Urban grid power forecasting is one of the important tasks of power system operators, which helps to analyze the development trend of the city. As the demand for electricity in various industries is affected by many f...Urban grid power forecasting is one of the important tasks of power system operators, which helps to analyze the development trend of the city. As the demand for electricity in various industries is affected by many factors, the data of relevant influencing factors are scarce, resulting in great deviations in the accuracy of prediction results. In order to improve the prediction results, this paper proposes a model based on Multi-Target Tree Regression to predict the monthly electricity consumption of different industrial structures. Due to few data characteristics of actual electricity consumption in Shanghai from 2013 to the first half of 2017. Thus, we collect data on GDP growth, weather conditions, and tourism season distribution in various industries in Shanghai, model and train the electricity consumption data of different industries in different months. The multi-target tree regression model was tested with actual values to verify the reliability of the model and predict the monthly electricity consumption of each industry in the second half of 2017. The experimental results show that the model can accurately predict the monthly electricity consumption of various industries.展开更多
Target tracking is very important in computer vision and related areas. It is usually difficult to accurately track fast motion target with appearance variations. Sometimes the tracking algorithms fail for heavy appea...Target tracking is very important in computer vision and related areas. It is usually difficult to accurately track fast motion target with appearance variations. Sometimes the tracking algorithms fail for heavy appearance variations. A multiple template method to track fast motion target with appearance changes is presented under the framework of appearance model with Kalman filter. Firstly, we construct a multiple template appearance model, which includes both the original template and templates affinely transformed from original one. Generally speaking, appearance variations of fast motion target can be covered by affine transformation. Therefore, the affine tr templates match the target of appearance variations better than conventional models. Secondly, we present an improved Kalman filter for approx- imate estimating the motion trail of the target and a modified similarity evaluation function for exact matching. The estimation approach can reduce time complexity of the algorithm and keep accuracy in the meantime. Thirdly, we propose an adaptive scheme for updating template set to alleviate the drift problem. The scheme considers the following differences： the weight differences in two successive frames; different types of affine transformation applied to templates. Finally, experiments demonstrate that the proposed algorithm is robust to appearance varia- tion of fast motion target and achieves real-time performance on middle/low-range computing platform.展开更多
针对北京市2016年12月16～21日的空气重污染过程进行了回报试验,探讨了该次事件预报的目标观测敏感区。使用新一代高分辨率中尺度气象模式(Weather Research Forecasting,WRF)和嵌套网格空气质量模式(Nested Air Quality Prediction Mod...针对北京市2016年12月16～21日的空气重污染过程进行了回报试验,探讨了该次事件预报的目标观测敏感区。使用新一代高分辨率中尺度气象模式(Weather Research Forecasting,WRF)和嵌套网格空气质量模式(Nested Air Quality Prediction Model System,NAQPMS),针对初始气象场的不确定性,通过4套初始场资料识别了影响北京地区细颗粒物(PM2.5)预报水平的目标观测敏感变量及其敏感区。结果表明:当综合考虑初始气象场的风场、温度、比湿不确定性的影响时,发现改善黑龙江区域上述气象要素的初始场精度,对北京地区PM2.5预报不确定的减小最显著;当分别考察风场、温度、比湿的不确定性的影响时,发现初始风场精度的改善,尤其是黑龙江区域风场精度的改善,能够更大程度地减小北京地区PM2.5的预报误差,对北京东南地区的PM2.5预报误差的减小甚至可达到40%以上。因此,优先对黑龙江区域的气象场,尤其是该区域的风场进行目标观测,并将其同化到预报模式的初始场中,将会有效提高初始气象场的质量,进而大大减小北京地区PM2.5浓度的预报误差,提高北京地区空气质量的预报技巧。初始风场代表了北京地区该次空气重污染事件预报的目标观测变量,而黑龙江地区则是该目标观测的敏感区域。展开更多
文摘Urban grid power forecasting is one of the important tasks of power system operators, which helps to analyze the development trend of the city. As the demand for electricity in various industries is affected by many factors, the data of relevant influencing factors are scarce, resulting in great deviations in the accuracy of prediction results. In order to improve the prediction results, this paper proposes a model based on Multi-Target Tree Regression to predict the monthly electricity consumption of different industrial structures. Due to few data characteristics of actual electricity consumption in Shanghai from 2013 to the first half of 2017. Thus, we collect data on GDP growth, weather conditions, and tourism season distribution in various industries in Shanghai, model and train the electricity consumption data of different industries in different months. The multi-target tree regression model was tested with actual values to verify the reliability of the model and predict the monthly electricity consumption of each industry in the second half of 2017. The experimental results show that the model can accurately predict the monthly electricity consumption of various industries.
基金Supported by the National Science Foundation of China(61472289)Hubei Province Science Foundation(2015CFB254)
文摘Target tracking is very important in computer vision and related areas. It is usually difficult to accurately track fast motion target with appearance variations. Sometimes the tracking algorithms fail for heavy appearance variations. A multiple template method to track fast motion target with appearance changes is presented under the framework of appearance model with Kalman filter. Firstly, we construct a multiple template appearance model, which includes both the original template and templates affinely transformed from original one. Generally speaking, appearance variations of fast motion target can be covered by affine transformation. Therefore, the affine tr templates match the target of appearance variations better than conventional models. Secondly, we present an improved Kalman filter for approx- imate estimating the motion trail of the target and a modified similarity evaluation function for exact matching. The estimation approach can reduce time complexity of the algorithm and keep accuracy in the meantime. Thirdly, we propose an adaptive scheme for updating template set to alleviate the drift problem. The scheme considers the following differences： the weight differences in two successive frames; different types of affine transformation applied to templates. Finally, experiments demonstrate that the proposed algorithm is robust to appearance varia- tion of fast motion target and achieves real-time performance on middle/low-range computing platform.
文摘针对北京市2016年12月16～21日的空气重污染过程进行了回报试验,探讨了该次事件预报的目标观测敏感区。使用新一代高分辨率中尺度气象模式(Weather Research Forecasting,WRF)和嵌套网格空气质量模式(Nested Air Quality Prediction Model System,NAQPMS),针对初始气象场的不确定性,通过4套初始场资料识别了影响北京地区细颗粒物(PM2.5)预报水平的目标观测敏感变量及其敏感区。结果表明:当综合考虑初始气象场的风场、温度、比湿不确定性的影响时,发现改善黑龙江区域上述气象要素的初始场精度,对北京地区PM2.5预报不确定的减小最显著;当分别考察风场、温度、比湿的不确定性的影响时,发现初始风场精度的改善,尤其是黑龙江区域风场精度的改善,能够更大程度地减小北京地区PM2.5的预报误差,对北京东南地区的PM2.5预报误差的减小甚至可达到40%以上。因此,优先对黑龙江区域的气象场,尤其是该区域的风场进行目标观测,并将其同化到预报模式的初始场中,将会有效提高初始气象场的质量,进而大大减小北京地区PM2.5浓度的预报误差,提高北京地区空气质量的预报技巧。初始风场代表了北京地区该次空气重污染事件预报的目标观测变量,而黑龙江地区则是该目标观测的敏感区域。
文摘对实验室前期Solexa结果进行深入挖掘,结合生物信息学分析从不同发育阶段猪皮下脂肪组织差异表达的miRNAs中筛选出高丰度差异极显著的候选miR-191.采用腺病毒过表达miR-191,实时定量PCR、Western blot及双荧光素酶报告基因检测等技术方法,初步研究miR-191对猪前体脂肪细胞分化的影响.结果发现,miR-191随着猪前体脂肪细胞的分化表达量逐渐增加.与对照组相比,过表达miR-191的猪前体脂肪细胞中miR-191转录本显著增加,并引起CCAAT增强子结合蛋白β(C/EBPβ)、PPARγ和aP2的mRNA水平降低,抑制了猪前体脂肪细胞分化.同时,Western blot结果显示,与对照组相比过表达miR-191的猪前体脂肪细胞在48 h C/EBPβ蛋白水平下降了55%.更重要的是,通过TargetScan等算法正向筛选以及MicroInspector反向筛选联合获得miR-191候选靶基因,经双荧光素酶报告基因检测结果证实,miR-191可直接作用于C/EBPβ3′UTR,从而降低萤火虫荧光素酶活性.综上所述,miR-191可能通过抑制脂肪细胞分化早期标志基因C/EBPβ的表达,从而抑制了猪前体脂肪细胞的分化.
