A contour shape descriptor based on discrete Fourier transform （DFT） and a K-means al- gorithm modified self-organizing feature map （SOFM） neural network are established for shape clus- tering. The given shape is ...A contour shape descriptor based on discrete Fourier transform （DFT） and a K-means al- gorithm modified self-organizing feature map （SOFM） neural network are established for shape clus- tering. The given shape is first sampled uniformly in the polar coordinate. Then the discrete series is transformed to frequency domain and constructed to a shape characteristics vector. Firstly, sample set is roughly clustered using SOFM neural network to reduce the scale of samples. K-means algo- rithm is then applied to improve the performance of SOFM neural network and process the accurate clustering. K-means algorithm also increases the controllability of the clustering. The K-means algo- rithm modified SOFM neural network is used to cluster the shape characteristics vectors which is previously constructed. With leaf shapes as an example, the simulation results show that this method is effective to cluster the contour shapes.展开更多
Spatial evolution in ancient Chinese villages is always one of the most interesting research topics in the field of architectural design, urban planning and history of architecture. Xi-di village exemplifies tradition...Spatial evolution in ancient Chinese villages is always one of the most interesting research topics in the field of architectural design, urban planning and history of architecture. Xi-di village exemplifies traditional settlements in ancient China, For many years, numerous researchers have explored its built form, origin and evolution process from different perspectives. This paper attempts to position the spatial evolution process of this village in the context of complex system theory, which views the process of space self-organization as a form of disequilibrium and nonlinear development process. Through analyzing the mechanism of village space changes, we develop the dynamic evolution modeling based on the theory of cellular automata. The purpose of the paper is to provide a new perspective for the conventional architectural research of space self-organization.展开更多
基金Supported by Guangdong Province Key Science and TechnologyItem(2011A010801005,2010A080402015)the National NaturalScience Foundation of China(61171142)
文摘A contour shape descriptor based on discrete Fourier transform （DFT） and a K-means al- gorithm modified self-organizing feature map （SOFM） neural network are established for shape clus- tering. The given shape is first sampled uniformly in the polar coordinate. Then the discrete series is transformed to frequency domain and constructed to a shape characteristics vector. Firstly, sample set is roughly clustered using SOFM neural network to reduce the scale of samples. K-means algo- rithm is then applied to improve the performance of SOFM neural network and process the accurate clustering. K-means algorithm also increases the controllability of the clustering. The K-means algo- rithm modified SOFM neural network is used to cluster the shape characteristics vectors which is previously constructed. With leaf shapes as an example, the simulation results show that this method is effective to cluster the contour shapes.
文摘Spatial evolution in ancient Chinese villages is always one of the most interesting research topics in the field of architectural design, urban planning and history of architecture. Xi-di village exemplifies traditional settlements in ancient China, For many years, numerous researchers have explored its built form, origin and evolution process from different perspectives. This paper attempts to position the spatial evolution process of this village in the context of complex system theory, which views the process of space self-organization as a form of disequilibrium and nonlinear development process. Through analyzing the mechanism of village space changes, we develop the dynamic evolution modeling based on the theory of cellular automata. The purpose of the paper is to provide a new perspective for the conventional architectural research of space self-organization.
