Search

• Spray swirling flame is modulated by both air and fuel. • Flame dynamic is determined by destructive/constructive modulation of air and fuel. • Flame coherent structures are revealed with POD method. Combustion instability is still a main challenge in developing advanced gas turbine combustors. It is essential to conduct instability control once it occurs. In this paper, liquid fuel modulation by a high-speed on/off valve is used to control flame oscillation generated by loudspeakers. Spray swirling flame dynamic under the combined modulation of air and fuel is experimentally studied. Flame dynamical characteristics and coherent structures at different excitation phase delays are discussed with nonlinear time-series analysis and proper orthogonal decomposition method. Experimental results show flame heat release rate fluctuation is decreased by 86% at 40° while flame instability is intensively enhanced at 220°. Phase portraits and recurrence plots show the flame is in a chaotic oscillation with low amplitude aperiodic oscillations at 40° while it suffers from the largest fluctuation at 220°. Difference in flame dynamic is caused by the destructive (out of phase) and constructive (in phase) interference of heat release rate fluctuations induced by air excitation and fuel modulation. Flame returns back to its nature dynamical behavior under out-of-phase modulation. Longitudinal modes of the spray flame are enhanced while the spiral modes are inhibited under in-phase modulation. [ABSTRACT FROM AUTHOR]

Wind speed prediction has received reasonable attention recently because of its clean and promising source of renewable energy. Recent studies have shown that developing efficient model to predict wind speed is a challenging task because of its nonlinear and stochastic characteristics. This paper aims to propose a new hybrid model to predict wind speed. For this purpose, Discrete Wavelet Transform (DWT), Phase Space Reconstruction (PSR) of chaos theory, Aquila Optimization Algorithm (AOA) and Backpropagation Neural Network (BPNN) are hybridised and a novel DWT-PSR-AOA-BPNN is proposed. To ascertain the proposed DWT-PSR-AOA-BPNN model performance, different hybrid model variants (DWT-PSR-GA-BPNN, DWT-PSR-PSO-BPNN, PSR-PSO-BPNN and PSR-AOA-BPNN) were developed for comparison. The comparison was done using statistical model evaluators of Mean Average Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and model efficiency of Loague and Green (ELG). The statistical results showed that the proposed DWT-PSR-AOA-BPNN model performed better and is therefore considered efficient wind speed prediction tool when compared with DWT-PSR-GA-BPNN, DWT-PSR-PSO-BPNN, PSR-PSO-BPNN and PSR-AOA-BPNN hybrid models. That is, the proposed DWT-PSR-AOA-BPNN had the lowest MAE, RMSE and MAPE values for the model testing (MAE = 1.1490, RMSE = 1.4190 and MAPE = 0.2743) and validation (MAE = 0.8122, RMSE = 0.9771 and MAPE = 0.1953). The DWT-PSR-AOA-BPNN also achieved the highest ELG values of 0.9904 (testing) and 0.99738 (validation) respectively. It is therefore concluded that considering the DWT-PSR-AOA-BPNN results, the indication corroborates the fact that this model can be utilized for efficient grid operations.

Abstract Background Atrial fibrillation (AF) represents the most common arrhythmia worldwide, related to increased risk of ischemic stroke or systemic embolism. It is critical to screen and diagnose AF for the benefits of better cardiovascular health in lifetime. The ECG-based AF detection, the gold standard in clinical care, has been restricted by the need to attach electrodes on the body surface. Recently, ballistocardiogram (BCG) has been investigated for AF diagnosis, which is an unobstructive and convenient technique to monitor heart activity in daily life. However, here is a lack of high-dimension representation and deep learning analysis of BCG. Method Therefore, this paper proposes an attention-based multi-scale features fusion method by using BCG signal. The 1-D morphology feature extracted from Bi-LSTM network and 2-D rhythm feature extracted from reconstructed phase space are integrated by means of CNN network to improve the robustness of AF detection. To the best of our knowledge, this is the first study where the phase space trajectory of BCG is conducted. Results 2000 segments (AF and NAF) of BCG signals were collected from 59 volunteers suffering from paroxysmal AF in this survey. Compared to the classical time and frequency features and the state-of-the-art energy features with the popular machine learning classifiers, AF detection performance of the proposed method is superior, which has 0.947 accuracy, 0.935 specificity, 0.959 sensitivity, and 0.937 precision, for the same BCG dataset. The experimental results show that combined feature could excavate more potential characteristics, and the attention mechanism could enhance the pertinence for AF recognition. Conclusions The proposed method can provide an innovative solution to capture the diverse scale descriptions of BCG and explore ways to involve the deep learning method to accurately screen AF in routine life.

In view of the difficulty of fault prediction for aviation hydraulic pumps and the poor realtime performance of state monitoring in practical applications, a hydraulic pump pressure signal prediction method is proposed to accomplish the monitoring and prediction of the health status of hydraulic pumps in advance. First, based on the on-line real-time acquisition of time series flight parameters and pressure signal data, the chaotic characteristics of the system are analyzed using chaos theory, so that the time series pressure signal is predictable. Second, phase space reconstruction (PSR) of the one-dimensional time series data is conducted. The embedding dimension m and time delay τ are obtained by the C-C method. The reconstructed matrix is used as the training set and test set of the support vector regression (SVR) algorithm model according to a certain proportion, and the genetic algorithm (GA) is then used to optimize the parameters of the SVR model. Finally, the SVR model optimized by the genetic algorithm based on phase space reconstruction (PSR-GA-SVR) is used to test the test set data. The results show that the prediction accuracy of the proposed method is higher than that of the BP neural network based on phase space reconstruction (PSR-BPNN) and the SVR model based on phase space reconstruction (PSR-SVR). Relative to PSR-BPNN and PSR-SVR, PSR-GA-SVR produces a minimum mean square error (MSE) reduced by 73.40% and 68.0%, respectively, and a mean absolute error (MAE) decreased by 90.41% and 90.87%, respectively. The confidence level for PSR-GA-SVR was increased, and the coefficient of determination was greater than 0.98.

The change and development of leakage current (LC) during the flashover process of insulators are closely related to the discharge process. Characteristic quantities of LC play an important role in the identification and prediction of ice-covered flashover development stages. In this paper, the feature extraction method for LC of ice-covered insulator is discussed based on the shape characteristics of chaotic attractor. Firstly, the power spectrum and the largest Lyapunov exponent are used to analyze the LC of ice-covered insulator qualitatively and quantitatively, which confirmed positive chaotic features during the flashover process. On this basis, the LC is reconstructed in phase space. To solve the problem that the attractor in the high-dimensional space cannot be observed in the phase space reconstruction, the principal vector analysis method is used to project the high-dimensional reconstruction vector into the three-dimensional space to reduce the dimension. Finally, the evolution law of chaotic attractor in each stage of LC under different pollution degree is analyzed and discussed. The results show that the LC of ice-covered insulator has obvious characteristics of attractor shape at different stages of pollution, and is sensitive to the degree of pollution and each stage of development. So the shape of the attractor of LC has important application value for the identification of pollution flashover state and prediction of pollution flashover process of ice-covered insulators.

In order to better characterize the performance degradation trend of rolling bearings, a new performance degradation evaluation method based on principal component analysis (PCA), phase space reconstruction (PSR) and kernel extreme learning machine (KELM), namely PAPRKM is proposed to evaluate the performance degradation of rolling bearings in this paper. In the PAPRKM method, the time-domain and frequency-domain features of the vibration signal are extracted to construct the high-dimension feature matrix. Then the PCA is used to reduce the dimension of the feature matrix in order to represent the running state and the declining trend of rolling bearings, so as to eliminate the redundancy and information conflict among these features. Nextly, the PSR is adopted to obtain more relevant information from the time series. By determining the delay time and embedding dimension, the time series are reconstructed to obtain a new performance degradation index, which is regarded as the input data to input into KELM, and the degradation trend prediction model is established to realize the performance degradation trend prediction. Finally, the actual vibration signals of rolling bearings are applied to prove the effectiveness of the PAPRKM. The obtained experimental results show that the PAPRKM method can effectively predict the performance degradation trend of rolling bearings. The predicted results are more accurate than the other compared methods.

• End chaos indicator provides feedback on operating conditions of pulsating heat pipe. • Mid-range correlation dimension identifies fluid purity. • Reconstructed attractor restores properties of original dynamics. • Soft-threshold wavelet noise reduction effective for attractors. As a result of their superior capillary effect, pulsating heat pipes containing nanofluids offer a great deal of potential for cooling tiny and medium-sized electronic equipment. The traditional analytical methods are unable to completely reveal the operating characteristics of the pulsating heat pipe because of the typical chaotic system's inherent randomness in the distribution of the number of vaporized cores, the speed of air plug rupture, the number of collisions of fluid microclusters, and the boiling time of fluid columns when the pipe is in its operating state. In order to achieve this, we present nonlinear chaos analysis tools, using ethanol-based copper oxide nanofluids as an example, and suggest a new characterization technique based on the phase space reconstruction theory, the Takens embedding theorem, the mutual information recursive selection method, and the correlation dimension estimation algorithm. The method reasonably estimates the chaos indicators, such as correlation dimension and K2-Kolmogorov Entropy, by calculating the optimal embedding dimension and the optimal delay time. It also reconstructs the attractor phase diagram, which provides a basis for feeding back the operating conditions of the nano-pulsating heat pipe. The evaporation section's results demonstrate that the end-related dimension and Kolmogorov entropy can intuitively reflect the stability of the system. The addition of CuO particles improves the system's stability at mass concentrations of 0–2.5 wt%, which decreases from 1.2834 to 1.2285 as the concentration increases; when the concentration of CuO particles exceeds 6 wt%, the system's complexity increases rather than decreases, rising to 1.2774; the mid-end correlation dimension performs better than other methods in determining fluid purity, and its value increases by 58 %, from 4.5767 to 7.2385 with the addition of CuO particles at a weight percentage of only 1 wt%. In contrast, the chaos indicators in the condensation section are relatively stable. The reconstructed attractors after wavelet noise reduction are more regular in shape, and the system can be judged whether it is in a "destabilized" state by the size of its volume and the density of its vectorial flow pattern. [ABSTRACT FROM AUTHOR]

The prediction of a stall precursor in an axial compressor is the basic guarantee to the stable operation of an aeroengine. How to predict and intelligently identify the instability of the system in advance is of great significance to the safety performance and active control of the aeroengine. In this paper, an aerodynamic system modeling method combination with the wavelet transform and gray wolf algorithm optimized support vector regression (WT-GWO-SVR) is proposed, which breaks through the fusion technology based on the feature correlation of chaotic data. Because of the chaotic characteristic represented by the sequence, the correlation-correlation (C-C) algorithm is adopted to reconstruct the phase space of the spatial modal. On the premise of finding out the local law of the dynamic system variety, the machine learning method is applied to model the reconstructed low-frequency components and high-frequency components, respectively. As the key part, the parameters of the SVR model are optimized by the gray wolf optimization algorithm (GWO) from the biological view inspired by the predatory behavior of gray wolves. In the definition of the hunting behaviors of gray wolves by mathematical equations, it is superior to algorithms such as differential evolution and particle swarm optimization. In order to further improve the prediction accuracy of the model, the multi-resolution and equivalent frequency distribution of the wavelet transform (WT) are used to train support vector regression. It is shown that the proposed WT-GWO-SVR hybrid model has a better prediction accuracy and reliability with the wavelet reconstruction coefficients as the inputs. In order to effectively identify the sign of the instability in the modeling system, a wavelet singular information entropy algorithm is proposed to detect the stall inception. By using the three sigma criteria as the identification strategy, the instability early warning can be given about 102r in advance, which is helpful for the active control.

Electrocardiogram (ECG) biometric provides an authentication to identify an individual on the basis of specific cardiac potential measured from a living body. Convolutional neural networks (CNN) outperform traditional ECG biometrics because convolutions can produce discernible features from ECG through machine learning. Phase space reconstruction (PSR), using a time delay technique, is one of the transformations from ECG to a feature map, without the need of exact R-peak alignment. However, the effects of time delay and grid partition on identification performance have not been investigated. In this study, we developed a PSR-based CNN for ECG biometric authentication and examined the aforementioned effects. Based on a population of 115 subjects selected from the PTB Diagnostic ECG Database, a higher identification accuracy was achieved when the time delay was set from 20 to 28 ms, since it produced a well phase-space expansion of P, QRS, and T waves. A higher accuracy was also achieved when a high-density grid partition was used, since it produced a fine-detail phase-space trajectory. The use of a scaled-down network for PSR over a low-density grid with 32 × 32 partitions achieved a comparable accuracy with using a large-scale network for PSR over 256 × 256 partitions, but it had the benefit of reductions in network size and training time by 10 and 5 folds, respectively.

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The airflow distribution characterised by different large- and small-scale eddies in the aircraft cabin is the most important factor to maintain passengers' thermal comfort and to remove contaminants. The airflow distributions in narrow-body aircraft cabins are based on the principle of mixing ventilation. Opposing jets from air diffusers can cause stream deflection and oscillation, which results in the asymmetry of large-scale instantaneous airflow structures. This dynamic airflow structure is very important for analysing time series parameters. Therefore, this study applied numerical simulation to examine the oscillation and asymmetry of instantaneous airflow field structures and phase space reconstruction and used spectrum analysis to evaluate the oscillation amplitude and period of dynamic airflow structure. We also studied the factors that influence the dynamic airflow structure, such as air supply speeds, air supply angles and the strength of the thermal plume. The results showed that as the air supply speed increased, the swing amplitude of the instantaneous airflow structure increased, while the period decreased. The air supply angle affected the jet attachment and collision angle, which in turn affected the swing amplitude and period. The thermal plume restrained the formation of large-scale swings and contributed to the appearance of small-scale structures. [ABSTRACT FROM AUTHOR]

Wind power is recognized as one of the most promising renewable and clean energy sources under the context of the increasing depletion of fossil fuels. The exact wind speed forecasting has great significance for the large-scale connection of wind farms with the power grid. In light of this, this paper contributes to establishing a novel hybrid model that can predict the future wind speed accurately. Firstly, the original wind speed time series is decomposed by the fast ensemble empirical mode decomposition into several sub-series that are further integrated by the runs test. The phase space reconstruction is used to dynamically choose each integrated sub-series' input and output vectors for the prediction model. Additionally, an improved whale optimization algorithm is exploited to optimize the weights and bias of the extreme learning machine. Finally, prediction results are obtained from the aggregation of each integrated sub-series prediction. To verify the accuracy and applicability of the proposed hybrid model, we apply several comparative models to conducted two case studies using different wind speed time series from Inner Mongolia that is Asia's largest gathering area of wind power farms. According to the experimental results, it can be concluded that the decomposition reduces the volatility and randomness of wind speed, and the runs test lowers the forecasting complexity. The phase space reconstruction can capture the chaotic property of wind speed series. The optimization for the whale optimization algorithm enhances its global and local optimization ability to further improve the performance of extreme learning machine. Overall, the proposed hybrid model can effectively capture the non-linear characteristics of wind speed series. • A novel hybrid short-term wind speed prediction model is proposed. •The model of FEEMD-RT-PSR is used for the data preprocessing. •Whale optimization algorithm is optimized. •The performance of extreme learning machine is improved. [ABSTRACT FROM AUTHOR]

Accurate wind speed forecasting has become an indispensable part in the dispatching and management of wind power generation. However, the volatility and intermittency of wind speed impede the improvement of forecasting accuracy. To figure out the problem and strengthen prediction performance, a novel hybrid framework consisting of variational mode decomposition (VMD), phase space reconstruction (PSR), improved beetle antenna search (BAS) and Volterra series model is established for multi-step ahead short-term wind speed forecasting. To confirm the performance of the proposed compound model, three datasets from the Sotavento Galicia wind farm are used for multi-step short-term wind speed forecasting experiment. The prediction results indicate that: (a) the prediction precision of the proposed model is significantly improved by employing VMD based on central frequency observation approach, which can weaken the non-stationarity of the original series; (b) the proposed DEBAS algorithm based on hierarchical strategy achieves the best performance compared with other comparative algorithms on the benchmark functions, because DEBAS can expand population diversity in the later iteration; (c) the forecasting performance of the hybrid model can be further enhanced by the proposed synchronous optimization strategy based on the improved DEBAS. • A fusion algorithm based on differential evolution and beetle antenna search is proposed by hierarchical strategy. • The proposed DEBAS performs well in optimization experiments for several benchmark functions. • Parameters of PSR and kernel identification coefficients of Volterra series model are optimized simultaneously by DEBAS. • Wind speed forecasting experiments as well as contrastive analysis verify the superiority of the proposed VMD-PSR-DEBAS-Volterra model. [ABSTRACT FROM AUTHOR]

Short-term traffic flow prediction is an important database for traffic management and real-time signal control. In order to improve the accuracy of traffic flow prediction, a Kalman filtering for short-term traffic flow prediction model based on phase space reconstruction is proposed by introducing the concept of chaos theory to solve the problem of chaotic traffic flow time series caused by shortened forecasting period. Firstly, the C-C method is used to reconstruct the phase space of one-dimensional time series, that is to say, the mapping relationship is generated in high-dimensional space to show the intrinsic information of the original sequence. Then, the predictability of traffic flow is judged by the characteristic index of a chaotic system. Finally, according to Kalman filtering theory, the reconstructed phase points are predicted as the initial value of Kalman filtering. The results show that compared with the 6 minute forecast, the relative error of 10 minute traffic flow forecasting is smaller, and it is 4-6%. And the prediction accuracy of a small sample size is within the acceptable range and lower than that of a large sample size. This model can meet the actual needs of ITS. [ABSTRACT FROM AUTHOR]

This study proposes the detection of ventricular fibrillation (VF) by wavelet transforms (WTs) and phase space reconstruction (PSR) from electrocardiogram (ECG) signals. A neural network with weighted fuzzy memberships (NEWFM) is used to detect VF as a classifier. In the first step, the WT was used to remove noise in ECG signals. In the second step, coordinates were mapped from the wavelet coefficients by the PSR. In the final step, NEWFM used the mapped coordinates-based features as inputs. The NEWFM has the bounded sum of weighted fuzzy memberships (BSWFM) that can easily appear the distinctness between the normal sinus rhythm (NSR) and VF in the graphical characteristics. The BSWFM can easily be set up in a portable automatic external defibrillator (AED) to detect VF in an emergency. [ABSTRACT FROM AUTHOR]

Stock market prediction is extremely important for investors because knowing the future trend of stock prices will reduce the risk of investing capital for profit. Therefore, seeking an accurate, fast, and effective approach to identify the stock market movement is of great practical significance. This study proposes a novel turning point prediction method for the time series analysis of stock price. Through the chaos theory analysis and application, we put forward a new modeling approach for the nonlinear dynamic system. The turning indicator of time series is computed firstly; then, by applying the RVFL-GMDH model, we perform the turning point prediction of the stock price, which is based on the fractal characteristic of a strange attractor with an infinite self-similar structure. The experimental findings confirm the efficacy of the proposed procedure and have become successful for the intelligent decision support of the stock trading strategy. [ABSTRACT FROM AUTHOR]

Due to the complexity of the interference operation environment of wire rope, the detection signals are usually weak and coupled in time-frequency domain, which makes the defect difficult to recognize, while the signal characterizations in phase space are also needed to be studied. Combining the nonlinear dynamic feature identification theories, phase space characteristics and chaotic features of wire rope defect detection signals are mainly investigated in this paper. First, principles of phase space reconstruction method for wire rope detection signals are presented by the chaotic dynamic indexes calculation of embedded dimension and delay time. Second, the change trends of the correlation dimension, approximate entropy and Lyapunov index of different phase space reconstructed wire rope defect detection signals are studied through the nonlinear simulation and analysis. Finally, a phase space reconstruction algorithm based on improved SVD is proposed, and the new algorithm is also compared with traditional signal processing methods. These results obtained by 6 groups of experiments were also evaluated and compared by the parameters of signal-to-noise ratio (SNR) and phase space trajectory chart, which manifests that the improved algorithm not only can increase the weak detection signal SNR to about 2.3dB of wire rope effectively, but also demonstrate the feasibility of the proposed methods in application. [ABSTRACT FROM AUTHOR]

This paper takes the time series of short-term traffic flow as research object. The delay time and embedding dimension are calculated by C-C algorithm, and the chaotic characteristics of the time series are verified by small data sets method.Then based on the neural network prediction model and the chaotic phase space reconstruction theory, the network topology is determined, and the prediction is conducted by the wavelet neural network and RBF neural network using Lan-Hai expressway experimental data. The results show that the prediction effect of RBF neural network is better. Due to the poor stability of the network caused by the initial parameters randomness, the genetic algorithm is used to optimize the initial parameters. The results show that the prediction error of the optimized wavelet neural network or RBF neural network is reduced by more than 10%, and prediction accuracy of the latter is better.

In order to address the problem that high energy consumption, high memory usage and low clustering effect in traditional data set high-dimensional clustering algorithms, we propose the high-dimensional clustering algorithm of incomplete mixed data set based on artificial intelligence. First, we construct the phase space reconstruction to ensure the invariance of features of incomplete mixed data set by analyzing the incomplete mixed data set and introduce the correlation dimension to obtain the feature correlation value. Second, we introduce the standard deviation and realize the extraction of features of incomplete mixed data set through calculating the sparsity of sample features. Third, we conduct repeated clustering for the mixed data set in the subspace according to the degree of correlation between incomplete mixed data sets in the multidimensional subspace. Last, we realize the design of high-dimensional clustering method for incomplete mixed data set in accordance with the stronger relevance in the mixed data sets. Experimental results show that the proposed algorithm has good correlation dimension processing effects, lower memory usage, time-consuming, lower and concentrated ensemble energy consumption (within 300J), good clustering effects, as high as 92%, which has some advantages and practical application value.

In order to deal with the dynamical degradation of logistic chaotic sequence caused by the structures and finite precision computing, we propose a modified algorithm based on phase space reconstruction and PCA. In the process of phase space reconstruction, it uses Cao theory after determining the delay time by the mutual information function and takes the first principal component as the result. Results of experiments demonstrate that: the algorithm erases the relevance among data points and enhances the randomness and complexity of the sequence.

Accurate wind speed forecasting exerts a critical role in energy conversion and management of wind power. In term of this purpose, a hybrid model based on multi-stage principal component extraction, kernel extreme learning machine (KELM) and gated recurrent unit (GRU) network is developed in this paper, where the multi-stage principal component extraction combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), singular spectrum analysis (SSA) and phase space reconstruction (PSR). Firstly, CEEMDAN is employed to decompose the raw wind speed data into a sequence of intrinsic mode functions (IMFs) and a residual component. Then the principal components and residual components of all IMFs are captured by SSA. Meanwhile, all residual components obtained by CEEMDAN decomposition and SSA processing are added to form a new component. Subsequently, PSR is utilized to construct each forecasting component obtained by CEEMDAN-SSA into the input and output of training set and testing set for the prediction model. Later, KELM and GRU neural network are conducted to predict the high-frequency and low-frequency components, respectively. Eventually, the prediction values of each component are accumulated to acquire the final prediction result. To evaluate the performance of the proposed model, four datasets from Sotavento Galicia wind farm are adopted to conduct experimental research. The experimental results manifest that the proposed model achieves higher accuracy of multi-step prediction than other comparative models.

Since traffic is a time-dependent and complicated non-linear system. Chaos Theory has been specifically designed to identify chaotic behaviour and properties for such systems. Previous researches have been restricted to single traffic parameter, which fails to get actual behaviour of the traffic systems. In this work, we have devised the multi-parameters chaos prediction method to describe the tendency of traffic from different aspects for prediction of single parameter chaotic time series. The fusion method is considered the relationship of different traffic parameters according to the phase space reconstruction. Taking the exit ramp of Xishanping expressway in Beibei district, Chongqing Municipality as the example, the feasibility and reliability of traffic state prediction were tested. The trail results indicates that more features of realistic traffic conditions are reflected by fusing multiple traffic parameters, and gain real accuracy improvements in traffic prediction. Compared with three single-parameter time series prediction methods, the mean absolute relative error of the multi-parameter prediction method decreased by 2.42, 2.39 and 0.8 respectively, the mean absolute relative error decreased by 2.33, 3.25 and 1.27, and the equal coefficient reached 0.9528 with a slight increase. Proposed method will definitely generates the opportunity for next generation of traffic control that are better able to detect the dynamic states of traffic, and therefore more effectively prevent the traffic congestion and pollution in the urban areas worldwide.

The evaporation duct is a particular type of atmospheric structure that always appears on the open ocean. Predicting the evaporation duct height (EDH) accurately and in a timely manner is of great significance for the practical application of marine wireless communication equipment. Understanding the characteristics of EDH time series is an essential prerequisite for establishing an appropriate prediction model. Moreover, the sampling timescales of EDH data may influence the dynamic characteristics of the EDH time series as well. In this study, EDH time series datasets at three timescales, hourly, daily, and monthly, were constructed as the case study. Statistical methods, namely the augmented Dickey–Fuller test and Ljung–Box test, were adopted to verify the stationary and white noise characteristics of the EDH time series. Then, rescaled range analysis was applied to calculate the Hurst exponent to study the fractal characteristics of the EDH time series. An extensive analysis and discussion of the chaotic dynamics of the EDH time series are provided. From the perspective of nonlinear dynamics, the phase space was constructed from the time delay τ and embedding dimension m, which were calculated from the mutual information method and the Grassberger–Procaccia algorithm, respectively. The maximum Lyapunov exponent was also calculated by the small data volume method to explore the existence of chaos in the EDH time series. According to our analysis, the EDH time series are stationary and have a non-white noise characteristic. The Hurst exponents for all three timescales were greater than 0.5, indicating the predictability of the EDH time series. The phase space diagrams exhibited strange attractors in a well-defined region for all the timescales, suggesting that the evolution of the EDH time series can possibly be explained by deterministic chaos. All of the maximum Lyapunov exponents were positive, confirming the chaos in the EDH time series. Further, stronger chaotic characteristics were found for the finer-resolution time series than the coarser-resolution time series. This study provides a new perspective for scholars to understand the fluctuation principles of the evaporation duct at different timescales. The findings from this study also lay a theoretical and scientific foundation for the future application of chaotic prediction methods in the research on the evaporation duct.

Early ventricular fibrillation (VF) prediction is critical for prevention of sudden cardiac death, and can improve patient survival. Generally, electrocardiogram (ECG) signal features are extracted to predict VF, a process which plays an important role in prediction accuracy. Therefore, this study first proposes a novel feature based on topological data analysis (TDA) to improve the accuracy of early ventricular fibrillation prediction. Firstly, the heart activity is regarded as a cardiac dynamical system, which is described by phase space reconstruction. Then the topological structure of the phase space is characterized with persistent homology, and its statistical features are further extracted and defined as TDA features. Finally, 60 subjects (30 VF, 30 healthy) from three public ECG databases are used to validate the prediction performance of the proposed method. Compared to heart rate variability features and box-counting features, TDA features achieve a superior accuracy of 91.7%. Additionally, the three types of features are combined as fusion features, achieving the optimal accuracy of 95.0%. The fusion features are then ranked, and the first seven components are all from the TDA features. It follows that the proposed features provide a significant effect in improving the predictive performance of early VF.

Due to the existence of observation noise such as multi-path error, the accuracy of deformation prediction results are affected by using the data series of GNSS deformation monitoring. In order to examine the influence of measurement error on the deformation prediction results, the GNSS derived surface displacement time series of Miaodian landslide in Jingyang Area of Shaanxi, and those after noise suppression in combination with chaos theory were analyzed. Firstly, the mutual information method was used to determine the time delay of the surface displacement time series, and the Cao method was used to determine the embedding dimension to obtain the phase space reconstruction parameters. Secondly, the maximum Lyapunov exponent method was used to identify the chaotic characteristics of the two surface displacement time series. Finally, the weighted first-order local prediction method, the largest Lyapunov exponent prediction method, and the BP neural network prediction method were used to predict the landslide surface displacements. The results show that the GNSS landslide surface displacement time series and the time series after noise suppression have chaotic characteristics. The BP neural network prediction method has good prediction performance with an MAE of 0.4 mm and an MRE of 11.9%. After S-transform noise suppression, the MAE and MRE are 0.1 mm and 4.12%, respectively. Compared with the original time series, the prediction performance has been significantly improved after noise suppression. [ABSTRACT FROM AUTHOR]

Based on phase space reconstruction (PSR) and hybrid VNS-SVR model, a remaining useful life (RUL) prediction method for aircraft engines is proposed. The proposed hybrid model combines support vector regression (SVR), which has been successfully adopted for regression problems, with the variable neighborhood search (VNS). First, the phase space reconstruction is used to transform the selected one-dimensional performance sequences of aircraft engines into matrix forms, which increases the data information and improve the learning efficiency of the model effectively. Then, SVR is used to construct the prediction model. Meanwhile, a VNS algorithm is proposed to optimize the kernel parameters. Finally, the hybrid model is used to RUL prediction of the aircraft engines. The experimental results show that the method has a good prediction performance. [ABSTRACT FROM AUTHOR]

Many meteorological systems are chaotic in nature, which inevitably limits its predictability. Accurate prediction of meteorological variables depends primarily on properly diagnosing the complex underlying dynamics. Nonlinear dynamic analysis has shown to be particularly useful for such purpose. In this study, the concept of recurrence analysis was extended to investigate and characterize the underlying dynamics of meteorological time series (i.e., wind speed, temperature, pressure and relative humidity) based on daily observation in Hong Kong during a period from 1998 to 2018. The existence of chaos was clearly identified based on the phase space reconstruction diagram and the recurrence plot. It was shown that the underlying dynamics associated with wind speed appears to indicate higher level of complexity as compared to those of pressure and temperature, which, in consequence, may lead to more irregular time‐dependent behaviour and lower predictability. Moreover, season‐to‐seasonal variability in the dynamics of meteorological time series was evident, particularly for wind speed and temperature. Overall, this study shows that the recurrence analysis can be well applied as a useful diagnostic tool to investigate the dynamics of meteorological systems, which is expected to provide a new avenue regarding the modelling and prediction of the behaviour of meteorological parameters. [ABSTRACT FROM AUTHOR]

The transient pulses caused by local faults of rolling bearings are an important measurement information for fault diagnosis. However, extracting transient pulses from complex nonstationary vibration signals with a large amount of background noise is challenging, especially in the early stage. To improve the anti-noise ability and detect incipient faults, a novel signal de-noising method based on enhanced time-frequency manifold (ETFM) and kurtosis-wavelet dictionary is proposed. First, to mine the high-dimensional features, the C-C method and Cao’s method are combined to determine the embedding dimension and delay time of phase space reconstruction. Second, the input parameters of the liner local tangent space arrangement (LLTSA) algorithm are determined by the grid search method based on Renyi entropy, and the dimension is reduced by manifold learning to obtain the ETFM with the highest time-frequency aggregation. Finally, a kurtosis-wavelet dictionary is constructed for selecting the best atom and eliminating the noise and reconstruct the defective signal. Actual simulations showed that the proposed method is more effective in noise suppression than traditional algorithms and that it can accurately reproduce the amplitude and phase information of the raw signal.

This paper describes a comparison of three types of feature sets. The feature sets were intended to classify 13 faults in a centrifugal pump (CP) and 17 valve faults in a reciprocating compressor (RC). The first set comprised 14 non-linear entropy-based features, the second comprised 15 information-based entropy features, and the third comprised 12 statistical features. The classification was performed using random forest (RF) models and support vector machines (SVM). The experimental work showed that the combination of information-based features with non-linear entropy-based features provides a statistically significant accuracy higher than the accuracy provided by the Statistical Features set. Results for classifying the 13 conditions in the CP using non-linear entropy features showed accuracies of up to 99.50%. The same feature set provided a classification accuracy of 97.50% for the classification of the 17 conditions in the RC.

Snow cover over the Tibetan Plateau plays a vital role in the regional and global climate system because it affects not only the climate but also the hydrological cycle and ecosystem. However, high-quality snow data are hindered due to the sparsity of observation networks and complex terrain in the region. In this study, a nonlinear time series analysis method called phase space reconstruction was used to obtain the Tibetan Plateau snow depth by combining the FY-3C satellite data and in situ data for the period 2014–2017. This method features making a time delay reconstruction of a phase space to view the dynamics. Both of the grids and their nearby in situ snow depth time series were reconstructed with two appropriate parameters called time delay and embedding dimension. The values of the snow depth for grids were averaged over the in situ observations and retrieval of the satellite if their two parameters were the same. That implies that the two trajectories of the time series had the same evolution trend. Otherwise, the snow depth values for grids were averaged over the in situ observation. If there were no in situ sites within the grids, the retrieval of the satellite remained. The results show that the integrated Tibetan Plateau snow depth (ITPSD) had an average bias of –1.35 cm and 1.14 cm, standard deviation of the bias of 3.96 cm and 5.67 cm, and root mean square error of 4.18 cm and 5.79 cm compared with the in situ data and FY-3C satellite data, respectively. ITPSD expressed the issue that snow depth is usually overestimated in mountain regions by satellites. This is due to the introduction of more station observations using a dynamical statistical method to correct the biases in the satellite data.

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to eliminate the wavelet boundary effect caused by the limitation of data length, a univariate predictive extension method based on phase space reconstruction and least squares support vector machine (LSSVM) is proposed. Compared with the traditional extension methods, whether in large scale or small scale, the predictive extension method can effectively eliminate the boundary effect of the wavelet transform. The predictive extension method is used to extend the average annual flow of Zamashik, Qilian, Yingluoxia, and Zhengyixia hydrological stations in the Hei River. Then, wavelet transform multiscale analysis is performed on the extended time series. The wavelet variance curve of the runoff time series after extending is obviously smoother than the unprocessed wavelet variance curve, and to a certain extent, the pseudo period caused by the wavelet boundary effect is eliminated. Analysis results show there are 10-year and 36-year cycles at Zamashik station, 10-year and 25-year cycles at Qilian station, 16-year and 38-year cycles at Yingluoxia station, and 9-year, 21-year, and 30-year cycles at Zhengyixia station. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• A novel HI is proposed through correlation warping radius tracking. • The proposed HI is applicable for variable operating conditions. • The proposed HI is effective to characterize the degradation process. • The proposed method is validated sensitive to incipient defect. Health indicators (HI) are crucial in early fault alarm and degradation monitoring of mechanical failures. In recent years, many HIs are developed and reported, however, a constructed HI which is more sensitive to incipient defect, robust to varying operating conditions and is interpretable to characterize the degradation process is still remain to be studied. To address this issue, a more powerful and effective HI applicable for nonstationary operating condition is studied in this paper. Inspired by chaos fractal theory (CFT) and slowly varying damage dynamics theory (SVDDT), this paper calculates the range of correlation warping radius (CWR) of each segment of signal through phase space reconstruction, and then finds that the boundary of correlation warping radius range (CWR-B) in normal conditions follows a fixed linear relationship. Through the application of this linear relationship, a new health monitoring indicator for vibration signals of rotating machinery has been proposed, through correlation warping radius tracking measurement (CWRTM). The proposed indicator not only has outstanding monitoring advantages for rotating machinery in a cyclo -stationary condition, but also applicable for machinery condition monitoring under variable operating conditions, achieving the unity of methods for condition monitoring in stationary and variable operating conditions. Compared with some typical health indicators, the experimental results on bearing run-to-failure dataset and wind turbine generator bearing field dataset show that the proposed indicator in this article is not only sensitive to early-stage fault, but also validated robust to varying operating conditions. [ABSTRACT FROM AUTHOR]

Wind power gains more and more attention from all over the world as a clean and renewable energy resource, and accurate prediction of wind speed has become a hot issue. This paper presents a novel incremental forecaster based on phase space reconstruction and broad learning network (BLN) for short-term prediction. First, time delay and embedding dimension which take an essential part in phase space reconstruction are determined by the C–C algorithm. Then, these optimal parameters are input to the BLN trained incrementally. Afterward, forecasting values are given by the output layer of BLN. Data collected from a wind farm is adopted for verifying the efficacy of this proposed model. Furthermore, five commonly used assessment indicators are applied to evaluate predictive performance of different models. Results show that the proposed model has the smallest prediction error, which performs better than the other models at one-step to three-step ahead forecasting, and this strength is attributed to address the problem of local optimum. Furthermore, the proposed model consumes less training time than the other models. Therefore, the proposed model tends to be promising for wind speed prediction of the big data era. • The proposed model is trained incrementally with adding enhancement nodes. • The inputting node number of BLN is determined by phase space reconstruction. • The C–C method is used to select the optimal parameters of phase space. [ABSTRACT FROM AUTHOR]

This paper aims at presenting a novel approach for the analysis of vibration signals detected from circuit breaker (CB) opening or closing operations and to mechanical fault identification. An adaptive signal decomposition approach and chaotic nonlinear dynamic technique are used for extracting fault sensitive features of CB. The fault sensitive frequency band is derived using variational mode decomposition (VMD) combining with Hilbert marginal spectrum (HMS). The obtained sensitive frequency band(s) are employed for the reconstruction of the dynamical attractor (chaotic dynamics viewed from a phase space perspective, namely, the attractor-based perspective) following the Taken's embedding theorem. The invariant measures and ergodic quantities such as the largest Lyapunov exponent, correlation dimension, and Kolmogorov entropy which can be estimated on the reconstructed attractor are presented as the fault sensitive features. The CB fault is estimated in a chaotic space using these sensitive features. The effectiveness of the developed method is evaluated by using data sets recorded from several vacuum CBs with the same type.

Considering the characteristics of network traffic on the data link layer, such as massive high-speed data flow, information camouflaged easily, and the phenomenon that abnormal traffic is much smaller than the normal one, an intrusion detection system (IDS) based on the quantitative model of interaction mode between ports is proposed. The model gives the quantitative expression of Port Interaction Mode in Data Link Layer (PIMDL), focusing on improving the accuracy and efficiency of the intrusion detection by taking the arrival time distribution of traffic. The feasibility of the model proposed is proved by the phase space reconstruction and visualization method. According to the characteristics of long and short sessions, a neural network based on CNN and LSTM is designed to mine the differences between normal and abnormal models. On this basis, an improved Intrusion Detection algorithm based on a multi-model scoring mechanism is designed to classify sessions in model space. And the experiments show that the quantitative model and the improved algorithm proposed can not only effectively avoid camouflage identity information, but also improve computational efficiency, as well as increase the accuracy of small sample anomaly detection.

The Conceptor network is a newly proposed reservoir computing (RC) model, which outperforms traditional classifiers, which can fail to model new classes of data for a supervised learning task. However, the reservoir structure design for the Conceptor is single, involving just a traditional random network, which has strong coupling between nodes and limits computing ability. This study focused on the reservoir topology design problem, and we propose a complex network Conceptor-based phase space reconstruction of time series. Several dynamical systems were chosen to build complex networks using a phase space reconstruction algorithm. The experiment results obtained using a mix of two irrational-period sines showed that the proposed phase space reconstruction reservoir topologies with the appropriate values of threshold provide Conceptors with extra reconstruction precision. Among them, the phase space reconstruction reservoir-based Lorenz system shows the best performance. Further experiments also identified the appropriate values of threshold of the phase space reconstruction method required to obtain optimal performance. The precision showed a non-linear decline with increase in memory load, and the proposed Lorenz phase space reconstruction reservoir maintained its advantages under different memory loads.

As a frequent natural disaster, red tide has attracted more and more attentions. In fact, red tide results from the joint actions of multiple complex marine environmental factors. Unfortunately, there is no work on the interaction analysis between these factors. To inaugurate a systematic research of this area, a novel machine learning based framework is developed for marine environmental series analysis. It combines cross recurrence plot (CRP), cross recurrence quantification analysis (CRQA) and statistical analysis. This framework provides a general way to transform two marine series into a high-dimensional space. CRP is used to visualize internal dynamics, while the influence of factors is quantitatively analyzed through CRQA. Finally, the representative factors in each field are statistically determined by boxplot. This is the first analysis framework attempting to reveal the similarity in intrinsic dynamics of marine factors. Experimental results show that the framework is competent to perform the visualization of marine time series. Besides, the results also demonstrate the degree of similarity between different marine factors through quantitative analysis.