Predicting mill load using partial least squares and extreme learning machines

Online prediction of mill load is useful to control system design in the grinding process. It is a challenging problem to estimate the parameters of the load inside the ball mill using measurable signals. This paper aims to develop a computational intelligence approach for predicting the mill load. Extreme learning machines (ELMs) are employed as learner models to implement the map between frequency spectral features and the mill load parameters. The inputs of the ELM model are reduced features, which are extracted and selected from the vibration frequency spectrum of the mill shell using partial least squares (PLS) algorithm. Experiments are carried out in the laboratory with comparisons on the well-known back-propagation learning algorithm, the original ELM and an optimization-based ELM (OELM). Results indicate that the reduced feature-based OELM can perform reasonably well at mill load parameter estimation, and it outperforms other learner models in terms of generalization capability.     

Inferential control system of distillation compositions using dynamic partial least squares regression

In order to control product compositions in a multicomponent distillation column, the distillate and bottom compositions are estimated from on-line measured process variables. In this paper, inferential models for estimating product compositions are constructed using dynamic Partial Least Squares (PLS) regression, on the basis of simulated time series data. It is found that the use of past measurements is effective for improving the accuracy of the estimation. The influence of selection of measurements and sampling intervals on the performance is also investigated. From the detailed dynamic simulation results, it is found that the cascade control system based on the proposed dynamic PLS model works much better than the usual tray temperature control system.     

OPLS在非线性偏最小二乘回归模型的应用

为了解决隐含潜变量回(implicit non-linear latent variable regression,INLR)建模方法中加入自变量的非线性项使系统中出现了与因变量y无关的信息或者噪音的问题,提出了基于正交投影(orthogonal projection to latent structures,OPLS)方法,时INLR数据进行预处理.OPLS算法能有效去除预测矩阵中与因变量y无关的信息,在不影响INLR建模效果的同时减少有意义的成分,改善了模型的解释性和真实性.模拟实验结果表明,改进后算法的建模效果优于INLR、偏最小二乘回归(partial least squares regression,PLSR)算法得到的模型.     

Human gait recognition using localized Grassmann mean representatives with partial least squares regression

Multimedia Tools and Applications - Gait recognition has become popular due to the rising demand for nonintrusive biometrics. At its nascent stage of development, gait recognition faces a number of...     

偏最小二乘回归分析与特征融合在图像识别中的应用

为了更有效地进行图像识别,对同一模式的不同特征进行融合是有效途径。讨论了偏最小二乘法及其改进算法、特征融合方法在图像识别中的应用。首先讨论了偏最小二乘法的基本原理和非迭代偏最小二乘法、基于共轭正交的偏最小二乘法用于特征抽取的原理和特点,给出了三种特征融合方法,在ORL与Yale人脸库上的实验结果表明进行对用PLS抽取的特征融合后可以有效地进行图像识别。     

二维共轭正交偏最小二乘分析及图像识别应用

偏最小二乘（PLS）是一种有效的图像特征抽取方法。不同于其他的多元数据分析方法,PLS综合了PCA与CCA的优点,抽取对样本具有最佳解释能力的成分。讨论了偏最小二乘法建模思想及非迭代算法、共轭正交算法和基于2D特征抽取时的算法原理和特点,以及PLS用于图像识别时类隶属矩阵的构造。在ORL与Yale人脸库上的实验结果表明用2DCOPLS抽取的特征进行图像识别的效果更好,更稳定。     

小波分析和偏最小二乘法相结合用于示波计时电位同时测定铅和铊中的研究

提出了基于小波分析和偏最小二乘(Partial Least Squares,PLS)基础上的化学计量学方法用于示波计时电位同时测定铅和铊的研究。利用小波变换可方便地从dE/dt-E信号中滤噪,提取与去极剂浓度变化有关的信号,获得利于多组分测定的示波图。该方法为示波过程分析奠定了一定的基础。     

小波分析和偏最小二乘法相结合用于示波计时电位同时测定铅和铊中的研究

提出了基于小波分析和偏最小二乘（Partal Least Squares,PLS)基础上的化学计量学方法用于示波计时电位同时测定铅和铊的研究。利用小波变换可方便地从dE/dt-E信号中滤噪，提取与去极剂浓度变化有关的信号，获得利于多组分测定的示波图。该方法为示波过程分析奠定了一定的基础。     

Multimedia retrieval based on non-linear graph-based fusion and partial least squares regression

Multimedia Tools and Applications - Heterogeneous sources of information, such as images, videos, text and metadata are often used to describe different or complementary views of the same...     

基于偏最小二乘回归的鲁棒性特征选择与分类算法

提出一种基于偏最小二乘回归的鲁棒性特征选择与分类算法（RFSC-PLSR）用于解决特征选择中特征之间的冗余和多重共线性问题。首先,定义一个基于邻域估计的样本类一致性系数;然后,根据不同k近邻（kNN）操作筛选出局部类分布结构稳定的保守样本,用其建立偏最小二乘回归模型,进行鲁棒性特征选择;最后,在全局结构角度上,用类一致性系数和所有样本的优选特征子集建立偏最小二乘分类模型。从UCI数据库中选择了5个不同维度的数据集进行数值实验,实验结果表明,与支持向量机（SVM）、朴素贝叶斯（NB）、BP神经网络（BPNN）和Logistic回归（LR）四种典型的分类器相比,RFSC-PLSR在低维、中维、高维等不同情况下,分类准确率、鲁棒性和计算效率三种性能上均表现出较强的竞争力。     

Large margin based nonnegative matrix factorization and partial least squares regression for face recognition

In this paper, we present a new method, called large margin based nonnegative matrix factorization (LMNMF), to encode latent discriminant information in training data. LMNMF seeks a nonnegative subspace such that k nearest neighbors of each sample always belong to same class and samples from different classes are separated by a large margin. In the subspace, the local separation structure of data is explicit. The large-margin criterion leads to a new objective function, and a convergency provable multiplicative nonnegative updating rule is derived to learn the basis matrix and encoding vectors. Then, partial least squares regression (PLSR) learns the mapping from the original data to low dimensional representations in order to capture local separation information. PLSR offers a unified solution to out-of-sample extension problem. Extensive experimental results demonstrate LMNMF with PLSR leads significant improvements on classification than several other commonly used NMF-based algorithms.     

Estimation of heavy-metal contamination in soil using reflectance spectroscopy and partial least-squares regression

Lead (Pb) poisoning from anthropogenic sources continues to threaten the health of urban children. Mapping Pb distribution on a large scale is imperative to identify hotspots and reduce Pb poisoning. To assess the feasibility of using reflectance spectroscopy to map soil Pb and other heavy metal abundance, the relationship between surface soil metal concentrations and hyperspectral reflectance measurements was examined via partial least-squares regression (PLSR) modelling. Soil samples were taken from four study sites. Metal concentrations were determined by inductively coupled plasma-atomic-emission spectrometry (ICP-AES) analysis, and reflectance was measured with an ASD (Analytical Spectral Devices) field spectrometer covering the spectral region of 350–2500 nm. Pb displayed an exponential decrease as a function of distance from the roadway, demonstrating the depositional patterns from leaded gas combustion which remain on the landscape 20 years after the phase-out of leaded gasoline. Calibration samples were used to derive the PLSR algorithm, and validation samples assessed the model's predictive ability. The correlation coefficients between the lab-determined abundance and the abundance predicted from PLSR calibration for all metals except copper were at or above 0.970, with the correlation coefficient for Pb the highest of all metals (0.992). Manganese, zinc and Pb had significant coefficients of determination (0.808, 0.760 and 0.746, respectively) for the validation samples. These results suggest that Pb and other heavy metal concentrations can be retrieved from spectral reflectance at high accuracy. Reflectance spectroscopy thus has potential to map the spatial distribution of Pb abundance with the aim of improving children's health in an urban environment.     

Simultaneous kinetic spectrophotometric determination of sulfide and sulfite ions by using an optode and the partial least squares (PLS) regression

The characterization of an optical sensor membrane is described for simultaneous determination of sulfite and sulfide ions based on the immobilization of crystal violet on a triacetylcellulose membrane. The absorbance of the membranes decreased by increasing sulfite and sulfide concentration. The partial least squares (PLS-1) calibration model based on spectrophotometric measurement for simultaneous determination of sulfite and sulfide ions was applied. This method is based on the difference between the rate of the reaction of sulfide and sulfite with membranes in pH 7.0 buffer solution and at 25 °C. The experimental calibration matrix for partial least squares (PLS-1) calibration was designed with 18 samples. The cross-validation method was used for selecting the number of factors. The results showed that simultaneous determination could be performed in the range of 200–2000 μg mL⁻¹ (2.5–25 mmol L⁻¹) and 80–900 μg mL⁻¹ (2.5–28.125 mmol L⁻¹) for sulfite and sulfide, respectively. The sensor can readily be regenerated with water and the color is fully reversible. The sensor was successfully applied to the simultaneous determination of sulfide and sulfite in water samples.     

Virtual metrology and feedback control for semiconductor manufacturing processes using recursive partial least squares

Virtual metrology (VM) is the prediction of metrology variables (either measurable or non-measurable) using process state and product information. In the past few years VM has been proposed as a method to augment existing metrology and has the potential to be used in control schemes for improved process control in terms of both accuracy and speed. In this paper, we propose a VM based approach for process control of semiconductor manufacturing processes on a wafer-to-wafer (W2W) basis. VM is realized by utilizing the pre-process metrology data and more importantly the process data from the underlying tools that is generally collected in real-time for fault detection (FD) purposes. The approach is developed for a multi-input multi-output (MIMO) process that may experience metrology delays, consistent process drifts, and sudden shifts in process drifts. The partial least squares (PLS) modeling technique is applied in a novel way to derive a linear regression model for the underlying process, suitable for VM purposes. A recursive moving-window approach is developed to update the VM module whenever metrology data is available. The VM data is then utilized to develop a W2W process control capability using a common run-to-run control technique. The proposed approach is applied to a simulated MIMO process and the results show considerable improvement in wafer quality as compared to other control solutions that only use lot-to-lot metrology information.     

Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design

The note proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.     

Influence and tuning of tunable screws for microwave filters using least squares support vector regression

This article presents an approach that can analyze the influence of tunable screws and perform a computer‐aided tuning for microwave filters. In the approach, a machine‐learning model that reveals the influence of tunable screws on the filter response is first developed by least squares support vector regression, according to some data from the tuning experience of filters. Then a computer‐aided tuning procedure based on the model is proposed, and the obtained adjusting amount of tunable screws can assist an unskilled operator to perform a fast and accurate tuning. The approach is validated by some experiments and the results confirm the effectiveness. The approach is particularly suitable to the computer‐aided tuning of volume‐producing filters. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.     

混合核函数PLS建模方法及在软测量中的应用

针对核函数方法中单个核函数的局限性,以及PLS非线性处理能力差的特点,提出混合核函数PLS建模方法,以提高模型的推广能力和非线性处理能力。混合核函数集中了多个局部和全局核函数,兼具局部和全局特性,并可以通过参数调节局部和全局核函数对混合核函数的作用,将过程的先验知识融入到核函数的确定,进而适合具有不同数据特征的工业过程。工业丙烯腈收率软测量建模的应用表明,混合核函数PLS软测量模型具有较好的数据适应性和非线性特性,满足了工业应用要求。     

RETRACTED ARTICLE: Texture-guided volumetric deformation and visualization using 3D moving least squares

Examining and manipulating the large volumetric data attract great interest for various applications. For such purpose, we first extend the 2D moving least squares (MLS) technique into 3D, and propose a texture-guided deformation technique for creating visualization styles through interactive manipulations of volumetric models using 3D MLS. Our framework includes focus+context (F+C) visualization for simultaneously showing the entire model after magnification, and the cut-away or illustrative visualization for providing a better understanding of anatomical and biological structures. Both visualization styles are widely applied in the graphics areas. We present a mechanism for defining features using high-dimensional texture information, and design an interface for visualizing, selecting and extracting features/objects of interest. Methods of the interactive or automatic generation of 3D control points are proposed for the flexible and plausible deformation. We describe a GPU-based implementation to achieve real-time performance of the deformation techniques and the manipulation operators. Different from physical deformation models, our framework is goal-oriented and user-guided. We demonstrate the robustness and efficiency of our framework using various volumetric datasets.