• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.69-77
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• 2022

Physical experiment through MBL has been used in many schools for a long time since students can check the experiment results immediately and conduct the experiment easily. However, conducting the experiment, not knowing the principle of the device or simply concentrating on the derived data has been raised as the problem of MBL experiment. To supplement this problem, this study measured the acceleration of gravity with the picket fence method, which is often used in MBL experiment, utilizing Arduino, calculated the error rate through a comparison to the actual acceleration of gravity and discussed the educational application of the experiment to measure it. As a result of the experiment, the error rate between the acceleration of gravity calculated by the experiment and the actual acceleration of gravity was about 1%, so it turned out that relatively accurate measurements were possible. Also, the sample mean of the experimental value was included in the confidence interval of 95%, so it could be concluded that it was a significant experiment. In addition, this study showed the possibility of the educational application of the experiment to measure it through the following: It can supplement the structural disadvantages of MBL; it can consider the interaction between Physics and Math; it is possible to converge with information course in STEAM education; and it is inexpensive to be equipped with the equipment. Hopefully, the physical experiment utilizing Arduino will further be revitalized in science gifted education based on this study.

• Journal of Industrial Convergence
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• v.20 no.10
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• pp.51-59
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• 2022

This study aims to investigate the changes in health status of inpatients before and after a fall accident, and it is a retrospective study using data from 328 inpatients who fell from January 1, 2016 to December 31, 2020, reported to the patient safety reporting system. The average age of the study subjects was 68.57(±14.13), and those in their 70s accounted for the most at 30.49%. Falls occurred on average 13.86(±25.03) days after hospitalization, and the time when the most falls occurred was between 22:30 and 06:59 with 42.99%. Before and after a fall during hospitalization, bowel problems (x²=314.0, p<.001), urination problems (x²=284.0, p<.001), intravenous fluid therapy (x²=85.16, p<.001), and walking (x²=69.77. p<.001), bedridden state (x²=51.60, p< .001), mental state and performance (x²=17.52, p<.001) patient's attitude (x²=220.17, p<.001), there was a statistically significant difference. It is necessary to develop an appropriate method and education program for fall prevention in hospital by considering the individual characteristics of inpatient.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.99-115
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• 2022

In this study, domestic academic journal papers on tax compliance, tax consciousness, and faithful tax payment (hereinafter referred to as "tax compliance") were comprehensively analyzed from an interdisciplinary perspective as a representative research topic in the field of tax science. To achieve the research purpose, topic modeling technique was applied as part of text mining. In the flow of data collection-keyword preprocessing-topic model analysis, potential research topics were presented from tax compliance related keywords registered by the researcher in a total of 347 papers. The results of this study can be summarized as follows. First, in the keyword analysis, keywords such as tax investigation, tax avoidance, and honest tax reporting system were included in the top 5 keywords based on simple term-frequency, and in the TF-IDF value considering the relative importance of keywords, they were also included in the top 5 keywords. On the other hand, the keyword, tax evasion, was included in the top keyword based on the TF-IDF value, whereas it was not highlighted in the simple term-frequency. Second, eight potential research topics were derived through topic modeling. The topics covered are (1) tax fairness and suppression of tax offenses, (2) the ideology of the tax law and the validity of tax policies, (3) the principle of substance over form and guarantee of tax receivables (4) tax compliance costs and tax administration services, (5) the tax returns self- assessment system and tax experts, (6) tax climate and strategic tax behavior, (7) multifaceted tax behavior and differential compliance intentions, (8) tax information system and tax resource management. The research comprehensively looked at the various perspectives on the tax compliance from an interdisciplinary perspective, thereby comprehensively grasping past research trends on tax compliance and suggesting the direction of future research.

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.385-391
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• 2022

Quality evaluation of beef carcasses is an important issue in the livestock industry. Recently, through the AI monitor system based on artificial intelligence, the quality manager can receive help in making accurate decisions based on the analysis of beef carcass images or result information. This artificial intelligence dataset is an important factor in judging performance. Existing datasets may have different surface orientation or resolution. In this paper, we proposed a two-stage classification model that can efficiently manage the grades of beef carcass image using deep learning. And to overcome the problem of the various conditions of the image, a new dataset of 1,300 images was constructed. The recognition rate of deep network for 5-grade classification using the new dataset was 72.5%. Two-stage evaluation is a method to increase reliability by taking advantage of the large difference between grades 1++, 1+, and grades 1 and 2 and 3. With two experiments using the proposed two stage model, the recognition rates of 73.7% and 77.2% were obtained. As this, The proposed method will be an efficient method if we have a dataset with 100% recognition rate in the first stage.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.57-65
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• 2021

A hyper-connected intelligence information society is emerging that creates new value by converging IoT, AI, and Bigdata, which are new technologies of the fourth industrial revolution, in all industrial fields. Everything is connected to the network and data is exploding, and artificial intelligence can learn on its own and even intellectual judgment functions are possible. In particular, the Internet of Things provides a new communication environment that can be connected to anything, anytime, anywhere, enabling super-connections where everything is connected. Artificial intelligence technology is implemented so that computers can execute human perceptions, learning, reasoning, and natural language processing. Artificial intelligence is developing advanced technologies such as machine learning, deep learning, natural language processing, voice recognition, and visual recognition, and includes software, machine learning, and cloud technologies specialized in various applications such as safety, medical, defense, finance, and welfare. Through this, it is utilized in various fields throughout the industry to provide human convenience and new values. However, on the contrary, it is time to respond as intelligent and sophisticated cyber threats are increasing and accompanied by potential adverse functions such as securing the technical safety of new technologies. In this paper, we propose a new data modeling method to enable IoT integrated security control by utilizing artificial intelligence technology as a way to solve these adverse functions.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.351-357
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• 2021

IoT devices, which are used in various ways in distributed environments, are becoming more important in data transmitted and received from IoT devices as fields of use such as medical, environment, transportation, bio, and public places are diversified. In this paper, as a method to ensure the reliability of IoT data, an autoencoder-based IoT-linked processing technique is proposed to classify and process numerous data by various important attributes. The proposed technique uses correlation indices for each IoT data so that IoT data is grouped and processed by blockchain by characteristics for IoT linkage processing based on autoencoder. The proposed technique expands and operates into a blockchain-based n-layer structure applied to the correlation index to ensure the reliability of IoT data. In addition, the proposed technique can not only select IoT data by applying weights to IoT collection data according to the correlation index of IoT data, but also reduce the cost of verifying the integrity of IoT data in real time. The proposed technique maintains the processing cost of IoT data so that IoT data can be expanded to an n-layer structure.

• Journal of Industrial Convergence
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• v.20 no.5
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• pp.83-92
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• 2022

As cases of social and ethical problems caused by artificial intelligence technology have occurred, artificial intelligence ethics are drawing attention along with social interest in the risks and side effects of artificial intelligence. Artificial intelligence ethics should not just be known and felt, but should be actionable and practiced. Therefore, this study proposes an artificial intelligence ethics education model to strengthen the practical ability of artificial intelligence ethics. The artificial intelligence ethics education model derived educational goals and problem-solving processes using artificial intelligence through existing research analysis, applied teaching and learning methods to strengthen practical skills, and compared and analyzed the existing artificial intelligence education model. The artificial intelligence ethics education model proposed in this paper aims to cultivate computing thinking skills and strengthen the practical ability of artificial intelligence ethics. To this end, the problem-solving process using artificial intelligence was presented in six stages, and artificial intelligence ethical factors reflecting the characteristics of artificial intelligence were derived and applied to the problem-solving process. In addition, it was designed to unconsciously check the ethical standards of artificial intelligence through preand post-evaluation of artificial intelligence ethics and apply learner-centered education and learning methods to make learners' ethical practices a habit. The artificial intelligence ethics education model developed through this study is expected to be artificial intelligence education that leads to practice by developing computing thinking skills.

• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.537-543
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• 2022

The purpose of this simulation study was to evaluate the possibility of pancreas detection through effective atomic number information using dual-energy computed tomography(CT). The effective atomic number of 10 tissue-equivalent materials were estimated through stoichiometric calibration. For stoichiometric calibration, HU values at low-energy (80 kV) and high-energy (140 kV) for 10 tissue-equivalent materials were used. Based on this method, the effective atomic number image of the tissue-equivalent material was extracted through an iterative algorithm. According to the results, the attenuation ratio in accordance with the effective atomic number was estimated to have an R² value of 0.9999, and the effective atomic number of Pancreas, Water, Liver, Blood, Spongiosa, and Cortical bone was overall within 1% accuracy compared to the theoretical value. Conventional pancreatic cancer examination uses a contrast medium, so there is a possibility of potential side effects of the contrast medium. In order to solve this problem, it is thought that it will be possible to contribute to an accurate and safe examination by extracting the effective atomic number using dual-energy CT without contrast enhancement. Based on this study, future research will be conducted on the detection of pancreatic cancer using the HU value of pancreatic cancer based on clinical images.

• Journal of Appropriate Technology
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• v.6 no.2
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• pp.94-102
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• 2020

Renewable energy (RE) is essential to comprise sustainable societies, especially, in rural villages of developing countries. Furthermore, application of off-grid RE systems to health care can improve the quality of life. In this research, a RE-based vaccination supply management system was constructed to enlarge the cold-chain in developing countries for the safe storage and delivery of vaccines. The system was comprised of the construction of RE plants and development of vaccine carriers. RE plants were constructed and connected to health posts in local villages. The cooling mechanism of vaccine carriers was improved and monitoring devices were installed. The effect of the system on vaccine cold-chain was evaluated from the field test and topographical analysis in the southern village of Nepal. RE plants were normally operated for the vaccine refrigerator in the health post. The modified vaccine carriers had a longer operation time and better temperature control via monitoring and RE-based recharging functionality. The topographical analysis estimated that the system can cover larger region. The system prototype showed great potential regarding the possibility of a sustainable and enlarged cold-chain. Thus, RE-based vaccine supply management is expected to facilitate vaccine availability while minimizing waste in the supply chain.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.281-289
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• 2021

The purpose of this study was to investigate the level of knowledge, preventive health behavior of the COVID-19 among nursing students. Data were collected from 190 nursing female students in B city and analyzed by t-test, ANOVA, Pearson correlation coefficient using SPSS/WIN 22.0. The degree of knowledge about COVID-19 in nursing female students was 9.18±1.95. The degree of preventive health behaviors on COVID-19 in nursing female students was 3.62±0.30. There were significant differences in knowledge about COVID-19 with respect to age(F=5.981, p=.001), grade(r=6.376, p<.001), college life satisfaction(F=3.632, p=.007). There were significant differences in preventive health behavior of the COVID-19 with respect to age(F=4.018, p=.008), grade(F=2.719, p=.046), health state(F=3.845, p=.005) college life satisfaction(F=3.875, p=.005), clinical experience satisfaction/expectation(F=4.337, p=.002), necessity COVID 19(t=2.801, p=.006). Through this research can be used as basic data by COVID 19 infection control education of nursing students.