• Journal of Gifted/Talented Education
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• v.25 no.5
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• pp.697-709
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• 2015

This paper aims at shaping remote observatory system environment for schools, developing astronomical observation program using that system and applying it to science-gifted elementary students in order to figure out effects on their scientific investigation ability and attitude. in order to figure out effects of astronomical observation program using remote observatory program on scientific investigation ability and attitude of science-gifted elementary students, test was conducted on gifted students class of 5th grade in A Elementary School(15) and those of 5th grade in B Elementary School(20). The summary of this paper's results are as follows. First, in order to compose remote observatory system, an astronomical telescope available for remote control to transfer actual observed images in real-time was manufactured. Second, learning program for using remote observatory system wad developed by selecting contents through analysis of the curriculum. Third, in order to figure out effects of astronomical observation program using remote observatory program on scientific investigation ability and attitude of science-gifted elementary students. As a result, both of basic investigation ability and integrated investigation abilit, sub-elements of scientific investigation ability, showed significant differences and scientific investigation ability combining basic and integrated investigation abilities showed significant differences as well. Effects of astronomical observation program applying remote observatory also showed significant differences and its sub-elements, openness, collaboration, patience and creativeness did not show significant differences while curiosity, critics and volunteering showed significant differences.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.119-125
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• 2015

A concentration meter is widely used at purification plants, sewage treatment plants and waste water treatment plants to sort and transfer high concentration sludge and to control the amount of chemical dosage. When the strange substance is contained in the sludge, however, the attenuation of ultrasonic wave could be increased or not be transmitted to the receiver. At that case, the value of concentration meter is higher than the actual density value or vibrated up and down. It has also been difficult to automate the residuals treatment process according to the problems as sludge attachment or damage of a sensor. Multi-beam ultrasonic concentration meter has been developed to solve these problems, but the failure of the ultrasonic beam of a specific concentration measurement value degrade the performance of the entire system. This paper proposes the method to improve the accuracy of sludge concentration rate by choosing reliable sensor values and learning them by proposed algorithm. The prediction algorithm is chosen as neuro-fuzzy model, which is tested by the various experiments.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.165-172
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• 2020

In this paper we propose an uses on-device-based edge computing technology and big data analysis methods through the use of on-device-based edge computing technology and analysis of big data, which are distributed computing paradigms that introduce computations and storage devices where necessary to solve problems such as transmission delays that occur when data is transmitted to central centers and processed in current general smart factories. However, even if edge computing-based technology is applied in practice, the increase in devices on the network edge will result in large amounts of data being transferred to the data center, resulting in the network band reaching its limits, which, despite the improvement of network technology, does not guarantee acceptable transfer speeds and response times, which are critical requirements for many applications. It provides the basis for developing into an AI-based facility prediction conservation analysis tool that can apply deep learning suitable for big data in the future by supporting intelligent facility management that can support productivity growth through research that can be applied to the field of facility preservation and smart factory industry with integrated hardware technology that can accommodate these requirements and factory management and control technology.

• Journal of the Korea Society for Simulation
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• v.31 no.1
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• pp.29-41
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• 2022

As one of the alternatives to solve the problem of unstable food supply and demand imbalance caused by abnormal climate change, the need for plant factories is increasing. Airflow in plant factory is recognized as one of important factor of plant which influence transpiration and heat transfer. On the other hand, Digital Twin (DT) is getting attention as a means of providing various services that are impossible only with the real system by replicating the real system in the virtual world. This study aimed to develop a digital twin model for airflow prediction that can predict airflow in various situations by applying the concept of digital twin to a plant factory in operation. To this end, first, the mathematical formalism of the digital twin model for airflow analysis in plant factories is presented, and based on this, the information necessary for airflow prediction modeling of a plant factory in operation is specified. Then, the shape of the plant factory is implemented in CAD and the DT model is developed by combining the computational fluid dynamics (CFD) components for airflow behavior analysis. Finally, the DT model for high-accuracy airflow prediction is completed through the validation of the model and the machine learning-based calibration process by comparing the simulation analysis result of the DT model with the actual airflow value collected from the plant factory.

• Journal of Appropriate Technology
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• v.6 no.2
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• pp.200-207
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• 2020

The purpose of the establishment of Fab Lab is to promote the sustainable development of local communities around the world. To this end, The Fab foundation are preparing a resource-circulating society that maintains a city's self-sufficiency rate of 50% or more by 2054. In developed countries, Fab Lab is not only a manufacturing space for startup support, but an open innovation space for learning and creation. In addition, in emerging countries, Fab Lab is playing a role as a digital production center to create and share appropriate new technologies by reflecting the needs of local communities. India has 70 Fab Labs, the largest emerging country, ahead of Russia's 48. India's Fab Lab is conducting a collaboration project through regular meetings held every six months. The subject of this study, Fab Lab Vigyan Ashram, is defined as a place to transfer the concept of digital lab to alternative schools in rural India. In this study, we looked at a case in which an alternative school for an agricultural community called Vigyan Ashram, the modern version of the Gurukula system, successfully combined with the digital fabrication called Fab Lab to become a new citizen-led making community of the 4th Industrial Revolution. Based on this, we explored the development model of the Indian Fab Lab that fits the local situation.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1137-1144
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• 2022

In this study, an artificial intelligence(AI) was developed to help with facial expression practice in order to express emotions. The developed AI used multimodal inputs consisting of sentences and facial images for deep neural networks (DNNs). The DNNs calculated similarities between the emotions predicted by the sentences and the emotions predicted by facial images. The user practiced facial expressions based on the situation given by sentences, and the AI provided the user with numerical feedback based on the similarity between the emotion predicted by sentence and the emotion predicted by facial expression. ResNet34 structure was trained on FER2013 public data to predict emotions from facial images. To predict emotions in sentences, KoBERT model was trained in transfer learning manner using the conversational speech dataset for emotion classification opened to the public by AIHub. The DNN that predicts emotions from the facial images demonstrated 65% accuracy, which is comparable to human emotional classification ability. The DNN that predicts emotions from the sentences achieved 90% accuracy. The performance of the developed AI was evaluated through experiments with changing facial expressions in which an ordinary person was participated.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.77-89
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• 2022

It has been proven through many previous researches that the pretrained language model with a large corpus helps improve performance in various natural language processing tasks. However, there is a limit to building a large-capacity corpus for training in a language environment where resources are scarce. Using the Cross-lingual Post-Training (XPT) method, we analyze the method's efficiency in Korean, which is a low resource language. XPT selectively reuses the English pretrained language model parameters, which is a high resource and uses an adaptation layer to learn the relationship between the two languages. This confirmed that only a small amount of the target language dataset in the relationship extraction shows better performance than the target pretrained language model. In addition, we analyze the characteristics of each model on the Korean language model and the Korean multilingual model disclosed by domestic and foreign researchers and companies.

• Journal of Christian Education in Korea
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• v.68
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• pp.175-216
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• 2021

This essay aims to seek an alternative model of catechesis, as this form of education faces various challenges from the Korean Church especially during COVID-19 pandemic. For a long time, catechesis in the Korean Church narrowly focused on the act of producing Christians who would be loyal to the local church, rather than focusing on nurturing members loyal to Christ, an issue that has been problematized in recent publications on catechesis. Thus, the loss of social trust in the Korean Church and the decline of its public image exemplify how this type of catechesis as disciple-making for local church's benefit, mostly nurtures a vertical dimension of faith. The current teaching and learning method mostly employs a unilateral transfer of doctrine from the teacher to the learner and emphasizes the memorization of doctrine. This type of instruction renders the catechesis as the most lackluster and outdated form of Christian education. This essay aims to reconceptualize the traditional model of catechesis. This essay first critically evaluates current situations of catechesis and presents several alternative meanings on the concept of doctrine. Then it explores the theories of catechesis through different models posed by Christian educators such as John Westerhoff III and Richard Osmer. The final section is devoted to presenting an alternative form of catechesis that focuses on seeking holistic faith.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.343-350
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• 2022

It is worth verifying the effectiveness of data integration between data with different features. This study investigated whether the data integration affects the accuracy of deep neural network (DNN), and which integration method shows the best improvement. This study used two different public datasets. One public dataset was taken in an actual farm in India. And another was taken in a laboratory environment in Korea. Leaf images were selected from two different public datasets to have five classes which includes normal and four different types of plant diseases. DNN used pre-trained VGG16 as a feature extractor and multi-layer perceptron as a classifier. Data were integrated into three different ways to be used for the training process. DNN was trained in a supervised manner via the integrated data. The trained DNN was evaluated by using a test dataset taken in an actual farm. DNN shows the best accuracy for the test dataset when DNN was first trained by images taken in the laboratory environment and then trained by images taken in the actual farm. The results show that data integration between plant images taken in a different environment helps improve the performance of deep neural networks. And the results also confirmed that independent use of plant images taken in different environments during the training process is more effective in improving the performance of DNN.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.21-29
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• 2023

In this paper, we propose an AI-based system for automatically classifying industry and occupation codes in the population census. The accurate classification of industry and occupation codes is crucial for informing policy decisions, allocating resources, and conducting research. However, this task has traditionally been performed by human coders, which is time-consuming, resource-intensive, and prone to errors. Our system represents a significant improvement over the existing rule-based system used by the statistics agency, which relies on user-entered data for code classification. In this paper, we trained and evaluated several models, and developed an ensemble model that achieved an 86.76% match accuracy in industry and 81.84% in occupation, outperforming the best individual model. Additionally, we propose process improvement work based on the classification probability results of the model. Our proposed method utilizes an ensemble model that combines transfer learning techniques with pre-trained models. In this paper, we demonstrate the potential for AI-based systems to improve the accuracy and efficiency of population census data classification. By automating this process with AI, we can achieve more accurate and consistent results while reducing the workload on agency staff.