• Smart Media Journal
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• v.6 no.4
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• pp.94-100
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• 2017

Procurement of development funds is a very difficult process in the Korean game industry where competition is fierce. Recently, crowdfunding has been used as a convenient platform to attract potential investors to secure the development cost of the game. This study explored the factors influencing the success of crowdfunding for game development projects. We collected data on 229 game development projects in Tumblebuck, a reward-based crowdfunding site in Korea. According to the results of the logistic regression model, the factors that have a positive effect on the success of funding are the average compensation amount relative to the target amount, the number of SNS shares, the number of updates, and the originality of the proposed game. Video playback time has been found to have a negative impact. Based on the results of this study, it is expected that planning a game development project considering the variables affecting the success of crowdfunding will help financing.

• Smart Media Journal
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• v.5 no.4
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• pp.124-130
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• 2016

Big data technologies are increasing the need for big data in many areas of the world. Recently, the health care industry has become increasingly aware of the importance of disease and health care services, as it has become increasingly immune to prevention and health care. To do this, we need a Big data system to collect and analyze the personal biometric data. In this paper, we design the biometric big data system using low cost wearable device. We collect basic biometric data, such as heart rate, step count and physical activity from Mi Band, and store the collected biometric data into MongoDB. Based on the results of this study, it is possible to build a big data system that can be used in actual medical environment by using Hadoop etc. and to use it in real medical service in connection with various wearable devices for medical information.

• Smart Media Journal
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• v.9 no.3
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• pp.130-141
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• 2020

The drone market in Korea is growing with a focus on the use of prevention, defense, exploration and surveying, search and rescue, video shooting, and facility management. However, the foreign dependence on drone's core technologies and components is high. Drone-powered countries such as the US and China are expanding the weaponization of drones, which can intensify trade wars between countries, such as strengthening import and export regulations and monopoly. Therefore, Korea should put R & D and localization of core technology, parts, and accessories of next generation drone first. For this, policy research and investment in infrastructure, equipment, and research personnel should be preceded. This study studied the evaluation of investment priorities by infrastructure sector (facility equipment, utilization field, and demand manpower) to foster small drone companies through literature studies. To this end, we expanded and reclassified e isting research, developed investment prioritization indicators through expert group interviews and reviews, derived future uncertainties, and selected investment priorities by infrastructure sector using AHP techniques. Finally, it proposed an infrastructure construction strategy to foster small drone companies in terms of drone development support, utilization support.

• Smart Media Journal
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• v.4 no.4
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• pp.86-92
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• 2015

Recently, it is entering the hyper connectivity age due to the development of sensor and communication technology. In particular, it is emerging new industries such as the IoT, bigdata, cloud by convergence with the ICT and other industries. Because these industries are high the gravity of the software, the demand for software manpower is increasing rapidly. But university curriculum don't deviate from the traditional curriculum, and lack of positive response to these changes is occurring a mismatch with the industry demand. In this paper, investigate a software curriculums of the four-year university, and will attempt to investigate the perception about the university software course of the corporate perspective. Also, we draw a on-site fitness of universities training course by analysis of importance on software training courses between universities and businesses. Finally, we propose a strategy model for software training course design appropriate for the field.

• Smart Media Journal
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• v.6 no.3
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• pp.15-20
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• 2017

Recently, the automotive industry has been studied about driver assistance systems for helping drivers to drive their cars easily by integrating them with the IT technology. This study suggests a method of detecting lanes, robust to road condition changes and applicable to lane departure warning and autonomous vehicles mode. The proposed method uses the method of detecting candidate areas by using the Gaussian filter and by determining the Otsu threshold value and edge. Moreover, the proposed method uses lane gradient and width information through the Hough transform to detect lanes. The method uses road lane information detected before to detect dashed lines as well as solid lines, calculates routes in which the lanes will be located in the next frame to draw virtual lanes. The proposed algorithm was identified to be able to detect lanes in both dashed- and solid-line situations, and implement real-time processing where applied to Raspberry Pi 2 which is open source hardware.

• Smart Media Journal
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• v.6 no.2
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• pp.50-56
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• 2017

Due to new social environment, expenditure on eating out has increased over the last few year, thereafter the food-tech industries have steadily grown as well. We have studied what variable would affect customer's choices when they plan to eat out or order in. There are two variables are taken into account to prove it. Firstly, it is climate changes, such as an amount of rainfall, snowfall and clouds. Secondly, it is days, such as seasons and holidays. Based on this, we looked up the SikSin user's behaviors patterns, then did analysis of the daily data provided by the Meteorological office. By the end of the study, it turned out that two variables, climate changes and days, both have a strong influence on customer's choices. It is considered that this research outcome will make contributions to small businesses founders who want to take the initiative, marketing managers and people who are engaged in the food-tech industry.

• Smart Media Journal
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• v.8 no.1
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• pp.67-73
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• 2019

This study investigates Active Learning and Facilitation Convergence Education Program which can improve core competency to cope with vocational education in the fourth industrial revolution era. I applied the integrated advantages of Active Learning which enhances 'problem solving skill' and those of Facilitation for creative thinking idea to application design process coursework and verified the effectiveness of such education method through student satisfaction survey. I also designed application contents for the students who are familiar with the mobile environments and UI contents for data visualization which can help those students to improve their skills in software. Every coursework was conducted as a team project. As a result, Active Learning and Facilitation Convergence Education Program is found to be helpful in improving the basic skills and competencies required in college education. I hope this work helps to reduce the educational gap between industry and professional colleges.

• Smart Media Journal
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• v.10 no.3
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• pp.23-30
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• 2021

Although the manufacturing time series data clustering technique is an important grouping solution in the field of detecting and improving manufacturing large data-based equipment and process defects, it has a disadvantage of low accuracy when applying the existing static data target clustering technique to time series data. In this paper, an evolutionary computation-based time series cluster analysis approach is presented to improve the coherence of existing clustering techniques. To this end, first, the image shape resulting from the manufacturing process is converted into one-dimensional time series data using linear scanning, and the optimal sub-clusters for hierarchical cluster analysis and split cluster analysis are derived based on the Pearson distance metric as the target of the transformation data. Finally, by using a genetic algorithm, an optimal cluster combination with minimal similarity is derived for the two cluster analysis results. And the performance superiority of the proposed clustering is verified by comparing the performance with the existing clustering technique for the actual manufacturing process image.

• Smart Media Journal
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• v.9 no.4
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• pp.162-169
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• 2020

Design is a key factor that determines the competitiveness of products in the textile and fashion industry. It is very important to measure the similarity of the proposed design in order to prevent unauthorized copying and to confirm the originality. In this study, a deep learning technique was used to quantify features from images of textile designs, and similarity was measured using Spearman correlation coefficients. To verify that similar samples were actually detected, 300 images were randomly rotated and color changed. The results of Top-3 and Top-5 in the order of similarity value were measured to see if samples that rotated or changed color were detected. As a result, the VGG-16 model recorded significantly higher performance than did AlexNet. The performance of the VGG-16 model was the highest at 64% and 73.67% in the Top-3 and Top-5, where similarity results were high in the case of the rotated image. appear. In the case of color change, the highest in Top-3 and Top-5 at 86.33% and 90%, respectively.

• Smart Media Journal
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• v.11 no.3
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• pp.26-30
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• 2022

Spiking neural network is a neural network that applies the working principle of real brain neurons. Due to the biological mechanism of neurons, it consumes less power for training and reasoning than conventional neural networks. Recently, as deep learning models become huge and operating costs increase exponentially, the spiking neural network is attracting attention as a third-generation neural network that connects convolution neural networks and recurrent neural networks, and related research is being actively conducted. However, in order to apply the spiking neural network model to the industry, a lot of research still needs to be done, and the problem of model retraining to apply a new model must also be solved. In this paper, we propose a method to minimize the cost of model retraining by extracting the weights of the existing trained deep learning model and converting them into the weights of the spiking neural network model. In addition, it was found that weight conversion worked correctly by comparing the results of inference using the converted weights with the results of the existing model.