• Title/Summary/Keyword: spatial optimization

검색결과 418건 처리시간 0.024초

Optimization of settlement layout based on parametric generation

  • Song, Jinghua;Xie, Xinqin;Yu, Yang
    • Advances in Computational Design
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    • 제3권1호
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    • pp.35-47
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    • 2018
  • Design of settlement space is a complicated process while reasonable spatial layout bears great significance on the development and resource allocation of a settlement. The study proposes a weighted L-system generation algorithm based on CA (Cellular Automation) model which tags the spatial attributes of cells through changes in their state during the evolution of CA and thus identifies the spatial growth mode of a settlement. The entrance area of the Caidian Botanical and Animal Garden is used a case study for the model. A design method is proposed which starts from the internal logics of spatial generation, explores possibility of spatial rules and realizes the quantitative analysis and dynamic control of the design process. Taking a top-down approach, the design method takes into account the site information, studies the spatial generation mechanism of settlements and further presents a engine for the generation of multiple layout proposals based on different rules. A optimal solution is acquired using GA (Genetic Algorithm) which generates a settlement spatial layout carrying site information and dynamically linked to the surround environment. The study aims to propose a design method to optimize the spatial layout of the complex settlement system based on parametric generation.

대용량 데이터의 분산 처리를 위한 클라우드 컴퓨팅 환경 최적화 및 성능평가 (Optimization and Performance Analysis of Cloud Computing Platform for Distributed Processing of Big Data)

  • 홍승태;신영성;장재우
    • Spatial Information Research
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    • 제19권4호
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    • pp.55-71
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    • 2011
  • 최근 IT 분야에서 인터넷을 기반으로 IT 자원들을 서비스 형태로 제공하는 클라우드 컴퓨팅에 대한 관심이 증대되고 있으며, 이에 따라 대규모 데이터를 수많은 서버들에 분산 저장하고 관리하기 위한 분산 데이터 처리 기법에 대한 연구가 활발히 진행되고 있다. 한편 GIS 기술의 성장과 더불어 급격히 증가하고 있는 공간 데이터를 효율적으로 활용하기 위해서는, 클라우드 컴퓨팅을 이용한 대용량 공간데이터의 분산 처리가 필수적이다. 이를 위해 본 논문에서는 대표적인 분산 데이터 처리 기법에 대해 살펴보고, 분산 데이터 처리 기법 성능 개선을 위한 최적화 요구사항을 분석한다. 마지막으로 Hadoop 기반 클러스터를 구축하고 이를 통해서 분산 데이터 처리 기법의 성능 최적화에 대한 성능평가를 수행한다.

A hierarchical Bayesian model for spatial scaling method: Application to streamflow in the Great Lakes basin

  • Ahn, Kuk-Hyun
    • 한국수자원학회:학술대회논문집
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    • 한국수자원학회 2018년도 학술발표회
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    • pp.176-176
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    • 2018
  • This study presents a regional, probabilistic framework for estimating streamflow via spatial scaling in the Great Lakes basin, which is the largest lake system in the world. The framework follows a two-fold strategy including (1) a quadratic-programming based optimization model a priori to explore the model structure, and (2) a time-varying hierarchical Bayesian model based on insights found in the optimization model. The proposed model is developed to explore three innovations in hierarchical modeling for reconstructing historical streamflow at ungaged sites: (1) information of physical characteristics is utilized in spatial scaling, (2) a time-varying approach is introduced based on climate information, and (3) heteroscedasticity in residual errors is considered to improve streamflow predictive distributions. The proposed model is developed and calibrated in a hierarchical Bayesian framework to pool regional information across sites and enhance regionalization skill. The model is validated in a cross-validation framework along with four simpler nested formulations and the optimization model to confirm specific hypotheses embedded in the full model structure. The nested models assume a similar hierarchical Bayesian structure to our proposed model with their own set of simplifications and omissions. Results suggest that each of three innovations improve historical out-of-sample streamflow reconstructions although these improvements vary corrsponding to each innovation. Finally, we conclude with a discussion of possible model improvements considered by additional model structure and covariates.

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TABU 알고리즘을 이용한 대공간 구조물의 최적설계 (Optimum Design of the Spatial Structures using the TABU Algorithm)

  • 조용원;이상주;한상을
    • 한국전산구조공학회:학술대회논문집
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    • 한국전산구조공학회 2005년도 춘계 학술발표회 논문집
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    • pp.273-280
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    • 2005
  • The design of structural engineering optimization is to minimize the cost. This problem has many objective functions formulating section and shape as a function of the included discrete variables. simulated annealing, genetic algerian and TABU algerian are searching methods for optimum values. The object of this reserch Is comparing the result of TABU algorithm, and verifying the efficiency of TABU algorithm in structural optimization design field. For the purpose, this study used a solid truss of 25 elements having 10 nodes, and size optimization for each constraint and load condition of Geodesic ome, and shape optimization of Cable Dome for verifying spatial structures by the application of TABU algorithm.

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효율적 구조최적화를 위한 유전자 알고리즘의 방향벡터 (Direction Vector for Efficient Structural Optimization with Genetic Algorithm)

  • 이홍우
    • 한국공간구조학회논문집
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    • 제8권3호
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    • pp.75-82
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    • 2008
  • 본 연구에서는 방향벡터(direction vector)를 이용한 지역 탐색법과 유전자 알고리즘을 결합한 새로운 알고리즘인 D-GA를 제안한다. 새로운 개체(individual)를 찾기 위한 방향벡터로는 진화과정 중에 습득되는 정보를 활용하기 위한 학습방향벡터(Loaming direction vector)와 진화와는 무관하게 한 개체의 주변을 탐색하는 랜덤방향벡터(random direction vector) 등 두 가지를 구성하였다. 그리고, 10 부재 트러스 설계 문제에 단순 유전자 알고리즘과 D-GA를 적용하여 최적화를 수행하였고, 그 결과를 비교 검토함으로써 단순 GA에 비하여 D-GA의 정확성 및 효율성이 향상되었음을 확인하였다.

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위상 최적화를 위한 생산적 적대 신경망 기반 데이터 증강 기법 (GAN-based Data Augmentation methods for Topology Optimization)

  • 이승혜;이유진;이기학;이재홍
    • 한국공간구조학회논문집
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    • 제21권4호
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    • pp.39-48
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    • 2021
  • In this paper, a GAN-based data augmentation method is proposed for topology optimization. In machine learning techniques, a total amount of dataset determines the accuracy and robustness of the trained neural network architectures, especially, supervised learning networks. Because the insufficient data tends to lead to overfitting or underfitting of the architectures, a data augmentation method is need to increase the amount of data for reducing overfitting when training a machine learning model. In this study, the Ganerative Adversarial Network (GAN) is used to augment the topology optimization dataset. The produced dataset has been compared with the original dataset.

A Form-finding Technique for Three-dimensional Spatial Structures

  • Lee, Sang Jin
    • Architectural research
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    • 제15권4호
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    • pp.207-214
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    • 2013
  • A form-finding technique is proposed for three-dimensional spatial structures. Two-step discrete finite element (FE) mesh generator based on computer aided geometric design (CAGD) is introduced and used to control the shape of three-dimensional spatial structures. Mathematical programming technique is adopted to search new forms (or shapes) of spatial structures. For this purpose, the strain energy is introduced as the objective function to be minimized and the initial volume (or the initial weight) is considered as constraint function. Numerical examples are carried out to test the capability of the proposed form-finding techniques and provided as benchmark tests.

공간최적화 모델을 활용한 환경계획의 공간화 방안 (Suggestion for Spatialization of Environmental Planning Using Spatial Optimization Model)

  • 윤은주;이동근;허한결;성현찬
    • 한국환경복원기술학회지
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    • 제21권2호
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    • pp.27-38
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    • 2018
  • Environmental planning includes resource allocation and spatial planning process for the conservation and management of environment. Because the spatialization of the environmental planning is not specifically addressed in the relevant statutes, it actually depends on the qualitative methodology such as expert judgement. The results of the qualitative methodology have the advantage that the accumulated knowledge and intuition of the experts can be utilized. However, it is difficult to objectively judge whether it is enough to solve the original problem or whether it is the best of the possible scenarios. Therefore, this study proposed a methodology to quantitatively and objectively spatialize various environmental planning. At first, we suggested a quantitative spatial planning model based on an optimization algorithm. Secondly, we applied this model to two kinds of environmental planning and discussed about the model performance to present the applicability. Since the models were developed based on conceptual study site, there was a limitation in showing possibility of practical use. However, we expected that this study can contribute to the fields related to environmental planning by suggesting flexible and novel methodology.