• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.309-309
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• 2018

기후변화에 따른 강우의 규모와 발생빈도 증가로 농촌유역의 홍수 피해는 지속적으로 증가하고 있다. 하지만 우리나라의 홍수 피해 저감 대책은 도시지역의 대하천 주변으로 집중되어있으며, 소하천 및 농촌유역의 홍수 피해 저감에 대한 관리와 투자 노력은 부족한 실정이다. 특히, 최근 들어 갑작스런 집중호우 등으로 인한 농촌유역 돌발홍수 피해 사례가 증가하고 있으며, 이에 대응하기 위해서는 홍수 발생 등을 신속하게 파악하기 위한 돌발홍수 예경보 시스템 개발이 필요하다. 한편, 최근 산업의 혁신과 생산성 향상을 위한 새로운 패러다임으로 4차 산업혁명이 대두되고 있으며, 빅데이터와 인공지능 (Artificial Intelligence, AI)을 비롯하여 사물인터넷 (Internet of Things, IoT), 드론, 슈퍼컴퓨팅 등의 이른바 4차 산업혁명 기술을 활용한 연구가 수행되고 있다. 본 연구에서는 기후변화에 따른 농촌유역 홍수 피해를 저감하고 또한 사전에 대비하기 위해 빅데이터와 인공지능 등 4차 산업혁명 기술을 적용한 농촌유역 돌발홍수 예경보 시스템을 개발하고 그 적용성을 평가하고자 한다. 우선, 농촌유역의 홍수와 관련된 빅데이터 (기상 자료, 수문 자료, 기후변화 자료, 농업용 수리구조물 자료 등)를 토대로 정형 빅데이터와 비정형 빅데이터를 구분 추출하고 이를 연계 해석할 수 있는 시스템을 개발하였다. 추출한 정형 및 비정형 빅데이터를 활용하여 딥러닝을 기반으로 농촌유역의 홍수를 예측하고 홍수 예경보 기준에 따른 평가를 수행할 수 있는 시스템을 개발하였다. 과거 강우사상을 홍수 예경보 시스템에 적용하여 홍수 모의 결과를 도출하였으며, 재해연보 등과 비교 분석하여 시스템의 적용성을 분석하였다.

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

• 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.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.33 no.3
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• pp.95-102
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• 2015

This study conducted the survey on the resources of traditional villages based on AHP in the subjects with the specialists in Korea and China to seek the resource evaluation standards to apply the preservation and development of traditional villages, and the differences of the importance on the resources among the specialists in both countries. We classified three levels of evaluation items to aim the deductions of the importance and priority in the resources of traditional villages. Upon the analysis results, natural resources were important in the level 1; environmental, historical, facility resources were important in the level 2; and the factors such as air, topography, traditional houses, agricultural landscape, shared community facilities, interchanges between urban and rural areas, family activities, and so on were important in the level 3. The factors that both Korean and Chinese groups evaluated as the most important ones were the same. In terms of overall importance by evaluation items, the factors such as air, water quality, noise, traditional houses, topography, shared community facilities, and so on were rated as relatively important in both Korean and Chinese groups. That is, the traditional villages have the necessity to preserve the cultural resources like their duties, however, it is required to control the natural environment with good quality preferentially. This study results can compare the importance on the resources of traditional village between Korea and China. Moreover, with calculation of the priority and scores for the preservation and management of traditional villages, they are expected to be used as the tool to apply the quantitative data in the evaluation process of traditional village resources in both countries.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.21-22
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• 2020

낙동강 하굿둑(이하 하굿둑)은 1987년 부산 사하구와 강서구 사이에 건설되어 하류 지역의 바닷물 유입을 막아 부산, 울산, 경남 등에 안정적으로 생활·농업·공업 등의 분야에 용수를 공급하는 역할을 해왔다. 현재, 하굿둑의 수문은 낙동강 상류로부터 하류로 흘러내려오는 민물(담수)을 방류하기 위해서만 하굿둑 수문을 개방하고 있다. 하구는 하천의 담수와 바다의 염수가 서로 만나는 구역으로 바닷물과 염수의 밀도차에 의한 혼합으로 자연상태의 하구에서는 담수와 염수가 섞이는 기수역이 형성되며, 이러한 특성으로 하구 인근의 지역에서는 일반적인 하천 및 해양, 연안과는 분명히 구별되는 생태계가 조성된다. 하굿둑 건설이후 바닷물(해수)과 민물(담수)이 만나는 낙동강 어귀에 기수생태계가 사라지면서 바닷물이 유입될 수 있도록 하여 생태계를 복원해야 한다는 필요성이 제기되어 왔으며, 하굿둑이 지역에 기여해온 사실은 분명하나 하굿둑으로 인해 생태계 단절이 발생하고 기수생태계가 파괴되었기 때문에 이를 해결하기 위해서는 하굿둑을 개방하여 과거 기수생태계를 복원해야 한다는 목소리가 높아지고 있다. 이에 따라 정부에서는 하굿둑의 기수생태계 복원을 위해서 관계기관 합동으로 의사결정을 하고 효율적인 개방 방안을 모색하는 실무협의회를 구성하여 운영 중이고, 실무협의회 논의를 통해 5개 주요 관계기관(환경부, 국토부, 해양수산부, 부산광역시, K-water) 공동으로 "낙동강하굿둑 운영개선 및 생태복원 방안 연구용역"을 추진 중이다. 2018년 1단계 용역이 완료되었으며, 2019년부터 2단계 연구용역을 추진 중이고 하굿둑 개방의 수준별로 각종 영향을 검토한 후 대책을 마련하여 기수생태계 복원 방안을 수립하는데 그 목적이 있다. 2단계 연구용역에서는 과학적이고 합리적인 기수생태계 복원방안 마련을 위해서 실제로 해수를 유입시키는 3차례의 실증실험 및 수리모형실험 등을 추진한다. 기존 연구들에서도 수문개방에 따른 해수유입 영향에 대해 모델링을 통해서 분석했지만 이는 검증이 이루어지지 않은 결과로 이번 용역에서는 실제 해수를 유입시키고 염분의 침투 및 각종 수생태 영향을 모니터링 한 후 그 결과를 반영하여 모델링을 고도화하고 있다. 최종적으로 고도화된 모델링 결과를 기반으로 기수생태계 조성 방안별로 염분, 수질, 수생태, 침퇴적 등 각종 분야에 대한 정확한 영향을 분석하고 이에 대한 대책을 포함하여 최종적으로 바람직한 기수생태계 복원 방안을 제시할 계획이다. 기수생태계 복원 방안이 계획에만 그치지 않고 실행으로 연결시키기 위해서 필요성에 대한 이해를 바탕으로 공감대를 형성해 나아가고 있으며 지역주민, 전문가, 관계기관 등 민(民)·관(官)·학(學) 다양한 의견을 수렴하여 하구지역내 수량-수질-수생태를 종합적으로 고려하여 복원 방안을 마련 후 사회적인 합의를 추진하여 확정할 예정이며, 하구의 안정적인 관리를 위해 AI 등 4차 산업혁명기술을 적극 적용하는 스마트한 하구물관리(Smart Estuary Watershed Management)"를 활용한 "하구통합물관리" (Estuary Integrated Watershed Management) 등 과학적인 관리를 추진할 계획이다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.11
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• pp.89-96
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• 2024

In this paper, we design an improved weed detection model using YOLOv10, a deep learning-based object detection algorithm. YOLOv10 improves its performance compared to previous versions by adding an attention module, the PSA module. PSA is strong at recognising complex patterns in large areas because it uses some features of its own attention to reduce computation and learn global information. However, it may be inefficient for certain problems, such as weeds, which are generally small objects. Therefore, in this paper, we propose an improved YOLOv10 by applying another attention module, SENet, instead of the PSA module. Since, SENet learns the importance between channels, it can learn the features of weeds in more detail than the PSA module. In addition, SENet is lighter, less computationally intensive, and faster than the PSA module, so we conducted experiments by replacing the PSA module with SENet, which is suitable for weed detection. The experiment consisted of 200 training runs with a total of 14 classes, and we compared the performance through various performance evaluations. The experimental results showed that the FPS increased from 476.19 to 526.32, which is about 9.52% processing speed improvement. The mAP50-95 value increased from 88.7% to 88.3%, which shows that the proposed model is lighter than the existing model and performs similarly to the existing model.