一种基于视频图像的挖掘机工作状态识别方法

为了实现违法用地现象的实时监测,对土地间的挖掘机等施工机械的工作状态识别是非常重要的.实际场景下,因随机噪声和光照变化的影响,给挖掘机工作状态识别带来极大的挑战.本文提出一种基于视频图像的挖掘机工作状态识别方法,首先,对同一品牌挖掘机工作装置的各种姿态建立混合local binary features (LBF)形状回归模型并进行离线训练;其次,利用上述模型预测输入视频帧中挖掘机工作装置的形状信息,构建挖掘机的工作状态特征描述子;最后,利用support vector machine (SVM)分类器自动判别挖掘机的工作状态—-工作状态或非工作状态.实验结果表明,该方法很好地克服了多姿态导致形状变化的影响,对挖掘机工作状态识别准确率达到了93.53%.     

大入射余角CFAR检测器

雷达在大入射余角高分辨率海杂波背景下检测时,等效后向散射面积增大,大部分海杂波能量投射到少数距离单元,能量分布不均,出现功率突然增大的杂波“异常单元”,导致检测器参考窗口所处的背景环境复杂多变,传统检测器检测概率降低,虚警率及误检率增加。为解决此问题,通过参考滑窗单元的协方差矩阵构造正定矩阵,求解其矩阵范数用以估计杂波功率水平,并采用支持向量机改进传统恒虚警率(constant false alarm rate,CFAR)检测器,得到基于正定矩阵杂波功率估计训练支持向量机的改进CFAR检测器。实验结果表明,新检测器在均匀杂波、多目标环境下检测性能稳定,在杂波边缘的虚警控制能力良好。     

基于支持向量机的轰炸机敏感性权衡优化

生存力中敏感性方案的设计及改进是一个效益和代价不断权衡的过程。针对轰炸机敏感性权衡中的效益-费用优化问题,研究了基于支持向量机的轰炸机敏感性权衡方法。建立支持向量机代理模型,简化了敏感性的计算,并利用交叉验证和网格搜索对模型参数进行优化;拟合了轰炸机敏感性参数与增加费用的函数关系,将灵敏度理论引入敏感性权衡中,从最优效费比的角度出发,确定敏感性参数的取值范围。最后通过仿真验证了方法的可行性。结果表明,该权衡优化方法能够在费用约束条件下对敏感性参数的取值范围进行优化,在保证精度的同时提升了计算效率。     

空中目标辐射源的个体识别方法

针对传统的辐射源个体识别方法在低信噪比环境下识别性能不佳的问题,提出了一种空中目标辐射源的个体识别方法,该方法利用经验模态分解和变分模态分解得到信号不同频率的模态分量,将各模态分量的多尺度排列熵作为特征,利用主成分分析对数据进行降维,并采用支持向量机分类器进行辐射源个体识别。仿真结果表明,该方法对相位噪声、频率漂移以及谐波失真等细微特征的识别性能明显优于传统方法,并具有良好的抗噪性。     

基于SVM的多特征图像隐秘分析

文章通过分析研究隐秘图像和正常图像小波子带系数高频部分的统计特征,从纹理统计矩、DCT系数直方图矩和上下文块之间的相关性方面来提取特征,组成特征向量,并采用SVM方法进行分类,实现了一种从不同类型、不同角度提取多个特征的图像隐秘分析算法,解决了现有通用性隐秘分析算法特征提取不足的问题。实验结果表明,这是一种有效的、高精度的及通用性的检测方法。     

基于支持向量机的信用评估模型及风险评价

运用基于支持向量机理来建立一个新的个人信用评估预测模型,以期取得更好的预测分类能力.并对SVM分类结果与三层全连接BPN分类结果进行了比较.结果表明,在判别潜在的贷款申请者中支持向量的判别结果比神经网络的要好.为了减小训练集偏差及为了验证两种方法的鲁棒性,基于两种策略(平衡样本与非平衡样本)交叉验证来进一步评价SVM分类准确性,并对两种方法基于两种策略的误分类作了风险代价分析.     

基于支持向量机的小波域自适应水印算法

提出一种小波域中基于小波系数方向树模型的支持向量机自适应水印算法.利用支持向量机在小样本的情况下具有良好的学习和泛化性能的特点,建立小波域内不同尺度上同方向子带内相同空间位置的小波系数间的关系模型;根据关系模型来嵌入和提取水印.为提高图像视觉效果,采用模糊聚类的方法自适应地选取水印的嵌入位置.实验结果表明:该方法对常见的图像攻击具有很好的鲁棒性,而且水印的隐蔽性好、安全性强.     

Short‐term salmon price forecasting

This study establishes a benchmark for short‐term salmon price forecasting. The weekly spot price of Norwegian farmed Atlantic salmon is predicted 1–5 weeks ahead using data from 2007 to 2014. Sixteen alternative forecasting methods are considered, ranging from classical time series models to customized machine learning techniques to salmon futures prices. The best predictions are delivered by k‐nearest neighbors method for 1 week ahead; vector error correction model estimated using elastic net regularization for 2 and 3 weeks ahead; and futures prices for 4 and 5 weeks ahead. While the nominal gains in forecast accuracy over a naïve benchmark are small, the economic value of the forecasts is considerable. Using a simple trading strategy for timing the sales based on price forecasts could increase the net profit of a salmon farmer by around 7%.     

Google Trends and the forecasting performance of exchange rate models

In this paper, we use Google Trends data for exchange rate forecasting in the context of a broad literature review that ties the exchange rate movements with macroeconomic fundamentals. The sample covers 11 OECD countries’ exchange rates for the period from January 2004 to June 2014. In out‐of‐sample forecasting of monthly returns on exchange rates, our findings indicate that the Google Trends search query data do a better job than the structural models in predicting the true direction of changes in nominal exchange rates. We also observed that Google Trends‐based forecasts are better at picking up the direction of the changes in the monthly nominal exchange rates after the Great Recession era (2008–2009). Based on the Clark and West inference procedure of equal predictive accuracy testing, we found that the relative performance of Google Trends‐based exchange rate predictions against the null of a random walk model is no worse than the purchasing power parity model. On the other hand, although the monetary model fundamentals could beat the random walk null only in one out of 11 currency pairs, with Google Trends predictors we found evidence of better performance for five currency pairs. We believe that these findings necessitate further research in this area to investigate the extravalue one can get from Google search query data.     

Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?

We study the performance of recently developed linear regression models for interval data when it comes to forecasting the uncertainty surrounding future stock returns. These interval data models use easy‐to‐compute daily return intervals during the modeling, estimation and forecasting stage. They have to stand up to comparable point‐data models of the well‐known capital asset pricing model type—which employ single daily returns based on successive closing prices and might allow for GARCH effects—in a comprehensive out‐of‐sample forecasting competition. The latter comprises roughly 1000 daily observations on all 30 stocks that constitute the DAX, Germany's main stock index, for a period covering both the calm market phase before and the more turbulent times during the recent financial crisis. The interval data models clearly outperform simple random walk benchmarks as well as the point‐data competitors in the great majority of cases. This result does not only hold when one‐day‐ahead forecasts of the conditional variance are considered, but is even more evident when the focus is on forecasting the width or the exact location of the next day's return interval. Regression models based on interval arithmetic thus prove to be a promising alternative to established point‐data volatility forecasting tools. Copyright ©2015 John Wiley & Sons, Ltd.