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.     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

动态高压微射流处理对方竹笋膳食纤维理化及结构特性的影响

以方竹笋中提取的膳食纤维为研究对象，采用动态高压微射流（dynamic high-pressure micro-fluidization, DHPM）在不同压力条件（0，50，100，150，200 MPa）下进行处理，探究其对竹笋膳食纤维（bamboo shoots dietary fiber, BSDF）理化和结构特性的影响。结果表明，随着处理压力的增大，BSDF粒径先增大后减小，当处理压力为150 MPa时，粒径最小，为（370±11） nm，此条件下BSDF的持水力、持油力和膨胀力达到最大，较对照组分别提高了47.74%，50.54%，61.27%，且差异显著（P<0.05）。红外光谱分析表明DHPM处理不会改变BSDF的官能团，但会使BSDF内部的部分氢键断裂和半纤维素、木质素等发生降解；X射线衍射和热重分析表明DHPM处理不会引起BSDF的晶体结构改变，但晶体有序度会下降，进而导致其热稳定性降低；微观结构分析显示DHPM处理会使BSDF颗粒尺寸减小、表面粗糙、组织松散，且当处理压力为200 MPa时，颗粒发生团聚。综上，DHPM可以有效改善BSDF的理化性质，在膳食纤维改性方面具有一定的应用价值。     

KDP/DKDP倍频晶体主截面定位方法

KDP/DKDP倍频晶体是惯性约束聚变系统中的关键元件,其主截面方向与晶体相位匹配角、晶体吸收系数紧密相关。为了实现KDP/DKDP倍频晶体主截面方向的高精度定位,本文提出一种光强测量间接定位方法。通过激光器结合稳功率仪及半波片输出稳定线偏振光,同时旋转相互正交的起偏器与检偏器可获得晶体的最佳消光位置即为主截面方向。推导了该测量系统光强的琼斯矩阵模型,给出了光强与起偏器、检偏器角度间的关系表达式。采用最小二乘方法拟合经过起偏器、倍频晶体及检偏器的光强变化曲线,从而可精确定位倍频晶体主截面的方向。通过计算机仿真模拟和实验验证了该方法的正确性和可行性。实验表明,该方法定位的重复测量精度优于0.02°,满足惯性约束聚变系统中KDP/DKDP倍频晶体主截面的定位控制精度要求。     

利用平面约束的月球车立体匹配方法

为了满足月球车视觉系统对检测障碍物检测的时效性和可靠性的需求，提出了一种基于平面约束和自适应惩罚参数的半全局立体匹配算法。首先，对极线校正后的两幅图像进行SIFT特征点提取与匹配，同时提取边缘特征；然后，利用匹配的SIFT特征点拟合空间平面，并根据平面估计左右图像所有像素点的视差搜索范围；最后，基于传统的半全局匹配算法，采用自适应惩罚参数的策略对左右图像进行立体匹配。实验结果表明：所提出的算法有效地降低了计算复杂度，其计算复杂度只有传统方法的19.9%，对于视差不连续区域以及遮挡区域都能够获得正确的匹配结果。较传统半全局匹配方法无论在速度还是匹配精度上都得到明显提高，为对立体匹配的实际应用奠定了基础。     

Recursive generalized extended least squares and RML algorithms for identification of bilinear systems with ARMA noise

Bilinear systems are considered as a particular class of nonlinear systems including the state variables which are typically used for online identification. By using a recursive identification method and the maximum likelihood principle, this paper presents two recursive-based algorithms to identify the parameters of bilinear in parameter systems with ARMA noise. In this regard, recursive generalized extended least squares (RGELS) and recursive Maximum Likelihood (RML) algorithms have been proposed for identification of bilinear systems. These algorithms can be used as an alternative choice in system identification with acceptable performance. The proposed algorithms estimate the correlated noise parameters with high accuracy by making full use of the measurement data. Simulation results indicate that the proposed algorithms are effective for online identification of bilinear in parameter systems with high convergence speed.     

基于激光三角法的汽车同步带齿形轮廓参数测量

针对目前汽车同步带齿形轮廓参数测量过程中存在的测量效率低和人为因素影响大等问题,提出了一种基于激光三角法的汽车同步带齿形轮廓参数非接触式测量方案,并设计了相应的汽车同步带齿形测量装置。该装置通过激光位移传感器与汽车同步带传动速度的配合,对汽车同步带齿形轮廓数据进行采集,并提出一种改进后的分割算法对采集到的齿形截面轮廓数据进行特征分割。通过对分割后齿形数据的拟合,实现了齿形轮廓曲线特征的分段重构并获得被测带齿的轮廓参数。基于激光三角法对汽车同步带齿形轮廓参数进行测量,实现了对齿形轮廓参数自动测量提高了测量效率避免了人为因素的影响,保证了测量精度,有望在汽车同步带实际生产中得到应用。     

Echo signal envelope fitting based signal processing methods for ultrasonic gas flow-meter

The echo signal of the ultrasonic gas flow-meter is difficult to locate, and the computation of signal processing methods proposed by others is extensive which affects the meter’s real-time performance. Aiming at solving these problems the echo signal envelopes under different flow rates are analyzed. And three kinds of signal processing methods based on echo signal envelope fitting are proposed. The shape of the echo signal remains the “approximate spindle shape” as the flow rate increases, and the envelope gradient of the middle parts of the upper envelope’s rising section and the lower envelope’s falling section remains the same at different flow rates, so the mathematical models of the two sections are established to obtain the envelope gradient curves of the two sections. With the envelope gradient curves, the ranges of envelope and peak points that linearly distributed are obtained. The least squares fitting is performed on those peak points to obtain two feature straight lines for respectively representing the upper envelope’s rising section and the lower envelope’s falling section. According to the spatial characteristics of the feature straight lines, the points on the feature straight lines are selected as the feature points for quickly locating the echo signal. According to the offline verification and comparison, the best one of three kinds of signal processing methods is selected, and realized on the hardware system of the two-channel ultrasonic gas flow-meter. The transmitter of two-channel ultrasonic gas flow-meter is developed based on FPGA & DSP dual core structure. It utilizes the parallel processing capacity and logic control ability of FPGA to realize the controlling of the high-speed ADC & DAC and the storage of the data. At the same time, it adopts the high-speed computing capacity of DSP to implement the digital signal processing method. The gas flow calibration experiments were carried out in a national accredited testing agency to verify the effectiveness of the signal processing methods and system. The experimental results show that the improved signal processing method based on echo signal upper and lower envelope fitting can quickly and accurately locate the echo signal. And the measurable range of the ultrasonic gas flow meter based on this signal processing method is broadened to 10 m³/h to 1,300 m³/h, and the turndown ratio is broadened to 1:130.