• Journal of Internet of Things and Convergence
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• v.5 no.1
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• pp.1-6
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• 2019

This study was intended to verify the impact on the quality of service for employees of youth training facilities. The purpose of this study is to provide basic data for improving service quality by analyzing the factors influencing service quality of youth training facility workers. Data were collected from 110 youth training facilities in Busan. The results showed that social support, emotional labor and self-efficacy had a static effect on the quality of service. Based on the results of this study, the following suggestions were made. First, in order to enhance social support, it is necessary to strengthen regular networking with other agency workers, interview with middle managers, and counseling. Second, to raise emotional labor, it is necessary to imprint a sense of mission as a youth leader. Psychological and emotional programs should also be developed and implemented. Third, in order to increase the self-efficacy, it is necessary to strengthen the administrative super vision and strengthen related education such as image making.

• Journal of Christian Education in Korea
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• v.62
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• pp.135-160
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• 2020

This study closely analyzed the curriculum of the Christian Education Counseling Department and the general Education Counseling Department, and found the current status and problems of the curriculum of the Christian Education Counseling Department and the general Education Counsel Department. This study presented a balanced curriculum of the Christian Education Counseling Department with above analysis. For this purpose, the analysis focused on the educational operation process of Christian education counseling departments and general education counseling departments, such as educational goals, subjects, and counseling practical training. The Christian Education Counseling Department and the general Education Counseling Department are often combined with departments such as Christian Education, Youth, Children and Youth, and Lifelong Education, with the characteristics of convergence majors, so the basic subjects of the department were analyzed to have a higher percentage of subjects than counseling subjects. The results of the analysis showed that both departments lacked a considerable number of subjects related to counseling practical training. In the counseling course, the subjects of personal analysis, education analysis, counseling ethics, and counseling case super-vision for the professional development of counselors are still lacking, according to the analysis. In order to train counselors, it was analyzed that the system of systematic clinical practice system, various counseling analysis for counselor education, and the expansion of super vision subjects were urgently needed. In a modern society where the demand for counseling and the need for counseling experts are increasing as society becomes more complex, it is hoped that Korean universities will be able to actively contribute and cooperate in developing models of counseling education and training counseling experts through them, focusing on standardized indicators for fostering counselors.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.71-82
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• 2022

In order to replace the sampling tensile test of products produced in the tab welding process, which is one of the automotive battery manufacturing processes, vision inspectors are currently being developed and used. However, the vision inspection has the problem of inspection position error and the cost of improving it. In order to solve these problems, there are recent cases of applying deep learning technology. As one such case, this paper tries to examine the usefulness of applying Faster R-CNN, one of the deep learning technologies, to existing product inspection. The images acquired through the existing vision inspection machine are used as training data and trained using the Faster R-CNN ResNet101 V1 1024x1024 model. The results of the conventional vision test and Faster R-CNN test are compared and analyzed based on the test standards of 0% non-detection and 10% over-detection. The non-detection rate is 34.5% in the conventional vision test and 0% in the Faster R-CNN test. The over-detection rate is 100% in the conventional vision test and 6.9% in Faster R-CNN. From these results, it is confirmed that deep learning technology is very useful for detecting welding error of lead tabs in automobile batteries.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.5
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• pp.163-173
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• 2020

In this study, on-site corporate instructors participated as student-cum-workers in a degree-linked high skills Meister course to improve job competency and practical ability as proposed in the Work-Study Career Vision. Evaluation questions were then developed and their validity was verified by assessing satisfaction related to expected goals in enhancing advanced training guidance and competency as an evaluator. Satisfaction assessment was conducted based on training preparation, training implementation, training effectiveness and training administration. The Delphi Method was adopted and a total of 48 items were developed in 6 categories under 4 main areas. There were 7 evaluation items on the satisfaction of training course development under training preparation, 21 evaluation items related to the satisfaction of Off-JT and OJT courses under training implementation, 16 evaluation items related to the satisfaction of increased competency as an on-site corporate instructor and the satisfaction of enhanced practical skills and skills application at work under training effectiveness, as well as 6 evaluation items to assess satisfaction with administrative support under training administration. The final conformity assessment was conducted based on the stability, content validity ratio, consensus and convergence indicators of the developed items. Results of this study do not only apply to quality management of the high skills Meister course which is being promoted as a pilot project for work-study programs, but also serves as a rationale that may be considered as a basic research tool in the collection of various opinions to derive overall system improvement factors for the work-study high skills Meister course.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.56 no.1
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• pp.55-60
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• 2020

In this study, the authors introduce a newly developed flatfish grading system. Owing to the features of flatfish with and wide body, the general types of grading system are not easy to apply for it. Furthermore, the flatfish to be graded is alive such that the existing measurement and grading systems cannot be used for it as well. This study gives a solution for measuring and grading the flatfish with high speed and good accuracy. For this object, the authors developed flatfish measurement and grading system. This system consist of the feeding, conveying, measurement part and sorting part. Especially, the measurement part is made by vision based measuring technique which satisfies the given specification. The result from the experiment shows that the developed system is applicable for measuring and grading the flatfish sizes in variety.

• Journal of KIISE:Software and Applications
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• v.26 no.1
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• pp.157-157
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• 1999

Researches on the gesture recognition have become a very interesting topic in the computer vision area, Gesture recognition from visual images has a number of potential applicationssuch as HCI (Human Computer Interaction), VR(Virtual Reality), machine vision. To overcome thetechnical barriers in visual processing, conventional approaches have employed cumbersome devicessuch as datagloves or color marked gloves. In this research, we capture gesture images without usingexternal devices and generate a gesture trajectery composed of point-tokens. The trajectory Is spottedusing phase-based velocity constraints and recognized using the discrete left-right HMM. Inputvectors to the HMM are obtained by using the LBG clustering algorithm on a polar-coordinate spacewhere point-tokens on the Cartesian space .are converted. A gesture vocabulary is composed oftwenty-two dynamic hand gestures for editing drawing elements. In our experiment, one hundred dataper gesture are collected from twenty persons, Fifty data are used for training and another fifty datafor recognition experiment. The recognition result shows about 95% recognition rate and also thepossibility that these results can be applied to several potential systems operated by gestures. Thedeveloped system is running in real time for editing basic graphic primitives in the hardwareenvironments of a Pentium-pro (200 MHz), a Matrox Meteor graphic board and a CCD camera, anda Window95 and Visual C++ software environment.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.417-420
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• 2006

Detection of faces on still color images with arbitrary backgrounds is attempted in this paper. The newly proposed method is invariant to arbitrary background, number of faces, scale, orientation, skin color, and illumination through the steps of color clustering, cluster scanning, sub-block processing, face area detection, and face verification. The sub-block method makes the proposed method invariant to the size and the number of faces in the image. The proposed method does not need any pre-training steps or a preliminary face database. The proposed method may be applied to areas such as security control, video and photo indexing, and other automatic computer vision-related fields.

• Journal of Korean society of Dental Hygiene
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• v.18 no.5
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• pp.643-652
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• 2018

Objectives: The purpose of this study was to review the systems used to evaluate dental hygiene education and to establish priorities for the evaluation index for accreditation to enhance competitiveness and facilitate quality control of dental hygiene education. Methods: A survey of priorities for accreditation evaluation was developed based on input from professors at 43 universities. Data were analyzed using the Analytic Hierarchy Process method with Expert Choice 2000 software. Results: The relative importance of each evaluation area, ranked in descending order, was as follows: vision and operating system; administration and finances; facilities and equipment; educational outcomes; professors; educational process; and students. The importance of the evaluation part was highest in field training at the education process part and scholarship at the student part. The importance after applying complex weights was highest in establishing a development plan for the vision and operating system. Conclusions: Practical accreditation evaluation based on objectivity and validity is needed to control the quality of dental hygiene education. Therefore, priorities in accreditation evaluation standard must be determined to establish a basis for quality improvement in education at dental hygiene departments.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.11
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• pp.1445-1454
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• 1995

In this study, we have developed a prototype of the automatic defects detection system for capsule inspection using the computer vision techniques. The subjects for inspection are empty hard capsules of various sizes which are made of gelatine. To inspect both sides of a capsule, 2-stage recognition is performed. Features we have used are various lengths of a capsule, area, linearity, symmetricity, head curvature and so on. Decision making is performed based on average value which is computed from 20 good capsules in training and permission bounds in factories. Most of time-consuming process for feature extraction is computed by hardware to meet the inspection speed of more than 20 capsules/sec. The main logic for control and arithmetic computation is implemented using EPLD for the sake of easy change of design and reduction in time for developement. As a result of experiment, defects on size or contour of binary images are detected over 95%. Because of dead zone in imaging system, detection ratio of defects on surface, such as bad joint, chip, speck, etc, is lower than the former case. In this case, detection ratio is 50-85%. Defects such as collet pinch and mashed cap/body seldom appear in binary image, and detection ratio is very low. So we have to process the gray-level image directly in partial region.

• Journal of Broadcast Engineering
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• v.25 no.7
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• pp.1081-1094
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• 2020

With the recent development of deep learning, most computer vision-related tasks are being solved with deep learning-based network technologies such as CNN and RNN. Computer vision tasks such as object detection or object segmentation use intermediate features extracted from the same backbone such as Resnet or FPN for training and inference for object detection and segmentation. In this paper, an experiment was conducted to find out the compression efficiency and the effect of encoding on task inference performance when the features extracted in the intermediate stage of CNN are encoded. The feature map that combines the features of 256 channels into one image and the original image were encoded in HEVC to compare and analyze the inference performance for object detection and segmentation. Since the intermediate feature map encodes the five levels of feature maps (P2 to P6), the image size and resolution are increased compared to the original image. However, when the degree of compression is weakened, the use of feature maps yields similar or better inference results to the inference performance of the original image.