• The Journal of Korean Philosophical History
• /
• no.57
• /
• pp.91-125
• /
• 2018

This study aims at attempting to make a new interpretation of Mencius from the point of social welfare. The thoughts of social welfare, found from Mencius, are temporal and humanistic and near to those in nowadays. Social welfare began under the name of philanthropic work, relief work, charitable work or social work from the Industrial Revolution in the 18th century on. Welfare means the whole social activities such as satisfying the fundamental desires of the social members, ensuring the conditions of their lives, and ultimately achieving social integration and stability. It means the conditions of their lives and wellbeing. Wellbeing means the minimal physical desires and psychological stability. The realization of the economic system through concept of steady livelihood and steady mind, tax system and well-field system, proposed by Mencius, can be ensured by the whole social activities such as ensuring the stable lives of the social members, enriching and satisfying their happiness and ultimately achieving social integration and stability. The thoughts of Mencius include people-as-root idea, which regards people as most important and tries to solve the instability and inequality that are the structural vulnerabilities in modern capitalist society. His concept of Way of the king means promoting people's sense of happiness through education of morality, based upon the people-as-root idea and filial and fraternal responsibilities. The main ideas of social welfare include living like a human being, ensuring minimal physical and psychological stabilities through social welfare system and welfare policy, enriching human dignity and freedom and enhancing the quality of lives. The thoughts of Mencius include all the above ideas. In particular, he desired to establish ethically and morally stable society by economically implementing the well-field system in Zhou dynasty, based upon politically benevolent governance of the politicians. That society was the people-as-root society, the realization of which was the ideal society Mencius desired to establish, and the goal of his thoughts on social welfare. This study, among the thoughts of Mencius, investigated his ideas on social welfare and the practical ethics for applying them to real society. In addition, to understand his ideas on social welfare, not only the social and economic backgrounds and conditions but also the political ideas at that time were also investigated. This will provide the opportunity to make more in-depth research of the elements of social welfare intrinsically contained in his thoughts.

• Journal of the Korean Society of Earth Science Education
• /
• v.14 no.2
• /
• pp.136-145
• /
• 2021

The Gifted Education Program is re-constructured into core competency-based program in line with fourth industrial revolution, where talented people with comprehensive ability are required. Therefore, competency-based elementary science gifted education program which is provided from Gifted Education Database(GED) is developed in accordance with 2015 revised edition in science and 5 main core-abilities; scientific thinking ability, scientific investigation ability, scientific problem solving ability, scientific communication ability and scientific participation and lifelong learning ability. This research, which is provided from GED, is focused on earth science area among competency-based elementary science gifted education program and analyse quantitatively and qualitatively how science and core-ability is appeared in 3 programs developed in science area. This research can be another guideline when someone would like to use competency-based earth science gifted education program in gifted education. Also, the purpose of this research is to help suggesting a right direction for competency-based earth science gifted education program. The conclusion based on research problem is as follow; Firstly, in competency-based earth science gifted education program, influence rates of scientific communication ability and scientific thinking ability are highest, where influence rates of scientific investigation ability, scientific problem solving ability and scientific participation and lifelong learning ability are relatively low. Secondly, in competency-based earth science gifted education program, single activity may includes several core-abilities. Following research is quite meaningful in aspect of giving out the information to choose topic in core-ability when using competency-based earth science gifted education program in gifted education. Also by supplementing lowly-influenced ability in competency-based earth science gifted education program, it is expected for gifted students to build scientific core-ability.

• Journal of Korean Society of Forest Science
• /
• v.108 no.3
• /
• pp.440-453
• /
• 2019

This study was carried out to investigate the effect of watching a two-dimensional (2D) forest video and a virtual reality (VR) forest video on stress reduction in adults. Experiments were conducted in an artificial climate room, and 40 subjects participated. After inducing stress in the subjects, subjects watched a 2D gray video, 2D forest video, or VR forest video for 5 mins. The autonomic nervous system activity was evaluated continuously in terms of measured heart rate variability during the experiment. After each experiment, the subject's psychological state was evaluated using a questionnaire. The 2D forest video decreased the viewer's stress index, increased HF, and reduced heart rate compared with the 2D gray video. The VR forest video had a greater stress index reduction effect, LF/HF increase effect, and heart rate reduction effect than the 2D gray video. Psychological measurements showed that subjects felt more comfortable, natural, and calm when watching the 2D gray video, 2D forest video or VR forest video. We also found that the 2D forest video and VR forest video increased positive emotions and reduced negative emotions compared to the 2D gray video. Based on these results, it can be concluded that watching the 2D forest and VR forest videos reduces the stress index and heart rate compared with watching the 2D gray video. Thus, it is considered that the 2D forest video increases the activity of the parasympathetic nervous system, and the VR forest video increases the activity of the sympathetic nervous system. The increased activity of the sympathetic nervous system upon watching the VR forest video is judged to be positive sympathetic nerve activity, such as novelty and curiosity, and not negative sympathetic activity, such as stress and tension. The results of this study are expected to be the basis for examining the visual effects of forest healing, with hope that the utilization of VR, the technology of the fourth industrial revolution in the forestry field, will broaden.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.43-61
• /
• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Journal of Intelligence and Information Systems
• /
• v.27 no.1
• /
• pp.177-190
• /
• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.24 no.5
• /
• pp.431-449
• /
• 2022

As many tunnels generally have been constructed, various experiences and techniques have been accumulated for tunnel design as well as tunnel construction. Hence, there are not a few cases that, for some usual tunnel design works, it is sufficient to perform the design by only modifying or supplementing previous similar design cases unless a tunnel has a unique structure or in geological conditions. In particular, for a tunnel blast design, it is reasonable to refer to previous similar design cases because the blast design in the stage of design is a preliminary design, considering that it is general to perform additional blast design through test blasts prior to the start of tunnel excavation. Meanwhile, entering the industry 4.0 era, artificial intelligence (AI) of which availability is surging across whole industry sector is broadly utilized to tunnel and blasting. For a drill and blast tunnel, AI is mainly applied for the estimation of blast vibration and rock mass classification, etc. however, there are few cases where it is applied to blast pattern design. Thus, this study attempts to automate tunnel blast design by means of machine learning, a branch of artificial intelligence. For this, the data related to a blast design was collected from 25 tunnel design reports for learning as well as 2 additional reports for the test, and from which 4 design parameters, i.e., rock mass class, road type and cross sectional area of upper section as well as bench section as input data as well as16 design elements, i.e., blast cut type, specific charge, the number of drill holes, and spacing and burden for each blast hole group, etc. as output. Based on this design data, three machine learning models, i.e., XGBoost, ANN, SVM, were tested and XGBoost was chosen as the best model and the results show a generally similar trend to an actual design when assumed design parameters were input. It is not enough yet to perform the whole blast design using the results from this study, however, it is planned that additional studies will be carried out to make it possible to put it to practical use after collecting more sufficient blast design data and supplementing detailed machine learning processes.

• Journal of Service Research and Studies
• /
• v.13 no.2
• /
• pp.82-98
• /
• 2023

This study investigates consumers' perceptions when customers participate in the process of innovating new products or new services essential to companies in the era of the 4th industrial revolution. Specifically, this study investigates how consumers' product evaluation varies depending on two types of innovation for a company's new product development (technology-based innovation and market-based innovation) and two customer roles (as information providers and as co-developers) participated in the development process. The research questions are as follows: As technology-based innovation and market-based innovation are different types of innovations, will consumers' product evaluation vary depending on these different types of innovation? If customers participate in the development process of a new product reflecting each innovation, how will the information that the customer participated be perceived by other consumers? In addition, this customer participation method can serve as an information provider and a co-developer, and will consumers' evaluation of new products vary depending on this role? As a result of verifying the hypothesis using an experimental method, it shows that consumers' product evaluation differs significantly depending on the role of customers who participated in the process of developing new product development process. In other words, the results indicate that the case where customers participated as market information providers in the process of developing new products is more favorable to the new product evaluation than the case where they participated as co-developers of the new products. In addition, there is an interaction effect between the type of product innovation and the role of customer participation. To be specific, when a product reflecting technological innovation is released, there is no difference in consumers' product evaluation according to the roles of two different customer participations. However, when a market-based innovation product is released, product evaluation is more favorably perceived when customers participated as information providers than they were involved in the new product development process as co-developers. This study is of theoretical significance in that it distinguishes each type of innovation and verified how other consumers' perceptions vary depending on their role when customers participate in the innovation process. Finally, limitations and future study directions are suggested along with practical implications.

• Journal of Navigation and Port Research
• /
• v.46 no.1
• /
• pp.1-10
• /
• 2022

This study aims to investigate changes in the demand for ship officers in response to changes in the shipping industry environment in which Maritime Autonomous Surface Ships (MASS) emerge according to the application of the fourth industrial revolution technology to ships, and it looks into changes in the skill of ship officer. It also analyzes and proposes a plan for nurturing ship officers accordingly. As a result of the degree of recognition and AHP analysis, this study suggests that a new training system is required because the current training and education system may cover the job competencies of emergency response, caution and danger navigation, general sailing, cargo handling, seaworthiness maintenance, emergency response, and ship maintenance and management, but tasks such as remote control, monitoring diagnosis, device management capability, and big data analysis require competency for unmanned and shore-based control. By evaluating the importance of change factors in the duties of ship officers in Maritime Autonomous Surface Ships, this study provides information on ship officer educational institutions' response strategies for nurturing ship officers and prioritization of resource allocation, etc. The importance of these factors was compared and evaluated to suggest changes in the duties of ship officers and methods of nurturing ship officers according to the introduction of Maritime Autonomous Surface Ships. It is expected that the findings of this study will be meaningful as it systematically derives the duties and competency factors of ship officers of Maritime Autonomous Surface Ships from a practical point of view and analyzed the perception level of each relevant expert to diagnose expert-level responses to the introduction of Maritime Autonomous Surface Ships.

• Journal of Cadastre & Land InformatiX
• /
• v.52 no.2
• /
• pp.171-187
• /
• 2022

Recently, the demand and development for non-face-to-face services are rapidly progressing due to the pandemic caused by the COVID-19, and attention is focused on the metaverse at the center. Entering the era of the 4th industrial revolution, Metaverse, which means a world beyond virtual and reality, combines various sensing technologies and 3D reconstruction technologies to provide various information and services to users easily and quickly. In particular, due to the miniaturization and economic increase of convergence sensors such as unmanned aerial vehicle(UAV) capable of high-resolution imaging and high-precision LiDAR(Light Detection and Ranging) sensors, research on digital-Twin is actively underway to create and simulate real-life twins. In addition, Game engines in the field of computer graphics are developing into metaverse engines by expanding strong 3D graphics reconstuction and simulation based on dynamic operations. This study constructed a mirror-world type metaverse that reflects real-world coordinate-based reality using Unreal Engine 5, a recently announced metaverse engine, with accurate 3D spatial information data of convergence sensors based on unmanned aerial system(UAS) and LiDAR. and then, spatial information contents and simulations for users were produced based on various public data to verify the accuracy of reconstruction, and through this, it was possible to confirm the construction of a more realistic and highly utilizable metaverse. In addition, when constructing a metaverse that users can intuitively and easily access through the unreal engine, various contents utilization and effectiveness could be confirmed through coordinate-based 3D spatial information with high reproducibility.

• Journal of Christian Education in Korea
• /
• v.74
• /
• pp.107-134
• /
• 2023

The purpose of this study is to provide a foundation for establishing the correct direction of education that utilizes artificial intelligence, a key technology of the Fourth Industrial Revolution, in the context of Christian education. To achieve this, theoretical and literature research was conducted. First, the research analyzed the historical development of artificial intelligence to understand its characteristics. Second, the research analyzed the use of artificial intelligence in convergence education from an educational perspective and examined the current policy direction in South Korea. Through this analysis, the research examined the direction of Christian education in the era of artificial intelligence. In particular, the research critically examined the perspectives of continuity and change in the context of Christian education in the era of artificial intelligence. The research reflected upon the fundamental educational purposes of Christian education that should remain unchanged despite the changing times. Furthermore, the research deliberated on the educational curriculum and teaching methods that should adapt to the changing dynamics of the era. In conclusion, this research emphasizes that even in the era of artificial intelligence, the fundamental objectives of Christian education should not be compromised. The utilization of artificial intelligence in education should serve as a tool that fulfills the mission permitted by God. Therefore, Christian education should remain centered around God, rooted in the principles of the Bible. Moreover, Christian education should aim to foster creative and convergent Christian nurturing. To achieve this, it is crucial to provide learners with an educational environment that actively utilizes AI-based hybrid learning environments and metaverse educational platforms, combining online and offline learning spaces. Moreover, to enhance learners' engagement and effectiveness in education, it is essential to actively utilize AI-based edutech that reflects the aforementioned educational environments. Lastly, in order to cultivate Christian learners with dynamic knowledge, it is crucial to employ a variety of teaching and learning methods grounded in constructivist theories, which emphasize active learner participation, collaboration, inquiry, and reflection. These approaches seek to align knowledge with life experiences, promoting a holistic convergence of faith and learning.