• The Journal of Bigdata
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• v.5 no.1
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• pp.29-39
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• 2020

Purpose The atmosphere of respect for individual tastes that have spread throughout society has changed the consumption trend. As a result, the travel industry is also seeing customized travel as a new trend that reflects consumers' personal tastes. In particular, there is a growing interest in 'film-induced tourism', one of the areas of travel industry. We hope to satisfy the individual's motivation for traveling while watching movies with customized travel proposals, which we expect to be a catalyst for the continued development of the 'film-induced tourism industry'. Design/methodology/approach In this study, we implemented a methodology through 'OCR' of extracting and suggesting film location information that viewers want to visit. First, we extract a scene from a movie selected by a user by using 'OpenCV', a real-time image processing library. In addition, we detected the location of characters in the scene image by using 'EAST model', a deep learning-based text area detection model. The detected images are preprocessed by using 'OpenCV built-in function' to increase recognition accuracy. Finally, after converting characters in images into recognizable text using 'Tesseract', an optical character recognition engine, the 'Google Map API' returns actual location information. Significance This research is significant in that it provides personalized tourism content using fourth industrial technology, in addition to existing film tourism. This could be used in the development of film-induced tourism packages with travel agencies in the future. It also implies the possibility of being used for inflow from abroad as well as to abroad.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.10
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• pp.941-950
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• 1991

channel is severely degraded by multipath delay time spread. In this paper. We propose a simple multipath delay time detection method, which has a merit of in serviceable, yet simple H/W realizability for $\pi/4$ shift QPSK by detecting cross channel interference. A $\pi/4$ shift QPSK signal originally has quadrature channel(Q-ch) component. Thus in order to measure CCI between in-phase channel(I-ch) and quadrature channel(Q-ch), which closely related to multipath delay time, Frequency doubling scheme(frequency doubler) and differential detector is proposed, which makes $\pi/4$ shift QPSK signal look like BPSK and also makes it possible for CCI to be detected at I-ch detector output. To get an information from time varying I-ch output signal under the multipath lading environment, a method for obtaining the mean of the absolute value$(V_{MABS}(t))$ and another one for obtaining the root mean square value$(V_{RMS}(t))$ of CCI are proposed. Furthermore, a relationship between delay spread and CCI is also analyzed. In order to confirm theoretical results, computer simulation has been carried out under the quasi-static and Reyleigh distributed two ray multipath fading environments. A fairly good result was obtained. However it was also shown that this method is sensitive to bandwidth restriction to some extent. In addition, some idea for a simple hardware realization for the frequency doubler are given.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.3
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• pp.301-315
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• 2010

Recently, competition between companies has intensified and so has the necessity of creating a new business value inventions has increased. A numbers of Business organizations are beginning to realize the importance of business process management. Processes however can often not go the way they were initially designed or non-efficient performance process model could be designed. This can be due to a lack of cooperation and understanding between business analysts and system developers. To solve this problem, business process mining which can be used as the basis of the business process re-engineering has been recognized to an important concept. Current process mining research has only focused their attention on extracting workflow-based process model from competed process logs. Thus there have a limitations in expressing various forms of business processes. The disadvantage in this method is process discovering time and log scanning time in itself take a considerable amount of time. This is due to the re-scanning of the process logs with each new update. In this paper, we will presents a modified FP-Tree algorithm for FP-Tree based business processes, which are used for association analysis in data mining. Our modified algorithm supports the discovery of the appropriate level of process model according to the user's need without re-scanning the entire process logs during updated.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.8
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• pp.2043-2050
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• 2014

As most the scanning systems developed until now provide radiation scan plane images of the inspected objects, there has been a limitation in judging exactly the shape of the objects inside a logistics container exactly with only 2-D radiation image information. As a radiation image is just the density information of the scanned object, the direct application of general stereo image processing techniques is inefficient. So we propose that a new volume-based 3-D reconstruction algorithm. Experimental results show the proposed new volume based reconstruction technique can provide more efficient visualization for X-ray inspection. For validation of the proposed shape reconstruction algorithm using volume, 15 samples were scanned and reconstructed to restore the shape using an X-ray stereo inspection system. Reconstruction results of the objects show a high degree of accuracy compared to the width (2.56%), height (6.15%) and depth (7.12%) of the measured value for a real object respectively. In addition, using a K-Mean clustering algorithm a detection efficiency of 97% is achieved. The results of the reconstructed shape information using the volume based shape reconstruction algorithm provide the depth information of the inspected object with stereo X-ray inspection. Depth information used as an identifier for an automated search is possible and additional studies will proceed to retrieve an X-ray inspection system that can greatly improve the efficiency of an inspection.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.409-418
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• 2021

Conformal coating is a technology that protects PCB(Printed Circuit Board) and minimizes PCB failures. Since the defects in the coating are linked to failure of the PCB, the coating surface is examined for air bubbles to satisfy the successful conditions of the conformal coating. In this paper, we propose an algorithm for detecting problematic bubbles in high-risk groups by applying image signal processing. The algorithm consists of finding candidates for problematic bubbles and verifying candidates. Bubbles do not appear in visible light images, but can be visually distinguished from UV(Ultra Violet) light sources. In particular the center of the problematic bubble is dark in brightness and the border is high in brightness. In the paper, these brightness characteristics are called valley and mountain features, and the areas where both characteristics appear at the same time are candidates for problematic bubbles. However, it is necessary to verify candidates because there may be candidates who are not bubbles. In the candidate verification phase, we used convolutional neural network models, and ResNet performed best compared to other models. The algorithms presented in this paper showed the performance of precision 0.805, recall 0.763, and f1-score 0.767, and these results show sufficient potential for bubble test automation.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.3
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• pp.383-391
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• 2017

In image processing, image segmentation has been studied by various methods in a long time. Image segmentation is the process of partitioning a digital image into multiple objects and face detection is a typical image segmentation field being used in a variety of applications that identifies human faces in digital images. In this paper, we propose a method for extracting the contours of faces included in images. Using the Viola-Jones algorithm, to do this, we detect the approximate locations of faces from images. But, the Viola-Jones algorithm could detected the approximate location of face not the correct position. In order to extract a more accurate face region from image, we use skin color in this paper. In details, face region would be extracted using the analysis of horizontal and vertical histograms on the skin area. Finally, the face contour is extracted using snake algorithm for the extracted face area. In this paperr, a modified snake energy function is proposed for face contour extraction based snake algorithm proposed by Williams et al.[7]

• Journal of Food Hygiene and Safety
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• v.26 no.3
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• pp.273-282
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• 2011

This study established hazards which may cause risk to human at farm during cultivation stage of paprika. Samples of plants (paprika, leaf, stem), cultivation environments (water, soil), personal hygiene (hand, glove, clothes), work utensils (carpet, basket, box) and airborne bacteria were collected from three paprika farms (A, B, C) located in Western Gyeongnam, Korea. The collected samples were assessed for biological (sanitary indications and major foodborne pathogens), chemical (heavy metals, pesticide residues) and physical hazards. In biological hazards, total bacteria and coliform were detected at the levels of 1.9~6.6 and 0.0~4.610g CFU/g, leaf, mL, hand or 100 $cm^2$, while Escherichia coli was not detected in all samples. In major pathogens, only Bacillus cereus were detected at levels of ${\leq}$ 1.5 log CFU/g, mL, hand or 100 $cm^2$, while Staphylococuus aureus, Listeria monocytogenes, E. coli O157 and Salmonella spp. were not detected in all samples. Heavy metal and pesticide residue as chemical hazards were detected at levels below the regulation limit, physical hazard factors, such as insects, pieces of metal and glasses, were also found in paprika farms. Proper management is needed to prevent biological hazards due to cross-contamination while physical and chemical hazards were appropriate GAP criteria.

• Journal of Life Science
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• v.16 no.2 s.75
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• pp.323-327
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• 2006

The Formation of acrylamide was studied in Maillard model reaction systems based on asparagine-glucose. The mixture of asparagine and glucose in equal molar ratio, and then heated at 125, 150, 175 and $200^{\circ}C$ for 10, 20 and 30 minute, respectively. The reaction products were extracted with ethyl acetate and methanol, and then isolated and detected on FFAP capillary column and HP-5MS 5% phenyl methyl siloxane column by using GC/MS. Acrylamide was detected only from methanol extracts and on FFAP capillary column, at retention time 23.53 min., and the detection limit was 4.6 ng. Acrylamide content mainly increased with increasing temperature and processing time till $175^{\circ}C$, therefore, maximal acrylamide formation occurred at $175^{\circ}C$ for 10 minute ($116{\mu}g/g$), while, above $175^{\circ}C$, higher temperatures or prolonged processing times caused a decrease of acrylamide levels, finally disappeared at $200^{\circ}C$ for 30 minute. Three major compounds were identified as 1,3-dihydroxypropanone, 2,3-dihydro-3,5-dihydroxy-6-methyl-4H-pyrane-4-one and 5-hydroxymethylfurfural, and three minor compounds also as 5-methylfurfural, 2-acetylpyrrole and N,N-dimethylcyclohexamine, from ethyl acetate or methanol extracts on FFAP or HP-5MS capillary column.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.11
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• pp.305-313
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• 2019

Recently, spatial information is being constructed actively based on the images obtained by drones. Because occlusion areas occur due to buildings as well as many obstacles, such as trees, pedestrians, and banners in the urban areas, an efficient way to resolve the problem is necessary. Instead of the traditional way, which replaces the occlusion area with other images obtained at different positions, various models based on deep learning were examined and compared. A comparison of a type of feature descriptor, HOG, to the machine learning-based SVM, deep learning-based DNN, CNN, and RNN showed that the CNN is used broadly to detect and classify objects. Until now, many studies have focused on the development and application of models so that it is impossible to select an optimal model. On the other hand, the upgrade of a deep learning-based detection and classification technique is expected because many researchers have attempted to upgrade the accuracy of the model as well as reduce the computation time. In that case, the procedures for generating spatial information will be changed to detect the occlusion area and replace it with simulated images automatically, and the efficiency of time, cost, and workforce will also be improved.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4B
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• pp.566-575
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• 2010

In recent, artificial immune system has become an important research direction in the anomaly detection of networks. The conventional artificial immune systems are usually based on the negative selection that is one of the computational models of self/nonself discrimination. A main problem with self and non-self discrimination is the determination of the frontier between self and non-self. It causes false positive and false negative which are wrong detections. Therefore, additional functions are needed in order to detect potential anomaly while identifying abnormal behavior from analogous symptoms. In this paper, we design novel network attack detection and response schemes based on artificial immune system, and evaluate the performance of the proposed schemes. We firstly generate detector set and design detection and response modules through adopting the interaction between dendritic cells and T-cells. With the sequence of buffer occupancy, a set of detectors is generated by negative selection. The detection module detects the network anomaly with a set of detectors and generates alarm signal to the response module. In order to reduce wrong detections, we also utilize the fuzzy number theory that infers the degree of threat. The degree of threat is calculated by monitoring the number of alarm signals and the intensity of alarm occurrence. The response module sends the control signal to attackers to limit the attack traffic.