为挖掘复杂环境因素对电力负荷预测效果的影响,提高电力负荷预测精确度,提出了一种基于k-shape时间序列聚类与STL季节趋势分解算法相结合的负荷曲线聚类预测模型(k-shape-seasonal and trend decomposition using loess-gradient boosti...为挖掘复杂环境因素对电力负荷预测效果的影响,提高电力负荷预测精确度,提出了一种基于k-shape时间序列聚类与STL季节趋势分解算法相结合的负荷曲线聚类预测模型(k-shape-seasonal and trend decomposition using loess-gradient boosting decision tree,k-shape-STL-GBDT)。首先分析用户用电时序特征,利用k-shape时间序列聚类算法根据负荷曲线划分用户聚类,其次,使用STL算法将不同簇的负荷数据划分为季节项、趋势项与随机项。然后,结合温度、湿度等影响因素搭建预测模型,以麻省大学smart*可再生能源项目的公开数据集为例进行分析,并与多种主流聚类分解预测模型进行对比。结果表明新提出的模型框架MAPE减少了4%以上,针对短期负荷预测表现出了较好的性能与预测精度。展开更多
A portable shape-shifting mobile robot system named as Amoeba Ⅱ（A-Ⅱ） is developed for the urban search and rescue application. It is designed with three degrees of freedom and two tracked drive systems. This robot...A portable shape-shifting mobile robot system named as Amoeba Ⅱ（A-Ⅱ） is developed for the urban search and rescue application. It is designed with three degrees of freedom and two tracked drive systems. This robot consists of two modular mobile units and a joint unit. The mobile unit is a tracked mechanism to enforce the propulsion of robot. And the joint unit can transform the robot shape to get high environment adaptation. A-Ⅱ robot can not only adapt to the environment but also change its body shape according to the locus space. It behaves two work states including the linear state （named as I state） and the parallel state （named as Ⅱ state）. With the linear state the robot can climb upstairs and go through narrow space such as the pipe, cave, etc. The parallel state enables the robot with high mobility on rough ground. Also, the joint unit can propel the robot to roll in sidewise direction. Two modular A-Ⅱ robots can be connected through jointing common interfaces on the joint unit to compose a stronger shape-shifting robot, which can transform the body into four wheels-driven vehicle. The experimental results validate the adaptation and mobility of A-Ⅱ robot.展开更多
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
文摘为挖掘复杂环境因素对电力负荷预测效果的影响,提高电力负荷预测精确度,提出了一种基于k-shape时间序列聚类与STL季节趋势分解算法相结合的负荷曲线聚类预测模型(k-shape-seasonal and trend decomposition using loess-gradient boosting decision tree,k-shape-STL-GBDT)。首先分析用户用电时序特征,利用k-shape时间序列聚类算法根据负荷曲线划分用户聚类,其次,使用STL算法将不同簇的负荷数据划分为季节项、趋势项与随机项。然后,结合温度、湿度等影响因素搭建预测模型,以麻省大学smart*可再生能源项目的公开数据集为例进行分析,并与多种主流聚类分解预测模型进行对比。结果表明新提出的模型框架MAPE减少了4%以上,针对短期负荷预测表现出了较好的性能与预测精度。
基金National Natural Science Foundation of China(No. 60375029)National Hi-tech Research and Development Program of China(863 Program,No.2006AA04Z254)
文摘A portable shape-shifting mobile robot system named as Amoeba Ⅱ（A-Ⅱ） is developed for the urban search and rescue application. It is designed with three degrees of freedom and two tracked drive systems. This robot consists of two modular mobile units and a joint unit. The mobile unit is a tracked mechanism to enforce the propulsion of robot. And the joint unit can transform the robot shape to get high environment adaptation. A-Ⅱ robot can not only adapt to the environment but also change its body shape according to the locus space. It behaves two work states including the linear state （named as I state） and the parallel state （named as Ⅱ state）. With the linear state the robot can climb upstairs and go through narrow space such as the pipe, cave, etc. The parallel state enables the robot with high mobility on rough ground. Also, the joint unit can propel the robot to roll in sidewise direction. Two modular A-Ⅱ robots can be connected through jointing common interfaces on the joint unit to compose a stronger shape-shifting robot, which can transform the body into four wheels-driven vehicle. The experimental results validate the adaptation and mobility of A-Ⅱ robot.
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.
