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.

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.

Experimental validation of a signal-based approach for structural earthquake damage detection using fractal dimension of time frequency feature

This article extends a signal-based approach formerly proposed by the authors, which utilizes the fractal dimension of time frequency feature （FDTFF） of displacements, for earthquake damage detection of moment resist frame （MRF）, and validates the approach with shaking table tests. The time frequency feature （TFF） of the relative displacement at measured story is defined as the real part of the coefficients of the analytical wavelet transform. The fractal dimension （FD） is to quantify the TFF within the fundamental frequency band using box counting method. It is verified that the FDTFFs at all stories of the linear MRF are identical with the help of static condensation method and modal superposition principle, while the FDTFFs at the stories with localized nonlinearities due to damage will be different from those at the stories without nonlinearities using the reverse-path methodology. By comparing the FDTFFs of displacements at measured stories in a structure, the damage-induced nonlinearity of the structure under strong ground motion can be detected and localized. Finally shaking table experiments on a 1：8 scale sixteen-story three-bay steel MRF with added frictional dampers, which generate local nonlinearities, are conducted to validate the approach.

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.

Analysis of OSA Syndrome from PPG Signal Using CART-PSO Classifier with Time Domain and Frequency Domain Features

Obstructive Sleep Apnea(OSA)is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation.The aim of this paper is to analyze the respiratory signal of a person to detect the Normal Breathing Activity and the Sleep Apnea(SA)activity.In the proposed method,the time domain and frequency domain features of respiration signal obtained from the PPG device are extracted.These features are applied to the Classification and Regression Tree(CART)-Particle Swarm Optimization(PSO)classifier which classifies the signal into normal breathing signal and sleep apnea signal.The proposed method is validated to measure the performance metrics like sensitivity,specificity,accuracy and F1 score by applying time domain and frequency domain features separately.Additionally,the performance of the CART-PSO(CPSO)classification algorithm is evaluated through comparing its measures with existing classification algorithms.Concurrently,the effect of the PSO algorithm in the classifier is validated by varying the parameters of PSO.

Feature Extraction and Recognition for Rolling Element Bearing Fault Utilizing Short-Time Fourier Transform and Non-negative Matrix Factorization

Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classitythe high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution（TFD） with non-negative matrix factorization（NMF）, and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform（STFT）. Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network（ANN）, NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space.

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.

Automatic Feature Point Detection and Tracking of Human Actions in Time-of-flight Videos

Detecting feature points on the human body in video frames is a key step for tracking human movements. There have been methods developed that leverage models of human pose and classification of pixels of the body image. Yet, occlusion and robustness are still open challenges. In this paper, we present an automatic, model-free feature point detection and action tracking method using a time-of-flight camera. Our method automatically detects feature points for movement abstraction. To overcome errors caused by miss-detection and occlusion, a refinement method is devised that uses the trajectory of the feature points to correct the erroneous detections. Experiments were conducted using videos acquired with a Microsoft Kinect camera and a publicly available video set and comparisons were conducted with the state-of-the-art methods. The results demonstrated that our proposed method delivered improved and reliable performance with an average accuracy in the range of 90 %.The trajectorybased refinement also demonstrated satisfactory effectiveness that recovers the detection with a success rate of 93.7 %. Our method processed a frame in an average time of 71.1 ms.

Discussion on the feature of strong earthquake: Orderly distribution in time, space and intensity before the Western Kunlun Mountain Pass M=8.1 earthquake

In the paper, the feature of strong earthquake orderly distribution in time, space and intensity before the Western Kunlun Mountain Pass M=8.1 earthquake is preliminarily studied. The modulation and triggering factors such as the earth rotation, earth tides are analyzed. The results show that: the giant earthquakes with the magnitude more than 8 occurred about every 24 years and the earthquakes with the magnitude more than 7 about every 7 years in Chinese mainland. The Western Kunlun Mountain M=8.1 earthquake exactly occurred at the expected time; The spatial distance show approximately the same distances between each two swarms. The earth rotation, earth tide, sun tide and sun magnetic field have played a role of modulation and triggering in the intensity. At last, the condi-tions for earthquake generation and occurrence are also discussed.

Peripheral arterial filling time and peripheral retina fluorescence features in ultra-widefield angiography

AIM:To evaluate the peripheral arterial filling time(PAFT)and venous filling time(VFT)in eyes without known diseases that may influence filling process using ultra-widefield(UWF)fluorescein angiography(FA),and to review the peripheral retina fluorescence features.METHODS:A total of 30 eyes of 30 patients were retrospectively reviewed in this observational study.UWFFA was performed using Optos 200Tx.PAFT and VFT was recorded.The interval between the arterial or venous filling completion and the previous photo was documented.The appearance of the far peripheral retina was described as either granular background fluorescence or mottled fluorescent band or vascular leakage.Terminal vascular patterns was described as loop pattern or branching pattern.Microvascular abnormalities such as arteriovenous shunting,vessels crossing the horizontal raphe,right angle vessels,terminal networks,capillary nonperfusion,drusen or microaneurysms were evaluated.RESULTS:The normal limits of PAFT was 3.397-8.984s and 4.399-11.753s for VFT.The appearance of the far peripheral retina,defined as granular background(63%),mottled fluorescence(20%),or vascular leakage(17%),was symmetrical between both eyes.Capillary nonperfusion(23%)and microaneurysms(40%)were more frequently found in eyes with loop pattern than in eyes with branching pattern.Other peripheral signs such as right-angle vessels(73%),and terminal networks(80%)were commonly seen on UWF-FA in the normal peripheral retina.CONCLUSION:The main courses of retinal artery and vein filling time are overlapping with each other on UWF-FA.Notably,the arterial filling process is completed in the arteriovenous phase rather than the traditionally named arterial phase.There are various manifestations in the peripheral retina of normal eyes.

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.

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.

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.

Digital modulation classification using multi-layer perceptron and time-frequency features

Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.

A CNN-Based Single-Stage Occlusion Real-Time Target Detection Method

Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.

Changing trends of clinicopathologic features and survival duration after surgery for gastric cancer in Northeast China

BACKGROUND Through analyzing the data from a single institution in Northeast China,this study revealed the possible clinicopathologic characteristics that influence the prognosis of patients with gastric cancer(GC).AIM To evaluate the changing trends of clinicopathologic features and survival duration after surgery in patients with GC in Northeast China,which is a highprevalence area of GC.METHODS The study analyzed the difference in clinicopathologic features and survival duration after surgery of 5887 patients who were histologically diagnosed with GC at the Harbin Medical University Cancer Hospital.The study mainly analyzed the data in three periods,2000 to 2004(Phase 1),2005 to 2009(Phase 2),and 2010 to 2014(Phase 3).RESULTS Over time,the postoperative survival rate significantly increased from 2000 to 2014.In the past 15 years,compared with Phases 1 and 2,the tumor size was smaller in Phase 3(P<0.001),but the proportion of high-medium differentiated tumors increased(P<0.001).The proportion of early GC gradually increased from 3.9%to 14.4%(P<0.001).A surprising improvement was observed in the mean number of retrieved lymph nodes,ranging from 11.4 to 27.5(P<0.001).The overall 5-year survival rate increased from 24%in Phase 1 to 43.8%in Phase 3.Through multivariate analysis,it was found that age,tumor size,histologic type,tumor-node-metastasis stage,depth of invasion,lymph node metastasis,surgical approach,local infiltration,radical extent,number of retrieved lymph nodes,and age group were independent risk factors that influenced the prognosis of patients with GC.CONCLUSION The clinical features of GC in Northeast China changed during the observation period.The increasing detection of early GC and more standardized surgical treatment effectively prolonged lifetimes.

Emotional Speech Synthesis Based on Prosodic Feature Modification

The synthesis of emotional speech has wide applications in the field of human-computer interaction, medicine, industry and so on. In this work, an emotional speech synthesis system is proposed based on prosodic features modification and Time Domain Pitch Synchronous OverLap Add (TD-PSOLA) waveform concatenative algorithm. The system produces synthesized speech with four types of emotion: angry, happy, sad and bored. The experiment results show that the proposed emotional speech synthesis system achieves a good performance. The produced utterances present clear emotional expression. The subjective test reaches high classification accuracy for different types of synthesized emotional speech utterances.

Wireless distributed computing for cyclostationary feature detection

Recently, wireless distributed computing （WDC） concept has emerged promising manifolds improvements to current wireless technotogies. Despite the various expected benefits of this concept, significant drawbacks were addressed in the open literature. One of WDC key challenges is the impact of wireless channel quality on the load of distributed computations. Therefore, this research investigates the wireless channel impact on WDC performance when the tatter is applied to spectrum sensing in cognitive radio （CR） technology. However, a trade- off is found between accuracy and computational complexity in spectrum sensing approaches. Increasing these approaches accuracy is accompanied by an increase in computational complexity. This results in greater power consumption and processing time. A novel WDC scheme for cyclostationary feature detection spectrum sensing approach is proposed in this paper and thoroughly investigated. The benefits of the proposed scheme are firstly presented. Then, the impact of the wireless channel of the proposed scheme is addressed considering two scenarios. In the first scenario, workload matrices are distributed over the wireless channel

A Minimal Subset of Features Using Feature Selection for Handwritten Digit Recognition

Many systems of handwritten digit recognition built using the complete set of features in order to enhance the accuracy. However, these systems lagged in terms of time and memory. These two issues are very critical issues especially for real time applications. Therefore, using Feature Selection (FS) with suitable machine learning technique for digit recognition contributes to facilitate solving the issues of time and memory by minimizing the number of features used to train the model. This paper examines various FS methods with several classification techniques using MNIST dataset. In addition, models of different algorithms (i.e. linear, non-linear, ensemble, and deep learning) are implemented and compared in order to study their suitability for digit recognition. The objective of this study is to identify a subset of relevant features that provides at least the same accuracy as the complete set of features in addition to reducing the required time, computational complexity, and required storage for digit recognition. The experimental results proved that 60% of the complete set of features reduces the training time up to third of the required time using the complete set of features. Moreover, the classifiers trained using the proposed subset achieve the same accuracy as the classifiers trained using the complete set of features.