• Communications of Mathematical Education
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• v.37 no.2
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• pp.233-256
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• 2023

This study conducted foundational research to derive ways to use ChatGPT in mathematics education by analyzing ChatGPT's responses to questions from the National Assessment of Educational Achievement (NAEA) and the College Scholastic Ability Test (CSAT). ChatGPT, a generative artificial intelligence model, has gained attention in various fields, and there is a growing demand for its use in education as the number of users rapidly increases. To the best of our knowledge, there are very few reported cases of educational studies utilizing ChatGPT. In this study, we analyzed ChatGPT 3.5 responses to questions from the three-year National Assessment of Educational Achievement and the College Scholastic Ability Test, categorizing them based on the percentage of correct answers, the accuracy of the solution process, and types of errors. The correct answer rates for ChatGPT in the National Assessment of Educational Achievement and the College Scholastic Ability Test questions were 37.1% and 15.97%, respectively. The accuracy of ChatGPT's solution process was calculated as 3.44 for the National Assessment of Educational Achievement and 2.49 for the College Scholastic Ability Test. Errors in solving math problems with ChatGPT were classified into procedural and functional errors. Procedural errors referred to mistakes in connecting expressions to the next step or in calculations, while functional errors were related to how ChatGPT recognized, judged, and outputted text. This analysis suggests that relying solely on the percentage of correct answers should not be the criterion for assessing ChatGPT's mathematical performance, but rather a combination of the accuracy of the solution process and types of errors should be considered.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.193-205
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• 2023

A large amount of floating debris from land-based sources during heavy rainfall has negative social, economic, and environmental impacts, but there is a lack of monitoring systems for floating debris accumulation areas and amounts. With the recent development of artificial intelligence technology, there is a need to quickly and efficiently study large areas of water systems using drone imagery and deep learning-based object detection models. In this study, we acquired various images as well as drone images and trained with You Only Look Once (YOLO)v5s and the recently developed YOLO7 and YOLOv8s to compare the performance of each model to propose an efficient detection technique for land-based floating debris. The qualitative performance evaluation of each model showed that all three models are good at detecting floating debris under normal circumstances, but the YOLOv8s model missed or duplicated objects when the image was overexposed or the water surface was highly reflective of sunlight. The quantitative performance evaluation showed that YOLOv7 had the best performance with a mean Average Precision (intersection over union, IoU 0.5) of 0.940, which was better than YOLOv5s (0.922) and YOLOv8s (0.922). As a result of generating distortion in the color and high-frequency components to compare the performance of models according to data quality, the performance degradation of the YOLOv8s model was the most obvious, and the YOLOv7 model showed the lowest performance degradation. This study confirms that the YOLOv7 model is more robust than the YOLOv5s and YOLOv8s models in detecting land-based floating debris. The deep learning-based floating debris detection technique proposed in this study can identify the spatial distribution of floating debris by category, which can contribute to the planning of future cleanup work.

• Journal of Service Research and Studies
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• v.11 no.1
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• pp.1-20
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• 2021

This study was conducted to derive a model of a sustainable economic system for humanity in the era of service economy that requires a paradigm shift. A new long-term sustainable development model has been built on the basis of thousands of years of economic operation experience. Currently, the world is operating the capitalism as the main economic system because there is no better alternative, and the changing economic and social environment such as the advent of the 4th Industrial Revolution is exacerbating the problems of the capitalism, such as job shortages and inequality. In this study, we analyzed the economic management system experienced by human society, and derived an economic system model that is ideal for the modern and future society and is sustainable in the long term. The conditions for a long-term sustainable economic system were presented first. It must be a model that can solve the problems of the current economic system. It must be a model that is faithful to the characteristics of the modern economic society and the nature of the economy itself. And since the new economic system is for humanity, it must be based on the common principles of human society. It should be a model that continuously guarantees core values such as equality and freedom required by human society. After analyzing the problems of the current economic system and analyzing the conditions required for the new system, the basic axioms that the new economic system should be based on were presented, and a desirable model was derived based on this. The structure of the derived model and the specific operation model were presented. In the future, research is needed to specify the operational model so that this model can be settled well in different environments for each country.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.31-39
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• 2023

The purpose of this study is to propose a scientific performance evaluation framework for measuring and managing the overall outcome of complex types of projects that are linked to public demand-based commercialization, such as information system projects and public procurement, in integrated national R&D projects. In the case of integrated national R&D projects that involve multiple research institutes to form a single final product, and in the case of demand-based demonstration and commercialization of the project results, the existing evaluation system that evaluates performance based on the short-term outputs of the detailed tasks comprising the R&D project has limitations in evaluating the mid- and long-term effects and practicality of the integrated research products. (Moreover, as the paradigm of national R&D projects is changing to a mission-oriented one that emphasizes efficiency, there is a need to change the performance evaluation of national R&D projects to focus on the effectiveness and practicality of the results.) In this study, we propose a performance evaluation framework from a structural perspective to evaluate the completeness of each national R&D project from a practical perspective, such as its effectiveness, beyond simple short-term output, by utilizing the Hourglass model. In particular, it presents an integrated performance evaluation framework that links the top-down and bottom-up approaches leading to Tool-System-Service-Effect according to the structure of R&D projects. By applying the proposed detailed evaluation indicators and performance evaluation frame to actual national R&D projects, the validity of the indicators and the effectiveness of the proposed performance evaluation frame were verified, and these results are expected to provide academic, policy, and industrial implications for the performance evaluation system of national R&D projects that emphasize efficiency in the future.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.523-541
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• 2023

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.

• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.7-7
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• 2023

Currently, the main technologies of various fourth industries are big data, the Internet of Things, artificial intelligence, blockchain, mixed reality (MR), and drones. In particular, "digital twin," which has recently become a global technological trend, is a concept of a virtual model that is expressed equally in physical objects and computers. By creating and simulating a Digital twin of software-virtualized assets instead of real physical assets, accurate information about the characteristics of real farming (current state, agricultural productivity, agricultural work scenarios, etc.) can be obtained. This study aims to streamline agricultural work through automatic water management, remote growth forecasting, drone control, and pest forecasting through the operation of an integrated control system by constructing digital twin data on the main production area of the nojinot industry and designing and building a smart farm complex. In addition, it aims to distribute digital environmental control agriculture in Korea that can reduce labor and improve crop productivity by minimizing environmental load through the use of appropriate amounts of fertilizers and pesticides through big data analysis. These open-field agricultural technologies can reduce labor through digital farming and cultivation management, optimize water use and prevent soil pollution in preparation for climate change, and quantitative growth management of open-field crops by securing digital data for the national cultivation environment. It is also a way to directly implement carbon-neutral RED++ activities by improving agricultural productivity. The analysis and prediction of growth status through the acquisition of the acquired high-precision and high-definition image-based crop growth data are very effective in digital farming work management. The Southern Crop Department of the National Institute of Food Science conducted research and development on various types of open-field agricultural smart farms such as underground point and underground drainage. In particular, from this year, commercialization is underway in earnest through the establishment of smart farm facilities and technology distribution for agricultural technology complexes across the country. In this study, we would like to describe the case of establishing the agricultural field that combines digital twin technology and open-field agricultural smart farm technology and future utilization plans.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.6
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• pp.173-189
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• 2023

As supreme technologies continue to be developed, industries such as artificial intelligence, biotechnology, robots, aerospace, electric vehicles, and solar energy are created, and the macro business environment is rapidly changing. Due to these large-scale changes and increased complexity, it is necessary to pay attention to the effect of social capital, which can create new value by utilizing capital increasing the importance of relationships rather than technology or asset ownership itself at the level of start-up strategy. Social capital is a concept first proposed by Hanifan in 1916, and refers to the overall sum of capabilities or resources that are latent or available for use in mutual, continuous, organic relationships or accumulated human relationship networks between individuals or social members. In addition, the diversity of start-up teams with diverse backgrounds, characteristics, and capabilities, rather than one exceptional founder, has been emphasized. Founding team diversity refers to the diversity of in-depth factors such as demographic factors, beliefs, and values of the founding team. In addition, changes in the macro environment are emphasizing the importance of technology start-ups and laboratory start-ups that lead industrial innovation and create the nation's core growth engines. This study focused on the I-Corps' program. I-Corps, which means innovation corps, is a laboratory startup program launched by the National Research Foundation (NSF) in 2011 to encourage entrepreneurship and commercialization of research results. It focuses on forming a startup team involving professors, researchers and market discovery activities. Taking these characteristics into account, this study empirically verified the impact of social capital from a network perspective and founding team diversity on I-Corps start-up performance. As a result of the analysis, the educational diversity of the founding team had a negative (-) effect on the financial performance of the founding team. On the other side, the gender diversity and the cognitive dimension of social capital had a positive (+) effect on the financial performance of the founding team. This study is expected to provide more useful theoretical and practical implications regarding the diversity, social capital, and performance interpretation of the I-Corps Lab startup team.

• Journal of Life Science
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• v.34 no.5
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• pp.339-355
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• 2024

The pulmonary system is a highly complex system that can only be understood by integrating its functional and structural aspects. Hence, in vivo animal models are generally used for pathological studies of pulmonary diseases and the evaluation of inhalation toxicity. However, to reduce the number of animals used in experimentation and with the consideration of animal welfare, alternative methods have been extensively developed. Notably, the Organization for Economic Co-operation and Development (OECD) and the United States Environmental Protection Agency (USEPA) have agreed to prohibit animal testing after 2030. Therefore, the latest advances in biotechnology are revolutionizing the approach to developing in vitro inhalation models. For example, lung organ-on-a-chip (OoC) and organoid models have been intensively studied alongside advancements in three-dimensional (3D) bioprinting and microfluidic systems. These modeling systems can more precisely imitate the complex biological environment compared to traditional in vivo animal experiments. This review paper addresses multiple aspects of the recent in vitro modeling systems of lung OoC and organoids. It includes discussions on the use of endothelial cells, epithelial cells, and fibroblasts composed of lung alveoli generated from pluripotent stem cells or cancer cells. Moreover, it covers lung air-liquid interface (ALI) systems, transwell membrane materials, and in silico models using artificial intelligence (AI) for the establishment and evaluation of in vitro pulmonary systems.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.9-18
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• 2024

The importance of active sonar systems is emerging due to the quietness of underwater targets and the increase in ambient noise due to the increase in maritime traffic. However, the low signal-to-noise ratio of the echo signal due to multipath propagation of the signal, various clutter, ambient noise and reverberation makes it difficult to identify underwater targets using active sonar. Attempts have been made to apply data-based methods such as machine learning or deep learning to improve the performance of underwater target recognition systems, but it is difficult to collect enough data for training due to the nature of sonar datasets. Methods based on mathematical modeling have been mainly used to compensate for insufficient active sonar data. However, methodologies based on mathematical modeling have limitations in accurately simulating complex underwater phenomena. Therefore, in this paper, we propose a sonar signal synthesis method based on a deep neural network. In order to apply the neural network model to the field of sonar signal synthesis, the proposed method appropriately corrects the attention-based encoder and decoder to the sonar signal, which is the main module of the Tacotron model mainly used in the field of speech synthesis. It is possible to synthesize a signal more similar to the actual signal by training the proposed model using the dataset collected by arranging a simulated target in an actual marine environment. In order to verify the performance of the proposed method, Perceptual evaluation of audio quality test was conducted and within score difference -2.3 was shown compared to actual signal in a total of four different environments. These results prove that the active sonar signal generated by the proposed method approximates the actual signal.

• The Korean Journal of Archival Studies
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• no.79
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• pp.5-32
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• 2024

The Korean government ranked 3rd among 193 UN member countries in the UN's 2022 e-Government Development Index. Korea, which has consistently been evaluated as a top country, can clearly be said to be a leading country in the world of e-government. The lubricant of e-government is data. Data itself is neither information nor a record, but it is a source of information and records and a resource of knowledge. Since administrative actions through electronic systems have become widespread, the production and technology of data-based records have naturally expanded and evolved. Technology may seem value-neutral, but in fact, technology itself reflects a specific worldview. The digital order of new technologies, armed with hyper-connectivity and super-intelligence, not only has a profound influence on traditional power structures, but also has an a similar influence on existing information and knowledge transmission media. Moreover, new technologies and media, including data-based generative artificial intelligence, are by far the hot topic. It can be seen that the all-round growth and spread of digital technology has led to the augmentation of human capabilities and the outsourcing of thinking. This also involves a variety of problems, ranging from deep fakes and other fake images, auto profiling, AI lies hallucination that creates them as if they were real, and copyright infringement of machine learning data. Moreover, radical connectivity capabilities enable the instantaneous sharing of vast amounts of data and rely on the technological unconscious to generate actions without awareness. Another irony of the digital world and online network, which is based on immaterial distribution and logical existence, is that access and contact can only be made through physical tools. Digital information is a logical object, but digital resources cannot be read or utilized without some type of device to relay it. In that respect, machines in today's technological society have gone beyond the level of simple assistance, and there are points at which it is difficult to say that the entry of machines into human society is a natural change pattern due to advanced technological development. This is because perspectives on machines will change over time. Important is the social and cultural implications of changes in the way records are produced as a result of communication and actions through machines. Even in the archive field, what problems will a data-based archive society face due to technological changes toward a hyper-intelligence and hyper-connected society, and who will prove the continuous activity of records and data and what will be the main drivers of media change? It is time to research whether this will happen. This study began with the need to recognize that archives are not only records that are the result of actions, but also data as strategic assets. Through this, author considered how to expand traditional boundaries and achieves reterritorialization in a data-driven society.