在线近红外光谱分析技术在汽油调和中的应用前景分析

本文介绍了近红外光谱分析技术的特点以及该技术在石化领域的应用。以汽油调和为例,对在线近红外光谱分析技术在其中的应用前景进行了分析,在线近红外光谱分析技术可以实时测定汽油调和组分以及调和后的成品汽油的多种物化性质指标,如辛烷值、烯烃、芳烃、苯含量、馏程等,该技术的应用可为炼厂带来可观的经济效益和社会效益。     

新型汽油辛烷值在线分析仪的研制

介绍了自主开发研制的汽油辛烷值近红外（NIR）光谱在线分析仪。它由光谱仪系统、电源电路控制系统、光纤及其附件与流通池系统、防爆系统、样品预处理系统、实时光谱测量和化学计量学软件等部分组成。介绍了叠加光谱的微分、光谱平滑、基线校正和标准归一化等预处理方法；并描述了通过化学计量学方法进行建模的过程。该分析仪已成功应用于某炼油厂重整装置生成油的辛烷值在线分析。     

近红外光谱快速测定催化裂化汽油重馏分选择性加氢脱硫产物的辛烷值

研究了用近红外光谱快速测定催化裂化汽油重馏分选择性加氢脱硫产物的辛烷值。该方法具有分析速度快，分析效率,高样品量少等特点，适合微反产物分析和催化剂筛选与评价。本分析方法能够满足标准方法精密度的要求。     

小波去噪-微分法用于近红外光谱分析汽油辛烷值

微分法可以有效消除光谱背景和基线漂移，同时会增加光谱噪音；小波变换具有很好去噪功能，本文将微分法和小波变换结合用于近红外光谱分析重整汽油辛烷值。考察了微分噪音对辛烷值分析精度的影响以及小波去噪对微分光谱的噪音扣除以及对辛烷值分析精度改善情况。结果表明，微分光谱可以扣除原始光谱的基线漂移，提高分析精度，同时增加光谱的噪音；噪音对分析精度影响很大。微分光谱经过小波去噪处理后信噪比增加，辛烷值分析精度得到改善。     

近红外光谱分析技术快速测定油品的辛烷值

介绍了用近红外光谱(NIR)分析模型对催化汽油样品辛烷值进行快速测定的一种方法。实验结果证明：用NIR光谱分析技术的分析结果能够完全满足生产中的精度要求，近红外分析的结果与实验室常规分析结果之间的差别远优于GB的再现性要求。而且该方法除有测量速度快的优点之外，还具有分析成本低、无污染、操作简单方便等特点，在所建立的模型样品范围内，能对样品进行准确、经济的分析。     

傅立叶变换近红外分析汽油辛烷值模型优化研究

利用傅立叶变换红外谱仪进行汽油样品辛烷值（RON）测定，采用仪器配备的软件进行模型优化，使用11种光谱预处理方法和不同的谱区组合，计算机自动处理优化过程并评价优化数据，50个汽油样品优化后得到最佳结果，光谱预处理方法为一阶导数，谱区范围为6100.4-4999.1cm^-1。得到的内部交互验证相关系数R2＝99．05％和均方差RMSECV＝0.167，优化后的结果说明，该方法完全能满足测试误差要求，从而为近红外模型优化提供参考。     

近红外光谱法测定汽油辛烷值

采用近红外光谱法测定汽油辛烷值，方法简便、快捷，具有好的重现性。近红外光谱法测定结果与标准试验方法（GB／T503和GB／T5487）测定结果进行比对，取得了令人满意的结果。     

阿达玛变换近红外光谱仪

本文总结了近红外光谱仪已有各种分光技术的优缺点,介绍了阿达玛变换分光技术的基本原理,并对HT100阿达玛变换近红外光谱仪的系统结构、组成和仪器技术指标进行了介绍,利用HT100型阿达玛变换近红外光谱仪对汽油研究法辛烷值进行了实证预测研究,研究表明预测结果满足国家标准方法的要求。阿达玛变换近红外光谱仪有望成为下一代便携式近红外光谱仪。     

采用相关分析解析石油产品近红外光谱

选择一系列结构上具有代表性的烃类化合物,配制不同浓度的溶液,以及它们不同浓度的重整汽油溶液,在中红外,短波近红外和长波近红外光谱仪上,分别测定溶液的中红外光谱、短波近红外光谱和长波近红外光谱。采用相关分析方法对中红外光谱和近红外光谱进行关联,确立重整汽油各结构基团吸收谱带在近红外短波区及长波区的精确位置,并考察化学环境和物理环境对谱带位置的影响。     

基于电容传感器的汽油辛烷值检测系统设计

为了解决汽油辛烷值检测成本高、准确度低等缺点,设计了一种基于电容传感器的汽油辛烷值检测系统。该检测系统以微处理器为控制核心,利用电容传感器测量汽油的介电常数,通过建立汽油辛烷值与介电常数的关系式,从而根据测得的介电常数反推出汽油的辛烷值。测试结果表明,设计的检测系统能有效检测出不同汽油的介电常数,并能根据介电常数计算得到汽油的辛烷值。系统对汽油辛烷值的检测相对误差小于2%。     

Interval type-2 fuzzy PID controller for uncertain nonlinear inverted pendulum system

In this paper, the interval type-2 fuzzy proportional–integral–derivative controller (IT2F-PID) is proposed for controlling an inverted pendulum on a cart system with an uncertain model. The proposed controller is designed using a new method of type-reduction that we have proposed, which is called the simplified type-reduction method. The proposed IT2F-PID controller is able to handle the effect of structure uncertainties due to the structure of the interval type-2 fuzzy logic system (IT2-FLS). The results of the proposed IT2F-PID controller using a new method of type-reduction are compared with the other proposed IT2F-PID controller using the uncertainty bound method and the type-1 fuzzy PID controller (T1F-PID). The simulation and practical results show that the performance of the proposed controller is significantly improved compared with the T1F-PID controller.     

A RESTful framework for Internet of things based on software defined network in modern manufacturing

In this paper, we first analyze the scalability and programming network architectures for Internet of things (IoT) in industries. Then, a representational state transfer(REST) framework for IoT based on software defined network (SDN) is proposed. Based on the proposed framework, control plane model and data plane model for IoT are proposed. In addition, a gateway model is provided to evaluate the proposed framework and use case results show that the proposed architectures are good for raw sensor data collecting. This framework provides a sharing mechanism for IoT in the modern manufacturing.     

Target Image Enhancement in Radar Imaging Using Fractional Fourier Transform

This paper presents a new Range-Doppler Algorithm based on Fractional Fourier Transform (RDA-FrFT) to obtain High-Resolution (HR) images for targets in radar imaging. The performance of the proposed RDA-FrFT is compared with the classical RDA algorithm, which is based on the Fast Fourier Transform (FFT). A closed-form expression for the range and azimuth compression of the proposed RDA-FrFT is mathematically derived and analyzed from the HR Synthetic Aperture Radar (SAR) imaging point of view. The proposed RDA-FrFT takes its advantage of the property of the FrFT to resolve chirp signals with high precision. Results show that the proposed RDA-FrFT gives low Peak Side-Lobe (PSL) and Integrated Side-Lobe (ISL) levels in range and azimuth directions for detected targets. HR images are obtained using the proposed RDA-FrFT algorithm.     

Shearlet regularization and dimensionality reduction for the temperature distribution sensing

In this paper, a new dimensionality reduction based temperature distribution sensing (TDS) method is proposed to reconstruct the temperature distribution via the limited number of the scattered temperature measurement data. The projective nonnegative matrix factorization (PNMF) method is developed to exact the basis vectors, and the augmented Lagrangian multipliers (ALM) method is proposed to solve the proposed PNMF model. A dimensionality reduction model is obtained via projecting the original temperature distribution onto the spaces spanned by a set of basis. An objective functional that considers the inaccurate properties of the reconstruction model and the measurement data, the Shearlet regularization and the total variation (TV) method is proposed to convert the TDS task into an optimization problem, where the temperature distribution is indirectly reconstructed via solving a low-dimensional vector. An iteration scheme is developed to solve the objective functional. Numerical simulation results validate the feasibility of the proposed reconstruction algorithm.     

基于 CSA-NARX 模型的电梯钢丝绳动态张力预测方法

为了预测曳引式电梯钢丝绳的动态张力,对带有外部输入的非线性自回归神经网络( NARX )进行研究,利用变色龙优化算法( CSA )对其关键参数进行优化,提出了 CSA-NARX 神经网络模型。该模型在计算速度以及预测精度方面皆优于 NARX 基础模型。最后,利用提出的神经网络模型对电梯上行过程中钢丝绳的动态张力进行预测,其预测精度达到了 97% 。以传统的非平稳时间序列分析模型 ARMA 和 LSTM 为对比,所提出模型的精度更高,验证了所提出模型的有效性。     

Geometric modeling and tool path generation of model propellers with a single setup change

A model propeller plays important roles in the design of the marine vehicles. The cavitations, the erosion, the pressure fluctuation, and the flow are measured by the model propeller. These measurements help to create the actual marine propeller. In this paper, geometry modeling and tool path generation for a model propeller are proposed, and the hub surface and lateral surface of the wing surfaces are generated by the proposed method. Considering the characteristics of the model propeller, efficient Cutter Location (CL) data are proposed: (1) the finish machining is completed with one setup posture. (2) A four-axis machining algorithm is proposed that makes it possible to the propeller with one setup and that minimizes machining error. (3) To minimize the maximum machining load, zigzag machining and an outside-to-inside tool path are introduced. A tilting guide curve is proposed to determine the tool axis vector. A smoothly changing tool axis vector is obtained through this curve, and the calculation is simple and fast. The results demonstrate that the proposed method is useful for the manufacturing of model propellers.     

Nonlinear system identification based on Takagi-Sugeno fuzzy modeling and unscented Kalman filter

This paper proposes two novel Kalman-based learning algorithms for an online Takagi-Sugeno (TS) fuzzy model identification. The proposed approaches are designed based on the unscented Kalman filter (UKF) and the concept of dual estimation. Contrary to the extended Kalman filter (EKF) which utilizes derivatives of nonlinear functions, the UKF employs the unscented transformation. Consequently, non-differentiable membership functions can be considered in the structure of the TS models. This makes the proposed algorithms to be applicable for the online parameter calculation of wider classes of TS models compared to the recently published papers concerning the same issue. Furthermore, because of the great capability of the UKF in handling severe nonlinear dynamics, the proposed approaches can effectively approximate the nonlinear systems. Finally, numerical and practical examples are provided to show the advantages of the proposed approaches. Simulation results reveal the effectiveness of the proposed methods and performance improvement based on the root mean square (RMS) of the estimation error compared to the existing results.     

高阶混合正则化图像盲复原方法

提出了一种高阶混合正则化图像盲复原方法,用于实现模糊噪声图像的清晰化盲复原。根据自然图像边缘的稀疏特性,对图像的边缘细节成分进行了全变差(total variation TV)正则化约束,根据自然图像同性质平滑区域内像素值的变化规律,将一种高阶的类Tikhonov正则化约束运用于图像的平滑区域中,提出了一种新的高阶混合正则化模型。最后,提出一种多变量分裂布雷格曼(Multi-variable Split Bregman MSB)最优化迭代策略对提出的模型进行最优化求解。实验结果表明,提出的方法能够很好地保护图像的边缘细节,同时有效地消除图像平滑区域内的阶梯和假边缘瑕疵。与近几年的一些较好的图像盲复原方法相比,本文方法的信噪比增量(increase of the signal to noise ratio ISNR)增加了0.03~2.5 dB。     

Fault level diagnosis for planetary gearboxes using hybrid kernel feature selection and kernel Fisher discriminant analysis

This paper introduces a hybrid dimension reduction method that combines kernel feature selection and kernel Fisher discriminant analysis (KFDA). In the first stage, a kernel feature selection method is proposed to remove redundant and irrelevant features for two purposes: (1) reducing computation burden of the entire fault diagnosis system and (2) alleviating the impact of irrelevant features on KFDA. In the second stage, KFDA is used to establish a more compact feature subset by extracting a smaller number of features. We use Gaussian radial basis function as the kernel function for the two kernel stages in the proposed method. A parameter selection method for this kernel is proposed to select the optimal values for the proposed method. Experimental results on fault level diagnosis demonstrate that the proposed hybrid dimension reduction method has advantages over other approaches that use feature selection or KFDA separately.     

改进蝙蝠算法优化支持向量机的故障诊断方法

提出了一种基于变分模态分解（VMD）和时移多尺度散布熵（TSMDE）的故障特征提取结合改进的蝙蝠算法（IBA）来优化支持向量机（SVM）的滚动轴承故障诊断方法。通过变分模态分解,避免了模式混叠问题,提取各模态分量的散布熵构造故障特征向量,作为故障诊断模型的输入;提出了一种新的自适应速度权重因子用于构建改进的蝙蝠算法以优化支持向量机（IBA-SVM）,实现了对不同故障类型的轴承进行分类;利用实验数据对提出的诊断方法进行验证,并与用粒子群算法（PSO）优化支持向量机（PSO-SVM）的诊断方法进行对比。结果表明所提出的方法分类准确率更高,用时更少。