Search Results (3,608)

This paper presents an innovative approach to wind energy forecasting through the implementation of an extended long short-term memory (xLSTM) model. This research addresses fundamental limitations in time-sequence forecasting for wind energy by introducing architectural enhancements to traditional LSTM networks. The xLSTM model incorporates two key innovations: exponential gating with memory mixing and a novel matrix memory structure. These improvements are realized through two variants, i.e., scalar LSTM and matrix LSTM, which are integrated into residual blocks to form comprehensive architectures. The xLSTM model was validated using SCADA data from wind turbines, with rigorous preprocessing to remove anomalous measurements. Performance evaluation across different wind speed regimes demonstrated robust predictive capabilities, with the xLSTM model achieving an overall coefficient of determination value of 0.923 and a mean absolute percentage error of 8.47%. Seasonal analysis revealed consistent prediction accuracy across varied meteorological patterns. The xLSTM model maintains linear computational complexity with respect to sequence length while offering enhanced capabilities in memory retention, state tracking, and long-range dependency modeling. These results demonstrate the potential of xLSTM for improving wind power forecasting accuracy, which is crucial for optimizing turbine operations and grid integration of renewable energy resources. Full article

In the present paper, we prove a new local convergence theorem with initial conditions and error estimates that ensure the Q-quadratic convergence of a modification of the famous Weierstrass method. Afterward, we prove a semilocal convergence theorem that is of great practical importance owing to its computable initial condition. The obtained theorems improve and complement all existing such kind of convergence results about this method. At the end of the paper, we provide three numerical examples to show the applicability of our semilocal theorem to some physics problems. Within the examples, we propose a new algorithm for the experimental study of the dynamics of the simultaneous methods and compare the convergence and dynamical behaviors of the modified and the classical Weierstrass methods. Full article

Energy management strategies (EMS) in the context of electric or hybrid vehicles can optimize the available energy by minimizing consumption. Most optimization-based EMS are not real-time-applicable for an accurate estimation of future consumption. The performance of these strategies also strongly depends on the driving patterns, which may be influenced by road and traffic conditions, among other factors such as driving style, weather, vehicle type, etc. The primary contribution of this work is to develop a novel two-layer driving pattern recognition (DPR) system for roadway type and traffic classification, thus enabling the identification of unknown patterns for the enhancement of the prediction of energy consumption of an electric vehicle (EV). The novelty of this work lies in the development of a strategy based on real-time data which is capable of classifying driving patterns and implementing an optimized EMS based on the results of the DPR. In the approach, first, labels are defined based on statistical features related to speed followed by the creation of representative driving patterns (RDPs). A neural network-based classifier is then employed for classification into six classes based on four features. A training accuracy of 97.7% is achieved with the classification of unknown speed profiles into the known RDPs. Testing with patterns from two different test routes shows an accuracy of 97.45% and 96.98% during morning and 96.65% and 94.12% during evening hours, respectively. Apart from the route and time of data collection, accuracy is also a function of sampling time horizon and the threshold values chosen for the features. A sensitivity analysis was also performed to evaluate the relative importance of each feature. An EMS based on sequential quadratic programming (SQP) was combined with DPR for the computation of optimal energy consumption. Simulation results show that maximum and minimum energy savings of 61% and 18% were obtained under suburban low traffic and highway high traffic conditions, respectively. An eco-driving or driver speed advisory system may further be developed based on information obtained from multiple routes and varying traffic scenarios. Full article

Parkinson’s disease lacks a cure, yet symptomatic relief can be achieved through various treatments. This study dives into the critical aspect of anomalous event detection in the activities of daily living of patients with Parkinson’s disease and the identification of associated movement disorders, such as tremors, dyskinesia, and bradykinesia. Utilizing the inertial data acquired from the most affected upper limb of the patients, this study aims to create an optimal pipeline for Parkinson’s patient monitoring. This study proposes a two-stage movement disorder detection and classification pipeline for binary classification (normal or anomalous event) and multi-label classification (tremors, dyskinesia, and bradykinesia), respectively. The proposed pipeline employs and evaluates manual feature crafting for classical machine learning algorithms, as well as an RNN-CNN-inspired deep learning model that does not require manual feature crafting. This study also explore three different window sizes for signal segmentation and two different auto-segment labeling approaches for precise and correct labeling of the continuous signal. The performance of the proposed model is validated on a publicly available inertial dataset. Comparisons with existing works reveal the novelty of our approach, covering multiple anomalies (tremors, dyskinesia, and bradykinesia) and achieving 93.03% recall for movement disorder detection (binary) and 91.54% recall for movement disorder classification (multi-label). We believe that the proposed approach will advance the field towards more effective and comprehensive solutions for Parkinson’s detection and symptom classification. Full article

Cardiovascular diseases (CVDs) are among the leading causes of mortality worldwide, particularly in Mexico, where rural regions face challenges due to limited access to medical equipment. This preliminary study proposes a low-cost cardiovascular disease classifier, Buazduino-001, which integrates machine learning (ML) techniques with Arduino-based technology to provide accessible and non-invasive risk assessment. Three classical ML models—logistic regression, random forest, and support vector machine—were implemented and evaluated using a dataset of 303 patients from the UCI Machine Learning Repository. This study introduces a six-stage methodology, including a novel step that prioritizes non-invasive attributes to optimize diagnostic time and cost. The random forest model demonstrated the best performance, achieving 87% classification accuracy, with a reduced feature set of five attributes (sex, age, chest pain, heart rate, and exercise-induced angina). In this preliminary study, the system was validated experimentally with 30 patients, confirming an 85% accuracy and an 80% reduction in diagnostic time compared to traditional medical assessments. The results highlight the practicality of combining ML with low-cost electronics to address healthcare gaps in resource-limited settings. While this study is preliminary, the Buazduino-001 system demonstrates potential for early CVD risk detection and could serve as a screening tool in rural clinics, complementing conventional diagnostic methods. Full article

It is known that fractional-order models can effectively represent complex high-order systems with fewer parameters. This paper focuses on the identification of a class of multiple-input single-output fractional Hammerstein models. When the commensurate order is assumed to be known, a greedy orthogonal least squares method is proposed to simultaneously identify the parameters and system orders, combined with a stopping rule based on the Bayesian information criterion. Subsequently, the commensurate order is determined by minimizing the normalized output error. The proposed method is validated by applying it to identify a CD-player arm system. Full article

Edge computing, characterized by its proximity to users and fast response times, is considered one of the key technologies for addressing low-latency demands in the future. An appropriate edge server deployment strategy can reduce costs for service providers and improve the quality of service for users. However, most previous studies have focused on server coverage or deployment solution consumption time, often neglecting the most critical aspect: minimizing user-request response latency. To address this, we propose an edge deployment strategy based on the queuing search algorithm (QSA), which models the edge deployment problem as a multi-constrained nonlinear optimization problem. The QSA mimics the logic of human queuing behavior and has the ability to perform faster global searches while avoiding local optima. Experimental results show that, compared to the genetic algorithm, simulated annealing algorithm, particle swarm optimization, and other recent algorithms, the average number of “hopping” iterations in QSA is 0.1 to 0.6 times fewer than in the other algorithms. Additionally, QSA is particularly suitable for edge computing environments with a large number of users and devices. Full article

This work introduces a novel and practical metaheuristic algorithm, the Gaslike Social Motility (GSM) algorithm, designed for optimization and image thresholding segmentation. Inspired by a deterministic model that replicates social behaviors using gaslike particles, GSM is characterized by its simplicity, minimal parameter requirements, and emergent social dynamics. These dynamics include: (1) attraction between similar particles, (2) formation of stable particle clusters, (3) division of groups upon reaching a critical size, (4) inter-group interactions that influence particle distribution during the search process, and (5) internal state changes in particles driven by local interactions. The model’s versatility, including cross-group monitoring and adaptability to environmental interactions, makes it a powerful tool for exploring diverse scenarios. GSM is rigorously evaluated against established and recent metaheuristic algorithms, including Particle Swarm Optimization (PSO), Differential Evolution (DE), Bat Algorithm (BA), Artificial Bee Colony (ABC), Artificial Hummingbird Algorithm (AHA), AHA with Aquila Optimization (AHA-AO), Colliding Bodies Optimization (CBO), Enhanced CBO (ECBO), and Social Network Search (SNS). Performance is assessed using 22 benchmark functions, demonstrating GSM’s competitiveness. Additionally, GSM’s efficiency in image thresholding segmentation is highlighted, as it achieves high-quality results with fewer iterations and particles compared to other methods. Full article

This article aims to examine the real-time precise point positioning (PPP) solution’s accuracy utilizing the low-cost dual-frequency multi-constellation U-blox ZED-F9P module and real-time GNSS orbit and clock products from five analysis centers, including Bundesamt für Kartographie und Geodäsie (BKG), Centre National d’Etudes Spatiales (CNES), International GNSS Service (IGS), Geo Forschungs Zentrum (GFZ), and GNSS research center of Wuhan University (WHU). Three-hour static quad-constellation GNSS measurements are collected from ZED-F9P modules and geodetic grade Trimble R4s receivers over a reference station in Aswan City, Egypt, for a period of three consecutive days. Since a multi-GNSS PPP processing model is applied in the majority of the previous studies, this study employs the single-constellation GNSS PPP solution to process the acquired datasets. Different single-constellation GNSS PPP scenarios are adopted, namely, GPS PPP, GLONASS PPP, Galileo PPP, and BeiDou PPP models. The obtained PPP solutions from the low-cost module are validated for the positioning and precipitable water vapor (PWV) domains. To provide a reference positioning solution, the post-processed dual-frequency geodetic-grade GNSS PPP solution is applied; additionally, as the station under investigation is not a part of the IGS reference station network, a new technique is proposed to estimate reference PWV values. The findings reveal that the GPS and Galileo 3D position’s accuracy is within the decimeter level, while it is within the meter level for both the GLONASS and BeiDou models. Additionally, millimeter-level PWV precision is obtained from the four PPP models. Full article

The large-scale integration of renewable energy and the serious safety issues faced by coal-fired units during peak regulation pose significant challenges to the reliable operation of power systems. Traditional probabilistic production simulation (PPS) methods fail to account for the fluctuations in load demand and the time-series variability of renewable energy, and they do not sufficiently consider the lifespan degradation of coal-fired units during actual peak regulation operations, which results in an inaccurate reflection of system reliability. This paper proposes an improved PPS method. First, this method rigorously considers the time-series fluctuations of load demand and renewable energy. Secondly, a multi-state model is established for coal-fired units, incorporating lifespan degradation and failure rates under different output conditions, which are dynamically updated based on load demand and operational status. An equivalent multi-state model for wind power output is also developed for different time periods. Finally, the Universal Generating Function (UGF) algorithm is used for PPS, enabling the calculation of system reliability indices, as well as dynamic costs such as unit start-up and shutdown, to assess the system’s capability to accommodate wind power. The impact of different peak regulation capabilities of units on system reliability is also studied. This paper presents the theoretical foundation, algorithm implementation, and related case studies, verifying the rationality and effectiveness of the proposed method in addressing the above-mentioned issues. Full article

Due to the challenges associated with the application of existing two-stage optimization methods in energy system capacity configuration, such as uncertainty scenario generation, multi-timescale coupling, and balancing economic and environmental benefits, this paper proposes a two-stage optimization configuration method based on Particle Swarm Optimization (PSO) for the capacity configuration of long-endurance hydrogen-powered hybrid unmanned aerial vehicles (UAVs). By constructing a hydrogen-powered hybrid UAV energy system model, an uncertainty model for the energy system, and multi-timescale comprehensive evaluation indicators and corresponding objective functions, the capacity configuration is determined using a two-stage stochastic programming model solved by CPLEX in MATLAB. The two-stage stochastic programming model consists of the first stage, which involves capacity optimization through PSO, and the second stage, which employs Monte Carlo method for random wind field sampling. The research provides a theoretical foundation for the application of the two-stage optimization capacity configuration method in the field of long-endurance hydrogen-powered hybrid UAVs. Full article

The incorporation of ${\mathsf{\ell }}_1$ regularization in Lasso regression plays a crucial role by inducing convexity to the objective function, thereby facilitating its minimization; when compared to non-convex regularization, the utilization of ${\mathsf{\ell }}_1$ regularization introduces bias through artificial coefficient shrinkage towards zero. Recently, the convex non-convex (CNC) regularization framework has emerged as a powerful technique that enables the incorporation of non-convex regularization terms while maintaining the overall convexity of the optimization problem. Although this method has shown remarkable performance in various empirical studies, its theoretical understanding is still relatively limited. In this paper, we provide a theoretical investigation into the prediction performance of the Lasso model with CNC sparse regularization. By leveraging oracle inequalities, we establish a tighter upper bound on prediction performance compared to the traditional ${\mathsf{\ell }}_1$ regularizer. Additionally, we propose an alternating direction method of multipliers (ADMM) algorithm to efficiently solve the proposed model and rigorously analyze its convergence property. Our numerical results, evaluated on both synthetic data and real-world magnetic resonance imaging (MRI) reconstruction tasks, confirm the superior effectiveness of our proposed approach. Full article

The application of connected automated vehicles (CAVs) provides new opportunities and challenges for optimizing and controlling urban intersections. To avoid collisions of vehicles in conflicting directions at intersections and improve the efficiency of intersections, an optimal scheduling model for CAVs at an unsignalized intersection is proposed. The model develops a linear programming model of intersection vehicle timing with the minimum average vehicle delay within the optimization time window as the optimization objective and the minimum safe time interval for vehicles to pass through the intersection as the constraint. A rolling optimization algorithm is designed to improve the efficiency of the algorithm solution. Finally, the effects of different traffic demand conditions on the results are investigated based on numerical simulation experiments. The results show that both the proposed algorithm and the Gurobi solver can significantly reduce the average vehicle delay compared with the first-come-first-served (FCFS) control method, and the proposed model and algorithm can reduce the average vehicle delay by 76.22% at most. Compared with the Gurobi solver, the proposed model and algorithm can reduce the solution time and ensure the optimization effect to the greatest extent. Therefore, the proposed model and algorithm provide theoretical support for managing CAVs at unsignalized intersections. Full article

The Cell Transmission Model (CTM) is a commonly used framework and cost-effective approach for evaluating transportation-related solutions, particularly for analyzing urban traffic congestion, due to its strong mathematical framework. Its effectiveness relies heavily on accuracy, making proper calibration essential for deriving reliable design decisions. This study utilizes CTM calibration techniques to design control strategies for mitigating accident-induced traffic congestion in two-way grid networks. By modifying the number of downstream cells and their vehicle capacity, we assess the impact of these adjustments on traffic flow efficiency within the grid structure. Additionally, we utilize MATLAB R2022a to design an intelligent transportation network simulation environment, providing a robust platform for testing and optimizing traffic management strategies specific to two-way grid networks. The findings of this research contribute to the introduction of a novel refinement to the traditional CTM by dividing only cell 9 into three smaller cells to accurately capture different movement directions, enhancing intersection modeling without increasing overall computational complexity. Full article

In this paper, a reliability assessment and prediction method based on bearing vibration signals is proposed, which combines Adaptive Cyclostationary Blind Deconvolution (ACYCBD) and AdaBoost-Mixed Kernel Relevance Vector Machine. Firstly, CYCBD parameters were optimized by the Ivy optimization algorithm to enhance the noise reduction effect, and then multidimensional features were extracted and dimensionalization was reduced by PaCMAP. Based on dimensionality reduction features, logistic regression was used to evaluate reliability, and AdaBoost-MKRVM was combined to predict reliability. The experimental results show that the mean absolute error (MAE) of the proposed method on the bearing life dataset of Xi’an Jiaotong University is 0.052, which is better than the traditional method, and provides a new idea for the performance prediction of rolling bearings. Full article