• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.2
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• pp.361-371
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• 2017

The increase in user data stored in the device implies the increase in threats of users' sensitive data. Currently, smartphone authentication mechanisms such as Pattern Lock, fingerprint recognition are widely used. Although, there exist disadvantages of inconvenience use and dependence that users need to depend on their own memory. User behavior based authentication mechanism have advantages of high convenience by offering continuous authentication when using the mobile device. However, these mechanisms show limitations on low accuracy of authentication and there are researches to improve the accuracy. This paper proposes improved authentication mechanism that uses user's smartphone application usage pattern which has not considered on earlier studies. Also, we analyze performance of proposed mechanism with collected datasets from actual use of smartphone applications.

• The Journal of Society for e-Business Studies
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• v.22 no.2
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• pp.79-98
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• 2017

Understanding the consumer-brand relationship in the IT market from the "emotional" or "love" perspective requires an urgent research effort to fully comprehend IT consumers beyond brand loyalty. This study introduces and attempts to empirically validate the concept and measurement of "Lovemarks," which capture the emotional relationship between consumers and IT brands, especially smartphone brands. Having a second-order confirmatory factor analysis for three different smartphone brands, this study extracted 18 final items with six constructs of mysteriousness, sensuality, intimacy, performance, trust, and reputation. The results indicate that the Lovemarks are psychometrically valid and reliably measure the emotional interaction of consumers and IT brands beyondbrand loyalty.

• Structural Engineering and Mechanics
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• v.87 no.5
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• pp.451-460
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• 2023

Recently, the use of smartphones for structural health monitoring in civil engineering has drawn increasing attention due to their rapid development and popularization. In this study, the structural responses and dynamic characteristics of a 421-m-tall skyscraper during the landfall of Typhoon Muifa are monitored using an iPhone 13. The measured building acceleration responses are first corrected by the resampling technique since the sampling rate of smartphone-based measurement is unstable. Then, based on the corrected building acceleration, the wind-induced responses (i.e., along-wind and across-wind responses) are investigated and the serviceability performance of the skyscraper is assessed. Next, the amplitude-dependency and time-varying structural dynamic characteristics of the monitored supertall building during Typhoon Muifa are investigated by employing the random decrement technique and Bayesian spectral density approach. Moreover, the estimated results during Muifa are further compared with those of previous studies on the monitored building to discuss its long-term time-varying structural dynamic characteristics. The paper aims to demonstrate the applicability and effectiveness of smartphones for structural health monitoring of high-rise buildings.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.68-80
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• 2022

Since the launch of AI, technology development to implement complete and sophisticated AI functions has continued. In efforts to develop technologies for complete automation, Machine Learning techniques and deep learning techniques are mainly used. These techniques deal with supervised learning, unsupervised learning, and reinforcement learning as internal technical elements, and use the Big-data Analysis method again to set the cornerstone for decision-making. In addition, established decision-making is being improved through subsequent repetition and renewal of decision-making standards. In other words, big data analysis, which enables data classification and recognition/recognition, is important enough to be called a key technical element of AI function. Therefore, big data analysis itself is important and requires sophisticated analysis. In this study, among various tools that can analyze big data, we will use a Python program to find out what variables can affect addiction according to smartphone use in a decision tree environment. We the Python program checks whether data classification by decision tree shows the same performance as other tools, and sees if it can give reliability to decision-making about the addictiveness of smartphone use. Through the results of this study, it can be seen that there is no problem in performing big data analysis using any of the various statistical tools such as Python and R when analyzing big data.

• The Journal of Information Systems
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• v.32 no.3
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• pp.225-251
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• 2023

Purpose The purpose of this study is to examine the future direction of in-vehicle app development and service, the relationship between potential customers' intention to use and the factors that affect it was explored. It was also checked whether the two types of app development platform and the experience of the existing smartphone app platform had a moderating effect on these relationships. Design/methodology/approach Data was gathered through surveys, collecting responses from 904 potential consumers of vehicle app services in Korea. Structural equation modeling was utilized to analyze the data. Findings According to the empirical analysis result, it was found that potential customers considered enjoyment as the most important benefit factor in in-vehicle app service, and the most important external factor affecting enjoyment was functional compatibility with smartphone. The type of vehicle app development platform did not have a meaningful moderating effect on the factor relationship, whereas the smartphone app platform experience showed a meaningful moderating effect on the relationship between factors. It was analyzed that the risk of app performance, personal information privacy, and driving safety data did not have a negative effect on the intention to use the vehicle app service.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1856-1864
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• 2015

In this paper, the localization algorithm for improving the accuracy of the positioning using the Wi-Fi fingerprint using the normal distribution probability and the built-in typed accelerometer sensor, the gyroscope sensor of smartphone in the indoor environment is proposed. The experiments for analyzing the performance of the proposed algorithm were carried out at the region of the horizontal and vertical 20m * 10m in the engineering school building of our university, and the performance of the proposed algorithm is compared with the fingerprint and the DR (dead reckoning) while user is moving according to the assigned region. As a result, the maximum error distance in the proposed algorithm was decreased to 2cm and 36cm compared with two algorithms, respectively. In addition to this, the maximum error distance was also less than compared with two algorithms as 16.64cm and 36.25cm, respectively. It can be seen that the fingerprint map searching time of the proposed algorithm was also reduced to 0.15 seconds compared with two algorithms.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.43-48
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• 2022

Recently, due to the rapid performance improvement of smartphones and the increase in background executions of mobile apps, multitasking has become common on mobile platforms. Unlike traditional desktop and server apps, response time is important in most mobile apps as they are interactive tasks, and some apps are classified as real-time tasks with deadlines. In this paper, we discuss how to meet the requirements of heterogeneous multitasking in managing memory of real-time and interactive tasks when they are executed together on a smartphone. To do so, we analyze the memory requirement of real-time tasks, and propose a model that has the ability of allocating memory to multitasking tasks on a smartphone. Trace-driven simulations with real-world storage access traces captured by heterogeneous apps show that the proposed model provides reasonable performance for interactive tasks while guaranteeing the requirement of real-time tasks.

• Journal of Digital Convergence
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• v.10 no.10
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• pp.247-259
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• 2012

The purpose of study is to advance current smartphone research past the limitation it faces being focused primarily on functional performance and to understand the causes of Smartphone diffusion. In order to do this, the dominant design concept was applied in examining the relationship between the determinants of the dominant design and the acceptance of smartphones. The research has yielded results which show that determinants such as technological superiority, number of installed based products, and availability of complementary goods have a positive effect on the intention to use smartphones and the attitude toward its use. Moreover, this study has contributed in broadening the understanding of dominant design by introducing the central concepts of dominant design which are not being dealt with in domestic studies and presenting the major variables which must be considered in the acceptance of smartphones.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.61-68
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• 2015

Recently, with increasing use of smartphones, the security threats also have increased rapidly. Especially, the compromised smartphone is very dangerous because it could be exploited in a DDOS attacks such as cyberterrorism as well as in the leakage of personal information. However, most bot detection mechanisms are still unsuitable for smartphone with its lower computing capability and limited battery capacity because they incur additional computational overheads or require pre-defined signatures. In this paper, we present an efficient bot detection mechanism in smartphones. Our mechanism detects effectively bots in outgoing traffic by using a correlation between user events and network traffic. We have implemented its prototype in Android smartphone and measured its performance. The evaluation results show that our mechanism provides low overhead to detect bots in smartphones.

• Journal of Digital Contents Society
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• v.11 no.3
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• pp.323-330
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• 2010

Due to the widespread smartphones and application stores, mobile application developments are now booming again in the same manner of PC history. The smartphone software development process, however, gives much inconvenience to developers because of the heterogeneous platform APIs and hardware incompatibility between different devices. To make clear these problems, mobile web applications are being accepted as an alternative to the native smartphone applications. Mobile web applications can be developed more simply and easily than native applications by using webkit engine's html, Java script, and CSS. Additionally developers can provide a platform-independent applications since web applications are going to run on web browsers. In this paper, we develop a personal applications store running on an android phone's browsers. We can accomplish this very simply by using webkit's various APIs such as OpenID. We can find out by implementing a real web application that development of web applications can surpass the native one in cost and time period without much loss of performance.