优化特征提取的互动式人脸活体检测研究

针对人脸识别系统中出现的通过照片或视频“欺诈”解锁问题，提出一种活体检测方法，通过随机指令判断被检测对象是否为真人。利用HOG特征和随机森林算法对摄像头采集的图像进行人脸检测，预测脸部68个特征点位置，把68个特征点位置和脸部位置的相对位置归一化后作为姿态的特征，提取的姿态特征与SVM分类器相结合，训练出分类效果较好的头部姿态估计分类器。通过多次实验，对特征提取方法进行优化，进一步提高检测准确率。最后，使用随机互动式命令序列对被检测人发出指令，实现活体检测。该方法对头部姿态估计具有较高的鲁棒性，并且可以有效地防范照片和视频认证攻击。     

一种图像校正的偏振光罗盘系统的研究

导航传感器在探险、应急、精确制导武器、船舶、航空器导航和定位系统中起着关键作用。针对偏振光罗盘在倾斜状态下误差较大的问题,提出了一种利用加速度计和陀螺仪对偏振光罗盘进行图像校正的方法,设计了一种基于陀螺仪校正的偏振光罗盘系统。首先利用加速度计和陀螺仪计算得到载体姿态角,对偏振光图像数据进行图像校正,采用Stokes矢量法解算大气偏振模式分布,进而提取导航特征点,最后对特征点进行拟合解算出航向角信息。并且进行了静态和动态测试实验,实验结果表明,该算法能够有效的对偏振光罗盘姿态变化引起的误差进行补偿,可以将偏振光罗盘的航向角测量误差控制在1.86°之内,平均误差为0.09°。     

图像分类中基于分类矢量量化的视觉词袋模型

特征表示是图像识别和分类的基础，视觉词袋是一种图像的特征表示方法。分析现有视觉词典构建方法的不足，提出一种新的视觉词典构建方法。首先利用梯度方差把特征矢量分为光滑类和边缘类，然后分别针对不同类别的特征矢量进行视觉词典的构建，最后根据两类视觉词典生成视觉词袋。图像分类实验表明，提出的新方法能提高分类准确率。     

鲸鱼优化支持向量机的短期风电功率预测

为提高风电预测的精度,提出一种鲸鱼优化支持向量机SVM(support vector machine)的组合预测模型。该模型针对风电序列的非平稳波动特性,首先应用集合经验模态分解技术EEMD(ensemble empirical mode de?composition)将原始风电序列分解为一系列不同特征尺度的子序列;并引入鲸鱼优化算法WOA(whales optimiza?tion algorithm)解决SVM中学习参数选择难的问题,进而对各子序列建立WOA_SVM预测模型;最后,叠加各子序列的预测值以得到最终预测值。仿真表明,所提EEMD_WOA_SVM模型具有较高的风电预测精度,显著优于其他基本模型。     

Benchmark value determination of energy efficiency indexes for coal-fired power units based on data mining methods

The operational optimisation of coal-fired power units is important for saving energy and reducing losses in the electric power industry. One of the key issues is how to determine the benchmark values of the energy efficiency indexes of the units. Therefore, a new framework for determining these benchmark values is proposed, based on data mining methods. First, the energy efficiency key performance indicators (KPIs) associated with the net coal consumption rate (NCCR) were selected based on the domain knowledge. Second, the decision-making samples with minimal NCCR were acquired with the fuzzy C-means (FCM) clustering algorithm, and the corresponding clustering centres were employed as the benchmark values. Finally, based on the support vector regression (SVR) algorithm, the target values of the NCCR were obtained with the KPIs as input, and the energy saving potential was evaluated by comparing the target values with the historical values of the NCCR. An actual on-duty 1000 MW unit was taken as study unit, and the results show that the energy saving potential is remarkable when the operators adjust the KPIs based on the calculated benchmark values.     

基于极点配置的推力矢量伺服系统控制策略研究

针对推力矢量伺服系统提出了一种采用状态反馈对系统极点进行配置的方法,同时采用该方法设计了Luenberger状态观测器,在此基础上实现了伺服系统的稳态控制。通过仿真和实验表明,采用该方法设计的推力矢量伺服系统的性能优于基于输出反馈的PI-陷波滤波控制的系统性能。     

A Minimalist Single-Layer Metasurface for Arbitrary and Full Control of Vector Vortex Beams

Vector vortex beams (VVBs) possess ubiquitous applications from particle trapping to quantum information. Recently, the bulky optical devices for generating VVBs have been miniaturized by using metasurfaces. Nevertheless, it is quite challenging for the metasurface-generated VVBs to possess arbitrary polarization and phase distributions. More critical is that the VVBs' annular intensity profiles demonstrated hitherto are dependent on topological charges and are hence not perfect, posing difficulties in spatially shared co-propagation of multiple vortex beams. Here, a single-layer metasurface to address all those aforementioned challenges in one go is proposed, which consists of two identical crystal-silicon nanoblocks with varying positions and rotation angles (i.e., four geometric parameters throughout). Those four geometric parameters are found to be adequate for independent and arbitrary control of the amplitude, phase, and polarization of light. Perfect VVBs with arbitrary polarization and phase distributions are successfully generated, and the constant intensity profiles independent of their topological charges and polarization orders are demonstrated. The proposed strategy casts a distinct perception that a minimalist design of just one single-layer metasurface can empower such robust and versatile control of VVBs. That provides promising opportunities for generating more complex vortex field for advanced applications in structural light, optical micromanipulation, and data communication.     

Using electroencephalogram to continuously discriminate feelings of personal thermal comfort between uncomfortably hot and comfortable environments

Thermal comfort is an important factor for the design of buildings. Although it has been well recognized that many physiological parameters are linked to the state of thermal comfort or discomfort of humans, how to use physiological signal to judge the state of thermal comfort has not been well studied. In this paper, the feasibility of continuously determining feelings of personal thermal comfort was discussed by using electroencephalogram (EEG) signals in private space. In the study, 22 subjects were exposed to thermally comfortable and uncomfortably hot environments, and their EEG signals were recorded. Spectral power features of the EEG signals were extracted, and an ensemble learning method using linear discriminant analysis or support vector machine as a sub-classifier was used to build the discriminant model. The results show that an average discriminate accuracy of 87.9% can be obtained within a detection window of 60 seconds. This study indicates that it is feasible to distinguish whether a person feels comfortable or too hot in their private space by multi-channel EEG signals without interruption and suggests possibility for further applications in neuroergonomics.     

Complementary Kalman Filter as a Baseline Vector Estimator for GPS-Based Attitude Determination

The Global Positioning System (GPS) offers the interferometer for attitude determination by processing the carrier phase observables. By using carrier phase observables, the relative positioning is obtained in centimeter level. GPS interferometry has been firstly used in precise static relative positioning, and thereafter in kinematic positioning. The carrier phase differential GPS based on interferometer principles can solve for the antenna baseline vector, defined as the vector between the antenna designated master and one of the slave antennas, connected to a rigid body. Determining the unknown baseline vectors between the antennas sits at the heart of GPS-based attitude determination. The conventional solution of the baseline vectors based on least-squares approach is inherently noisy, which results in the noisy attitude solutions. In this article, the complementary Kalman filter (CKF) is employed for solving the baseline vector in the attitude determination mechanism to improve the performance, where the receiversatellite double differenced observable was utilized as the measurement. By using the carrier phase observables, the relative positioning is obtained in centimeter level. Employing the CKF provides several advantages, such as accuracy improvement, reliability enhancement, and real-time assurance. Simulation results based on the conventional method where the least-squares approach is involved, and the proposed method where the CKF is involved are compared and discussed.     

Nonlinear Factor-Augmented Predictive Regression Models with Functional Coefficients

This article introduces a new class of functional-coefficient predictive regression models, where the regressors consist of auto-regressors and latent factor regressors, and the coefficients vary with certain index variable. The unobservable factor regressors are estimated through imposing an approximate factor model on high dimensional exogenous variables and subsequently implementing the classical principal component analysis. With the estimated factor regressors, a local linear smoothing method is used to estimate the coefficient functions (with appropriate rotation) and obtain a one-step ahead nonlinear forecast of the response variable, and then a wild bootstrap procedure is introduced to construct the prediction interval. Under regularity conditions, the asymptotic properties of the proposed methods are derived, showing that the local linear estimator and the nonlinear forecast using the estimated factor regressors are asymptotically equivalent to those using the true latent factor regressors. The developed model and methodology are further generalized to the factor-augmented vector predictive regression with functional coefficients. Finally, some extensive simulation studies and an empirical application to forecast the UK inflation are given to examine the finite-sample performance of the proposed model and methodology.