• Journal of information and communication convergence engineering
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• v.18 no.2
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• pp.75-81
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• 2020

Approximately 1.3 million people die from traffic accidents each year, and smartphone usage while driving is one of the main causes of such accidents. Therefore, detection of smartphone usage by drivers has become an important part of distracted driving detection. Previous studies have used single camera-based methods to collect the driver images. However, smartphone usage detection by employing a single camera can be unsuccessful if the driver occludes the phone. In this paper, we present a driver smartphone usage detection system that uses multiple cameras to collect driver images from different perspectives, and then processes these images with ensemble convolutional neural networks. The ensemble method comprises three individual convolutional neural networks with a simple voting system. Each network provides a distinct image perspective and the voting mechanism selects the final classification. Experimental results verified that the proposed method avoided the limitations observed in single camera-based methods, and achieved 98.96% accuracy on our dataset.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.195-204
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• 2020

The purpose of this study was to explore factors associated with smartphone dependence among adults in their 20s. The data were derived from the 2017 Survey on Smartphone Over-dependence conducted by the Ministry of Science and ICT and the National Information Society Agency. There were 3,684 participants. The data were analyzed by frequency, percentage, mean, standard deviation, independent t-test, Pearson's correlation coefficient, and weighted hierarchical multiple regression analysis. For the results, factors related with higher smartphone dependence of participants were duration (β=.18, p=.000) and frequency (β=.04, p=.000) of usage, gaming (β=.15, p=.000), watching videos (β=.09, p=.000), mobile shopping (β=.05, p=.000), working (β=.05, p=.010), e-mailing (β=.13, p=.000), and sports betting (β=.07, p=.000). Music (β=-.07, p=.000) and adult content (β=-.07, p=.000) significantly reduced their smartphone dependence. SNS (Social Networking Services) (β=.01, p=.358) and instant messengers (β=-.02, p=.330) were not factors related to smartphone dependence. However, instant messengers were the most used by participants and had a strong correlation with working (r=.55, p=.000). This study shows that smartphone usage patterns related with smartphone dependence among adults in their 20s are different from those of children and adolescents. These results could be used to more deeply understand smartphone dependence among adults in their 20s and plan early detection and prevention and care of dependence.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.6
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• pp.1347-1359
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• 2017

SQLite database is a file-based DBMS(Database Management System) that provides transactions, and it is loaded on smartphone because it is appropriate for lightweight platform. AS the usage of smartphone increased, SQLite-related crimes can occur. In this paper, we proposed a new concealing method for SQLite db file and a detection method against it. As a result of concealing experiments, it is possible to intentionally conceal 70bytes in the DB file header and conceal original data by inserting artificial pages. But it can be detected by parsing 70bytes based on SQLite structure or using the number of record and index. After that, we proposed detection algorithm for concealed data.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.9
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• pp.953-958
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• 2015

Raspberry Pi is a credit card-sized computer with support for a large number of input and output peripherals. This makes it the perfect platform for interaction with many different devices and for usage in a wide range of applications. When combined with Wi-Fi, it can communicate remotely, therefore increasing its suitability for the construction of wireless sensor nodes. In addition, data processing and decision-making can be based on artificial intelligence, what is performed in developed testbed on the example of monitoring and determining the confidence of fire. In this paper, we demonstrated the usage of Raspberry Pi as a sensor web node for fire-safety monitoring in a building. When the UV-flame sensors detect a flame as thin as that of a candle, the Raspberry Pi sends a push-message to notify the assigned smartphone of the on-site situation through the GCM server. A mobile app was developed to provide a real-time video streaming service in order to determine a false alarm. If an emergency occurs, one can immediately call for help.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.254-260
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• 2019

Recently, a self-camera that generates and distributes a large amount of moving images has been rapidly increasing due to the appearance of SNS such as Facebook, Instagram, and Tweet using mobile. In particular, the amount of SNS connections using mobile phones is significantly increasing in terms of usage, number of connections, and usage time. However, the use of a self-recording system using a smartphone by itself is extremely limited not only in terms of usage but also in frequency of use. In addition, the conventional unattended recording system is a very expensive system that automatically records and tracks an object to be photographed using an infrared signal. Therefore, this paper developed a low cost unmanned recording system using mobile phone. The system consists of a commercial mobile camera, a servomotor for moving the camera from side to side, a microcontroller for controlling the motor, and a commercial wireless Bluetooth earset for video audio input. And it is an unmanned automation system using mobile, and anyone can record image by self image tracking.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.157-163
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• 2022

In this paper, we propose to develop a Bluetooth Health Device Profile (HDP)-based smartphone system to utilize it for early detection of urinary tracts diseases that occur a lot in cats. Therefore, based on Bluetooth HDP, we developed a device and mobile application system (Mycatner®) that can monitor cat activity, toilet usage, urinary disease, and health status, and evaluated its availability to monitor cat health status. The specific feature of this system is that it can measure the number of cat urination frequencies to identify abnormal conditions suspected of urinary tract diseases early, and second, it can be tested with urine test paper and shared with animal hospitals, reducing time and cost. As a result, it is evaluated that the developed device capable of wireless monitoring the urinary system health status of cats is the first in our knowledge.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.584-590
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• 2020

Long-term use of digital screens in daily life can lead to computer vision syndrome including symptoms such as eye strain, dry eyes, and headaches. To prevent computer vision syndrome, it is important to limit screen usage time and take frequent breaks. There are a variety of applications that can help users know the screen usage time. However, these apps are limited because users see various screens such as desktops, laptops, and tablets as well as smartphone screens. In this paper, we propose and evaluate machine learning-based models that detect the screen device in use using color, IMU and lidar sensor data. Our evaluation shows that neural network-based models show relatively high F1 scores compared to traditional machine learning models. Among neural network-based models, the MLP and CNN-based models have higher scores than the LSTM-based model. The RF model shows the best result among the traditional machine learning models, followed by the SVM model.