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The Innovation Ecosystem and Implications of the Netherlands. (네덜란드의 혁신클러스터정책과 시사점)

  • Kim, Young-woo
    • Journal of Venture Innovation
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    • v.5 no.1
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    • pp.107-127
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    • 2022
  • Global challenges such as the corona pandemic, climate change and the war-on-tech ensure that the demand who the technologies of the future develops and monitors prominently for will be on the agenda. Development of, and applications in, agrifood, biotech, high-tech, medtech, quantum, AI and photonics are the basis of the future earning capacity of the Netherlands and contribute to solving societal challenges, close to home and worldwide. To be like the Netherlands and Europe a strategic position in the to obtain knowledge and innovation chain, and with it our autonomy in relation to from China and the United States insurance, clear choices are needed. Brainport Eindhoven: Building on Philips' knowledge base, there is create an innovative ecosystem where more than 7,000 companies in the High-tech Systems & Materials (HTSM) collaborate on new technologies, future earning potential and international value chains. Nearly 20,000 private R&D employees work in 5 regional high-end campuses and for companies such as ASML, NXP, DAF, Prodrive Technologies, Lightyear and many others. Brainport Eindhoven has a internationally leading position in the field of system engineering, semicon, micro and nanoelectronics, AI, integrated photonics and additive manufacturing. What is being developed in Brainport leads to the growth of the manufacturing industry far beyond the region thanks to chain cooperation between large companies and SMEs. South-Holland: The South Holland ecosystem includes companies as KPN, Shell, DSM and Janssen Pharmaceutical, large and innovative SMEs and leading educational and knowledge institutions that have more than Invest €3.3 billion in R&D. Bearing Cores are formed by the top campuses of Leiden and Delft, good for more than 40,000 innovative jobs, the port-industrial complex (logistics & energy), the manufacturing industry cluster on maritime and aerospace and the horticultural cluster in the Westland. South Holland trains thematically key technologies such as biotech, quantum technology and AI. Twente: The green, technological top region of Twente has a long tradition of collaboration in triple helix bandage. Technological innovations from Twente offer worldwide solutions for the large social issues. Work is in progress to key technologies such as AI, photonics, robotics and nanotechnology. New technology is applied in sectors such as medtech, the manufacturing industry, agriculture and circular value chains, such as textiles and construction. Being for Twente start-ups and SMEs of great importance to the jobs of tomorrow. Connect these companies technology from Twente with knowledge regions and OEMs, at home and abroad. Wageningen in FoodValley: Wageningen Campus is a global agri-food magnet for startups and corporates by the national accelerator StartLife and student incubator StartHub. FoodvalleyNL also connects with an ambitious 2030 programme, the versatile ecosystem regional, national and international - including through the WEF European food innovation hub. The campus offers guests and the 3,000 private R&D put in an interesting programming science, innovation and social dialogue around the challenges in agro production, food processing, biobased/circular, climate and biodiversity. The Netherlands succeeded in industrializing in logistics countries, but it is striving for sustainable growth by creating an innovative ecosystem through a regional industry-academic research model. In particular, the Brainport Cluster, centered on the high-tech industry, pursues regional innovation and is opening a new horizon for existing industry-academic models. Brainport is a state-of-the-art forward base that leads the innovation ecosystem of Dutch manufacturing. The history of ports in the Netherlands is transforming from a logistics-oriented port symbolized by Rotterdam into a "port of digital knowledge" centered on Brainport. On the basis of this, it can be seen that the industry-academic cluster model linking the central government's vision to create an innovative ecosystem and the specialized industry in the region serves as the biggest stepping stone. The Netherlands' innovation policy is expected to be more faithful to its role as Europe's "digital gateway" through regional development centered on the innovation cluster ecosystem and investment in job creation and new industries.

Changes in Textural Properties of Korean Radish and relevant Chemical, Enzymatic Activities during Salting (염장과정 중 무의 조직감과 이와 관련된 화학적, 효소활성 변화)

  • Rhee, Hee-Seoup;Lee, Gui-Ju
    • Journal of the Korean Society of Food Culture
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    • v.8 no.3
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    • pp.267-274
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    • 1993
  • This study was aimed to investigate the changes in textural properties of Korean radish and relevant chemical, enzymatic activities during salting. During salting, pH was decreased and total acidity was increased. The maximum compression and puncture forces of Korean radish were decreased significantly whereas cutting force was increased. From the force-distance curves, the break point and maximum force point disappeared in salted Korean radish whereas these appeared apparently in fresh one. Also, the number of peak obtained by three types of test from salted Korean radish was decreased. Hot water soluble pectin and 0.4% Na-hexametaphosphate soluble pectin were increased whereas 0.05 N-HCl soluble pectin were decreased significantly. Polygalacturonase activity were increased in Korean radish solid(RS) and Korean radish juice(RJ) until 4 days of salting. Pectin esterase activity were decreased in RS and RJ. Cx-cellulase activity did not appear initially, however, they began to show their activities after 2 days of salting and were increased in RJ although it was low.

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Application and Analysis of Remote Sensing Data for Disaster Management in Korea - Focused on Managing Drought of Reservoir Based on Remote Sensing - (국가 재난 관리를 위한 원격탐사 자료 분석 및 활용 - 원격탐사기반 저수지 가뭄 관리를 중심으로 -)

  • Kim, Seongsam;Lee, Junwoo;Koo, Seul;Kim, Yongmin
    • Korean Journal of Remote Sensing
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    • v.38 no.6_3
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    • pp.1749-1760
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    • 2022
  • In modern society, human and social damages caused by natural disasters and frequent disaster accidents have been increased year by year. Prompt access to dangerous disaster sites that are inaccessible or inaccessible using state-of-the-art Earth observation equipment such as satellites, drones, and survey robots, and timely collection and analysis of meaningful disaster information. It can play an important role in protecting people's property and life throughout the entire disaster management cycle, such as responding to disaster sites and establishing mid-to long-term recovery plans. This special issue introduces the National Disaster Management Research Institute (NDMI)'s disaster management technology that utilizes various Earth observation platforms, such as mobile survey vehicles equipped with close-range disaster site survey sensors, drones, and survey robots, as well as satellite technology, which is a tool of remote earth observation. Major research achievements include detection of damage from water disasters using Google Earth Engine, mid- and long-term time series observation, detection of reservoir water bodies using Sentinel-1 Synthetic Aperture Radar (SAR) images and artificial intelligence, analysis of resident movement patterns in case of forest fire disasters, and data analysis of disaster safety research. Efficient integrated management and utilization plan research results are summarized. In addition, research results on scientific investigation activities on the causes of disasters using drones and survey robots during the investigation of inaccessible and dangerous disaster sites were described.

A Study on the evaluation technique rubric suitable for the characteristics of digital design subject (디지털 디자인 과목의 특성에 적합한 평가기법 루브릭에 관한 연구)

  • Cho, Hyun Kyung
    • The Journal of the Convergence on Culture Technology
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    • v.9 no.6
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    • pp.525-530
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    • 2023
  • Digital drawing subjects require the subdivision of evaluation elements and the graduality of evaluation according to the recent movement of the innovative curriculum. The purpose of this paper is to present the criteria for evaluating the drawing and to propose it as a rubric evaluation. In the text, criteria for beginner evaluation were technical skills such as the accuracy and consistency of the line, the ratio and balance of the picture, and the ability to effectively utilize various brushes and tools at the intermediate levels. In the advanced evaluation section, it is a part of a new perspective or originality centered on creativity and originality, and a unique perspective or interpretation of a given subject. In addition, as an understanding of design principles, the evaluation of completeness was derived focusing on the ability to actively utilize various functions of digital drawing software through design principles such as placement, color, and shape. The importance of introducing rubric evaluation is to allow instructors to make objective and consistent evaluations, and the key to research in rubric evaluation in these art subjects is to help learners clearly grasp their strengths and weaknesses, and learners can identify what needs to be improved and develop better drawing skills accordingly through feedback on each item.

A Study on Wearable Augmented Reality-Based Experiential Content: Focusing on AR Stone Tower Content (착용형 증강현실 기반 체험형 콘텐츠 연구: AR 돌탑 콘텐츠를 중심으로)

  • Inyoung Choi;Hieyong Jeong;Choonsung Shin
    • Smart Media Journal
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    • v.13 no.4
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    • pp.114-123
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    • 2024
  • This paper proposes AR stone tower content, an experiential content based on wearable augmented reality (AR). Although wearable augmented reality is gaining attention, the acceptance of the technology is still focused on specialized applications such as industrial sites. On the other hand, the proposed AR stone tower content is based on the material of 'stone tower' so that general users can relate to it and easily participate in it, and it is organized to utilize space in a moving environment and find and stack stones based on natural hand gestures. The proposed AR stone tower content was implemented in the HoloLens 2 environment and evaluated by general users through a pilot exhibition in a small art museum. The evaluation results showed that the overall satisfaction with the content averaged 3.85, and the content appropriateness for the stone tower material was very high at 4.15. In particular, users were highly satisfied with content comprehension and sound, but somewhat less satisfied with object recognition, body adaptation, and object control. The above user evaluations confirm the resonance and positive response to the material, but also highlight the difficulties of the average user in experiencing and interacting with the wearable AR environment.

Deep Learning-based Fracture Mode Determination in Composite Laminates (복합 적층판의 딥러닝 기반 파괴 모드 결정)

  • Muhammad Muzammil Azad;Atta Ur Rehman Shah;M.N. Prabhakar;Heung Soo Kim
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.37 no.4
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    • pp.225-232
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    • 2024
  • This study focuses on the determination of the fracture mode in composite laminates using deep learning. With the increase in the use of laminated composites in numerous engineering applications, the insurance of their integrity and performance is of paramount importance. However, owing to the complex nature of these materials, the identification of fracture modes is often a tedious and time-consuming task that requires critical domain knowledge. Therefore, to alleviate these issues, this study aims to utilize modern artificial intelligence technology to automate the fractographic analysis of laminated composites. To accomplish this goal, scanning electron microscopy (SEM) images of fractured tensile test specimens are obtained from laminated composites to showcase various fracture modes. These SEM images are then categorized based on numerous fracture modes, including fiber breakage, fiber pull-out, mix-mode fracture, matrix brittle fracture, and matrix ductile fracture. Next, the collective data for all classes are divided into train, test, and validation datasets. Two state-of-the-art, deep learning-based pre-trained models, namely, DenseNet and GoogleNet, are trained to learn the discriminative features for each fracture mode. The DenseNet models shows training and testing accuracies of 94.01% and 75.49%, respectively, whereas those of the GoogleNet model are 84.55% and 54.48%, respectively. The trained deep learning models are then validated on unseen validation datasets. This validation demonstrates that the DenseNet model, owing to its deeper architecture, can extract high-quality features, resulting in 84.44% validation accuracy. This value is 36.84% higher than that of the GoogleNet model. Hence, these results affirm that the DenseNet model is effective in performing fractographic analyses of laminated composites by predicting fracture modes with high precision.