Photophysical investigation of the formation of defect levels in P doped ZnO thin films

Zinc oxide (ZnO) offers a major disadvantage of asymmetry doping in terms of reliability, stability, and reproducibility of p-type doping, which is the main hindrance in realization of optoelectronic devices. The problem is even more complicated due to formation of various native defects in unintentionally doped n-type ZnO. The realization of p-type conductivity in doped ZnO requires an in-depth understanding of the formation of an effective shallow acceptor, as well as donor-acceptor compensation. Photophysical properties such as photoconductivity along with photoluminescence (PL) studies have unprecedentedly and effectively been utilized in this work to monitor the evolution of various in-gap defects. Phosphorus (P) doped ZnO thin films have been grown by RF magnetron sputtering under various Ar to O₂ gas ratios to investigate the effect of O₂ on the donor-acceptor compensation by comprehensive photoconductivity measurements supported by the PL studies. Initial elemental analyses indicate presence of abundant zinc vacancies (V_Zn) in O-rich ambience. The results predict that P sits in the zinc (Zn) site rather than the oxygen (O) site causing the formation of P_Zn–2V_Zn acceptor-like defects, which compensates the donor defects in P doped ZnO films. Photocurrent spectra uniquely reveal presence of more oxygen vacancies (V_O) defects states in lower O₂ flow, which gets compensated with an increase in the O₂ flow. Successive photocurrent transients indicate probable presence of more V_O in the films grown with lower O₂ flow and more V_Zn in higher O₂ flow. Overall the photosensitivity measurements clearly present that O-rich ambience expedites the formation of acceptor defects which are compensated, thereby lowering the dark current and enhancing the ultraviolet photosensitivity.     

Friction compensation controller for load varying machine tool feed drive

The load applied to a machine tool feed drive changes during the machining process as material is removed. This load change alters the Coulomb friction of the feed drive. Because Coulomb friction accounts for a large part of the total friction the friction compensation control accuracy of the feed drives is limited if this nonlinear change in the applied load is not considered. This paper presents a new friction compensation method that estimates the machine tool load in real time and considers its effect on friction characteristics. A friction observer based on a Kalman filter with load estimation is proposed for friction compensation control considering the applied load change. A specially designed feed drive testbed that enables the applied load to be modified easily was constructed for experimental verification. Control performance and friction estimation accuracy are demonstrated experimentally using the testbed.     

Stress distribution in thick-walled cylinder due to non-uniform radial pressure on the example of reinforcement corrosion in concrete

This paper presents an analytical solution to the non-uniform pressure on thick-walled cylinder. The formulation is based on the linear elasticity theory (plain strain) and stress function method. As an example, the proposed solution is used to model the stress distribution due to non-uniform steel reinforcement corrosion in concrete. The model is formulated considering different scenarios of corrosion pressure distribution. It is validated against the finite element model for different cases of non-uniform pressure distributions. The results show that the corrosion-induced cracks are likely to start just beyond the anodic zone. This is confirmed by the experimental tests on concrete cylinder exposed to non-uniform accelerated corrosion of steel reinforcement. The model can be effectively used to calculate the distribution of corrosion-induced stresses in concrete.     

Design and validation of robotic bionic eye with multiple flexible ropes parallel mechanism inspired by oculomotor law

Producing a stable and agile bionic eye for visual image acquisition in robotics is a challenging task. In this paper, we design a bionic eye with mirror-symmetric distribution and cross-connection of flexible ropes. This mechanism is based on oculomotor law and the physiological structure of the extraocular muscles (EOMs). Specifically, the basic structural parameters are determined by Listing’s law, and the unique connection of the flexible ropes can realize the functions of the recti and oblique muscles. Furthermore, to mimic the trochlea structure, a pulley mechanism is constructed to permit the free movement of the flexible ropes. Through simulation and physical experiments, it is demonstrated that the bionic eye mechanism can move with agility under the structural parameters. The experimental results indicate that the proposed bionic eye mechanism has a superior motion accuracy of 2.798 mm, which is 6.7% of the maximum motion distance, and the repeatable accuracy of the mechanism can up to 0.210 mm.     

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.     

Detection of correlation characteristics between financial time series based on multi-resolution analysis

Interactions between financial time series are complex and changeable in both time and frequency domains. To reveal the evolution characteristics of the time-varying relations between bivariate time series from a multi-resolution perspective, this study introduces an approach combining wavelet analysis and complex networks. In addition, to reduce the influence the phase lag between the time series has on the correlations, we propose dynamic time-warping (DTW) correlation coefficients to reflect the correlation degree between bivariate time series. Unlike previous studies that symbolized the time series only based on the correlation strength, the second-level symbol is set according to the correlation length during the coarse-graining process. This study presents a novel method to analyze bivariate time series and provides more information for investors and decision makers when investing in the stock market. We choose the closing prices of two stocks in China’s market as the sample and explore the evolutionary behavior of correlation modes from different resolutions. Furthermore, we perform experiments to discover the critical correlation modes between the bull market and the bear market on the high-resolution scale, the clustering effect during the financial crisis on the middle-resolution scale, and the potential pseudo period on the low-resolution scale. The experimental results exactly match reality, which provides powerful evidence to prove that our method is effective in financial time series analysis.     

论精神损害的国家赔偿责任

国家的侵权行为造成相对人的精神损害应当承担赔偿责任是当今世界国家赔偿制度的发展潮流与趋势.确立精神损害的国家赔偿责任对社会公平正义的实现、人权的保护及促使国家公职人员依法行使职权具有重大的意义,因而应在基于我国国情的基础上,构建我国的精神损害国家赔偿制度.     

DISPERSION COMPENSATION CFBG IN100km 10Gbit／s RECIRCULATING SYSTEMS

A 10Gbit/s recirculating system is configured with Chirped Fiber Bragg Grating (CFBG) for the dispersion compensation. For the first time, the transmission distance in the loop reaches 1000km with bit error rate of 10-9. The effect of the group delay ripple of the fiber grating is also investigated in the recirculating systems, and it is shown that the transmission distance is limited to 4 cycles (4×167.1km ) in the loop with the power penalty fluctuation below 1.0dB. Thus the group delay ripple should be reduced to allow for the wavelength drift of±5GHz. At the end of this letter, the principles are given for designing long haul recirculating systems with dispersion compensation CFBG.     

一种单相串联混合电力有源滤波器的研究

文中对串联混合电力滤波器抑制谐波的原理进行了分析。为了检测单项系统的谐波电流,提出一种基于虚拟三相法和瞬时无功理论的谐波检测方法,并且对串联滤波逆变器的直流侧电压的控制进行了讨论。仿真结果证明了其有效性。     

色散补偿及EDFA增益均衡的一体化

提出一种新的色散补偿及EDFA增益均衡的一体化方法。在10Gb／s的光通信系统和600km G652光纤中首次实现了基于16个碉啾光纤布拉格光栅(FBG)的色散补偿及掺饵光纤放大器(EDFA)的增益均衡。16个FBG的波长间隔为1．6nm，波长覆盖C波段1529～1556nm约27nm的范围。经600km G652光纤传输后，10Gb／s的NRZ码传输功率代价小于2dB。