Search Results (84)

This paper presents a novel data-driven framework for real-time State of Charge (SOC) estimation in lithium-ion battery systems using a data-driven Prognostics and Health Management (PHM) approach. The method leverages an optimized bidirectional Long Short-Term Memory (Bi-LSTM) network, trained with enhanced datasets filtered via exponentially weighted moving averages (EWMAs) and refined through SHAP-based feature attribution. Compared against a Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) across ten diverse drive cycles, the proposed model consistently achieved superior performance, with mean absolute errors (MAEs) as low as 0.40%, outperforming EKF (0.66%) and UKF (1.36%). The Bi-LSTM model also demonstrated higher R² values (up to 0.9999) and narrower 95% confidence intervals, confirming its precision and robustness. Real-time implementation on embedded platforms yielded inference times of 1.3–2.2 s, validating its deployability for edge applications. The framework’s model-free nature makes it adaptable to other nonlinear, time-dependent systems beyond battery SOC estimation. Full article

Psychological momentum dynamics in tennis have triggered interest for a long time, but measuring their impact presents substantial obstacles. In this paper, we present an approach to quantify momentum that combines real-time winning probabilities, leverage, and an exponentially weighted moving average (EWMA). We test the method on a high-profile match between Carlos Alcaraz and Novak Djokovic, demonstrating how changes in leverage affect momentum. Furthermore, we use feature extraction methods from time series analysis to derive momentum-related characteristics, which are critical inputs for creating an eXtreme Gradient Boosting (XGBoost) binary classification model to predict game winners. The algorithm has an average accuracy of 84% and provides real-time predictions of each player’s chances of winning the match. Our findings indicate that momentum is a somewhat relevant element in forecasting match outcomes, highlighting its potential value in improving match prediction systems. Full article

Conventional vision-based sensors face limitations such as low update rates, restricted applicability, and insufficient robustness in dynamic environments with complex object motions. Single-pixel tracking systems offer high efficiency and minimal data redundancy by directly acquiring target positions without full-image reconstruction. This paper proposes a single-pixel detection system for adaptive multi-target tracking based on the geometric moment and the exponentially weighted moving average (EWMA). The proposed system leverages geometric moments for high-speed target localization, requiring merely $\mathit{3}\mathit{N}$ measurements to resolve centroids for $\mathit{N}$ targets. Furthermore, the output values of the system are used to continuously update the weight parameters, enabling adaptation to varying motion patterns and ensuring consistent tracking stability. Experimental validation using a digital micromirror device (DMD) operating at 17.857 kHz demonstrates a theoretical tracking update rate of 1984 Hz for three objects. Quantitative evaluations under 1920 × 1080 pixel resolution reveal a normalized root mean square error (NRMSE) of 0.00785, confirming the method’s capability for robust multi-target tracking in practical applications. Full article

This paper provides a comprehensive review of the Screening Robust Estimation Method (SREM) for normal processes in Phase I. Particular emphasis is placed on the exponentially weighted moving average (EWMA) control chart, which is employed in the retrospective monitoring stage. A central concern addressed in this discussion is that the EWMA chart is said to be based on the false alarm rate (FAR) design criterion, but it is accurately implemented. It is well established that FAR-based methods for Phase I monitoring assume that the distribution of the monitoring statistic remains invariant at each sampling instance. However, the EWMA statistic inherently introduces a sequential dependence among all sampling moments, which contradicts this assumption. This paper shows that a modified EWMA chart, incorporating probability-based control limits rather than the conventional formulation utilized in the SREM, enhances the monitoring of normal processes in Phase I. Through simulations, it is established that the new design proposal of an EWMA chart for Phase I monitoring has a slightly narrower decision threshold among the control methodologies included in the study. The new chart is as effective as the traditional $\overline{X}$ charts in detecting localized increases in the target value of the mean of a normal process, and outperforms them when other types of anomalies are present in the available preliminary samples. Full article

In crop growth, irrigation has to be adjusted according to developmental stages. Smart agriculture requires the accurate prediction of growth status and timely intervention to improve the quality of agricultural products, but this task faces significant challenges due to variable environmental factors. To address this issue, this study proposes a data-driven irrigation method to enhance crop yield. Our approach harvests extensive datasets to train and optimize an integrated deep-learning architecture combining Informer, Long Short-Term Memory (LSTM) networks, and Exponential Weighted Moving Average (EWMA) models. Controlled greenhouse experiments validated the reliability and practicality of the proposed prediction and intervention strategy. The results showed that the model accurately issued irrigation warnings 3–5 days in advance. Compared to traditional fixed irrigation, the model significantly reduced irrigation frequency while maintaining the same or even better growth conditions. In terms of plant quantity, the experimental group increased by 410.0%, while the control group grew by 50.0%. Additionally, the experimental group’s average plant height was 21.8% higher than that of the control group. These results demonstrate the efficacy of the proposed irrigation prediction method in enhancing crop growth and yield, providing a novel strategy for future agricultural planning and management. Full article

Predicting asset return volatility is one of the central problems in quantitative finance. These predictions are used for portfolio construction, calculation of value at risk (VaR), and pricing of derivatives such as options. Classical methods of volatility prediction utilize historical returns data and include the exponentially weighted moving average (EWMA) and generalized autoregressive conditional heteroskedasticity (GARCH). These approaches have shown significantly higher rates of predictive accuracy than corresponding methods of return forecasting, but they still have vast room for improvement. In this paper, we propose and test several methods of volatility forecasting on the S&P 500 Index using tree ensembles from machine learning, namely random forest and gradient boosting. We show that these methods generally outperform the classical approaches across a variety of metrics on out-of-sample data. Finally, we use the unique properties of tree-based ensembles to assess what data can be particularly useful in predicting asset return volatility. Full article

This study introduces an innovative two-stage framework for monitoring and diagnosing high-dimensional data streams with sparse changes. The first stage utilizes an exponentially weighted moving average (EWMA) statistic for online monitoring, identifying change points through extreme value theory and multiple hypothesis testing. The second stage involves a fault diagnosis mechanism that accurately pinpoints abnormal components upon detecting anomalies. Through extensive numerical simulations and electron probe X-ray microanalysis applications, the method demonstrates exceptional performance. It rapidly detects anomalies, often within one or two sampling intervals post-change, achieves near 100% detection power, and maintains type-I error rates around the nominal 5%. The fault diagnosis mechanism shows a 99.1% accuracy in identifying components in 200-dimensional anomaly streams, surpassing principal component analysis (PCA)-based methods by 28.0% in precision and controlling the false discovery rate within 3%. Case analyses confirm the method’s effectiveness in monitoring and identifying abnormal data, aligning with previous studies. These findings represent significant progress in managing high-dimensional sparse-change data streams over existing methods. Full article

A new statistical control chart denoted as CHEWMA (Cultural Heritage EWMA) is proposed for microclimate monitoring in preventive conservation. This tool is a real-time detection method inspired by the EN 15757:2010 standard, serving as an alternative to its common adaptations. The proposed control chart is intended to detect short-term fluctuations (STFs) in temperature (T) and relative humidity (RH), which would enable timely interventions to mitigate the risk of mechanical damage to collections. The CHEWMA chart integrates the Exponentially Weighted Moving Average (EWMA) control chart with a weighting mechanism that prioritizes fluctuations occurring near extreme values. The methodology was validated using RH time series recorded by seven dataloggers installed at the Alava Fine Arts Museum, and, from these, seventy simulated time series were generated to enhance the robustness of the analyses. Sensitivity analyses demonstrated that, for the studied dataset, the CHEWMA chart exhibits stronger similarity to the application of EN 15757:2010 than other commonly used real-time STF detection methods in the literature. Furthermore, it provides a flexible option for real-time applications, enabling adaptation to specific conservation needs while remaining aligned with the general framework established by the standard. To the best of our knowledge, this is the first statistical process control chart designed for the field of preventive conservation of cultural heritage. Beyond assessing CHEWMA’s performance, this study reveals that, when adapting the procedures of the European norm by developing a new real-time approach based on a simple moving average (herein termed SMA-FT), a window of approximately 14 days is more appropriate for STF detection than the commonly assumed 30-day period in the literature. Full article

The Statistical Process Control (SPC) approach using mathematical modeling proves effective for correlated data, with applications in healthcare, finance, and technology to enhance quality and efficiency. Here, we provide a novel SPC method using mathematical modeling and discuss its use in simulation tests to assess its applicability for tracking processes containing correlated data operating on sophisticated control charts. Particularly, an approach for detecting small shifts in the mean of a process running on the double-modified exponentially weighted moving average (DMEWMA) control chart, which is symmetric about the center line with upper and lower control limits, is of special interest. The computations showed exceptional accuracy, with ARL from the explicit formula closely matching that from the NIE method. Simulation tests assess its applicability in detecting small mean shifts and compare its performance with exponentially weighted moving average (EWMA) and modified exponentially weighted moving average (MEWMA) control charts across various scenarios. For several values of the design parameters, the performances of these three control charts are also compared in terms of the relative average index and relative standard deviation index. The results show that the DMEWMA chart outperforms others for several process mean shifts. The method’s practical use is demonstrated with stock data, highlighting its superior effectiveness in enhancing process monitoring. Full article

Control charts are frequently used instruments for process quality monitoring. Another name for the NEEWMA control chart is the new extended exponentially weighted moving average (new extended EWMA) control chart. The lower control limit (LCL) and upper control limit (UCL) are equally spaced from the center line, giving it a symmetrical design. Because of its symmetry, the NEEWMA chart is very good at identifying even the tiniest changes in operation by detecting deviations from the target in both upward and downward directions. This study derives an explicit formula for the average run length (ARL) of the NEEWMA control chart based on the autoregressive (AR) model with exponential white noise. The focus is on the zero-state performance of the NEEWMA control chart, which is derived using explicit formulas. Banach’s fixed-point theorem was used to prove existence and uniqueness of this formula. The accuracy of this formula is validated by comparing it to the numerical integral equation (NIE) method using percentage accuracy (%Acc). The results show that the NEEWMA control chart is more efficient than the ARL evaluated by the NIE method, particularly regarding computation time. The performance of the NEEWMA control chart is compared with the EWMA and extended EWMA control charts by evaluating both the ARL and standard deviation run length (SDRL). The NEEWMA control chart outperforms the others in detection performance, followed by the extended EWMA and EWMA control charts. Further verification of its superior performance is provided through comparisons using the average extra quadratic loss (AEQL) and the performance comparison index (PCI), which confirm that it outperforms both the EWMA and extended EWMA control charts across various parameters and shift sizes. Finally, an illustrative example using real-life economic data demonstrates its efficiency. Full article

The global outbreak of coronavirus disease 2019 (COVID-19) has posed a severe threat to public health and caused widespread socioeconomic disruptions in the past several years. While the pandemic has subsided, it is essential to explore effective disease surveillance tools to aid in controlling future pandemics. Several studies have proposed methods to capture the epidemic trend and forecast new daily confirmed cases. In this study, we propose the use of exponentially weighted moving average (EWMA) control charts integrated with time series models to monitor the number of daily new confirmed cases of COVID-19. The conventional EWMA control chart directly monitors the number of daily new confirmed cases. The proposed methods, however, monitor the residuals of time series models fitted to these data. In this study, two time series models—the auto-regressive integrated moving average (ARIMA) model and the vector auto-regressive moving average (VARMA) model—are considered. The results are compared with those of the conventional EWMA control chart using three datasets from India, Malaysia, and Thailand. The findings demonstrate that the proposed method can detect disease outbreak signals earlier than conventional control charts. Full article

High-quality processes, characterized by low defect rates, typically exhibit an exponential distribution for time-between-events (TBE) data. To effectively monitor TBE data, one-sided exponential Adaptive Exponentially Weighted Moving Average (AEWMA) schemes are introduced. However, the run length (RL) distribution varies with the magnitude of the process mean shift, rendering the median run length (MRL) a more pertinent performance metric. This paper investigates the RL properties of one-sided exponential AEWMA schemes using a Markov chain method. An optimal design procedure based on MRL is developed to enhance the one-sided exponential AEWMA scheme. Comparative analyses reveal that the one-sided exponential AEWMA scheme provides better balanced protection against both minor and major shifts in the process mean compared to EWMA-type and Shewhart schemes. Finally, two practical case studies illustrate the application of AEWMA schemes in manufacturing. Full article

In this work, a novel model for hourly PM2.5 time series imputation is proposed for the estimation of missing values in different gap sizes, including 1, 3, 6, 12, and 24 h. The proposed model is based on statistical techniques such as moving averages, linear interpolation smoothing, and linear interpolation. For the experimentation stage, two datasets were selected in Ilo City in southern Peru. Also, five benchmark models were implemented to compare the proposed model results; the benchmark models include exponential weighted moving average (EWMA), autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU). The results show that, in terms of average MAPEs, the proposed model outperforms the best deep learning model (GRU) between 26.61% and 90.69%, and the best statistical model (ARIMA) between 2.33% and 6.67%. So, the proposed model is a good alternative for the estimation of missing values in PM2.5 time series. Full article

When using the Transformer model for wind power prediction, the presence of noise in wind power data and the model’s final layer relying solely on a simple linear output reduces the model’s ability to capture nonlinear relationships, leading to a decrease in prediction accuracy. To address these issues, this paper proposes an ultrashort-term wind power prediction model based on exponential weighted moving average (EWMA) data processing and Kolmogorov–Arnold Network (KAN)-Transformer. First, multiple variable features are smoothed using EWMA, which suppresses noise while preserving the original data trends. Then, the EWMA-processed data is input into the Encoder and Decoder modules of the Transformer model to extract features. The output from the Decoder layer is then passed through the KAN layer, built using a cubic B-spline function, to enhance the model’s ability to capture nonlinear relationships, thereby improving the prediction accuracy of the Transformer model for wind power. Finally, experimental analysis is conducted, and it shows that the proposed model achieves the highest prediction accuracy, with a mean absolute error of 4.38 MW, a root mean squared error of 7.37 MW, and a coefficient of determination of 98.73%. Full article

The prediction of patient attendance in emergency departments (ED) is crucial for effective healthcare planning and resource allocation. This paper proposes an early warning system that can detect emerging trends in ED attendance, offering timely alerts for proactive operational planning. Over 13 years of historical ED attendance data (from January 2010 till December 2022) with 1,700,887 data points were used to develop and validate: (1) a Seasonal Autoregressive Integrated Moving Average with eXogenous factors (SARIMAX) forecasting model; (2) an Exponentially Weighted Moving Average (EWMA) surge prediction model, and (3) a trend persistence prediction model. Drift detection was achieved with the EWMA control chart, and the slopes of a kernel-regressed ED attendance curve were used to train various machine learning (ML) models to predict trend persistence. The EWMA control chart effectively detected significant COVID-19 events in Singapore. The surge prediction model generated preemptive signals on changes in the trends of ED attendance over the COVID-19 pandemic period from January 2020 until December 2022. The persistence of novel trends was further estimated using the trend persistence model, with a mean absolute error of 7.54 (95% CI: 6.77–8.79) days. This study advanced emergency healthcare management by introducing a proactive surge detection framework, which is vital for bolstering the preparedness and agility of emergency departments amid unforeseen health crises. Full article