FY-3气象卫星MERSI数据快速预处理的IDL实现

作为新型卫星数据源,FY\|3/MERSI(风云三号中分辨率光谱成像仪)影像的快速预处理方法与模块目前较少见。采用基于三角网的几何校正算法,根据研究区Shapefile文件和FY\|3/MERSI自带定位数据提取处理区域,实现对应区域全部20个通道几何校正,并利用后向映射重采样输出各通道图像纠正后像元点DN值,随后进行辐射定标、太阳高度角订正等预处理。该系列过程用IDL编写相关程序实现了用户界面化交互操作。运行结果表明:设计的算法流程占用内存小,计算速度快。对于一个面积约45万km²的区域,所有通道处理耗时仅450 s,且校正后影像质量和精度均较好。相较于相同区域借助ENVI软件下地理位置查找表法(GLT)仅实现单通道几何校正耗时超30 min,本文开发的模块大大提高了FY\|3/MERSI数据处理效率,为其在城市热环境、积雪等领域的应用做好准备工作。     

A novel deep learning scheme for multi-condition remaining useful life prediction of rolling element bearings

The remaining useful life (RUL) prediction of a rolling element bearing is important for more reasonable maintenance of machinery and equipment. Generally, the information of a failure can hardly be acquired in advance while running and the degradation process varies in terms of different faults. Thus, fault identification is indispensable for a multi-condition RUL prediction, where, however, the fault identification and RUL prediction are separated in most studies. A new hybrid scheme is proposed in this paper for the multi-condition RUL prediction of rolling element bearings. The proposed scheme contains both classification and regression, where the 2D-DCNN based classifier and predictors are built concerning typical fault conditions of a bearing. For the online prediction, the raw signals are spanned in the time-frequency domain and then transferred into images as the input of the scheme. The classifier is used to monitor the vibration of rolling bearings for online fault recognition and excite the corresponding predictor for RUL prediction once a fault is detected. The output from the predictor is amended by the proposed adaptive delay correction method as the final prediction results. A demonstration is performed based on the XJTU-SY datasets and the results are compared with those from the state-of-the-art methods, which proves the superiority of the proposed scheme in improving the accuracy and linearity of RUL prediction. The time cost of the proposed online prediction scheme is also investigated and the results indicate high time effectiveness.     

Response correction techniques for surrogate‐based design optimization of microwave structures

Simulation‐based optimization has become an important design tool in microwave engineering. However, using electromagnetic (EM) solvers in the design process is a challenging task, primarily due to a high‐computational cost of an accurate EM simulation. In this article, we present a review of EM‐based design optimization techniques exploiting response‐corrected physically based low‐fidelity models. The surrogate models created through such a correction can be used to yield a reasonable approximation of the optimal design of the computationally expensive structure under consideration (high‐fidelity model). Several approaches using this idea are reviewed including output space mapping, manifold mapping, adaptive response correction, and shape‐preserving response prediction. A common feature of these methods is that they are easy to implement and computationally efficient. Application examples are provided. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2012.     

一种制做三维牙——颌骨光弹性模型的新方法

本文提出一种用“整体浇注法”制做三维牙——颌骨光弹性模型的新方法,即用价格便宜的石膏做阴模,先制出与真实牙几何相似的牙光弹性模型和模拟牙周膜的乳胶套,将它们粘接好,然后将其放入颌骨石膏阴模的适当位置,浇入环氧树脂混合液,用“二次固化法”进行固化,即可制成三维牙——颌骨整体光弹性模型。此法能保证模型与实物的严格几何相似和牙周的边界条件相似,使实验精度大大提高且工艺简单、成本低。     

一种虚拟式的自动控制原理实验平台设计

目前多用MATLAB进行自动控制原理的各类仿真,而它所需编写代码繁琐且界面不够灵活。鉴于此,提供了利用基于G语言的LABVIEW设计的自动控制原理虚拟实验平台。从模块组成上介绍了虚拟平台所完成的设计及分析功能,并以频域响应分析、频率法校正为例详细阐述了平台设计、分析系统的原理,最后经过与MATLAB仿真结果的对比验证,表明该平台系统分析的准确性与可靠性。     

Edge diffraction correction for 3D scattering center modeling based on the shooting and bouncing ray technique

In the traditional scattering center extraction method based on the shooting and bouncing ray(SBR) technique,only the physical optics contribution of the target is considered.However,the physical optics method is unable to describe the contribution of the actual edge diffraction.By comprehensively considering the specular reflection and edge diffraction contribution of the target,an edge diffraction correction method for 3D scattering center modeling based on the SBR technique is proposed.Using the SBR technique and combining the image domain ray tube integration and equivalent edge currents method,the edge diffraction correcting formula for the 3D inverse synthetic aperture radar image is derived.Simulation results show that the proposed method is able to effectively improve the modeling accuracy of the 3D scattering center.     

基于门控循环单元与误差修正的短期负荷预测

针对负荷预测模型迭代训练过程中存在误差积累的问题,提出结合叠式双向门控循环单元（SBiGRU）、完整自适应噪声集成经验模态分解（CEEMDAN）和误差修正的组合预测模型. 建立SBiGRU模型学习在气温、日期类型影响下负荷序列的时序特征,误差特征体现在SBiGRU模型预测产生的误差序列中;使用CEEMDAN算法将误差序列分解为数个本征模态函数（IMF）分量与趋势分量,对每项分量再次建立SBiGRU模型进行学习与预测,并对各分量的预测值进行序列重构,得到误差的预测结果;对预测结果进行求和以修正误差. 模型评估结果表明,组合模型的预测准确精度为98.86%,与SBiGRU、BiRNN、支持向量回归等方法相比,该模型具有更好的精度.     

一种计及空间相关性的光伏电站历史出力数据的修正方法

针对光伏系统渗透率增高对电力系统稳定运行带来的严峻挑战,考虑到光伏功率预测技术精度高度依赖于数据精度的问题,提出一种基于人工神经网络的光伏电站历史出力数据修正方法。利用人工神经网络在建立复杂非线性映射关系的优越性,引入皮尔逊相关系数对数据进行降维处理,选择与目标光伏电站出力相关性高的电站作为基准光伏电站,并结合光伏出力的空间相关性特征与基准光伏电站的出力数据对目标光伏电站失准及缺失数据进行修正,以解决由人为因素或数据采集系统老旧带来的光伏数据失准问题,并通过山东省聊城市的光伏历史出力数据对所提方法进行分析验证。     

基于小波的实时烟雾检测*

传统的离子式、吸气式、光电式等烟雾检测器在大空间中检测烟雾时,会受到发射信号与接收信号之间的距离、平面角度、精确对准等限制,无法对整个空间的烟雾状况进行描述.新的方法通过监测区域的摄像机拍摄的视频图像序列,进行小波变换,分析图像帧在时域和空域的频率特性,来确定被监测区域是否有火灾烟雾的发生.实验证明该方法不受空间高度、热障、易爆、有毒等环境的限制,并且有灵敏度高、抗干扰力强、适用范围广等特点.     

A novel arc welding inverter with unit power factor based on DSP control

A novel inverter power source is developed characterized with constant output current and unit power factor input. Digital signal processor （ DSP ） is used to realize power factor correction and control of back-stage inverter bridge of the arc welding inverter. The fore-stage adopts double closed loop proportion and integration （PI） rectifier technique and the back- stage adopts digital pulse width modulation （ PWM） technique. Simulated waves can be obtained in Matlab/Simulink and validated by experiments. Experiments of the prototype showed that the total harmonic distortion （THD） can be controlled within 10% and the power factor is approximate to 1.