• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.1
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• pp.103-109
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• 2016

The performance of a multi-head, computerized combination scaling system to automatically identify a group of agricultural products having a total weight within the target range has been optimized to reduce the package cycle time of the merchandise. First, the structure of the scale was modified to enable faster measurement by enhancing the dynamic stability during the process. Second, the high frequency noise in the measured signal was eliminated by a high frequency filter to provide more accurate weight data. Finally, the algorithm to identify a group of products with a total weight within the target range was modified to enable a user to select an optimal number of scales. According to the experimental verifications, this modified system reduced the package cycle time significantly and also was accurate in measuring the total weight of the selected products.

• 한국전산유체공학회:학술대회논문집
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• 2005.10a
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• pp.49-53
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• 2005

This paper presents a multi-scale hybrid simulation for the design of a catalytic multi-tubular reactor with high performance. The multi-tubular reactor consists of shell and a large number of tubes in which various catalytic chemical reactions occur. To consider fluid dynamics in the shell-side and kinetics in the tube-side at the same time, commercial CFD package and process simulation tool are coupled. This hybrid approach allowed us to predict many kinds of meaningful results such as tube center temperature profile, heat transfer coefficients on the tube wall, temperature rise of cooling medium, pressure drop through shell and tube side, concentration profile of each chemical species along the tube, and so on., and to achieve the optimal reactor design.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1596-1604
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• 2013

Coordinating operation between large-scale wind power and thermal units in multiple time scale is an important problem to keep power balance, especially for the power grids mainly made up of large coal-fired units. The paper proposes a novel operation mode of multi-scale unit commitment (abbr. UC) that includes mid-term UC and day-ahead UC, which can take full advantage of insufficient flexibility and improve wind power accommodation. First, we introduce the concepts of multi-scale UC and then illustrate the benefits of introducing mid-term UC to the wind-coal intensive grid. The paper then formulates the mid-term UC model, proposes operation performance indices and validates the optimal operation mode by simulation cases. Compared with day-ahead UC only, the multi-scale UC mode could reduce the total generation cost and improve the wind power net benefit by decreasing the coal-fired units' on/off operation. The simulation results also show that the maximum total generation benefit should be pursued rather than the wind power utilization rate in wind-coal intensive system.

• The Journal of the Korea Contents Association
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• v.14 no.3
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• pp.11-21
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• 2014

Multi-scale image segmentation is important in many applications such as image stylization and medical diagnosis. This paper proposes a novel segmentation algorithm based on MSER(maximally stable extremal region) which captures multi-scale structure and is stable and efficient. The algorithm collects MSERs and then partitions the image plane by redrawing MSERs in specific order. To denoise and smooth the region boundaries, hierarchical morphological operations are developed. To illustrate effectiveness of the algorithm's multi-scale structure, effects of various types of LOD control are shown for image stylization. The proposed technique achieves this without time-consuming multi-level Gaussian smoothing. The comparisons of segmentation quality and timing efficiency with mean shift-based Edison system are presented.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.1
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• pp.1-6
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• 2018

In this paper, we propose the method to eliminate repetitive processes for key-point detection on multi-scale image space. The proposed method detects key-points from the original image, and select a good key-points using the cluster filters, and create the key-point clusters. And it select reference objects by using direction angles of the key-point clusters, predict the scale of the original image by using the distributed distance ratio. It transform the scale of the reference image, and apply the detection of key-points to the transformed reference image. In the results of the experiment, the proposed method can be found to improve the key-points detection time by 75 % and 71 % compared to SIFT method and scaled ORB method using the multi-scale images.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.221-229
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• 2004

The Continuous Wavelets Transform project signal f(t) to "Time-scale"plan utilizing the time varied function which called "wavelets". This Transformation permit to analyze scale time dependence of signal f(t) thus the local or global scale properties can be extracted. Moreover, the signal f(t) can be reconstructed stably by utilizing the Inverse Continuous Wavelets Transform. In this paper, the EEG signal is analyzed by wavelets coherence method and the De-noising procedure is represented.

• Journal of Advanced Marine Engineering and Technology
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• v.29 no.2
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• pp.217-224
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• 2005

A new roof edge detection method based on multi level scales of wavelet function is proposed in this paper roof edge and its direction are obtained in this new methods at one time. Besides. scale characteristics of detecting roof edge is analyzed. And a few new methods on fingerprint image pre-processing are described. A method segmenting foreground/background of fingerprint images is proposed, in which Prior estimation of direction field is not required any more. A segmentation method based on multi-scale roof edges is implemented. and the valid scale range of the method is defined. too. And the method is used to segment ridges and valleys in fingerprint images simultaneously The exact direction fields made up of the direction of each point in ridges can be obtained when detecting ridges exactly based on the roof edge detector, in comparison with the traditional coarse estimation of direction fields. Obviously. it will establish a solid foundation for the sequent fingerprint identification.

• Structural Engineering and Mechanics
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• v.41 no.4
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• pp.559-573
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• 2012

An adaptive modeling and simulation technique is introduced for the effective and reliable fluid-structure interaction analysis using MSC/Dytran for large-scale complex pressurized liquid containment. The proposed method is composed of a series of the global rigid sloshing analysis and the locally detailed fluid-structure analysis. The critical time at which the system exhibits the severe liquid sloshing response is sought through the former analysis, while the fluid-structure interaction in the local region of interest at the critical time is analyzed by the latter analysis. Differing from the global coarse model, the local fine model considers not only the complex geometry and flexibility of structure but the effect of internal pressure. The locally detailed FSI problem is solved in terms of multi-material volume fractions and the flow and pressure fields obtained by the global analysis at the critical time are specified as the initial conditions. An in-house program for mapping the global analysis results onto the fine-scale local FSI model is developed. The validity and effectiveness of the proposed method are verified through an illustrative numerical experiment.

• Nuclear Engineering and Technology
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• v.42 no.6
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• pp.620-635
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• 2010

In this paper we describe current activities on the project Multi-Scale Modeling and Analysis of convective boiling (MSMA), conducted jointly by the Paul Scherrer Institute (PSI) and the Swiss Nuclear Utilities (Swissnuclear). The long-term aim of the MSMA project is to formulate improved closure laws for Computational Fluid Dynamics (CFD) simulations for prediction of convective boiling and eventually of the Critical Heat Flux (CHF). As boiling is controlled by the competition of numerous phenomena at various length and time scales, a multi-scale approach is employed to tackle the problem at different scales. In the MSMA project, the scales on which we focus range from the CFD scale (macro-scale), bubble size scale (meso-scale), liquid micro-layer and triple interline scale (micro-scale), and molecular scale (nano-scale). The current focus of the project is on micro- and meso-scales modeling. The numerical framework comprises a highly efficient, parallel DNS solver, the PSI-BOIL code. The code has incorporated an Immersed Boundary Method (IBM) to tackle complex geometries. For simulation of meso-scales (bubbles), we use the Constrained Interpolation Profile method: Conservative Semi-Lagrangian $2^{nd}$ order (CIP-CSL2). The phase change is described either by applying conventional jump conditions at the interface, or by using the Phase Field (PF) approach. In this work, we present selected results for flows in complex geometry using the IBM, selected bubbly flow simulations using the CIP-CSL2 method and results for phase change using the PF approach. In the subsequent stage of the project, the importance of effects of nano-scale processes on the global boiling heat transfer will be evaluated. To validate the models, more experimental information will be needed in the future, so it is expected that the MSMA project will become the seed for a long-term, combined theoretical and experimental program.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.786-799
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• 2021

Road damage detection is important for road maintenance. With the development of deep learning, more and more road damage detection methods have been proposed, such as Fast R-CNN, Faster R-CNN, Mask R-CNN and RetinaNet. However, because shallow and deep layers cannot be extracted at the same time, the existing methods do not perform well in detecting objects with fewer samples. In addition, these methods cannot obtain a highly accurate detecting bounding box. This paper presents a Multi-level Feature Pyramids method based on M2det. Because the feature layer has multi-scale and multi-level architecture, the feature layer containing more information and obvious features can be extracted. Moreover, an attention mechanism is used to improve the accuracy of local boundary boxes in the dataset. Experimental results show that the proposed method is better than the current state-of-the-art methods.