• The Journal of the Convergence on Culture Technology
• /
• v.9 no.5
• /
• pp.685-695
• /
• 2023

This study aimed to investigate the current utilization and implications of digital learning support media in the field of French studies, and to explore future research directions. To achieve this, we conducted a comprehensive review of the use of digital media in various learning processes within French studies. Additionally, we examined the direct application of ChatGPT, an emerging technology, to learning by extending its use to foreign language and education fields. Our findings indicate that the application of digital learning support media in French studies is somewhat limited, with selective use in processes such as online class support media, pre-class learning, efficient learning and interaction, and self-directed learning. In the case of ChatGPT, our research found that no studies have been conducted within French studies, and very few studies have been conducted on its practical application in other educational fields. While ChatGPT has a wide range of applications and has shown positive effects on learners, ethical concerns have been raised regarding the quality, source, and reliability of information. Therefore, future research in French studies should focus on educational application and effectiveness verification in university teaching and learning situations, as well as interdisciplinary convergence with digital learning support media.

• Journal of the Korea Convergence Society
• /
• v.12 no.12
• /
• pp.39-47
• /
• 2021

The construction of high-quality input features through effective segmentation is essential for increasing the sentence comprehension of a language model. Improving the quality of them directly affects the performance of the downstream task. This paper comparatively studies the segmentation that effectively reflects the linguistic characteristics of Korean regarding word and sentence classification. The segmentation types are defined in four categories: eojeol, morpheme, syllable and subchar, and pre-training is carried out using the RoBERTa model structure. By dividing tasks into a sentence group and a word group, we analyze the tendency within a group and the difference between the groups. By the model with subchar-level segmentation showing higher performance than other strategies by maximal NSMC: +0.62%, KorNLI: +2.38%, KorSTS: +2.41% in sentence classification, and the model with syllable-level showing higher performance at maximum NER: +0.7%, SRL: +0.61% in word classification, the experimental results confirm the effectiveness of those schemes.

• Journal of Convergence for Information Technology
• /
• v.11 no.8
• /
• pp.36-41
• /
• 2021

Although the technology for prediction or analysis using artificial intelligence is constantly developing, a black-box problem does not interpret the decision-making process. Therefore, the decision process of the AI model can not be interpreted from the user's point of view, which leads to unreliable results. We investigated the problems of artificial intelligence and explainable artificial intelligence using Blockchain to solve them. Data from the decision-making process of artificial intelligence models, which can be explained with Blockchain, are stored in Blockchain with time stamps, among other things. Blockchain provides anti-counterfeiting of the stored data, and due to the nature of Blockchain, it allows free access to data such as decision processes stored in blocks. The difficulty of creating explainable artificial intelligence models is a large part of the complexity of existing models. Therefore, using the point cloud to increase the efficiency of 3D data processing and the processing procedures will shorten the decision-making process to facilitate an explainable artificial intelligence model. To solve the oracle problem, which may lead to data falsification or corruption when storing data in the Blockchain, a blockchain artificial intelligence problem was solved by proposing a blockchain-based explainable artificial intelligence model that passes through an intermediary in the storage process.

• Advanced Industrial SCIence
• /
• v.3 no.2
• /
• pp.31-37
• /
• 2024

This paper presents a single and 2-patch microstrip array antenna operated on a frequency of 10.3GHz(x-band). It outlines the process of designing a microstrip patch array antenna using CST MWS. Initially, a single microstrip antenna was designed, followed by optimization using CST MWS to attain optimal return losses and gain. Subsequently, the design was expanded to create a 2×1 microstrip inset-fed array antenna for the X-band applications. The construction material is Roger RO4350B, with specific dimensions (h=0.79mm, 𝜖_r = 3.54). The achieved results include an S11 of -18dB at the resonant frequency (10.3GHz), a gain of 9.82dBi, a bandwidth of 0.165GHz, and a 3-dB beamwidth of 30°, 121° in Az(𝜑=0) and El(𝜑=90) plane, respectively. The future plan involves the fabrication of this array antenna and further expansion to a 4×4 array of microstrip antennas. It is then incorporated on the X-band applications for practical uses.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.2
• /
• pp.537-545
• /
• 2024

The importance of University Archives Management is increasing. In this study, we researched cases of collecting and managing educational archival contents in universities. Developed archival contents for software practical education, and implemented it to the Capstone Design course and overseas program. The effect of applying the model was analyzed through surveys and interviews. The Capstone Design Survey, involving 349 participants, indicated the highest satisfaction with the University-Industry Cooperation type. The experience of dissemination and enhancement was aggregated as the second highest satisfaction. In the second survey, 62 students who had participated in the overseas program over the span of two years took part. All nine Likert-type questions showed high satisfaction scores of more than 4 points. The top three satisfaction factors-content, program type, and advanced experience-showed high satisfaction scores of 4.85, 4.74, and 4.71, respectively. Through interviews with professors, mentors, and students, it was also confirmed that instructional methods utilizing archival contents are effective. And the model we developed is applicable for convergence education.

• Advanced Industrial SCIence
• /
• v.1 no.2
• /
• pp.24-29
• /
• 2022

Recently, smart farm research that applies IoT technology to various farms is being actively conducted to improve agricultural cooling power and minimize cost reduction. In particular, methods for automatically and remotely controlling environmental information data around smart farms through IoT devices are being studied. This paper proposes a processing model that can maintain an optimal growth environment by monitoring environmental information data collected from smart farms in real time based on machine learning. Since the proposed model uses machine learning technology, environmental information is grouped into multiple blockchains to enable continuous data collection through rich big data securing measures. In addition, the proposed model selectively (or binding) the collected environmental information data according to priority using weights and correlation indices. Finally, the proposed model allows us to extend the cost of processing environmental information to n-layer to a minimum so that we can process environmental information in real time.

• Advanced Industrial SCIence
• /
• v.3 no.3
• /
• pp.14-19
• /
• 2024

Currently, micro LEDs (Light Emitting Diode) are attracting attention in the lighting field along with next-generation displays and have advantages such as high luminance, operating speed, energy efficiency, and long-term driving. It is predicted to bring new innovations in smartphones, televisions, and wearable electronic devices. These micro displays are self-luminous displays that emit light by themselves by being implemented as pixels composed of micrometer-sized LED devices. The main manufacturing processes can be divided into crystal growth, patterning and etching, chip separation and transfer, bonding and wiring, panel assembly and encapsulation, inspection, and quality management. Recently, this technology has developed at a rapid pace, and companies are expanding their investments in these fields. According to recent market research results, the micro LED display market is expected to continue to grow, and the main development direction of development can be summarized as manufacturing process improvement, material innovation, and driving technology development. It is believed that commercialization will accelerate through these studies and lead to innovation in the display industry with high performance and various application possibilities.

• Journal of Convergence for Information Technology
• /
• v.11 no.12
• /
• pp.1-13
• /
• 2021

This study tried to suggest ways to improve the indicator system to strengthen the personal information protection. For this purpose, the components of indicator system are derived through domestic and foreign literature, and it was selected as main the diagnostic indicators through FGI/Delphi analysis for personal information protection experts and a survey for personal information protection officers of public institutions. As like this, this study was intended to derive an inspection standard that can be reflected as a separate index system for personal information protection, by classifying the specific IT technologies of the 4th industrial revolution, such as big data, cloud, Internet of Things, and artificial intelligence. As a result, from the planning and design stage of specific technologies, the check items for applying the PbD principle, pseudonymous information processing and de-identification measures were selected as 2 common indicators. And the checklists were consisted 2 items related Big data, 5 items related Cloud service, 5 items related IoT, and 4 items related AI. Accordingly, this study expects to be an institutional device to respond to new technological changes for the continuous development of the personal information management level diagnosis system in the future.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.33 no.6
• /
• pp.265-274
• /
• 2021

Outlier detection research in ocean data has traditionally been performed using statistical and distance-based machine learning algorithms. Recently, AI-based methods have received a lot of attention and so-called supervised learning methods that require classification information for data are mainly used. This supervised learning method requires a lot of time and costs because classification information (label) must be manually designated for all data required for learning. In this study, an autoencoder based on unsupervised learning was applied as an outlier detection to overcome this problem. For the experiment, two experiments were designed: one is univariate learning, in which only SST data was used among the observation data of Deokjeok Island and the other is multivariate learning, in which SST, air temperature, wind direction, wind speed, air pressure, and humidity were used. Period of data is 25 years from 1996 to 2020, and a pre-processing considering the characteristics of ocean data was applied to the data. An outlier detection of actual SST data was tried with a learned univariate and multivariate autoencoder. We tried to detect outliers in real SST data using trained univariate and multivariate autoencoders. To compare model performance, various outlier detection methods were applied to synthetic data with artificially inserted errors. As a result of quantitatively evaluating the performance of these methods, the multivariate/univariate accuracy was about 96%/91%, respectively, indicating that the multivariate autoencoder had better outlier detection performance. Outlier detection using an unsupervised learning-based autoencoder is expected to be used in various ways in that it can reduce subjective classification errors and cost and time required for data labeling.

• Science of Emotion and Sensibility
• /
• v.26 no.3
• /
• pp.69-82
• /
• 2023

High-tech sensibility textile and fashion, in which consumers' emotions and various textile and fashion technologies are converged, is an important industrial group. It is important to develop the ability to apply in practice by gathering the creative by understanding other fields and exchanging ideas through interdisciplinary collaboration in the field of emotional engineering. Through interdisciplinary research and collaboration, talent must be nurtured of individuals who would lead the era of the 4^th Industrial Revolution with the ability to empathize with others as well as the creative convergence-type intellectual ability necessary for the rapidly changing society. To determine content-creation methods, basic research is conducted. Additionally, this study investigates on the current status and educational process of the emotional textile-fashion industry worldwide. To nurture talents in the textile and fashion sensibility science, the basic contents are created to manage the knowledge that delivers sensibility science and the ICT related to this field, as well as in the intensive, PB-style conceptual design based on sensibility. The process from derivation of consumer emotion analysis and product development can be experienced through smart kit practice. Moreover, various methods are developed to set up intellectual property rights generated while developing ICT convergence products as start-ups. The study also covers new knowledge rights to develop emotional textile fashion.