• Steel and Composite Structures
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• v.48 no.1
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• pp.27-41
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• 2023

Under the long-term effect of corrosive environment, many cold-formed steel (CFS) structures have serious corrosion problems. Corrosion leads to the change of surface morphology and the loss of section thickness, which results in the change of instability mode and failure mechanism of CFS structure. This paper mainly investigates the elastic local buckling behavior of corroded CFS columns. The surface morphology scanning test was carried out for eight CFS columns accelerated corrosion by the outdoor periodic spray test. The thin shell finite element (FE) eigen-buckling analysis was also carried out to reveal the influence of corrosion surface characteristics, corrosion depth, corrosion location and corrosion area on the elastic local buckling behaviour of the plates with four simply supported edges. The accuracy of the proposed formulas for calculating the elastic local buckling stress of the corroded plates and columns was assessed through extensive parameter studies. The results indicated that for the plates considering corrosion surface characteristics, the maximum deformation area of local buckling was located at the plates with the minimum average section area. For the plates with localized corrosion, the main buckling shape of the plates changed from one half-wave to two half-wave with the increase in corrosion area length. The elastic local buckling stress decreased gradually with the increase in corrosion area width and length. In addition, the elastic local buckling stress decreased slowly when corrosion area thickness was relatively large, and then tends to accelerate with the reduction in corrosion area thickness. The distance from the corrosion area to the transverse and longitudinal centerline of the plate had little effect on the elastic local buckling stress. Finally, the calculation formula of the elastic local buckling stress of the corroded plates and CFS columns was proposed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.9
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• pp.1531-1540
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• 2016

In this paper, we propose vehicle license plate recognition system based on Radial Basis Function Neural Networks (RBFNNs) with the use of local binarization functions and canny edge algorithm. In order to detect the area of license plate and also recognize license plate numbers, binary images are generated by using local binarization methods, which consider local brightness, and canny edge detection. The generated binary images provide information related to the size and the position of license plate. Additionally, image warping is used to compensate the distortion of images obtained from the side. After extracting license plate numbers, the dimensionality of number images is reduced through Principal Component Analysis (PCA) and is used as input variables to RBFNNs. Particle Swarm Optimization (PSO) algorithm is used to optimize a number of essential parameters needed to improve the accuracy of RBFNNs. Those optimized parameters include the number of clusters and the fuzzification coefficient used in the FCM algorithm, and the orders of polynomial of networks. Image data sets are obtained by changing the distance between stationary vehicle and camera and then used to evaluate the performance of the proposed system.

• Journal of Astronomy and Space Sciences
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• v.10 no.2
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• pp.163-188
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• 1993

On the framework of Tolman spacetime model, the caustics around a local perturbed region in redshift is due to the local expansion rate induced by a local density inhomogeneity in real space. We have compared the caustics in redshift space, which are analytically obtained, with the observed redshift-distance patterns of galaxies which are belonging to Coma and Perseus clusters. For the Abell density distribution model and polytropic density profiles which are well-fitting the optical and X-ray observations, respectively, the size of caustics which is defined by "turnaround radius" of a local density perturbed region should give constraints on the sizes and masses of rich clusters and give also a clue to understand the state of hot X-ray emitting gas.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.4
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• pp.231-238
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• 2017

Detecting small infrared targets from the low-SCR images at a long distance is very hard. The previous Local Contrast Method (LCM) algorithm based on the human visual system shows a superior performance of detecting small targets by a background suppression technique through local contrast measure. However, its slow processing speed due to the heavy multi-scale processing overhead is not suitable to a variety of real-time applications. This paper presents a lightweight real-time small target detection algorithm, called by the Improved Selective Local Contrast Method (ISLCM), to reduce the scale-invariant computational overhead. The proposed ISLCM applies the improved local contrast measure to the predicted selective region so that it may have a comparable detection performance as the previous LCM while guaranteeing low scale-invariant computational load by exploiting both adaptive scale estimation and small target feature feasibility. Experimental results show that the proposed algorithm can reduce its computational overhead considerably while maintaining its detection performance compared with the previous LCM.

• Journal of Agricultural Extension & Community Development
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• v.23 no.3
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• pp.321-334
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• 2016

Food is one of the essential elements of the tourist experiences. The study aims to segment food tourists for benefit sought. This study attempted to segment tourists who had experienced local food at tourist destinations by their benefit sought to meet local food. A self-administered survey was obtained from 498 visitors in the study areas. Results from the factor analysis show that the most explained variances of benefit sought were food taste (17.2%) and refresh (12.9%). Five distinct segments were identified based on the benefits; family seeker (17.6%), passive seeker (15.1%), want-it-all seeker (23.1%), raw material seeker (31.8%), gastronomic seeker (12.4%). In addition, a significant difference in the characteristics of tourists who had tasted local food at tourist destinations was observed in terms of occupation, income, education, expenditure for food, and tour distance. Implications are discussed relative to marketing strategies.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.4
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• pp.316-320
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• 2016

In this paper, we presents a pattern recognition by considering the spatial co-occurrence among micro-patterns of texture images. The micro-patterns of texture image have been extracted by local binary pattern based on median(MLBP) of block image, and the recognition process is based on co-occurrence among MLBPs. The MLBP is applied not only to consider the local character but also analyze the pattern in order to be robust noise, and spatial co-occurrence is also applied to improve the recognition performance by considering the global space of image. The proposed method has been applied to recognized 17 RGB images of 120*120 pixels from Mayang texture image based on Euclidean distance. The experimental results show that the proposed method has a texture recognition performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.15-19
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• 2004

Ant Colony System(ACS) is a meta heuristic approach based on biology in order to solve combinatorial optimization problem. It is based on the tracing action of real ants which accumulate pheromone on the passed path and uses as communication medium. In order to search the optimal path, ACS requires to explore various edges. In existing ACS, the local updating rule assigns the same pheromone to visited edge. In this paper, our local updating rule gives the pheromone according to the number of visiting times and the distance between visited cities. Our approach can have less local optima than existing ACS and find better solution by taking advantage of more informations during searching.

• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.775-779
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• 2011

As the technology of multimedia sensor networks is desired in large numbers of applications nowadays, real-time service becomes one of the most important research challenges. Even though lots of related works have been conducted to meet this requirement in several ways, the specific traffic model for real-time has not been taken yet. Thus, it causes lack of adaptability of those approaches in real deployment. To solve this problem, in this paper, we model the application via (m,k)-firm streams which have weakly hard real-time property. And then, a novel forwarding scheme based on modified DBP (Distance-Based Priority) is proposed by considering local-DBP and stream DBP together. Local-DBP can contribute to identify the detailed causes of unsatisfied quality, that is, network congestion or wireless link failure. Simulation results reveal that (m,k)-firm is a good traffic model for multimedia sensor networks and the proposed scheme can contribute to guarantee real-time requirement well.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.623-628
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• 2008

We present a new method of extracting foreground object from background image for vision-based game interface. Background Subtraction is an important preprocessing step for extracting the features of tracking objects. The image is divided into the cells where the Local Histogram with Gaussian kernel is computed and compared with the corresponding one using Bhattacharyya distance measure. The histogram-based method is partially robust against illumination change, noise and small moving objects in background. We propose a Multi-Scaled Integral Histogram approach for noise suppression and fast computation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.4
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• pp.819-833
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• 2013

Computing global features based on local features using a bio-inspired framework has shown promising performance. However, for some tough applications with large intra-class variances, a single local feature is inadequate to represent all the attributes of the images. To integrate the complementary abilities of multiple local features, in this paper we have extended the efficacy of the bio-inspired framework, HMAX, to adapt heterogeneous features for global feature extraction. Given multiple global features, we propose an approach, designated as parameterized metric learning, for high dimensional feature fusion. The fusion parameters are solved by maximizing the canonical correlation with respect to the parameters. Experimental results show that our method achieves significant improvements over the benchmark bio-inspired framework, HMAX, and other related methods on the Caltech dataset, under varying numbers of training samples and feature elements.