• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.1
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• pp.177-186
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• 2011

Mobile services which are applied PC performance and mobile characteristics are increased with spread of the smartphone. Recently, mobile cloud service is getting the spotlight as a solution of mobile service problems that mobile device is lack of memory, computing power and storage and mobile services are subordinate to a particular mobile device platform. However, mobile cloud service has more potential security threats by the threat inheritance of mobile service, wireless network and cloud computing service. Therefore, security threats of mobile cloud service has to be removed in order to deploy secure mobile cloud services and user and manager should be able to respond appropriately in the event of threat. In this paper, We define mobile cloud service threats by threat analysis of mobile device, wireless network and cloud computing and we propose mobile cloud service countermeasures in order to respond mobile cloud service threats and threat scenarios in order to respond and predict to potential mobile cloud service threats.

• Journal of Digital Convergence
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• v.19 no.6
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• pp.1-9
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• 2021

Artificial intelligence (AI) devices are rapidly emerging as a core platform of next-generation information and communication technology (ICT), this study investigated consumer usage behavior and user experience through AI devices that are widely applied to consumers' daily lives. To this end, data was collected from 600 consumers with experience in using AI devices were derived to recognize the attributes and behavior of AI devices. The analysis results are as follows. First, music listening was the most used among various attributes and it was found that simple functions such as providing weather information were usefully recognized. Second, the main devices used by AI device users were identified as AI speakers, smartphone, PC and laptops. Third, associative images of AI devices appeared in the order of fun, useful, novel, smart, innovative, and friendly. Therefore, practical implications are suggested to contribute to provision of user services using AI devices in the future by analyzing usage behaviors that reflect the characteristics of AI devices.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1376-1383
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• 2020

Although studies on fire evacuation systems are increasing, studies on the evacuation of evacuees in indoor spaces are insufficient. According to the latest research, it has been suggested that the use of high frequency might be effective for identifying the location of evacuees indoors. Accordingly, in this paper, the authors intend to develop evacuation location recognition technology and fire evacuation guidance system using high-frequency and a smartphone. The entire system was developed, including an app server, evacuees location recognition unit, an evacuation route search, an output unit, and a speaker unit based on Wi-Fi communication. The experimental results proved the possibility of the effectiveness of the system in the fire situation data. It is expected that this study could be used as an essential study of a fire evacuation guidance system using high frequency data in case of fire.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.11-22
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• 2021

The COVID-19 pandemic has led to distance learning in primary and secondary school. Little has been known whether home ICTs environment is appropriate for the distance learning. This paper aims to assess the current state of ICTs environment at home for the distance learning of children. Using 2012 and 2019 Korean Media Panel Survey, we investigated the number of smart devices owned by households and found differences in ownership by household characteristics. The results showed that the majority of household owned more than one smart devices per child. However, the difference in the proportion of households with less than one device per child varied depending on whether smartphone was included in smart devices. These results imply that public intervention is needed to prevent educational inequality caused by the home ICTs environment for the distance learning.

• Journal of Korean Biological Nursing Science
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• v.23 no.4
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• pp.287-297
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• 2021

Purpose: This study aimed to evaluate the correlation and agreement of physical activity (PA) between data obtained from wearable Actigraph devices and self-reporting questionnaires, and to investigate the relationship between psychological state (depression, anxiety, and fatigue) and PA. Methods: A descriptive study was conducted using physical measurements and surveys. PA was measured through both the International Physical Activity Questionnaire (IPAQ) and the Actigraph GT3X+ device. The demographic characteristics of the subjects, as well as their depression, anxiety, and fatigue scores, were collected with structured questionnaires. The Spearman's rank correlation coefficient and the Bland-Altman plot method were employed. Results: Data from 36 healthy adults were analyzed. The overall levels of PA measured using the IPAQ and the Actigraph were 1,891.69 MET min/week and 669.96 MET/day, respectively. Total levels of PA did not show a significant correlation between the two measurement methodologies. However, the moderate-intensity PA resulting from the IPAQ scores showed a significant positive correlation with the light-intensity PA recorded by the Actigraph. The Bland-Altman plot analysis demonstrated that the levels of PA as measured by the two different methods did not match. In addition, PA measured using the Actigraph showed a significant negative correlation with depression and anxiety whereas PA measured using the IPAQ showed a significant positive correlation with fatigue. Conclusion: The conclusion of this study is that the data obtained from the subjective self-reporting questionnaire and the wearable Actigraph do not correlate or match in healthy adults. Future research should investigate the relationship between depression and PA intensity through the Actigraph, or other wearable devices equipped with smartphone apps.

• Journal of the Korea Institute of Building Construction
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• v.22 no.2
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• pp.219-228
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• 2022

In this study, to improve educational motivation and learning outcomes, a mobile app using game elements was developed, and the effect of its application in rebar work education was analyzed. Using the 4F(Figure out-Focus-Fun design-Finalize) process, which is a game development model, a mobile learning app for rebar work was developed that considers the characteristics of college students familiar with smartphone use, and the app was developed in a manner that utilizes game mechanics such as learning missions and points to stimulate a learner's interest and improve educational motivation. The results show that the proposed app for rebar work is positively evaluated in terms of interface style, perceived usefulness, perceived ease of use, perceived enjoyment, attitude toward using, and intention to use. Therefore, it can be concluded that using the learning game app for rebar work in classes can contribute to improving a learner's performance in various aspects.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.249-257
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• 2022

In recent years, if we order food by easily accessing the online market with our smartphone, we can receive the product in a fresh state at dawn the next day. Cold chain is an industry that can create high added value because it has both the characteristics of general logistics and sensitivity to temperature. Based on the electrical specifications derived from the reefer container capacity requirement investigation, we proved that power supply to up to 33 reefer containers can be made by using three additional auxiliary power supplies which are applied for freight trains in Korea. In this paper, we conducted a research on a design of power supply system for freight train reefer container based on simulation as a basic research necessary for low-temperature distribution and cold chain construction based on the reefer container railroad. Consequently, the simulation was conducted using the three-phase inverter diagram in PSIM and the SVPWM (3-harmonic injection method) control technique, and it was verified that the required power voltage was satisfied with 622V_dc, which is lower than the input voltage of general SPWM of 718V_dc. The details of this paper could be used as a foundational study for constructing cold chains based on a reefer container dedicated to freight trains in the future.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.4
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• pp.27-39
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• 2024

Soil texture is determined by the proportions of sand, silt, and clay within the soil, which influence characteristics such as porosity, water retention capacity, electrical conductivity (EC), and pH. Traditional classification of soil texture requires significant sample preparation including oven drying to remove organic matter and moisture, a process that is both time-consuming and costly. This study aims to explore an alternative method by developing an AI model capable of predicting soil texture from images of pre-sorted soil samples using computer vision and deep learning technologies. Soil samples collected from agricultural fields were pre-processed using sieve analysis and the images of each sample were acquired in a controlled studio environment using a smartphone camera. Color distribution ratios based on RGB values of the images were analyzed using the OpenCV library in Python. A convolutional neural network (CNN) model, built on PyTorch, was enhanced using Digital Image Processing (DIP) techniques and then trained across nine distinct conditions to evaluate its robustness and accuracy. The model has achieved an accuracy of over 80% in classifying the images of pre-sorted soil samples, as validated by the components of the confusion matrix and measurements of the F1 score, demonstrating its potential to replace traditional experimental methods for soil texture classification. By utilizing an easily accessible tool, significant time and cost savings can be expected compared to traditional methods.

• Information Systems Review
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• v.18 no.3
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• pp.31-50
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• 2016

This study investigates the impact of social-face sensitivity of smartphones on the adoption of iPhone in China. Social-face sensitivity is divided into three dimensions, namely, other-directed sensitivity, self-directed sensitivity, and formality-directed sensitivity. We surveyed 218 university students in China through an online survey site. The results showed that formality-directed and other-directed sensitivity have significant impacts on iPhone preferences. Self-directed sensitivity was not significant. We investigated two moderate variables, namely, financial ability and brand sensitivity. Both variables showed significantly moderate impacts on the intention to purchase iPhone. The impact of social-face sensitivity on iPhone preferences implies that the iPhone has dual characteristics to the Chinese, namely, as utility and luxury goods. This finding offers managerial implications for Apple and other mobile service companies in terms of production and marketing strategies.

• Nutrition Research and Practice
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• v.18 no.2
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• pp.269-281
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• 2024

BACKGROUND/OBJECTIVES: The objective of this study was to compare dietary habits and mental health among middle school students in urban and rural areas based on bedtime, and to provide evidence supporting appropriate bedtime for Korean middle school students in relation to their healthy dietary habits and mental well-being. SUBJECTS/METHODS: The study population consisted of 25,681 second-year middle school students who participated in the Korea Youth Risk Behavior Survey in 2020-2022. Participants were asked about their bedtime and wake-up time during the past 7 days and were classified into five categories. The study compared the general characteristics, academic factors, dietary habits, and mental health of urban and rural students based on their bedtime. RESULTS: Bedtime was found to be later in the following order: urban female students, rural female students, urban male students, and rural male students. As bedtime got later, the rates of smoking and alcohol consumption increased. Students who went to bed before 11 p.m. had lower academic performance, while rural male students who went to bed after 2 a.m. had lower academic performance. Later bedtime was associated with increased smartphone usage, skipping breakfast, consuming fast food, and drinking carbonated beverages. Later bedtime was also associated with higher perceived stress levels, particularly among students who went to bed after 2 a.m., higher rates of suicidal ideation, experiencing sadness and despair, as well as the prevalence of clinically significant anxiety disorders. CONCLUSION: These results suggest that middle school students who go to bed too late have higher rates of smoking and alcohol drinking, as well as unhealthy eating habits, stress, suicidal ideation, sadness, and anxiety. Therefore, it is necessary to provide educational and social institutional support to promote adequate sleep for the health of adolescents.