Autonomous Kernel Based Models for Short-Term Load Forecasting

The application of support vector machines to forecasting problems is becoming popular, lately. Several comparisons between neural networks trained with error backpropagation and support vector machines have shown advantage for the latter in different domains of application. However, some difficulties still deteriorate the performance of the support vector machines. The main one is related to the setting of the hyperparameters involved in their training. Techniques based on meta-heuristics have been employed to determine appropriate values for those hyperparameters. However, because of the high noneonvexity of this estimation problem, which makes the search for a good solution very hard, an approach based on Bayesian inference, called relevance vector machine, has been proposed more recently. The present paper aims at investigating the suitability of this new approach to the short-term load forecasting problem.

Diversity Sampling Based Kernel Density Estimation for Background Modeling

A novel diversity-sampling based nonparametric multi-modal background model is proposed. Using the samples having more popular and various intensity values in the training sequence, a nonparametric model is built for background subtraction. According to the related intensifies, different weights are given to the distinct samples in kernel density estimation. This avoids repeated computation using all samples, and makes computation more efficient in the evaluation phase. Experimental results show the validity of the diversity- sampling scheme and robustness of the proposed model in moving objects segmentation. The proposed algorithm can be used in outdoor surveillance systems.

Comparison of Uniform and Kernel Gaussian Weight Matrix in Generalized Spatial Panel Data Model

Panel data combine cross-section data and time series data. If the cross-section is locations, there is a need to check the correlation among locations. ρ and λ are parameters in generalized spatial model to cover effect of correlation between locations. Value of ρ or λ will influence the goodness of fit model, so it is important to make parameter estimation. The effect of another location is covered by making contiguity matrix until it gets spatial weighted matrix (W). There are some types of W—uniform W, binary W, kernel Gaussian W and some W from real case of economics condition or transportation condition from locations. This study is aimed to compare uniform W and kernel Gaussian W in spatial panel data model using RMSE value. The result of analysis showed that uniform weight had RMSE value less than kernel Gaussian model. Uniform W had stabil value for all the combinations.

基于Kernel特征空间分解的组分仪递推模型

A recursive Kernel eigenspace updating algorithm was proposed to build the soft sensor for end-product quality.The updating procedure was composed of two sub-stages,i.e.firstly performing forward increasing updating and then followed by backward decreasing updating,which drastically decreased the required computation workload.Further,the whole Kernel matrix did not need to be stored.Simulation study on the Tennessee Eastman process showed that the consequent impurity component model had satisfying precision under both normal and faulty operations,which was obviously superior to the offline batch model and meanwhile approximated the performance of model obtained by successively applying the time-consuming traditional eigenvalue numerical algorithm.

我国矿产资源型产业技术创新能力分布的动态演进研究——基于Kernel密度和马尔可夫链分析

依据矿产资源型产业1996-2012年技术创新指标的面板数据,运用非参数Kernel密度估计及马尔可夫链分析模型,对我国矿产资源型产业技术创新能力的分布及发展演变趋势进行研究,研究结果表明,我国矿产资源型产业技术创新能力经历了"单峰——双峰——单峰"的演变过程,不同产业之间技术创新能力发展速度存在不平衡性,产业链后续产业对上游产业技术进步具有明显的拉动效应,矿产资源型产业技术创新能力表现出向高水平状态转移的收敛性。

测量误差数据下单指标模型的非参数模拟外推估计

研究了测量误差数据下一类单指标模型的估计问题.结合局部线性平滑法和核密度估计法,提出一种基于非参数模拟外推的模型估计方法 .所提出的非参数模拟外推估计,不需要假定观测变量的具体分布并且不需要假定测量误差的方差已知,具有较广的适应性.在一些正则条件下,证明了非参数模拟外推方法给出的参数估计量的渐近正态性.

浙江省普惠金融包容发展指数演化及其影响因素分析——基于Kernel非参数估计方法及面板模型

在对浙江省普惠金融包容发展指数进行测算基础上进一步进行Kernel密度估计分析,不同地区差异性比较明显。同时利用静态面板和动态面板模型分析影响普惠金融包容发展水平的相关因素,人均国民经济发展水平、国际互联网用户、受教育程度、对外出口、人均社会消费品零售等对普惠金融包容发展具有正影响。城镇与农村收入比对普惠金融包容发展影响不确定。可以从提高信息化水平,深化互联网金融的普惠制服务,协调各个地区之间的经济发展,缩小城乡收入差距等方面提高浙江省普惠金融包容发展的整体水平。

基于各向异性混合核函数高斯过程回归的RC柱概率抗剪承载力模型

针对钢筋混凝土(RC)柱抗剪承载力传统预测模型的非线性逼近能力不足且无法合理描述不确定性所存在的缺陷,提出一种基于各向异性混合核函数高斯过程回归的RC柱概率抗剪承载力预测模型。首先,基于核函数相加性和自动相关性,构造出一种新型的各向异性混合核函数;然后,结合高斯过程回归原理和各向异性混合核函数,建立了RC柱的概率抗剪承载力模型;进而采用极大似然估计法,确定了RC柱概率抗剪承载力模型的超参数;最后,基于91组剪切破坏RC柱的试验数据,通过与传统核函数形式和传统模型进行对比分析,验证了该模型的有效性。结果表明:与传统核函数相比,各向异性混合核函数的确定性预测指标均方根误差R_(MSE)和平均绝对误差M_(AE)分别降低约16%和19%,概率性预测值指标负对数预测密度N_(LPD)和平均标准化对数损失M_(SLL)分别降低约15%和23%;与传统机器学习模型相比,本文模型的均方根误差R_(MSE)和平均绝对误差M_(AE)分别降低约38%和39%;根据所提出的概率模型能够建立概率密度函数曲线和置信区间,从而合理描述抗剪承载力的不确定性并校准分析传统模型的预测精度。

GIS-based evaluation of landslide susceptibility using a novel hybrid computational intelligence model on different mapping units

Landslide susceptibility mapping is significant for landslide prevention.Many approaches have been used for landslide susceptibility prediction,however,their performances are unstable.This study constructed a hybrid model,namely box counting dimension-based kernel logistic regression model,which uses fractal dimension calculated by box counting method as input data based on grid cells mapping unit and terrain mapping unit.The performance of this model was evaluated in the application in Zhidan County,Shaanxi Province,China.Firstly,a total of 221 landslides were identified and mapped,and 11 landslide predisposing factors were considered.Secondly,the landslide susceptibility maps(LSMs) of the study area were obtained by constructing the model on two different mapping units.Finally,the results were evaluated with five statistical indexes,sensitivity,specificity,positive predictive value(PPV),negative predictive value(NPV) and Accuracy.The statistical indexes of the model obtained on the terrain mapping unit were larger than those based on grid cells mapping unit.For training and validation datasets,the area under the receiver operating characteristic curve(AUC) of the model based on terrain mapping unit were 0.9374 and 0.9527,respectively,indicating that establishing this model on the terrain mapping unit was advantageous in the study area.The results show that the fractal dimension improves the prediction ability of the kernel logistic model.In addition,the terrain mapping unit is a more promising mapping unit in Loess areas.

QSAR Studies on PCDD/Fs by Kernel PLS

QSPR models of PCDD/Fs were generated by means of kernel partial least squares. The molecular distance-edge vector method was used as descriptors to get model I for predicting PCDD/Fs retention behavior. The chlorinated positions were also used and model II was obtained. In studied cases, the predictive ability of the KPLS model is comparable or superior to those of PLS and ANN. The results indicate that KPLS can be used as an alternative powerful modeling tool for QSPR studies.

Composite Quantile Regression for Nonparametric Model with Random Censored Data

The composite quantile regression should provide estimation efficiency gain over a single quantile regression. In this paper, we extend composite quantile regression to nonparametric model with random censored data. The asymptotic normality of the proposed estimator is established. The proposed methods are applied to the lung cancer data. Extensive simulations are reported, showing that the proposed method works well in practical settings.

Simulation of drop breakage in liquid–liquid system by coupling of CFD and PBM: Comparison of breakage kernels and effects of agitator configurations

This work focuses on drop breakage for liquid-liquid system with an adoption of numerical simulation by using computational fluid dynamics and population balance model (PBM) coupled with two-fluid model (TFM). Two different breakage kernels based on identical breakage mechanism but different descriptions of breaking time are take n into account in this work. Eight cases corresp on ding to distinct configurations of agitator are carried out to validate numerical predictions, namely agitators with different porosity and hole diameters, respectively implemented in Cases 1 to 5 and Cases 6 to 8. The results are compared with experimental data for testing the applicability of both kernels. Simulations are implemented, in this work, with an approach of class method for the solution of population balance model by the special-purpose computational fluid dynamics solver Fluent 16.1 based on finite volume method, and the grids used for meshing the solution domain are accomplished in a commercial software Gambit 2.4.6. The effects of configurations of agitator corresponding to different parameters mentioned above on final Sauter mean diameter are equally concentrated in this work. Analysis of both kernels and comparisons with experimental results reveal that, the second kernel has more decent agreement with experiments, and the results of investigations on effects of agitator configurations show that the in fluences of these parameters on Sauter mean diameter are marginal, but appropriate porosity and hole diameter are actually able to decrease Sauter mean diameter. These outcomes allow us to draw general conclusions and help investigate performances of liquid-liquid system.

Selective ensemble modeling based on nonlinear frequency spectral feature extraction for predicting load parameter in ball mills

Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.

ESTIMATION FOR THE AYMPTOTIC VARIANCE OF PARAMETRIC ESTIMATES IN PARTIAL LINEAR MODEL WITH CENSORED DATA

Consider tile partial linear model Y=Xβ+ g(T) + e. Wilers Y is at risk of being censored from the right, g is an unknown smoothing function on [0,1], β is a 1-dimensional parameter to be estimated and e is an unobserved error. In Ref[1,2], it wes proved that the estimator for the asymptotic variance of βn(βn) is consistent. In this paper, we establish the limit distribution and the law of the iterated logarithm for,En, and obtain the convergest rates for En and the strong uniform convergent rates for gn(gn).

改进RHGSO-FC算法的RGB-D图像GMM聚类分割

随着低成本深度图像传感器的引入,在RGB-D图像中进行可靠的图像分割是计算机视觉的一个目标,而如何对杂乱的场景进行图像分割具有挑战性。基于随机亨利气体溶解度优化算法的模糊聚类(RHGSO-FC),提出一种新的RGB-D图像分割方法。对亨利气体溶解度优化算法(HGSO)进行改进,提出改进的亨利气体溶解度优化算法(LRHGSO),并利用基于改进亨利气体溶解度优化算法的核模糊聚类(LRHGSO-KFC)生成初始化标签。将初始化标签传入到高斯混合(GMM)聚类中,得到多个聚类结果。最后对这些聚类结果通过聚集超像素方法进行分割合并,得到最终分割结果。实验数据集采用NYU depth V2室内图像,与现有的一些分割方法:阈值分割算法、硬C-均值、模糊C-均值、高斯混合聚类、核模糊聚类、模糊子空间聚类、混沌Kbest引力搜索算法和随机亨利气体溶解度优化算法进行比较,结果表明提出的RGB-D分割算法优于其他比较的算法。

中国省域工业碳排放效率的空间马尔可夫链分析

选取2010—2020年中国30个省份(除西藏、港澳台外)的面板数据,结合工业生产的特点,使用非期望SBM模型测算中国省域工业碳排放效率,结合莫兰指数、核密度、空间马尔可夫链模型对中国省域工业碳排放效率的分布及发展趋势进行分析解读。结果发现:样本期间,工业碳排放效率呈先降低后升高的走势;2013年起我国工业碳排放效率在省域空间中呈现显著的空间正相关性,工业碳排放效率的省域分布情况为分散聚集分散,地区差异增大有两极化趋势,且存在明显的时间滞后性和空间溢出效应。

“双碳”战略下绿色金融对我国碳排放强度的影响研究

基于我国30个省份2010—2019年的面板数据,运用基于加速遗传算法的投影寻踪模型测度绿色金融发展水平,通过空间杜宾模型实证研究了绿色金融对碳排放强度的影响及空间溢出效应,并利用核密度估计图刻画出我国东部、中部、西部三大区域碳排放强度的空间分布动态特征。研究发现:第一,绿色金融对碳排放强度有着显著的负向影响以及负的空间溢出效应;第二,低碳技术水平的创新与发展以及能源消费结构的优化有利于降低碳排放强度;第三,我国东部地区碳排放强度水平较低,中西部地区相对较高,整体水平在考察期内逐年降低。基于以上结论,论文提出了有针对性的政策建议,以期为绿色金融助推我国“双碳”目标如期实现提供有力的支持。

Performance Evaluation of Various Functions for Kernel Density Estimation

There have been vast amount of studies on background modeling to detect moving objects. Two recent reviews[1,2] showed that kernel density estimation(KDE) method and Gaussian mixture model(GMM) perform about equally best among possible background models. For KDE, the selection of kernel functions and their bandwidths greatly influence the performance. There were few attempts to compare the adequacy of functions for KDE. In this paper, we evaluate the performance of various functions for KDE. Functions tested include almost everyone cited in the literature and a new function, Laplacian of Gaussian(LoG) is also introduced for comparison. All tests were done on real videos with vary-ing background dynamics and results were analyzed both qualitatively and quantitatively. Effect of different bandwidths was also investigated.

Forecast Urban Air Pollution in Mexico City by Using Support Vector Machines: A Kernel Performance Approach

The development of forecasting models for pollution particles shows a nonlinear dynamic behavior;hence, implementation is a non-trivial process. In the literature, there have been multiple models of particulate pollutants, which use softcomputing techniques and machine learning such as: multilayer perceptrons, neural networks, support vector machines, kernel algorithms, and so on. This paper presents a prediction pollution model using support vector machines and kernel functions, which are: Gaussian, Polynomial and Spline. Finally, the prediction results of ozone (O3), particulate matter (PM10) and nitrogen dioxide (NO2) at Mexico City are presented as a case study using these techniques.

四川省种植业碳排放现状、动态演进及预测

加强种植业碳排放测算,可为推进种植业绿色低碳转型提供重要依据。本文针对种植业从投入到产出的全过程,基于农地利用碳排放、稻田CH4排放和农地N2O排放3类主要来源,运用碳排放系数法测算2010—2021年四川省种植业碳排放量,从时序特征和地区差异揭示四川省种植业碳排放特征,采用核密度分析法剖析四川省种植业碳排放动态演进趋势,运用灰色预测模型预测2022—2030年四川省种植业碳排放量和碳排放强度。结果表明:1)四川省种植业碳排放量在2010—2016年间呈波动上升趋势,2016年以后呈波动下降趋势,其碳排放强度在2010—2021年间持续降低,四川省种植业碳排放以农地利用和稻田CH_(4)碳排放为主,2021年较2010年农地利用碳排放和稻田CH_(4)排放比重均略有减少,而农地N_(2)O排放比重有所上升。2)四川省各市(自治州)种植业碳排放量和强度差异明显,2021年碳排放量最高的南充市比最低的甘孜高24.69倍,碳排放强度最高的巴中市比最低的甘孜高2.8倍。3)四川省五大区域种植业碳排放动态演进呈差异化特征,成都平原、川南、川东北、攀西和川西北五大区域种植业碳排放强度总体均呈下降趋势,但下降速度和变化幅度各异,总体上五大区域内碳排放强度的差距在逐步缩小。4)四川省种植业碳排放量和碳排放强度预计保持稳步下降态势,估计到2025年和2030年四川省种植业碳排放量将分别减少约68.72万t和137.84万t,碳排放强度将分别减少约0.13 t·万元^(-1)和0.26 t·万元^(-1)。基于此,四川省种植业碳减排应主要关注源于化肥投入和稻田CH4碳排放,因此需因地制宜采取差异化的减排措施,强化农业科技创新和推广,提升四川省种植业绿色低碳整体发展水平。