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Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny 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 wind power generation capacity is increasing rapidly every year. There needs to be a corresponding development in the management of wind power. Accurate wind speed forecasting is essential for a wind power management system. However, it is not easy to forecast wind speed precisely since wind speed time series data are usually nonlinear and fluctuant. This paper proposes a novel combined wind speed forecasting model that based on PSR (phase space reconstruction), NNCT (no negative constraint theory) and a novel GPSOGA (a hybrid optimization algorithm that combines global elite opposition-based learning strategy, particle swarm optimization and the genetic algorithm) optimization algorithm. SSA (singular spectrum analysis) is firstly applied to decompose the original wind speed time series into IMFs (intrinsic mode functions). Then, PSR is employed to reconstruct the intrinsic mode functions into input and output vectors of the forecasting model. A combined forecasting model is proposed that contains a CBP (cascade back propagation network), RNN (recurrent neural network), GRU (gated recurrent unit), and CNNRNN (convolutional neural network combined with recurrent neural network). The NNCT strategy is used to combine the output of the four predictors, and a new optimization algorithm is proposed to find the optimal combination parameters. In order to validate the performance of the proposed algorithm, we compare the forecasting results of the proposed algorithm with different models on four datasets. The experimental results demonstrate that the forecasting performance of the proposed algorithm is better than other comparison models in terms of different indicators. The DM (Diebold–Mariano) test, Akaike's information criterion and the Nash–Sutcliffe efficiency coefficient confirm that the proposed algorithm outperforms the comparison models. [ABSTRACT FROM AUTHOR]

The extraction of effective classification features from electroencephalogram (EEG) signals in motor imagery is a popular research topic. The Common Spatial Pattern (CSP) algorithm is widely employed in this field. However, the performance of the traditional CSP method depends significantly on the choice of a specific frequency band and channel number of EEG data. Furthermore, inter-class variance among these frequency bands and the limited number of available EEG channels can adversely affect the CSP algorithm's ability to extract meaningful features from the relevant signal frequency bands. We hypothesize that multiple Intrinsic Mode Functions (IMFS), into which the raw EEG signal is decomposed, can better capture the non-Gaussian characteristics of the signal, thus compensating for the limitations of the CSP algorithm when dealing with nonlinear and non-Gaussian distributed data with few channels. Therefore, this paper proposes a novel method that integrates Variational Mode Decomposition (VMD), Phase Space Reconstruction (PSR), and the CSP algorithm to address these issues. VMD is used to filter and enhance the quality of the collected data, PSR is employed to increase the effective data channels (data augmentation), and the subsequent CSP filtering can obtain signals with spatial features, which are decoded by Convolutional Neural Networks (CNN) for action decoding. This study utilizes self-collected EEG data to demonstrate that the new method can achieve a good classification accuracy of 82.30% on average, confirming the improved algorithm's effectiveness and feasibility. Furthermore, this study conducted validation on the publicly available BCI Competition IV dataset 2b, demonstrating an average classification accuracy of 87.49%. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower 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.)

Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body-surrounding rock combination under high-stress conditions. Current monitoring data processing methods cannot fully consider the complexity of monitoring objects, the diversity of monitoring methods, and the dynamics of monitoring data. To solve this problem, this paper proposes a phase space reconstruction and stability prediction method to process heterogeneous information of backfill-surrounding rock combinations. The three-dimensional monitoring system of a large-area filling body-surrounding rock combination in Longshou Mine was constructed by using drilling stress, multipoint displacement meter, and inclinometer. Varied information, such as the stress and displacement of the filling body-surrounding rock combination, was continuously obtained. Combined with the average mutual information method and the false nearest neighbor point method, the phase space of the heterogeneous information of the filling body-surrounding rock combination was then constructed. In this paper, the distance between the phase point and its nearest point was used as the index evaluation distance to evaluate the stability of the filling body-surrounding rock combination. The evaluated distances (ED) revealed a high sensitivity to the stability of the filling body-surrounding rock combination. The new method was then applied to calculate the time series of historically ED for 12 measuring points located at Longshou Mine. The moments of mutation in these time series were at least 3 months ahead of the roadway return dates. In the ED prediction experiments, the autoregressive integrated moving average model showed a higher prediction accuracy than the deep learning models (long short-term memory and Transformer). Furthermore, the root-mean-square error distribution of the prediction results peaked at 0.26, thus outperforming the no-prediction method in 70% of the cases. [ABSTRACT FROM AUTHOR]

Epilepsy is a type of brain disorder triggered by an abrupt electrical imbalance of neuronal networks. An electroencephalogram (EEG) is a diagnostic tool to capture the underlying brain mechanisms and detect seizure onset in epileptic patients. To detect seizures, neurologists need to manually monitor EEG recordings for long periods, which is challenging and susceptible to errors depending on expertise and experience. Therefore, automatic identification of seizure and seizure-free EEG signals becomes essential. This study introduces a method based on the features extracted from the phase space reconstruction for classifying seizure and seizure-free EEG signals. The computed features are derived from the elliptical area and interquartile range of the Euclidean distance by varying percentage values of data points ranging from 50 to 100%. We consider two public datasets and evaluate these features in each EEG epoch that includes the healthy, interictal, preictal, and ictal stages of epileptic subjects, utilizing the K-nearest neighbor classifier for classification. Results show that the features have higher values during the seizure than the seizure-free EEG signals and healthy subjects. Furthermore, the proposed features can effectively discriminate seizure EEG signals from the seizure-free and normal subjects with 100% accuracy, sensitivity, and specificity in both datasets. Likewise, the classification between the preictal stage and seizure EEG signals attains 98% accuracy. Overall, the reconstructed phase space features significantly enhance the accuracy of detecting epileptic EEG signals compared with existing methods. This advancement holds great potential in assisting neurologists in swiftly and accurately diagnosing epileptic seizures from EEG signals. [ABSTRACT FROM AUTHOR]

The examination of fluctuations in the correlations between δ13C and δ18O is of significant importance for the reconstruction of the Earth’s climate history. A key challenge in paleoclimatology is finding a suitable method to represent the correlated fluctuation system between δ13C and δ18O. The method must be able to handle data sets with missing or inaccurate values, while still retaining the full range of dynamic information about the system. The non-linear and complex correlations between δ13C and δ18O poses a challenge in developing reliable and interpretable approaches. The transition network, which involves embedding the δ13C and δ18O sequence into the network using phase space reconstruction, is a coarse-grained based approach. This approach is well-suited to nonlinear, complex dynamic systems, and is particularly adept at emerging knowledge from low-quality datasets. We have effectively represented the fluctuations in the correlation between δ13C and δ18O since 66 million years ago (Ma) using a system of complex network. This system, which has topological dynamical structures, is able to uncover the stable modes and key patterns in Cenozoic climate dynamics. Our findings could help to improve climate models and predictions of future climate change.

Electricity is an essential energy resource in the industrial, commercial and housing sector, having a very important role in the development of societies. Urbanization and industrialization implies a great demand of energy for developing economies. In the search to be able to know how much electrical energy is consumed, a modeling of the electrical energy demand is carried out. However, the inherent intricacy and nonlinear nature of electricity consumption patterns present a significant obstacle to achieve precise modeling. In this article, a chaos theory approach is carried out to analyze the behavior of the system and to obtain properties of its dynamic system. A network consisting of a convolutional part, a hidden part and an output part is proposed. Convolutional operations are employed for dimensionality reduction in transformed data sets by reconstruction of the phase space. A radial basis function neural is used in the hidden part. The dynamic analysis approach using chaos theory, and the proposed neural network is compared with the radial basis function neural network for the modeling of electrical energy consumption. [ABSTRACT FROM AUTHOR]

Predictions of variations in exchange rates of other currencies to a vehicle currency such as the Dollar (USD) are vital in order to reduce the risks for international transactions. In this study, we use a heuristic algorithm of Harris Hawks' optimization (HHO) along with phase space reconstructions (PSRs) coupled to the ANN (PSR-ANNHHO) to predict the daily data of GBP/USD and CAD/USD exchange rates. In this new hybrid model, unlike the previous ones, the input of the model is based on the two parameters of time delay and the embedding dimension. The HHO algorithm increases the performance of ANN, which has can model non-linear systems in a natural manner. The performance of the PSR-ANNHHO model can be compared with the ANN and the ANN hybridized with metaheuristic Algorithm of Innovative Gunner (AIG) model (ANN-AIG). The Modified Diebold–Mariano test indicates the statistical difference between the accuracy of the models. Based on the statistical measures and graphs, the PSR-ANNHHO model predicts exchange rates considerably better than stand-alone ANN and ANN-AIG model in each case. Hence, implementing PSR along with using the heuristic algorithms could increase the accuracy of the model. This model's precise performance supports the case for it to be employed to predict future exchange rate variations, in order to decrease transactions risks in the global markets. [ABSTRACT FROM AUTHOR]

Investigation of the behaviour and complexity of hard-to-measure parameter time series is not explored to a greater extent before modelling a wastewater treatment facility (WWTF). In this context, A dynamic non-linear chaotic approach, namely the False nearest neighbour (FNN) algorithm, is employed for the first time to investigate the influent and effluent quality parameters of a WWTF. The primary objective of this research is to analyze the parameters of a WWTF located in India for its behaviour and complexity using the FNN algorithm. The autocorrelation function and average mutual information time lags are used as the delay time (τ) in the algorithm for phase space reconstruction and further FNN analysis. The optimum embedding dimensions (mopt) from FNN plots indicate the complexity or number of optimum variables required to model the time series. For influent and effluent parameters, the mopt values fall within a range of 4–15 and 4–17, respectively, and the τ value influences this range. Wastewater time series behaviour differs, such as pure stochastic or chaotic or chaotic series with noise, which is highly dependent on τ. The future scope of the study involves integrating the retrieved behaviour and complexity into state-of-the-art artificial intelligence or data-driven techniques to forecast hard-to-measure parameters. [ABSTRACT FROM AUTHOR]

In this study, we propose a method based on phase space reconstruction to estimate the short-term future behavior of pressure signals in pipelines. The pressure time series data were obtained from an IoT experimental model conducted in the laboratory. The proposed hydraulic system demonstrated the presence of traces of weak chaos in the time series of the pressure signal. Fractal dimension analysis revealed a complex fractal structure in the data, indicating the existence of nonlinear dynamics. Similarly, Lyapunov coefficients, divergent trajectories, and autocorrelation analysis confirmed the presence of weak chaos in the time series. The results demonstrated the existence of apparently chaotic patterns that follow the theory proposed by Kolmogorov for deterministic dynamic systems that exhibit apparently random behaviors. Phase space reconstruction allowed us to show the dynamic characteristics of the signal so that short-term predictions were stable. Finally, the study of strange attractors in pipeline pressure time series can have significant contributions to anomaly detection. • A methodology is proposed for the reconstruction of the phase space to estimate the short-term future behavior of pressure signals in pipelines in real time. • The analysis of the proposed hydraulic system revealed some indications of weak chaos in the time series of the pressure signal obtained experimentally. • The methodology implemented and the results of this study showed that the short-term predictions were very accurate and consistent; Chaotic patterns were also identified that support the theory proposed by Kolmogorov.

The Long Short-Term Memory neural network is a specialized architecture designed for handling time series data, extensively applied in the field of predicting gas concentrations. In the harsh conditions prevalent in coal mines, the time series data of gas concentrations collected by sensors are susceptible to noise interference. Directly inputting such noisy data into a neural network for training would significantly reduce predictive accuracy and lead to deviations from the actual values. The Empirical Mode Decomposition method, commonly employed in gas concentration prediction, faces challenges in practical engineering applications due to the substantial influence of newly acquired data on the initial decomposition subsequence values. Consequently, it is difficult to use this method as intended. Conversely, the Wavelet Threshold Denoising method does not encounter this issue. Furthermore, gas concentration sequences exhibit chaotic characteristics. Performing phase space reconstruction allows for the extraction of additional valuable hidden information. In light of these factors, a prediction model is proposed, integrating WTD, Phase Space Reconstruction, and LSTM neural networks. Initially, the gas concentration sequence itself is subjected to wavelet threshold denoising. Subsequently, phase space reconstruction is performed, and the resulting reconstructed phase space matrix serves as the input for the LSTM neural network. The outcomes from the final LSTM neural network reveal that the PS method indeed extracts more valuable information. The Mean Absolute Error and Root Mean Square Error are reduced by 35.1% and 25%, respectively. Additionally, when compared to the PS-LSTM model without utilizing the WTD method, the WTD-PS-LSTM predictive model showcases reductions of 77.1% and 80% in MAE and RMSE, respectively. Compared with the LSTM model, the MAE and RMSE of the WTD-PS-LSTM prediction model were reduced by 81.4% and 82.6%, respectively. This greatly improves the credibility of whether or not a response related to coal mine safety management is implemented.

Understanding the variability of wind pressure on tall building surfaces is of essential importance regarding the control of wind-induced response. In this study, advanced time series analysis techniques, e.g., phase space reconstruction and recurrence analysis, were applied to diagnose the wind pressure variability from a nonlinear dynamic perspective. It is shown that, the wind pressure acting on tall building surfaces exhibit distinct chaotic nature. Due to the complex flow patterns around the building, the underlying dynamics of wind pressure is subject to pronounced region-to-region variability. For windward surface, the wind pressure dynamics at the stagnation region is more deterministic than those of downstream. For the side surfaces, the distribution of recurrence quantification analysis (RQA) indicators follows a clear pattern, where the values are larger at the near-front edge, and decreases towards the rear. The distribution of RQA indicators on the leeward surface is opposite to that of windward surface, in which larger values occur more often at near-ground level. Further upstream, the values are found to decrease. The outcomes provide valuable insights to the wind-structural interaction, thus could help wind-resistant design. [ABSTRACT FROM AUTHOR]

Traffic flow prediction is an effective strategy to assess traffic conditions and alleviate traffic congestion. Influenced by external non-stationary factors and road network structure, traffic flow sequences have macro spatiotemporal characteristics and micro chaotic characteristics. The key to improving the model prediction accuracy is to fully extract the macro and micro characteristics of traffic flow time sequences. However, traditional prediction model by only considers time features of traffic data, ignoring spatial characteristics and nonlinear characteristics of the data itself, resulting in poor model prediction performance. In view of this, this research proposes an intelligent combination prediction model taking into account the macro and micro features of chaotic traffic data. Firstly, to address the problem of time-consuming and inefficient multivariate phase space reconstruction by iterating nodes one by one, an improved multivariate phase space reconstruction method is proposed by filtering global representative nodes to effectively realize the high-dimensional mapping of chaotic traffic flow. Secondly, to address the problem that the traditional combinatorial model is difficult to adequately learn the macro and micro characteristics of chaotic traffic data, a combination of convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM) is utilized for capturing nonlinear features of traffic flow more comprehensively. Finally, to overcome the challenge that the combined model performance degrades due to subjective empirical determined network parameters, an improved lightweight particle swarm is proposed for improving prediction accuracy by optimizing model hyperparameters. In this paper, two highway datasets collected by the Caltrans Performance Measurement System (PeMS) are taken as the research objects, and the experimental results from multiple perspectives show that the comprehensive performance of the method proposed in this research is superior to those of the prevalent methods. [ABSTRACT FROM AUTHOR]

Quantifying the dynamical features of discrete tasks is essential to understanding athletic performance for many sports that are not repetitive or cyclical. We compared three dynamical features of the (i) bow hand, (ii) drawing hand, and (iii) center of mass during a single bow-draw movement between professional and neophyte archers: dispersion (convex hull volume of their phase portraits), persistence (tendency to continue a trend as per Hurst exponents), and regularity (sample entropy). Although differences in the two groups are expected due to their differences in skill, our results demonstrate we can quantify these differences. The center of mass of professional athletes exhibits tighter movements compared to neophyte archers (6.3 < 11.2 convex hull volume), which are nevertheless less persistent (0.82 < 0.86 Hurst exponent) and less regular (0.035 > 0.025 sample entropy). In particular, the movements of the bow hand and center of mass differed more between groups in Hurst exponent analysis, and the drawing hand and center of mass were more different in sample entropy analysis. This suggests tighter neuromuscular control over the more fluid dynamics of the movement that exhibits more active corrections that are more individualized. Our work, therefore, provides proof of principle of how well-established dynamical analysis techniques can be used to quantify the nature and features of neuromuscular expertise for discrete movements in elite athletes. [ABSTRACT FROM AUTHOR]

Fuel flow of aero-engine is a very important gas path performance monitoring parameter. It is a highly nonlinear time series data with a large amount of noise. Accurate prediction of fuel flow is a great challenge. Levenberg–Marquardt (LM) neural networks have become a popular method of time series prediction due to its high optimization efficiency. However, in the process of network training, the highly nonlinear data make the LM neural networks generate a large truncation error when calculating the falling direction of loss. This error makes the loss large and cannot be effectively reduced and even continues to increase the loss to cause iterative divergence. In order to solve this problem, a LM neural network with improved optimization strategy, namely reconstructed double regulation LM neural network, is proposed in this paper. First, the aero-engine fuel flow sequence is decomposed based on the phase space reconstruction technology to reduce the nonlinearity of the original data to improve the prediction accuracy of the neural networks model. Secondly, a double regulation optimization algorithm for network weights is proposed, which combines the method of adaptively changing the gradient descent direction and the convergence step size to prevent the neural networks from easily falling into local minimum. The reconstructed double regulation LM neural network is used to predict the fuel flow of a certain type of engine. It can be seen from the simulation results that this improved neural network has the highest prediction accuracy and can reduce the mean absolute error and mean absolute percentage error of engine fuel flow prediction to 36.41 pounds per hour and 1.18, respectively. At the same time, the double regulation optimization algorithm can greatly improve the error convergence speed and save the calculation time according to the trend chart of the neural networks prediction error. [ABSTRACT FROM AUTHOR]

Marine life is very sensitive to changes in pH. Even slight changes can cause ecosystems to collapse. Therefore, understanding the future pH of seawater is of great significance for the protection of the marine environment. At present, the monitoring method of seawater pH has been matured. However, how to accurately predict future changes has been lacking effective solutions. Based on this, the model of bidirectional gated recurrent neural network with multi-headed self-attention based on improved complete ensemble empirical mode decomposition with adaptive noise combined with phase space reconstruction (ICPBGA) is proposed to achieve seawater pH prediction. To verify the validity of this model, pH data of two monitoring sites in the coastal sea area of Beihai, China are selected to verify the effect. At the same time, the ICPBGA model is compared with other excellent models for predicting chaotic time series, and root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2) are used as performance evaluation indicators. The R2 of the ICPBGA model at Sites 1 and 2 are above 0.9, and the prediction errors are also the smallest. The results show that the ICPBGA model has a wide range of applicability and the most satisfactory prediction effect. The prediction method in this paper can be further expanded and used to predict other marine environmental indicators. [ABSTRACT FROM AUTHOR]

In this paper, the fusion teaching model was constructed, and the clustering of music elements was statistically calculated by calculating the intra-class distance of music elements. The feature vector of big data clustering is extracted, the traditional music element data is partitioned using linear FM signal, and the spatial matrix of traditional music element data is obtained after initializing the clustering center. To form the information flow model of big data time series, the phase space reconstruction analysis method is used to process piano data in nonlinear mapping. To achieve the objective function after clustering, adjust the weights within the fitness function, and then output the optimal program of the integrated teaching model. The results show that the post-test scores of students in the experimental group are higher than those of students in the control group, and the scores of tuning and composition have been improved by 2 and 2.5 points to reach the full score of 10 compared with those of the control group, which demonstrates the validity and feasibility of the fusion teaching mode of traditional music elements and college piano.

In this paper, the constraints faced by the power sector in terms of energy, economy and ecology are first studied in depth, and the relationship between investment efficiency and new power development is explored by using the Data Envelopment Analysis (DEA) method. Secondly, the parameters of the least squares support vector machine (LSSVM) model are optimized based on phase space reconstruction and the artificial bee colony (ABC) algorithm, and an optimized ABC- LSSVM wind abandonment power assessment model is proposed. Finally, the proposed wind power abandonment assessment model’s accuracy and precision are verified by comparing it to the traditional maximum probability method. The results show that the evaluation result obtained by using the traditional maximum probability method is 4580.15 MWh, and the evaluation result of the ABC-LSSVM algorithm is 5123.12 MWh, while the actual output power is calculated as 4975.63 MWh, which indicates that the ABC-LSSVM algorithm is better than the traditional maximum probability method model in the evaluation of wind power abandonment. This paper accurately evaluates the wind power abandoned by wind turbines, which is of guiding significance for realizing wind reuse and rational planning of the power grid.

Aiming at the chaotic characteristics of wind power sequence and combined with meteorological information, a short-term prediction method of wind power based on phase space reconstruction and bidirectional long short-term memory neural network (Re-BiLSTM) is proposed. Firstly, the embedding dimension m and time delay τ of the time series are determined by the C–C method, and the wind power data is reconstructed based on the embedding theorem. The reconstructed data and normalized meteorological data (wind speed, wind direction) are then used as inputs, and bidirectional long short-term memory neural network (BiLSTM) is used to make short-term prediction of wind power. The results show that compared with artificial neural networks, BiLSTM, Random forest, and K-Nearest Neighbor, Re-BiLSTM has lower prediction error, which fully proves the effectiveness of the model.

Obtaining mine pressure data through traditional monitoring methods and using statistical or machine learning algorithms to predict mine pressure can no longer meet the requirements of intelligent development in mines. It is necessary to seek new methods to improve the accuracy and real-time performance of mine pressure data monitoring and prediction. Based on three-dimensional similar physical model experiments, a distributed fiber optic monitoring system is constructed. The distributed fiber optic cables are pre-embedded along the model's direction and height. Pressure data is collected during the simulated mining process of the working face, and the optical fiber Brillouin frequency shift mean variation degree is introduced as an indicator to determine whether the pressure is coming. By preprocessing the optical fiber monitoring data such as noise removal, normalization and phase space reconstruction, the one-dimensional initial monitoring data is converted into three-dimensional data. The method uses Bayesian algorithm to iteratively optimize the parameters of the CatBoost algorithm. After reaching the maximum number of iterations, the optimal parameter combination is loaded into the CatBoost algorithm. The prediction model for mine pressure appearance is obtained by training. The results show that the Bayesian algorithm has fewer iterations and smaller errors than traditional grid search methods. Compared with random forest (RF), gradient boosting decision tree (GBDT) and extreme gradient boosting (XGBoost), the CatBoost algorithm has higher prediction accuracy and stronger generalization capability. The CatBoost mine pressure appearance prediction model optimized by the Bayesian algorithm can accurately predict the three weighting in the test set. The overall prediction trend is in line with the measured value, with mean absolute error of 0.0091, root-mean-square error of 0.0077, and determination coefficient of 0.933 9.

Vibration signals associated with on-load tap-changer (OLTC) gear switching is closely related to its mechanical state. Based on the high-dimensional phase point spatial distribution of the vibration signal of OLTC, the vibration signals at multiple positions of OLTC are represented by tensor quantization to capture as rich as possible the mechanical status information of OLTC. Then, the third order tensor in the phase space is decomposed into Tucker tensor to obtain the core tensor, and a discriminative model of OLTC mechanical fault based on convolutional neural network is established. Taking the vibration signal of a certain CM type OLTC as an example for analysis, the results show that the phase space core tensor information of the vibration signal of OLTC is comprehensive and less redundant when the OLTC acts. The mechanical fault diagnosis model based on the convolutional neural network has good performance, with an accuracy rate of more than 95%, which can provide a reference for fault identification and condition maintenance of OLTC.

Rock burst is one of serious disasters that inhibit safe and high efficient coal mining. The realization of intelligent pre-warning of rock burst is the critical path to ensure coal mine intelligent and safe mining. As the b value is an effective monitoring indicator of rock burst, it is of great significance for a timely pre-warning of rock burst to grasp the evolution trend of b value in the process of mining. Therefore, based on the phase space reconstruction (PSR) and deep learning, a short-term forecast method for the b value of time sequence in mine exploitation is proposed. The b value of time sequence identified by CNN and denoised is extended to a high-dimensional space through phase space reconstruction technique, and then the long short-term memory (LSTM) network optimized by the genetic algorithm (GA) learns the high-dimensional data feature, which constructs the b value prediction Model (PSR−GA−LSTM). Combined with the W1123 fully mechanized mining face of the Kuangou coal mine identified rock burst mine, the b value of time sequence denoised is reconstructed using the optimized parameters of PSR. The prediction performance of different models is evaluated and the case research of the optimal prediction model is carried out. The research results show that after the b value of time sequence is processed by noise reduction technology, the learning ability of the model for the b value trend feature can be enhanced and the interference of noise to the precursory information of rock burst can be reduced. After the b value of time sequence is reconstructed in phase space and the hyperparameters of the LSTM are optimized, the prediction accuracy of the model can be significantly improved. Compared with other models, the residual fluctuation range of the PSR−GA−LSTM model is the smallest and stable within 0.005, and its root mean square error (RMSE), mean absolute error (MAE) and the mean absolute percentage error (MAPE) is 0.001 51, 0.001 33, 0.29%, which are lower than other models. After the PSR−GA−LSTM model is trained on the b value of time sequence, the predicted b value trend contains the precursory information of rock burst, which can provide b value pre-warning indicators for the occurrence of rock burst events in advance. The model has a better ability to predict the trend development of the b value of rock burst mine with uniform advance, and the method used in this paper can provide a reference for the prediction and pre-warning research on the evolution of rock burst in time.

In this paper, a weak impulse signal detection method based on phase space reconstruction of chaotic time series and long-term and short-term memory neural network (LSTM) in the deep learning model is proposed. Reconstructing the phase space of chaotic signals can effectively extract the chaotic information in the sequence, and constructing LSTM neural network can effectively distinguish the signal points from the non-signal points to achieve better detection results. When detecting the weak pulse signal in Lorenz chaotic system and Rossler chaotic system, the model has high detection accuracy and can still maintain the detection performance when the signal-to-noise ratio is low. This paper compares it with other machine learning models and deep learning models, such as Support Vector Machine (SVM), Recurrent Neural Network (RNN), Extreme Learning Machine (ELM), and so on. The results show that the detection accuracy of this model is higher than other comparable models under different signal-to-noise ratios and has strong detection performance. In addition, the weak pulse signal in the sunspot sequence is detected, and the fault signal in the rolling bearing is diagnosed. These results show that this model can accurately detect the weak pulse signal in the chaotic background when the signal-to-noise ratio is low and is suitable for dealing with the weak signal detection problem in the chaotic background in real life and the fault diagnosis problem in the engineering application field. This not only reduces the detection threshold of weak signal detection but also widens the application field of weak signal detection. [ABSTRACT FROM AUTHOR]

Due to the environmental protection of electric buses, they are gradually replacing traditional fuel buses. Several previous studies have found that accidents related to electric vehicles are linked to Unintended Acceleration (UA), which is mostly caused by the driver pressing the wrong pedal. Therefore, this study proposed a Model for Detecting Pedal Misapplication in Electric Buses (MDPMEB). In this work, natural driving experiments for urban electric buses and pedal misapplication simulation experiments were carried out in a closed field; furthermore, a phase space reconstruction method was introduced, based on chaos theory, to map sequence data to a high-dimensional space in order to produce normal braking and pedal misapplication image datasets. Based on these findings, a modified Swin Transformer network was built. To prevent the model from overfitting when considering small sample data and to improve the generalization ability of the model, it was pre-trained using a publicly available dataset; moreover, the weights of the prior knowledge model were loaded into the model for training. The proposed model was also compared to machine learning and Convolutional Neural Networks (CNN) algorithms. This study showed that this model was able to detect normal braking and pedal misapplication behavior accurately and quickly, and the accuracy rate on the test dataset is 97.58%, which is 9.17% and 4.5% higher than the machine learning algorithm and CNN algorithm, respectively. [ABSTRACT FROM AUTHOR]

The chaotic behaviour of monthly rainfall data of Benin, Cote d'Ivoire, Cameroon, Ghana, Niger, Nigeria, Senegal and Togo between January 1901 and December 2015 were investigated using wavelet transformation analysis and time series techniques. Wavelet power spectrum was used to split the time series into different scales. Power concentrations between 8 and 16 months were observed for the selected locations. The embedding dimension, delay and largest Lyapunov exponent (LE) were calculated. We observed positive LE ranging from 0.13 to 0.36, indicating the rainfall was chaotic. Ghana had the highest values of LE, while the lowest LE was observed at Niger.. [ABSTRACT FROM AUTHOR]

Selection of suitable general circulation models (GCMs) increases the reliability in the assessment of the impact of climate change. In a recent study, we introduced the concepts of complex networks for evaluating the performance of GCMs in simulating rainfall using clustering coefficient, which quantifies the tendency of a network to cluster. However, since networks have different properties, it is important to use any additional measure to verify the consistency in the outcomes for more reliable interpretations and conclusions. To this end, in the present study, we employ the shortest path length to evaluate the performance and ranking of GCMs for rainfall simulation. The shortest path length quantifies the efficiency of a network in transmitting information between the nodes in the network. It is generally a better measure, as its calculation involves every pair of nodes in the entire network, rather than only the nodes that are in 'clusters' with a given node of interest. We evaluate the ability of 49 GCMs from the Coupled Model Intercomparison Project phase 6 (CMIP6) to simulate the monthly rainfall in India for the period 1961–2014. We consider each grid, of the 288 grids of 1° × 1° spatial resolution across India, as a network. For each network, we treat the higher-dimensional vectors reconstructed from the scalar rainfall time series as the nodes and the connections between the vectors as the links. We determine the optimum dimension for reconstruction using the false nearest neighbor method. We consider two cases for the evaluation of GCMs: (1) Case 1—whole-year rainfall (January-December); and (2) Case 2—summer monsoon rainfall (June–September). For each grid, we rank the 49 GCMs based on the difference in the average shortest path length between the observed rainfall network and the GCM-simulated rainfall network. For each of Case 1 and Case 2, we employ the group decision-making (GDM) methodology to rank the GCMs for the entire study area, considering all the 288 grids. We then use the comprehensive rating metric (RM) value to combine the ranks obtained for the GCMs for Case 1 and Case 2 and to identify the final ranking of the GCMs. For the whole-year rainfall, the models CMCC-ESM2, NorCPM1, GFDL-ESM4, CMCC-CM2-SR5, and CESM2-WACCM, in order, occupy the top five positions. For the summer monsoon rainfall, the models CESM2-FV2, E3SM-1-1-ECA, CMCC-ESM2, EC-Earth3-Veg-LR and CMCC-CM2-HR4, in order, are the top five. The results from the RM values suggest that the models CMCC-ESM2, CESM2-WACCM, CMCC-CM2-HR4, E3SM-1-1-ECA, and BCC-ESM1 are, in order, the five best-performing models. We find that the ranks obtained for the GCMs based on the shortest path length analysis are in reasonably good agreement and consistent with those obtained using clustering coefficient, especially for the whole-year rainfall. [ABSTRACT FROM AUTHOR]

To address the problem of mechanical defect identification in a high-voltage circuit breaker (HVCB), this paper studies the circuit breaker vibration signal and proposes a method of feature extraction based on phase-space reconstruction of the vibration substages. To locate mechanical defects in circuit breakers, vibration signals are divided into different substages according to the time sequence of the parts of the circuit breakers. The largest Lyapunov exponent (LLE) of the vibration signals' substages is calculated, and then the substages are reconstructed in high-dimensional phase space. The geometric features of the phase trajectory mean center distance (MCD) and vector diameter offset (VDO) are calculated, and the LLE, MCD, and VDO are selected as the three fault identification features of the vibration substages. The eigenvalue anomaly rate of each substage of the vibration signal under defect state are calculated and analyzed to locate the vibration substage of the mechanical defect. Finally, a fault diagnosis model is constructed by a support vector machine (SVM), and the common mechanical defects of circuit breakers simulated in the laboratory are effectively identified. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press 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.)

This paper investigates the dynamics of the time-series of water temperature of the Skokomish River (2019–2020) at hourly time scale by employing well-known nonlinear methods of chaotic data analysis including average mutual information, false nearest neighbors, correlation exponent, and local divergence rates. The delay time and the embedding dimension were calculated as 1400 and 9, respectively. The results indicated that the thermal regime in this river is chaotic due to the correlation dimension (1.38) and the positive largest Lyapunov exponent (0.045). Furthermore, complex networks have been applied to study the periodicity of thermal time-series throughout a year. A special algorithm is then used to find the so-called communities of the nodes. The algorithm found three communities which have been called Cold, Intermediate, and Warm. The temperatures in these three communities are, respectively, in the intervals (0.8, 5.8), (5.8, 11.63), and (11.63, 15.8). This analysis indicates that highest variations in water temperature occur between warm and cold seasons, and complex networks are highly capable to analyze hydrothermal fluctuations and classify their time-series. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology 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.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department 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.)

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics 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 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. [ABSTRACT FROM AUTHOR]

The short-term forecast of photovoltaic (PV) power is crucial for the security and economics of power system operations. However, the fluctuation characteristics of the PV power, which are closely related to the meteorological factors, introduce inaccuracies in its forecast. Towards this end, the paper studies the effects of clustering analysis at long time scale and data reconstruction technique at short time scale on capturing PV power fluctuation characteristics. A short-term PV power forecasts method based on multi-scale fluctuation characteristics extraction (MFCE), which employs a path analysis to identify the relevance of meteorological factors with PV power at long time scale and a phase space reconstruction to analyze PV power fluctuation characteristics at short time scale, is proposed in this paper. The proposed MFCE methodology deploys a widely-used extreme gradient boosting (XGBoost) model to output the forecasting results. Both the effectiveness and accuracy of the proposed methodology are verified by using the real data under the conditions of sunny and cloudy days of four seasons compared to traditional methodologies. • The multi-scale fluctuation characteristics are analyzed for PV power forecast. • Identify dominant meteorological factors affecting PV power by path analysis. • The hour-level fluctuation is reduced by using path analysis-based clustering. • The phase space reconstruction is used to reduce the minute-level fluctuation. [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.)

Precipitation prediction is an important technical mean for flood and drought disaster early warning, rational utilization, and the development of water resources. Complementary ensemble empirical mode decomposition (CEEMD) can effectively reduce mode aliasing and white noise interference; extreme learning machines (ELM) can predict non-stationary data quickly and easily; and the fruit fly optimization algorithm (FFOA) has better local optimization ability. According to the multi-scale and non-stationary characteristics of precipitation time series, a new prediction approach based on the combination of complementary ensemble empirical mode decomposition (CEEMD), extreme learning machine (ELM), and the fruit fly optimization algorithm (FFOA) is proposed. The monthly precipitation data measured in Zhengzhou City from 1951 to 2020 was taken as an example to conduct a prediction experiment and compared with three prediction models: ELM, EMD-HHT, and CEEMD-ELM. The research results show that the sum of annual precipitation predicted by the CEEMD-ELM-FFOA model is 577.33 mm, which is higher than the measured value of 572.53 mm with an error of 4.80 mm. The average absolute error is 0.81 and the average relative error is 1.39%. The prediction value of the CEEMD-ELM-FFOA model can closely follow the changing trend of precipitation, which shows a better prediction effect than the other three models and can be used for regional precipitation prediction. [ABSTRACT FROM AUTHOR]

Aiming at the problem that it is difficult to identify the load state due to the inherent characteristics of ball mill vibration signal such as strong randomness, non-smoothness and non-linearity, a load state identification method for ball mill based on phase space reconstruction and PSO-K-means was proposed. Firstly, numerical simulation of two chaotic time series of Lorenz and Rossler used the improved before-and-after autocorrelation coefficient algorithm and derived an accurate and efficient method for calculating the delay time and embedding dimension. Then, feature extraction was performed for phase space attractors under three different load states, and the variation law of the associated dimensional feature quantity was analyzed. Finally, Classification and identification of ball mill load states by inputting the correlation dimensions as feature vectors into the PSO-K-means clustering model. The results show that the PSO-K-means clustering model has high accuracy in load state identification, with 94. 2%, 96. 3% and 94. 8% accuracy under underload, normal load and overload, respectively. The above results confirm that the method can achieve effective identification of ball mill load states. [ABSTRACT FROM AUTHOR]

The process of styrene polymerization is a typical hazardous chemical process. Due to its high reaction temperature and high product viscosity, it is very prone to thermal runaway accidents. In order to reduce the occurrence of such accidents, this work investigates the thermal hazard of the polymerization process by reaction calorimetry experiments and deduces the thermal runaway criterion for the styrene polymerization reaction by combining the results of gas chromatography experiments. The phase space of the system was reconstructed by the delayed coordinate method to obtain the reconstructed divergence criterion (re-div). The results showed that the exothermic heat and adiabatic temperature rise of the polymerization reaction showed a logarithmic increase with the increase of the initial reaction temperature. Compared with the div calculated by numerical method and analytical method, the analytical method div is more accurate and effective. The analytical divergence criterion (div) curve is strictly decreasing in the post-polymerization case, which indicates that it can be used as an off-line criterion to determine the safety of the reaction process by the initial reaction condition parameters. After the experimental verification, it can be confirmed that the re-div can predict whether the reaction is out of control in advance and accurately. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower 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.)