57
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• 2016

Rectification is used as a preprocessing to reduce the computation complexity of disparity estimation. However, rectification also requires a complex computation. To minimize the computing complexity, rectification using a lookup-table (R-LUT) has been introduced. However, since, the R-LUT consumes large amount of memory, rectification with compressed LUT (R-CLUT) has been introduced. However, the more we reduce the memory consumption, the more we need decoding overhead. Therefore, we need to attain an acceptable trade-off between the size of LUT and decoding overhead. In this paper, we present such a trade-off by adaptively combining simple coding methods, such as differential coding, modified run-length coding (MRLE), and Huffman coding. Differential coding is applied to transform coordinate data into a differential form in order to further improve the coding efficiency along with Huffman coding for better stability and MRLE for better performance. Our experimental results verified that our coding scheme yields high performance with maintaining robustness. Our method showed about ranging from 1 % to 16 % lower average inverse of compression ratio than the existing methods. Moreover, we maintained low latency with tolerable hardware overhead for real-time implementation.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.149-154
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• 2022

In recent years, the application of deep learning method to computer vision has shown to achieve great performances. Thus, many research projects have also applied deep learning technology to railroad defect detection. In this paper, we have reviewed the researches that applied computer vision based deep learning method to railroad defect detection and inspection, and have discussed the current trend and the direction of those researches. Many research projects were targeted to operate automatically without visual inspection of human and to work in real-time. Therefore, methods to speed up the computation were also investigated. The reduction of the number of learning parameters was considered important to improve computation efficiency. In addition to computation speed issue, the problem of annotation was also discussed in some research projects. To alleviate the problem of time consuming annotation, some kinds of automatic segmentation of the railroad defect or self-supervised methods have been suggested.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.3
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• pp.372-379
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• 2014

This paper surveys past and state-of-the-art SLAM technologies. The standard methods for solving the SLAM problem are the Kalman filter, particle filter, graph, and bundle adjustment-based methods. Kalman filters such as EKF (Extended Kalman Filter) and UKF (Unscented Kalman Filter) have provided successful results for estimating the state of nonlinear systems and integrating various sensor information. However, traditional EKF-based methods suffer from the increase of computation burden as the number of features increases. To cope with this problem, particle filter-based SLAM approaches such as FastSLAM have been widely used. While particle filter-based methods can deal with a large number of features, the computation time still increases as the map grows. Graph-based SLAM methods have recently received considerable attention, and they can provide successful real-time SLAM results in large urban environments.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.281-283
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• 1993

A new method of EEG spindle waveform detection i s presented. The method combines the signal conditioning in the time-domin and the analysis of local spectrum in the frequency-domain. Fast computation methods, utilizing some effective approximations, are also suggested for the desist and implementation of the filter as well as for the computation of the local spectrum. The presented approach is especially useful for the real-time implementation of the waveform detection system under a general purpose microcomputer environment. The overall detection system is implemented and tested on-line with the total 24 hour data of selected four subjects. The result show the average agreement of 86.7% with the visually inspected result.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.24 no.2
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• pp.161-197
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• 2020

Total variation minimization is standard in mathematical imaging and there have been numerous researches over the last decades. In order to process large-scale images in real-time, it is essential to design parallel algorithms that utilize distributed memory computers efficiently. The aim of this paper is to illustrate recent advances of domain decomposition methods for total variation minimization as parallel algorithms. Domain decomposition methods are suitable for parallel computation since they solve a large-scale problem by dividing it into smaller problems and treating them in parallel, and they already have been widely used in structural mechanics. Differently from problems arising in structural mechanics, energy functionals of total variation minimization problems are in general nonlinear, nonsmooth, and nonseparable. Hence, designing efficient domain decomposition methods for total variation minimization is a quite challenging issue. We describe various existing approaches on domain decomposition methods for total variation minimization in a unified view. We address how the direction of research on the subject has changed over the past few years, and suggest several interesting topics for further research.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.304-309
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• 2015

On construction sites, it is important to monitor the performance of construction equipment and workers to achieve successful construction project management; especially, vision-based detection methods have advantages for the real-time site data collection for safety and productivity analyses. Although many researchers developed vision-based detection methods with acceptable performance, there are still limitations to be addressed: 1) sensitiveness to the shape and appearance changes of moving objects in difference working postures, and 2) high computation time. To deal with the limitations, this paper proposes a detection algorithm of construction equipment based on Integral Channel Features. For validation, 16,850 frames of video streams were recorded and analyzed. The results showed that the proposed method worked in high performance in terms of accuracy and processing time. In conclusion, the developed method can help to understand useful site information including working pattern, working time and input manpower analyses.

• 연구논문집
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• s.27
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• pp.47-55
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• 1997

Fatigue life prediction of mechanical components is necessary to develop new products, which is very expensive and time-consuming. This paper reviews technologies proposed for computation of dynamic stress in mechanical components. The methods based on multibody dynamics are considering more real operational conditions than other methods. The technology for fatigue life prediction without the prototype for experiment results in cost and time saving. This technology can be applied to design of various mechanical components like carbody.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.7
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• pp.865-870
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• 2018

A machine vision based industrial inspection includes defects detection and classification. Fast inspection is a fundamental problem for many applications of real-time vision systems. It requires little computation time and localizing defects robustly with high accuracy. Deep learning technique have been known not to be suitable for real-time applications. Recently a couple of fast region-based CNN algorithms for object detection are introduced, such as Faster R-CNN, and YOLOv2. We apply these methods for an industrial inspection problem. Three CNN based detection algorithms, VOV based CNN, Faster R-CNN, and YOLOv2, are experimented for defect detection on metal surface. The results for inspection time and various performance indices are compared and analysed.