• Journal of the Korean Society for Precision Engineering
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• v.22 no.1
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• pp.82-88
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• 2005

The damage inspection of container surface is performed by the expert inspectors at the container terminal gate of harbor. In this paper, we substitute the expert's capability with the damage inspection system using the artificial intelligent control algorithm and vision system, so we can improve the work environment and effectively decrease the inspection time and cost. Firstly, using six CCD cameras attached to the terminal gate, whole container is partially captured according to eleven sensors aligned with the entering direction of container. Captured partial images are inspected by the fuzzy system which the expert's technology is embedded. Next, we compose partial images to be a complete container image through the correlation coefficient method. Complete container image is saved to solve future troublesome problems. In this paper, the effectiveness of the proposed system was verified through the field test.

• Journal of Information Technology Services
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• v.7 no.4
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• pp.139-149
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• 2008

We propose expert system for restoration the original character form of ancient relics based on image processing and computer graphics. Letters engraved in relief like relics and intaglio like curved tombstones and letters engraved in plane or curved part of cultural asset may have been broken by a lot of rubbed copy, a long time and tide. In this paper, we suggest a new method for extracting and recovering the broken letters of cultural asset into an original form by using Z-map, morphological filter, and high frequency filter. Based on the suggested method. we develop the character recovering system.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1886-1898
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• 2015

In today’s era of advanced technological developments, the threats to the authenticity and integrity of digital images, in a nutshell, the threats to the Image Forensics Research communities have also increased proportionately. This happened as even for the ‘non-expert’ forgers, the availability of image processing tools has become a cakewalk. This image forgery poses a great problem for judicial authorities in any context of trade and commerce. Block matching based image cloning detection system is widely researched over the last 2-3 decades but this was discouraged by higher computational complexity and more time requirement at the algorithm level. Thus, for reducing time need, various dimension reduction techniques have been employed. Since a single technique cannot cope up with all the transformations like addition of noise, blurring, intensity variation, etc. we employ multiple techniques to a single image. In this paper, we have used Fuzzy logic approach for decision making and getting a global response of all the techniques, since their individual outputs depend on various parameters. Experimental results have given enthusiastic elicitations as regards various transformations to the digital image. Hence this paper proposes Fuzzy based cloning detection and classification system. Experimental results have shown that our detection system achieves classification accuracy of 94.12%. Detection accuracy (DAR) while in case of 81×81 sized copied portion the maximum accuracy achieved is 99.17% as regards subjection to transformations like Blurring, Intensity Variation and Gaussian Noise Addition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.678-700
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• 2023

Diagnosing breast diseases using ultrasound (US) images remains challenging because it is time-consuming and requires expert radiologist knowledge. As a result, the diagnostic performance is significantly biased. To assist radiologists in this process, computer-aided diagnosis (CAD) systems have been developed and used in practice. This type of system is used not only to assist radiologists in examining breast ultrasound images (BUS) but also to ensure the effectiveness of the diagnostic process. In this study, we propose a new approach for breast lesion localization and segmentation using a multi-scale pyramid of the ultrasound image of a breast organ and a convolutional semantic segmentation network. Unlike previous studies that used only a deep detection/segmentation neural network on a single breast ultrasound image, we propose to use multiple images generated from an input image at different scales for the localization and segmentation process. By combining the localization/segmentation results obtained from the input image at different scales, the system performance was enhanced compared with that of the previous studies. The experimental results with two public datasets confirmed the effectiveness of the proposed approach by producing superior localization/segmentation results compared with those obtained in previous studies.

• Journal of the Semiconductor & Display Technology
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• v.21 no.2
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• pp.117-122
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• 2022

This paper proposes a method for evaluating the work of manufacturing workers using MediaPipe as a risk factor for musculoskeletal diseases. Recently, musculoskeletal disorders (MSDs) caused by repeated working attitudes in industrial sites have emerged as one of the biggest problems in the industrial health field while increasing public interest. The Korea Occupational Safety and Health Agency presents tools such as NIOSH Lifting Equations (NIOSH), OWAS (Ovako Working-posture Analysis System), Rapid Upper Limb Assessment (RULA), and Rapid Entertainment Assessment (REBA) as ways to quantitatively calculate the risk of musculoskeletal diseases that can occur due to workers' repeated working attitudes. To compensate for these shortcomings, the system proposed in this study obtains the position of the joint by estimating the posture of the worker using the posture estimation learning model of MediaPipe. The position of the joint is calculated using inverse kinetics to obtain an angle and substitute it into the REBA equation to calculate the load level of the working posture. The calculated result was compared to the expert's image-based REBA evaluation result, and if there was a result with a large error, feedback was conducted with the expert again.

• International Journal of Advanced Culture Technology
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• v.3 no.2
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• pp.77-86
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• 2015

In this paper there is an overview of some standards and security models which are implemented in such an IP-based and heterogeneous networks and we also present some security models in an open environment and finally we obtain that as a result of the nature of 4G networks there are still more security holes and open issues for expert to notice. Our survey shows that a number of new security threats to cause unexpected service interruption and disclosure of information will be possible in 4G due mainly to the fact that 4G is an IP-based, heterogeneous network. Other than that, it tells about the security issues and vulnerabilities present in the above 4G standards are discussed. Finally, we point to potential areas for future vulnerabilities and evaluate areas in 4G security which warrant attention and future work by the research and advanced technology industry.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.3
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• pp.305-314
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• 2001

In this paper, we construct an automatic evaluation system of psychological feeling for corporate identity (CI) symbol mark based on a fuzzy neural network technique. The system is modelled by trainable fuzzy inference rules with several input variables (qualitative and quantitative design components of CI symbol mark) and a single output variable (consumer's feeling). The back propagation learning algorithm, which is a conventional learning method of multilayer feedforward neural networks, is used for parameter identification of the fuzzy inference system. The learning ability to train data and the generalization ability to test data are evaluated for the proposed evaluation system by computer simulations.

• The Journal of the Korea Contents Association
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• v.5 no.1
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• pp.37-44
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• 2005

In this study, we integrated 40 kinds of aerobic bacteria that has a higher appearance rate and data for experts and educations, and constructed the aerobic bacterial images database. Constructed system is useful to culture of bacteria for novice without expert's heuristics and deep knowledge and to decrease time and money for handling of patient's examination results. Moreover, it can add new bacterial information in database and contribute to raise a medical quality and it is useful to support a expert's technology in department of laboratory medicine.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.27-32
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• 2020

Along with the aging of the population, the number of dementia patients is increasing, and the social and economic burden is also increasing. Currently, the effective way to manage dementia patients is to identify patients with dementia early. However, in rural and island areas where medical staff are scarce, there is a problem that it is difficult to visit a hospital and get an early examination. Therefore, we propose a remote early detection system for dementia to solve the problems. The remote dementia early diagnosis system is a system that allows a patient to receive examination and treatment from a remote dementia expert using remote medical technology based on real-time image communication. The remote early diagnosis system for dementia consists of a local client system used by medical staff at health centers in the island, an image server that transmits, stores and manages images, and an expert client used by remote dementia experts. The local client subsystem satisfies the current medical law's remote collaboration by allowing the patient to use it with the health center's medical staff. In addition, expert clients are used by dementia experts, and can store/manage patient information, analyze patient history information, and predict the degree of dementia progression in the future.

• Journal of The Korean Society of Integrative Medicine
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• v.8 no.3
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• pp.103-112
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• 2020

Purpose : This study aims to investigate social welfare and students' image of social workers and determine the relevance of these factors to the academic system, gender, and their motivations when selecting a major. Methods : For this study, social welfare students from two two-year colleges and two four-year universities were randomly selected, and 320 students from social welfare departments who understood and agreed to participate in the research took the survey. The data analysis of this study was conducted using SPSS 23.0. Results : The Social Welfare Department students' perception of a social worker's image was shown to be positive. In the social worker image based on academic background, the first grade was found to be more positive than the second, third, and fourth grades. The professional image was more positive than the traditional, social, and vocational images. The gender-based social worker image showed that females were more positive than males. Females were more positive for the professional image, and males were more positive for the traditional image. Regarding the image of social workers based on students' motivation when choosing their major, it was found that volunteer jobs were more positive in terms of traditional images, social images through recommendations, and professional images with aptitude and interest. Conclusion : In sum, most of the students in the social welfare departments had a positive perception of the social worker position. They had a more positive image at the time of admission. Finally, students who entered the school with an expert awareness of social welfare were more positive.