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%。     

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.     

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.     

风速时间序列混沌判定方法比较研究

混沌识别是对非线性时间序列进行混沌预测的前提。针对时间序列风速确定性与随机性相结合的复杂非线性特征,研究了不同的混沌识别方法,并对风速时间序列进行混沌特征识别。应用随机噪声、周期运动及经典混沌系统的时间序列对所选方法进行可靠性验证。对美国国家风能研究中心M2测风塔实测时间序列风速数据进行非线性混沌特征识别。结果表明:风速时间序列具有明显的混沌特征;各风速时间序列表现出不同程度的混沌特征;各混沌识别方法对风速时间序列混沌特征的表达形式不同,互为补充,相互验证。     

基于混沌理论的弧形闸门支臂振动特性

为了研究弧形闸门支臂的振动问题,采用混沌理论对时间序列进行处理,确定延迟时间τ、嵌入维数m进行相空间重构,并计算时间序列的混沌特征值,进而研究弧形闸门在启闭过程中振动的复杂性。以某水电站弧形闸门支臂振动为研究对象,根据支臂振动加速度原型观测资料,分析不同开度的振动情况,发现启门振动的复杂程度高于闭门工况,小开度的振动复杂程度更高,且支臂振动的复杂程度为与面板连接位置最大,与闸墩连接位置最小,中间位置介于前两者之间。为弧形闸门短期振动预测提供了支持。     

Nature inspired 21 branches sneezewort/Achillea ptarmica plant growth pattern–shaped antenna for Ku‐band applications

This reported paper presents the design and analysis of wideband nature inspired planar antenna of 46 × 18 × 1.6 mm³ size, designed using FR4 (Flame Retardant) substrate. The antenna patch is delineated from the growth pattern of sneezewort plant with supplementary notion of golden ratio in branching and branch width. The total number of branches in each stage follows the Fibonacci series and the branch width is calculated from the golden ratio concept. Starting from one feed line and two branches, the maximum numbers of branches are increased to 20. There has been significant enhancement in the radiation performance of proposed geometry by increasing the number of branches to 21 and by modifying ground with T‐shaped slot. The antenna covers wide impedance bandwidth of 8.2 to 16.5 GHz by possessing stable radiation characteristics.     

基于混合时间序列卷积神经网络的轴承故障诊断

在传统滚动轴承故障诊断中,绝大多数方法采用了从振动信号提取特征的诊断模式,但是这种模式必然会使原始信号降维进而导致故障信息的丢失。卷积神经网络(CNN)通过权重共享和稀疏连接直接对原始信号进行操作,实现自适应特征提取,最大化保留故障信息。受CNN原理启发,开发出了一种基于工业振动信号特征的新型诊断框架,称之为混合时间序列CNN(HTS-CNN)。首先,利用估计总体比例的方法自适应确定模型训练样本数目;其次,通过对时间序列片段进行随机组合的方式,使模型能够提取非相邻信号特征;最后,利用Softmax激活函数在模型输出端执行多分类任务。通过对凯斯西储大学及CUT-2平台轴承数据进行分析,实验结果表明:该方法能够准确、有效的对滚动轴承故障进行分类。     

Anomalous crystal structure of γ" phase in the Mg-RE-Zn(Ag) series alloys:Causality clarified by ab initio study

The crystal structure of the single-unit-cell thickness γ " phase,as a key strengthening phase in Mg-REZn(Ag) series alloys,has been extensively studied,and several structural models have been proposed in the past two decades.However,these reported models,and even the lattice constants at the same proposed structure,are scattered severely,which has led to considerable confusion and not available for further mechanical property simulation and prediction of Mg alloys containing this phase.In this study,by using first-principles calculations,the crystal structure of y" phase is clarified,resolving the discrepancies among different experiments,and its intrinsic mechanical properties have also been studied for the first time.It is verified that the γ " phase contains quasi-five atomic layers,instead of the previously reported tri-layer,and surprisingly,its crystal structure has many variants,which would change with the alloy composition.Besides,with the help of the simulated selected area electron diffraction(SAED) patterns,it is found that the atoms in the central layer remain partially ordered distribution,and this ordered extent primarily depends on the atomic ratio of RE:Zn(Ag) and the solute content in an alloy.That is,the ordered extent increases with decreasing the atomic ratio of RE:Zn(Ag) and/or increasing solute content of alloy,and vice versa.Ag and Zn dissolved in the γ" phase would produce almost opposed mechanical anisotropy for the γ " phase under the identical crystal structure,and the addition of Ag shows more efficient on increasing the shear modulus of γ" phase.