• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1565-1572
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• 2015

In this paper, we analyze the polio incidence data based on the Poisson autoregressive models, focusing particularly on change-point detection. Since the data include some strongly deviating observations, we employ the robust cumulative sum (CUSUM) test proposed by Kang and Song (2015) to perform the test for parameter change. Contrary to the result of Kang and Lee (2014), our data analysis indicates that there is no significant change in the case of the CUSUM test with strong robustness and the same result is obtained after ridding the polio data of outliers. We additionally consider the comparison of the forecasting performance. All the results demonstrate that the robust CUSUM test performs adequately in the presence of seemingly outliers.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.523-535
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• 2017

Local intelligent services aim at controlling local services such as cooling or lightening services in a certain local area, using Internet-of-Things (IoT) sensor data in the area. As the IoT paradigm has evolved, local intelligent services have gained increasing attention. However, most of the local intelligent service mechanism proposed so far do not directly take the users' presence and service preference information into account for controlling local services. This study proposes an individual presence-and-preference-based local service system (IPP-LISS). We present a intelligent service control algorithm and implement a prototype system of IPP-LISS. Typically, the intelligence part of IPP-LISS including the prediction models, is generated on remote server in the cloud because of their compute-intense aspect. However, this can cause huge data traffic between IoT devices and servers in the cloud. The emerging mobile edge computing technology will be a promising solution of this challenge of IPP-LISS. In this paper, we implement IPP-LISS in the cloud, and then, based on the implementation result, we discuss applying the mobile edge computing technology to the IPP-LISS application.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1407-1413
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• 2019

In this paper, we assume that another transmitter can transmit its data signal in the same frequency band in order to maximize the frequency efficiency of non-orthogonal multiple access(NOMA) systems. We also focus on the improvement in fairness performance of receiver's data rates, not the maximization of sum data rate for NOMA systems. Thus, in this paper, we propose a power allocation scheme to enhance the fairness performance of average data rates of receivers considering the NOMA systems in the presence of interference. Assuming Rayleigh fading channels, the average data rates of receivers are defined, and the power allocation coefficients to improve the data-rate fairness are derived by using high signal-to-noise power ratio approximation. In addition, through simulations, it is shown that the proposed power allocation scheme can improve the data-rate fairness in the NOMA system with interference.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.413-416
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• 2008

In the presence of multiple continuous queries, multi-query optimizing is a new challenge to process multiple stream data in real-time. So, in this paper, we proposed an approach to optimize multi-query of sliding window on network traffic data streams and do some comparisons to traditional queries without optimizing. We also detail some method of scheduling on different data streams, while different scheduling made different results. We test the results on variety of multi-query processing schedule, and proofed the proposed method is effectively optimized the data stream similar multi-queries.

• ETRI Journal
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• v.44 no.2
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• pp.208-219
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• 2022

Crime amount prediction is crucial for optimizing the police patrols' arrangement in each region of a city. First, we analyzed spatiotemporal correlations of the crime data and the relationships between crime and related auxiliary data, including points-of-interest (POI), public service complaints, and demographics. Then, we proposed a crime amount prediction model based on 2D convolution and long short-term memory neural network (2DCONV-LSTM). The proposed model captures the spatiotemporal correlations in the crime data, and the crime-related auxiliary data are used to enhance the regional spatial features. Extensive experiments on real-world datasets are conducted. Results demonstrated that capturing both temporal and spatial correlations in crime data and using auxiliary data to extract regional spatial features improve the prediction performance. In the best case scenario, the proposed model reduces the prediction error by at least 17.8% and 8.2% compared with support vector regression (SVR) and LSTM, respectively. Moreover, excessive auxiliary data reduce model performance because of the presence of redundant information.

• Journal of Korean society of Dental Hygiene
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• v.21 no.5
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• pp.611-620
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• 2021

Objectives: This study investigated health behavior and nutritional status according to the presence or absence of periodontal disease using data from the Sixth National Health Nutrition Survey. Methods: Data from the 2013-2015 National Health Nutrition Survey were analyzed by frequency analysis, chi-square test, and Pearson's correlation analysis using SPSS 21.0. Results: Periodontal disease was more frequent in the group of patients who perceived that their subjective health status was "very bad" or were underweight according to the BMI classification. In men, periodontal disease occurred more frequently in those who were taking calcium (p<0.05), and periodontal disease occurred more frequently in the age group of 20-39, regardless of sex (p<0.05). In women, periodontal disease was less frequent in the group taking vitamin A. In both men and women, the presence of periodontal disease correlated with high fasting blood sugar and glycated hemoglobin levels. Conclusions: A customized program should be developed for each patient to identify factors that affect the relationship among periodontal disease, nutrient intake, and blood parameters. Such information will help improve the oral health of the population.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.38-46
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• 2022

This study examined the relationship of academic achievement, learning motivation and self-directed learning ability and cognitive presence of nursing students in online class. Data were collected through self reported structured questionnaire in 202 nursing students from October 19 to 30, 2020. Data were analyzed using SPSS/WIN 21.0. As a result this study, academic achievement was positively correlated with learning motivation(r=.45, p<.001), self-directed learning ability(r=.50, p<.001) and cognitive presence(r=.64, p<.001). As a result regression analysis, subjective academic grades, self-directed learning ability and cognitive presence explained 57.0% of the academic achievement in nursing students(F=91.00, p<.001). Therefore, it is necessary to develop various interventions to enhance the self-directed learning ability and to design the classes that increase the cognitive presence.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2128-2147
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• 2021

Cognitive radio systems are being implemented recently to tackle spectrum underutilization problems and aid efficient data traffic. Spectrum sensing is the crucial step in cognitive applications in which cognitive user detects the presence of primary user (PU) in a particular channel thereby switching to another channel for continuous transmission. In cognitive radio systems, the capacity to precisely identify the primary user's signal is essential to secondary user so as to use idle licensed spectrum. Based on the inherent capability, a new spectrum sensing technique is proposed in this paper to identify all types of primary user signals in a cognitive radio condition. Hence, a spectrum sensing algorithm using improved convolutional neural network and long short-term memory (CNN-LSTM) is presented. The principle used in our approach is simulated annealing that discovers reasonable number of neurons for each layer of a completely associated deep neural network to tackle the streamlining issue. The probability of detection is considered as the determining parameter to find the efficiency of the proposed algorithm. Experiments are carried under different signal to noise ratio to indicate better performance of the proposed algorithm. The PU signal will have an associated modulation format and hence identifying the presence of a modulation format itself establishes the presence of PU signal.

• Journal of Distribution Science
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• v.17 no.2
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• pp.101-107
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• 2019

Purpose - The purpose of this study is to analyze the user requirements and developer requirements in virtual reality(VR), and suggest ways to increase the satisfaction of users who experience virtual reality. Specifically, focused on experience store of VR as a distribution retailer, user requirements were analyzed according to the results of previous research that the characteristics of Affordance, Presence, and Head Mounted Display (HMD) affect user satisfaction. Research, design, data, and methodology - Based on previous studies, in this study, we analyzed the importance of immerse, interaction, perception, and presence for the satisfaction of the user through the questionnaire, and the user experiences virtual reality (VR) The importance of HMD, Presence, Affordance, and experience satisfaction were analyzed. Based on this, we analyzed the user requirements and the developer requirements based on the questionnaires and interviews of the experts, starting from the user's desire or preference using QFD technique. Results - As a result of the analysis, the development factors that occupied a big part in the virtual reality field appeared to be visual. In detail, this showed that there was a lack of sense of presence for users. This should be improved. This paper shows that the requirements for visual, auditory, and tactile development are necessary. For the satisfaction of the users of virtual reality (VR) users, it is necessary to develop them according to their priority in consideration of importance and difficulty in the future. Conclusions - In this study, it was suggested that development according to priority should be considered, considering the difficulty level. Based on the results of this study, the following suggestions are made. First, we tried to search the list of user requirements as much as possible, but it is likely to change due to the development of VR technology. Therefore, it is necessary for developers to continually supplement user requirements with recent research literature. Second, development priorities according to difficulty level should be considered. This aspect should be considered in the actual VR development process, so if the numerical value of the difficulty is calculated, it should be presented in the research.

• Journal of The Korean Association of Information Education
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• v.22 no.3
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• pp.307-316
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• 2018

In this study, we conduct SW education using robot in elementary school, to examine the effects of SW education using robot and identify differences dependeing on the presence of assistance teacher. In order to achieve the purpose of this study, SW education using robot was conducted to 29 students. After collecting data, we examined mean difference using matched pair t-test and ANCOVA. Our results show that SW education using robot significantly improved CT and attitudes toward robots. CT, however, was not significantly improved in control group. Further, although improvement in attitudes toward robots was not associated with presence of assistance teacher, presence of assistance teacher did play a role in improvement of CT. This study was investigated effects of the SW education using robot and identify differences depending on the presence of assistant teacher, and expanded the understanding of the SW education using robot.