Price forecast in the competitive electricity market by support vector machine

The electricity market has been widely introduced in many countries all over the world and the study on electricity price forecast technology has drawn a lot of attention. In this paper, with different parameter C_i and ε_i assigned to each training data, the flexible C_i Support Vector Regression (SVR) model is developed in terms of the particularity of the price forecast in electricity market. For Day Ahead Market (DAM) price forecast, the load, time of use index and index of day type are taken as the major factors to characterize the market price, therefore, they are selected as the inputs for the flexible SVR forecast model. For the long-term price forecast, we take the reserve margin R^m, HHI and the fuel price index as the inputs, since they are the major factors that drive the market price variation in long run. For short-term price forecast, besides the detailed analysis with the young Italian electricity market, the new model is tested on the experimental stage of the Spanish market, the New York market and the New England market. The long-term forecast with the SVR model presented is justified by the forecast with the data from the Long Run Market Simulator (LREMS).     

Multisensor long distance target detection using support vector machine

Multisensor image fusion could improve system performances such as detection, tracking, and identification greatly. In this paper, a long distance target detection approach is presented based on multisensor image features fusion. This method extracts two different features from visual and infrared (IR) image sequences respectively to detect regions of motion information content. Temporal change feature is extracted from the visual image sequence using temporal decomposition based on wavelet, which reflects the dynamical content variation at a pixel at any time. And correlation features between local regions are extracted from IR image sequence to distinguish regions with potential moving targets. All these features are merged into a multi-dimensional space and the support vector machine is trained to select regions that have the potential target at each pixel location. The method is robust and feasible to detect long distance targets in clutter background scene.     

基于平均场支持向量机的混沌时间序列预测

提出了一种新的基于支持向量机的混沌时间序列预测方法，该方法利用平均场理论使支持向量机的学习过程变得简单高效。同时由于该方法将支持向量机的参数近似为高斯分布的，因此采用平均场理论能够容易的求解这些参数，这样获得的支持向量机的参数比传统的基于二次规划的算法更加精确，而且学习速度更快。最后利用该方法对嵌入维数与模型的泛化能力关系进行了探讨，并利用Mackey-Glass时间序列对该方法进行了验证，结果表明：该预测方法能精确地预测混沌时间序列,而且在混沌时间序列的嵌入维数未知时也能取得比较好的预测效果.这一结论预示着平均场支持向量机是一种研究混沌时间序列的有效方法.     

Multimodal biometric authentication based on score level fusion using support vector machine

Fusion of multiple biometrics for human authentication performance improvement has received considerable attention. This paper presents a novel multimodal biometric authentication method integrating face and iris based on score level fusion. For score level fusion, support vector machine (SVM) based fusion rule is applied to combine two matching scores, respectively from Laplacianface based face verifier and phase information based iris verifier, to generate a single scalar score which is used to make the final decision. Experimental results show that the performance of the proposed method can bring obvious improvement comparing to the unimodal biometric identification methods and the previous fused face-iris methods.     

基于在线最小二乘支持向量机回归的混沌时间序列预测

提出了一种基于在线最小二乘支持向量机（LS-SVM）回归的混沌时间序列的预测方法.与离线支持向量机相比，在线最小二乘支持向量机预测方法即使当混沌系统的参数随时间变化时仍然有效.以Chen's混沌系统、Rssler混沌系统、Hénon映射及脑电(EEG)信号四种混沌时 间序列为例评估本文提出的预测方法，结果验证了其混沌时间序列预测的有效性. 关键词： 混沌时间序列 预测 在线学习 支持向量机     

基于选择性支持向量机集成的海杂波背景中的微弱信号检测

基于复杂非线性系统相空间重构理论,提出了一种混沌背景中微弱信号检测的选择性支持向量机集成的方法,为了提高支持向量机集成的泛化能力,采用K均值聚类算法选择每簇中精度最高的子支持向量机进行集成,建立了混沌背景噪声的一步预测模型,从预测误差中检测湮没在混沌背景噪声中的微弱目标信号（包括周期信号和瞬态信号）,最后分别以Lorenz系统和实测的IPIX雷达数据作为混沌背景噪声进行实验研元结果表明该方法能够有效地将混沌背景噪声中极其微弱的信号检测出来,抑制噪声对混沌背景信号的影响,与神经网络和传统支持向量机方法相比,预测精度和检测门限方面的性能有显著提高．     

基于支持向量机的微波链路雨强反演方法

为提高微波链路雨致衰减反演雨强精度, 在Mie散射理论、气体吸收衰减模型以及Gamma雨滴谱分布的基础上, 将支持向量机引入到微波链路测量降水中, 提出了基于支持向量机的微波链路雨强反演方法, 并开展了15–20 GHz频段的视距微波链路与地面雨滴谱仪的同步观测降雨实验. 实验结果表明, 基于支持向量机的微波链路雨强反演模型的反演雨强与实测雨强的相关系数全部高于0.6, 最高达到0.9674; 雨强的均方根误差最小值为0.5780 mm/h, 累积降雨量的绝对最小误差仅为0.0080 mm; 相对偏差大部分在10%以内, 最小偏差为0.7425%. 实验结果验证了基于支持向量机的微波链路雨强反演方法的有效性、准确性和适用性, 对于进一步提高微波链路反演降雨精度、改善降水监测效果具有重要意义.     

时空混沌序列的局域支持向量机预测

结合局域预测法计算速度快的优点和支持向量机的泛化性能好、全局最优、稀疏解等特性，用局域支持向量机预测研究了时空混沌序列的局域预测性能，并用局域支持向量机预测模型讨论了嵌入维数、邻近个数选择以及时空混沌的耦合方式和格子间的耦合强度变化对时空混沌局域预测性能的影响.研究结果表明：局域支持向量机不仅比全局支持向量机、局域零阶预测、局域线性预测等方法具有更好的预测性能，且具有对嵌入维数和邻近个数不敏感的优点；时空混沌的耦合方式和格子间的耦合强度对时空混沌序列的预测性能有明显影响.     

Infrasound signal classification based on spectral entropy and support vector machine

The operation speed of the algorithm is the critical factor in the real-time monitoring of infrasound signals. The existing methods mainly focus on how to improve the accuracy of classification and can’t be used in real time monitoring because of their slow running speed. We adopt spectral entropy into the feature extraction of infrasound signals. Combined with the support vector machine algorithm, the proposed method effectively extracted the signal features meanwhile greatly improved the operation efficiency. Experimental results show that the running speed of the proposed method is 1.0 s, which is far less than 4.7 s of the comparison method. So the proposed method can be applied in real-time monitoring of earthquakes, tsunamis, landslides and other infrasound events.     

Automatic knee cartilage segmentation from multi-contrast MR images using support vector machine classification with spatial dependencies

Accurate segmentation of knee cartilage is required to obtain quantitative cartilage measurements, which is crucial for the assessment of knee pathology caused by musculoskeletal diseases or sudden injuries. This paper presents an automatic knee cartilage segmentation technique which exploits a rich set of image features from multi-contrast magnetic resonance (MR) images and the spatial dependencies between neighbouring voxels. The image features and the spatial dependencies are modelled into a support vector machine (SVM)-based association potential and a discriminative random field (DRF)-based interaction potential. Subsequently, both potentials are incorporated into an inference graphical model such that the knee cartilage segmentation is cast into an optimal labelling problem which can be efficiently solved by loopy belief propagation. The effectiveness of the proposed technique is validated on a database of multi-contrast MR images. The experimental results show that using diverse forms of image and anatomical structure information as the features are helpful in improving the segmentation, and the joint SVM-DRF model is superior to the classification models based solely on DRF or SVM in terms of accuracy when the same features are used. The developed segmentation technique achieves good performance compared with gold standard segmentations and obtained higher average DSC values than the state-of-the-art automatic cartilage segmentation studies.     

Neural specificity of acupuncture stimulation from support vector machine classification analysis

Acupoint specificity, as a crucial issue in acupuncture neuroimaging studies, is still a controversial topic. Previous studies have generally adopted a block-based general linear model (GLM) approach, which predicts the temporal changes in the blood oxygenation level-dependent signal conforming to the “on-off” specifications. However, this method might become impractical since the precise timing and duration of acupuncture actions cannot be specified a priori. In the current study, we applied a data-driven multivariate classification approach, namely, support vector machine (SVM), to explore the neural specificity of acupuncture at gall bladder 40 (GB40) using kidney 3 (KI3) as a control condition (belonging to different meridians but the same nerve segment). In addition, to verify whether the typical GLM approach is sensitive enough in exploring the neural response patterns evoked by acupuncture, we also employed the GLM method to the same data sets. The SVM analysis detected distinct neural response patterns between GB40 and KI3 — positive predominantly for the GB40, while negative following the KI3. By contrast, group analysis from the GLM showed that acupuncture at these different acupoints can both evoke similar widespread signal decreases in multiple brain regions, and most of these regions were spatially overlapped, mainly distributing in the limbic and subcortical structures. Our findings may provide additional evidence to support the specificity of acupuncture, relevant to its clinical efficacy. Moreover, we also proved that GLM analysis is prone to be susceptible to errors and is not appropriate for detecting neural response patterns evoked by acupuncture stimulation.     

Performance evaluation for epileptic electroencephalogram (EEG) detection by using Neyman-Pearson criteria and a support vector machine

The diagnosis of several neurological disorders is based on the detection of typical pathological patterns in electroencephalograms (EEGs). This is a time-consuming task requiring significant training and experience. A lot of effort has been devoted to developing automatic detection techniques which might help not only in accelerating this process but also in avoiding the disagreement among readers of the same record. In this work, Neyman-Pearson criteria and a support vector machine (SVM) are applied for detecting an epileptic EEG. Decision making is performed in two stages: feature extraction by computing the wavelet coefficients and the approximate entropy (ApEn) and detection by using Neyman-Pearson criteria and an SVM. Then the detection performance of the proposed method is evaluated. Simulation results demonstrate that the wavelet coefficients and the ApEn are features that represent the EEG signals well. By comparison with Neyman-Pearson criteria, an SVM applied on these features achieved higher detection accuracies.     

Quantum-enhanced least-square support vector machine: Simplified quantum algorithm and sparse solutions

Quantum algorithms can enhance machine learning in different aspects. Here, we study quantum-enhanced least-square support vector machine (LS-SVM). Firstly, a novel quantum algorithm that uses continuous variable to assist matrix inversion is introduced to simplify the algorithm for quantum LS-SVM, while retaining exponential speed-up. Secondly, we propose a hybrid quantum-classical version for sparse solutions of LS-SVM. By encoding a large dataset into a quantum state, a much smaller transformed dataset can be extracted using quantum matrix toolbox, which is further processed in classical SVM. We also incorporate kernel methods into the above quantum algorithms, which uses both exponential growth Hilbert space of qubits and infinite dimensionality of continuous variable for quantum feature maps. The quantum LS-SVM exploits quantum properties to explore important themes for SVM such as sparsity and kernel methods, and stresses its quantum advantages ranging from speed-up to the potential capacity to solve classically difficult machine learning tasks.     

基于多模板子空间的支持向量机多类自然形状识别

自然物体的检测与识别是机器视觉以及模式识别的重要任务。由于自然物体形状的多样性与柔性以及视觉判别的复杂性,使基于计算机的自然形状物体的准确检测与识别变得比较困难。提出了基于多模板子空间的支持向量机(SVM)多类自然形状识别方法。利用广义Hough变换表示自然形状物体轮廓,针对每个类别通过训练得到多个匹配模板;检测时利用多模板最近邻相关匹配进行粗检测,使用支持向量机进行分类。在相关匹配限定的子空间内收集训练样本,有效地降低了训练样本数目。实验结果证明所提出的自然形状检测与识别方法是十分有效的,大大改进了经典检测算法的检测效果以及自动化程度。