• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.15-22
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• 2012

For the construction of safe and clear urban environment, it is necessary that we identify the rubbish waste volume and we know the accuracy volume. In this paper, we developed the algorithm which computes the waste volume using the stereo camera for enhancing the environment of waste repository. Using the stereo vision camera, we first computed the distortion parameters of stereo camera and then we obtained the points cloud of the object surface by measuring the target object. Regarding the points cloud as the input of the volume calculation algorithm, we obtained the waste volume of the target object. For this purpose, we suggested two volume calculation algorithm based on the uniform meshing method. The difference between the measured volume such as today's one and yesterday's one gives the reposit of waste volume. Using this approach, we can get the change of the waste volume repository by reading the volume reports weekly, monthly and yearly, so we can get quantitative statistics report of waste volume.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.1
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• pp.102-107
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• 2011

The current blood control system is using barcode and scanning one by one to manage blood bags. To have better management and accuracy, an RFID BIS (blood information system) is implemented with an UHF RFID tag antenna using a reflecter for a blood bag has been used.. The UHF RFID tag for blood bag, attached on the high permittivity blood, is designed and fabricated. The tag antenna is optimized and fabricated with the simulation tests such as the existence and nonexistence of the reflector, various distance between the reflector and the dipole tag, the different widths of the reflector and the existence and nonexistence of the T-matching structure. The characteristics and the reading range patterns of the tag antennas are measured. The BIS is implemented with the new tag design.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.06a
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• pp.9-18
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• 2010

This study investigated the cadastral non-coincidence between real land using and cadastral book using Google Earth Web for difficult area to access that is more efficient method compared with field survey for saving time and money. An reading error has occurred eight parcels about dry field and paddy field but this method is more powerful in case of a danger area of steep, unregistered cemeteries of cadastral book using Google Earth Web of image interpretation that method takes 1 day, the accuracy is 96% and improved 20% more than field survey takes 5 days by 40 parcels. It's possible to reduce the manpower, time and budget could be minimized. In particular, it is need to land alteration of forests and fields category that finds 47 locations a burial ground of non register cadastre book. Google Earth Web method is enabling easy visual analysis of the future land administration of local governments to improving the reliability of temporal and economic costs can be very useful to reduce.

• Journal of the Korean Society of Food Science and Nutrition
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• v.38 no.12
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• pp.1801-1810
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• 2009

This study was carried out to investigate food-nutrition labeling perception and utilization classified by age in Busan. The survey was conducted from March 26 to April 30, 2008 by questionnaires and data analyzed by SPSS program. The results are summarized as follows: reasons for purchase of the processed food was 'delicious' in elementary school children and middle & high school students, but was 'easy to eat and cook' in the adults groups (p<0.001). The criteria for choice of the processed foods was 'taste' in all of the subjects. Eighty seven point five percent of the over 60's do not know about food labeling and 70.1% of them did not check the food label. The first confirmed items for buying the processed foods was 'expiration date' in all of the subjects (71.1%). In elementary school children, middle & high school students, 20's & 30's group, the ratio of awareness of nutrition label was higher than the 40's & 50's and over 60's group. For reading of nutrition label, all of the subjects except elementary group replied 'often' (p<0.001). For the experience of education and publicity on food-nutrition labeling, 54.3% of the subjects replied 'often', and there was a significant difference by age. For the necessity of education and publicity on food-nutrition labeling, 49.5% of the subjects replied 'necessary'. There was significant positive correlation between degree of checking of nutrition label and degree of checking of food label, accuracy of knowledge of processed food, necessity of education and publicity. Therefore, education and publicity on food-nutrition labeling for the subjects are required to encourage them to choose more nutritious food and have healthier dietary pattern.

• Progress in Medical Physics
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• v.24 no.3
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• pp.140-144
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• 2013

In this research, the glass dosimeter was calibrated to measure the standard absorbed dose of the Cs-137 irradiator and absorbed dose in a biological sample. Absorbed dose in water for Cs-137 gamma ray was determined by the IAEA TRS-277 protocol. The PTW-TM30013 ion chamber and the PTW-TM41023 water phantom were utilized for measuring absorbed dose and the value was compared with the reading from DoseAce GD-302M glass dosimeter from Asahi Techno Glass Corporation for its calibration. The uncertainty of measurement ($1{\sigma}$) of the calibrated glass dosimeter was 2.7% and this result would be applied to improve the accuracy in measurement of absorbed dose in a biological sample.

• Journal of Korean Library and Information Science Society
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• v.43 no.2
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• pp.179-201
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• 2012

Currently, most academic libraries provide OPAC 2.0 service which applies Web 2.0 based-internet search engine to OPAC. The new service is for the users' convenience and so the considerations for the users' opinions are of great importance for each of services. The objectives of the study were to investigate perceptions of the students as the main user for academic library on the importance for the main services of OPAC 2.0 and analyzed information sources and the reasons for book search by the virtue of this service. The study showed that there were the users' high expectations on accuracy and satisfaction for OPAC 2.0, and the main searching information source was the OPAC of the universities to which the users belong due to familiar screen as well as convenient reading and book loan through it. The OPAC 2.0 service needs to be improved with the consideration of the users' opinions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.43-48
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• 2019

While reading text-only documents and finding unknown words, readers will become the focus disturbed and not be able to understand the content of the documents. Because children have little experience, it is difficult to understand correctly if the description in context is unfamiliar or ambiguous. In this paper, in order to help understand the text and increase the interest of the readers, we analyze the texts of documents and select the contents that are considered important, and implement a system that displays the most relevant images automatically from the web and links the texts and the images together. The implementation of the system divides the article into paragraphs, analyzes the text, selects important sentences for each paragraph and the important words that best represent the meaning of the important sentences, searches for images related to the words on the web, and then links the images to each of the previous paragraphs. Experiments have shown how to select important sentences and how to select important words in the sentences. As a result of the experiment, we could get 60% performance by evaluating the accuracy of the relation between three selected images and corresponding important sentences.

• Imaging Science in Dentistry
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• v.52 no.2
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• pp.181-186
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• 2022

Purpose: This study aimed to investigate inter-observer reliability among observers with different levels of proficiency and the diagnostic imaging reliability of cone-beam computed tomography (CBCT) images of the retromolar canal. Materials and Methods: CBCT images of 307 patients were assessed for the presence of retromolar canals(RMCs) by 3 observers independently. Diagnoses were made twice by each observer at intervals of more than 3 weeks. Interobserver reliability was assessed using the kappa coefficient. One observer had no experience in diagnosis using CBCT images. Therefore, a specialist in diagnostic imaging explained the CBCT images for interpretation and practiced diagnostic imaging together with this observer, while the other observer interpreted the images independently. Thereafter, the observers re-evaluated the images. Results: The interobserver kappa coefficients (including bilateral RMCs) calculated at the first reading were low, ranging from 0.21 to 0.61. Their values ranged from 0.95 (right side) to 1.00 (left side) after one-on-one practice with a diagnostic imaging specialist, while the values ranged from 0.65 (right side) to 0.66 (left side) without one-on-one practice. Conclusion: Diagnostic accuracy was improved through diagnostic imaging practice. To improve the anatomical interpretation of images, it is important to practice diagnostic imaging with a specialist in diagnostic imaging. One-on-one instruction about diagnostic imaging was an effective method of training.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2390-2406
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• 2022

Scene text recognition has important application value and attracted the interest of plenty of researchers. At present, many methods have achieved good results, but most of the existing approaches attempt to improve the performance of scene text recognition from the image level. They have a good effect on reading regular scene texts. However, there are still many obstacles to recognizing text on low-quality images such as curved, occlusion, and blur. This exacerbates the difficulty of feature extraction because the image quality is uneven. In addition, the results of model testing are highly dependent on training data, so there is still room for improvement in scene text recognition methods. In this work, we present a natural scene text recognizer to improve the recognition performance from the feature level, which contains feature representation and feature enhancement. In terms of feature representation, we propose an efficient feature extractor combined with Representative Batch Normalization and ResNet. It reduces the dependence of the model on training data and improves the feature representation ability of different instances. In terms of feature enhancement, we use a feature enhancement network to expand the receptive field of feature maps, so that feature maps contain rich feature information. Enhanced feature representation capability helps to improve the recognition performance of the model. We conducted experiments on 7 benchmarks, which shows that this method is highly competitive in recognizing both regular and irregular texts. The method achieved top1 recognition accuracy on four benchmarks of IC03, IC13, IC15, and SVTP.

• Advances in nano research
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• v.12 no.2
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• pp.185-195
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• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.