基于MCGS的洗油空穴反应实验装置计算机控制系统设计

介绍了基于MCGS的洗油空穴反应实验装置远程计算机自动控制系统的硬件、软件设计及其实现过程,为后续进一步的研究工作提供理论和实践基础。     

基于UG软件的手机模具设计及数控加工仿真

数控设备在加工制造精密复杂零件方面得到了广泛应用.数控加工要达到预期的加工效果,需要编制高质量的数控程序.重点介绍了UG软件中模具设计及数控加工模块,通过手机实例分析了设计、加工步骤.利用UG软件进行数控加工,可以方便建立实体模型,直观观察刀具的运动过程,提高程序的正确性和安全性,提高工作效率.     

Nonlinear adaptive filtering using kernel‐based algorithms with dictionary adaptation

Nonlinear adaptive filtering has been extensively studied in the literature, using, for example, Volterra filters or neural networks. Recently, kernel methods have been offering an interesting alternative because they provide a simple extension of linear algorithms to the nonlinear case. The main drawback of online system identification with kernel methods is that the filter complexity increases with time, a limitation resulting from the representer theorem, which states that all past input vectors are required. To overcome this drawback, a particular subset of these input vectors (called dictionary) must be selected to ensure complexity control and good performance. Up to now, all authors considered that, after being introduced into the dictionary, elements stay unchanged even if, because of nonstationarity, they become useless to predict the system output. The objective of this paper is to present an adaptation scheme of dictionary elements, which are considered here as adjustable model parameters, by deriving a gradient‐based method under collinearity constraints. The main interest is to ensure a better tracking performance. To evaluate our approach, dictionary adaptation is introduced into three well‐known kernel‐based adaptive algorithms: kernel recursive least squares, kernel normalized least mean squares, and kernel affine projection. The performance is evaluated on nonlinear adaptive filtering of simulated and real data sets. As confirmed by experiments, our dictionary adaptation scheme allows either complexity reduction or a decrease of the instantaneous quadratic error, or both simultaneously. Copyright © 2015 John Wiley & Sons, Ltd.     

Fault estimation and accommodation for networked control systems with nonuniform sampling periods

This paper deals with the problem of fault estimation and accommodation for a class of networked control systems with nonuniform uncertain sampling periods. Firstly, the reason why the adaptive fault diagnosis observer cannot be applied to networked control systems is analyzed. Based on this analysis, a novel robust fault estimation observer is constructed to estimate both continuous‐time fault and system states by using nonuniformly discrete‐time sampled outputs. Furthermore, using the obtained states and fault information, a nonuniformly sampled‐data fault tolerant control law is designed to preserve the stability of the closed‐loop system. The proposed scheme can not only guarantee the impact of continuous‐time uncertainties and discrete‐time sampled estimation errors on the faulty system to satisfy a H _∞ performance index but also repress the negative effect of the unknown intersample behavior of continuous‐time fault by use of an inequality technique. Finally, simulation results are included to demonstrate the feasibility of the proposed method. Copyright © 2014 John Wiley & Sons, Ltd.     

Prediction of rock mass P wave velocity using blasthole drilling information

In this paper, the development of the models for the prediction of rock mass P wave velocity is presented. For model development, the database of 53 cases including widely used and recorded drilling parameters and P wave velocity was constructed from the field studies conducted in 13 open pit lignite mines. Both conventional linear, non-linear multiple regression and Adaptive Neuro Fuzzy Inference System (ANFIS) were used for model development. Prediction performance indicators showed that ANFIS model presented the best performance and it can successfully be used for the preliminary prediction of P wave velocities of rock masses.     

Terrain classification and adaptive locomotion for a hexapod robot Qingzhui

Frontiers of Mechanical Engineering - Legged robots have potential advantages in mobility compared with wheeled robots in outdoor environments. The knowledge of various ground properties and...     

Phenyl Ethylammonium Iodide introduction into inverted triple cation perovskite solar cells for improved VOC and stability

The efficiency of more than 25% in organic-inorganic hybrid perovskite solar cells has made them very attractive in the pursuit of cheaper alternatives to Si-based devices. However, the stability of the perovskite solar cells was challenging, given their high susceptibility to moisture. Very few reports have emerged in this regard that investigated the influence of introducing large cations into a triple cation perovskite (TC-PVS), with several studies limited to single and dual cation perovskites. Further, the crystallization of TC-PVS on a polymer surface such as PEDOT is not straightforward, and their inclusion in inverted solar cell devices was limited. In this work, we investigated the impact of incorporating Phenyl ethyl ammonium cation into FAMACs triple cation composition. We demonstrated improvements in the crystallinity and more uniform coverage with little to no pinholes and smooth morphology for an optimum PEA amount of 1.67% in the precursor solution. The superior morphology, along with a passivation effect from a quasi 2D phase, led to increased photoluminescence and minority carrier lifetimes. Corresponding inverted photovoltaic devices prepared with PEA showed increased open-circuit voltage from 0.89 V for a control sample to 0.95 V for 1.67% PEA and 0.98 V for 5% PEA, doped devices in an inverted configuration. The efficiency, as a result, increased from 11.27% for a control device to 14.85% for a 1.67% PEA doped device. Further, PEA doped devices showed improved operational and thermal stability attributed to the higher moisture tolerance and light-soaking ability of the PEA doped TC-PVS compared to the undoped TC-PVS.     

A review on modeling of solar photovoltaic systems using artificial neural networks,fuzzy logic,genetic algorithm and hybrid models

The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.