Search Results (7,157)

This paper delves into a comprehensive analysis of a generalized impulsive discrete reaction–diffusion system under periodic boundary conditions. It investigates the behavior of reactant concentrations through a model governed by partial differential equations (PDEs) incorporating both diffusion mechanisms and nonlinear interactions. By employing finite difference methods for discretization, this study retains the core dynamics of the continuous model, extending into a discrete framework with impulse moments and time delays. This approach facilitates the exploration of finite-time stability (FTS) and dynamic convergence of the error system, offering robust insights into the conditions necessary for achieving equilibrium states. Numerical simulations are presented, focusing on the Lengyel–Epstein (LE) and Degn–Harrison (DH) models, which, respectively, represent the chlorite–iodide–malonic acid (CIMA) reaction and bacterial respiration in Klebsiella. Stability analysis is conducted using Matlab’s LMI toolbox, confirming FTS at equilibrium under specific conditions. The simulations showcase the capacity of the discrete model to emulate continuous dynamics, providing a validated computational approach to studying reaction-diffusion systems in chemical and biological contexts. This research underscores the utility of impulsive discrete reaction-diffusion models for capturing complex diffusion–reaction interactions and advancing applications in reaction kinetics and biological systems. Full article

A major priority for practicing engineers in an electric power system is preserving the stability of frequency and voltage levels. Any change in these two factors will impact the efficiency and lifespan of the machines connected to the power supply. Therefore, this paper provides a control approach utilizing the Interval Type-2 Fuzzy Sets- Proportional Integral Derivative (IT2FSs-PID) controller and Advanced Thyristor Controlled Series Capacitor (ATCSC) with a combined Load Frequency Control-Automatic Voltage Regulator (LFC-AVR). Several inspections were implemented to demonstrate the controller’s strength, including various disturbances in the power system. The LFC-AVR was studied using two different dynamic models, referred to as open and closed loops on the Generation Rate Constraint (GRC) forms. A comparison was made using different techniques from the literature using the same model. Before using the approach, the frequency deviation of area-1 had a very large settling time value, which was caused by system instability. However, after implementing the approach, this value decreased to 4.9236 s. Finally, an additional ATCSC was added to the proposed model to observe its effect on the power system. The simulation was implemented using MATLAB/SIMULINK tools. Full article

When a multi-inverter grid-connected system is influenced by the parasitic parameters of LCL-type inverters and the impedance of the connected system’s lines, its resonance characteristics become more complex and difficult to predict. For LCL-type multi-inverter grid-connected systems, a mathematical model that considers the effects of parasitic parameters and line impedance has been established, leading to the derivation of the system’s Norton equivalent circuit and a general expression for the inverter output current. The resonance characteristics of multi-inverter grid-connected systems composed of inverters with the same and different parameters were analyzed under the influence of parasitic parameters and line impedance. To suppress the resonance in multi-inverter grid-connected systems and address the issue of traditional PI control not meeting grid requirements for LCL-type grid-connected inverters, a strategy combining superhelical sliding mode control with active damping was adopted. To verify the practical performance of the adopted resonance suppression strategy in complex environments, a grid-connected system model containing two LCL-type inverters was constructed using the MATLAB/Simulink software platform, followed by simulation analysis. The simulation results strongly confirm the feasibility and effectiveness of the adopted resonance suppression strategy, considering the effects of parasitic parameters and line impedance. This strategy demonstrates significant suppression effects in addressing resonance issues caused by parasitic parameters and line impedance, effectively enhancing the quality of grid-connected electrical energy. Full article

Urban airspace, characterized by densely packed high-rise buildings, presents complex and dynamically changing environmental conditions. It brings potential risks to UAV flights, such as the risk of collision and accidental entry into no-fly zones. Currently, mainstream path planning algorithms, including the PSO algorithm, have issues such as a tendency to converge to local optimal solutions and poor stability. In this study, an improved particle swarm optimization algorithm (LGPSO) is proposed to address these problems. This algorithm redefines path planning as an optimization problem, constructing a cost function that incorporates safety requirements and operational constraints for UAVs. Stochastic inertia weights are added to balance the global and local search capabilities. In addition, asymmetric learning factors are introduced to direct the particles more precisely towards the optimal position. An enhanced Lévy flight strategy is used to improve the exploration ability, and a greedy algorithm evaluation strategy is designed to evaluate the path more quickly. The configuration space is efficiently searched using the corresponding particle positions and UAV parameters. The experiments, which involved mapping complex urban environments with 3D modeling tools, were carried out by simulations in MATLAB R2023b to assess their algorithmic performance. The results show that the LGPSO algorithm improves by 23% over the classical PSO algorithm and 18% over the GAPSO algorithm in the optimal path distance under guaranteed security. The LGPSO algorithm shows significant improvements in stability and route planning, providing an effective solution for UAV path planning in complex environments. Full article

This paper presents a novel machine learning framework useful for optimizing energy consumption in households. Home appliances have a great potential to optimize electricity consumption by mitigating peaks in the grid load or peaks in renewable energy generation. However, such functionality of home appliances requires their users to change their behavior regarding energy consumption. One of the criteria that could encourage electricity users to change their behavior is the cost of energy. The introduction of dynamic energy prices can significantly increase energy costs for unsuspecting consumers. In order to be able to make the right decisions about the process of electricity use in households, an algorithm based on machine learning is proposed. The presented proposal for optimizing electricity consumption takes into account dynamic changes in energy prices, energy production from renewable energy sources, and home appliances that can participate in the energy optimization process. The proposed model uses data from smart meters and dynamic price information to generate personalized recommendations tailored to individual households. The algorithm, based on machine learning and historical household behavior data, calculates a metric to determine whether to send a notification (message) to the user. This notification may suggest increasing or decreasing energy consumption at a specific time, or may inform the user about potential cost fluctuations in the upcoming hours. This will allow energy users to use energy more consciously or to set priorities in home energy management systems (HEMS). This is a different approach than in previous publications, where the main goal of optimizing energy consumption was to optimize the operation of the power system while taking into account the profits of energy suppliers. The proposed algorithms can be implemented either in HEMS or smart energy meters. In this work, simulations of the application of machine learning with different characteristics were carried out in the MATLAB program. An analysis of machine learning algorithms for different input data and amounts of data and the characteristic features of models is presented. Full article

The Empatica EmbracePlus is a recent innovation in medical-grade wristband wearable sensors that enable unobtrusive continuous measurement of pulse rate, electrodermal activity, skin temperature, and various accelerometry-based actigraphy measures using a minimalistic smartwatch design. The advantage of this lightweight wearable is the potential for holistic longitudinal recording and monitoring of physiological processes that index a suite of autonomic functions, as well as to provide ecologically valid insights into human behaviour, health, physical activity, and psychophysiological processes. Given the longitudinal nature of wearable recordings, EmbracePlus data collection is managed by storing raw timeseries in short ‘chunks’ in avro file format organised by universal standard time. This is memory-efficient but requires programming expertise to compile the raw data into continuous file formats that can be processed using standard techniques. Currently, there are no accessible tools available to compile and analyse raw EmbracePlus data over user-defined time periods. To address that, we introduce the LOTUS toolkit, an open-source graphical user interface that allows users to reconstitute and process EmbracePlus datasets over select time intervals. LOTUS is available on GitHub, and currently allows users to compile raw EmbracePlus data into unified timeseries stored in more familiar Excel or Matlab file formats to facilitate signal processing and analysis. Future work will expand the toolkit to process Empatica E4 and other wearable signal data, while also integrating more sophisticated functions for feature extraction and analysis. Full article

In this work, we present various novelty methods by employing the fractional differential quadrature technique to solve the time and space fractional nonlinear Benjamin–Bona–Mahony equation and the Benjamin–Bona–Mahony–Burger equation. The novelty of these methods is based on the generalized Caputo sense, classical differential quadrature method, and discrete singular convolution methods based on two different kernels. Also, the solution strategy is to apply perturbation analysis or an iterative method to reduce the problem to a series of linear initial boundary value problems. Consequently, we apply these suggested techniques to reduce the nonlinear fractional PDEs into ordinary differential equations. Hence, to validate the suggested techniques, a solution to this problem was obtained by designing a MATLAB code for each method. Also, we compare this solution with the exact ones. Furthermore, more figures and tables have been investigated to illustrate the high accuracy and rapid convergence of these novel techniques. From the obtained solutions, it was found that the suggested techniques are easily applicable and effective, which can help in the study of the other higher-D nonlinear fractional PDEs emerging in mathematical physics. Full article

This article focuses on generalized octonions which include real octonions, split octonions, semi octonions, split semi octonions, quasi octonions, split quasi octonions and para octonions in special cases. We make a classification according to the inner product and vector parts and give the polar forms for lightlike generalized octonions. Furthermore, the matrix representations of the generalized octonions are given and some properties of these representations are achieved. Also, powers and roots of the matrix representations are presented. All calculations in the article are achieved by using MATLAB R2023a and these codes are presented with an illustrative example. Full article

This study presents the results of an original study on the influence of selected parameters on the thermal efficiency of a vertical ground heat exchanger (VGHE) in a ground-source heat pump (GSHP) system. The research objective was an analysis of the specific thermal efficiency of a vertical ground heat exchanger q, received by a U-shaped element made of plastic pipes placed in a borehole, depending on seven direct influencing factors: the ground temperature T_g; the soil thermal conductivity coefficient λ_g; the thermal conductivity coefficient of the well material λ_m; the temperature of the heating medium (glycol) T_w at the feed to the ground heat exchanger and its flow rate M; the internal diameter of the pipes of the ground heat exchanger d_w; and the distances between the external walls of the pipes of the ground heat exchanger L. The analysis was carried out for the climatic conditions of the Podlasie Voivodeship (Poland). Based on the results of the computational experiment obtained using the TRNSYS numerical environment, a deterministic mathematical model of this relationship was developed, and the effects of the influence of selected factors on the specific thermal efficiency q of the vertical ground heat exchanger, received by the U-shaped element, were analysed. Based on the model, the contribution of each parameter to the efficiency of the heat exchanger was determined. It turned out that changes in the values of the factors T_g (X₁), λ_g (X₂), λ_m (X₃), M (X₅), d_w (X₆) and L (X₇) from the lower to the upper level caused an increase in the specific efficiency q of the heat exchanger by 34.04, 7.90, 15.20, 55.42, 6.58 and 24.26%. Only factor T_w (X₄), with such a change, caused a decrease in the thermal efficiency of the tested heat exchanger by 44.22%. The parameters of the tested element of the geothermal heating system were also optimized according to the energy criterion using a numerical method in the Matlab environment. The information may be useful for scientists, designers, producers and consumers of heating systems based on heat pumps with a vertical ground heat exchanger as the lower heat source. Full article

In response to the widespread issue of fake comments on e-commerce platforms, this study aims to analyze and propose a blockchain-based solution to incentivize authentic user feedback and reduce the prevalence of fraudulent reviews. Specifically, this paper constructs a tripartite evolutionary game model between sellers, buyers, and e-commerce platforms to study the real comment mechanism of blockchain. The strategy evolution under different incentive factors is simulated using replication dynamic equation analysis and Matlab software simulation. The study found that introducing smart contracts and “tokens” for incentives not only increased incentives for real comments but also reduced the negative experiences caused by “speculative” sellers, thereby influencing buyers to opt for authentic reviews. By structuring interactions through blockchain, the mechanism helped lower informational entropy thus reducing disorder and unpredictability in buyer and seller behavior and contributing to system stability. Further, by increasing penalties for dishonest behavior under the “credit on the chain” system, the platform lowered entropy in the system by promoting trust and reducing fraudulent activities. The real comment mechanism based on blockchain proposed in this paper can effectively enhance the order and transparency within the comment ecosystem. These findings contribute to theory and practice by providing strategic insights for e-commerce platforms to encourage genuine feedback, reduce informational entropy, and mitigate fake comments, ultimately fostering a more reliable online marketplace. Full article

The abundance of powered semiconductor devices has increased with the introduction of renewable energy sources into the grid, causing power quality disturbances (PQDs). This represents a huge challenge for grid reliability and smart city infrastructures. Accurate detection and classification are important for grid reliability and consumers’ appliances in a smart city environment. Conventionally, power quality monitoring relies on trivial machine learning classifiers or signal processing methods. However, recent advancements have introduced Deep Convolution Neural Networks (DCNNs) as promising methods for the detection and classification of PQDs. These techniques have the potential to demonstrate high classification accuracy, making them a more appropriate choice for real-time operations in a smart city framework. This paper presents a voting ensemble approach to classify sixteen PQDs, using the DCNN architecture through transfer learning. In this process, continuous wavelet transform (CWT) is employed to convert one-dimensional (1-D) PQD signals into time–frequency images. Four pre-trained DCNN architectures, i.e., Residual Network-50 (ResNet-50), Visual Geometry Group-16 (VGG-16), AlexNet and SqeezeNet are trained and implemented in MATLAB, using images of four datasets, i.e., without noise, 20 dB noise, 30 dB noise and random noise. Additionally, we also tested the performance of ResNet-50 with a squeeze-and-excitation (SE) mechanism. It was observed that ResNet-50 with the SE mechanism has a better classification accuracy; however, it causes computational overheads. The classification performance is enhanced by using the voting ensemble model. The results indicate that the proposed scheme improved the accuracy (99.98%), precision (99.97%), recall (99.80%) and F1-score (99.85%). As an outcome of this work, it is demonstrated that ResNet-50 with the SE mechanism is a viable choice as a single classification model, while an ensemble approach further increases the generalized performance for PQD classification. Full article

Path planning and collision avoidance issues are key to the autonomous navigation of unmanned surface vehicles (USVs). This study proposes an adaptive differential evolution algorithm model integrated with the analytic hierarchy process (AHP-ADE). The traditional differential evolution algorithm is enhanced by introducing an elite archive strategy and adaptively adjusting the scale factor F and the crossover factor $CR$ to balance global and local search capabilities, preventing premature convergence and improving the search accuracy. Additionally, the collision risk index (CRI) model is optimized and combined with the quaternion ship domain, enhancing the precision of CRI calculations and USV autonomous collision avoidance capabilities. The improved CRI model, the International Regulations for Preventing Collisions at Sea, and the optimal collision avoidance distance were incorporated as evaluation factors in a fitness function assessment, with weights determined through the AHP to enhance the rationality and accuracy of the fitness function. The proposed AHP-ADE algorithm was compared with the improved particle swarm algorithm, and the performance of the algorithm was comprehensively evaluated using safety, economy, and operational efficiency. Simulation experiments on the MATLAB platform demonstrated that the proposed AHP-ADE algorithm exhibited better performance in scenarios involving multiple ship encounters, thus proving its effectiveness. Full article

The air void system in concrete significantly affects its mechanical, thermal, and frost durability properties. This study explored the use of ChatGPT, an AI tool, to generate Python code for analyzing air void parameters in hardened concrete, such as total air void content (A), specific surface (α), and air void spacing factor (L). Initially, Python scripts were created by requesting ChatGPT-3.5 to convert MATLAB scripts developed by Fonseca and Scherer in 2015. The results from Python closely matched those from MATLAB when applied to polished sections of seven different concrete mixes, demonstrating ChatGPT’s effectiveness in code conversion. However, generating accurate code without referencing the original MATLAB scripts required detailed prompts, highlighting the need for a strong understanding of the test method. Finally, a Python script was applied to modify void reconstruction in 2D images into 3D by stereology, and comparing this with (3D) CT scanner results, showing comparable results. Full article

As a crucial component of CRH (China Railway High-speed) trains, the safety and stability of the suspension system are of paramount importance to the overall vehicle system. Based on the framework of probabilistic relevant principal component analysis (PRPCA), this paper proposes a novel method for incipient fault diagnosis in the CRH suspension system using PRPCA and support vector machine (SVM). Firstly, simulation data containing multiple types of fault information are obtained from the Simpack2018.1-Matlab2016a/Simulink co-simulation platform. Secondly, the nonlinear PRPCA approach, based on the Wasserstein distance, is employed for fault detection and data preprocessing in the suspension system. Furthermore, SVM is used for fault recognition, and the F1-Measure index is utilized for a comprehensive evaluation to assess the fault diagnosis performance more intuitively. Finally, based on the comparison results with traditional principal component analysis (PCA) and SVM-based methods, the proposed incipient fault diagnosis method demonstrates superior efficiency in fault detection and recognition. However, the proposed method is not very sensitive to sensor faults, and the performance of sensor fault diagnosis needs to be further improved in subsequent research. Full article

Due to its high maturity and low cost, alkaline water electrolysis (AWE) technology has been widely integrated with large-scale renewable energy systems (RESs) for green hydrogen (H₂) production. Here, to evaluate the operational performance of a 50 kW AWE electrolyzer under different operation conditions, we developed an empirical modeling and experimental validation approach. The model particularly focuses on the polarization curve and the hydrogen to oxygen ratio (HTO). The relevant parameters of the empirical model were obtained by fitting the experimental data with MATLAB. The validity and accuracy of the established model and parameters were verified by comparing the fitted values with experimental values, and a good correlation was found. Since the experiments were performed in the sub-cell of 5 MW scale AWE electrolyzers, this model can also predict the performance of industrial MW-scale AWE electrolyzers and serve as a tool for the optimal design and control of industrial AWE electrolyzers. The results demonstrated that the models can achieve an accuracy with an R² value exceeding 0.95 across a range of operational conditions. Full article