• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.2
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• pp.94-103
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• 2008

This study measured technology transfer efficiency for public institutes. The study made use of DEA being one of the non-parametric linear programming to evaluate technology transfer efficiency for public institutes and to measure technology efficiency, pure technical efficiency and scale efficiency. The measurement of the technology transfer efficiency for public institutes was as follows: The cause of the technology transfer inefficiency was affected by pure technical inefficiency more than by scale inefficiency. Public institutes' RTS(Return To Scale) value varied depending upon the features of the organizations than the features of the regions. Public research institutes' RTS value is more effective than universities' RTS value. We compared the RTS group with the RTS of Projected DMU groups. The RTS group had constant returns to scale effect while the RTS of the Projected DMU had increasing returns to scale effect. The technology transfer efficiency of public institutes varied depending upon the features of the organizations and regions : The technology transfer efficiency of public institutes were as follows : public research institutes at the metropolitan area, public research institutes at the local areas, universities at the metropolitan area and universities at the local areas. In other words, the technology transfer efficiency was affected by organizational characteristics more than by regional characteristics at the place where public institutes were located.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.2
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• pp.30-37
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• 2003

To effectively control a vast amount of postal packages, we need the automatic system for extracting the address region from CCD scanner images. In this paper, we propose a address region extraction algorithm in the standard postal label. We used geometric characteristics of the underlying address regions and defined several criteria for fast detection of address regions. As a result, we accomplished a successful detection and classification of the postal package labels in real time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1116-1121
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• 2006

We studied on some techniques which are able to discriminate the difference between normal and infarcted myocardium by consecutive image analysis in B-mode echocardiogram. In order to analyze two-dimensional echocardiogram, we presented inter-frame and intra-frame analysis method throughout the complete heart cycle in closed-chest human and proposed new analysis parameters which are named HGE and LT. The analysis technique using the proposed parameters revealed quantitatively dominant features between normal and infarcted regions. The infarcted areas yield regions of higher intensity throughout the entire cardiac cycle, but normal tissue demonstrates greater variability throughout the entire cardiac cycle. Hence, we could verify that these parameters varying over the entire cardiac cycle are good indicators for the state of myocardium.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1167-1174
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• 2006

Text region detection from a natural scene is useful in many applications such as vehicle license plate recognition. Therefore, in this paper, we propose a text region extraction method using pattern histogram of character-edge maps. We create 16 kinds of edge maps from the extracted edges and then, we create the 8 kinds of edge maps which compound 16 kinds of edge maps, and have a character feature. We extract a candidate of text regions using the 8 kinds of character-edge maps. The verification about candidate of text region used pattern histogram of character-edge maps and structural features of text region. Experimental results show that the proposed method extracts a text regions composed of complex background, various font sizes and font colors effectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1231-1236
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• 2006

The contrast control of images with narrow dynamic range is a simple method among enhancement methods for low intensity of image. Histogram equalization is the most common method for this purpose, which stretches the dynamic range of intensity Conventional methods would fail to enhance images with extremely dark and bright regions, because of not considering the shape of histogram. In this paper, we propose a novel adaptive histogram equalization based on histogram matching with multiple Gaussian transformation function. As a result, output images with a couple of peaks of histogram could be improved and the details such as edges in dark regions could be appeared better than conventional method subjectively.

• Microbiology and Biotechnology Letters
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• v.52 no.2
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• pp.122-134
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• 2024

Levan is a functional fructooligosaccharide that belongs to the class of fructans and displays a range of physiological and dietary benefits. Levansucrase, classified as a fructosyltransferase, catalyzes the synthesis of levan fructans through the transfer of d-fructosyl residues from sucrose. Recombinant levansucrase exhibits a significantly high level of expression and activity in E. coli, however, its industrial applicability is limited due to its relatively poor thermal stability. In this study, we generated six single mutants by targeting either the high flexibility regions (242-250) or the 382Glu residue within the -TEAP- motif, and further developed two double mutants featuring concurrent mutations encompassing both regions. We expressed multiple levansucrase mutant proteins in E. coli DH5α using site-directed mutagenesis and measured the enzymatic activity and thermal stability of each mutant by levan formation reactions. The E246Y mutant displayed half-time 5.02-fold and 1.89-fold higher at 55℃ and 56℃, respectively, when compared to the WT, whereas the E382L mutant exhibited a 1.06-fold enhancement in half-time at 55℃. Furthermore, E246Y/E382L exhibited 1.95-fold and 1.86-fold higher thermal stability at 55℃ and 56℃, respectively. These findings suggest the potential for the developed thermally stable levansucrases to be applied in industrial settings, highlighting the effectiveness of our targeted mutagenesis approach.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.7
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• pp.4508-4515
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• 2014

Real-time body detection has been researched actively. On the other hand, the detection rate of color distorted images is low because most existing detection methods use static skin color model. Therefore, this paper proposes a new method for detecting the skin color region using a gradient map and saturation features. The basic procedure of the proposed method sequentially consists of creating a gradient map, extracting a gradient feature of skin regions, noise removal using the saturation features of skin, creating a cluster for extraction regions, detecting skin regions using cluster information, and verifying the results. This method uses features other than the color to strengthen skin detection not affected by light, race, age, individual features, etc. The results of the detection rate showed that the proposed method is 10% or more higher than the traditional methods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.697-704
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• 2018

Since the face in image content corresponds to individual information that can distinguish a specific person from other people, it is important to accurately detect faces not hidden in an image. In this paper, we propose a method to accurately detect a face from input images using a deep learning algorithm, which is one of the machine learning methods. In the proposed method, image input via the red-green-blue (RGB) color model is first changed to the luminance-chroma: blue-chroma: red-chroma ($YC_bC_r$) color model; then, other regions are removed using the learned skin color model, and only the skin regions are segmented. A CNN model-based deep learning algorithm is then applied to robustly detect only the face region from the input image. Experimental results show that the proposed method more efficiently segments facial regions from input images. The proposed face area-detection method is expected to be useful in practical applications related to multimedia and shape recognition.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.2
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• pp.267-272
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• 2007

This paper proposes a new approach fur the detection of facial feature regions using the characteristic of DCT(discrete cosine transformation) thatconcentrates the energy of an image into lower frequency coefficients. Since the facial features are pertained to relatively high frequency in a face image, the inverse DCT after removing the DCT's coefficients corresponding to the lower frequencies generates the image where the facial feature regions are emphasized. Thus the facial regions can be easily segmented from the inversed image using any differential operator. In the segmented region, facial features can be found using face template. The proposed algorithm has been tested with the image MIT's CBCL DB and the Yale facedatabase B. The experimental results have shown superior performance under the variations of image size and lighting condition.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.3
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• pp.2185-2191
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• 2015

With the advance of the environment where we can get various multimedia contents, adult image detection has become an important issue these days. In this paper, we suggest a method of robustly detecting navel areas from input images which can be usefully utilized in adult image detection. The suggested algorithm first extracts face regions and extracts candidate nipple areas using a nipple map. Our method then selects only actual nipple regions by filtering candidate areas with geometrical features and an average nipple filter. Subsequently, the method robustly detects navel areas by using the structural relation with the nipple areas and applying edge and saturation images. Experimental results show that the suggested algorithm can effectively detect navel regions.