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In this paper, we propose a novel method for converting the existing 2D processes in the design and construction phase of civil engineering, to the future BIM-based processes. First, we compare and analyze the actual application processes of the outputs of the existing 2D method and the outputs of the 3D BIM method, for the whole process of BIM design of earthworks and road structures and integrated utilization of quantity, process(4D) and construction cost(5D), in view of life-cycle. The proposed method acquire the outputs of the design phase integrating IFC international common standard file information and CBS/OBS/WBS standard classification scheme information, and acquire the outputs of the construction stage by using an integrated utilization module for quantity, process(4D) and construction cost(5D). Ultimately, we intend to commercialize the step by step technologies for BIM design and construction in civil engineering by using the proposed method.

As the number of households with companion animals increases, the demand to monitor the health of companion animals in remote locations far away is increasing. We are going to put a wearable device on a companion animal so that it can monitor the heart beat signal from a remote location. However, the monitoring method using Bluetooth has some disadvantages. it can be accessed only in a short distances. In case of WiFi, large power consumption is the problem. To overcome these issues, we propose a system to reduce power consumption by indirectly receiving a user's request using Bluetooth at a time when the user does not need it, and sending sensor data through WiFi when the user makes a monitoring request.

In this paper, we empirically compare the effectiveness of training models to recognize beauty-related skin disease using supervised deep learning algorithms. Recently, deep learning algorithms are being actively applied for various fields such as industry, education, and medical. For instance, in the medical field, the ability to diagnose cutaneous cancer using deep learning based artificial intelligence has improved to the experts level. However, there are still insufficient cases applied to disease related to skin beauty. This study experimentally compares the effectiveness of identifying beauty-related skin disease by applying deep learning algorithms, considering CNN, ResNet, and SE-ResNet. The experimental results using these training models show that the accuracy of CNN is 71.5% on average, ResNet is 90.6% on average, and SE-ResNet is 95.3% on average. In particular, the SE-ResNet-50 model, which is a SE-ResNet algorithm with 50 hierarchical structures, showed the most effective result for identifying beauty-related skin diseases with an average accuracy of 96.2%. The purpose of this paper is to study effective training and methods of deep learning algorithms in consideration of the identification for beauty-related skin disease. Thus, it will be able to contribute to the development of services used to treat and easy the skin disease.

In this study, we analyze how stock trend determination affects trend prediction accuracy. In stock markets, successful investment requires accurate stock price trend prediction. Therefore, a volume of research has been conducted to improve the trend prediction accuracy. For example, information extracted from SNS (social networking service) and news articles by text mining algorithms is used to enhance the prediction accuracy. Moreover, various machine learning algorithms have been utilized. However, stock trend determination has not been properly analyzed, and conventionally used methods have been employed repeatedly. For this reason, we formulate the trend determination as a moving average-based procedure and analyze its impact on stock trend prediction accuracy. The analysis reveals that trend determination makes prediction accuracy vary as much as 47% and that prediction accuracy is proportional to and inversely proportional to reference window size and target window size, respectively.

The purpose of this paper is to reduce the ratio of the patient accidents that may occur in nursing hospitals. In other words, it determines whether the person approaching the dangerous area is a elderly (patient uniform) group or a practitioner(Casual Clothing) group, based on the clothing displayed by CCTV. We collected the basic learning data from web crawling techniques and nursing hospitals. Then model training data was created with Image Generator and Labeling program. Due to the limited performance of CCTV, it is difficult to create a good model with both high accuracy and speed. Therefore, we implemented the ResNet model with relatively excellent accuracy and the YOLO3 model with relatively excellent speed. Then we wanted to allow nursing hospitals to choose a model that they wanted. As a result of the study, we implemented a model that can distinguish patient and casual clothes with appropriate accuracy. Therefore, it is believed that it will contribute to the reduction of safety accidents in nursing hospitals by preventing the elderly from accessing the danger zone.

Genetic Algorithm(GA) is applied to a problem that could not figure out its solution in a straightway. It is called as NP-hard problem. GA requires a high-performance system to be run on since the high-cost operations are needed such as crossover, selection, and mutation. Moreover, the scale of the problem domain is normally huge. That is why the straightway cannot be applied. To reduce the drawback of high-cost requirements, we try to answer if all the operations including mutation are necessary for all cases. In the experiment, we set up two cases of with/without mutation operations and gather the number of generations and the fitness of a solution. The subject in the experiment is Travelling Salesman Problem(TSP), which is one of the popular problems solved by GA. As a result, the cases with mutation operation are not faster and the solution is fitter than the case with mutation operation. From the result, the conclusion is that mutation operation does not always need for a better solution in a faster way.

In this paper, we propose a fire detection technology using YOLOv3 and EfficientDet, the most reliable artificial intelligence detection algorithm recently, an alert service that simultaneously transmits four kinds of notifications: text, web, app and e-mail, and an AWS system that links fire detection and notification service. There are two types of our highly accurate fire detection algorithms; the fire detection model based on YOLOv3, which operates locally, used more than 2000 fire data and learned through data augmentation, and the EfficientDet, which operates in the cloud, has conducted transfer learning on the pretrained model. Four types of notification services were established using AWS service and FCM service; in the case of the web, app, and mail, notifications were received immediately after notification transmission, and in the case of the text messaging system through the base station, the delay time was fast enough within one second. We proved the accuracy of our fire detection technology through fire detection experiments using the fire video, and we also measured the time of fire detection and notification service to check detecting time and notification time. Our AI fire detection and notification service system in this paper is expected to be more accurate and faster than past fire detection systems, which will greatly help secure golden time in the event of fire accidents.

Most process date such as semiconductor and petrochemical processes, autocorrelation often exists between observed data, but when the existing SPC(Statistical process control) is applied to these processes, it is not possible to effectively detect the average change of the process. In this paper, when the average change of a certain size occurs in the process data following a specific time series model, the average of the residuals changes according to the passage of time, and the change pattern of the average is introduced around the ARMA(1,1) process. Based on this result, the reference value required in the design process of the CUSUM (Cumulative sum) control chart is appropriately considered by considering the type of the time series model of the process data of the CUSUM control chart that can detect small mean changes in the process and the width of the process mean change of interest. It was confirmed through simulation that it should be selected and used.

This study aims to identify research topics and examine the trend of Covid19-related papers on DBpia. Applying latent Dirichlet allocation (LDA), we have extracted seven research topics, each of which concerns "International Dynamics", "Technology & Security", "Psychological Impact", "Biomedical-Related", "Economic Impact", "Online Education", and "Religion-Related". In addition, we used the multinomial logistic model to examine the trend of research topics. We found that the papers mainly cover topics related to "International Dynamics" and "Biomedical-Related" before June 2020, but the topics have become diverse since then. In particular, topics regarding "Economic Impact", "Online Education" and "Psychological Impact" has drawn increased attention of researchers. The findings would provide a guideline for collaboration in Covid19-related research, and could serve as a reference work for active research.

This study aims to design and develop effective digital contents to improve the ability for artificial intelligence literacy. First, we defined AI literacy and analyzed the competencies required for artificial intelligence literacy. After selecting the educational elements for AI ability, we composed 10 educational programs. To confirm the appropriateness of designed contents, we verified through content validity test by 10 experts. The CVI value was over 0.75, which was highly valid. The developed content was installed on the online system and applied to 55 AI beginners for 4 weeks. The learners showed a positive result of at least 3.85 in the items of content difficulty, understanding, effectiveness, and learning challenge. As a result of this analysis, we can see that the developed content is positive for helping many people understand AI and improving AI literacy.

This paper aims to compare and analyze the major keywords according to the political orientation of progressive and conservative newspapers by utilizing the big data of the four major domestic daily newspapers related to COVID-19, which has entered a long-term war. To this end, 93,917 news reports from Jan. 20 to Sept. 15, 2020 were divided into four stages and the major keywords of the four newspapers were implemented and analyzed in WordCloud. According to the analysis, the conservative newspaper focused on the government's response, criticism, and China's responsibility by mentioning the keywords "government," "president," "state of affairs" and "mask" more than the progressive newspaper, while the progressive newspaper uses keywords that emphasize the seriousness of the disease and the occurrence of a dangerous situation. The Chosun Ilbo found that the use of various keywords during the massive outbreak of collective infections (2.18-5.15), and that the JoongAng Ilbo used keywords criticizing government policies in relation to reports of infectious diseases such as COVID-19, but also used keywords that emphasize the seriousness of diseases used by progressive newspapers and the occurrence of dangerous situations.

In this paper, we analyze LADN(Local Area Data Network) that provides high throughput, low latency and service localization for 5G wireless networks and propose an enhanced control protocol design for LADN in 5G wireless networks. The concept of LADN is newly introduced in 3GPP 5G communication system and the LADN is a data network to which the UE(User Equipment) can connect with a specific LADN session only when the UE is located in a certain service area. If the LADN information between the UE and core network is not identical, the LADN session cannot be properly established. The proposed approach promplty synchronizes the LADN information between the UE and core network by using the specific registration procedure and appropriately establishes the LADN session, when the establishment of the LADN session is failed. Consequently, the proposed enhanced control protocol design(ECP) can prevent unnecessary signalling overhead and communication delay for LADN in 5G wireless networks.

In this paper, we design and implement a product information guidance system Based on BLE beacons. This system is designed to provide various functions such as contactless entrance service through mobile ticket, seat guidance service, event push service, content push service, and public service. This system consists of three modules: a beacon recognition module, a server linkage module, and a content transmission module. The beacon recognition module implements a function of transmitting a specific ID of a nearby beacon when a customer with a smartphone approaches the available service area. The server linkage module recognizes the beacon ID value, transfers it to the server, and implements the function to check the location of the server having the ID. The content transmission module implements a function of transmitting event or service information set at a corresponding location to a smartphone. The beacon-based product information guidance system implemented in this paper has the advantage of being able to quickly and easily implement various product information guidance systems.

Recently, Harmful sites, including pornographic videos, drugs, personal information and hacking tool distribution sites, have caused serious social problems. However, due to the nature of the Internet environment where anyone can use it freely, it is difficult to control the user effectively. And the site operator operates by changing the domain to bypass the blockage. Therefore, even once identified sites have low persistence. In this paper, we propose multi-channel domain tracking technology, a technique that can effectively track changes in the domain addresses of harmful sites, including the same or similar content, by tracking changes in these harmful sites. Proposed technology is a technology that can continuously track information in a domain using OSINT technology. We tested and verified that the proposed technology was effective for domain tracking with a 90.4% trace rate (sensing 66 changes out of 73 domains).

In graph theory, the least-cost path is discovered by searching the shortest path between a start node and destination node. The least cost is calculated as a one-dimensional value that represents the difference in distance or price between two nodes, and the nodes and edges that comprise the lowest sum of costs between the linked nodes is the shortest path. However, it is difficult to determine the shortest path if each node has multiple attributes because the number of cost types that can appear is equal to the number of attributes. In this paper, a shortest path search scheme is proposed that considers multiple attributes using the Euclidean distance to satisfy various user requirements. In simulation, we discovered that the shortest path calculated using one-dimensional values differs from that calculated using the Euclidean distance for two-dimensional attributes. The user’s preferences are reflected in multi attributes and it was different from one-dimensional attribute. Consequently, user requirements could be satisfied simultaneously by considering multiple attributes.

Recently, keyword extraction from social big data has been widely used for the purpose of extracting opinions or complaints from the user’s perspective. Regarding this, our previous work suggested a method to improve accuracy of keyword extraction based on the notion of cohesion scoring, but its accuracy can be degraded when the number of input reviews is relatively small. This paper presents a method to resolve this issue by applying simplified morphological analysis as a postprocessing step to extracted keywords generated from the algorithm discussed in the previous work. The proposed method enables to add analysis rules necessary to process input data incrementally whenever new data arrives, which leads to reduction of a dictionary size and improvement of analysis efficiency. In addition, an interactive rule adder is provided to minimize efforts to add new rules. To verify performance of the proposed method, experiments were conducted based on real social reviews collected from online, where the results showed that error ratio was reduced from 10% to 1% by applying our method and it took 450 milliseconds to process 5,000 reviews, which means that keyword extraction can be performed in a timely manner in the proposed method.

This study developed a system that can collect, manage, and utilize the travel routes of individuals who tested positive for coronavirus disease 2019 (COVID-19) based on the data standardization and quality management principles and presented the analysis data collected from the existing system. Unlike many other countries in the world, Korea demonstrated a rapid response by conducting epidemiological investigations. Further, the local governments have actively shared the travel routes of individuals who tested positive for COVID-19 to facilitate proactive prevention of the infectious disease per the Infectious Disease Control and Prevention Law. However, currently, there is no standard protocol for the local governments to share the information, thus complicating the process of sharing, managing, and utilizing the collected data. Therefore, this study developed a system that can facilitate sharing of the travel routes of individuals who tested positive for COVID-19 by establishing database construction procedures and using the travel route of COVID-19 patients as per the Disaster & Safety Information Sharing Platform and developing a data processing guideline, a data entry system with default templates, and Open API. Although this sharing system was designed to communicate the travel routes of COVID-19 patients, it can also be utilized in case of other infectious diseases. Therefore, it can be used as a response strategy for future outbreaks of infectious diseases.

Recently, due to exponential growth of access information on the web, the importance of predicting a user’s next web page use has been increasing. One of the methods that can be used for predicting user’s next web page is deep learning. To predict next web page, web logs are analyzed by data preprocessing and then a user’s next web page is predicted on the output of the analyzed web logs using a deep learning algorithm. In this paper, we propose a framework for web page prediction that includes methods for web log preprocessing followed by deep learning techniques for web prediction. To increase the speed of preprocessing of large web log, a Hadoop based MapReduce programming model is used. In addition, we present a web prediction system that uses an efficient deep learning technique on the output of web log preprocessing for training and prediction. Through experiment, we show the performance improvement of our proposed method over traditional methods. We also show the accuracy of our prediction.

In this paper, we design and implement a service system for mobile devices to utilize the integrated underground geospatial information map in underground exploration fields. The field utilization service system for mobile devices is designed to visualize tiled maps, 3D terrain information, underground structures, underground facilities, and ground information provided by the Integrated Underground Geospatial Information Map Management System according to current position. And It is designed to reflect the results obtained from field exploration in real time. Also, the proposed system is implemented to transfer and visualize the integrated underground geospatial information map in the form of a glTF format due to constraints on wireless networks and device characteristics of mobile devices. Implemented mobile service systems can prevent accidents in underground exploration field from occurring by providing users with accurate and integrated underground geospatial data by visualizing maps and geospatial objects in three dimensions at underground exploration fields. In addition, updated underground geospatial data is transmitted in real time to the Integrated Underground Geospatial Information Map Management Systems, which can maintain up to date and accuracy.

In today's process-oriented industries, such as semiconductor and petrochemical processes, autocorrelation exists between observed data. As a management method for the process where autocorrelation exists, a method of using the observations is to construct a batch so that the batch mean approaches to independence, or to apply the EWMA (Exponentially Weighted Moving Average) statistic of the observed value to the EWMA control chart. In this paper, we propose a method to determine the batch size of UBM (Unweighted Batch Mean), which is commonly used as a management method for observations, and a method to determine the optimal batch size based on ARL (Average Run Length) We propose a method to estimate the standard deviation of the process. We propose an improved control chart for processes in which autocorrelation exists.

The control system for the efficiency of resource utilization in sensor network environment based on object detection and environmental sensor requires active control function which based on sensor data acquisition and transmission functions and server’s data analysis. Using active rule-based mobile agent middleware, this paper proposes a new distributed control framework that reduces the load of central sensor data server in sensor network environment by implementing remote data sensing and Zigbee-based communication with server and data analysis method of server. In addition, we implemented a power-saving system prototype using active rule-based distributed control methods that applied consumer’s demand and environmental variables, and verified the validity of the proposed system through experiments and evaluations in the mobile agent middleware environment. The proposed system is a system framework that can efficiently autonomously control distributed objects in the sensor network environment, and it can be applied effectively to the development of demand response service based on optimal power control for the smart power system in the future.

Memory-based collaborative filtering is one of the representative types of the recommender system, but it suffers from the inherent problem of data sparsity. Although many works have been devoted to solving this problem, there is still a request for more systematic approaches to the problem. This study exploits distribution of user ratings given to items for computing similarity. All user ratings are utilized in the proposed method, compared to previous ones which use ratings for only common items between users. Moreover, for similarity computation, it takes a global view of ratings for items by reflecting other users’ ratings for that item. Performance is evaluated through experiments and compared to that of other relevant methods. The results reveal that the proposed demonstrates superior performance in prediction and rank accuracies. This improvement in prediction accuracy is as high as 2.6 times more than that achieved by the state-of-the-art method over the traditional similarity measures.

This study is to find out the effectiveness by adult CPR method of a single rescuer. For patients with acute cardiac arrest, CPR is the only first aid, and chest compression is the most important of these techniques. The subjects of this study were freshman, 120 subjects who did not learn CPR, and for data analysis, the SPSS 22.0 Version was used. The accuracy of chest compression site selection was significantly higher in the upper part of the two fingers in the pit of the stomach(p<.001), and the ease was also high(p<.001). In the case of the general public, they select the chest compression area easily and quickly, it turns out that it is easier to designate the landmark toward the pit of the stomach. In the future, through various job groups, research to select the chest compression site is needed, and additional studies should be conducted to confirm the maintenance and accuracy during chest compression.

This study conducted an online survey of university students based in Jeolla-do province from September 1 to 10, 2020 using Naver survey form to find out the perception and attitude of paramedic students about child abuse. A total of 293 students were studied, and the collected data was analyzed using SPSS WIN 20.0. The analysis of general characteristics, recognition of child abuse reporting obligations and activation plans, and awareness of child abuse education were calculated with frequency and percentage, and the degree of child abuse awareness was calculated by the average and standard deviation. Differences in perception of child abuse behavior based on the general characteristics of respondents were used T-test and ANOVA.

The purpose of this study is to compare the speed, confidence, and ease of endotracheal intubation in a difficult posture by using the SALT(Supraglottic Airway Laryngopharyngeal Tube), and a direct laryngoscope to improve the ability to implement professional airway management. The subject of the study was an experimental study by a randomized crossover design, targeting 30 first-class emergency medical technicians working in J-do fire station, and the SPSS 20.0 version was used for data analysis. The endotracheal intubation by using SALT showed a significant difference in speed compared to endotracheal intubation by using a direct laryngoscope(p<.001), and also showed a significant difference in confidence and ease(p<.001). If it is difficult to endotracheal intubation by direct laryngoscope, or in the case of trauma patients, if SALT is used, safe and rapid intubation will be possible. In addition, to improve the efficiency of professional airway management, it is believed that the introduction of SALT in domestic sites is necessary.

This is descriptive research aimed at identifying the factors for nursing productivity in nurses at a tertiary hospital. The data were collected from a survey conducted in 257 nurses at a single hospital in Seoul from May to June 2020. The collected data were analyzed through t-test, ANOVA, Scheffe test, Pearson’s correlation, and multiple regression analysis by using an SPSS 25.0 program. The total score was an average of 25.40±5.49 (range: 6-36) for gratitude disposition, 65.15±9.18 (range: 21-105) for job satisfaction, 87.64±12.05 (range: 23-115) for nursing performance, and 35.68±6.79 (range: 0-45) for nursing productivity. Nursing productivity was positively correlated with gratitude disposition (r=.219, p<.001), job satisfaction (r=.440, p<.001), and nursing performance (r=.494, p<.001). Nursing productivity was affected by gratitude disposition (β=.144), nursing performance (β=.394), and job satisfaction (β=.278). On the basis of these results, it is necessary to develop and apply education programs aimed at promoting work productivity for nursing manpower.

This study is intended to verify the efficiency of the information system by using the navigation application in case of fire. In the demonstration scenario simulation, it was assumed that a fire occurred 6th floor in Korea Institute of Industrial Design Promotion located in C city, K province. Eighty juniors students from K University's department of paramedics of science participated. As a result of the experiment, the demonstration simulation using the navigation application showed that the evacuation time was faster than the case of evacuation guidance in a fire situation. The need for a fire prevention system and fire prevention awareness before and after the implementation of the demonstration scenario simulation increased the need for a fire prevention system and fire prevention awareness score after the experiment.

In this paper, we propose to confirm the perceptions of vocational rehabilitation of the mentally disabled by mental health service providers. The survey was conducted using a questionnaire consisting of vocational ability for the mentally disabled, family support, worker competence, and worker needs, targeting 132 workers at the mental health welfare center and mental rehabilitation facility, which are mental health service institutions in A Metropolitan City. The collected data were analyzed using the technical knowledge, t-test, ANOVA, Scheffé test, Pearson's correlation coefficient, and multiple regression using the SPSS 21.0 program. In the end, the workers' desire for vocational rehabilitation for the mentally disabled of workers in mental health service institutions was higher in mental rehabilitation facilities than in mental health welfare centers, and the competence of workers had a significant effect on the vocational skills and family support of the mentally disabled people. Therefore, it will be possible to ultimately promote vocational rehabilitation for the mentally disabled by strengthening the capacity for vocational rehabilitation for the mentally handicapped of mental health service providers in the future.

In this study, we identify promising technologies for Piggyback transportation system by analyzing the relevant patent information. In order for this, we first develop the patent database by extracting relevant technology keywords from the pioneering research papers for the Piggyback flactcar system. We then employed textmining to identify the frequently referred words from the patent database, and using these words, we applied the LDA (Latent Dirichlet Allocation) algorithm in order to identify “topics” that are corresponding to “key” technologies for the Piggyback system. Finally, we employ the ARIMA model to forecast the trends of these “key” technologies for technology forecasting, and identify the promising technologies for the Piggyback system. with keyword search method the patent analysis. The results show that data-driven integrated management system, operation planning system and special cargo (especially fluid and gas) handling/storage technologies are identified to be the “key” promising technolgies for the future of the Piggyback system, and data reception/analysis techniques must be developed in order to improve the system performance. The proposed procedure and analysis method provides useful insights to develop the R&D strategy and the technology roadmap for the Piggyback system.

This study looked at the World Martial Arts Mastership's 'non-face promotional media strategies and methods' after the Corona-19 fandom, and found out if they could be applied to other sports events. As a method of research, the promotional media strategy of martial arts mastership was divided into types and in-depth interviews with media agents were conducted. According to the study, hosting an online martial arts competition, building an untact convention platform, and a base education facility can be a successful contest with budget support. Martial arts branding, popularization and industrialization can be a new attempt if ideas and execution personnel are sufficient. If IT technology and Chungbuk martial arts infrastructure are combined, they will be able to pioneer a new model called 'non-face-to-face-to K-sports'.

It has already been a few years since the word Fintech in Korea started to attract attention. These days, they believed that Fintech was just a boom, but these days it is recognized as a catalyst for financial transformation. Large venture companies are also launching demonstration experiments by creating new organizations that can respond to Fintech. It feels like a big tide is coming to the cautious and conservative financial industry. Finance is made up of digital information. Fintech is an evolutionary process in which finance, expressed by digital information, is transformed into information technology (IT) and human economic activities are reorganized. It is FinTech. You won’t be able to understand the real effects of Fintech by sticking to individual applications like remittance payments or household account book services. Fintech is an innovation that changes the structure of economic activity itself. In fact, it is from now on that a big impact will come. In other words, now is the time when we are thinking of a dream that we have not yet dreamed of. In this paper, I will examine how fintech originated, spread to Korea, and how it intends to change Korea’s finance in the future. Financial institutions have used the fruits of information technology advances in the direction of pursuing stability and stability, without major changes in the way they work. However, the movement of Fintech that started in Silicon Valley in the United States shows that the fruit can be used in other directions. The fruit of technological progress is expected to expand year by year in the future. It is a request of the times to use it to improve user convenience and to pursue innovation that is beneficial to society. We expect the flow of Fintech to accelerate innovation in the Korean financial industry.

The purpose of this study is to confirm the use of smartphones by children aged 3 to 5 years old and to propose a plan for prevention of overdependence. To this end, the data of 909 people aged 3 to 5 years old were used in the study from the raw data of the '2019 Smartphone Overdependence Survey' conducted by the National Information Society Agency. The research results are as follows : Infants aged 3 to 5 were using their smartphone for an average of more than an hour a day, it was found that smartphones were used in the order of video services and games, listening to music, educational contents and search services. Parents were found to be tolerant of their children's smartphone usage time and dependence. In particular, despite the fact that most infants and toddlers use smartphones to watch videos and use game content, they are not concerned. Therefore, parental education content for the use of smartphones by infants and toddlers needs to include comprehensive content on media use.

Currently, electric furnace oxide slag is mostly used for soil or road use due to its nature. Although electric furnace oxidation slag is an industrial byproduct, not a circulating aggregate, the shortcomings of electric furnace oxidation slag are gradually being resolved due to the development of technology, and it is said that electric furnace oxidation slag is enough to be used as aggregates in light of research and technology conditions outside of Korea. However, there are difficulties in expanding construction and application, given that the current standard for electric furnace oxid slag only defines recycling purposes and does not have specific regulations. Therefore, institutional supplementation is needed to utilize oxidation slag as electricity. In this study, the laws and system related to oxidation slag by electricity are reviewed, laws related to recycled aggregate are examined, and measures for improvement are proposed.

In this paper, We Propose a ways how franchise service of delivery app should be made by analyzing the effect of delivery app usage properties of food service franchisee on satisfaction, business performance and intention of reusing, and suggest support plan of delivery app for franchise by food service franchisee. This study conducted survey on 212 franchises that have experience using delivery app among food service franchisees. To verify the hypothesis of the study, single regression and multiple regression analysis were conducted for verifying the relationship between variables. Key results of the study are as follows. First, it was found that the delivery app usage properties of food service franchisee had a positive relationship with satisfaction. Second, it was found that the delivery app usage properties of food service franchisee had positive relationship with business performance. Third, the satisfaction was found to affect business performance. Fourth, it was found that the satisfaction didn't affect the intention of reusing, and business performance was found to have a positive effect on the intention of reusing. It is necessary to increase the satisfaction of using of franchises through continuous management and reasonable pricing policies, and the headquarter of franchisee should be able to contribute to energetic sales of franchises through effective cooperation with delivery app companies.

The purpose of this study is to analyze and interpret the current situation of private security companies·guards for the past three years, security companies by size, general·special (new education), and qualification system provided by the Police Agency, Security Association, etc. It provides a theoretical foundation for private security and provides a new perspective for interpreting private security. As a result, through the current situation, this private security has a concentration of metropolitan area and facility security, an abnormal personal protection company contrast, the number of personal protection institutes, there is a special security shift to regular jobs, and the current continuous education On the other hand, the education of special security guards has been shown to be limited. In the qualification system, the utilization of security instructor qualifications and the utilization and public relations of personal probation officer qualifications will appear. The current state of typical private security is as follows. The first is the balanced development of private security and the clarity of business divisions. Second, the quality of private security education and educational institutions must be high. Third is the recognition of the qualification system and active public relations.

The purpose of this study is to explore the impact of home stay platform on guests' willingness to comment online under the Shared home stay business model. Shared platform of home stay facility in addition to providing a variety of support services, help the landlord to the tenant do offline accommodation services, implementation, trading, will need to take some measures to actively promote the tenant groups to the landlord, the evaluation is objective, effective and sufficient number in order to better promote the sharing credit ecological establishment of home stay facility. In this study, consumers who have used the Shared home stay platform are taken as the research objects. The survey method adopts network questionnaire survey and Likert seven subscales. The statistical software SPSS24.0 program is used to process the data. Firstly, descriptive statistical analysis was conducted, followed by validity analysis and reliability analysis. After the reliability and validity of the questionnaire were determined, correlation analysis and regression analysis were used to verify the proposed hypothesis. The research results of this study are summarized as follows :(1) the usability of platform comment function, guest satisfaction and platform reward have a positive impact on the guest online comment willingness; (2) The credit mechanism of the platform has a positive regulating effect on the process of tenant satisfaction influencing tenant comment intention.

This study investigated the determinants of debt ratio in public institutions. For this purpose, we analyzed the impact of external and internal factors on debt ratio. In this paper, external factors included government grants and deficits compensation, and internal factors included inefficient management. The results of this study are as follows. First, we find that there is a significant positive relation between government grants and debt ratio. This result means that the higher government grants, the higher debt ratio. Second, we also find that there is a significant positive relation between deficits compensation and debt ratio. This implies that the institutions subject to deficits compensation have higher debt ratio. Third, we can not find a significant relation between welfare benefit and debt ratio. This finding implies that inefficient management is not a factor on debt ratio of public institutions. The results documented in this paper provide important policy implications for investigating the determinants of debt ration in public institutions.

In this paper, we propose to discover the certification items and to study the content system for SW education instructors, including SW education based on basic teaching-learning capabilities and Computational Thinking(CT). To this end, SW education instructor qualification were divided into three classes using methods such as prior case studies, Delphi surveys, and expert meetings, and the certification evaluation areas were divided into large areas of 'Teaching and Learning Method' and 'Software Education' reflecting primary and secondary curriculum. Sub-areas and content elements for each series were set and verified through expert Delphi survey. Such research is expected to contribute to the spread and dissemination of SW education by being used meaningfully when establishing a system that fosters SW education instructors and maintains and manages the quality of instructors.

In this paper, I propose a presentation examples of the development of teaching materials using Arduino. For this purpose, a six-step low-cost microcontroller teaching-learning development model was used, the steps being topic selection, exploration of implementation methods, experimentation, production of teaching and learning materials, implementing lesson plans, and improvement. After analyzing the composition of the source code and circuits introduced in the existing Arduino book, this content was reconstructed to fit the programming education context. A simple method of constructing a circuit using materials such as Arduino and a piezo buzzer is proposed to save time on circuit composition. Using this circuit, examples of use in teaching-learning activities for various programming content elements are presented. The core concept of this study is that it provides a direct experience of the content of C language programming exercises that can only be found on existing screens.