Earticle

Since 2019, elementary schools have been teaching software to students, so pre-service teachers should have the ability to teach software. Also, in the COVID-19 situation, pre-service teachers need the ability to teach software online. The purpose of this study is to investigate the effectiveness of online software education for preservice teachers. After providing online software education to preservice teachers, we analyse the results and examines whether online software education is effective. In this study, we define 55 learning elements by analyzing the achievement standards that can evaluate the software education ability of preservice teachers. We figure out whether pre-service teachers have acquired the ability to provide online software education to elementary school students. As a result of the study, we concluded that pre-service teachers who received this online education could conduct software education online in elementary school.

Data mining has broad applications that reach beyond scholarly and scientific research and provide internet search engine services that are commonly used forms of Text and Data Mining(‘TDM’) of websites. The exceptions and limitations for data mining provide a competitive advantage in the global race for policy innovation because it permits researchers to conduct computational analysis - TDM on any materials to which they have access. For this purpose, Japan and the EU added limitations on copyright to legalize some TDM research through amendments to copyright law, and the U.S. copyright law has allowed data mining by the fair use provision. On the other hand, there are no explicit exceptions and limitations for data mining under the Korean Copyright Act, and there are no cases considering data mining fair use. We review comparatively exceptions and limitations on copyright which will help to encourage AI-related business by using more data smoothly through the mining process and extracting more valuable information.

COVID-19 vaccines have been developed worldwide in order to prevent the spread of coronavirus infection- 19, but some people tend to refuse to be vaccinated against COVID-19. Therefore, we will investigate how people's understanding of COVID-19 vaccines affects their attitude to accept COVID-19 vaccination information. Therefore, the purpose of this study is to examine the determinants that affect the acceptability of COVID-19 vaccine through the informational characteristics of COVID-19 vaccine and the individual health belief theory. This study conducted an offline survey of 215 adult men and women living in Seoul and Gyeonggido Province during the period from September 1 to September 10, 2022, and we have conducted a final analysis using a total of 212 questionnaires. The results of our study were as follows. First, among the information characteristics of COVID-19 vaccine, it was confirmed that the amount of information had a significant positive effect on susceptibility, severity, and barriers in health belief theory, respectively. Second, among the information characteristics of COVID-19 vaccine, it was found that the quality of information had a significant positive effect on the susceptibility in health belief theory. Third, susceptibility and barriers in the health belief theory significantly had a positive effect on voluntary attitude and involuntary attitude in acceptance attitude, respectively. And finally, it was found that the severity of the health belief theory had a positive effect on the involuntary attitude in acceptance attitude. The results of this study suggest that policy efforts are needed to make accurate information about COVID-19 vaccine known to the people.

Many parts of life have been changed due to the unprecedented coronavirus outbreak, and Noncontact has now become a general culture of society around the world. Also, many years later, after the Fourth Industrial Revolution, it is now deeply embedded in the human lifestyle. The purpose of this paper's research is to investigate the metaverse perception before and after Corona. It was confirmed that the number of metaverse, the central keyword, was 70971 before Corona, but 261767 after Corona, which was more than three times the frequency. In addition, it was confirmed that the number of COVID-19, the reference point of this study, increased significantly to 1,9236 during the pre-COVID-19 period. Through this, it can be inferred that the metaverse accelerated and developed significantly after the corona. Metaverse about Keywords such as cryptocurrency, cryptocurrency, coin, and exchange appeared before Corona, and the word frequency ranking for blockchain, which is an underlying technology, was high, but after Corona, the word frequency ranking fell significantly as mentioned above. As such, it was confirmed that keywords for metaverse were changing before and after Corona, and as such, Consumers' perceptions were also changing.

The purpose of this study is to confirm and analyze the impact on consumers through big data keyword analysis on weak food. For data collection, web documents, blogs, news, cafes, intellectuals, academic information, and Google Web, news, and Facebook provided by Naver and Daum were used as analysis targets. The data analysis period was set from January 2018 to December 2021. For data collection and analysis, the frequency and matrix of keywords were extracted through Textom, a social matrix site, and the relationship and connection centrality between keywords were analyzed and visualized using the Netdraw function among UCINET6 programs. In addition, CONCOR analysis was conducted to derive clusters for similar keywords. As a result of analyzing yakseon food with keywords, a total of 35,985 cases of collected data were derived. Through this, it was confirmed that medicinal food affects consumers. Furthermore, if a business model is created and developed through yakseon food, it will be possible to lead the popularization of yakseon food.

The purpose of this study is to investigate the views on utilizing ICT material, especially the reasons for ICT material choice. Numerous recent studies have demonstrated increased use of electronic devices including smartphones in educational settings. But the reasons for using it as a learning tool have not been relatively researched from the point of view of pre-service teachers. Therefore, this study analyzed the pre-service teachers’ thinking about utilizing ICT material and their learning experience. In that regard, data was collected in various ways such as surveys, interviews, and brief writings of participants. According to the study result, pre-service teachers were motivated in utilizing ICT material rather than text material. That is the reason why, as adult learners, especially pre-service teachers could be more motivated in self-directed learning environments. This study will be useful in considering ideas for the instructional design of pre-service teachers.

In this study, keywords from representative online portal sites such as Naver, Google, and Youtube were collected based on text mining analysis technique using Textom to check the changes in metqaverse after COVID- 19. before Corona, it was confirmed that social media platforms such as Kakao Talk, Facebook, and Twitter were mentioned, and among the four metaverse, consumer awareness was still concentrated in the field of life logging. However, after Corona, keywords from Roblox, Fortnite, and Geppetto appeared, and keywords such as Universe, Space, Meta, and the world appeared, so Metaverse was recognized as a virtual world. As a result, it was confirmed that consumer perception changed from the life logging of Metaverse to the mirror world. Third, keywords such as cryptocurrency, cryptocurrency, coin, and exchange appeared before Corona, and the word frequency ranking for blockchain, which is an underlying technology, was high, but after Corona, the word frequency ranking fell significantly as mentioned above.

Recently, the improvement of computational processing ability due to the rapid development of computing technology has greatly advanced the field of artificial intelligence, and research to apply it in various domains is active. In particular, in the national defense field, attention is paid to intelligent recognition among machine learning techniques, and efforts are being made to develop object identification and monitoring systems using artificial intelligence. To this end, various image processing technologies and object identification algorithms are applied to create a model that can identify friendly and enemy weapon systems and personnel in real-time. In this paper, we conducted image processing and object identification focused on tanks among various weapon systems. We initially conducted processing the tanks' image using a convolutional neural network, a deep learning technique. The feature map was examined and the important characteristics of the tanks crucial for learning were derived. Then, using YOLOv5 Network, a CNN-based object detection network, a model trained by labeling the entire tank and a model trained by labeling only the turret of the tank were created and the results were compared. The model and labeling technique we proposed in this paper can more accurately identify the type of tank and contribute to the intelligent recognition system to be developed in the future.

The concept of Virtual Character has been developed for a long time with people's demand for cultural and entertainment products such as games, animations, and movies. In recent years, with the rapid development of concepts and industries such as social media, self-media, web3.0, artificial intelligence, virtual reality, and Metaverse, Virtual Character has also expanded new derivative concepts such as Virtual Idol, Virtual YouTuber, and Virtual Digital Human. With the development of technology, people's life is gradually moving towards digitalization and virtualization. At the same time, under the global environment of the new crown epidemic, human social activities are rapidly developing in the direction of network society and online society. From the perspective of digital media content, this paper studies the production technology of Virtual Character related products in the Chinese market, and analyzes the future development direction and possibility of the Virtual Character industry in combination with new media development directions and technical production methods. Consider and provide reference for the development of combined applications of digital media content industry, Virtual Character and Metaverse industry.

Lack of sufficient battery capacity is one of the most important challenges impeding the development of wireless sensor networks (WSNs). Recent innovations in the areas of wireless energy transfer and rechargeable batteries have made it possible to advance WSNs. Therefore, in this article, we propose an energy-efficient charging of sensors in a WSN scenario. First, we have formulated the problem as an integer linear programming (ILP) problem. Then a utility function-based greedy algorithm named UGreedy/UF1 is proposed for solving the problem. Finally, the performance of UGreedy/UF1 is analyzed along with other baseline algorithms: UGreedy/UF2, 2-opt TSP, and Greedy TSP. The simulation results show that UGreedy/UF1 performs better than others both in terms of the deadline missing ratio of sensors and the total energy consumption of UAVs.

Detecting anomalies in human movement is an important task in industrial applications, such as monitoring industrial disasters or accidents and recognizing unauthorized factory intruders. In this paper, we propose an abnormal worker movement detection system based on data stream processing and hierarchical clustering. In the proposed system, Apache Spark is used for streaming the location data of people. A hierarchical clusteringbased anomalous trajectory detection algorithm is designed for detecting anomalies in human movement. The algorithm is integrated into Apache Spark for detecting anomalies from location data. Specifically, the location information is streamed to Apache Spark using the message queuing telemetry transport protocol. Then, Apache Spark processes and stores location data in a data frame. When there is a request from a client, the processed data in the data frame is taken and put into the proposed algorithm for detecting anomalies. A real mobility trace of people is used to evaluate the proposed system. The obtained results show that the system has high performance and can be used for a wide range of industrial applications.

In this paper, we define EMRA-based USN metadata to describe sensor device, sensor node, and sensor network information at the application level. And the proposed method for effectively storing and retrieving USN metadata based on EMRA uses agent technology. As the sensor metadata proposed in this paper is based on SensorML, interoperability can be maintained in the USN environment, and the metadata management system can be directly utilized for metadata management in USN middleware or applications.

In severe disaster situations, Emergency alert provide information for people to survive. However, the only device that can receive the Cell Broadcasting Service (CBS) of the Korean Public Alert System (KPAS), the Public Warning System (PWS), is Smart Phone and Receiving the disaster information is very limited. Therefore, this paper proposes a solution to the problem. First, we discover problems through the analysis of KPAS, and second, we propose Smart Watch and Navigation as devices to receive Emergency alert. The devices can receive disaster information through Bluetooth communication from Smart Phone and provide content such as disaster-related images and videos. Based on the proposed items, the application was developed, and as a result of the test, Smart Phone provided more disaster information like images and videos than current emergency alert, and the method of notification of emergency alert was varied. And Smart Watch and Navigation received disaster information successfully through Smart Phone, which also provided various disaster information like images and videos. The developed application has expanded functionally than the existing emergency alert and can benefit many people in emergency situations.

We look at the initial stage of Meta (previous Facebook)’s new metaverse platform and investigate its platform design in pre-launch and ignition life stage. From the Rocket Model (RM)’s theoretical logic, the results reveal that Meta firstly focuses on investing in key content developers by acquiring virtual reality (VR), video, music content firms and offering production support platform of the augmented reality (AR) content, ‘Spark AR’ last three years (2019~2021) for attracting high-potential developers and users. In terms of three matching criteria, Meta develops an Artificial Intelligence (AI) powered translation software, partners with Microsoft (MS) for cloud computing and AI, and develops an AI platform for realistic avatar, MyoSuite. In ‘connect’ function, Meta curates the game concept submitted by game developers, welcomes other game and SNS based metaverse apps, and expands Horizon Worlds (HW) on VR devices to PCs and mobile devices. In ‘transact’ function, Meta offers ‘HW Creator Funding’ program for metaverse, launches the first commercialized Meta Avatar Store on Meta’s conventional SNS and Messaging apps by inviting all fashion creators to design and sell clothing in this store. Mata also launches an initial test of non-fungible token (NFT) display on Instagram and expands it to Facebook in the US. Lastly, regarding optimization, especially in the face of recent data privacy issues that have adversely affected corporate key performance indicators (KPIs), Meta assures not to collect any new data and to make its privacy policy easier to understand and update its terms of service more user friendly.

The development of the fourth industrial revolution and logistics 4.0 related technology, the growth of the e-commerce market, and the transition to a non-face to face society due to the pandemic are accelerating the growth of the logistics industry. Due to the growth of the logistics industry, various services are emerging to meet the requirements of the market, and research and technology development related to the parcel storage, which is an important element of the last mile service, is also underway. In the past, if it was difficult to deliver the goods directly to the recipient, the parcel storage installed near the delivery location was used, but the usability was not good and the storage of the goods was limited. In addition, the existing parcel storage has a lot of functional limitations compared to the advanced logistics technology, so it is necessary to develop a device that improves it. Therefore, this study conducted to secure safety for unmanned parcel storage devices with robot technology to improve usability and functionality in line with the advanced logistics industry. Based on ISO 10218, an industrial robot related standard, risk identification studies were conducted to derive results that contribute to the development of devices under development.

In this study, in order to efficiently detect data patterns and outliers in time series data, outlier detection processing is performed for each section based on a smart contract in the data preprocessing process, and parameters for the ARIMA model are determined by generating and reflecting the significance and outlierrelated parameters of the data. It was created and applied to the modified arithmetic expression to lower the data abnormality. To evaluate the performance of this study, the normality of the data was compared and evaluated when the parameters of the general ARIMA model and the ARIMA model through this study were applied, and a performance improvement of more than 6% was confirmed.

In keeping with the fourth industrial revolution, IoT-based environments are being implemented everywhere, and as a result, there is an increasing number of cases in which various types of sensed data are utilized efficiently. In particular, the number of multimedia application cases using smart devices is increasing, and there is an increasing number of cases in which large amounts of data are used as fast and convenient as small amounts of data. Given the increasing size of files used in smart devices, the study designed a modified Htree based on journaling meta data management in Ext4-based smart devices and analyzed performance through experiments of various methods. Performance evaluation analysis using IOZone and Hdread shows that the data size of 100Mbyte compared to the data input / output of existing htree is 10% or more, and that of the super large capacity file of 500Mbyte or more is 5% or more.

This study aimed to systematically review the available body awareness interventions in patients with musculoskeletal disorders. A total of 3,462 studies were searched in relevant databases using the search keywords, and six studies that met the selection criteria after review were included in the final analysis. The qualitative level of the studies was classified using the five levels of evidence-based classification, and systematic analysis was conducted according to PICO model (patient/population, intervention, comparison and outcomes). Of the six studies included in the final analysis, there were one, three, and two randomized controlled trials, non-randomized two-group studies, and case reports, respectively. Body awareness therapy was applied to patients with idiopathic scoliosis, non-specific musculoskeletal disorder, hip osteoarthritis, and amputation, who showed positive improvements after the interventions. Our results suggested that body awareness therapy is an important intervention with positive effects on the recovery of patients with musculoskeletal disorders.

The purpose of this study is to investigate the changes in the number of repetitions and the repetition rate according to the exercise mode when performing 65%1RM resistance exercise at the 1-minute rest interval and the 3-minute rest interval. Sixteen healthy male subjects were treated with Bench press and Biceps curl of 65%1RM intensity at 1 and 3 minute rest intervals. The number of repetitions for each set of 1 minute rest interval showed a significant decrease from 1set to 5set in bench press. biceps curl showed a significant decrease from 1set to 4set. The repetition rate according to the exercise mode with a 1-minute rest interval showed a significant difference from 2sets to 4sets. In the repetition rate for each set, bench press showed a significant decrease from 1set to 5set. biceps curl showed a significant decrease from set 1 to set 4. The number of repetitions according to the exercise mode with a 3-minute rest interval showed a significant difference from 2sets to 5sets. In the number of repetitions for each set, bench press showed a significant decrease from 1set to 5set. biceps curl showed a significant decrease from 1set to 4set. The repetition rate according to the exercise mode with a 3-minute rest interval showed a significant difference from 2sets to 5sets. In the repetition rate for each set, bench press showed a significant decrease from 1 set to 5 sets. biceps curl showed a significant decrease from 1set to 4set. In summary, the decrease in the number of repetitions according to the set progression in the resistance exercise of the endurance depends on the exercise mode, and the increase of the rest interval or the decrease of the weight-intensity should be considered when aiming for more exercise.

In the naked eye observation, the health of livestock can be controlled by the range of activity, temperature, pulse, cough, snot, eye excrement, ears and feces. In order to confirm the health of livestock, this paper uses calf face image data to classify the health status by image shape, color and texture. A series of images that have been processed in advance and can judge the health status of calves were used in the study, including 177 images of normal calves and 130 images of abnormal calves. We used GLCM calculation and Convolutional Neural Networks to extract 6 texture attributes of GLCM from the dataset containing the health status of calves by detecting the image of calves and learning the composite image of Convolutional Neural Networks. In the research, the classification ability of GLCM-CNN shows a classification rate of 91.3%, and the subsequent research will be further applied to the texture attributes of GLCM. It is hoped that this study can help us master the health status of livestock that cannot be observed by the naked eye.

The purpose of this study is to identify selection attributes, functional benefits, psychological benefits, and values according to crossover shopping patterns (showrooming and webrooming). To achieve objectives of this study, a survey was designed based on the means-end chain theory, using the in-depth laddering technique and APT laddering technique which understanding the linkage of A(attributes)-FB(functional benefits)-PB(psychological benefit)-V(value). These two laddering techniques were used to construct a hierarchical value map (HVM) by linking selection attributes, functional benefits, psychological benefits, and value levels. The selection attribute items that showrooming shoppers consider important are ‘price conformity’, ‘product information’, ‘product variety’, and ‘delivery service’. Functional benefit items were ‘free purchase’, ‘economic benefit’, ‘communication’, ‘safety’, and ‘accurate Information’, and psychological benefit items were ‘convenience’, ‘relaxation’, ‘pleasure’, ‘rational consumption’, and ‘stability’. Finally, the value items were ‘self-satisfaction’, ‘abundant life’, ‘achievement’, ‘happiness’, and ‘reasonable life’. Next, the selection attribute items that webrooming shoppers consider important are ‘price conformity’, ‘product information’, ‘product variety’, ‘AS’, ‘shopping atmosphere’, and ‘seller service’. Functional benefit items were ‘free purchase’, ‘economic profit’, ‘expression opinion’, ‘safety’, and ‘accurate information’, and psychological benefit items were ‘convenience’, ‘relaxation’, ‘rational consumption’, and ‘stability’. Finally, the value items were ‘self-satisfaction’, ‘abundant life’, ‘happiness’, and ‘reasonable life’.

Today, we are exposed to various text-based media such as newspapers, Internet articles, and SNS, and the amount of text data we encounter has increased exponentially due to the recent availability of Internet access using mobile devices such as smartphones. Collecting useful information from a lot of text information is called text analysis, and in order to extract information, it is performed using technologies such as Natural Language Processing (NLP) for processing natural language with the recent development of artificial intelligence. For this purpose, a morpheme analyzer based on everyday language has been disclosed and is being used. Pre-learning language models, which can acquire natural language knowledge through unsupervised learning based on large numbers of corpus, are a very common factor in natural language processing recently, but conventional morpheme analysts are limited in their use in specialized fields. In this paper, as a preliminary work to develop a natural language analysis language model specialized in the railway field, the procedure for construction a corpus specialized in the railway field is presented.

Along with a variety of coding education, physical computing education for controlling various sensors is being actively conducted for elementary, middle, and high school students in line with the era of the fourth industrial revolution. A problem with physical computing education using Arduino is pin connection errors between Arduino and various sensors. Most of the students who come into contact with the Arduino for the first time often do not know the purpose of the Arduino pin and the connection position of the pin. Also, hardware built with incorrect pin connections to the Arduino board often does not work properly. If this case continues, students will lose interest in coding education. Therefore, in this paper, we implemented an augmented reality application that informs the connection process of the Arduino board and the sensor during physical computing coding education using Arduino, and designed and implemented educational content for the Arduino pin position and connection process. First, we explain the role of the Arduino board and the sensor and the location of the pins. After that, the students run the educational augmented reality educational content using their smartphones and check the correct pin connection process between the Arduino and the sensor. In the physical computing education, augmented reality content is used to increase the understanding and immersion of the class. It is expected that the educational effect will also increase by inducing fun and interest in physical computing coding education.

Recently, the world is facing many difficulties in all areas as a result of price instability and the Russian- Ukraine war. Personally, as many jobs disappear, household income is declining sharply. In particular, the possibility of such bankruptcies is relatively increasing in small and medium-sized enterprises (SMEs), and it is highly likely that this will continue for the time being. In this situation, countries around the world are continuously making efforts to improve corporate productivity. In Korea, the smart factory support project implemented by the Smart Manufacturing Innovation Promotion Team has been continuously promoted for the past several years, providing opportunities for SMEs in need to improve productivity and change the corporate structure. In this study, the current status of smart factory promotion at home and abroad was examined, and problems and improvement measures were studied for the insufficient efficiency in the smart factory promotion system implemented in Korea.

Currently, issues related to freight at Vietnamese logistics companies are becoming more and more urgent because of typical problems in Vietnam such as traffic, infrastructure, and application of information technology. This problem has been studied by applying many different approaches such as Integer Programming (LP), Mixed Integer Programming (MIP), hybrid, meta search, … In this paper, we applied the ILP model in order to deal with the VRP problem in a small size logistics company which is very popular in Vietnam. The experiments showed promising results with some optimal solutions with some small extra costs.

The purpose of this study was to investigate changes in heart rate according to recovery methods after circuit weight training exercise. Fourteen men in their twenties were selected as subjects, and three sets of circuit weight training were performed by cycling six sports, and two recovery conditions (dynamic and static) were performed immediately after exercise. Changes in heart rate did not have an interactive effect according to recovery method and time, and both conditions showed significant changes between sets 1 and 2, and between sets 3 and after recovery. In this study, the high heart rate of 2 sets and 3 sets was seen as a result of exercise stimulation, and the low heart rate of 1 set was thought to be due to the decrease in vagus nerve activity rather than the role of catecholamines. On the other hand, the heart rate after 20 minutes of exercise did not show any difference according to the recovery method, which could mean that the recovery process due to the aquatic environment can act more strongly than the process of dynamic recovery and static recovery. It is thought that the characteristics affected the sensory and circulation of the body, and thus the change of the afferent signal and the level of metabolic products generated in the active muscle.

Many countries around the world using ICT technologies have various technologies to keep pace with the 4th industrial revolution, and various algorithms and systems have been developed accordingly. Among them, many industries and researchers are investing in unmanned automation systems based on AI. At the time when new technology development and algorithms are developed, decision-making by big data analysis applied to AI systems must be equipped with more sophistication. We apply, Pearson's correlation analysis is applied to six independent variables to find out the job satisfaction that office workers feel according to their job characteristics. First, a correlation coefficient is obtained to find out the degree of correlation for each variable. Second, the presence or absence of correlation for each data is verified through hypothesis testing. Third, after visualization processing using the size of the correlation coefficient, the degree of correlation between data is investigated. Fourth, the degree of correlation between variables will be verified based on the correlation coefficient obtained through the experiment and the results of the hypothesis test