• Proceedings of the Korea Information Processing Society Conference
• /
• 2021.11a
• /
• pp.104-106
• /
• 2021

도시의 인구 밀집도가 증가함에 따라 도시의 단위 면적당 건물 밀집도 역시 증가하고 있으며, 이에 도시 화재는 대규모 화재로 발전할 가능성이 높다. 도시 내 대규모 화재로 인한 인명 및 경제적인 피해를 최소화하기 위해 시뮬레이션 기반의 화재 대응 방안들이 널리 연구되고 있으며, 최근에는 시뮬레이션에서 효과적인 화재 대응 방안을 탐색하기 위해 강화학습 기술을 활용하는 연구들이 소개되고 있다. 그러나, 시뮬레이션의 규모가 커지는 경우, 상태 정보 및 화재 대응을 위한 행위 공간의 크기가 증가함으로 인해 강화학습의 복잡도가 증가하며, 이에 따라 학습 확장성이 저하되는 문제가 발생한다. 본 논문에서는 시뮬레이션 규모 증가 시 강화학습의 학습 확장성을 유지하기 위해, 화재 상황 정보와 재난 대응을 위한 행위 공간을 변환하는 기법을 제안한다. 실험 결과를 통해 기존에 강화학습 모델의 학습이 어려웠던 대규모 도시 재난시뮬레이션에서 본 기법을 적용한 강화학습 모델은 학습 수행이 가능하였으며, 화재 피해가 없는 상황의 적합도를 100%로 하고, 이것 대비 99.2%의 화재 대응 적합도를 달성했다.

• The Journal of Korean Association of Computer Education
• /
• v.10 no.4
• /
• pp.61-71
• /
• 2007

The Lifelong Learning City Project whose aim is to encourage regional innovation and development by the Ministry of Education & HRD. As of in 2006, there are 57 Lifelong Learning Cities which are providing its citizens with various lifelong learning programs since the project got started in 2001. The Lifelong Learning City Project has been increasing across the nation since it has been regarded as a key strategy for the sustainable reg ional development. Accordingly, there has been a lot of discussion on how to develop the project qualitatively as well as quantitatively. In this context, this study aims to draw policy implications of the Cyber Network System for the lifelong learning cities. With this context, this paper suggests to set up the Cyber Network System as the developmental strategy of learning cities and intends to search how to structure the Cyber network System. For this purpose, we survey the learners' needs and suggest the directions for the further Cyber network System of the Learning Cities.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.38 no.1
• /
• pp.141-148
• /
• 2018

In recent years, there has been a movement to create a learning city where people can learn and enjoy what they want whenever, wherever, and whenever, so that the self-realization of individuals and the quality of life can be enhanced to improve the competitiveness of the city as a whole, It is becoming active. Many developed countries in the world are supporting projects to build learning cities by utilizing schools and public facilities, thereby providing local residents with opportunities for self-growth and solving community problems. In Korea, too, there are various programs using idle facilities. However, there is a lack of education programs for local residents and learning programs by partnership with local communities. It is when spatial and software strategies are needed to build a successful learning city. Therefore, we want to systematically organize the spatial data of the facilities that can be learned, analyze the current problems, and explore various ways to utilize them. We also analyze the programs that residents need to implement real and efficient learning cities.

• Korean Journal of Comparative Education
• /
• v.26 no.4
• /
• pp.105-124
• /
• 2016

This research aims to analyse the features and meaning of students' participation in lifelong learning city project in Germany. Lifelong learning city project has been running for several years to develop a new learning culture in the grass root level. In Germany, many students in elementary, middle and high school have actively participated in diverse lifelong learning projects such as learning support, career development, community participation. This case study shows us to reshape a new model of lifelong learning and widen the range of target group for lifelong learning in Korea. Student participation can enrich the quality of lifelong learning city project, which could make a significant contribution to embodying the lifelong learning society by creating a new learning culture.

• Electronics and Telecommunications Trends
• /
• v.34 no.4
• /
• pp.1-14
• /
• 2019

Recent trends in deep reinforcement learning (DRL) have revealed the considerable improvements to DRL algorithms in terms of performance, learning stability, and computational efficiency. DRL also enables the scenarios that it covers (e.g., partial observability; cooperation, competition, coexistence, and communications among multiple agents; multi-task; decentralized intelligence) to be vastly expanded. These features have cultivated multi-agent reinforcement learning research. DRL is also expanding its applications from robotics to natural language processing and computer vision into a wide array of fields such as finance, healthcare, chemistry, and even art. In this report, we briefly summarize various DRL techniques and research directions.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.97-97
• /
• 2022

AI 모형을 적용한 도시지역 침수예측에 대한 연구는 꾸준히 수행되어 왔다. AI 모형을 이용해 도시침수예측을 하기 위해서는 모형에 강우자료를 학습시키게 되는데, 시계열 강우분포 자료를AI 모형의 학습자료로 사용하기에 자료의 양이 너무 많기 때문에 총 강우량만을 이용하여 도시침수예측을 수행한 바 있다(Kim et al., 2021). 하지만 총 강우량만을 AI 모형에 학습시킬 경우, 지속기간 동안 강우가 고르게 분포하는지 불규칙적으로 분포하는지에 대한 정보가 포함되지 않았기 때문에 침수예측력이 떨어질 수 있다. 따라서 본 연구에서는 시계열 강우자료의 통계치를 산정하여 AI 모형에 학습시킴으로써 강우분포특성을 고려한 침수예측을 통해 예측력을 높이고자 한다. 총 강우량만을 학습시킬 경우, 같은 지속시간에 같은 양의 강우가 내리더라도 고른 분포를 가진 강우에 의해서는 실제 침수는 작게 일어나므로 과대예측을, 전체 지속시간 중 특정 시간대에 편향된 분포를 가진 강우에 의해서는 실제 침수가 크게 일어나므로 과소예측을 하는 문제가 발생할 수 있다. 따라서 표준편차를 평균 강우량으로 나눈 값인 변동계수, 강우분포의 뾰족한 정도를 나타내는 첨도, 평균값에 대해 어느 방향으로 비대칭인지를 나타내는 왜도 값을 추가로 학습시킴으로써 시계열 강우자료 전체를 학습시키지 않고도 강우분포를 학습시키지 않았을 때 발생하는 과소·과대예측 문제를 해결할 수 있다. 또한 변동계수 대신 표준편차를 학습시키는 모형, 변동계수와 표준편차를 모두 학습시키지 않는 모형, 변동계수와 표준편차를 모두 학습시키는 모형과의 침수예측 결과 비교를 통해 표준편차와 변동계수 중 어떤 통계치를 학습시키는 것이 적합한지와 비슷한 통계치 자료를 모두 학습시켰을 때의 과적합 문제 등에 대한 결론를 얻을 수 있다.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.36 no.1
• /
• pp.151-156
• /
• 2016

Making a learning city which allows unlimited accessibility to a chance of learning plays an important role to accomplish individuals'self-realizaton and advance quality of life so that improve whole competition of the city. Although securing enough space is necessary to realize the learning city, our reality has an imbalanced city structure which could hamper it. The purpose of this study is to examine the way to resolve the spatial imbalance by utilizing unused facilities located in elementary schools. The resulting conclusions provide implications that current low effectiveness is originated from passive participation of schools and leading better participation is needed to improve it.

• 열린충남
• /
• s.36
• /
• pp.90-110
• /
• 2006

• Journal of the Korean association of regional geographers
• /
• v.16 no.5
• /
• pp.572-589
• /
• 2010

With the advent of lifelong learning society, most of the cities in Korea have been promoting the construction of lifelong learning city. At this point, the purpose of this study is to configure regional learning programs and apply them to the actual world. Important results of this study can be outlined as follows; First, the regional learning programs in the lifelong learning society are desirable to be configurated with the situated learning method focused on the characteristics of learners, instructors and the local contexts. In addition, the active training of regional human resources such as organizing learning circles and SERVICE-LEARNING through the participation of them is effective as a teaching method. Secondly, the consequence through the field excursion calling itself 'the geography exploration group', organized by the researcher and involved with participants together provoked the paradigm shift on their region and the regional identity. Thirdly, the active participation of local experts including geography teachers should be needed for the popularization of geography education. Hence, if we continue to develop the local learning programs tailored to regional traits and apply them to the actual world, geography education will play an important role to build the stable foundation in the lifelong learning society.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2021.05a
• /
• pp.441-442
• /
• 2021

본 논문에서는 고해상도의 시계열 위성영상을 딥러닝 알고리즘으로 학습하여 도시 변화탐지를 수행한다. 고해상도 위성영상을 활용한 서비스는 4 차 산업혁명 융합 신사업 중 하나인 스마트시티에 적용하여 도시 노후화, 교통 혼잡, 범죄 등 다양한 도시 문제 해결 및 효율적인 도시를 구축하는데 활용이 가능하다. 이에 본 연구에서는 도시 변화탐지를 위한 딥러닝 알고리즘으로 DeepLabV3+를 사용한다. 이는 인코더-디코더 구조로, 공간 정보를 점진적으로 회복함으로써 더욱 정확한 물체의 경계면을 찾을 수 있다. 제안하는 방법은 DeepLabV3+의 레이어와 loss function 을 수정하여 기존보다 좋은 결과를 얻었다. 객관적인 성능평가를 위해, 공개된 데이터셋 LEVIR-CD 으로 학습한 결과로 평균 IoU 는 0.87, 평균 Dice 는 0.93 을 얻었다.