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.     

Flower-shaped ZnO nanomaterials for low-temperature operations in NOX gas sensors

In this study, we synthesized nanostructured zinc oxide (ZnO) by using various concentrations (0–0.05 M) of cetyltrimethylammonium bromide (CTAB) as a surfactant to optimize its morphology for gas sensor applications. The optimization process was used to elucidate the morphology effects (rod-shaped and flower-shaped morphologies). The morphologies were investigated through scanning electron microscopy, in which the assembly of nanorods leading to a spherical microstructure with a CTAB concentration of 0.005 M was observed. Brunauer–Emmett–Teller isotherm measurements revealed a surface area of 7.928 g/m² for the flower-like morphology, which was relatively higher than those of other CTAB-assisted morphologies. Such morphological features were expected to contribute toward high-performance gas-sensing. The effect of morphology variation on the resistance of ZnO microstructures was used for gas measurements. Among the varied morphologies, a sample with a spherical flower-shaped morphology exhibited a very high response at low temperatures (~29 at 25 °C) toward NO_X gas (0.75 ppm) and a high selectivity toward NO_x among ammonia (NH₃), toluene (C₆H₅CH₃), carbon monoxide (CO), acetone (CH₃COCH₃), and ethanol (C₂H₅OH). Raman and photoluminescence spectroscopy analyses unraveled the presence of a high density of oxygen vacancies in the sample, thereby suggesting a close link between the defective nature of the sample and the high response of the flower-like ZnO at low temperatures.     

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.     

基于深度学习的传感器优化布置方法

针对传感器优化布置(optimal sensor placement,简称OSP)问题,提出了一种新的使用深度神经网络的解决方案,并以简化的桥梁形状的桁架结构中的振动测试传感器优化为例进行了验证。首先,选择一种传统的传感器优化布置方法,对自动化生成的大量不同的桁架结构分别进行传感器优化布置计算,将所得优化布置结果在进行数据预处理后构建出深度学习方法所需要的训练集与验证集;其次,使用Python语言和深度学习框架TensorFlow设计实现与本研究问题适配的深度神经网络模型并训练;然后,随机生成了新的桁架结构参数;最后,将深度神经网络输出的传感器布置结果和传统方法的计算结果进行了比较,验证了本研究方法的有效性以及在速度上、可移植性与可扩展性方面的性能优势。     

Pressure sensor matrix for indirect measurement of grip and push forces exerted on a handle

To date, a large part of workers is exposed to vibrations (23% in Europe) which can negatively impact on their health. This work discusses the importance of measuring grip and push forces in the context of hand-arm vibration tests, bearing in mind the state-of-art of current standards. It proposes a method for indirect measurement of coupling forces using a matrix of polymeric pressure capacitive sensors and discusses the model used for defining these quantities. The matrix of pressure sensors is wrapped around the tool handle and the acting forces, exchanged with the handle, are derived from the pressure values measured by the matrix. Calibration is presented and the effect of curvature is discussed. The work continues with the experimental validation of the model proposed for push force measurements carried out through lifting tests using known masses with a cylindrical handle. An experimental correction coefficient is defined in correlation to the type of grip. The method for measuring the push force, thus corrected, is assessed by means of push force tests on an instrumented handle. Finally the experimental data are analysed in order to assess the uncertainty of the proposed method for measuring the push force, highlighting the contribution of the different sources of uncertainty. The proposed measurement method allows to measure the push and the grip force (known influencing quantities for the measurement of the hand-arm vibration) during tool test and without modifying the handles.     

双弹性板矩形空腔内噪声的有源力控制

研究了在弹性板上施加不同参数的有源力对腔内噪声进行控制的问题。构建双弹性板构成的封闭矩形声腔模型，对封闭声腔的结构 声耦合特性进行分析，推导了在封闭声腔有源力控制作用下声压计算公式，给出采用有源力控制的最优控制模型。分析了弹性板在不同参数的次级力源激励下腔内局部声场的声压级响应，并建立了仿真模型对局部声场的减噪效果进行分析。结果表明，合理选择次级力源的参数对控制效果的影响较为明显，且次级力源的个数并非越多越好。     

Input variable scaling for statistical modeling

Input variable scaling is one of the most important steps in statistical modeling. However, it has not been actively investigated, and autoscaling is mostly used. This paper proposes two input variable scaling methods for improving the accuracy of soft sensors. One method statistically derives the input variable scaling factors; the other one uses spectroscopic data of a material whose content is estimated by the soft sensor. The proposed methods can determine the scales of the input variables based on their importance in output estimation. Thus, it can reduce the negative effects of input variables which are not related to an output variable. The effectiveness of the proposed methods was confirmed through a numerical example and industrial applications to a pharmaceutical and a distillation processes. In the industrial applications, the proposed methods improved the estimation accuracy by up to 63% compared to conventional methods such as autoscaling with input variable selection.