This paper proposes an effective Photovoltaic (PV) Power Forecasting (PVPF) technique based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with exogenous input (NARXNN) Long Short-Term Memory (LSTM) model. First, the NARXNN model acquires data to generate a residual error vector. Then, stacked LSTM model, optimized by Tabu search algorithm, uses correction associated original produce point and interval PVPF. The performance of proposed PVPF was investigated...

There are a variety of maximum power point tracking (MPPT) algorithms for improving the energy efficiency solar photovoltaic (PV) systems. The mode implementation (digital or analog), design simplicity, sensor requirements, convergence speed, range efficacy, and hardware costs primary distinctions between these algorithms. Selecting an appropriate algorithm is critical users, as it influences electrical PV systems lowers by reducing number panels required to achieve desired output. This...

Short-term Photovoltaic (PV) Power Forecasting (STPF) is considered a topic of utmost importance in smart grids. The deployment STPF techniques provides fast dispatching the case sudden variations due to stochastic weather conditions. This paper presents an efficient data-driven method based on enhanced Random Forest (RF) model. proposed employs ensemble attribute selection manage bias/variance optimization for application and enhance forecasting quality results. overall architecture...

The recent COVID-19 pandemic has highlighted all the weaknesses of manufacturing systems and supply chains. In this challenging context, smallholders have faced several crises mainly related to difficulty finding manpower for harvesting activities impossibility distributing food, due forced closure many distribution channels. main consequences were lost sales wasted food. With aim increasing responsiveness in face COVID-like crises, paper provides an overview methodologies approaches...

Time-Frequency Distributions (TFDs) support the heart sound characterisation and classification in early cardiac screening. However, despite frequent use of TFDs signal analysis, no comprehensive study has been conducted to compare their performances deep learning for automatic diagnosis. This is first investigate optimal single/combined using learning. The main contribution this that it provides practical insights into selection as convolutional neural network (CNN) inputs design CNN...

Smart grid systems require an accurate energy prediction from renewable sources to ensure high sustainability and power quality. For PV plants, a precise estimation of the generated is crucial for reduction production/demand unbalance. This essential need comes variability weather parameters during electricity generation. Long Short Term Memory (LSTM) Gated Recurrent Unit (GRU) recurrent neural networks proved their efficiency in forecasting applications. Thus, this paper proposes...

<title>Abstract</title> The transition toward sustainable agri-food supply chains demands holistic strategies that harmonize environmental stewardship, economic viability, and social equity. Grounding our analysis in a systematic review of 75 peer-reviewed studies (2014–2024), we reveal how multi-capital integration the dynamic interplay natural, financial, social, human capitals—propels sustainability forward. Natural capital emerges as bedrock resilient systems, yet its potential remains...

This paper develops a novel approach to characterise muscle force from electromyography (EMG) signals, which are the electric activities generated by muscles. Based on nonlinear Hammerstein-Wiener model, first part of this study outlines estimation different sub-models mimic diverse profiles. The second fixes appropriate multimodel library and computes contribution estimate desired force. pre-existing dataset, obtained results show effectiveness proposed EMG signals with reasonable accuracy....

Despite standards on electrocardiogram (ECG) monitoring in medical diagnostics, signal acquisition is prone to noisy artifacts and relies greatly the quality of skin contact transducing interference. Electrodes, serving as indispensable conduits ECG acquisition, act crucial interface between human body recording instrumentation. The usage traditional gel electrodes may provoke irritation, by contrast, advent embroidered electrodes, a contemporary innovation, holds promise more comfort...

This paper proposes a new estimation scheme for both speed and acceleration signals in order to include them control loop dynamic system. The proposed consists improve higher sliding modes estimator where it can cope with measurement noise better than the classic one. Then, parameters are online updated respect measured signal variations reach good compromise between precision robustness. tuning formulas of gains developed by Lyapunov analysis. Simulation experimental results presented...

The high variability of weather parameters is making photovoltaic energy generation intermittent and narrowly controllable. Threatened by the sudden discontinuity between load grid, management for smart grid systems highly require an accurate PV power forecasting model. In this regard, Nonlinear autoregressive exogenous (NARX) one few potential models that handle time series analysis long-horizon prediction. This later efficient high-performing. However, model often suffers from vanishing...

This paper deals with online numerical differentiation of a noisy time signal where new higher order sliding mode differentiators are proposed. The key point these algorithms is to include dynamic on the differentiator parameters. These dynamics tune-up automatically algorithm gains in real-time. Convergence properties schemes derived using Lyapunov approach. Their effectiveness illustrated via simulations and experimental tests, comparative studies performed between classical ones. Such...

The integration of Photovoltaic (PV) systems requires the implementation potential PV power forecasting techniques to deal with high intermittency weather parameters. In prediction process, Genetic Programming (GP) based on Symbolic Regression (SR) model has a widespread deployment since it provides an effective solution for nonlinear problems. However, during training SR models might miss optimal solutions due large search space leaf generations. This paper proposes novel hybrid that...

Time-Frequency Distributions (TFDs) support the heart sound characterisation and classification in early cardiac screening. However, despite frequent use of TFDs signal analysis, no study comprehensively compared their performances on deep learning for automatic diagnosis. Furthermore, combination processing methods as inputs Convolutional Neural Networks (CNNs) has been proved a practical approach to increasing performance. Therefore, this aimed investigate optimal TFD/ combined input CNNs....

In this paper, a dynamic identification method for robot manipulator is investigated. A robust adaptive differentiator based on higher order sliding modes used to estimate the state of system. theoretical proof convergence given. frequency analysis in term filtering provided and comparison with filter classical carried out. The parameters are estimated using recursive least squares solution linear system obtained from sampling model along closed loop tracking trajectory. An experimental...

Despite standards on electrocardiogram (ECG) monitoring in medical diagnostics, signal acquisition is prone to noisy artefacts and rely greatly the quality of skin contact transducing interference. Electrodes, serving as indispensable conduits ECG acquisition, act crucial interface between human body recording instrumentation. The usage traditional gel electrodes may provoke irritation, by contrast, advent embroidered electrodes, a contemporary innovation, holds promise more comfort...

Temperature forecasting based on meteorological data is the key stage for an accurate estimation of PV power production and demand-side management leading to better grid stability. Typically, weather prediction parameters seconds until months ahead historical database. Thus, researchers create several approaches maximize accuracy these predictions increase period estimation. This paper proposes a new medium long-term temperature approach Multi Inputs Single Output (MISO) model base empirical...

This paper investigates the replacement of surface electrodes (physical sensors) measuring electrocardiogram (ECG) signals by forecasting linear algorithms. The aim is to test ability overcome loss information in case failure any electrode. From real ECG measured different auscultation sites, predict signal one site depending on another evaluated 3 methods. In this paper, based quantitative criteria, a comparative study between Linear regression (LR) model, K-nearest neighbors model (KNN)...

Electro-hydraulic actuators have been widely applied in industry with position or pressure control. Nevertheless, the conventional linear control technology is not able to satisfy requirement of improving precision due nonlinearities hydraulic systems. This paper presents two strategies based on intelligent-PI order track a electro- system. The aim here compare three controllers: proportional controller, free-model restricted model controller partial analysis obtained results shows that this...