Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series data.Due to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly detection.However,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time series.In this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as CAFFN.Specifically,a series and feature mixing block is introduced to learn representations in 1D space.Additionally,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature extraction.Finally,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly detection.Experimental results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.

AUTO-EXTRACTING TECHNIQUE OF DYNAMIC CHAOS FEATURES FOR NONLINEAR TIME SERIES

The main purpose of nonlinear time series analysis is based on the rebuilding theory of phase space, and to study how to transform the response signal to rebuilt phase space in order to extract dynamic feature information, and to provide effective approach for nonlinear signal analysis and fault diagnosis of nonlinear dynamic system. Now, it has already formed an important offset of nonlinear science. But, traditional method cannot extract chaos features automatically, and it needs man＇s participation in the whole process. A new method is put forward, which can implement auto-extracting of chaos features for nonlinear time series. Firstly, to confirm time delay r by autocorrelation method; Secondly, to compute embedded dimension m and correlation dimension D; Thirdly, to compute the maximum Lyapunov index λmax; Finally, to calculate the chaos degree Dch of Poincare map, and the non-circle degree Dnc and non-order degree Dno of quasi-phase orbit. Chaos features extracting has important meaning to fault diagnosis of nonlinear system based on nonlinear chaos features. Examples show validity of the proposed method.

New supervised learning classifiers for structural damage diagnosis using time series features from a new feature extraction technique

The motivation for this article is to propose new damage classifiers based on a supervised learning problem for locating and quantifying damage.A new feature extraction approach using time series analysis is introduced to extract damage-sensitive features from auto-regressive models.This approach sets out to improve current feature extraction techniques in the context of time series modeling.The coefficients and residuals of the AR model obtained from the proposed approach are selected as the main features and are applied to the proposed supervised learning classifiers that are categorized as coefficient-based and residual-based classifiers.These classifiers compute the relative errors in the extracted features between the undamaged and damaged states.Eventually,the abilities of the proposed methods to localize and quantify single and multiple damage scenarios are verified by applying experimental data for a laboratory frame and a four-story steel structure.Comparative analyses are performed to validate the superiority of the proposed methods over some existing techniques.Results show that the proposed classifiers,with the aid of extracted features from the proposed feature extraction approach,are able to locate and quantify damage;however,the residual-based classifiers yield better results than the coefficient-based classifiers.Moreover,these methods are superior to some classical techniques.

An Improved Time Series Symbolic Representation Based on Multiple Features and Vector Frequency Difference

Symbolic Aggregate approXimation (SAX) is an efficient symbolic representation method that has been widely used in time series data mining. Its major limitation is that it relies exclusively on the mean values of segmented time series to derive the symbols. So, many important features of time series are not considered, such as extreme value, trend, fluctuation and so on. To solve this issue, we propose in this paper an improved Symbolic Aggregate approXimation based on multiple features and Vector Frequency Difference (SAX_VFD). SAX_VFD discriminates between time series by adopting an adaptive feature selection method. Furthermore, SAX_VFD is endowed with a new distance that takes into account the vector frequency difference between the symbolic sequence. We demonstrate the utility of the SAX_VFD on the time series classification task. The experimental results show that the proposed method has a better performance in terms of accuracy and dimensionality reduction compared to the so far published SAX based reduction techniques.

Comparison of River Terraces in the Middle Reach Valleys of the Yellow River and Analysis on the Multi-Gradational Features of Tectonism in the Formation of Terrace Series

Where the Yellow River flows through the Haiyuan-Tongxin arc-form tectonic region on the northeastern side of the Qinghai-Xizang (Tibet) Plateau, as many as 10～21 basis and erosion terraces have been produced, among which the biggest altitude above river level is 401m and the formation age of the highest terrace is 1.57 Ma B.P. Based on comparative analysis of the Yellow River terraces located separately in the Mijiashan mountain, the Chemuxia gorge, the Heishanxia gorge and the other river terraces in the vast extent of the northern part of China, it has been found that the tectonic processes resulting in the formation of the terrace series is one of multi-gradational features, i.e., a terrace series can include the various terraces produced by tectonic uplifts of different scopes or scales and different ranks. The Yellow River terrace series in the study region can be divided into three grades. Among them, in the first grade there are 6 terraces which were formed separately at the same time in the vast extent of the northern part of China and represent the number and magnitude of uplift of the Qinghai-Xizang Plateau since 1.6 Ma B. P.; in the second grade there are 5 terraces which were separately and simultaneously developed within the Haiyuan-Tianjingshan tectonic region and represent the number and magnitude of uplift of this tectonic region itself since 1.6Ma B.P.; in the third grade there are 10 terraces which developed on the eastern slope of the Mijiashan mountain and represent the number and amplitude of uplift of the Haiyuan tectonic belt itself since 1.6Ma B.P. Comparison of the terrace ages with loess-paleosoil sequence has also showed that the first grade terraces reflecting the vast scope uplifts of the Qinghai-Xizang Plateau are very comparable with climatic changes and their formation ages all correspond to the interglacial epochs during which paleosoils were formed. This implies that the vast extent tectonic uplifts resulting in river down-cutting are closely related to the warm-humid climatic periods which can also result in river downward erosion after strong dry and cold climatic periods, and they have jointly formed the tectonic-climatic cycles. There exists no unanimous and specific relationship between the formation ages of the second and third grade terraces and climatic changes and it is shown that the formation of those terraces was most mainly controlled by tectonic uplifts of the Tianjingshan block and the Haiyuan belt. The river terraces in the study region, therefore, may belong to 2 kinds of formation cause. One is a tectonic-climatic cyclical terrace produced jointly by vast extent tectonic uplifts and climatic changes, and the terraces of this kind are extensively distributed and can be well compared with each other among regions. Another is a pulse-tectonic cyclical terrace produced by local tectonic uplifts as dominant elements, and their distribution is restricted within an active belt and can not be compared with among regions.

Feature extraction and damage alarming using time series analysis

Aiming at the problem of on-line damage diagnosis in structural health monitoring （SHM）, an algorithm of feature extraction and damage alarming based on auto-regressive moving-average （ARMA） time series analysis is presented. The monitoring data were first modeled as ARMA models, while a principalcomponent matrix derived from the AR coefficients of these models was utilized to establish the Mahalanobisdistance criterion functions. Then, a new damage-sensitive feature index DDSF is proposed. A hypothesis test involving the t-test method is further applied to obtain a decision of damage alarming as the mean value of DDSF had significantly changed after damage. The numerical results of a three-span-girder model shows that the defined index is sensitive to subtle structural damage, and the proposed algorithm can be applied to the on-line damage alarming in SHM.

Deep learning framework for time series classification based on multiple imaging and hybrid quantum neural networks

Time series classification(TSC)has attracted a lot of attention for time series data mining tasks and has been applied in various fields.With the success of deep learning(DL)in computer vision recognition,people are starting to use deep learning to tackle TSC tasks.Quantum neural networks(QNN)have recently demonstrated their superiority over traditional machine learning in methods such as image processing and natural language processing,but research using quantum neural networks to handle TSC tasks has not received enough attention.Therefore,we proposed a learning framework based on multiple imaging and hybrid QNN(MIHQNN)for TSC tasks.We investigate the possibility of converting 1D time series to 2D images and classifying the converted images using hybrid QNN.We explored the differences between MIHQNN based on single time series imaging and MIHQNN based on the fusion of multiple time series imaging.Four quantum circuits were also selected and designed to study the impact of quantum circuits on TSC tasks.We tested our method on several standard datasets and achieved significant results compared to several current TSC methods,demonstrating the effectiveness of MIHQNN.This research highlights the potential of applying quantum computing to TSC and provides the theoretical and experimental background for future research.

Mapping winter wheat using phenological feature of peak before winter on the North China Plain based on time-series MODIS data

By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.

Feature Selection for Time Series Modeling

In machine learning, selecting useful features and rejecting redundant features is the prerequisite for better modeling and prediction. In this paper, we first study representative feature selection methods based on correlation analysis, and demonstrate that they do not work well for time series though they can work well for static systems. Then, theoretical analysis for linear time series is carried out to show why they fail. Based on these observations, we propose a new correlation-based feature selection method. Our main idea is that the features highly correlated with progressive response while lowly correlated with other features should be selected, and for groups of selected features with similar residuals, the one with a smaller number of features should be selected. For linear and nonlinear time series, the proposed method yields high accuracy in both feature selection and feature rejection.

Dwarfi ng apple rootstock responses to elevated temperatures: A study on plant physiological features and transcription level of related genes

The aim of this study was to investigate the impact of heat stress on physiological features, together with endogenous hormones and the transcription level of related genes, to estimate the heat resistance ability and stress injury mechanism of different dwarfing apple rootstocks. Among the six rootstocks, the rootstocks of native Shao series（SH series） showed better heat stress resistance than those of Budagovski 9（B9）, Cornell-Geneva 24（CG24）, and Malling 26（M26） from abroad. Among SH series rootstocks, SH1 and SH6 showed higher heat stress resistance than SH40. M26 demonstrated the lowest adaption ability to heat stress, showing higher leaf conductivity and lower liquid water content（LWC） with the increase in temperature. Heat stress also resulted in the suppression of photosynthesis, which showed no significant restoration after 7-day recovery. It should be noted that although a higher temperature led to a lower LWC and photosynthetic efficiency（P_n） of CG24, there was no significant increase in leaf conductivity, and 7 days after the treatment, the P_n of CG24 recovered. The extremely high temperature tolerance of SH series rootstocks could be related to the greater osmotic adjustment（OA）, which was reflected by smaller reductions in leaf relative water content（RWC） and higher turgor potentials and leaf gas exchange compared with the other rootstocks. Determination of hormones indicated multivariate regulation, and it is presumed that a relatively stable expression levels of functional genes under high-temperature stress is necessary for heat stress resistance of rootstocks.

Time Series Analysis for Vibration-Based Structural Health Monitoring:A Review

Structural health monitoring(SHM)is a vast,interdisciplinary research field whose literature spans several decades with focusing on condition assessment of different types of structures including aerospace,mechanical and civil structures.The need for quantitative global damage detection methods that can be applied to complex structures has led to vibration-based inspection.Statistical time series methods for SHM form an important and rapidly evolving category within the broader vibration-based methods.In the literature on the structural damage detection,many time series-based methods have been proposed.When a considered time series model approximates the vibration response of a structure and model coefficients or residual error are obtained,any deviations in these coefficients or residual error can be inferred as an indication of a change or damage in the structure.Depending on the technique employed,various damage sensitive features have been proposed to capture the deviations.This paper reviews the application of time series analysis for SHM.The different types of time series analysis are described,and the basic principles are explained in detail.Then,the literature is reviewed based on how a damage sensitive feature is formed.In addition,some investigations that have attempted to modify and/or combine time series analysis with other approaches for better damage identification are presented.

Feature Layer Fusion of Linear Features and Empirical Mode Decomposition of Human EMG Signal

To explore the influence of the fusion of different features on recognition,this paper took the electromyography(EMG)signals of rectus femoris under different motions(walk,step,ramp,squat,and sitting)as samples,linear features(time-domain features(variance(VAR)and root mean square(RMS)),frequency-domain features(mean frequency(MF)and mean power frequency(MPF)),and nonlinear features(empirical mode decomposition(EMD))of the samples were extracted.Two feature fusion algorithms,the series splicing method and complex vector method,were designed,which were verified by a double hidden layer(BP)error back propagation neural network.Results show that with the increase of the types and complexity of feature fusions,the recognition rate of the EMG signal to actions is gradually improved.When the EMG signal is used in the series splicing method,the recognition rate of time-domain+frequency-domain+empirical mode decomposition(TD+FD+EMD)splicing is the highest,and the average recognition rate is 92.32%.And this rate is raised to 96.1%by using the complex vector method,and the variance of the BP system is also reduced.

基于VMD-GA-BiLSTM的月降水量预测方法

利用辽宁省气象局提供的地面观测降水资料,构建了具有多元时间特征的降水数据,采用变分模态分解方法(variational mode decomposition,VMD)组合遗传算法(genetic algorithm,GA)对双向长短时记忆神经网络(bidirectional long short-term memory,BiLSTM)进行优化,建立基于VMD-GA-BiLSTM的月降水量预测模型,并与BiLSTM、VMD-BiLSTM和GA-BiLSTM进行实验对比,应用均方根误差(root mean square error,RMSE)、平均绝对误差(mean absolute error,MAE)和R 2决定系数作为模型评价指标。实验结果表明:VMD-GA-BiLSTM模型的R 2决定系数达到0.98,RMSE和MAE表现更低,验证了VMD-GA-BiLSTM模型在时间序列预测方面的优势。

基于组合时域特征提取和Stacking集成学习的燃煤锅炉NO_(x)排放浓度预测

为提高火电厂锅炉出口NO_(x)排放浓度的预测精度,提出一种考虑组合时域特征的Stacking集成学习模型。首先,为挖掘数据深层信息,采用时序分析、完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise analysis,CEEMDAN)和统计学计算数据标准差、偏度等特征的方法进行组合时域特征提取以构建重构数据;其次,考虑到重构数据中存在的冗余变量对模型的精度有所影响,利用遗传算法(genetic algorithm,GA)对重构数据进行特征降维;最后,为充分发挥各个模型的优势以提高模型的预测精度,构建以极限学习机(extreme learning machines,ELM)、深度神经网络(deep neural networks,DNN)、多层感知器(multilayer perceptron,MLP)、极限梯度提升算法(extreme gradient boosting,XGBoost)为基模型和以回声状态网络(echo state network,ESN)为元模型的Stacking集成学习NOx排放浓度预测模型。实验结果表明:该预测模型在不同数据集下都有着不错的预测效果,预测误差均小于2%,能够对锅炉NOx排放浓度实现精准预测。

多源时序数据特征优选的南方丘陵山区农作物分类研究

以广东省揭阳市揭西县为例,基于Sentinel-2多光谱影像、Sentinel-1时序雷达数据、时序植被指数和地形数据,通过计算J-M距离和特征分析对18个特征波段进行特征组合优选,并对比分析了支持向量机(SVM)、随机森林(RF)、最大似然比(MLC) 3种分类器对南方丘陵山区农作物的分类效果。结果表明,时序植被指数特征中NDVI效果最优,时序雷达特征中VH极化方式最优,地形特征中DEM最优;从不同农作物类型来看,时序植被指数特征和时序雷达特征均能提升晚稻、秋玉米的分类精度;对于晚稻而言,时序雷达特征和地形特征对其分类精度均有提升作用。不同分类器对比结果表明,SVM的总体精度比RF和MLC分别高4.67%和7.84%;Kappa系数分别高5.88%和10.48%,可为南方丘陵山区农作物分类提供有效思路和方法参考。

考虑时序特征的深圳港集装箱吞吐量组合方法预测

集装箱吞吐量预测对港口企业运营及决策具有重要的作用。传统集装箱吞吐量预测方法存在预测精度不高的缺点。为解决这一问题,提出了一种考虑季节性和不确定性的SARIMA-XGBoost组合预测方法。针对集装箱吞吐量的季节性特征,选取季节性自回归移动平均模型(seasonal autoregressive integrated moving average model,SARIMA)捕捉周期性特征和线性特征;针对集装箱吞吐量中的不确定性因素,选取极致梯度提升树算法(extreme gradient boosting,XGBoost)自适应学习时间序列数据中的复杂模式和非线性特征。通过选取优化指标并计算分配权重的方式实现了预测模型中线性和非线性特征的有效融合,从而提升预测精度。通过对深圳港2013—2022年集装箱吞吐量月度数据进行实证研究和对比分析,结果表明SARIMA-XGBoost组合方法预测精度最高、稳定性好,验证了该组合方法在集装箱吞吐量预测中的有效性。

论苏轼的纪行组诗

苏轼纪行组诗一百零六组,在文本形态、内容表现与风格特征三方面有显著的个人特色。这些组诗以记录行旅之间的单个事件为主,除纪行外,频繁叙及游览和社交。在宋诗重说理的风尚影响下,苏轼往往在纪行组诗中抒发一己之情感、阐发深刻的人生哲理。此外,他的纪行组诗不仅能突破传统纪行组诗对于诗人个体的关注,还能做到“情”“景”“事”“理”四者兼备,为唐宋纪行组诗的创作开辟新途。

基于注意力机制CNN-LSTM的毫米波雷达点云特征数据预测生成

在智能驾驶的环境感知领域,毫米波雷达是一种关键的传感器技术。然而,因数据量有限,其特征数据的采集具有一定的挑战性,这限制了环境感知分类模型的训练效果。针对这一难题,提出了一种融合自注意力机制的卷积长短期记忆网络模型,旨在预测并生成毫米波雷达点云的特征数据,以此来扩展雷达特征数据集。首先采集道路目标的运动状态数据,对数据进行二维快速傅里叶变换、恒虚警率检测,并利用多输入多输出(Multiple-Input Multiple-Output,MIMO)技术提升方位分辨率;接着执行点云聚类及特征提取;最后采用含注意力机制的卷积长短期记忆网络对特征数据进行进一步处理与预测。在真实采集的3类道路目标数据集上,与其他模型相比,该方法在不同道路目标运动特征的预测R^(2)上提高了1%~7%。

应用n-LSTM的云平台任务CPU负载预测方法

云平台任务的CPU负载预测有助于云平台资源的优化配置,以改善资源利用率.它是有效管理云资源的重要手段.为提高任务CPU负载预测精度,本文主要做了以下工作:1)利用热度图提取用于进行CPU负载预测的资源使用特征;2)设计并实现了一种基于n-LSTM的云平台任务的CPU负载预测方法DPFE-n-LSTM;3)分别在阿里云平台数据集和Google云平台数据集上进行了实验,结果表明,相对于目前已经提出的CPU负载预测模型BP、LSTM和CNN-LSTM,DPFE-n-LSTM方法具有更好的预测性能.

基于深度学习的公交行驶轨迹预测研究综述

公交行驶轨迹预测是对公交车到达线路上的重要轨迹点,如站点和道路交叉口等,进行到达时间预测。准确预测公交车到达站点和道路交叉口的时间,可以提高城市公交系统的运行效率和服务质量,对于城市公共交通规划和公交调度至关重要。从公交行驶轨迹预测方法的发展现状入手,分析了影响公交运行的相关因素,归纳并探讨了不同类型的相关数据集以及数据预处理方法。依照其发展脉络将公交行驶轨迹预测方法分为基于历史数据的模型、以时间序列模型为代表的参数模型以及包括机器学习和深度学习方法的非参数模型三大类,并总结分析了不同方法的优势和局限性。由于基于深度学习的相关模型在时间序列预测任务中表现出了优越性能,因此越来越多的学者开始采用基于深度学习的模型来解决公交行驶轨迹预测问题,同时考虑将城市道路所展现的空间特征与时间特征相结合以进一步提高预测精度。最后,阐述了公交行驶轨迹预测研究领域中面临的挑战,并对该领域未来的发展进行总结分析与趋势展望。