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.     

基于技术进化趋势的裁剪方法研究

裁剪是TRIZ中一种有效解决问题的方法,其通过删除问题元件,重组系统有用功能进行产品创新设计,达到简化系统与优化产品的目的.为了启发和引导设计者在裁剪过程中有效地利用资源,在使用裁剪规则引导创新设计的基础上,以具有预测性和启发性的技术进化趋势作为知识源,综合分析进化趋势解题与裁剪规则引导功能重组的资源来源,构建了进化趋势与裁剪规则的映射关系,提出了基于裁剪规则的多层次进化趋势选择策略,利用进化趋势辅助功能重组,构建了基于进化趋势的裁剪方法过程模型.最后通过钢带铠装机裁剪实例验证了此方法的合理性.     

Wavelet-based hybrid natural image modeling using generalized Gaussian and α-stable distributions

Natural image is characterized by its highly kurtotic and heavy-tailed distribution in wavelet domain. These typical non-Gaussian statistics are commonly described by generalized Gaussian density (GGD) or α-stable distribution. However, each of the two models has its own deficiency to capture the variety and complexity of real world scenes. Considering the statistical properties of GGD and α-stable distributions respectively, in this paper we propose a hybrid statistical model of natural image’s wavelet coefficients which is better in describing the leptokurtosis and heavy tails simultaneously. Based on a clever fusion of GGD and α-stable functions, we establish the optimal parametric hybrid model, and a close-formed Kullback–Leibler divergence of the hybrid model is derived for evaluating model accuracy. Experiment results and comparative studies demonstrate that the proposed hybrid model is closer to the true distribution of natural image’s wavelet coefficients than the single modeling using GGD or α-stable, while is beneficial for applications such as image comparison.     

Analysis of specific discharge rate functions in industrial scale cement grinding multi-compartment ball mills to assist ball mill modeling

Specific discharge rates in industrial scale multi-compartment cement grinding ball mills were determined using industrial scale data to assist modeling of multi-compartment ball mills. Multi-compartment ball mill model structures were proposed to estimate normalized specific discharge rate functions. Multi-compartment mills were divided into perfectly mixed mill segments to describe material transport mechanism in the mill. Specific discharge rate functions were determined by using the estimated mill hold-up for perfectly mixed mill segments at the steady state condition of the cement grinding circuits. Estimated normalized discharge rate functions were found to be different than the basic pattern observed in semi-autogenous mills due to the differences in design and operational conditions of the mills. Coarse particle accumulation was found to have a significant effect on discharge rate function pattern. Estimated normalized discharge rate functions could be used in modeling of multi-compartment ball mills.     

Simultaneous enhancement of the efficiency and stability of organic solar cells using PEDOT:PSS grafted with a PEGME buffer layer

We report a simple processing method to simultaneously improve the efficiency and stability of organic solar cells (OSCs). Poly(4-styrene sulfonate)-doped poly(3,4-ethylenedioxy-thiophene (PEDOT:PSS), widely used as hole transport layer (HTL) in OSCs, tends to accelerate the degradation of devices because of its hygroscopic and acidic properties. In this regard, we have modified PEDOT:PSS to reduce its hygroscopic and acidic properties through a condensation reaction between PEDOT:PSS and poly(ethylene glycol) methyl ether (PEGME) in order to improve the efficiency and stability of OSCs. As a result, the power conversion efficiency (PCE) increased by 21%, from 2.57% up to 3.11%. A better energy level alignment by the reduced work function of the modified PEDOT:PSS with a highest occupied molecular orbital (HOMO) level of poly(3-hexylthiophene-2,5-diyl) (P3HT) is considered the origin of the improved the efficiency. The half-life of OSCs with PEDOT:PSS modified with PEGME buffer layer also increased up to 3.5 times compared to that of devices with pristine PEDOT:PSS buffer layer.     

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.     

A neural network based technique for vibration characterization using Gaussian laser beams

This paper presents a neural network technique combined with an optical measurement system for the characterization of mechanical vibrations in industrial machinery. In the proposed system, the Gaussian beam of a laser source illuminates on an array of photodetectors. If either the laser source or the photodetector array is coupled with a vibrating system, then the optical powers detected by the photodetectors will vary accordingly, and are expected to reflect the magnitude and frequency of the X–Y planar vibrations of the monitored system. The time-varying optical powers are input to an artificial neural network-based vibration monitoring system which maps the power distributions to the X–Y position of the laser beam center. An experimental setup of the system is built and used for training and testing purposes. The obtained experimental results demonstrate the adequacy of combining optical techniques with neural networks to estimate the vibration frequency and magnitude. Estimated frequencies were within 1% of the actual ones, and the estimated magnitudes were within 29% of the actual magnitudes when using a chirp signal in the training phase. The magnitude estimation percentage error was further reduced below 12% when the neural network was trained with a decaying chirp signal.     

Fast Measurements of the Gas‐Liquid Diffusion Coefficient in the Gaussian Wake of a Spherical Bubble

A fast method is proposed for determining the oxygen gas‐liquid diffusion coefficient from measurements of the fluorescence quenching behind a bubble. The approach consists of capturing pictures of the concentration field at micro‐scale in the laminar bubble wake. The Gaussian concentration profiles measured in the wake are demonstrated to be systematically equivalent to an instantaneous plane diffusion case. The approach permits to accurately evaluate the gas‐liquid diffusivity in a very short time of around one second.     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

Multi-objective optimization of shield construction parameters based on random forests and NSGA-II

Metro shield construction will inevitably cause changes in the stress and strain state of the surrounding soil, resulting in stratum deformation and surface settlement (SS), which will seriously endanger the safety of nearby buildings, roads and underground pipe networks. Therefore, in the design and construction stage, optimizing the shield construction parameters (SCP) is the key to reducing the SS rate and increasing the safe driving speed (DS). However, optimization of existing SCP are challenged by the need to construct a unified multiobjective model for optimization that are efficient, convenient, and widely applicable. This paper innovatively proposes a hybrid intelligence framework that combines random forest (RF) and non-dominant classification genetic algorithm II (NSGA-II), which overcomes the shortcomings of time-consuming and high cost for the establishment and verification of traditional prediction models. First, RF is used to rank the importance of 10 influencing factors, and the nonlinear mapping relationship between the main SCP and the two objectives is constructed as the fitness function of the NSGA-II algorithm. Second, a multiobjective optimization framework for RF-NSGA-II is established, based on which the optimal Pareto front is calculated, and reasonable optimized control ranges for the SCP are obtained. Finally, a case study in the Wuhan Rail Transit Line 6 project is examined. The results show that the SS is reduced by 12.5% and the DS is increased by 2.5% with the proposed framework. Meanwhile, the prediction results are compared with the back-propagation neural network (BPNN), support vector machine (SVM), and gradient boosting decision tree (GBDT). The findings indicate that the RF-NSGA-II framework can not only meet the requirements of SS and DS calculation, but also used as a support tool for real-time optimization and control of SCP.