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Since system identification plays a crucial role in controlling systems, it is essential to have access to appropriate identification methods. In this paper, two novel identification methods are proposed for estimating Controlled Auto-Regressive Moving Average (CARMA) systems: the multi-innovation gradient-based iterative algorithm and the two-stage multi-innovation gradientbased iterative algorithm. Our primary objective is to estimate the unknown parameters of a tumor model using these methods. To evaluate the effectiveness of the proposed methods, various factors are considered, such as convergence rate and estimation error. By conducting simulations, the practical applicability and performance of the introduced algorithms are demonstrated. The obtained results are presented through tables and figures, providing a comprehensive analysis of the estimation outcomes. The multi-innovation gradient-based iterative algorithm and the twostage multi-innovation gradient-based iterative algorithm offer valuable contributions to the field of system identification, particularly in the context of CARMA systems. These methods offer an innovative approach to estimate the parameters of complex systems, specifically focusing on tumor models. The convergence rate and estimation error analysis highlight the reliability and accuracy of the proposed methods, indicating their potential for practical implementation. In conclusion, this paper presents novel identification methods for estimating CARMA systems in the context of tumor models. The proposed algorithms demonstrate promising results in terms of convergence rate and estimation accuracy. These findings contribute to the development of effective and reliable identification techniques, offering valuable insights for controlling and understanding complex systems. [ABSTRACT FROM AUTHOR]

Exponentially weighted moving average (EWMA) control charts are well-established devices for monitoring process stability. Typically, control charts are evaluated by considering their Average Run Length (ARL), that is the expected number of observations or samples until the chart signals. Because of the limitations of an average, various papers also dealt with the run length distribution and quantiles. Going beyond these papers, we develop algorithms for and evaluate the quantile performance of EWMA control charts with variance adjusted control limits and with fast initial response features, of EWMA charts based on the sample variance, and of EWMA charts simultaneously monitoring mean and variance. Additionally, for the mean charts we consider medium, late and very late process changes and their impact on appropriately conditioned run length quantiles. It is demonstrated that considering run length quantiles can protect from constructing distorted EWMA designs while optimising theirzero-stateARL performance. The implementation of all the considered measures in the R package ‘spc’ allows any control chart user to consider EWMA schemes from the run length quantile prospective in an easy way. [ABSTRACT FROM PUBLISHER]

Purpose: The Wiener-Hammerstein nonlinear system is made up of two dynamic linear subsystems in series with a static nonlinear subsystem, and it is widely used in electrical, mechanical, aerospace and other fields. This paper considers the parameter estimation of the Wiener-Hammerstein output error moving average (OEMA) system. Design/methodology/approach: The idea of multi-population and parameter self-adaptive identification is introduced, and a multi-population self-adaptive differential evolution (MPSADE) algorithm is proposed. In order to confirm the feasibility of the above method, the differential evolution (DE), the self-adaptive differential evolution (SADE), the MPSADE and the gradient iterative (GI) algorithms are derived to identify the Wiener-Hammerstein OEMA system, respectively. Findings: From the simulation results, the authors find that the estimation errors under the four algorithms stabilize after 120, 30, 20 and 300 iterations, respectively, and the estimation errors of the four algorithms converge to 5.0%, 3.6%, 2.7% and 7.3%, which show that all four algorithms can identify the Wiener-Hammerstein OEMA system. Originality/value: Compared with DE, SADE and GI algorithm, the MPSADE algorithm not only has higher parameter estimation accuracy but also has a faster convergence speed. Finally, the input–output relationship of laser welding system is described and identified by the MPSADE algorithm. The simulation results show that the MPSADE algorithm can effectively identify parameters of the laser welding system. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent control system technique is proposed to model and control of a nonlinear coupled three tank system. Two pumps fed the tank 1 and tank 2 and a fractional flow of these two pumps fed tank 3. The main aim of this paper is to make a set point tracking experiments of the tanks level using a nonlinear autoregressive moving average L-2 (NARMA L-2) and neural network predictive controllers. The proposed controllers are designed with the same neural network architecture and algorithm. Comparison of the system with the proposed controllers for tracking a step and random level set points for a fixed and variable flow parameter and some good results have been obtained. [ABSTRACT FROM AUTHOR]

For nearly a decade, quality-related fault detection algorithms have been widely used in industrial systems. However, the majority of these detection strategies rely on static assumptions of the operating environment. In this paper, taking the time series of variables into consideration, a dynamic kernel entropy component regression (DKECR) framework is proposed to address the instability of quality-related fault detection due to the existing dynamic characteristics. Compared with the typical kernel entropy component analysis method, the proposed method constructs the relationship between process states and quality states to further interpret the direct effect on the product taken by the fault. In the proposed approach, process measurements are converted to a lower-dimensional subspace with a specific angular structure that is more comprehensive than traditional subspace approaches. In addition, the angular statistics and their relevant thresholds are exploited to enhance the quality-related fault detection performance. Finally, the proposed method will be compared with three methods by means of a numerical example and two industrial scenarios to demonstrate its practicality and effectiveness. Note to Practitioners—This paper studies a quality-related fault detection problem for the dynamic nonlinear industrial system. Controlling and measuring the quality state is challenging for the high-level monitoring system of the manufacturing process due to the nonlinear dynamic feature in states. This paper proposes a new data-driven method based on the kernel entropy component analysis method to assess the correlation between the quality and fault in the industrial system, reducing unnecessary overhaul and maintenance. Based on the autoregressive moving average exogenous algorithm, the proposed method captures the dynamic interaction between the process states to decrease false alarms. In the experimental section, the DKECR method outperforms the compared approaches, which can provide stable fault detection results. Additionally, the unique angle structure of the proposed method can supply more information for engineers’ monitoring needs. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we use the maximum likelihood principle and the negative gradient search principle to study the identification issues of the multivariate equation‐error systems whose outputs are contaminated by an moving average noise process. The model decomposition technique is used to decompose the system into several regressive identification subsystems based on the number of the outputs. In order to improve the parameter estimation accuracy, a decomposition‐based multivariate maximum likelihood gradient iterative algorithm is proposed by means of the maximum likelihood principle and the iterative identification method. The numerical simulation example indicates that the proposed method has better parameter estimation results than the compared decomposition‐based multivariate maximum likelihood gradient algorithm. [ABSTRACT FROM AUTHOR]

The controlled autoregressive autoregressive moving average (CARARMA) models are of popularity to describe the evolution characteristics of dynamical systems. To overcome the identification obstacle resulting from colored noises, this paper studies the identification of the CARARMA models by forming an intermediate correlated noise model. In order to realize the real-time prediction function of the models, the on-line identification scheme is developed by constructing the dynamical objective functions based on the real-time sampled observations. Firstly, a rolling optimization cost function is built based on the observation at a single sampling instant to catch the modal information at a single time point and a generalized extended stochastic gradient (GESG) algorithm is proposed through the stochastic gradient optimization. Secondly, a rolling window cost function is built in accordance with the dynamical batch observations within data window by extending the proposed GESG algorithm and the multi-innovation generalized extended stochastic gradient algorithm is derived. Thirdly, from the perspective of theoretical analysis, the convergence proof of the proposed algorithm is provided based on the stochastic martingale convergence theory. Finally, the simulation analysis and comparison studies are provided to show the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Software-based technical indicators have been widely used for the stock market forecasting, aiming to predict market direction. Even though many algorithms for the software based technical indicators are presented, there are almost no hardware implementations reported in the literature. In this paper, the hardware implementation is presented for three commonly used technical indicators: Moving Average Convergence/Divergence (MACD), Relative Strength Index (RSI), and Aroon. Latency evaluation is conducted for Bitcoin and Ethereum within a single-day timeframe, utilizing the Xilinx Zynq-7000 programmable SoC XC7Z020-CLG484-1 platform. Additionally, various hardware/software (HW/SW) partitioning strategies are explored to leverage the flexibility of software alongside the performance advantages of hardware via the Zynq SoC platform. The results show that the best performing technical indicator is MACD with a speedup of 30 times over its software only counterpart. Furthermore, a hybrid design integrating multiple technical indicators is proposed, pairing MACD with RSI due to their competitive throughput values, differing by only 0.38 microseconds. This hybrid approach capitalizes on the parallel processing capabilities of hardware, enabling multiple systems to operate simultaneously. • Proposed SoC framework boosts HFT system efficiency. • HW/SW partitioning on Zynq-7000 SoC enhances flexibility. • MACD, RSI, Aroon indicators implemented for forecasting. • MACD outperforms others on ZYNQ SoC, 30x faster than software. • MACD and RSI combination optimal for hybrid designs. [ABSTRACT FROM AUTHOR]

Range-based positioning using wireless signal has gained remarkable attention in past decades. Accurately extracting the distance information, i.e., estimating parameters of individual multipath, is of interest. It has been shown that diffuse multipath component (DMC) in wireless propagation channel has significant influence on channel estimation and, thus, the ranging performance. In addition to specular component (SC), DMC should be considered in order to improve the estimated accuracy of channel paths’ parameters and, thus, the ranging performance. In this paper, we extend the quasi-maximum likelihood space-alternating generalized expectation-maximization (SAGE) algorithm by incorporating an autoregressive moving average (ARMA) filter to estimate the channel parameters under the presence of DMC. The parameters of both DMC and SC are estimated in an iterative approach, where the DMC is assumed as colored noise and the ARMA-filter is used to estimate the contributions from DMC. The validation of the algorithm based on measured data shows that both SC and DMC can be accurately estimated by the proposed approach. A comparison with standard SAGE and AR-SAGE is conducted, where the results reveal that the proposed method outperforms other two approaches in terms of ranging accuracy. [ABSTRACT FROM AUTHOR]

This paper combines discrete wavelet transform (DWT), autoregressive moving average (ARMA), and XGBoost algorithm to propose a weighted hybrid algorithm named DWTs-ARMA-XGBoost (DAX) on long-time temperature series analysis. Firstly, this paper chooses the temperature data of February 1 to 20 from 1967 to 2016 of northern mountainous area in North China as the observed data. Then, we use 10 different discrete wavelet functions to decompose and reconstruct the observed data. Next, we build ARMA models on all the reconstructed data. In the end, we regard the calculations of 10 DWT-ARMA (DA) algorithms and the observed data as the labels and target of the XGBoost algorithm, respectively. Through the data training and testing of the XGBoost algorithm, the optimal weights and the corresponding output of the hybrid DAX model can be calculated. Root mean squared error (RMSE) was followed as the criteria for judging the precision. This paper compared DAX with an equal-weighted average (EWA) algorithm and 10 DA algorithms. The result shows that the RMSE of the two hybrid algorithms is much lower than that of the DA algorithms. Moreover, the bigger decrease in RMSE of the DAX model than the EWA model represents that the proposed DAX model has significant superiority in combining models which proves that DAX has significant improvement in prediction as well. [ABSTRACT FROM AUTHOR]

Summary: The major aim of data center (DC) is to optimize the three parameters, viz., energy consumption, resource utilization, and quality of service (QoS). Our objective is to balance these three parameters by managing the virtual machine (VM) efficiently. Therefore, this paper presents a holistic view of VM consolidation and explains its constituent subprocesses: (i) host overload detection, (ii) VM selection for migration from the overloaded host, and (iii) VM placement for provisioning selected VMs on a new set of hosts. Later, this paper attempts to optimize the parameters mentioned above by proposing a host overload detection algorithm based on exponential weighted moving average (EWMA) and its variants. The performance of the proposed EWMA is compared with the state‐of‐the‐art algorithms based on (i) local regression, (ii) median absolute deviation, and (iii) interquartile range. The efficacy of the proposed EWMA is evaluated in combination with four different existing VM selection policies: (i) minimum migration time, (ii) minimum utilization, (iii) maximum correlation, and (iv) random selection. The evaluation metrics comprise energy consumption, VM migration count from the overloaded hosts representing the resource utilization, service level agreement (SLA) violations describing the QoS, and the average execution time. Simulation experiments are carried out on the CloudSim simulator using PlanetLab real cloud trace as workload. The proposed EWMA consumes 18.33% less energy, causes 47.81% less VM migration, makes 9.91% less SLA violation, and takes 43.44% less average‐time for the execution of the entire workload as compared to state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

This paper considers the parameter estimation problem for bilinear stochastic systems with autoregressive moving average (ARMA) noise using the stochastic gradientmethod. First, the identification model is derived by eliminating the state variables. Based on the obtained identification model, a multi-innovation generalized extended stochastic gradient (MI-GESG) algorithm is proposed using the multi-innovation identification theory. Furthermore, to enhance the parameter estimation accuracy, a maximum likelihood based MI-GESG (ML-MI-GESG) algorithm is developed by using the maximum likelihood identification principle. Finally, an illustrative simulation example is provided to testify the proposed algorithms. The simulation results show the effectiveness of the proposed algorithms for identifying bilinear systems. [ABSTRACT FROM AUTHOR]

In this paper, we study smooth stochastic multilevel composition optimization problems, where the objective function is a nested composition of T functions. We assume access to noisy evaluations of the functions and their gradients, through a stochastic first-order oracle. For solving this class of problems, we propose two algorithms using moving-average stochastic estimates, and analyze their convergence to an e-stationary point of the problem. We show that the first algorithm, which is a generalization of [S. Ghadimi, A. Ruszczynski, and M. Wang, SIAM J. Optim., 30 (2020), pp. 960-979] to the T level case, can achieve a sample complexity of OT(1/ε6) by using minibatches of samples in each iteration, where OT hides constants that depend on T. By modifying this algorithm using linearized stochastic estimates of the function values, we improve the sample complexity to OT(1/ε4). This modification not only removes the requirement of having a minibatch of samples in each iteration, but also makes the algorithm parameter-free and easy to implement. To the best of our knowledge, this is the first time that such an online algorithm designed for the (un)constrained multilevel setting obtains the same sample complexity of the smooth single-level setting, under standard assumptions (unbiasedness and boundedness of the second moments) on the stochastic first-order oracle. [ABSTRACT FROM AUTHOR]

The monitoring data of landslide deformation are characterized by non-smooth, nonlinear and random changes, and the cumulative changes of the monitored objects have both monotonous growth trends and short-term fluctuations. The GM(1,1) model can get better results only when the data series are monotonous. Due to the limitations of the model, the prediction accuracy of the GM(1,1) model is limited to a certain extent. An improved algorithm based on the GM(1,1) model and the empirical mode decomposition (EMD-GM(1,1) model) for deformation prediction was presented to improve the forecast accuracy in this paper. Firstly, EMD was used to effectively separate the nonlinear high-frequency and low-frequency components hidden in the deformation sequence; then the moving average method was applied to build a prediction model for high-frequency component, and the GM(1,1) was applied to build the prediction model for low-frequency one according to the characteristics of each component; finally, the predicted value of each component was superimposed. The experimental results indicate that the optimized EMD-GM(1,1) model combines the advantages of the two models to separate effectively the different frequency components of the deformation sequence, which has higher prediction accuracy. Compared with the conventional GM(1,1) model, DGM(2,1) model and the Linear fitting model, the proposed model could satisfactorily describe the landslide deformation prediction practically. [ABSTRACT FROM AUTHOR]

An adaptive clamping method (SGD-MS) based on the radius of curvature is designed to alleviate the local optimal oscillation problem in deep neural network, which combines the radius of curvature of the objective function and the gradient descent of the optimizer. The radius of curvature is considered as the threshold to separate the momentum term or the future gradient moving average term adaptively. In addition, on this basis, we propose an accelerated version (SGD-MA), which further improves the convergence speed by using the method of aggregated momentum. Experimental results on several datasets show that the proposed methods effectively alleviate the local optimal oscillation problem and greatly improve the convergence speed and accuracy. A novel parameter updating algorithm is also provided in this paper for deep neural network. [ABSTRACT FROM AUTHOR]

Summary: The simultaneous analysis of several financial time series is salient in portfolio setting and risk management. This paper proposes a novel alternating expectation conditional maximisation (AECM) algorithm to estimate the vector autoregressive moving average (VARMA) model with variance gamma (VG) error distribution in the multivariate skewed setting. We explain why the VARMA‐VG model is suitable for high‐frequency returns (HFRs) because VG distribution provides thick tails to capture the high kurtosis in the data and unbounded central density further captures the majority of near‐zero HFRs. The distribution can also be expressed in normal‐mean‐variance mixtures to facilitate model implementation using the Bayesian or expectation maximisation (EM) approach. We adopt the EM approach to avoid the time‐consuming Markov chain Monto Carlo sampling and solve the unbounded density problem in the classical maximum likelihood estimation. We conduct extensive simulation studies to evaluate the accuracy of the proposed AECM estimator and apply the models to analyse the dependency between two HFR series from the time zones that only differ by one hour. [ABSTRACT FROM AUTHOR]

Efficient R peaks detection is the key to the accurate analysis of electrocardiogram (ECG) signals which is a benefit to the early detection of cardiovascular diseases. In recent years, many effective R peaks detection methods have been proposed, however, the false detection rate is relatively high when the noisy ECG signal is involved. Based on the property of MTEO that it could enhance the features of signal, a novel R peaks detection algorithm is proposed in this paper to deal with ECG signals with low SNR. The algorithm includes two stages. In the first stage, a band-pass filter is used for eliminating noise, then the first-order forward differentiation and MTEO are used to transform the ECG signals, at last, the output of MTEO is smoothed with a Moving Averaging filter. In the second stage, the adaptive thresholds method and efficient decision rules are applied to detect the true R peaks. The efficiency and robustness of the proposed method are substantiated on MIT-BIH Arrhythmia Database (MITDB), Fantasia Database and MIT-BIH Normal Sinus Rhythm Database. The testing of the proposed method on the MITDB showed the following results: Sensitivity (Se) = 9 9. 8 8 % , Positive predictivity (+P) = 9 9. 7 8 % and Accuracy (Acc) = 9 9. 6 7 %. On Fantasia Database involvement, Se = 9 9. 9 9 % , +P = 9 9. 9 8 % and Acc = 9 9. 9 7 %. On MIT-BIH Normal Sinus Rhythm Database involvement, Se = 9 9. 9 9 % , +P = 9 9. 9 9 % and Acc = 9 9. 9 9 %. Compared with other R peaks detection methods, the proposed algorithm is simple, efficient and robust. [ABSTRACT FROM AUTHOR]

Heartbeat detection is the first step in automatic analysis of the electrocardiogram (ECG). For mobile and wearable devices, the detection process should be both accurate and computationally efficient. In this paper, we present a QRS detection algorithm based on moving average filters, which affords a simple yet robust signal processing technique. The decision logic considers the rhythmic and morphological features of the QRS complex. QRS enhancing is performed with channel-specific moving average cascades selected from a pool of derivative systems we designed. We measured the effectiveness of our algorithm on well-known benchmark databases, reporting F1 scores, sensitivity on abnormal beats and processing time. We also evaluated the performances of other available detectors for a direct comparison with the same criteria. The algorithm we propose achieved satisfying performances on par with or higher than the other QRS detectors. Despite the performances we report are not the highest that have been published so far, our approach to QRS detection enhances computational efficiency while maintaining high accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we develop the multifractal detrending weighted average algorithm of historical volatility (MF-DHV) for one-dimensional multifractal measure based on the classical multifractal detrended fluctuation analysis (MF-DFA). In the calculation process of getting a local trend for MF-DHV, historical volatility is taken to develop an moving average algorithm, which is different from the simple moving average function in multifractal detrended moving average (MF-DMA). We assess the performance of three methods such as MF-DFA, MF-DMA, and MF-DHV based on the p-model multiplicative cascading constructed time series. The computational results show that all the estimated generalized Hurst exponent H (q) , the scaling exponent τ (q) , and the singularity spectrum f (α) of MF-DHV are in good agreement with the theoretical values. In addition, we also calculate the standard deviations of H err and τ err for three methods, and the lowest errors in MF-DHV provides the most accurate estimates. To avoid the accidental selection of parameters, we change the total length of the generated multifractal simulation data and p-value, respectively. It is found that in all the cases, the MF-DHV outperforms the other two methods. [ABSTRACT FROM AUTHOR]

This paper presents Gradient-based Iterative (GI) and Two-Stage Gradient-based Iterative (2S-GI) identification algorithms for the Controlled Auto-Regressive Moving Average (CARMA) form of a multivariable tumor model. The mathematical proof of the 2S-GI algorithm for multivariable CARMA systems is provided, demonstrating its effectiveness in parameter estimation. The step-by-step introduction of the algorithm facilitates further studies and implementation. A comprehensive comparison between the GI and 2S-GI algorithms is conducted, evaluating their performance in terms of convergence rate and estimation accuracy. The introduced multivariable tumor model serves as a testbed for the algorithms’ effectiveness. The results of the comparison, supported by simulated data, demonstrate the superiority of the 2S-GI algorithm in accurately estimating the parameters of the CARMA system. This research provides valuable insights into the application of gradient-based iterative algorithms in controlling multivariable tumor models, paving the way for improved control strategies in cancer treatment. [ABSTRACT FROM AUTHOR]

Time series analysis is becoming essential in different areas for the observation and monitoring of time-sequential data sets to extract relevant statistics and predict the series' behaviour. Current approaches effectively detect changes in the data streams. Still, most of these techniques are limited to noise interference and the inability to identify the most significant parameter values for productive abnormality detection in time series. In this paper, we improve on the previous moving median of the martingale sequence and the Gaussian moving average of the martingale sequence approaches by implementing various optimisation algorithms such as G-mean enumeration, genetic algorithms and particle swarm optimisation. The use of these methods allows us to find the optimal parameter set for each algorithm. The proposed system can reduce noise in the data and estimate the change degree in time series scenarios. Results show that the proposed approaches perform better than the previous martingale approaches. [ABSTRACT FROM AUTHOR]

In this paper a new method of frequency jumps detection in data from atomic clock comparisons is proposed. The presented approach is based on histogram analysis for different time intervals averaging phasetime data recorded over a certain period of time. Our method allows identification of multiple frequency jumps for long data series as well to almost real-time jump detection in combination with advanced filtering. Several methods of preliminary data processing have been tested (simple averaging, moving average and Vondrak filtration), to achieve flexibility in adjusting the algorithm parameters for current needs which is the key to its use in determining ensemble time scale or to control systems of physical time scales, such as UTC(PL). The best results have been achieved with the Vondrak filter. [ABSTRACT FROM AUTHOR]

This paper considers the underdetermined blind separation of multiple input multiple output (MIMO) radar signals that are insufficiently sparse in both time and frequency domains under noisy conditions, while traditional algorithms are usually applied in the ideal sparse environment. An effective separation method based on single source point (SSP) identification and time-frequency smoothed l0 norm (TF-SL0) is proposed. Firstly, a preprocessing step of the moving average filter and a novel argument-based time-frequency SSPs detection are employed to improve the signal-to-noise ratio and signal sparsity of the observed signals, respectively. Then, the mixing matrix is obtained by using clustering algorithms. Secondly, to obtain the optimal solution of underdetermined sparse component analysis, the smoothed l0 norm (SL0) is introduced to preliminarily achieve signal separation in the time-frequency domain. Finally, time-frequency ridge estimation is proposed to jointly enhance the reconstruction accuracy of the MIMO radar signals, and the time domain waveforms are recovered by the model of the signals. Simulations illustrate the validity of the method and show that the proposed method outperforms the traditional methods in source separation, especially in the non-cooperative electromagnetic case where the prior information is unknown. [ABSTRACT FROM AUTHOR]

In this paper, we introduce an iterative learning control (ILC) scheme based on an iteratively moving average operator for nonlinear dynamic systems with randomly varying trial lengths. By using the iteratively moving average operator, the proposed ILC algorithm overcomes the limitation of traditional ILC that all trial lengths must be identical. It is shown that for nonlinear affine and non-affine systems, the proposed learning algorithm works effectively to nullify the tracking error. In the end, two illustrative examples are presented to demonstrate the performance and the effectiveness of the proposed ILC scheme for nonlinear dynamic systems. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper focuses on the parameter identification problem for Wiener nonlinear dynamic systems with moving average noises. In order to improve the convergence rate, the gradient-based iterative algorithm is presented by replacing the unmeasurable variables with their corresponding iterative estimates, and to compute iteratively the noise estimates based on the obtained parameter estimates. The simulation results show that the proposed algorithm can effectively estimate the parameters of Wiener systems with moving average noises. [ABSTRACT FROM AUTHOR]

Shadow removing is an important issue in performance of intelligent transportation systems. Moving shadows cause confusion and errors in moving object detection, since they cannot be removed from foreground using commercial background subtraction methods. In this paper, a synthetic shadow detection algorithm using combination of Hessenberg decomposition (HD) and principal component analysis (PCA) is proposed. The HD and PCA are applied to improve the performance of shadow detection. At first, the background image is obtained using moving average method and adaptive background estimation. The moving objects in the foreground image can be accurately obtained via the background subtraction method. In this algorithm, the candidate shadow region is estimated by using HD. With regard to ability of HD in the decomposition of foreground image into two parts as shadow and object, we should calculate the HD of foreground image and determine the shadow and object regions. In this procedure, some parts of vehicles like dark region and windshield that is similar to shadow are detected as moving shadow. Therefore, PCA is applied to compensate misclassified regions in the previous stage. The proposed algorithm is evaluated on real and operational conditions such as different perspectives and illuminations. Experimental results demonstrate the efficiency and effectiveness of our proposed algorithm in the intelligent transportation systems. [ABSTRACT FROM PUBLISHER]

• An effective compression algorithm was proposed for real-time transmission data. • A mixed prediction model of LSTM and XGBoost was established for time series data. • High performance of the proposed algorithm was veried by comparative experiments. • Almost 50% compression rate can be reached under one in ten thousand distortions. In this paper, a compression and decompression algorithm is proposed for real-time transmission data by combining discrete wavelet decomposition and reconstruction, hybrid model prediction of long short-term memory (LSTM) and extreme gradient boosting (XGBoost), and the development of quantization sequences. The forward compression and reverse decompression are achieved by continuously acquiring the prediction error and data series with quantization error through the iterative shifting approach. To accurately forecast data series with quantization errors, three classes of data with specific distribution characteristics collected from the measurement-while-drilling (MWD) operation field are decomposed into approximate data and detailed data, which are then predicted by autoregressive integrated moving average (ARIMA), support vector regression (SVR), XGBoost, backpropagation neural network (BPNN), radial basis function neural network (RBFNN), and LSTM models. Particularly, the LSTM model and the XGBoost model are proven to be suitable strategies for working with approximate data and detailed data separately by comparing with conventional approaches in terms of the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2) predictive indexes. Besides, experiment studies show that almost 50% compression performance can be reached under one in ten thousand distortions. It is verified that the proposed algorithm is appropriate and efficient for real-time data transmission applications with high performance. [ABSTRACT FROM AUTHOR]

The coordination of modern transportation system depends heavily on intelligent techniques, information assortment, and its analysis. Sensors play a crucial role in information assortment in charging scheduling of electric vehicles (EVs). EVs are destined to become inevitable due to their innate economic contribution, climate improvement, and social attributes as per United Nation's sustainable development goals. Innovation in EV has gained the interest of many researchers since it is one of the novel green transportation sectors. Moreover, EVs are essential to preserve conventional fuels and to maximize the utilization of renewable sources. Nevertheless, EVs have short driving ranges due to their battery limitation, which hinders the reliability. The charging stations (CS) for EVs are also unevenly distributed. This paper presents a novel strategy to schedule the charging points in EV CSs. The goal is to determine the convenient CS for EVs through Vehicular Ad‐hoc Network (VANET) model. In this model, the CSs are determined and prioritized using four phases, such as driving, charge planning, charging scheduling, and battery charging. Charging scheduling was designed using a newly developed optimization strategy, exponential Harris Hawks optimization (Exponential HHO) algorithm, which combines two algorithms, Harris Hawks optimization (HHO) and exponential weighted moving average (EWMA). Furthermore, the fitness function was also newly devised by considering parameters such as average waiting time, remaining energy, number of EVs, and distance. The proposed Exponential HHO was validated using VANET simulation and the performance was improved with maximum remaining energy of 52.709 Whr, minimal distance of 27.256 km, and a maximum average waiting time of 0.352 min in comparison with existing methods. To be specific, the proposed Exponential HHO yielded better improvement, especially when considering a large number of vehicles. [ABSTRACT FROM AUTHOR]

Aimed at the problem of order determination of short-term power consumption in a time series model, a new method was proposed to determine the order p and the moving average q of the ARMA model by particle swarm optimization (PSO).According to the difference between the predicted value and the real value of the ARMA model, the fitness function of the particle swarm optimization algorithm is constructed, while the optimal solution which satisfies the ARMA model is confirmed by adjusting the inertia weight, population size, particle velocity, and iteration number. Finally, SVR regression is performed by using a support vector machine to correct the residual sequence obtained after the prediction of ARMA. The final prediction result is obtained by adding the predicted values and corrected residual. Based on the data of historical electricity load of a residential district in 2016~2017, the proposed method is compared with the traditional models. The result of the use of MATLAB simulation shows that the method is simple and feasible, greatly improves the model prediction accuracy, and implements the new method for short-term load forecasting of a small sample. [ABSTRACT FROM AUTHOR]

A lack of adequate consideration of underwater image enhancement gives room for more research into the field. The global background light has not been adequately addressed amid the presence of backscattering. This paper presents a technique based on pixel differences between global and local patches in scene depth estimation. The pixel variance is based on green and red, green and blue, and red and blue channels besides the absolute mean intensity functions. The global background light is extracted based on a moving average of the impact of suspended light and the brightest pixels within the image color channels. We introduce the block-greedy algorithm in a novel Convolutional Neural Network (CNN) proposed to normalize different color channels' attenuation ratios and select regions with the lowest variance. We address the discontinuity associated with underwater images by transforming both local and global pixel values. We minimize energy in the proposed CNN via a novel Markov random field to smooth edges and improve the final underwater image features. A comparison of the performance of the proposed technique against existing state-of-the-art algorithms using entropy, Underwater Color Image Quality Evaluation (UCIQE), Underwater Image Quality Measure (UIQM), Underwater Image Colorfulness Measure (UICM), and Underwater Image Sharpness Measure (UISM) indicate better performance of the proposed approach in terms of average and consistency. As it concerns to averagely, UICM has higher values in the technique than the reference methods, which explainsits higher color balance. The μ values of UCIQE, UISM, and UICM of the proposed method supersede those of the existing techniques. The proposed noted a percent improvement of 0.4%, 4.8%, 9.7%, 5.1% and 7.2% in entropy, UCIQE, UIQM, UICM and UISM respectively compared to the best existing techniques. Consequently, dehazed images have sharp, colorful, and clear features in most images when compared to those resulting from the existing state-of-the-art methods. Stable σ values explain the consistency in visual analysis in terms of sharpness of color and clarity of features in most of the proposed image results when compared with reference methods. Our own assessment shows that only weakness of the proposed technique is that it only applies to underwater images. Future research could seek to establish edge strengthening without color saturation enhancement. [ABSTRACT FROM AUTHOR]

The willow tree algorithm, first developed by Curran in 1998, provides an efficient option pricing procedure. However, it leads to a large bias through Curran’s sampling strategy when the number of points at each time step is not large. Thus, in this paper, a new sampling strategy is proposed. Compared with Curran’s sampling strategy, the new strategy gives a much better estimation of the standard normal distribution with a small number of sampling points. We then apply the willow tree algorithm with the new sampling strategy to price path-dependent options such as American, Asian and American moving-average options. The numerical results illustrate that the willow tree algorithm is much more efficient than the least-squares Monte Carlo method and binomial tree method with higher precision. [ABSTRACT FROM AUTHOR]

Rerouting has become an important challenge to Wireless Multimedia Sensor Networks (WMSNs) due to the constraints on energy, bandwidth, and computational capabilities of sensor nodes and frequent node and link failures. In this paper, we propose a traffic prediction-based fast rerouting algorithm for use between the cluster heads and a sink node in WMSNs (TPFR). The proposed algorithm uses the autoregressive moving average (ARMA) model to predict a cluster head's network traffic. When the predicted value is greater than the predefined network traffic threshold, both adaptive retransmission trigger (ART) that contributes to switch to a better alternate path in time and trigger efficient retransmission behaviors are enabled. Performance comparison of TPFR with ant-based multi-QoS routing (AntSensNet) and power efficient multimedia routing (PEMuR) shows that they: (a) maximize the overall network lifespan by load balancing and not draining energy from some specific nodes, (b) provide high quality of service delivery for multimedia streams by switching to a better path towards a sink node in time, (c) reduce useless data retransmissions when node failures or link breaks occur, and (d) maintain lower routing overhead. [ABSTRACT FROM AUTHOR]

In this paper we study parameter estimation problems for the output error moving average systems. In order to reduce calculation loads of the existing identification methods, an interactive stochastic gradient (ISG) algorithm is presented to estimate the parameters of the system model and the noise model, respectively, based on the interactive estimation theory. Since the ISG algorithm possesses a slow convergence rate and poor estimation accuracy, interactive gradient-based and interactive least-squares-based iterative algorithms are derived to enhance the parameter estimation performances of the ISG algorithm. The simulation results illustrate the effectiveness of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

Monitoring driver attention using the gaze estimation is a typical approach used on road scenes. This indicator is of great importance for safe driving, specially on Level 3 and Level 4 automation systems, where the take over request control strategy could be based on the driver's gaze estimation. Nowadays, gaze estimation techniques used in the state-of-the-art are intrusive and costly, and these two aspects are limiting the usage of these techniques on real vehicles. To test this kind of application, there are some databases focused on critical situations in simulation, but they do not show real accidents because of the complexity and the danger to record them. Within this context, this paper presents a low-cost and non-intrusive camera-based gaze mapping system integrating the open-source state-of-the-art OpenFace 2.0 Toolkit to visualize the driver focalization on a database composed of recorded real traffic scenes through a heat map using NARMAX (Nonlinear AutoRegressive Moving Average model with eXogenous inputs) to establish the correspondence between the OpenFace 2.0 parameters and the screen region the user is looking at. This proposal is an improvement of our previous work, which was based on a linear approximation using a projection matrix. The proposal has been validated using the recent and challenging public database DADA2000, which has 2000 video sequences with annotated driving scenarios based on real accidents. We compare our proposal with our previous one and with an expensive desktop-mounted eye-tracker, obtaining on par results. We proved that this method can be used to record driver attention databases. [ABSTRACT FROM AUTHOR]

A low-cost machine learning (ML) algorithm is proposed and discussed for spatial tracking of unknown, correlated signals in localized, ad-hoc wireless sensor networks. Each sensor is modeled as one neuron and a selected subset of these neurons are called to identify the spatial signal. The algorithm is implemented in two phases of spatial modeling and spatial tracking. The spatial signal is modeled using its M iso-contour lines at levels { ℓ j } j = 1 M and those sensors that their sensor observations are in Δ margin of any of these levels report their sensor observations to the fusion center (FC) for spatial signal reconstruction. In spatial modeling phase, the number of these contour lines, their levels and a proper Δ are identified. In this phase, the algorithm may either use adaptive-weight stochastic gradient or scaled stochastic gradient method to select a proper Δ. Additive white Gaussian noise (AWGN) with zero mean is assumed along with the sensor observations. To reduce the observation noise's effect, each sensor applies moving average filter on its observation to drastically reduce the effect of noise. The modeling performance, the cost and the convergence of the algorithm are discussed based on extensive computer simulations and reasoning. The algorithm is proposed for climate and environmental monitoring. In this paper, the percentage of wireless sensors that initiate a communication attempt is assumed as cost. The performance evaluation results show that the proposed spatial tracking approach is low-cost and can model the spatial signal over time with the same performance as that of spatial modeling. [ABSTRACT FROM AUTHOR]

In this paper, we propose a prediction algorithm, the combination of Long Short-Term Memory (LSTM) and attention model, based on machine learning models to predict the vision coordinates when watching 360-degree videos in a Virtual Reality (VR) or Augmented Reality (AR) system. Predicting the vision coordinates while video streaming is important when the network condition is degraded. However, the traditional prediction models such as Moving Average (MA) and Autoregression Moving Average (ARMA) are linear so they cannot consider the nonlinear relationship. Therefore, machine learning models based on deep learning are recently used for nonlinear predictions. We use the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network methods, originated in Recurrent Neural Networks (RNN), and predict the head position in the 360-degree videos. Therefore, we adopt the attention model to LSTM to make more accurate results. We also compare the performance of the proposed model with the other machine learning models such as Multi-Layer Perceptron (MLP) and RNN using the root mean squared error (RMSE) of predicted and real coordinates. We demonstrate that our model can predict the vision coordinates more accurately than the other models in various videos. [ABSTRACT FROM AUTHOR]

Weighted moving average (WMA) is extensively used for short term forecasting to remove random fluctuations in cyclical data. However, it is susceptible to the number of previous observations (window size), used to predict the next element. A large value of window size increases the model risk whereas a smaller one poses parameter risk. For training data size of N, checking the window size ranging from 2 to N-1 needs O(N 3 ) time. In this paper, we propose MRF (Map Reduce based forecasting algorithm), which checks the possible window values in parallel and reduces the time complexity to O(N 2 ). The algorithm meticulously designs < key, value > for each window size, which distributes the job equally in the reduce phase. We have shown MRF to be the correct implementation of sequential algorithm. Extensive experiments were performed on MRF. The predicted values for real life meteorological and computing datasets generated using MRF are highly correlated to actual ones. Real and predicted are also validated using paired sample t tests. Results on synthetic data exhibits scalability of MRF algorithm. [ABSTRACT FROM AUTHOR]

There are many algorithms that can be used for the time-series forecasting problem, ranging from simple (e.g. Moving Average) to sophisticated Machine Learning approaches (e.g. Neural Networks). Most of these algorithms require a number of user-defined parameters to be specified, leading to exponential explosion of the space of potential solutions. Since the trial-and-error approach to finding a good algorithm for solving a given problem is typically intractable, researchers and practitioners need to resort to a more intelligent search strategy, with one option being to constraint the search space using past experience - an approach known as Meta-learning. Although potentially attractive, Meta-learning comes with its own challenges. Gathering a sufficient number of Meta-examples, which in turn requires collecting and processing multiple datasets from each problem domain under consideration is perhaps the most prominent issue. In this paper, we are investigating the situations in which the use of additional data can improve performance of a Meta-learning system, with focus on cross-domain transfer of Meta-knowledge. A similarity-based cluster analysis of Meta-features has also been performed in an attempt to discover homogeneous groups of time-series with respect to Meta-learning performance. Although the experiments revealed limited room for improvement over the overall best base-learner, the Meta-learning approach turned out to be a safe choice, minimizing the risk of selecting the least appropriate base-learner. [ABSTRACT FROM AUTHOR]

This paper considers the combined parameter and state estimation problem of a bilinear state space system with moving average noise. There are product terms of state variables and control variables in bilinear systems, which brings difficulties to parameter and state estimation. By designing a bilinear state estimator based on Kalman filter and using input–output data to estimate the state, a hierarchical multi-innovation stochastic gradient (i.e., H-MISG) algorithm based on the state estimator is proposed to jointly estimate unknown states and parameters. In addition, compared with the hierarchical stochastic gradient algorithm, H-MISG algorithm introduces the innovation length parameter, makes full use of the system input and output data information, and improves the accuracy of parameter estimation. Numerical simulation examples verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

To solve the problem of secondary path mutation and external disturbance abrupt changes during helicopter maneuver flight, the previous research proposed a hybrid active vibration control law. To improve the engineering applicability, the original algorithm is ameliorated to the least mean square-input-output-based robust (LMS-IOBR) algorithm. The system model within the target frequency band can be identified through the input-output data to avoid constructing complex state observers. In addition, the output form of the feedback controller is constructed by an autoregressive moving average model with extra input, which is beneficial to improve operational efficiency. Numerical simulations demonstrate that compared with the original algorithm, controller real-time computation can be reduced by 52% with control effects guaranteed at the same time. Furthermore, to verify the effectiveness and adaptability of LMS-IOBR, multi-input multioutput vibration control experiments are carried out on a specially developed simple platform for simulating helicopter maneuver states. Comparative tests in various typical states are performed between the LMS-IOBR and the multichannel least mean square algorithm. Under the complex circumstances of simulating continuous subduction uplift, the peak response of closed-loop system attenuates by 80% and 70%, and the vibration of two points is reduced to 15% and 20%, respectively, within 3 s. The experimental results demonstrate that the proposed LMS-IOBR algorithm shows stronger transient adaptability and robustness against external disturbance excitation and secondary channel mutation in helicopter maneuver flight. [ABSTRACT FROM AUTHOR]

In this article, we present a synthetic inertia (SI) algorithm that allows for the simulation of the inertia response of a traditional generator to an electrical power system. To obtain the algorithm, detailed dynamic calculations were performed using a large real-system dynamic model in Siemens PSS/E modeling packages (PSS/E). Output error (OE), autoregressive moving average model with exogenous inputs (ARMAX), and Box–Jenkins (BJ) models of parametric identification were used to obtain the SI algorithm. The dynamic calculation results such as active power output, frequency variation in the presence of the active power deficit, surplus, and short circuit in the power system were used to compare the algorithm accuracy with comparable generator results. For this purpose, the power system stabilizer (PSS) and the turbine governor were not evaluated to obtain the most accurate possible active power change due to the characteristics of the generator. The errors were evaluated by using the models to determine the error estimates for the correlation coefficient (Ryŷ), root mean square deviation (RMSE), and coefficient of determination (R2). Based on the obtained results, we established that the OE mathematical model should be used, as it is more efficient compared to the ARMAX and BJ models. [ABSTRACT FROM AUTHOR]

Accurate and fast synchrophasor measurement is the key to the wide applications of PMUs in the system-wide monitoring and reliable operation of smart grid. To address this issue, a fixed length moving average filter-based synchrophasor measurement algorithm for P class phasor measurement units (PMUs) (FA-PSMA) is proposed in this paper. Firstly, a novel fixed length adaptive moving average filter (FAMAF) is proposed. The FAMAF has an adaptive filter capability with a fixed data window length. Then, the FAMAF is applied after a phase-locked loop (PLL) for enhanced disturbance rejection capability under frequency drifts. Finally, a detailed performance assessment is presented to validate the performance of the proposed FA-PSMA. Theoretical analysis and simulation results validate that the proposed FA-PSMA can track the grid frequency and phasor accurately under distorted grid conditions. The response time and measurement accuracy satisfy the requirements specified in IEC/IEEE 60255-118-1. [ABSTRACT FROM AUTHOR]

This article considers the parameter estimation problems of two‐input single‐output Hammerstein output‐error moving average systems. The system is decomposed into two subsystems based on the hierarchical principle. The first model is used to identify the linear parameters and the parameters of the unknown measurable information vector. The second model is for identifying non‐linear parameters. By using the auxiliary model, we introduce a forgetting factor to improve the parameter estimation accuracy. The auxiliary model‐based forgetting factor recursive least squares algorithm and the auxiliary model‐based forgetting factor multi‐innovation recursive least squares algorithm are presented. The simulation results indicate that the proposed algorithms are effective. [ABSTRACT FROM AUTHOR]

Multifractal detrended fluctuation analysis (MFDFA) and multifractal detrended moving average (MFDMA) algorithm have been established as two important methods to estimate the multifractal spectrum of the one-dimensional random fractal signal. They have been generalized to deal with two-dimensional and higher-dimensional fractal signals. This paper gives a brief introduction of the two-dimensional multifractal detrended fluctuation analysis (2D-MFDFA) and two-dimensional multifractal detrended moving average (2D-MFDMA) algorithm, and a detailed description of the application of the two-dimensional fractal signal processing by using the two methods. By applying the 2D-MFDFA and 2D-MFDMA to the series generated from the two-dimensional multiplicative cascading process, we systematically do the comparative analysis to get the advantages, disadvantages and the applicabilities of the two algorithms for the first time from six aspects such as the similarities and differences of the algorithm models, the statistical accuracy, the sensitivities of the sample size, the selection of scaling range, the choice of the q -orders and the calculation amount. The results provide a valuable reference on how to choose the algorithm from 2D-MFDFA and 2D-MFDMA, and how to make the schemes of the parameter settings of the two algorithms when dealing with specific signals in practical applications. [ABSTRACT FROM AUTHOR]

T-wave alternans (TWA) is hypothesized to be related with patients at an increased risk for ventricular arrhythmias. A novel TWA assessment method based on least squares curve fitting techniques (LSCF) is presented in this paper. Real clinical ECG recordings and simulated ECG signals containing additive TWAs and noise are used to test the efficiency of the LSCF. The results are also compared with those by the Modified Moving Average (MMA) algorithm. The experimental results demonstrate that the LSCF is efficiency and promising in detecting and measuring TWAs. [ABSTRACT FROM AUTHOR]

Radar target characteristics need to be accurately extracted to enhance the role of high-frequency (HF) radar target recognition technology in modern radar, sea and air monitoring, and other applications. The pole characteristics of radar targets have become a mainstream research focus because of their inherent advantages for target recognition. However, existing pole extraction methods for complex targets generally have problems in early- and late-time responses aliasing and target information loss. To avoid this problem, this study proposes a new method to extract radar target poles based on the special particle swarm optimization algorithm (SPSO) and an autoregressive moving average (ARMA) model. This method, which does not involve the distinction between early-and late-time responses, is used to estimate an approximation of the entire scattering echo of the target. Then the parameters of the model are precisely optimized with the help of a particle swarm optimization algorithm combined with opposition-based learning and inertia weight decreasing. Strategy. Owing to the characteristics of the azimuth consistency of the target poles, a sliding window is used to calibrate the positions of multi-azimuth poles in the complex plane. The method was demonstrated to be feasible with good performance when it was applied to extract the pole features of ships at different azimuths in the high-frequency band. [ABSTRACT FROM AUTHOR]