• Communications of Mathematical Education
• /
• v.35 no.4
• /
• pp.445-473
• /
• 2021

The purpose of this study is to derive implications for the design of the next curriculum by analyzing the textbooks designed as a new subject in the 2015 revised curriculum. In the mathematics curriculum documents of , 'related learning elements' are presented instead of 'learning elements'. 'Related learning elements' are defined as mathematical concepts or principles that can be used in the context of artificial intelligence, but there are no specific restrictions on the amount and scope of dealing with 'related learning elements'. Accordingly, the aspects of 'related learning elements' reflected in the textbooks were analyzed focusing on the textbook format, the amount and scope of contents, and the ways of using technological tools. There were differences in the format of describing 'related learning elements' in the textbook by textbook and the amount and scope of handling mathematics concepts. Although similar technological tools were dealt with in each textbook so that 'related learning elements' could be used in the context of artificial intelligence, the focus was on computations and interpretation of results. In order to fully reflect the intention of the curriculum in textbooks, a systematic discussion on 'related learning elements' will be necessary. Additionally, in order for students to experience the use of mathematics in artificial intelligence, substantialized activities that can set and solve problems using technological tools should be included in textbooks.

• Journal of Korea Entertainment Industry Association
• /
• v.15 no.5
• /
• pp.175-188
• /
• 2021

The purpose of this study was to grasp the influence of mental health upon the lifelong drinking experience in adolescents of non-capital area and capital area through using the 2019 Youth Health Behavior Survey Data. The research subjects included total 57,303 adolescents who participated in the survey among 60,100 adolescents at totally 800 schools with 400 middle schools and 400 high schools. Out of these, 29,384 middle school students and 27,919 high school students were selected. As a result of the research, the mental health that has significant influence upon the lifelong drinking and smoking experience in case of non-capital area appeared to be significant in the perceived subjective health (p<.01), cognitive stress(p<.001), relief from fatigue(p<.001), sadness & despair experience(p<.001), suicide ideation(p<.001), suicide plan(p<.01), and suicide attempt(p<.001). In case of capital area, the mental health of having a significant impact on the lifelong drinking and smoking experience was indicated to be significant in cognitive stress(p<.01), relief from fatigue(p<.001), sadness & despair experience(p<.001), suicide ideation(p<.001), and suicide attempt(p<.001). Based on this outcome, adolescents' drinking problem has influence upon mental health characteristic. Hence, an effort is needed for developing the intervention & education program aiming at the more effective youth drinking prevention through establishing direction and revaluing education program in consideration of mental health factors by region in adolescents of capital area and non-capital area.

• Journal of Intelligence and Information Systems
• /
• v.28 no.4
• /
• pp.211-227
• /
• 2022

Illegal gambling through online gambling sites has become a significant social problem. The development of Internet technology and the spread of smartphones have led to the proliferation of illegal gambling sites, so now illegal online gambling has become accessible to anyone. In order to mitigate its negative effect, the Korean government is trying to detect illegal gambling sites by using self-monitoring agents or reporting systems such as 'Nuricops.' However, it is difficult to detect all illegal sites due to limitations such as a lack of staffing. Accordingly, several scholars have proposed intelligent illegal gambling site detection techniques. Xu et al. (2019) found that fake or illegal websites generally have unique features in the HTML tag structure. It implies that the HTML tag structure can be important for detecting illegal sites. However, prior studies to improve the model's performance by utilizing the HTML tag structure in the illegal site detection model are rare. Against this background, our study aimed to improve the model's performance by utilizing the HTML tag structure and proposes Tag2Vec, a modified version of Doc2Vec, as a methodology to vectorize the HTML tag structure properly. To validate the proposed model, we perform the empirical analysis using a data set consisting of the list of harmful sites from 'The Cheat' and normal sites through Google search. As a result, it was confirmed that the Tag2Vec-based detection model proposed in this study showed better classification accuracy, recall, and F1_Score than the URL-based detection model-a comparative model. The proposed model of this study is expected to be effectively utilized to improve the health of our society through intelligent technology.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.15 no.4
• /
• pp.257-270
• /
• 2022

In this paper, we design a smart factory risk improvement system and risk improvement method using AI edge devices. The smart factory risk improvement system collects, analyzes, prevents, and promptly responds to the worker's work performance process in the smart factory using AI edge devices, and can reduce the risk that may occur during work with improving the defect rate when workers perfom jobs. In particular, based on worker image information, worker biometric information, equipment operation information, and quality information of manufactured products, it is possible to set an abnormal risk condition, and it is possible to improve the risk so that the work is efficient and for the accurate performance. In addition, all data collected from cameras and IoT sensors inside the smart factory are processed by the AI edge device instead of all data being sent to the cloud, and only necessary data can be transmitted to the cloud, so the processing speed is fast and it has the advantage that security problems are low. Additionally, the use of AI edge devices has the advantage of reducing of data communication costs and the costs of data transmission bandwidth acquisition due to decrease of the amount of data transmission to the cloud.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.26 no.3
• /
• pp.39-47
• /
• 2022

In this paper, we proposed a thickness measurement method of concrete slab using GPR, and the verification of the suggested algorithm was carried out through real-scale experiment. The thickness measurement algorithm developed in this study is to set the relative dielectric constant based on the unique shape of parabola, and time series data can be converted to thickness information. GPR scanning were conducted in four types of slab structure for noise reduction, including finishing mortar, autoclaved lightweight concrete, and noise damping layer. The thickness obtained by GPR was compared with Boring data, and the average error was 1.95 mm. In order to investigate the effect of finishing materials on the slab, additional three types of finishing materials were placed, and the following average error was 1.70 mm. In addition, sampling interval from device, the effect of radius on the shape of parabola, and Boring error were comprehensively discussed. Based on the experimental verification, GPR scanning and the suggested algorithm have a great potential that they can be applied to the thickness measurement of finishing mortar from concrete slab with high accuracy.

• Korean Journal of Plant Resources
• /
• v.36 no.1
• /
• pp.100-106
• /
• 2023

In order to control the fire blight disease, all plants within the radius of the diseased orchard were removed in the early stage of the outbreak, or antibiotics control was performed for prevention. Since the beginning of antibiotics use on plants, the potential for development of resistance to antibiotics by the plant pathogen and unintended detrimental effects on the fruit trees and environment has become a problem. The purpose of this study is to determine the degree of phytotoxicity to fruit trees caused by excessive spraying of the fire blight disease disinfectant and to establish basic data for safe disinfectant guide. We analyzed whether damage to the fruit tree and the maximum residual limit of fruit was exceeded when three kinds of the fire blight disease disinfectants were continuously sprayed in excess of the number of safe use during the growing season. There was no phytotoxicity in apple 'Fuji' and pear 'Niitaka', and oxolinic acid was detected beyond the limit of quantitation in 'Fuji' grown without a bag, and the other disinfectants were detected below the maximum residue limit. When these disinfectants are continuously sprayed in excess of the number of safe, phytotoxicity may remain on the fruit. Therefore, it is necessary to observe the prescribed dilution factor and observe the safe frequency and the timing of use.

• Journal of Korea Society of Industrial Information Systems
• /
• v.28 no.4
• /
• pp.45-58
• /
• 2023

In a world of uncertainty and complexity, leadership is essential to lead collaborative and positive interactions among employees. In other words, if members share opinions and work through voluntary leadership, they will respond more effectively to uncertain challenges and get closer to the targeted management performance. Therefore, in this study, we would like to elucidate the importance of shared leadership, which has recently become an issue. We will examine the impact of shared leadership on team efficacy, team organizational citizenship behavior, and turnover intention. A survey was conducted among members working in a team organization in Busan, and the results were as follows. First, the effects of shared leadership on team efficacy were found to have significant positive(+) effects, such as the hypotheses set at planning and organizing 0.202(C.R.=2.853), problem solving 0.463(C.R.=5.620), support and caring 0.237(C.R.=3.326), and development and mentoring 0.366(C.R.=5.132), respectively. Second, the effects of team efficacy on team organizational citizenship behavior and turnover intention were 0.545(C.R.=5.895) and -0.143(C.R.=-0.817), respectively, and team efficacy was found to have a positive(+)positive(+) effect on team organizational citizenship behavior, but team efficacy did not have a significant effect on turnover intention.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.26 no.3
• /
• pp.12-36
• /
• 2023

Graph databases, which store different types of data and their relationships modeled as a graph, can be effective in managing and analyzing real estate spatial data linked by complex relationships. However, they are not widely used due to the limited spatial functionalities of graph databases. In this study, we propose a uniform grid-based real estate spatial data management approach using a graph database to respond to various real estate-related spatial questions. By analyzing the real estate community to identify relevant data and utilizing national point numbers as unit grids, we construct a graph schema that linking diverse real estate data, and create a test database. After building a test database, we tested basic topological relationships and spatial functions using the Jackpine benchmark, and further conducted query tests based on various scenarios to verify the appropriateness of the proposed method. The results show that the proposed method successfully executed 25 out of 29 spatial topological relationships and spatial functions, and achieved about 97% accuracy for the 25 functions and 15 scenarios. The significance of this study lies in proposing an efficient data integration method that can respond to real estate-related spatial questions, considering the limited spatial operation capabilities of graph databases. However, there are limitations such as the creation of incorrect spatial topological relationships due to the use of grid-based indexes and inefficiency of queries due to list comparisons, which need to be improved in follow-up studies.

• Journal of the Korea Society for Simulation
• /
• v.32 no.3
• /
• pp.33-41
• /
• 2023

As carbon-free has been getting interest, renewable energy sources have been increasing. However, renewable energy is intermittent and variable so it is difficult to predict the produced electrical energy from a renewable energy source. In this study, digital-twin concept is applied to solve difficulties in predicting electrical energy from a renewable energy source. Considering that rotation of wind turbine has high correlation with produced electrical energy, a model which simulates rotation in the drivetrain of a wind turbine is developed. The base of a drivetrain simulation model is set with well-known state equation in mechanical engineering, which simulates the rotating system. Simulation based machine learning is conducted to get unknown parameters which are not provided by manufacturer. The simulation is repeated and parameters in simulation model are corrected after each simulation by optimization algorithm. The trained simulation model is validated with 27 real wind turbine operation data set. The simulation model shows 4.41% error in average compared to real wind turbine operation data set. Finally, it is assessed that the drivetrain simulation model represents the real wind turbine drivetrain system well. It is expected that wind-energy-prediction accuracy would be improved as wind turbine digital twin including the developed drivetrain simulation model is applied.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.8
• /
• pp.371-380
• /
• 2023

Relation extraction is to extract relationships between named entities from text. Traditionally, relation extraction methods only extract relations between predetermined subject and object entities. However, in end-to-end relation extraction, all possible relations must be extracted by considering the positions of the subject and object for each pair of entities, and so this method uses time and resources inefficiently. To alleviate this problem, this paper proposes a method that sets directions based on the positions of the subject and object, and extracts relations according to the directions. The proposed method utilizes existing relation extraction data to generate direction labels indicating the direction in which the subject points to the object in the sentence, adds entity position tokens and entity type to sentences to predict the directions using a pre-trained language model (KLUE-RoBERTa-base, RoBERTa-base), and generates representations of subject and object entities through probabilistic crossover operation. Then, we make use of these representations to extract relations. Experimental results show that the proposed model performs about 3 ~ 4%p better than a method for predicting integrated labels. In addition, when learning Korean and English data using the proposed model, the performance was 1.7%p higher in English than in Korean due to the number of data and language disorder and the values of the parameters that produce the best performance were different. By excluding the number of directional cases, the proposed model can reduce the waste of resources in end-to-end relation extraction.