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).     

基于频谱分析与支持向量机的车型音频识别研究

机动车车型识别是城市道路交通流监测统计的一个重要方面。本文基于频谱分析与支持向量机方法提出一种车型音频识别方法,以1/3倍频程频谱数据作为特征数据,并使用支持向量机方法完成不同车型分类下的车型识别,同时还分析比较了不同训练样本量及不同单个样本数据量大小对识别结果的影响。在将车型细分的情况下,对小汽车、大型公交车、水泥车、摩托车四种车型的样本外识别结果达到96.9%的准确率,验证了方法的有效性。     

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.     

Variational quantum support vector machine based on Hadamard test

Classical machine learning algorithms seem to be totally incapable of processing tremendous amounts of data, while quantum machine learning algorithms could deal with big data with ease and provide exponential acceleration over classical counterparts. Meanwhile, variational quantum algorithms are widely proposed to solve relevant computational problems on noisy, intermediate-scale quantum devices. In this paper, we apply variational quantum algorithms to quantum support vector machines and demonstrate a proof-of-principle numerical experiment of this algorithm. In addition, in the classification stage, fewer qubits, shorter circuit depth, and simpler measurement requirements show its superiority over the former algorithms.     

Curvelet based image compression via core vector machine

In this paper, a novel curvelet based digital image compression scheme is proposed. Aiming at achieving high compression ratio, the proposed scheme embeds a representative machine learning method, core vector machine (CVM), in the encoding process of the image compression technique. The core vector machine (CVM) has been introduced as an extremely fast classifier which is demonstrably superior to standard support vector machine (SVM) on very large datasets. In this scheme, we appropriately utilize the characteristic of CVM to reduce huge numbers of curvelet coefficients. Compared with image compression algorithms do not use CVM and methods based on wavelet transform, experimental results show that the compression performance of our method gains much improvement in peak-signal-to-noise-ratio (PSNR) and CPU time. Moreover, the algorithm works fairly well for declining block effect at higher compression ratios.     

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.     

基于支持向量机的电磁逆散射方法

为解决电磁逆散射问题,提出了一种实时逆散射方法,该方法利用支持向量机(SVM)将逆散射问题转化为一个回归估计问题. 基于SVM的电磁逆散射方法成功地解决了逆散射问题中的非线性和不适定性.利用穿墙问题测试了该方法的可行性和有效性, 测试结果表明,不论是无噪声还是有噪声的情况,该方法都能很好地对墙后目标进行探测与定位.此外, 在穿墙环境下用SVM预测模型讨论了接收天线的采样位置数对预测结果的影响.最后对多源设置下的预测误差进行了分析和研究, 研究表明,相比于单源情况多源设置有利于对墙后目标的识别.     

基于多分类支持向量机的船舶桨叶数识别研究

分析了目前常用的支持向量机多分类方法以及存在的不足,本文提出了一种混合纠错输出编码的多分类支持向量机改进算法,并应用于利用船舶目标辐射噪声DEMON谱进行船舶桨叶数分类的实验。理论分析与实验结果表明,该改进算法编码明确、具备纠错能力,是一种有效的多分类支持向量机方法,在船舶桨叶数识别中,其分类性能优于一对余、一对一及最小输出编码支持向量机等多分类方法,可适用于船舶桨叶数的分类识别。     

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.     

基于选择性支持向量机集成的混沌时间序列预测

提出了一种基于聚类的选择性支持向量机集成预测模型.为提高支持向量机集成的泛化能力，采用自组织映射和K均值聚类算法结合的聚类组合算法，从每簇中选择出精度最高的子支持向量机进行集成，可以保证子支持向量机有较高精度并提高了子支持向量机之间的差异度.该方法能以较小的代价显著提高支持向量机集成的泛化能力.采用该方法对Mackey-Glass混沌时间序列和Lorenz系统生成的混沌时间序列进行预测实验，结果表明可以对混沌时间序列进行准确预测，验证了该方法的有效性. 关键词： 支持向量机 集成 混沌时间序列 聚类     

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.     

基于支撑向量机算法的抗干扰混合波束成形系统

针对复杂电磁环境下毫米波阵列的空间辐射干扰抑制问题,设计了一种低成本的自适应波束成形系统。首先建立了复杂电磁环境下通信系统模型,建立目标函数。然后利用机器学习中支持向量机算法将原非凸约束问题转化为二阶锥优化问题,获得理想编码矢量。最后利用梯度追踪算法对理想波束矢量进行稀疏重构,完成抗干扰波束的低复杂度实现。仿真结果表明,提出的波束成形系统能够对干扰作出有效抑制,能提升通信质量。     

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。     

基于改进支持向量机的水声目标-杂波不平衡分类研究

针对水声目标-杂波数据集在有限样本下的类不平衡特性导致代价敏感支持向量机难以逼近贝叶斯最优决策的问题,该文提出了一种基于能量统计方法的支持向量机(En-SVM).该算法通过度量原始数据空间与有限样本空间特征函数之间的加权平方距离,量化少数类样本不完全采样过程中的信息损失,来补偿再生核希尔伯特空间中机器学习算法所需的少数...     

Characterization of Iran electricity market indices with pay-as-bid payment mechanism

Market data analysis in Iran’s electricity market as a market with a pay-as-bid payment mechanism has been considered in this paper. The analysis procedure includes both predictability and correlation analysis of the most important load and price indices. The experimental data from Iran’s electricity market has been employed in its real size which is long enough to take properties such as non-stationarity of the market into account. For predictability, the characteristics of the hourly accepted Weighted Average Price (WAP) as the topmost price index of this market is analyzed. The analysis tools are time series analysis methods such as power spectral density analysis, phase space reconstruction and test of surrogates, the fractional dimension and the slope of integral sums and the recurrence plots. The results indicate a deterministic, un-stationary and seasonal behavior in addition to unstable periodic orbits and even chaotic behavior in WAP time series. These observations imply just short-term predictability of WAP behavior. The interactive behavior of WAP with the hourly required load (RL) is also considered. For this interaction analysis, in addition to the common correlation methods, cross and joint recurrence plot are also employed. The joint behavioral analysis represents an un-stationary mimic correlation between WAP and RL.     

基于支持向量机的~(252)Cf中子裂变信号时频特征分析及识别

基于裂变中子(252Cf)对裂变链(235U系统)依存关系,在对252Cf中子裂变信号的测量原理及信号特点分析基础上,开展了基于支持向量机的中子裂变信号时频特征分析及识别研究工作。采用小波分解和去噪小波包分解方法,提取不同状态下随机核信号的时频能量特征,借助于统计学习理论的支持向量机(SVM)分类器原理进行训练和分类。研究结果表明:通过直接小波分解或去噪小波包分解,以获取核信号特征的方法是有效的;去噪小波包分解特征提取方式,较之直接小波分解特征提取方式更能反映中子裂变核系统的内部特征和规律;基于SVM核信号样本的分类,训练后的SVM分类器有着大于70%以上的正确率,且较好地克服了训练样本数较少的问题,验证了方法的可行性和有效性。     

Small-time scale network traffic prediction based on a local support vector machine regression model

In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.     

支持向量机算法在激光诱导击穿光谱技术塑料识别中的应用研究

基于支持向量机 (support vector machines, SVM) 算法采用激光诱导击穿光谱技术对11种塑料进行了识别. 每种塑料各采集100个光谱, 其中50个光谱作为训练集, 用于建立支持向量机模型, 剩下的50 个光谱作为测试集, 用于测试所建立支持向量机模型的识别精度. 结果表明测试集550个光谱中有543个光谱识别正确,算术平均识别精度达到了98.73%. 其中有6个聚氨酯 (PU) 光谱被误判为有机玻璃 (PMMA), 原因主要是受空气中氮气的影响, 使得有机玻璃和聚氨酯两种塑料在氮元素含量上的差异不能通过N I 746.87 nm, C-N(0,0) 388.3 nm两条谱线的强度准确表征. 本结果为LIBS技术塑料分类提供了方法和数据参考. 关键词： 支持向量机 激光诱导击穿光谱 塑料识别     

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.