• Journal of the Korean Society of Visualization
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• v.20 no.1
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• pp.45-51
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• 2022

Airborne particulate matter(PM) has been a global environmental problem. PM whose diameter is smaller than 10 ㎛ can permeate respiratory organs and has harmful effects on human health. Therefore, PM monitoring systems are necessary for management of PM and prevention of PM-induced negative effects. Conventional PM monitoring techniques are expensive and cumbersome to handle. In the present study, two types of PM monitoring devices were designed for measuring indoor PM concentration, portably. We experimentally investigated the performance of three commercial PM concentration measurement sensors in a closed test chamber. As a result, PM2008 sensor showed the best PM concentration measurement accuracy. Linear regression method was applied to convert PM concentration value acquired from PM2008 sensor into ground truth value. A mobile application(app.) was also created for users to check the PM concentration, easily. The mobile app. also provides safety alarm when the PM₁₀ concentration exceeds 81 ㎛/m³. The developed hand-held system enables the facile monitoring of surrounding air quality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3723-3737
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• 2022

Google Play is one of the largest Android phone app markets and it contains both free and paid apps. It provides a variety of categories for every target user who has different needs and purposes. The customer's rate every product based on their experience of apps and based on the average rating the position of an app in these arch varies. Fraudulent behaviors emerge in those apps which incorporate search rank maltreatment and malware proliferation. To distinguish the fraudulent behavior, a novel framework is structured that finds and uses follows left behind by fraudsters, to identify both malware and applications exposed to the search rank fraud method. This strategy correlates survey exercises and remarkably joins identified review relations with semantic and behavioral signals produced from Google Play application information, to distinguish dubious applications. The proposed model accomplishes 90% precision in grouping gathered informational indexes of malware, fakes, and authentic apps. It finds many fraudulent applications that right now avoid Google Bouncers recognition technology. It also helped the discovery of fake reviews using the reviewer relationship amount of reviews which are forced as positive reviews for each reviewed Google play the android app.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.141-149
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• 2023

Repacked mobile apps constitute about 78% of all malware of Android, and it greatly affects the technical ecosystem of Android. Although many methods exist for repacked app detection, most of them suffer from performance issues. In this manuscript, a novel method using the Constant Key Point Selection and Limited Binary Pattern (CKPS: LBP) Feature extraction-based Hashing is proposed for the identification of repacked android applications through the visual similarity, which is a notable feature of repacked applications. The results from the experiment prove that the proposed method can effectively detect the apps that are similar visually even that are even under the double fold content manipulations. From the experimental analysis, it proved that the proposed CKPS: LBP method has a better efficiency of detecting 1354 similar applications from a repository of 95124 applications and also the computational time was 0.91 seconds within which a user could get the decision of whether the app repacked. The overall efficiency of the proposed algorithm is 41% greater than the average of other methods, and the time complexity is found to have been reduced by 31%. The collision probability of the Hashes was 41% better than the average value of the other state of the art methods.

• Korean Journal of Remote Sensing
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• v.27 no.1
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• pp.33-41
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• 2011

In the information communication technology, it is world-widely apparent that trend movement from internet web to smartphone app by users demand and developers environment. So it needs kinds of appropriate technological responses from geo-spatial domain regarding this trend. However, most cases in the smartphone app are the map service and location recognition service, and uses of geo-spatial contents are somewhat on the limited level or on the prototype developing stage. In this study, app for extraction of corner point features using geo-spatial imagery and their linkage to database system are developed. Corner extraction is based on Harris algorithm, and all processing modules in database server, application server, and client interface composing app are designed and implemented based on open source. Extracted corner points are applied LOD(Level of Details) process to optimize on display panel. Additional useful function is provided that geo-spatial imagery can be superimposed with the digital map in the same area. It is expected that this app can be utilized to automatic establishment of POI (Point of Interests) or point-based land change detection purposes.

• The Journal of Industrial Distribution & Business
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• v.9 no.12
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• pp.63-72
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• 2018

Purpose - In this study, it is classified the service quality dimension of mobile shopping app using Kano model. In addition, it is evaluated quality factors suitable for strategic management from the viewpoint of service provider through mobile application through binary dimension analysis. Research design, data, and methodology - In this study, seven quality dimensions such as information quality, reliability, immediacy, convenience, design, security and customer service were derived through related studies to make binary shopping quality app quality measurement. 37 sub-variables were set by each quality dimensions. Each questionnaire was composed of positive and negative items like Kano's proposed method, and the satisfaction coefficient suggested by Timko(1993) was examined to understand the influence of each factors on customer satisfaction. Results - As a result of research, shopping app users perceived unity quality factor in most items of service quality dimension such as information quality, reliability, immediacy, convenience and customer service. In addition, the satisfaction coefficient showed a good impression, quick response of the result, fast delivery, and the unsatisfactory coefficient showed more interest in personal information such as payment method safety, and transaction security. As a result of research, shopping app users perceived unity quality factor in most items of service quality dimension such as information quality, reliability, immediacy, convenience and customer service. And, in information quality, the information overload was classified as an apathetic quality component, while the related information provision belonged to an attractive quality component. In reliability quality, customized service provision was classified as an attractive quality component. In instant connectivity, the quality of the connection during transport was classified as an attractive quality component. In convenience quality, access to product information was classified as a one-way quality component. All components of designs quality were classified as attractive quality components, and in security quality, all of their components were all classified as one quality component. Lastly, in customer service, they components were all classified as a single quality component. In addition, the satisfaction coefficient showed a good impression, quick response of the result, fast delivery, and the unsatisfactory coefficient showed more interest in personal information such as payment method safety, and transaction security. Conclusion - In the online service environment, which is difficult to differentiate in terms of upward upgrading only by technological implementation and function, the results of this study can be suggested as a differentiating factor for major channels with customers rather than improve the brand image.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.377-380
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• 2022

An application called an app can be downloaded and used on mobile devices. Among them, Android-based apps have the disadvantage of being implemented on an open source basis and can be exploited by anyone, but unlike iOS, which discloses only a small part of the source code, Android is implemented as an open source, so it can analyze the code. However, since anyone can participate in changing the source code of open source-based Android apps, the number of malicious codes increases and types are bound to vary. Malicious codes that increase exponentially in a short period of time are difficult for humans to detect one by one, so it is efficient to use a technique to detect malicious codes using AI. Most of the existing malicious app detection methods are to extract Features and detect malicious apps. Therefore, three ways to select the optimal feature to be used for learning after feature extraction are proposed. Finally, in the step of modeling with optimal features, ensemble techniques are used in addition to a single model. Ensemble techniques have already shown results beyond the performance of a single model, as has been shown in several studies. Therefore, this paper presents a plan to select the optimal feature and implement a learning model for Android app-based malicious code detection.

• Journal of the Korea Fashion and Costume Design Association
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• v.22 no.3
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• pp.139-153
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• 2020

Today, the proliferation of smart-phones and other mobile devices is bringing many changes to people's daily lives. Recently, the mobile shopping market has grown rapidly and has become a center of distribution. Furthermore, the consumer mobile fashion market is expected to expand and mobile fashion consumers' shopping tendencies will gradually become segmented. Differentiated marketing strategies for mobile fashion companies are to become essential. This study intends to understand the propensity of mobile fashion shopping and the importance of fashion app attributes, and their impact on mobile fashion shopping customer satisfaction and loyalty. The research aims to identify shopping trends and buying behaviors of mobile fashion consumers, find ways to increase mobile fashion shopping purchases, and help develop the mobile fashion market. The results of this study are summarized as follows. First, the compilation and the economics of the mobile fashion shopping propensity factors were shown to have a significant impact on product information, product reviews, and service quality, which are in turn important factors for fashion app attributes, whereas convenience only had a significant impact on service quality. Second, product information and service quality, which are also important factors for fashion app attributes, have a significant impact on mobile fashion shopping. Third, customer satisfaction concerning mobile fashion shopping has a significant impact on loyalty. Mobile fashion shopping customer satisfaction increases loyalty. Increasing the satisfaction and loyalty of mobile fashion shopping will lead to increased sales using mobile fashion shopping apps(Site) and become apparent in the results of mobile fashion companies. Therefore, various efforts by mobile fashion companies will be needed to satisfy their customers.

• Journal of the Korea Convergence Society
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• v.7 no.5
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• pp.23-28
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• 2016

In this paper, smart devices using a 4 to 6 years old, infants and parents to a user layer of training and with games or studying infants by integrating them. Not losing interest and concentration, Mobile infants to learning to learn Korean, English, the estimated budget. design the app tainer. For parents of infants and a variety of media concentration and learning Korean, English, English word cards that can increase the interest of the design and, to find a picture Memory game, had provided games are various kinds such as learning to do puzzles. Also, infants and tries to help study a visual learning and auditory learning at the same time can be achieved by mothers of children is much more conveniently. Learning to guide implementation to maximize the availability, convenience and mobility.

• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.357-364
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• 2018

This study was attempted to develop education programs through mobile apps to promote breastfeeding for high-risk mothers. The development of mobile apps was carried out in four stages, including analysis, design, implementation and evaluation, by referring to the software development life cycle. The subjects of this study were cesarean delivery mother, premature baby and twin delivery mother, and contents of education included difficulty in breastfeeding by high risk mother. Experts and users evaluated the program and found it appropriate as an educational mobile app. The education through mobile app is not limited by time and space. Therefore, it will help knowledge and continuous practice of breastfeeding by high risk mothers. It is necessary to directly test the effects of applying the breastfeeding app developed in this study.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.4
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• pp.141-152
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• 2013

The applications and concerned technologies of cloud computing services, one of major trends in the information communication technology, are widely progressing and advancing. OpenStack, one of open source cloud computing platforms, is comprised of several service components; using these, it can be possible to build public or private cloud computing service for a given target application. In this study, a remote sensing image analysis processing service on cloud computing environment has designed and implemented as an operational test application in the private cloud computing environment based on OpenStack. The implemented service is divided into instance server, web service, and mobile app. A instance server provides remote sensing image processing and database functions, and the web service works for storage and management of remote sensing image from user sides. The mobile app provides functions for remote sensing images visualization and some requests.