• Agribusiness and Information Management
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• v.1 no.2
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• pp.23-42
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• 2009

As technology develops, more advanced technologies involving GPS, GIS, RFID and sensor networks have been adopted in agriculture sector for u-Farm. However, technology adoptions have been evaluated as ineffective. Farmers and agri-business have low level of understanding on technology so it is not efficiently utilized. This study introduces a case of RFID/sensor networks of mushroom farm as a u-Farm case study, focusing on developing a framework for analysis of u-Farm investment returns. RFID and sensor networks improve real-time production control, processing management, and traceability. Integration of RFID and sensor networks leads to innovation into the mushroom farm, reducing labor cost, increasing productivity, and improving quality of the mushroom. The ROI which is used as an indicator of performance indicator is 413%.

• Transactions of the Korean hydrogen and new energy society
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• v.33 no.4
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• pp.318-329
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• 2022

This study analyzed keyword networks and Author's Affiliation networks of hydrogen-related papers published in Korea Citation Index (KCI) journals from 2016 to 2020. The study investigated co-occurrence patterns of institutions over time to examine collaboration trends of hydrogen scholars. The study also conducted frequency analysis of keyword networks to identify key topics and visualized keyword networks to explore topic trends. The result showed Collaborative research between institutions has not yet been extensively expanded. However, collaboration trends were much more pronounced with local universities. Keyword network analysis exhibited continuing diversification of topics in hydrogen research of Korea. In addition centrality analysis found hydrogen research mostly deals with multi-disciplinary and complex aspects like hydrogen production, transportation, and public policy.

• Journal of Welding and Joining
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• v.11 no.2
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• pp.27-41
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• 1993

The estimation of nugget sizes was attempted by utilizing the artificial neural networks method. Artificial neural networks is a highly simplified model of the biological nervous system. Artificial neural networks is composed of a large number of elemental processors connected like biological neurons. Although the elemental processors have only simple computation functions, because they are connected massively, they can describe any complex functional relationship between an input-output pair in an autonomous manner. The electrode head movement signal, which is a good indicator of corresponding nugget size was determined by measuring the each test specimen. The sampled electrode movement data and the corresponding nugget sizes were fed into the artificial neural networks as input-output pairs to train the networks. In the training phase for the networks, the artificial neural networks constructs a fuctional relationship between the input-output pairs autonomusly by adjusting the set of weights. In the production(estimation) phase when new inputs are sampled and presented, the artificial neural networks produces appropriate outputs(the estimates of the nugget size) based upon the transfer characteristics learned during the training mode. Experimental verification of the proposed estimation method using artificial neural networks was done by actual destructive testing of welds. The predicted result by the artifficial neural networks were found to be in a good agreement with the actual nugget size. The results are quite promising in that the real-time estimation of the invisible nugget size can be achieved by analyzing the process variable without any conventional destructive testing of welds.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.107-110
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• 2000

In this paper, we propose a method of constructing neural networks using bio-inspired emergent and evolutionary concepts. This method is algorithm that is based on the characteristics of the biological DNA and growth of plants. Here is, we propose a constructing method to make a DNA coding method for production rule of L-system. L-system is based on so-called the parallel rewriting mechanism. The DNA coding method has no limitation in expressing the production rule of L-system. Evolutionary algorithms motivated by Darwinian natural selection are population based searching methods and the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it to one step ahead prediction of Mackey-Glass time series.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.124-125
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• 2023

Defect detection during liquid crystal display (LCD) manufacturing has always been a critical challenge. This study aims to address this issue by proposing a data augmentation method based on generative adversarial networks (GAN) to improve defect identification accuracy in LCD production. By leveraging synthetically generated image data from GAN, we effectively augment the original dataset to make it more representative and diverse. This data augmentation strategy enhances the model's generalization capability and robustness on real-world data. Compared to traditional data augmentation techniques, the synthetic data from GAN are more realistic, diverse and broadly distributed. Experimental results demonstrate that training models with GAN-generated data combined with the original dataset significantly improves the detection accuracy of critical defects in LCD manufacturing, compared to using the original dataset alone. This study provides an effective data augmentation approach for intelligent quality control in LCD production.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.197-200
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• 1996

This paper considers feedback error learning neural networks for robot manipulator control. Feedback error learning proposed by Kawato [2,3,5] is a useful learning control scheme, if nonlinear subsystems (or basis functions) consisting of the robot dynamic equation are known exactly. However, in practice, unmodeled uncertainties and disturbances deteriorate the control performance. Hence, we presents a robust feedback error learning scheme which add robustifying control signal to overcome such effects. After the learning rule is derived, the stability is analyzed using Lyapunov method.

• East Asian Economic Review
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• v.17 no.1
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• pp.55-79
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• 2013

The objective of this article is to explore the economic relationship between China and the surrounding dynamic Asian economies. It delves into China's influence over the Asian economies and whether this relationship is a market-led or de facto symbiosis. The three principal channels of regional integration analyzed in this article are trade, FDI and vertically integrated production networks. They are essentially based on the activities of the private-sector in these economies. China methodically expanded and deepened its economic ties with the regional neighbors. At the present juncture, China's integration with the surrounding Asia is deep. Another issue that this article explores is the so-called China "threat" or "fear" in Asia. It implies that China is crowding out exports of the other Asian economies in the world market place. Also, as China has become the most attractive FDI destination among the developing countries, it is apprehended that China is receiving FDI at the expense of the Asian economies. These concerns were examined by several empirical studies, and the inference is that they are exaggerated. This article concludes that the private-sector business activities in China and other rapidly growing Asian economies were (and are) instrumental in bringing together the production structures and real economies. The result is both convergence and integration among the dynamic Asian economies. Over the years China and its Asian neighbors has developed a close and symbiotic economic relationship and a de facto regional integration.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.617-618
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• 2015

IT convergence with agriculture is expected to bring more efficiency and quality improvement in pre-production stage, Production stage, post-production stage of agricultural products with the aid of information processing and autonomous control technologies of the IT area. This paper describes the actualized convergence service for agriculture, namely Smart Farming as a solution to cope various problems caused by severe conditions or the gap of viewpoints between the people engaged in farming and the IT engineers. In particular this defines service capabilities for Smart Farming, provides a reference model for Smart Farming, and identifies network capabilities required to produce an infrastructure which supports Smart Farming.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.99-105
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• 2021

In addition to the OTT video production service represented by Nexflix and YouTube, a personalized recommendation system for content with artificial intelligence has become common. YouTube's personalized recommendation service system consists of two neural networks, one neural network consisting of a recommendation candidate generation model and the other consisting of a ranking network. Netflix's video recommendation system consists of two data classification systems, divided into content-based filtering and collaborative filtering. As the online platform-led content production is activated by the Corona Pandemic, the field of virtual influencers using artificial intelligence is emerging. Virtual influencers are produced with GAN (Generative Adversarial Networks) artificial intelligence, and are unsupervised learning algorithms in which two opposing systems compete with each other. This study also researched the possibility of developing AI platform based on individual recommendation and virtual influencer (metabus) as a core content of OTT in the future.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.3
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• pp.41-49
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• 2024

In this paper, we validate simulation results for the design optimization of a Superconducting Fault Current Limiter (SFCL) intended for use in Medium Voltage Direct Current systems (MVDC). With the increasing integration of renewable energy and grid connections, researchers are focusing on medium-voltage systems for balancing energy in new and renewable energy networks, rather than traditional transmission or distribution networks. Specifically, for DC distribution networks dealing with fault currents that must be rapidly blocked, current-limiting systems like superconducting current limiters offer distinct advantages over the operation of DC circuit breakers. The development of such superconducting current limiters requires finite element analysis (FEM) and an extensive design process before prototype production and evaluation. To expedite this design process, the design outcomes are assimilated using a Reduced Order Model (ROM). This approach enables the verification of results akin to finite element analysis, facilitating the optimization of design simulations for production and mass production within existing engineering frameworks.