• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.565-575
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• 2022

Recently, in the field of water resource engineering, interest in predicting time series water levels and flow rates using deep learning technology that has rapidly developed along with the Fourth Industrial Revolution is increasing. In addition, although water-level and flow-rate prediction have been performed using the Long Short-Term Memory (LSTM) model and Gated Recurrent Unit (GRU) model that can predict time-series data, the accuracy of flow-rate prediction in rivers with rapid temporal fluctuations was predicted to be very low compared to that of water-level prediction. In this study, the Paldang Bridge Station of the Han River, which has a large flow-rate fluctuation and little influence from tidal waves in the estuary, was selected. In addition, time-series data with large flow fluctuations were selected to collect water-level and flow-rate data for 2 years and 7 months, which are relatively short in data length, to be used as training and prediction data for the LSTM and GRU models. When learning time-series water levels with very high time fluctuation in two models, the predicted water-level results in both models secured appropriate accuracy compared to observation water levels, but when training rapidly temporal fluctuation flow rates directly in two models, the predicted flow rates deteriorated significantly. Therefore, in this study, in order to accurately predict the rapidly changing flow rate, the water-level data predicted by the two models could be used as input data for the rating curve to significantly improve the prediction accuracy of the flow rates. Finally, the results of this study are expected to be sufficiently used as the data of flood warning system in urban rivers where the observation length of hydrological data is not relatively long and the flow-rate changes rapidly.

• Membrane Journal
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• v.32 no.5
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• pp.292-303
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• 2022

An eco-friendly energy conversion device without the emission of pollutants has gained much attention due to the rapid use of fossil fuels inducing carbon dioxide emissions ever since the first industrial revolution in the 18th century. Polymer electrolyte membrane fuel cells (PEMFCs) that can produce water during the reaction without the emission of carbon dioxide are promising devices for automotive and residential applications. As a key component of PEMFCs, polymer electrolyte membranes (PEMs) need to have high proton conductivity and physicochemical stability during the operation. Currently, perfluorinated sulfonic acid-based PEMs (PFSA-PEMs) have been commercialized and utilized in PEMFC systems. Although the PFSA-PEMs are found to meet these criteria, there is an ongoing need to improve these further, to be useful in practical PEMFC operation. In addition, the well-known drawbacks of PFSA-PEMs including low glass transition temperature and high gas crossover need to be improved. Therefore, this review focused on recent trends in the development of high-performance PFSA-PEMs in three different ways. First, control of the side chain of PFSA copolymers can effectively improve the proton conductivity and thermal stability by increasing the ion exchange capacity and polymer crystallinity. Second, the development of composite-type PFSA-PEMs is an effective way to improve proton conductivity and physical stability by incorporating organic/inorganic additives. Finally, the incorporation of porous substrates is also a promising way to develop a thin pore-filling membrane showing low membrane resistance and outstanding durability.

• Journal of Navigation and Port Research
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• v.46 no.1
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• pp.42-56
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• 2022

Due to the 4th Industrial Revolution announced at the Davos Forum, the World Economic Forum, in 2016, industrial trends around the world are changing rapidly and intelligently. Among them, the digital twin is drawing attention from all industries as a groundbreaking technology that reduces unnecessary costs and trial and error by implementing real objects, systems, and environments in the same way in the real virtual world and using them to perform simulation analysis. In particular, there is a lot of interest in the application of digital twin technology in solving ports safety and efficiency challenges at once. However, there is a lack of in-depth research for the application of digital twin technology in the port, and in particular, there is a lack of research on measurable data for the implementation of the digital twin in ports. The purpose of this study was to increase granularity and connectivity through measurable data investigation for the application of digital twin technology at container ports. Based on the study results, data factors for container port application were classified into crane data, operational data, physical data, and transportation data, and factor composition, correlation with factors, and fitness were confirmed through confirmatory factor analysis.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.286-293
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• 2022

Recently, the importance of elemental technologies constituting smart factories is increasing due to the 4th Industrial Revolution, and simulation is widely used as a tool to learn these technologies. In particular, PID control is an automatic control technique used in various fields, and most of them analyze mathematical models in certain situations or research on application development with built-in controllers. In actual educational environment requires PID simulator training as well as PID control principles. In this paper, we propose a model that enables education and practice of various PID controls through 3D simulation. The proposed model implemented virtual balls and Fan and implemented PID control by configuring a system so that the force can be lifted by the air pressure generated in the Fan. At this time, the height of the ball was expressed in a graph according to each gain value of the PID controller and then compared with the actual system, and through this, satisfactory results sufficiently applicable to the actual class were confirmed. Through the proposed model, it is expected that the rapidly increasing elemental technology of smart factories can be used in various ways in a remote classroom environment.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.383-384
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• 2022

Following the development of technologies such as digital twin, IoT, and AI after the 4th industrial revolution, decision-making problems are being solved based on high-dimensional data analysis. This has recently been applied to the port logistics sector, and a number of studies on big data analysis, deep learning predictions, and simulations have been conducted on container terminals to improve port productivity. These high-dimensional data analysis techniques generally require a large number of data. However, the global port environment has changed due to the COVID-19 pandemic in 2020. It is not appropriate to apply data before the COVID-19 outbreak to the current port environment, and the data after the outbreak was not sufficiently collected to apply it to data analysis such as deep learning. Therefore, this study intends to present a port data augmentation method for data analysis as one of these problem-solving methods. To this end, we generate the container stowage situation of the yard through a generative adversarial neural network model in terms of container terminal operation, and verify similarity through statistical distribution verification between real and augmented data.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.57-58
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• 2023

The working time for loading and transporting containers in the container terminal is one of the factors directly related to port productivity, and minimizing working time for these operations can maximize port productivity. Among working time for container operations, the working time of yard tractors(Y/T) responsible for the transportation of containers between berth and yard is a significant portion. However, it is difficult to estimate the working time of yard tractors quantitatively, although it is possible to estimate it based on the practical experience of terminal operators. Recently, a technology based on IoT(Internet of Things), one of the core technologies of the 4th industrial revolution, is being studied to monitoring and tracking logistics resources within the port in real-time and calculate working time, but it is challenging to commercialize this technology at the actual port site. Therefore, this study aims to develop yard tractor working time prediction model to enhance the operational efficiency of the container terminal. To develop the prediction model, we analyze actual port operation data to identify factors that affect the yard tractor's works and predict its working time accordingly.

• Information Systems Review
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• v.22 no.1
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• pp.17-31
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• 2020

With the start of the fourth industrial revolution era, technologies of various fields are merged and new types of technologies and products are being developed. In addition, the importance of the registration of intellectual property rights and patent registration to gain market dominance of them is increasing in oversea as well as in domestic. Accordingly, the number of patents to be processed per examiner is increasing every year, so time and cost for prior art research are increasing. Therefore, a number of researches have been carried out to reduce examination time and cost for patent-pending technology. This paper proposes a method to calculate the degree of similarity among patent documents of the same priority claim when a plurality of patent rights priority claims are filed and to provide them to the examiner and the patent applicant. To this end, we preprocessed the data of the existing irregular patent documents, used Word2vec to obtain similarity between patent documents, and then proposed recommendation model that recommends a similar patent document in descending order of score. This makes it possible to promptly refer to the examination history of patent documents judged to be similar at the time of examination by the examiner, thereby reducing the burden of work and enabling efficient search in the applicant's prior art research. We expect it will contribute greatly.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.71-79
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• 2024

ChatGPT, an interactive artificial intelligence (AI) chatbot developed by Open AI in the U.S., gaining popularity with great repercussions around the world. Some academia are concerned that ChatGPT can be used by students for plagiarism, but ChatGPT is also widely used in a positive direction, such as being used to write marketing phrases or website phrases. There is also an opinion that ChatGPT could be a new future for "search," and some analysts say that the focus should be on fostering rather than excessive regulation. This study analyzed consciousness about ChatGPT for college students through a survey of their perception of ChatGPT. And, plagiarism inspection systems were prepared to establish an education support model using ChatGPT and ChatGPT. Based on this, a university education support model using ChatGPT was constructed. The education model using ChatGPT established an education model based on text, digital, and art, and then composed of detailed strategies necessary for the era of the 4th industrial revolution below it. In addition, it was configured to guide students to use ChatGPT within the permitted range by using the ChatGPT detection function provided by the plagiarism inspection system, after the instructor of the class determined the allowable range of content generated by ChatGPT according to the learning goal. By linking and utilizing ChatGPT and the plagiarism inspection system in this way, it is expected to prevent situations in which ChatGPT's excellent ability is abused in education.

• Information Systems Review
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• v.26 no.1
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• pp.251-268
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• 2024

In the Fourth Industrial Revolution era, global powers are enhancing R&D support to leverage innovations like AI, IoT, and big data for productivity gains and structural economic and social reforms. Yet, the declining R&D budget growth rate and the forecasted sharp cut in South Korea's R&D budget in 2024 highlight the critical need for national R&D performance management system discussions. Diverging from previous studies focused on quantitative analysis of performance determinants, this research utilizes fuzzy set qualitative comparative analysis(fsQCA) to explore the interplay of factors affecting research institutions' outcomes comprehensively. Analyzing data from 2018 to 2022, it examines three outcome types of research institutions, identifying factor combinations crucial for success. By pinpointing these factors' configurations, the study offers institution-specific performance enhancement guidelines and insights for national R&D policy management and performance evaluation efficiency.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.4
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• pp.161-182
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• 2024

The study analyzed the rapidly growing food tech startup in South Korea, focusing on industry classification, core technological domains, investment stages, and growth trajectories. Utilizing the ERIS model, two innovative food tech startups, MyChef and CatchTable, were examined as case studies. Results revealed food tech startups are focusing on information technology and smart distribution technology-oriented solutions rather than traditional food production. This study also found that robotics and AI integration were key technology areas. Analyzing the emergence of food tech startups, investment stages, and cumulative investment amounts based on founding years revealed a trend of scaling operations through rounds of funding, especially after securing SERIES A and B funding. The period between 2014 and 2018 saw a dense concentration of food tech startup establishments, likely influenced by favorable conditions for technological innovation amid the Fourth Industrial Revolution. The high rate of strategic mergers and acquisitions and bankruptcy can be interpreted as the complexity inherent in the food tech industry. The case study of MyChef, which grew into HMR manufacturing, and Wad(CatchTable), which expanded into a restaurant reservation platform, derived the entrepreneurs, resources, industry, and strategic factors that served as success factors for food tech startups. This study has practical implications in that it provides entrepreneurs, investors, and policymakers in the food tech industry with insight and direction to develop strategies in line with market trends and technological changes and promote sustainable growth.