• Journal of the Korea Society of Computer and Information
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• v.18 no.6
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• pp.21-28
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• 2013

This paper proposes an effective fire detection approach that includes the following multiple heterogeneous algorithms: moving region detection using grey level histograms, color segmentation using fuzzy c-means clustering (FCM), feature extraction using a grey level co-occurrence matrix (GLCM), and fire classification using support vector machine (SVM). The proposed approach determines the optimal threshold values based on grey level histograms in order to detect moving regions, and then performs color segmentation in the CIE LAB color space by applying the FCM. These steps help to specify candidate regions of fire. We then extract features of fire using the GLCM and these features are used as inputs of SVM to classify fire or non-fire. We evaluate the proposed approach by comparing it with two state-of-the-art fire detection algorithms in terms of the fire detection rate (or percentages of true positive, PTP) and the false fire detection rate (or percentages of true negative, PTN). Experimental results indicated that the proposed approach outperformed conventional fire detection algorithms by yielding 97.94% for PTP and 4.63% for PTN, respectively.

• Smart Structures and Systems
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• v.20 no.5
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• pp.563-572
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• 2017

The safety of structures is closely associated with the structural out-of-plane behavior. In particular, long and slender beam structures have been increasingly used in the design and construction. Therefore, an evaluation of the lateral and torsional behavior of a structure is important for the safety of the structure during construction as well as under service conditions. The current contact measurement method using displacement meters cannot measure independent movements directly and also requires caution when installing the displacement meters. Therefore, in this study, a vision-based system was used to measure the in-plane and out-of-plane displacements of a structure. The image processing algorithm was based on reference objects, including multiple targets in Lab color space. The captured targets were synchronized using a load indicator connected wirelessly to a data logger system in the server. A laboratory beam test was carried out to compare the displacements and rotation obtained from the proposed vision-based measurement system with those from the current measurement method using string potentiometers. The test results showed that the proposed vision-based measurement system could be applied successfully and easily to evaluating both the in-plane and out-of-plane movements of a beam including twisting rotation.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.33 no.4
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• pp.303-309
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• 2020

This study proposes a crack identification algorithm to analyze the surface condition of porcelain insulators and to efficiently visualize cracks. The proposed image processing algorithm for crack identification consists of two primary steps. In the first step, the brightness is eliminated by converting the image to the lab color space. Then, the background is removed by the K-means clustering method. After that, the optimum image treatment is applied using morphological image processing and median filtering to remove unnecessary noise, such as blobs. In the second step, the preprocessed image is converted to grayscale, and any cracks present in the image are identified. Next, the region properties, such as the number of pixels and the ratio of the major to the minor axis, are used to separate the cracks from the noise. Using this image processing algorithm, the precision of crack identification for all the sample images was approximately 80%, and the F1 score was approximately 70. Thus, this method can be helpful for efficient crack monitoring.

• Food Science and Preservation
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• v.15 no.2
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• pp.191-196
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• 2008

We examined the effects of four different types of cutting on the quality characteristics of sweet pumpkin. Two hundred grams of each of four samples were packed individually in polypropylene mm and stored at $10^{\circ}C$ for 9 days. Samples were evaluated for weight loss, change in hardness, color change, pH change, water-soluble materials, gas changes, and sensory evaluation. $CO_2$concentration increased during storage, whereas $O_2$ concentration rapidly decreased and then stabilized after 3 days. $C_2H_4$ was detected only after 3 days of storage, and steadily increased thereafter. The rate of weight loss steadily increased Analysis of Lab color space indicated no significant change in the L and b values, but an increase in the a value at the end of storage. Waster-soluble solids increased, but hardness showed no change. All the samples underwent a steady increase in pH. Samples cut into 8 pieces had the highest sensory scores.