Spectral radiance reconstruction from trichromatic camera responses based on orthogonal test and regularized algorithm

Reconstruction of spectral information based on multi‐channel image system is a significant problem in color reproduction, detection, and recognition. A spectral radiance reconstruction from trichromatic digital camera responses is researched in this article. The mapping relationship between the trichromatic imaging system response and the incident spectral radiance is analyzed. Then, in order to remove the ill‐posedness of the problem, a regularized constraint solution model of spectral radiance reconstruction matrix is established. And the spectral radiance can be reconstructed by spectral radiance reconstruction matrices and trichromatic imaging system response. Finally, the spectral radiance reconstruction matrix is estimated by the system radiometric calibration experiment. The input radiance is offered by a LCD display. A 3‐factor and 9‐level orthogonal test is designed for the calibration experiment, and a test set of 24 colors is used for precision analysis. The results show that the average relative mean error of our method is 8.69%, it is lower than that of Wiener filtering method by 2.84%. The method can reconstruct spectral radiance information effectively.     

Meso-structure construction and effective modulus simulation of PBXs

ABSTRACT

The polymer-bonded explosive (PBX) is a kind of multi-phase composite consisting of the polymeric binder and embedded energetic particles, in which the particle volume fraction (PVF) is often higher than 90%. In the present work, by using the Voronoi-polygon generation method along with the concept of gradation to generate Voronoi particles with given gradation, and with further operations including modification, shrinking, smoothing, etc. to the particles, a new meso-structure construction method for PBXs is proposed. The constructed meso-structures possess good gradation relationship and have high PVFs (94.99% in maximum) simultaneously. The strict periodicity on the boundaries of the meso-structure can also be achieved. To verify the constructed PBX meso-structures, the numerical manifold method (NMM) is used to simulate the effective modulus of the constructed meso-structures by considering different influencing factors such as the size of meso-structure, PVF, gradation, and initial defects, etc. The simulation results are analyzed qualitatively, and the causes of differences between the simulation results and available experimental results or other numerical results are discussed. The validity of the proposed method for the construction of PBX meso-structures is verified. This work also provides foundations for the further numerical studies of the mechanical and thermal behaviors of PBXs at the mesoscale.     

深水水下管汇的结构设计概述

以南海某油气田的深水水下管汇为例,结合管汇的功能需求、结构组成、结构计算等,对深水水下管汇的结构设计进行了概述。     

基于流场分析的Y型对接式压裂管汇布局研究

为提高压裂管汇的使用寿命,在传统的直角对接式布局基础上,设计了一种Y型对接式压裂管汇撬。运用Eulerian-Lagrangian方法与流体分析软件,分析获得不同结构参数下Y型对接式压裂管汇的流场压力、流场速度、冲蚀分布规律,并对Y型对接式布局进行优化。研究表明,随着对接角的增大,Y型对接式压裂管汇的流场压力、速度分布越不稳定,最大冲蚀速率随之减少,平均冲蚀速率随之增大; 在对接四通拐角处设置过渡倒圆后,随着倒圆半径的增大,最大冲蚀速率及平均冲蚀速率随之减小; 在对接角45°、倒圆半径20 mm时,最大冲蚀速率减小37.6%,平均冲蚀速率减少49.7%; Y型对接式压裂管汇的冲蚀速率比直角对接式结构有显著减小,提高管汇使用寿命。     

A new linearly constrained minimum variance beamformer for reconstructing EEG sparse sources

Brain source imaging based on EEG aims to reconstruct the neural activities producing the scalp potentials. This includes solving the forward and inverse problems. The aim of the inverse problem is to estimate the activity of the brain sources based on the measured data and leadfield matrix computed in the forward step. Spatial filtering, also known as beamforming, is an inverse method that reconstructs the time course of the source at a particular location by weighting and linearly combining the sensor data. In this paper, we considered a temporal assumption related to the time course of the source, namely sparsity, in the Linearly Constrained Minimum Variance (LCMV) beamformer. This assumption sounds reasonable since not all brain sources are active all the time such as epileptic spikes and also some experimental protocols such as electrical stimulations of a peripheral nerve can be sparse in time. Developing the sparse beamformer is done by incorporating L1-norm regularization of the beamformer output in the relevant cost function while obtaining the filter weights. We called this new beamformer SParse LCMV (SP-LCMV). We compared the performance of the SP-LCMV with that of LCMV for both superficial and deep sources with different amplitudes using synthetic EEG signals. Also, we compared them in localization and reconstruction of sources underlying electric median nerve stimulation. Results show that the proposed sparse beamformer can enhance reconstruction of sparse sources especially in the case of sources with high amplitude spikes.     

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%。     

基于流形学习的光学遥感图像分类

随着光学遥感图像技术的快速发展与广泛应用,对光学遥感图像的准确分类具有深远的研究意义。传统特征提取方式提取的高维特征中夹杂着许多冗余信息,分类过程可能导致过拟合现象,针对传统的线性降维算法不足以保持原始数据的内部结构,容易造成数据失真这一问题,提出基于流形学习的光学遥感图像分类算法。该算法首先提取出图像的SIFT特征,然后将流形学习运用于特征降维,最后结合支持向量机进行训练和识别。实验结果表明,在Satellite、NWPU和UCMerced实验数据中,冰川、建筑群和海滩分类精度得到了有效提高,达到85%左右;针对沙漠、岩石、水域等特殊环境遥感图像,分类精度提高了10%左右。总而言之,基于流形学习的分类算法对通过降维之后的数据能够保持在原高维空间中的拓扑结构,相似特征点能得到有效聚合,预防了"维数灾难",减少了计算量,保证了分类精度。     

大地电磁测深阶段式自适应正则化反演

如何合理确定正则化因子一直是地球物理反演的研究热点和难点。从提高反演稳定性的角度对正则化因子的选择进行考量，是确定正则化因子取值的新思路。此外，以往的正则化反演研究对非线性优化算法的随机性考虑不足。基于此，以Zhdanov提出的自适应算法为框架，提出了一种新的自适应正则化算法，即阶段式自适应算法，按照“阶段”自适应地调整正则化因子。将此方法分别应用于大地电磁测深（MT）的共轭梯度和差分进化算法（DE）反演。模型试验表明该算法可以提高反演的稳定性，在一定程度上降低衰减因子的影响，而且该算法具有同时适用于线性和非线性优化算法的特点。     

船体结构冰载荷反演方法及试验验证

冰载荷是影响船舶冰区航行期间结构安全的重要环境载荷。船舶的冰压监测通常采用应变传感器，合理地布放传感器是识别冰载荷的基础。通过对比船体外板结构试验中的冲击载荷和不同测试方案下的应变信号，确定了最佳应变传感器布放方案；采用Green核函数方法建立了船体外板结构应变冲击载荷间的响应关系，并对采集信号在噪声影响下反演的不适定性进行了分析；采用Tikhonov正则化方法克服了载荷反演过程中出现的数值不稳定问题；最后将试验中的响应用到载荷识别分析中，反演的载荷可以较为准确地反映冲击载荷的时域特征并且载荷识别精度良好。