基于机器视觉的手机电池表面缺陷检测

针对手机电池表面质量人工检测情况,开发了电池表面缺陷无损检测系统软件。首先电池表面经过倾斜矫正、感兴趣区域提取和字符灰度值修改等预处理操作,通过基于灰度密度分布和灰度差的自适应阈值亮度法对感兴趣区域进行子图像遍历,融合有重合区域的缺陷子图像并滤除没有明显缺陷的区域;然后采用支持向量机多种类分类法,提取二值图像像素分布规律作为训练特征,识别电池表面缺陷种类;最后设计了人机交互界面,确定最佳的可变参数,实验测试缺陷识别率达95%以上。     

随钻测量用MIMU信号处理

为提高随钻测量系统的姿态估计精度,克服传统算法对于非平稳信号处理的局限性,提出基于ARIMA模型的卡尔曼滤波算法。该算法采用时间序列分析方法,建立了随机漂移误差的ARIMA模型,根据实测数据的ACF和PACF,确定微机械陀螺仪模型的各个参数,利用卡尔曼滤波算法进行滤波处理。实验对模型的拟合残差均值与方差作了比较分析,通过卡尔曼滤波处理,ARIMA模型数据零偏减小了1个数量级,滤波方差相比传统AR模型减小了42%。所建立的ARIMA模型与传统时间序列方法建模相比,降低了模型预测误差,滤除MEMS陀螺仪随机漂移误差更为明显,模型对提高陀螺仪输出精度更为有效。     

Spatial filtering velocimeter using frequency shifting by the method of rotating kernel

A new approach of frequency shifting by rotating kernel is proposed to improve the performance of a spatial filtering velocimeter, used to provide accurate velocity information for a vehicle self-contained navigation system. A linear CMOS image sensor was employed both as a spatiotemporal differential spatial filter and as a photodetector. The filtering operation was fully performed in FPGA and is realized by applying a rotating kernel to the pixel values of the image. Theoretical analysis showed this method could double the maximum measurable velocity. The power spectrum of the output signal was obtained by fast Fourier transform (FFT), and was corrected by a frequency spectrum correction algorithm, named energy centrobaric correction. This velocimeter was used to measure the moving velocities of a conveyor belt. Experimental results verified the method’s ability of reducing the output signal frequency and standard uncertainty of velocity measurement. What is more, the undesired output introduced by frequency shifting to the power spectrum of the output signal was deeply investigated and a new method was proposed to eliminate the undesired component in output signals. This velocimeter aims at providing accurate velocity information for vehicle autonomous navigation system.     

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.     

A New Time-Aware Collaborative Filtering Intelligent Recommendation System

Aiming at the problem that the traditional collaborative filtering recommendation algorithm does not fully consider the influence of correlation between projects on recommendation accuracy, this paper introduces project attribute fuzzy matrix, measures the project relevance through fuzzy clustering method, and classifies all project attributes. Then, the weight of the project relevance is introduced in the user similarity calculation, so that the nearest neighbor search is more accurate. In the prediction scoring section, considering the change of user interest with time, it is proposed to use the time weighting function to improve the influence of the time effect of the evaluation, so that the newer evaluation information in the system has a relatively large weight. The experimental results show that the improved algorithm improves the recommendation accuracy and improves the recommendation quality.     

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...