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.     

Metal-free catalyst fabrication by incorporating oxygen groups on the surface of the carbonaceous sample and efficient hydrogen production from NaBH4 methanolysis

In the present study, metal-free catalysts for efficient H₂ generation from NaBH₄ methanolysis was produced for the first time from apricot kernel shells with two-step activation. The first stage of the two-stage activation includes the production of activated carbon with the KOH agent (AKOH), and the second stage includes hydrothermally HNO₃ activation with oxygen doping (O doped AKOH + N). The hydrogen production rate (HGR) and the activation energy (Ea) of the reaction with the obtained metal-free catalyst (10 mg) were determined as 14,444 ml min^?1 g^?1 and 7.86 kJ mol^?1, respectively. The structural and physical-chemical properties of these catalysts were characterized by XRD (X-ray diffraction), SEM (scanning electron microscopy), elemental CHNS analysis, FT-IR (Fourier transform infrared spectroscopy), and nitrogen adsorption analysis. Also, the reusability results of this metal-free catalyst for H₂ production are promising.     

A three-stage graphics processing unit-based finite element analyses matrix generation strategy for unstructured meshes

With the development of parallel computing architectures, larger and more complex finite element analyses (FEA) are being performed with higher accuracy and smaller execution times. Graphics processing units (GPUs) are one of the major contributors of this computational breakthrough. This work presents a three-stage GPU-based FEA matrix generation strategy with the key idea of decoupling the computation of global matrix indices and values by use of a novel data structure referred to as the neighbor matrix. The first stage computes the neighbor matrix on the GPU based on the unstructured mesh. Using this neighbor matrix, the indices and values of the global matrix are computed separately in the second and third stages. The neighbor matrix is computed for three different element types. Two versions for performing numerical integration and assembly in the same or separate kernels are implemented and simulations are run for different mesh sizes having up to three million degrees of freedom on a single GPU. Comparison with GPU-based parallel implementation from the literature reveals speedup ranging from 4× to 6× for the proposed workload division strategy. Furthermore, the same kernel implementation is found to outperform the separate kernel implementation by 70% to 150% for different element types.     

Nitrogen-doping microporous adsorbents prepared from palm kernel with excellent CO2 capture property

In post-combustion CO₂ capture, waste biomass is a favourable precursor to prepare porous carbons due to its low cost, renewability, and unique microstructures. This study presents a facile method for preparing N-doping porous carbons. Palm kernel shell was selected as carbon precursor, and its inherent silica species acted as a natural template to form hierarchical pores. The obtained samples exhibit predominant characteristics with highly developed micropores and a high N content providing an important contribution to CO₂ adsorption capacity, which can reach up to 5.29 and 2.30 mmol/g under 100 kPa at 25 and 60 °C, respectively. Moreover, the resultant porous carbons also exhibit excellent cycling stability after 20 cycles. Furthermore, the activation mechanism was investigated by the quantitative thermogravimetry-mass spectrometry (TG-MS) method.     

A process fault diagnosis method using multi-time scale dynamic feature extraction based on convolutional neural network

Unlike many other techniques used in process control, which are widely applied in practice and play significant roles, abnormal situation management (ASM) still relies heavily on human experience, not least because the problem of fault detection and diagnosis (FDD) has not been well addressed. In this paper, a process fault diagnosis method using multi-time scale dynamic feature extraction based on convolutional neural network (CNN) consisting of similarity measurement, variable ranking, and multi-time scale dynamic feature extraction is proposed. The CNN-based model containing the fixed multiple sampling (FMS) layer can extract dynamic characteristics of process data at different time scales. The benchmark Tennessee Eastman (TE) process is used to verify the performance of the proposed method.     

基于混合时间序列卷积神经网络的轴承故障诊断

在传统滚动轴承故障诊断中,绝大多数方法采用了从振动信号提取特征的诊断模式,但是这种模式必然会使原始信号降维进而导致故障信息的丢失。卷积神经网络(CNN)通过权重共享和稀疏连接直接对原始信号进行操作,实现自适应特征提取,最大化保留故障信息。受CNN原理启发,开发出了一种基于工业振动信号特征的新型诊断框架,称之为混合时间序列CNN(HTS-CNN)。首先,利用估计总体比例的方法自适应确定模型训练样本数目;其次,通过对时间序列片段进行随机组合的方式,使模型能够提取非相邻信号特征;最后,利用Softmax激活函数在模型输出端执行多分类任务。通过对凯斯西储大学及CUT-2平台轴承数据进行分析,实验结果表明:该方法能够准确、有效的对滚动轴承故障进行分类。     

基于VMD和BSA-KELM的高陡边坡位移预测模型研究

边坡位移的时间序列曲线存在复杂的非线性特性,传统的预测模型精度不足以满足预测要求。为此提出了基于变分模态分解的鸟群优化-核极限学习机的预测模型,并用于河北省某水泥厂的边坡位移预测。该方法首先采用VMD把边坡位移序列分解为一系列的有限带宽的子序列,再对各子序列分别采用相空间重构并用核极限学习机预测,采用鸟群算法优化相空间重构的嵌入维度和KELM中惩罚系数和核参数三个数值,以取得最优预测模型。最后将各个子序列预测值叠加,得到边坡位移的最终预测值。结果表明：和KELM、BSA-KELM、EEMD-BSA-KELM模型相比,基于VMD的BSA-KELM预测精度更高,为边坡位移的预测提供一种有效的方法。     

High-speed,two-dimensional digital image correlation algorithm using heterogeneous (CPU-GPU) framework

Two-dimensional digital image correlation (2D-DIC) is an experimental technique used to measure in-plane displacement of a test specimen. Real-time measurement of full-field displacement data is challenging due to enormous computational load of the algorithm. In order to improve the computational speed, the focus of recent research works has been on the approach of parallelization across subsets within image pairs using graphics processing unit (GPU). But alternate GPU-based parallelization approaches to improve the performance of this algorithm as per the order of data processing have not been explored. To address this research gap, our method utilizes parallelism within a subset as well as across subsets for each computation step in an iteration cycle. A heterogeneous (CPU-GPU) framework in combination with a pyramid-based initial values estimation for subsets (in parallel) is proposed in this work. The precompute steps of the proposed framework are implemented using CPU, whereas the main iterative steps are realized using GPU. It is demonstrated that the overall computational speed of the proposed heterogeneous framework improves by compared to a sequential CPU-based implementation for a pair of gray-scale images with a resolution of pixels. As an important milestone, feasibility to measure deformations in real time ( 1 s) is manifested in this study.     

Estimating posterior inference quality of the relational infinite latent feature model for overlapping community detection

Overlapping community detection has become a very hot research topic in recent decades, and a plethora of methods have been proposed. But, a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefinedmanually. We propose a flexible nonparametric Bayesian generative model for count-value networks, which can allow K to increase as more and more data are encountered instead of to be fixed in advance. The Indian buffet process was used to model the community assignment matrix Z, and an uncollapsed Gibbs sampler has been derived.However, as the community assignment matrix Z is a structured multi-variable parameter, how to summarize the posterior inference results and estimate the inference quality about Z, is still a considerable challenge in the literature. In this paper, a graph convolutional neural network based graph classifier was utilized to help to summarize the results and to estimate the inference quality about Z. We conduct extensive experiments on synthetic data and real data, and find that empirically, the traditional posterior summarization strategy is reliable.