• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.2
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• pp.189-200
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• 2012

With increasing the number of smart device such as Tablet PC, smart phone and netbook, information security which based on smart device in mobile environment have become the issue. It is important to enter a password safety. In various types of mobile devices, because of hardware limitation of device, it is difficult that to equip secondary input device such as keyboard and mouse. Also, a loss of accuracy becomes a problem because input information was entered by touch screen. Because of problem mentioned above it can be predicted to change password scheme text based password scheme to graphical password scheme, graphical password scheme is easy to use and is resistant to shoulder surfing attack. So this paper proposes new graphical password scheme based 5 strokes which are made by decomposed the Korean to defend against shoulder surfing attack.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2128-2132
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• 2017

Smart mobility is rapidly emerging as a next-generation eco-friendly transportation system, and the market is booming every year. However, due to the characteristics of the devices that use electricity as the power source, the motor and the battery are different from the performance and actual performance indicated by the manufacturer depending on the user's weight and driving environment. Therefore, The frequency of the overload is increased and the failure and damage of the device are increasing. In this paper, we propose an application that provides personalized recommended driving guidance and overloaded driving situation notification at the actual driving separately from the recommended driving provided by the manufacturer after measuring the driving environment of the user, so as to prevent malfunction and damage of the smart mobility device, To ensure safety.

• Journal of The Korean Association of Information Education
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• v.19 no.2
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• pp.197-206
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• 2015

The dissemination of smart appliances, along with the development of the latest information and communication technology has an effect on society. Their application can be seen in the smart learning environment, by use of digital textbooks with smart devices in the classroom. Hence in this study, the 4L-ACC activity using digital textbooks was applied in the third grade social studies class to improve critical thinking and problem solving skills of elementary school students. This study compared and analyzed the impact in critical thinking and problem-solving skills using digital textbooks versus standard social studies classes. As a result, in social studies classes that used the 4L-ACC activity compared to common social studies classes, elementary student's problem understanding, critical thinking, problem resolution, reflective thinking, and all lower areas of critical thinking and problem-solving were improved. In conclusion, using digital textbooks enhance student's ability to think critically and problem solve. This should provide learners a more comprehensive learning experience and therefore be integrated into more education environment.

• The Journal of the Korea Contents Association
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• v.15 no.9
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• pp.621-630
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• 2015

The purpose of study is to comprehend learning satisfaction and academic achievement about the mobile program of nursing English learning constructed as a solution of supporting smart education in J college. The study was conducted from March to December in 2014. The subjects of study are 342 freshmen (39 males, 303 females) of nursing department. Learning satisfaction was analyzed through questionnaires, and academic achievement was investigated through test produced by 3 professors who majored in nursing. Resources analysis was implemented by SPSS 21.0. The study leads to 3 results from the macroscopic perspective. First, there is a difference of satisfaction with whole program between all factors such as mainly used devices, available times, available days, average hours of use per day, available places. Also, items under program satisfaction (subject or content, availability, academic ability) has a difference. Lastly, change of achievement scores by each time was significant.

• Journal of Internet Computing and Services
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• v.12 no.4
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• pp.135-143
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• 2011

The smartphone allows learners to be involved in learning environments in which students actively study from anywhere and at anytime. Because learners can keep engaged in the environment where they can access to the internet, they can efficiently study in transit using various features and functions of smartphone. Smart learning is a unique learning based on mobility and functions of mobile digital devices including searching and sharing information and using various applications. For the effective use of smartphones in e-learning systems, the contents and learning management systems should be designed to meet effective teaching and learning principles, such as interactivity and collaborations. In smart learning, learning contents for effective learning need to be integrated with typical functions of smartphones and to develop small pieces of learning contents according to learning topics. In the case of learning management systems, it should reflect understanding of learners' environment using a PA agent program and provide personalized learning services.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.1
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• pp.47-53
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• 2006

Localization is a essential technology for mobile robot to work well. Until now expensive sensors such as laser sensors have been used for mobile robot localization. We suggest RFID tag based localization system. RFID tag devices, antennas and tags are cheap and will be cheaper in the future. The RFID tag system is one of the most important elements in the ubiquitous system and RFID tag will be attached to all sorts of goods. Then, we can use this tags for mobile robot localization without additional costs. So, in this paper, the smart floor using passive RFID tags is proposed and, passive RFID tags are mainly used for identifying mobile robot's location and pose in the smart floor. We discuss a number of challenges related to this approach, such as tag distribution (density and structure), typing and clustering. When a mobile robot localizes in this smart floor, the localization error mainly results from the sensing range of the RFID reader, because the reader just ran know whether a tag is in the sensing range of the sensor. So, in this paper, we suggest two algorithms to reduce this error. We apply the particle filter based Monte Carlo localization algorithm to reduce the localization error. And with simulations and experiments, we show the possibility of our particle filter based Monte Carlo localization in the RFID tag based localization system.

• Journal of IKEEE
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• v.24 no.2
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• pp.632-637
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• 2020

The non-intrusive load monitoring (NILM) is an important way to cost-effective real-time monitoring the energy consumption and time of use for each appliance in a home or business using aggregated energy from a single recording meter. In this paper, we collect from the smart farm's power consumption data acquisition system to the server via an LTE modem, converted the total power consumption, and the power of individual electric devices into HDF5 format and performed NILM analysis. We perform NILM analysis using open source denoising autoencoder (DAE), long short-term memory (LSTM), gated recurrent unit (GRU), and sequence-to-point (seq2point) learning methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.320-322
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• 2021

As the world's elderly population increases, the proportion of visually impaired is also increasing, and there are still many restrictions on the use of outside activities, such as safety problems and lack of guidance information. To solve this problem, research on smart devices such as smart glasses with optical character recognition (OCR) function is being actively conducted. In this paper, we propose a system that recognizes obstacles ahead and informs information by voice, and also guides the way to the destination. Using the deep learning object recognition model Yolo, it let them to recognize the risk factors as obstacles such as stairs and Larva cones. and it also deliver the information with a voice. so you can expect that the visually impaired can do a lot of different activity even more now that system takes the visually impaired to the destination by using the directions API, voice recognition, TTS library.

• Smart Media Journal
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• v.3 no.1
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• pp.16-21
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• 2014

The demand for food rises proportionally as population grows. To be able to achieve sustainable supply of livestock products, efficient farm management is a necessity. With the advancement in technology it also brought innovations that could be harness in order to achieve better productivity in animal production and agriculture. Precision Livestock Farming (PLF) is a budding concept of making use of smart sensors or available devices to automatically and continuously monitor and manage livestock production. With this concept, this paper introduces a swine management system that integrates image processing technique for weight monitoring. This system captures pig images using camera, evaluate and estimate the weight base on the captured image. It is comprised of Pig Module, Breeding Module, Health and Medication Module, Weighr Module, Data Analysis Module and Report Module to help swine farm administrators better understand the performance and situation of the swine farm. This paper aims to improve the management in both small and big livestock raisers.

• Smart Media Journal
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• v.6 no.3
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• pp.83-88
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• 2017

The data analysis system using Big-data is worthy to be used in various fields such as politics, traffic, natural disaster, shopping, customer management, medical care, and weather information. Particularly, the analysis of the momentum of an individual using an acceleration signal collected from a wearable device has already been widely used. However, since the data used in such a system stores only the data necessary for measuring the individual activity, it does not provide various analysis results other than the exercise amount of the individual. In this paper, I propose a system that analyzes collective behavior pattern and activity based on the acceleration signal that can be collected from personal smartphones for 24 hours a day and stored in big data. I also propose a system that sends acceleration signals and receives analysis results using standard messaging to use on various smart devices.