Detailed analysis and simulation verification for reconstruction of plasma optical boundary with spectrometric technique on HL-2M tokamak

The plasma optical boundary reconstruction technique based on Hommen's theory is promising for future tokamaks with high parameters. In this work, we conduct detailed analysis and simulation verification to estimate the ‘logic loophole' of this technique. The finite-width effect and unpredictable errors reduce the technique's reliability, which leads to this loophole. Based on imaging theory, the photos of a virtual camera are simulated by integrating the assumed luminous intensity of plasma. Based on Hommen's theory, the plasma optical boundary is reconstructed from the photos. Comparing the reconstructed boundary with the one assumed, the logic loophole and its two effects are quantitatively estimated. The finite-width effect is related to the equivalent thickness of the luminous layer, which is generally about 2-4 cm but sometimes larger. The level of unpredictable errors is around 0.65 cm. The technique based on Hommen's theory is generally reliable, but finite-width effect and unpredictable errors have to be taken into consideration in some scenarios. The parameters of HL-2M are applied in this work.

Blind reconstruction of convolutional code based on segmented Walsh-Hadamard transform

Walsh-Hadamard transform （WriT） can solve linear error equations on Field F2, and the method can be used to recover the parameters of convolutional code. However, solving the equations with many unknowns needs enormous computer memory which limits the application of WriT. In order to solve this problem, a method based on segmented WriT is proposed in this paper. The coefficient vector of high dimension is reshaped and two vectors of lower dimension are obtained. Then the WriT is operated and the requirement for computer memory is much reduced. The code rate and the constraint length of convolutional code are detected from the Walsh spectrum. And the check vector is recovered from the peak position. The validity of the method is verified by the simulation result, and the performance is proved to be optimal.

Sample based 3D face reconstruction from a single frontal image by adaptive locally linear embedding

In this paper, we propose a highly automatic approach for 3D photorealistic face reconstruction from a single frontal image. The key point of our work is the implementation of adaptive manifold learning approach. Beforehand, an active appearance model (AAM) is trained for automatic feature extraction and adaptive locally linear embedding (ALLE) algorithm is utilized to reduce the dimensionality of the 3D database. Then, given an input frontal face image, the corresponding weights between 3D samples and the image are synthesized adaptively according to the AAM selected facial features. Finally, geometry reconstruction is achieved by linear weighted combination of adaptively selected samples. Radial basis function (RBF) is adopted to map facial texture from the frontal image to the reconstructed face geometry. The texture of invisible regions between the face and the ears is interpolated by sampling from the frontal image. This approach has several advantages: (1) Only a single frontal face image is needed for highly automatic face reconstruction; (2) Compared with former works, our reconstruction approach provides higher accuracy; (3) Constraint based RBF texture mapping provides natural appearance for reconstructed face.

Extracting useful high-frequency information from wide-field electromagnetic data using time-domain signal reconstruction

The wide-field electromagnetic method is widely used in hydrocarbon exploration,mineral deposit detection,and geological disaster prediction.However,apparent resistivity and normalized field amplitude exceeding 2048 Hz often exhibit upward warping in data,making geophysical inversion and interpretation challenging.The cumulative error of the crystal oscillator in signal transmission and acquisition contributes to an upturned apparent resistivity curve.To address this,a high-frequency information extraction method is proposed based on time-domain signal reconstruction,which helps to record a complete current data sequence;moreover,it helps estimate the crystal oscillator error for the transmitted signal.Considering the recorded error,a received signal was corrected using a set of reconstruction algorithms.After processing,the high-frequency component of the wide-field electromagnetic data was not upturned,while accurate high-frequency information was extracted from the signal.Therefore,the proposed method helped effectively extract high-frequency components of all wide-field electromagnetic data.

High-resolution Image Reconstruction by Neural Network and Its Application in Infrared Imaging

As digital image techniques have been widely used, the requirements for high-resolution images become increasingly stringent. Traditional single-frame interpolation techniques cannot add new high frequency information to the expanded images, and cannot improve resolution in deed. Multiframe-based techniques are effective ways for high-resolution image reconstruction, but their computation complexities and the difficulties in achieving image sequences limit their applications. An original method using an artificial neural network is proposed in this paper. Using the inherent merits in neural network, we can establish the mapping between high frequency components in low-resolution images and high-resolution images. Example applications and their results demonstrated the images reconstructed by our method are aesthetically and quantitatively (using the criteria of MSE and MAE) superior to the images acquired by common methods. Even for infrared images this method can give satisfactory results with high definition. In addition, a single-layer linear neural network is used in this paper, the computational complexity is very low, and this method can be realized in real time.

基于观测方程重构滤波算法的锂离子电池荷电状态估计

滤波算法中观测方程的准确性在电池状态评估中起着决定性作用。然而,该文通过试验发现,由于温度、工作电流和荷电状态(SOC)的影响,即使使用精度较高的电池模型,扩展卡尔曼滤波(EKF)算法中观测方程的输出值与实际电压之间仍会存在较大误差,即产生了较大的新息。该文提出一种基于观测方程重组的增强型扩展卡尔曼滤波(E-EKF)算法。该算法的核心思想是利用具有温度、SOC和电流自适应能力的误差修正策略对观测方程进行重组,实现算法中新息的降低,进而提高SOC估计的准确性。使用两种不同温度下的典型工况试验对E-EKF算法的性能进行了验证。试验结果表明,该算法能够适应不同的温度和工况,并具有较高的SOC估计精度。

基于曲面控制点重构的加工误差在机测量方法

为了提高汽车车身外覆盖件模具的加工精度,提出一种在机测量自由曲面加工误差的方法。首先基于改进波前法生成三角网格,提取理论曲面网格节点的坐标数据,根据在机测量得到实际加工曲面的采样数据;然后基于T-splines的小波控制点法进行曲面重构,拟合加工曲面;最后基于广义牛顿法计算重构的实际曲面控制点到理论曲面的法向距离,获得曲面的加工误差分布,并对实验加工的凹凸曲面样件的轮廓度误差进行分析。实验结果表明,基于T-splines控制点法的曲面重构方法能够在机、有效地获得自由曲面的加工误差。

相空间重构与改进SMA优化SVR的网络流量预测

为提高网络流量预测精度,提出结合相空间重构与改进黏菌优化支持向量回归的预测模型。为解决黏菌算法收敛慢、易得局部最优的不足,引入3种形态对立学习对种群进行初始化,提高种群多样性;利用非线性反馈因子更新机制,均衡全局搜索与局部开发;设计柯西-高斯混合变异对最优解变异,扩展搜索空间,避免陷入局部最优。利用改进黏菌算法对支持向量回归优化调参,有效解决超参初值敏感缺陷,提高学习精度和收敛速度,以此构建网络流量预测模型。实验结果表明,改进模型预测误差更小,能够实现高精度和实时性预测要求。

基于在机测量的曲面共形电路3D打印误差补偿方法

基于3D打印技术制备曲面共形电路具有广阔的应用前景,然而,由于曲面基材加工误差和导电线路定位误差的影响,导致曲面电路3D打印头高度难以精确控制,进而影响打印精度。为此,本文以气动式直写打印为研究对象,提出了一种“在机测量+线路模型重构”的打印头高度误差补偿方法。设计了一种曲率自适应测量点选取方法,利用NURBS曲线反算法对测量数据进行重构,还原真实线路模型。通过生成真实线路的打印轨迹,替换原始理论打印轨迹,补偿了打印头高度误差。自主搭建了一套集成了在机测量系统的曲面共形电路3D打印平台,设计了直线、圆弧与NURBS自由曲线3种形状的线路,进行补偿前后电路打印的对比实验。结果表明,未进行补偿前,在线路曲率较大的地方,线路在基板上的波峰处会产生拉丝现象,波谷处会产生堆叠现象,而补偿后的导线线宽比较均匀;进一步对每条导线的电阻进行测量,发现各条线路补偿前的电阻值波动较大,补偿后的电路的电阻值均匀,且相较补偿前平均降低65.99%,最大电阻降幅达85.75%。

基于NSGA-Ⅱ传感位置优化的曲面重构及误差补偿方法

通过优化光纤布拉格光栅形状传感技术中传感点位置和补偿重构结果来提高薄层合金板三维形状重构精度。通过ANSYS workbench建立合金板仿真模型,提取应变和位移模态振型,根据模态置信准则、转换矩阵稳定性和模态振型相似性分别设计了三个目标函数,采用快速和精英机制的多目标遗传算法优化传感器位置。将镍钛合金板弯曲成不同曲率半径的弧形,利用光纤布拉格光栅中心波长漂移量和线性插值算法计算得出不同形状下的结构应变,重构合金板形状,均方根误差和最大误差相较于单目标优化算法分别减小30%和15%。利用粒子群优化径向基函数神经网络算法拟合误差与位移的关系实现误差补偿,均方根误差和最大误差比无补偿时分别减小了90%和70%,最大相对百分比误差仅为5%,提高了三维形状重构算法精度。

基于重构误差和极端模式识别的综合能源系统短期负荷预测

综合能源系统的运行场景存在极端模式,且含有异常数据,亟剧增加了综合能源负荷预测的难度。该文提出基于极端模式识别和误差重构的综合能源系统极端模式短期负荷预测方法,通过极端模式的识别,异常数据的检测,提高综合能源负荷预测的精度。首先,基于最小累积距离的综合能源负荷数据聚类,识别系统的极端模式;然后,利用深度学习模型的残差和聚类误差进行误差重构,检测异常数据;最后,采用改进的Stacking集成学习方法,进行极端模式的综合能源负荷预测。将所提方法应用于典型的综合能源系统,并与已有方法比较,实验结果表明,所提方法能够很好地解决极端模式的综合能源系统短期负荷预测问题。

一种复杂曲面的精密测量逆向与误差评定方法

目的研究叶片类零件精密测量、逆向与误差评定方法,为该类零件的精密加工提供数字化误差分布模型。方法针对接触式测头存在的空间径向偏置问题,基于四点标定法,建立空间数学模型,完成工具中心点(TCP)的标定;针对叶片点云分布散乱、无序等问题,提出基于多级B样条插值方法,进行叶片三维点云的曲面重建;最后基于迭代法,对叶片误差进行精确评定。结果对测量所得叶片点云,基于多级B样条与控制网格差异化细分的重建算法可以重建出高保真的连续曲面;误差评定算法可以完成测量结果与设计模型之间不小于0.01 mm的误差计算并得到分层分区的误差可视化云图。结论机器人接触式测量系统可以对叶片类复杂曲面完成高精度测量,采用基于B样条的三维重建算法可以快速的重建出叶片三维曲面;笔者提出的误差评定算法可以进行高精度叶片点云的误差计算,其分层分区结果对于叶片抛磨精加工阶段具有指导意义。

基于EEMD与曲面重构的自由曲面加工误差分解与补偿

为了减小自由曲面零件的加工误差和提高加工精度,提出了集合经验模态分解(Ensemble Empirical Mode Decomposition, EEMD)与曲面重构结合对加工误差进行分解和补偿的方法。利用EEMD对在机检测得到的第一次切削加工后的加工误差进行分解,得到若干固有模态函数(Intrinsic Mode Functions, IMFs);根据系统误差特征,通过设置相关系数阈值筛选出加工误差中含有系统误差的IMFs分量(即分解出的系统误差);根据该系统误差,利用曲面重构法得到新的加工曲面,并生成新的NC代码,再次进行切削加工,对系统误差进行补偿。通过EEMD加工误差分解仿真分析可以得出,EEMD具有较为理想的加工误差分解能力。自由曲面零件加工实例结果显示,采用本文提出的方法对加工误差进行补偿后的最大绝对加工误差由0.0632mm降为0.0139mm,平均绝对加工误差由0.0491mm降为0.0052mm,加工精度提高了89.4%,证明了该方法在自由曲面零件加工误差补偿方面的有效性。

基于动态受控主元分析模型的故障检测

为了提高故障检测准确率,提出了基于动态受控主元分析(dynamic controlled principal component analysis,DCPCA)模型的故障检测方法。首先,利用DCPCA提取动态受控主元(dynamic controlled principal component,DCPC),所得DCPC包含过程的自回归特性和与控制输入之间的动态因果关系,使得构建的DCPCA模型更精确。然后,针对传统方法只对过程变量进行静态空间结构的故障检测,忽略了动态特性的问题,基于DCPCA模型适时应用检测综合指标,对系统进行静态重构误差和动态模型误差的双重检测,使得检测结果更全面。最后,基于田纳西-伊斯曼(Tennessee-Eastman,TE)过程的仿真结果验证了所提方法的可行性和有效性。

方位多波束弧形阵列SAR成像方法研究

弧形阵列合成孔径雷达(Synthetic Aperture Radar,简称SAR)是一种广域观测的新体制微波成像系统,相比于线性阵列合成孔径雷达,弧形阵列SAR能够有效克服常规成像雷达前视、侧视、下视等观测视角单一的问题,弥补了常规成像模式的不足。方位多波束技术是星载合成孔径雷达系统获取高分辨宽覆盖测绘能力的重要技术途径。本文首先构建了多波束弧形阵列SAR系统的方位向多通道信号模型,并通过传统的方位多通道重建方法进行成像仿真,成像结果出现虚假目标,由于传统的多通道重建方法适用于大多数线性阵列,所以通过分析回波斜距的数学几何模型推导出了多波束弧形阵列SAR相比于线性阵列SAR多了一个附加相位,与弧形阵列天线结构相结合,附加相位由子孔径角度引起,因此方位多通道回波数据中相位误差是由多通道弧形阵列SAR的子孔径角度引起,从而导致多通道不均衡,所以传统的多通道重建方法不能够直接用于弧形阵列SAR的成像处理中,为了实现弧形阵列SAR系统的静止目标聚焦成像,提出了适用于弧形阵列SAR系统的方位多通道重建方法,在进行多通道重构和合并之前,对方位通道间的附加相位进行补偿,避免方位欠采样,然后根据弧形阵列方位多通道脉冲响应重构方位多通道数据,该方法可以很好的抑制了虚假目标,最后通过仿真实验验证了该方法的可行性。

基于JADE-斜投影的鲁棒波束形成算法

针对矩阵重构类波束形成算法对阵列幅相误差敏感的问题,提出一种基于盲源信号分离和斜投影的矩阵重构鲁棒波束形成算法。首先,依靠盲源分离技术得到接收信号和混合矩阵,结合期望信号先验信息完成混合矩阵中信号导向矢量的搜索。然后,利用盲源分离得到的信号协方差矩阵完成阵列幅相误差估计。最后,基于幅相误差校准的混合矩阵和斜投影思想,构建各干扰的斜投影算子,将接收数据分别向干扰斜投影空间进行投影,得到对应的干扰信号,完成干扰噪声协方差矩阵重构。仿真结果表明,所提方法对阵列幅相误差具有较好的鲁棒性,验证了算法的有效性。

基于关联规则与重构误差的二次系统故障检测方法

二次系统是否可靠直接关系整个变电站乃至系统能否安全可靠运行。随着高比例新能源并网,如何有效检测二次系统故障愈发重要。针对现有逻辑回路的故障方法对数据完备性要求较高而难以实际应用,现有二次设备的故障检测方法难以辨识正常数据和故障数据的微小差异导致计算精度无法保障的问题,提出基于关联规则与重构误差的二次系统故障检测方法。首先,利用Apriori算法求出故障报警信息与逻辑回路中故障装置的关联规则,实现逻辑回路故障快速诊断;然后,利用正常二次设备的运行数据训练个体判别器,通过衡量待判别数据的重构误差来判别二次设备运行状态,并利用集成学习模型量化设备当前故障检测概率;最后,对集成学习模型进行集成优化,以提高二次设备异常预警的可信度。利用河南省某变电站实际运行数据集进行仿真测试,验证了所提方法的有效性和准确性。

基于深度展开ADMM网络的稳健自适应波束形成

阵列通道间的幅相误差导致导向矢量失配,会严重退化自适应波束形成的性能。现有稳健自适应波束形成(RAB)方法通过引入最差导向矢量失配误差约束或联合估计幅相误差和波束形成器权值矢量,以改善波束形成性能,但这些方法的计算复杂度高,且在有限快拍下性能有限。为此,文中在深度展开框架下提出一种基于交替方向乘子法(ADMM)的RAB网络,以快速实现幅相误差和干扰协方差矩阵的联合估计。首先,建立存在阵列通道幅相误差时的干扰信号稀疏表示模型;然后,根据基于ADMM的幅相误差和干扰稀疏表示系数联合估计算法,设计一种深度展开ADMM(DU-ADMM)网络,该网络的输入为接收到达的干扰信号,输出为幅相误差和干扰稀疏表示系数;最后,利用该网络的输出重构出干扰加噪声协方差矩阵,并生成稳健自适应波束形成器。仿真结果表明,DU-ADMM网络可在单快拍场景下实现RAB,且能够以较少的网络层数更精确地估计出幅相误差,有效降低了计算量,同时可获得更高的输出信干噪比。

基于CP重构的高时间传输效率CP-UFMC接收方法

针对循环前缀(cyclic prefix,CP)不足的情况对循环前缀通用滤波多载波(cyclic prefix-universal filtered multi-carrier,CP-UFMC)系统的影响进行了数学推导,提出了一种基于CP重构的高时间传输效率CP-UFMC接收方法来改善系统性能,并进一步研究了选择性重构方法的性能。之后仿真研究了不同CP长度,不同载波频率偏移和不同正交振幅调制阶数时的系统误符号率(symbol error rate,SER)表现。仿真结果表明,基于CP重构的CP-UFMC接收方法可以改善CP不足导致的系统SER恶化问题,使其接近CP充足时的SER,并优于对比的其他通用滤波多载波系统接收方法。

Learning Sequential and Structural Dependencies Between Nucleotides for RNA N6-Methyladenosine Site Identification

N6-methyladenosine(m6A)is an important RNA methylation modification involved in regulating diverse biological processes across multiple species.Hence,the identification of m6A modification sites provides valuable insight into the biological mechanisms of complex diseases at the post-transcriptional level.Although a variety of identification algorithms have been proposed recently,most of them capture the features of m6A modification sites by focusing on the sequential dependencies of nucleotides at different positions in RNA sequences,while ignoring the structural dependencies of nucleotides in their threedimensional structures.To overcome this issue,we propose a cross-species end-to-end deep learning model,namely CR-NSSD,which conduct a cross-domain representation learning process integrating nucleotide structural and sequential dependencies for RNA m6A site identification.Specifically,CR-NSSD first obtains the pre-coded representations of RNA sequences by incorporating the position information into single-nucleotide states with chaos game representation theory.It then constructs a crossdomain reconstruction encoder to learn the sequential and structural dependencies between nucleotides.By minimizing the reconstruction and binary cross-entropy losses,CR-NSSD is trained to complete the task of m6A site identification.Extensive experiments have demonstrated the promising performance of CR-NSSD by comparing it with several state-of-the-art m6A identification algorithms.Moreover,the results of cross-species prediction indicate that the integration of sequential and structural dependencies allows CR-NSSD to capture general features of m6A modification sites among different species,thus improving the accuracy of cross-species identification.