Location Estimation via Support Vector Regression

Location estimation using the global system for mobile communication (GSM) is an emerging application that infers the location of the mobile receiver from multiple signals measurements. While geometrical and signal propagation models have been deployed to tackle this estimation problem, the terrain factors and power fluctuations have confined the accuracy of such estimation. Using support vector regression, we investigate the missing value location estimation problem by providing theoretical and empirical analysis on existing and novel kernels. A novel synthetic experiment is designed to compare the performances of different location estimation approaches. The proposed support vector regression approach shows promising performances, especially in terrains with local variations in environmental factors     

一种求解支持向量回归波束形成的算法

在对传统求解支持向量回归算法研究与分析的基础上,针对支持向量回归模型,结合支持向量回归的波束形成技术,提出了一种利用迭代重加权最小二乘支持向量回归波束形成的算法,并对具有严重干扰的接收信号进行了数值仿真试验和对比分析。结果表明：基于迭代重加权最小二乘支持向量回归波束形成的算法不同于传统的标准二次型算法,收敛速度快,干扰抑制强,计算量小,降低了计算复杂度,避免了二次规划技术的高计算成本,提高了算法效率,并保持了良好的泛化能力,具有一定的参考价值。     

Support vector regression-based study of interference in absorption spectral lines of mixed gases

When measuring the concentration of multi-component gas mixtures based on supercontinuum laser absorption spectroscopy (SCLAS), there are interferences between the absorption spectral lines. For the spectral interference problem of CO2 and CH4 at 1 432 nm, a method based on support vector regression (SVR) is proposed in this paper. The SVR model, the k-nearest neighbor (KNN) model and the least squares (LS) model are used to analyze and predict the absorption spectral data, and the prediction accuracies were 96.29%, 88.89% and 85.19%, respectively, with the highest prediction accuracy of the SVR model. The results show that the method can accurately measure the concentration of gas mixtures, realize the detection of mixed gases using a single waveband, and provide a solution to the overlapping spectral line interference of multi-component gas mixtures.     

Support Vector Regression Model for Measuring the Permittivity of Asphalt Concrete

The support vector regression (SVR) model for waveguide method of measuring the permittivity of asphalt concrete is presented in this letter. To validate the SVR model, simulated and measured data are employed. The training and testing data for the simulated SVR model are obtained by the reflection coefficient expression. While the testing data for the measured SVR model is obtained by HFSS. Experimental results suggest that the SVR model has a better performance in predicting the permittivity among microwave band. This SVR model could be applied to microwave industry as a kind of permittivity measurement tool.     

基于小波变换与支持向量机回归的冬小麦叶面积指数估算

叶面积指数(LAI)是作物长势诊断及产量预测的重要参数。通过对冬小麦采样点的高光谱曲线进行连续小波变换(CWT),然后利用小波系数与LAI 建立支持向量机回归(SVR)模型,实现冬小麦不同生育时期的叶面积指数估算。通过对所研究方法与选取的植被指数、偏最小二乘(PLS)回归等5种方法的反演结果进行统计分析。结果表明:利用连续小波变换确定的LAI 的敏感波段为680、739、802、895 nm,对应尺度分别为8、4、9 和8,对应小波系数的LAI 回归确定系数(R2)明显高于冠层反射率的回归确定系数;利用小波系数与LAI 建立的SVR 模型的反演精度最高,模型实测值与预测值的检验精度(R2)为0.86,均方根误差(RMSE)为0.43;而常用植被指数(归一化植被指数,NDVI;比值植被指数,RVI)建立的估测模型对冬小麦多个生育时期LAI 反演精度最低(R2 0.76,RMSE0.56)。因此利用连续小波变换进行数据预处理,能更好地筛选出对叶面积指数敏感的信息,LAI 回归方法比较结果表明,SVR 比PLS 更适合于LAI 的估测,通过将CWT 与SVR 结合(CWT-SVR)能实现不同生育时期冬小麦叶面积指数的遥感估算。     

基于机动特征辅助的MFR状态预测方法

针对多功能雷达（Multi-Function Radar,MFR）状态预测方法存在的鲁棒性、预测正确率不佳的问题,提出一种基于机动特征辅助的MFR状态预测方法.该方法将载机机动信息与常规侦收参数共同作为预测特征集,一方面利用支持向量回归（Support Vector Regression,SVR）和侦收信号特征集,得到常规预测模型,另一方面通过SVR和机动特征集,得到MFR各个状态间的转变概率模型;然后利用D-S证据理论得到最终预测状态.实验结果表明,与SVR和LSR方法相比,平均预测精度分别提高了6.97%和7.2%,同时具备更优异的鲁棒性.此外,提出的预测方法通过进一步的拓展,可应用于机械设备、道路交通等领域.     

用机器学习方法进行电力负荷宏观预测

分析了电力负荷宏观预测的模型和相关技术，引入支持向量回归方法(SVR)解决问题，并通过计算实例，比较分析了SVR与神经网络方法用于预测的效果，提出SVR广阔应用前景。     

基于气象因素的PM10浓度预测

为建立准确高效的空气质量预报系统,建立以污染物、气象因素、污染物混合气象因素的三种预测因子模式,并将该三种预测因子模式作为支持向量机回归 (Support vector machine regression, SVR)的输入变量进行PM10浓度的每日预测,寻找最优预测因子模式。并使用灰狼优化算法 (Grey wolf optimization, GWO)对支持向量机回归模型进行优化,形成GWO-SVR模型。实验结果表明,污染物混合气象因素作为输入变量为最优预测因子模式, SVR和GWO-SVR模型测试集确定系数分别达到$R^2$=0.79和$R^2$=0.81,预测精度较高,经比较发现GWO-SVR模型预测性能较好。之后,依据风向条件对数据进行分类,使用较优的GWO-SVR进行PM10浓度预测,预测结果显示盛行西南风时, 预测集评测指标为$R$=0.91、$M_{\rm SE}$=47.15,优于盛行东北风时的$R$=0.87、$M_{\rm SE}$=125.80和所有数据下的$R$=0.90、$M_{\rm SE}$=107.94。     

Support Vector Regression Model for Millimeter Wave Transitions

In this paper, we introduce a new method, support vector regression (SVR) method, to model millimeter wave transitions. SVR is based on the structural risk minimization (SRM) principle, which leads to good generalization ability for regression problem. The SVR model can be electromagnetically developed with a set of training data and testing data which produced by the electromagnetic simulation. Two Ka-band millimeter wave transitions, i.e., waveguide to microstrip transition and coaxial to waveguide adapter, are used as examples to validate the method. Experimental results show that the developed SVR models have a good predictive ability, and they are useful for interactive CAD of millimeter wave transitions.     

一种基于SVR的数字图像水印算法

以回归型支持向量机为基础,提出一种彩色数字图像水印算法。在小波域内选取特征向量并获得支持向量机训练模型,进而利用该训练模型嵌入和提取水印信息。该算法以保证不可感知性和鲁棒性的良好平衡为前提,实现了水印的盲检测。实验仿真表明,该算法不仅具有较好的不可感知性,而且对叠加噪声、JPEG压缩、锐化、平滑滤波、对比度增强、剪切等常规处理具有较好的鲁棒性,其整体性能优于一般基于支持向量机的图像水印方案。     

边缘网络中一种VNF需求预测方法

针对当前虚拟网络功能（Virtualization Network Functions,VNF）需求预测方法准确率较低且不适用于边缘网络的问题,提出了一种在边缘网络中基于支持向量回归（Support Vector Regression,SVR）与门控循环单元（Gated Recurrent Unit,GRU）神经网络模型结合的VNF需求预测方法。考虑到网络边缘流量具有突发性、自相似性及长相关性等特点,结合SVR和GRU两种模型的优点,利用计算复杂度较低的SVR和GRU模型分别提取网络服务历史时序数据的短期特征和长期特征,以提高VNF需求预测准确率,实现边缘网络中VNF的提前放置。实验表明,所提出的预测方法在边缘网络中针对不同网络服务的预测较于传统方法、循环神经网络（Recurrent Neural Networks,RNN）、长短期记忆网络（Long Short-Term Memory,LSTM）模型能够降低20%~30%的误差,有更佳的预测效果。     

Image superresolution using support vector regression.

A thorough investigation of the application of support vector regression (SVR) to the superresolution problem is conducted through various frameworks. Prior to the study, the SVR problem is enhanced by finding the optimal kernel. This is done by formulating the kernel learning problem in SVR form as a convex optimization problem, specifically a semi-definite programming (SDP) problem. An additional constraint is added to reduce the SDP to a quadratically constrained quadratic programming (QCQP) problem. After this optimization, investigation of the relevancy of SVR to superresolution proceeds with the possibility of using a single and general support vector regression for all image content, and the results are impressive for small training sets. This idea is improved upon by observing structural properties in the discrete cosine transform (DCT) domain to aid in learning the regression. Further improvement involves a combination of classification and SVR-based techniques, extending works in resolution synthesis. This method, termed kernel resolution synthesis, uses specific regressors for isolated image content to describe the domain through a partitioned look of the vector space, thereby yielding good results.     

Inverter Nonlinearity Compensation in the Presence of Current Measurement Errors and Switching Device Parameter Uncertainties

This paper proposes a compensation strategy for the unwanted disturbance voltage due to inverter nonlinearity. We employ an emerging learning technique called support vector regression (SVR). SVR constructs a motor dynamic voltage model by a linear combination of the current samples in real time. The model exhibits fast observer dynamics and robustness to observation noise. Then the disturbance voltage is estimated by subtracting the constructed voltage model from the current controller output. The proposed method compensates for all of the inverter nonlinearity factors at the same time. All the processes in estimating distortions are independent of the dead time and power device parameters. From the analysis of the effect on current measurement errors, we confirmed that the sampling error had little negative impact on the proposed estimation method. Experiments demonstrate the superiority of the proposed method in suppressing voltage distortions caused by inverter nonlinearity     

一种石墨烯可重构贴片天线电磁响应快速预测方法

针对传统石墨烯可重构天线辐射特性全波模拟耗时问题,将支持向量回归(Support Vector Regression,SVR)这一机器学习方法用于石墨烯贴片天线参数快速重构预测.将石墨烯贴片天线不同参数(贴片尺寸、化学势、频率等)下的电磁响应转化为一个回归估计问题.以天线单元参数为输入,相应S参数为输出,建立回归模型,...     

基于支持向量回归的供应链合作伙伴核心竞争力评价

供应链管理是一种先进、新颖的管理方法,在供应链中,科学的评价合作伙伴核心竞争力是优化选择最佳合作伙伴的关键.本文将基于支持向量回归的数据挖掘方法,用于核心竞争力综合评价研究中,给出应用实例,结果表明支持向量回归不仅具有较高的训练效率,而且有更高的精确度.     

No-reference image quality assessment based on hybrid model

The aim of research on the no-reference image quality assessment problem is to design models that can predict the quality of distorted images consistently with human visual perception. Due to the little prior knowledge of the images, it is still a difficult problem. This paper proposes a computational algorithm based on hybrid model to automatically extract vision perception features from raw image patches. Convolutional neural network (CNN) and support vector regression (SVR) are combined for this purpose. In the hybrid model, the CNN is trained as an efficient feature extractor, and the SVR performs as the regression operator. Extensive experiments demonstrate very competitive quality prediction performance of the proposed method.     

自适应误差惩罚支撑向量回归机

该文提出一种支撑向量回归机AEPSVR。它首先用 -SVR求得一个近似的支撑向量回归函数,在此基础上,引入一种新自适应误差惩罚函数,通过迭代,得到鲁棒的支撑向量回归机。该方法因以 -SVR为基础,故可以应用各种求解SVR的优化算法。实验表明,该支撑向量回归机AEPSVR能显著地降低离群点的影响,具有良好的泛化性能。     

Cyclosporine concentration prediction using clustering and supportvector regression methods

A combined strategy of clustering and support vector regression (SVR) methods is proposed to predict Cyclosporine A (CyA) concentration in renal transplant recipients. Clustering combats the high variability and non-stationarity of the time series and reports knowledge gain in the problem. The SVR outperforms other classical neural networks     

A novel prediction method based on the support vector regression for the remaining useful life of lithium-ion batteries

Traditional approaches to lithium-ion battery health management mostly focus on the state of charge (SOC) estimation issues, whereas the state of health (SOH) estimation is also critical to lithium-ion batteries for safe operation. For online battery prognostics, it is critical to make timely and accurate response to SOH. The loss of rated capacity of a battery is usually used to determine the battery SOH, whereas the measurement of the capacity of an operating battery is quite challenging. Normally, the rated capacity fading largely relies on laboratory measurements and offline analysis. In this paper, two real-time measurable health indicators (HI) - one is the time interval of an equal charging voltage difference (TIECVD), and the other is the time interval of an equal discharging voltage difference (TIEDVD) - are extracted. A novel method which combines feature vector selection (FVS) with SVR is utilized to model the relationship between these two HIs and capacity, then the online capacity can be evaluated, more accurate prognostics of SOH and remaining useful life (RUL) can be made. Besides, compared to standard SVR, the proposed method takes FVS to cut down the training data size, which improves the efficiency of model training and prediction. In the end, two datasets demonstrated this approach performs both well in accuracy and efficiency.     

激光诱导击穿光谱的飞灰碳含量定量分析方法

燃煤飞灰碳含量是影响锅炉工作效率的重要特性指标之一,文中开展激光诱导击穿光谱技术（LIBS）实现飞灰未燃碳的定量分析方法研究,为LIBS应用于飞灰含碳量的快速/在线检测奠定基础。根据所探测的LIBS特征光谱,将线性和非线性化学计量学方法,包括多元线性回归（MLR）和偏最小二乘回归（PLSR）线性分析分析方法,以及非线性的极限学习机（ELM）和支持向量机回归（SVR）模型应用于飞灰未燃碳的预测分析中,结合交叉验证法对模型进行验证。对比线性和非线性模型的结果可以看出,非线性模型的预测结果明显优于线性模型,其中采用基于K-CV参数优化的非线性SVR模型具有比较理想的分析结果,有助于提高飞灰碳含量分析的精确度和准确度,采用三折叠交叉验证法对模型进行验证,得到模型的决定系数R2均为0.99,相对偏差的平均值ARD分别为1.54%、3.45%、3.51%,相对标准误差RSD的平均值分别为7.53%、2.89%、7.18%。