• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.21-27
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• 2019

Recently, embedded systems have been introduced in various fields such as smart factories, industrial drones, and medical robots. Since sensor data collection and IoT functions for machine learning and big data processing are essential in embedded systems, it is essential to port the operating system that is suitable for the function requirements. However, in embedded systems, it is necessary to separate the hard real-time system, which must process within a fixed time according to service characteristics, and the flexible real-time system, which is more flexible in processing time. It is difficult to port the operating system to a low-performance embedded device such as 8BIT MCU to perform simultaneous real-time. When porting a real-time OS (RTOS) to a low-specification MCU and performing a number of tasks, the performance of the real-time and general processing greatly deteriorates, causing a problem of re-designing the hardware and software if a hard real-time system is required for an operating system ported to a low-performance MCU such as an 8BIT MCU. Research on the technology that can process real-time processing system requirements on RTOS (ported in low-performance MCU) is needed.

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

The purpose of this study is to find a way to increase the examination rate by presenting the current state of national health examination. To this end, demographic factors(Gender, age, income level, education level) and health factors(Smoking, alcohol, obesity, subjective health status) were selected, and whether the selected factors were different for each group, and the examinees according to the number of hospitals and income level were visualized. As a result, except for subjective health status in health factors, population factors, and number of hospitals were all related to the examination. In addition, among the age factors of the demographic factor, those in their twenties and those with a low income level and those who were underweight among the health factors of obesity had a high rate of non-testing. Therefore, it is considered necessary to promote, support, and educate those untested by these groups.

• Journal of Information Technology Applications and Management
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• v.28 no.3
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• pp.49-58
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• 2021

Due to the increasing interest in wellness aroused by the aging population and the pursuing feature of active old age, Korean elderly set importance on long life with their healthy condition. Following the change in the paradigm of the medical delivery system from hospital-oriented, treatment-oriented to personal-centered and self-care, Service design application of Smart Healthcare for the elderly became valuable. Smart Healthcare is a healthcare service provided through the fusion of ICT technologies including mobile/wearable devices, IoT, big data, and information technology, and it is utilized to prevent diseases managing abundant health information and living habits. As a methodology for delivering such Smart Healthcare to the elderly, Service design can be adopted. Therefore, this study would like to present the perquisites of Smart Healthcare design for the elderly through analyzing the results from in-depth interview methods between the elderly and medical staff. As a result of this study, guidelines for Service design application of health vulnerability management for the elderly utilizing smart phones were presented. Therefore, this study presented four prerequisites composed of 'high level of supplementation and ethical decision making', 'improvement of inequality in accessibility and experience', 'resolving problems in policy implementation' and 'user-friendliness' for the Smart Healthcare service design for the elderly. Overall, Service design is expected to play an innovative role in improving the quality of life for the elderly through the process of collecting and delivering information on Smart Healthcare centered on the experience of the elderly.

• 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.

• Journal of the korean academy of Pediatric Dentistry
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• v.50 no.3
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• pp.292-306
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• 2023

Antibiotics are used for the prevention and treatment of infections. This study aimed to investigate the patterns of dental antibiotic prescription in children and adolescents. The Health Insurance Review and Assessment Service provided data on patients who visited medical institutions. It was categorized according to year, sex, age, insurance type, dental institution, and region. Chi-square tests, Fisher's exact tests, and one-way analyses of variance were performed. Statistical analyses were performed using SAS software (ver. 9.2; SAS Institute, Cary, NC, USA). Amoxicillin and cephalosporins, the most commonly used antibiotics, accounted for approximately 96% of the prescriptions. The younger the child, the more antibiotics were prescribed for trauma, pulpitis, and dental abscesses. However, closer to adolescence, the antibiotics were primarily prescribed to manage impacted teeth and periodontal problems. Antibiotics were prescribed for 3.13 days on average. There were significant differences in the prescription rates according to age, sex, type of insurance, type of medical institution, and region (p < 0.05). This study suggested that antibiotic prescriptions should be closely monitored to ensure appropriate usage of antibiotics.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.69-74
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• 2022

Even after the drunk driving law was revised through the Yoon Chang-ho Act in 2019, the proportion of habitual offenders among all drunk drivers in 2021 was 4.7%, up 0.5% from 2018. In addition, drunk driving is not easily stopped due to the addiction of alcohol, and there is a high probability of recidivism in accidents as it is often driven again. Therefore, in this paper, to prevent this, when alcohol is measured using its own sensor rather than a manual police measure, the vehicle stops and related data such as the current location and time are automatically saved. Since it is not possible to develop directly on the car, this system was developed by converging various technologies and sensors such as Arduino board, Firebase, and GPS based on the IoT environment in consideration of the simulation environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.236-239
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• 2021

With the growth of AI technology, the scale of Knowledge Graphs continues to be expanded. Knowledge Graphs are mainly expressed as RDF representations that consist of connected triples. Many RDF storages compress and transform RDF triples into the condensed IDs. However, if we try to transform a large scale of RDF triples, it occurs the high processing time and memory overhead because it needs to search the large ID mapping table. In this paper, we propose the method of converting RDF triples using Hash-based ID mapping tables with MapReduce, which is the software framework with a parallel, distributed algorithm. Our proposed method not only transforms RDF triples into Integer-based IDs, but also improves the conversion speed and memory overhead. As a result of our experiment with the proposed method for LUBM, the size of the dataset is reduced by about 3.8 times and the conversion time was spent about 106 seconds.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.24-31
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• 2017

As the concept of sustainability becomes more and more popular, a large amount of literature have been recorded recently on intelligent green building and smart city (IGB&SC). It is therefore needed to systematically analyse the existing knowledge structure as well as the future new development of this domain through the identification of the thematic trends, landmark articles, typical keywords together with co-operative researchers. In this paper, Citespace software package is applied to analyse the citation networks and other relevant data of the past eleven years (from 2006 to 2016) collected from Web of Science (WOS). Through this, a series of professional document analysis are conducted, including the production of core authors, the influence made by the most cited authors, keywords extraction and timezone analysis, hot topics of research, highly cited papers and trends with regard to co-citation analysis, etc. As a result, the development track of the IGB&SC domains is revealed and visualized and the following results reached: (i) in the research area of IGB&SC, the most productive researcher is Winters JV and Caragliu A is most influential on the other hand; (ii) different focuses of IGB&SC research have been emerged continually from 2006 to 2016 e.g. smart growth, sustainability, smart city, big data, etc.; (iii) Hollands's work is identified with the most citations and the emerging trends, as revealed from the bursts analysis in document co-citations, can be concluded as smart growth, the assessment of intelligent green building and smart city.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.229-239
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• 2023

This study aims to explore the impact of algorithmic price discrimination on negative word-of-mouth (NWOM) through the lens of attribution theory. It also examines the mediating roles of intentional attributions and negative emotions, as well as the moderating effect of price sensitivity. For this study, 772 consumers who had purchased flight tickets completed a questionnaire survey, and the collected data were analyzed and tested using SPSS 27.0 and AMOS 24.0 software. The research findings reveal that algorithmic price discrimination has a significant positive impact on intentional attributions, negative emotions, and NWOM. Specifically, deliberate attributions and negative emotions mediate the relationship between algorithmic price discrimination and NWOM, while price sensitivity positively moderates the relationship between negative emotions and NWOM. Therefore, companies should consider disclosing algorithm details transparently in their marketing strategies to mitigate consumers' negative emotions and implement targeted strategies for consumers with different levels of price sensitivity to enhance positive word-of-mouth.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.99-105
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• 2023

In the 4th industrial revolution, the field of criminal justice is paying attention to Legaltech using artificial intelligence to provide efficient legal services. This paper attempted to create a crime prediction model that can apply Recurrent Neural Network(RNN) to increase the potential for using legal technology in the domestic criminal justice field. To this end, the crime process was divided into pre, during, and post stages based on the criminal facts described in the judgment, utilizing crime script analysis techniques. In addition, at each time point, the method and evidence of crime were classified into objects, actions, and environments based on the sentence composition elements and the 8 principles of investigation. The case summary analysis framework derived from this study can contribute to establishing situational crime prevention strategies because it is easy to identify typical patterns of specific crime methods. Furthermore, the results of this study can be used as a useful reference for research on generating crime situation prediction data based on RNN models in future follow-up studies.