基于GA-BP和POS-BP神经网络的光伏电站出力短期预测

当前在光伏电站出力短期预测方面较多的采用BP或者优化的BP神经网络算法,存在采用的优化算法单一、缺乏多种优化算法比较选优、预测误差大的问题。基于本地5 k W小型分布式光伏电站,综合考虑影响光伏出力的太阳光辐射强度、环境温度、风速气象相关因素和光伏电站历史发电数据,分别采用BP以及遗传算法和粒子群算法优化的BP神经网络算法—GA-BP和POS-BP构建了晴天、多云、阴雨三种天气条件下光伏出力短期预测模型。实测结果表明,三种神经网络算法预测模型在三种不同天气条件下均达到了一定的预测精度。其中GA-BP、POS-BP相比传统的BP预测模型降低了预测误差,且POS算法相比GA算法对于BP神经网络预测模型的优化效果更好,进一步降低了预测误差,适用性更强。

卫星钟差超短期预报模型分析

采用线性模型、二次多项式模型、灰色模型和基于历元间差的二次多项式模型对不同卫星的钟差进行预报分析。结果表明,在超短期钟差预报中,四种方法预报精度相当,且均能满足实时精度单点定位的需要。在小样本的超短期预报中,钟差预报的精度与卫星的相关性很小,主要取决于钟差在不同时段内的小尺度变化以及所预报时间的长短。

基于WT和MGFPE混合模式的船舶横摇运动长期预测

为了获得横摇运动在不同时间尺度下的演变规律,提出了利用小波变换和均生函数周期外推混合模式进行船舶横摇运动非线性时间序列长期预测的方法.该方法通过小波变换对横摇运动时间序列进行多分辨率分析,将原序列分解为多个相对简单的准周期信号,信号的趋势项、周期项和随机项被分离出来,然后采用均生函数周期外推预报模式对这些准周期信号进行预报和集成.仿真结果表明,此方法能有效地提高预报长度,并能获得较高的建模及预报精度,该方法亦可用于其他非线性时间序列长期预测.

2016年8月24日意大利佩鲁贾M_W6.2地震的震情和灾情基本情况介绍

2016年8月24日01点36分33秒意大利佩鲁贾发生M_W6.2地震,造成重大人员伤亡、引起国际社会广泛关注。鉴于意大利佩鲁贾M_W6.2地震的灾害特点和对我国防震减灾工作的可能的借鉴意义,本文从灾情概况、构造及地震活动背景、监测网络、应急救援机构及意大利中长期地震预测工作等方面对此次地震进行了介绍,以期反映此次地震的基本情况,为相关读者和工作提供参考。

Seismic pattern treatment method through calculation of seismic density at grid nodes

Analysis of seismic data and seismicity characteristics in China, we gave a method to deal with seismic patterns by calculating density at grid nodes. Number of earthquakes and epicenter distribution are considered comprehensively in this method. Effect of datum accuracy is stressed on parameter confirmation. Seismic patterns from this method are stable and can reflect seismic characteristics reliably. These seismic patterns are the base of quantitative analysis of seismicity. It can be applied in seismic tendency analysis and medium-long term earthquake prediction, earthquake countermeasure and risk mitigation.

A comparative study on the medium-long term results of endovascular repair and open surgical repair in the management of ruptured abdominal aortic aneurysms

Background Although it is generally acknowledged that patients with ruptured abdominal aortic aneurysm （rAAA） obtain the greatest benefit from endovascular repair （EVAR）, convincing evidence on the medium-long term effect is lacking. The aim of this study was to compare and summarize published results of rAAA that underwent EVAR with open surgical repair （OSR）. Methods A search of publicly published literature was performed. Based on an inclusion and exclusion criteria, a systematic meta-analysis was undertaken to compare patient characteristics, complications, short term mortality and medium-long term outcomes. A random-effects model was used to pool the data and calculate pooled odds ratios and weighted mean differences. A quantitative method was used to analyze the differences between these two methods. Results A search of the published literature showed that fourteen English language papers comprising totally 1213 patients with rAAA （435 EVAR and 778 OSR） would be suitable for this study. Furthermore, 13 Chinese studies were included, including 267 patients with rAAA totally, among which 238 patients received operation. The endovascular method was associated with more respiratory diseases before treatment （OR=1.81, P=0.01）, while there are more patients with hemodynamic instability before treatment in OSR group （OR=1.53, P=0.031）. Mean blood transfusion was 1328 ml for EVAR and 2809 ml for OSR （weighted mean difference （WMD） 1500 ml, P=0.014）. The endovascular method was associated with a shorter stay in intensive care （WMD 2.34 days, P 〈0.001） and a shorter total post- operative stay （WMD 6.27 days, P 〈0.001）. The pooled post-operative complication rate of respiratory system and visceral ischemia seldom occurred in the EVAR group （OR=0.48, P 〈0.001 and OR=0.28, P=0.043, respectively）. The pooled 30-day mortality was 25.7% for EVAR and 39.6% for OSR, and the odds ratio was 0.53 （95% confidence interval （CI） 0.41-0.70, P 〈0.001）. There was not, however, any significant reduction in the medium-long all-cause mortality rate （HR=1.13, P=0.381） and re-intervention rate （OR= 2.19, ,~=-0.243） following EVAR. In EVAR group, nevertheless, incidence of type I endoleak was significantly lower than type II endoleak （OR=0.33, P=0.039） at late follow-up period. Conclusions On the basis of this systematic review, rAAA EVAR results in less blood use for transfusion, shorter operation time, shorter intensive care unit and hospital stays, and lower 30-day mortality. However, in the medium-long term, it is not associated with a reduction in all-cause mortality.

A new inter‑system double‑difference RTK model applicable to both overlapping and non‑overlapping signal frequencies

Aiming at the problem that the traditional inter-system double-difference model is not suitable for non-overlapping signal frequencies,we propose a new inter-system double-difference model with single difference ambiguity estimation,which can be applied for both overlapping and non-overlapping signal frequencies.The single difference ambiguities of all satellites and Differential Inter-System Biases(DISB)are first estimated,and the intra-system double difference ambiguities,which have integer characteristics,are then fixed.After the ambiguities are successfully fixed,high-precision coordinates and DISB can be obtained with a constructed transformation matrix.The model effectively avoids the DISB parameter filtering discontinuity caused by the reference satellite transformation and the low precision of the reference satellite single difference ambiguity calculated with the code.A zero-baseline using multiple types of receivers is selected to verify the stability of the estimated DISB.Three baselines with different lengths are selected to assess the positioning performance of the model.The ionospheric-fixed and ionospheric-float models are used for short and medium-long baselines,respectively.The results show that the Differential Inter-System Code Biases(DISCB)and Differential Inter-System Phase Biases(DISPB)have good stability regardless of the receivers type and the signal frequency used and can be calibrated to enhance the strength of the positioning model.The positioning results with three baselines of different lengths show that the proposed inter-system double-difference model can improve the positioning accuracy by 6–22%compared with the intra-system double-difference model which selects the reference satellite independently for each system.The Time to First Fix(TTFF)of the two medium-long baselines is reduced by 30%and 29%,respectively.

Comparative Experiments on the Effect of Radar Data Assimilation and Increasing Horizontal Resolution on Short-term Numerical Weather Prediction

To examine the effect of radar data assimilation and increasing horizontal resolution on the short-term numerical weather prediction, comparative numerical experiments are conducted for a Huabei （North China） torrential rainfall event by using the Advanced Regional Prediction System （ARPS） and ARPS Data Anal- ysis System （ADAS）. The experiments use five different horizontal grid spacings, i.e., 18, 15, 9, 6, and 3 km,respectively, under the two different types of analyses： one with radar data, the other without. Results show that, when radar data are not used in the analysis （i.e., only using the conventional observation data）, increasing horizontal resolution can improve the short-term prediction of 6 h with better representation of the frontal structure and higher scores of the rainfall prediction, particularly for heavy rain situations. When radar data are assimilated, it significantly improves the rainfall prediction for the first 6 h, especially the locality and intensity of precipitation. Moreover, using radar data in the analysis is more effective in improving the short-term prediction than increasing horizontal resolution of the model alone, which is demonstrated by the fact that by using radar data in the analysis and a coarser resolution of the 18-km grid spacing, the predicted results are as good as that by using a higher resolution of the 3-km grid spacing without radar data. Further study of the results under the radar data assimilation with grid spacing of 18-3 km reveals that the rainfall prediction is more sensitive to the grid spacing in heavy rain situations （more than 40 mm） than in ordinary rain situations （less than 40 mm）. When the horizontal grid spacing reduces from 6 to 3 km, there is no obvious improvement to the prediction results. This suggests that there is a limit to how far increasing horizontal resolution can do for the improvement of the prediction. Therefore, an effective approach to improve the short-term numerical prediction is to combine the radar data assimilation with an optimal horizontal resolution.