基于VMD和改进TCN的短期光伏发电功率预测

光伏发电功率存在波动性,且光伏出力易受各种气象特征影响,传统TCN网络容易过度强化空间特性而弱化个体特性。针对上述问题,文中提出一种基于VMD和改进TCN的短期光伏发电功率预测模型。通过VMD将原始光伏发电功率时间序列分解为若干不同频率的模态分量,将各个模态分量以及相对应的气象数据输入至改进TCN网络进行建模学习。利用中心频率法确定VMD的最优分解模态分解个数。在传统TCN预测模型的基础上,使用DropBlock正则化取代Dropout正则化以达到抑制卷积层中信息协同的效果,并引入注意力机制自主挖掘并突出关键气象输入特征的影响,量化各气象因素对光伏发电的影响,从而提高预测精度。以江苏省某光伏电站真实数据为例进行仿真实验,结果表明所提预测方法的RMSE为0.62 MW,MAPE为2.03%。     

Enhanced wide-activated residual network for efficient and accurate image deblocking

To save bandwidth and storage space as well as speed up data transmission, people usually perform lossy compression on images. Although the JPEG standard is a simple and effective compression method, it usually introduces various visually unpleasing artifacts, especially the notorious blocking artifacts. In recent years, deep convolutional neural networks (CNNs) have seen remarkable development in compression artifacts reduction. Despite the excellent performance, most deep CNNs suffer from heavy computation due to very deep and wide architectures. In this paper, we propose an enhanced wide-activated residual network (EWARN) for efficient and accurate image deblocking. Specifically, we propose an enhanced wide-activated residual block (EWARB) as basic construction module. Our EWARB gives rise to larger activation width, better use of interdependencies among channels, and more informative and discriminative non-linearity activation features without more parameters than residual block (RB) and wide-activated residual block (WARB). Furthermore, we introduce an overlapping patches extraction and combination (OPEC) strategy into our network in a full convolution way, leading to large receptive field, enforced compatibility among adjacent blocks, and efficient deblocking. Extensive experiments demonstrate that our EWARN outperforms several state-of-the-art methods quantitatively and qualitatively with relatively small model size and less running time, achieving a good trade-off between performance and complexity.     

A recurrent neural network using historical data to predict time series indoor PM2.5 concentrations for residential buildings

Due to the severe outdoor PM2.5 pollution in China, many people have installed air-cleaning systems in homes. To make the systems run automatically and intelligently, we developed a recurrent neural network (RNN) that uses historical data to predict the future indoor PM2.5 concentration. The RNN architecture includes an autoencoder and a recurrent part. We used data measured in an apartment over the course of an entire year to train and test the RNN. The data include indoor/outdoor PM2.5 concentration, environmental parameters and time of day. By comparing three different input strategies, we found that a strategy employing historical PM2.5 and time of day as inputs performed best. With this strategy, the model can be applied to predict the relatively stable trend of indoor PM2.5 concentration in advance. When the input length is 2 h and the prediction horizon is 30 min, the median prediction error is 8.3 µg/m³ for the whole test set. For times with indoor PM2.5 concentrations between (20,50] µg/m³ and (50,100] µg/m³, the median prediction error is 8.3 and 9.2 µg/m³, respectively. The low prediction error between the ground-truth and predicted values shows that the RNN can predict indoor PM2.5 concentrations with satisfactory performance.     

On Two‐Stage Estimation of the Spectral Density with Assigned Risk in Presence of Missing Data

Estimation with assigned risk is a classical statistical problem, and the theory is well developed for the case of directly observed (no missing) data. In this article a more complicated problem of estimation of the spectral density in presence of missing data is considered. First, the corresponding theory of sequential estimation with minimal expected stopping time is developed. Then it is shown that a two‐stage estimator may be used and it yields the minimal stopping time. Sample size of the first stage may be deterministic and in order smaller than a minimal stopping time, and then the first stage defines the size of the second stage. Furthermore, the estimator adapts to unknown smoothness of an underlying spectral density and to an underlying missing mechanism.     

“四步法”在岩性油藏砂体连通性研究中应用

岩性油藏砂体规模小、非均质性强、油水分布复杂等特点造成了砂体间的连通关系复杂,砂体展布特征不明显。在"旋回对比、分级控制、不同相带区别对待"的单油层对比方法确定井间砂体连通关系,研究"对、拆、统、验"四步法,提高了高邮凹陷黄珏油田戴南组砂体连通性研究的准确度,为岩性油藏的油田注采层系调整、流线场调整等措施提供地质依据。     

Eliciting and measuring hesitance in decision-making

Hesitance is an innate human psychological phenomenon pervasive in our daily life. Although it is very complex and still not fully understood, there have been many studies related to this interesting topic. In this study, we proposed two fundamental problems about hesitance: the first is the generating problem, which is related to how to elicit hesitance information from human thinking; the second is the measuring problem, which is related to how to devise effective and reasonable methods to measure hesitance degrees from given hesitance information. With these two fundamental questions, we first discussed and analyzed several examples in real life involving hesitance, showing that it can indeed be recorded and represented. Then we roughly classified the hesitance information into two major classes: static hesitance and dynamic hesitance. Some simple and interesting methods were proposed to elicit hesitance, and more reasonable methods were proposed to measure the hesitance degree from the two classes of hesitance information. All methods that have been discussed in this study try to avoid complexity in every aspect while reserving strictness and reasonability; therefore, the present study provides suitable information for practitioners with different backgrounds.     

气相色谱-串联质谱法测定植物性食品中50种农药残留

目的建立QuEChERS前处理、气相色谱-串联质谱法(gas chromatography-tandem mass spectrometry,GC-MS/MS)结合基质匹配标准系列测定植物性食品中50种农药的分析方法。方法样品经乙腈提取,QuEChERS净化,采用正构烷烃保留指数法确定待测农药保留时间,最后经三重四极杆质谱仪结合基质匹配标准系列定性及定量检测。结果在0.05～2.00μg/mL范围内50种农药线性良好,相关系数r均大于0.995,方法检出限在0.002～0.007 mg/kg,加标回收率为77.7%～122%,相对标准偏差(relative standard deviation, RSD)为1.9%～12.1%。结论本方法具有简便、快速、检测结果较准确的特点,适合测定植物性食品中50种农药残留。     

基于风速时间序列分形与传统插值对比研究

风速时间序列具有波动强烈、数学上处处连续、处处不可导特征,为解决风速时间序列的插值问题及提高其精度,采用数学分形学理论的分形插值方法,基于Kaimal和NWTCUP湍流风谱模型进行风场模拟。从得到的风速时间序列中随机抽取数据作为初始点,将分形插值方法与三次样条和Hermite传统插值方法进行对比。结果表明:分形插值方法不仅具有保持原始风速时间序列所具有的本质和内在联系,如自相似性、长程相关性和标度不变性等非线性动力学系统特征信息,而且比三次样条和Hermite传统插值方法更适合剧烈震荡的风速时间序列的插值。