• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.3B
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• pp.287-296
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• 2011

The white stick mainly used for visually impaired person has difficulty in providing location information and effective countermeasures for emergency situations encountered during walking as well as detecting floating obstacles on the ground. In this paper, we propose a machine-to-machine based intelligent walking assistance system for safe and convenient walking of the visually impaired. The proposed system consists of a walking assistance stick used by the visually impaired and a server supporting multiple stick users in remote places through mobile communication networks. The stick equipped with ultrasonic sensors, GPS(global positioning system) receiver and vibrator not only detects floating obstacles, but also offers stick users with present location identification utilizing a text-to-voice conversion technology. Besides providing geographic information, the server notifies the emergency locations of users to guardian and aid agency, and it provides log information during walking such as the place, time and the number of accidents. Test results with a developed prototype system have shown that the system properly performs the functions and satisfies overall system performance.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.361-366
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• 2022

Recently, with the development of virtual and augmented reality technology, metaverse is emerging as a new paradigm that will lead the next-generation internet era, and social and economic activities are spreading around the game, entertainment, music, and content industries. Moreover, as non-face-to-face conversion accelerated after the outbreak of COVID-19, lifestyles and industrial sites are becoming untact and further rapidly becoming a metaverse. In particular, the application of metaverse to the education field is attracting attention because realistic classes using real-time voice conversations using avatars, 3D objects, and 360-degree images can increase immersion and overcome the limitations of distance education. This study examines the concept of metaverse and examines that education using metaverse can be an alternative that can increase the efficiency of education in the non-face-to-face era. In particular, it shows that it is effective in language education and suggests an actual metaverse-based Korea language education program.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.4
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• pp.106-116
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• 2016

MTO (Make to Order) is a manufacturing process in which manufacturing starts only after a customer's order is received. Manufacturing after receiving customer's orders means to start a pull-type supply chain operation because manufacturing is performed when demand is confirmed, i.e. being pulled by demand (The opposite business model is to manufacture products for stock MTS (Make to Stock), which is push-type production). There are also BTO (Build to Order) and ATO (Assemble To Order) in which assembly starts according to demand. Lean manufacturing by MTO is very efficient system. Nevertheless, the process industry, generally, which has a high fixed cost burden due to large-scale investment is suitable for mass production of small pieces or 'mass customization' defined recently. The process industry produces large quantities at one time because of the lack of manufacturing flexibility due to long time for model change or job change, and high loss during line-down (shutdown). As a result, it has a lot of inventory and costs are increased. In order to reduce the cost due to the characteristics of the process industry, which has a high fixed cost per hour, it operates a stock production system in which it is made and sold regardless of the order of the customer. Therefore, in a business environment where the external environment changes greatly, the inventory is not sold and it becomes obsolete. As a result, the company's costs increase, profits fall, and it make more difficult to survive in the competition. Based on the customer's order, we have built a new method for order system to meet the characteristics of the process industry by producing it as a high-profitable model. The design elements are designed by deriving the functions to satisfy the Y by collecting the internal and external VOC (voice of customer), and the design elements are verified through the conversion function. And the Y is satisfied through the pilot test verified and supplemented. By operating this make to order system, we have reduced bad inventories, lowered costs, and improved lead time in terms of delivery competitiveness. Make to order system in the process industry is effective for the display glass industry, for example, B and C groups which are non-flagship models, have confirmed that the line is down when there is no order, and A group which is flagship model, have confirmed stock production when there is no order.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.53-60
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• 2021

The number of visually impaired and blind people is increasing, but braille translation textbooks for them are insufficient, which violates their rights to education despite their will. In order to guarantee their rights, this paper develops a learning system, HunMinJeomUm, that helps them access textbooks, documents, and photographs that are not available in braille, without the assistance of others. In our system, a smart phone app and web pages are designed to promote the accessibility of the blind, and a braille kit is produced using Arduino and braille modules. The system supports the following functions. First, users select documents or pictures that they want, and the system extracts the text using OCR. Second, the extracted text is converted into voice and braille. Third, a membership registration function is provided so that the user can view the extracted text. Experiments have confirmed that our system generates braille and audio outputs successfully, and provides high OCR recognition rates. The study has also found that even completely blind users can easily access the smart phone app.

• Education of Primary School Mathematics
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• v.22 no.4
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• pp.261-280
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• 2019

This study is a qualitative study on the case of teaching mathematics between parents and children. 12 lesson units were applied to the 5th grade elementary school child for the first semester, 2019. The purpose of this study was to identify conceptual understanding in the area, the types of problems that child felt difficult during the learning and parents' advantages and difficulties in this setting. For this study, video recording and voice recording were collected for each lesson class. The concept of the area was recognized correctly, the awareness of reconstruction became clear, and the concept of partitioning, unit iteration and structuring an array was more clearly rebuilt. He showed difficulty in conversion between units of the area, in displaying height of the shape whose height is displayed outside and drawing type of figure with same area after the value of the area was offered. In the learning situation of parents and children, parents who are researchers have the advantage of being able to customize up to their children and being free from time and cost constraints. There were difficulties in controlling negative emotion toward the child, determining the level of the children, distribution the class time and deciding the degree of intervention. Furthermore, research on parenting and child-to-parent teaching in mathematics is recommended.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.