• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.737-742
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• 2011

Navigation problems include the task of determining the control input under various constraints for systems such as mobile robots subject to uncertain disturbance. Such tasks can be modeled as constrained stochastic control problems. In order to solve these control problems, one may try to utilize the dynamic programming(DP) methods which rely on the concept of optimal value function. However, in most real-world problems, this trial would give us many difficulties; for examples, the exact system model may not be known; the computation of the optimal control policy may be impossible; and/or a huge amount of computing resource may be in need. As a strategy to overcome the difficulties of DP, one can utilize ADP(approximate dynamic programming) methods, which find suboptimal control policies resorting to approximate value functions. In this paper, we apply recently proposed ADP methods to a class of navigation problems having complex constraints, and observe the resultant performance characteristics.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.70-75
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• 2022

Accurately measuring location is necessary to provide a variety of services. The data for indoor positioning measures the RSSI values from the WiFi device through an application of a smartphone. The measured data becomes the raw data of machine learning. The feature data is the measured RSSI value, and the label is the name of the space for the measured position. For this purpose, the machine learning technique is to study a technique that predicts the exact location only with the WiFi signal by applying an efficient technique to classification. Ensemble is a technique for obtaining more accurate predictions through various models than one model, including backing and boosting. Among them, Boosting is a technique for adjusting the weight of a model through a modeling result based on sampled data, and there are various algorithms. This study uses Xgboost among the above techniques and evaluates performance with other ensemble techniques.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.359-373
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• 2022

The new crown pneumonia (COVID-19) has become a global epidemic. The disease has spread to most countries and poses a challenge to the healthcare system. Contact tracing technology is an effective way for public health to deal with diseases. Many experts have studied traditional contact tracing and developed digital contact tracking. In order to better understand the field of contact tracking, it is necessary to analyze the development of contact tracking in the field of computer science by bibliometrics. The purpose of this research is to use literature statistics and topic analysis to characterize the research literature of contact tracking in the field of computer science, to gain an in-depth understanding of the literature development status of contact tracking and the trend of hot topics over the past decade. In order to achieve the aforementioned goals, we conducted a bibliometric study in this paper. The study uses data collected from the Scopus database. Which contains more than 10,000 articles, including more than 2,000 in the field of computer science. For popular trends, we use VOSviewer for visual analysis. The number of contact tracking documents published annually in the computer field is increasing. At present, there are 200 to 300 papers published in the field of computer science each year, and the number of uncited papers is relatively small. Through the visual analysis of the paper, we found that the hot topic of contact tracking has changed from the past "mathematical model," "biological model," and "algorithm" to the current "digital contact tracking," "privacy," and "mobile application" and other topics. Contact tracking is currently a hot research topic. By selecting the most cited papers, we can display high-quality literature in contact tracking and characterize the development trend of the entire field through topic analysis. This is useful for students and researchers new to field of contact tracking ai well as for presenting our results to other subjects. Especially when comprehensive research cannot be conducted due to time constraints or lack of precise research questions, our research analysis can provide value for it.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.157-167
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• 2023

The use of big data for transportation often involves using data that includes personal information, such as the driver's driving routes and coordinates. This study explores the creation of a route choice prediction model using a large dataset from mobile navigation apps using federated learning. This privacy-focused method used distributed computing and individual device usage. This study established preprocessing and analysis methods for driver data that can be used in route choice modeling and compared the performance and characteristics of widely used learning methods with federated learning methods. The performance of the model through federated learning did not show significantly superior results compared to previous models, but there was no substantial difference in the prediction accuracy. In conclusion, federated learning-based prediction models can be utilized appropriately in areas sensitive to privacy without requiring relatively high predictive accuracy, such as a driver's preferred route choice.

• Nutrition Research and Practice
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• v.11 no.3
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• pp.247-256
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• 2017

BACKGROUND/OBJECTIVES: Easy access to intervention and support for certain behaviors is important for obesity prevention and management. The available technology such as smartphone applications can be used for intervention regarding healthy food choices for obesity prevention and management in elementary-school students. The transtheoretical model (TTM) is comprised of stages and processes of change and can be adopted to tailored education for behavioral change. This study aims to develop TTM-based nutrition contents for mobile applications intended to change eating behaviors related to weight gain in young children. SUBJECTS/METHODS: A synthesized algorithm for tailored nutrition messages was developed according to the intake status of six food groups (vegetables, fruits, sugar-sweetened beverages, fast food and instant food, snacks, and late-night snacks), decision to make dietary behavioral changes, and self-confidence in dietary behavioral changes. The messages in this study were developed from December 2014 to April 2015. After the validity evaluation of the contents through expert consultation, tailored nutrition information messages and educational contents were developed based on the TTM. RESULTS: Based on the TTM, stages of subjects are determined by their current intake status, decision to make dietary behavioral changes, and self-confidence in dietary behavioral changes. Three versions of tailored nutrition messages at each TTM stage were developed so as to not send the same messages for three weeks at most, and visual materials such as figures and tables were developed to provide additional nutritional information. Finally, 3,276 tailored nutrition messages and 60 nutrition contents for applications were developed. CONCLUSIONS: Smartphone applications may be an innovative medium to deliver interventions for eating behavior changes directly to individuals with favorable cost-effectiveness. In addition, using the TTM for tailored nutrition education for healthy eating is an effective approach.

• Science of Emotion and Sensibility
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• v.14 no.1
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• pp.7-16
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• 2011

This is a study on operation system's application method for leading-in public bike operation system through researching on case study at home and abroad, and situation research in Naju city. In the 1st research, studied about the main problems showed at application cases of public bike system at domestic and overseas, such as using time, danger for be stolen and damaged. 2nd research focuses on necessarily of leading-in operation system to be used easily by city residents and travelers of Naju City which is the scheduled city for leading-in public bike system. 3rd research is on the basis of the result showed in 1st and 2nd research, supplied some problems' solving method for France Veblib System, such as lending and rental process using mobile, system operation process, communication process. Also, supplied the application method for problems showed at 1st and 2nd research and civil service proposal. The leading-in method study on public bike operation system was done through above research, also case study at home and abroad, situation study, and rental program module development, and this operation system is worked as an model operation system in Naju City. The future study of leading-in operation system will be more effective by means of summing up test running result.

• Communications of Mathematical Education
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• v.29 no.4
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• pp.573-588
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• 2015

In this paper, we introduce development process and application of a simple and effective model of a statistics laboratory using open source software R, one of leading language and environment for statistical computing and graphics. This model consists of HTML files, including Sage cells, video lectures and enough internet resources. Users do not have to install statistical softwares to run their code. Clicking 'evaluate' button in the web page displays the result that is calculated through cloud-computing environment. Hence, with any type of mobile equipment and internet, learners can freely practice statistical concepts and theorems via various examples with sample R (or Sage) codes which were given, while instructors can easily design and modify it for his/her lectures, only gathering many existing resources and editing HTML file. This will be a resonable model of laboratory for studying statistics. This model with bunch of provided materials will reduce the time and effort needed for R-beginners to be acquainted with and understand R language and also stimulate beginners' interest in statistics. We introduce this interactive statistical laboratory as an useful model for beginners to learn basic statistical concepts and R.

• Journal of Korean Library and Information Science Society
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• v.54 no.2
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• pp.23-42
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• 2023

The library tour program is a new type of cultural program that was first introduced and operated by J City, and library tourists travel to specialized libraries in the city according to a set course and experience various experiences. This study aims to build a customized course recommendation model that considers the characteristics of individual participants in addition to the existing fixed group travel format so that more users can enjoy the opportunity to participate in library tours. To this end, the characteristics of library travelers were categorized to establish traveler personas, and library evaluation items and evaluation criteria were established accordingly. We selected 22 libraries targeted by the library travel program and measured library data through actual visits. Based on the collected data, we derived the characteristics of suitable libraries and developed a persona-based library tour course recommendation model using a decision tree algorithm. To demonstrate the feasibility of the proposed recommendation model, we build a mobile application mockup, and conducted user evaluations with actual library users to identify satisfaction and improvements to the developed model.

• Journal of the Korean Society for information Management
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• v.38 no.1
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• pp.243-262
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• 2021

The present study aims to identify the factors that affect the continuous use and discontinuance of the hospital information seeking applications(hospital apps thereafter) by employing the post acceptance model. The surveys were conducted with people who used the hospital apps from October 11 to 18, 2019. Researchers collected 125 valid data and analyzed them by using the structural equation model. The study found that the satisfaction and confirmation of expectation for the hospital apps users had significant effects on intention for continuous use and perceived usefulness, respectively. However, the perceived usefulness did not have a significant effect on the intention for continue use. The present study has identified the variables that influence the continuous use of these innovative technologies. The findings of the study confirmed the post acceptance model by observing the adoption and use of the hospital apps and extended the literature of the post acceptance model by discussing the unique characteristics of the hospital apps that satisfy the urgent help-seekers under emergency situations or the information needs emphasizing promptness. In addition, based on the benefits and limitations of hospital apps reported by consumers, the study provided practical implications for designing more user-friendly apps to hospital app developers or managers.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.1-17
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• 2012

Personalized smart devices such as smartphones and smart pads are widely used. Unlike traditional feature phones, theses smart devices allow users to choose a variety of functions, which support not only daily experiences but also business operations. Actually, there exist a huge number of applications accessible by smart device users in online and mobile application markets. Users can choose apps that fit their own tastes and needs, which is impossible for conventional phone users. With the increase in app demand, the tastes and needs of app users are becoming more diverse. To meet these requirements, numerous apps with diverse functions are being released on the market, which leads to fierce competition. Unlike offline markets, online markets have a limitation in that purchasing decisions should be made without experiencing the items. Therefore, online customers rely more on item-related information that can be seen on the item page in which online markets commonly provide details about each item. Customers can feel confident about the quality of an item through the online information and decide whether to purchase it. The same is true of online app markets. To win the sales competition against other apps that perform similar functions, app developers need to focus on writing app descriptions to attract the attention of customers. If we can measure the effect of app descriptions on sales without regard to the app's price and quality, app descriptions that facilitate the sale of apps can be identified. This study intends to provide such a quantitative result for app developers who want to promote the sales of their apps. For this purpose, we collected app details including the descriptions written in Korean from one of the largest app markets in Korea, and then extracted keywords from the descriptions. Next, the impact of the keywords on sales performance was measured through our econometric model. Through this analysis, we were able to analyze the impact of each keyword itself, apart from that of the design or quality. The keywords, comprised of the attribute and evaluation of each app, are extracted by a morpheme analyzer. Our model with the keywords as its input variables was established to analyze their impact on sales performance. A regression analysis was conducted for each category in which apps are included. This analysis was required because we found the keywords, which are emphasized in app descriptions, different category-by-category. The analysis conducted not only for free apps but also for paid apps showed which keywords have more impact on sales performance for each type of app. In the analysis of paid apps in the education category, keywords such as 'search+easy' and 'words+abundant' showed higher effectiveness. In the same category, free apps whose keywords emphasize the quality of apps showed higher sales performance. One interesting fact is that keywords describing not only the app but also the need for the app have asignificant impact. Language learning apps, regardless of whether they are sold free or paid, showed higher sales performance by including the keywords 'foreign language study+important'. This result shows that motivation for the purchase affected sales. While item reviews are widely researched in online markets, item descriptions are not very actively studied. In the case of the mobile app markets, newly introduced apps may not have many item reviews because of the low quantity sold. In such cases, item descriptions can be regarded more important when customers make a decision about purchasing items. This study is the first trial to quantitatively analyze the relationship between an item description and its impact on sales performance. The results show that our research framework successfully provides a list of the most effective sales key terms with the estimates of their effectiveness. Although this study is performed for a specified type of item (i.e., mobile apps), our model can be applied to almost all of the items traded in online markets.