• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.25-30
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• 2018

Pattern classification system is often an important component of intelligent systems. In this paper, we present a pattern classification system consisted of the feature selection module, knowledge base construction module and decision module. We introduce a feature impact evaluation selection method based on fuzzy cluster analysis considering computational approach and generalization capability of given data characteristics. A fuzzy neural network, OFUN-NET based on unsupervised learning data mining technique produces knowledge base for representative clusters. 240 blemish pattern images are prepared and applied to the proposed system. Experimental results show the feasibility of the proposed classification system as an automating defect inspection tool.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.47-56
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• 2022

Recently bank card number recognition plays an important role in improving payment efficiency. In this paper we propose a new bank-card number identification algorithm. The proposed algorithm consists of three modules which include edge detection, candidate region generation, and recognition. The module of 'edge detection' is used to obtain the possible digital region. The module of 'candidate region generation' has the role to expand the length of the digital region to obtain the candidate card number regions, i.e. to obtain the final bank card number location. And the module of 'recognition' has Convolutional deep learning Neural Network (CNN) to identify the final bank card numbers. Experimental results show that the identification rate of the proposed algorithm is 95% for the card numbers, which shows 20% better than that of conventional algorithm or method.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.5
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• pp.259-265
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• 2023

Pedestrian detection is used to effectively detect pedestrians in various situations based on deep learning. Pedestrian detection has difficulty detecting pedestrians due to problems such as camera performance, pedestrian description, height, and occlusion. Even in the same pedestrian, performance in detecting them can differ according to the height of the pedestrian. The height of general pedestrians encompasses various scales, such as those of infants, adolescents, and adults, so when the model is applied to one group, the extraction of data becomes inaccurate. Therefore, this study proposed a pedestrian detection method that fine-tunes the pedestrian area by Refining Layer and Feature Concatenation to consider various heights of pedestrians. Through this, the score and location value for the pedestrian area were finely adjusted. Experiments on four types of test data demonstrate that the proposed model achieves 2-5% higher average precision (AP) compared to Faster R-CNN and DRPN.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.4
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• pp.309-315
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• 2016

NCS is the nation-wide standard designed to standardize and systematize the capability(knowledge, technology and attitude) required for personnel to undertake their task in industrial site according to industry field and level of the task. In order to cope with rapidly changing labor market, individual has to be equipped with ability required for each relevant duty field. And company also requires worker to have basic working ability, professional knowledge or technical capability for relevant duty. Especially, the core competency, a fundamental element for all tasks, has been recognized as critical part not just in social level but also in company level. In that sense, the necessity of adding the core competence into regular education program has been vastly demanded. This study mainly deals with development of NCS-based learning module for information capability that most of industrial field want their employee to be equipped as basic skill. And, in particular, education achievement analysis for computer application capability, a subordinate capabilities of information capability, and its application method are mainly described.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.21-29
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• 2020

Accurate estimation of human pose relies on backbone method in which its role is to extract feature map. Up to dated, the method of backbone feature extraction is conducted by the plain convolutional neural networks named by CNN and the residual neural networks named by Resnet, both of which have various architectures and performances. The CNN family network such as VGG which is well-known as a multiple stacked hidden layers architecture of deep learning methods, is base and simple while Resnet which is a bottleneck layers architecture yields fewer parameters and outperform. They have achieved inspired results as a backbone network in human pose estimation. However, they were used then followed by different pose estimation networks named by pose parsing module. Therefore, in this paper, we present a comparison between the plain CNN family network (VGG) and bottleneck network (Resnet) as a backbone method in the same pose parsing module. We investigate their performances such as number of parameters, loss score, precision and recall. We experiment them in the bottom-up method of human pose estimation system by adapted the pose parsing module of openpose. Our experimental results show that the backbone method using VGG network outperforms the Resent network with fewer parameter, lower loss score and higher accuracy of precision and recall.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.18 no.3
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• pp.392-400
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• 2011

Purpose: This study was done to provide fundamental data to develop a simulation application working practice module and to develop a strategy that would improve team efficacy of students, as well as interpersonal understanding, and proactivity in problem solving after using the team based learning simulation. Methods: The participants were students in fourth year in C University and they participated in the simulation learning for 8 weeks from October to December 2010. The variables of team efficacy, interpersonal understanding, and proactivity in problem solving were measured and data were analyzed using SPSS WIN 17.0 program. Results: After applying the team based simulation learning, students' team efficacy, interpersonal understanding, and proactivity in problem solving improved significantly. Conclusion: The results indicate that the simulation module in this study gave the students experience in providing available and safe nursing care under conditions similar to reality and also underlined the importance of team competency for student nurses in caring for patients.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.613-617
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• 2021

According to the growth of the service industry, stresses from emotional labor workers have been emerging as a social problem, thereby so-called the Emotional Labor Protection Act was implemented in 2018. However, insufficient substantial protection systems for emotional workers emphasizes the necessity of a digital stress management system. Thus, in this paper, we suggest a stress detection system for customer service representatives based on deep learning facial expression recognition. This system consists of a real-time face detection module, an emotion classification FER module that deep-learned big data including Korean emotion images, and a monitoring module that only visualizes stress levels. We designed the system to aim to monitor stress and prevent mental illness in emotional workers.

• Journal of Korea Multimedia Society
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• v.12 no.5
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• pp.757-766
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• 2009

This study presents an adaptive hypermedia learning system which can provide individual learning environment using a learner behavioral model. This system proposes a LBML which can manage learners' learning behavioral information by tracking down such information real-time. The system consists of a collecting system of learning behavioral information and an adaptive learning support system. The collecting system of learning behavioral information uses Web 2.0 technologies and collects learners' learning behavioral information real-time based on a SCORM CMI data model. The collected information is stored as LBML instances of individual learners based on a LBML schema. With the adaptive learning support system, a rule-based learning supporting module and an interactive learning supporting module are developed by analysing LBML instances.

• Journal of Korean Elementary Science Education
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• v.31 no.2
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• pp.135-145
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• 2012

In this study, we developed an efficient in-service teachers training program, which could help the professional development of science gifted teachers. The characteristic feature of this in-service training program was to put great emphasis on interaction between fellow teachers. With this program, teachers could share their experiences and informations about gifted education. The program consisted of 4 interaction modules: 'the module of interaction at the pre-planning', 'the module of interaction in the small group', 'the module of interaction at the plan of application', 'the module of interaction at the practical exercise'. In this study, we analyzed the interaction between science gifted education teachers in 'the module of interaction at the pre-planning'. We analyzed the interaction between science gifted education teachers in 'the module of interaction at the pre-planning'. Each teachers got 17.2 correction opinions from peer teachers. They accepted 79.2% opinions among them and refused the other opinions (20.8% opinions). In the analysis of 'program process', the interactions for the improvement about 'the acquirements of knowledge and function' step were 41.9% and the interactions for the improvement about 'plan and exploration' step were 30.5%. In the analysis of 'program domain', the interactions about 'method of teaching and learning' were 41.9%. The interactions about 'program step' were 28.6% and the interactions about 'learning contentsh were 24.8%. With these results, we discussed the features of interaction between science gifted education teachers and proposed the improvement of in-service training program for elementary science gifted teachers.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.381-388
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• 2022

In instance segmentation, Mask-RCNN is mostly used as a base model. Increasing the performance of Mask-RCNN is meaningful because it affects the performance of the derived model. Mask-RCNN has a transform module for unifying size of input images. In this paper, to improve the Mask-RCNN, we apply deep-learning-based ASSR to the resizing part in the transform module and inject calculated scale information into the model using IM(Integration Module). The proposed IM improves instance segmentation performance by 2.5 AP higher than Mask-RCNN in the COCO dataset, and in the periment for optimizing the IM location, the best performance was shown when it was located in the 'Top' before FPN and backbone were combined. Therefore, the proposed method can improve the performance of models using Mask-RCNN as a base model.