Statistical Piezotronic Effect in Nanocrystal Bulk by Anisotropic Geometry Control

Utilizing inner-crystal piezoelectric polarization charges to control carrier transport across a metal-semiconductor or semiconductor–semiconductor interface, piezotronic effect has great potential applications in smart micro/nano-electromechanical system (MEMS/NEMS), human-machine interfacing, and nanorobotics. However, current research on piezotronics has mainly focused on systems with only one or rather limited interfaces. Here, the statistical piezotronic effect is reported in ZnO bulk composited of nanoplatelets, of which the strain/stress-induced piezo-potential at the crystals’ interfaces can effectively gate the electrical transport of ZnO bulk. It is a statistical phenomenon of piezotronic modification of large numbers of interfaces, and the crystal orientation of inner ZnO nanoplatelets strongly influence the transport property of ZnO bulk. With optimum preferred orientation of ZnO nanoplatelets, the bulk exhibits an increased conductivity with decreasing stress at a high pressure range of 200–400 MPa, which has not been observed previously in bulk. A maximum sensitivity of 1.149 µS m⁻¹ MPa⁻¹ and a corresponding gauge factor of 467–589 have been achieved. As a statistical phenomenon of many piezotronic interfaces modulation, the proposed statistical piezotronic effect extends the connotation of piezotronics and promotes its practical applications in intelligent sensing.     

Trajectories of knowledge economy in SSA and MENA countries

In the first critical assessment of knowledge economy dynamic paths in Africa and the Middle East, but for a few exceptions, we find overwhelming support for diminishing cross-country disparities in knowledge-based economy dimensions. The paper employs all the four components of the World Bank's Knowledge Economy Index (KEI): economic incentives, innovation, education, and information infrastructure. The main finding suggests that sub-Saharan African (SSA) and the Middle East and North African (MENA) countries with low levels of KE dynamics and catching-up their counterparts of higher KE levels. We provide the speeds of integration and time necessary to achieve full (100%) integration. Policy implications are also discussed.     

Dielectric,ferroelectric, and piezoelectric properties of lead-free Ba0.95Ca0.05Ti1-xZrxO3 ceramics

Abstract

Ba_0.95Ca_0.05Ti_1-xZr_xO₃ (BCTZO) ceramics were prepared by a solid state reaction method. The samples were characterized by X-ray diffraction (XRD), Scanning electron microscopy (SEM) and X-ray absorption near edge structure (XANES). The ceramics exhibit a pure perovskite structure. The average grain size gradually decreases with increasing Zr concentration. XANES results indicate that the intensities of pre-edge peaks dropped with increasing Zr concentration. The BCTZO ceramic of x?=?0.05 has the optimum electrical properties with the maximum dielectric constant (ε'_m), remanent polarization (2P_r), coercive electric field (2E_c) and piezoelectric charge constant (d₃₃) of 7,244, 12.54 (μC/cm²), 5.29 (kV/cm) and 288 (pC/N), respectively.     

Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.     

混凝土结构的压电声波检测分析

本文介绍了混凝土结构的压电体波和表面波检测的主要进展,对两种压电声波检测的优缺点进行了总结。体波检测设备一般埋入混凝土内部,需要选择合理的检测部位,检测结果较为精确;声表面波检测无需选择特定的部位,但是检测深度有限。在实际检测工作过程中,可以联合两种方法相互验证。     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

PMU based voltage security assessment of power systems exploiting principal component analysis and decision trees

The need for feature selection and dimension reduction is felt as a fundamental step in security assessment of large power systems in which the number of features representing the state of power grids dramatically increases. These large amounts of attributes are not proper to be used for computational intelligence (CI) techniques as inputs, because it may lead to a time consuming procedure with insufficient results and they are not suitable for on-line purposes and updates.This paper proposes a combined method for an online voltage security assessment in which the dimension of the token data from phasor measurement units (PMUs) is reduced by principal component analysis (PCA). Then, the features with different stability indices are put into several categories and feature selection is done by correlation analysis in each category. These selected features are then given to decision trees (DTs) for classification and security assessment of power systems.The method is applied to 39-bus test system and a part of Iran power grid. It is seen from the results that the DTs with reduced data have simpler splitting rules, better performance in saving time, reasonable DT error and they are more suitable for constant updates.     

Electrical and mechanical properties of BZT − xBCT lead-free piezoceramics

In this study, lead-free (1 − x)Ba(Zr_0.2Ti_0.8)O₃ − x(Ba_0.7Ca_0.3)TiO₃ compositions are synthesized via conventional solid oxide route, and the ceramics are fabricated with normal sintering in air. The effects of composition fluctuations on dielectric, piezoelectric, and mechanical properties are investigated. The phase structure and the microstructure are analyzed with X-ray diffraction and scanning electron microscopy. The best dielectric and piezoelectric properties of ε_r = 11 207 and d₃₃ = 330 pC/N were obtained for BZT−0.35BCT and BZT−0.5BCT ceramics, respectively. The mechanical behavior—in terms of Vickers hardness and compressive and flexural strengths—was investigated, and the best mechanical behavior was found in the vicinity of the phase transition boundary with x values between 0.5 and 0.6.     

Experimental research on dynamic response of PZT-5H under impact load

In order to improve the stability of PZT-based sensors, the mechanical, dielectric, ferroelectric and piezoelectric properties of PZT-5H under impact load were studied experimentally by using the separated Hopkinson pressure bar (SHPB) with an electrical output measurement device. At the same time, the experimental study on the material properties of PZT-5H before and after the impact was carried out. The effect of impact cracks on the output voltage of PZT-5H was also analyzed. The results show that the dynamic piezoelectric constants of PZT-5H under low stress impact (10–50 MPa) are different from those under quasi-static state, and the empirical relationship between them and the peak stress is obtained through experiments. The dielectric properties of PZT-5H did not change under low stress impact, but micro-cracks occurred in the material and dielectric loss increased at high frequencies. Under short circuit, the residual polarization intensity of PZT-5H decreases sharply due to impact load. While the impact load causes the secondary polarization and the increase of the residual polarization intensity of PZT under open circuit. When the stress is over 45 MPa, the PZT-5H breaks. The formation of cracks causes abnormal discharge voltage and gap discharge.