MATLAB Platform Research Articles

Efficient Energy Management System for AC–DC Microgrid and Electric Vehicles Utilizing Renewable Energy With HCO Approach

ABSTRACTThe reliability of various energy sources can be increased and distributed production and renewable energy can be fully integrated into the power grid on a wide scale through the growth and development of the microgrid (MG). Global energy difficulties are brought about by the finite supply of fossil fuels and the world's expanding energy consumption. Due to these challenges, the electric power system has to convert to a renewable energy‐based power generation system to produce clean, green energy. However, because of the unpredictable nature of the environment, the shift toward the use of renewable energy sources raises uncertainty in the production, control, and power system operation. This manuscript proposes a renewable energy‐based energy management system for electric vehicles and AC–DC MGs. The proposed method is Hermit Crab Optimizer (HCO). The major goal of the proposed strategy is to supply steady power regardless of generation disparity, which should stop the storage devices from degrading too quickly. The HCO approach provides a stable power balance for MG operation. The proposed technique efficiently strikes a power balance to meet load requirements and recharge electric cars. By then, the proposed strategy is implemented in the MATLAB platform and the execution is computed with the existing procedure. The proposed technique displays better outcomes in all existing systems like biogeography‐based optimization (BBO) algorithm, particle swarm optimization (PSO) algorithm, genetic algorithm (GA), and artificial neural network (ANN). The existing technique shows the cost of 25$, 30$, 35$, 40$, and the proposed technique displays the cost of 20$ which is lower than the other existing techniques.

Joint Noniterative Beamforming Schemes for BER Minimization in Next‐Generation Wireless Sensor Networks

This research paper proposes three joint transmit and receive (TX/RX) beamforming schemes for minimizing the bit error rates of next‐generation wireless sensor networks. Based on minimizing the optimization problem of the total mean squared error between the transmitted and received data symbols, the first beamforming scheme is proposed. The second beamforming scheme is based on the projection of the effective channel on the space spanned by the channel matrix. A third practical beamforming scheme based on the strongest eigenvalues of the channel matrix is introduced. This beamforming scheme proves its optimality in its operation under the condition that the number of TX antennas is equal to or larger than the total number of RX antennas. The proposed joint TX/RX beamforming schemes are formulated in closed form. Numerous numerical results are provided using the MATLAB platform. The proposed beamforming schemes are compared with the widely adopted block diagonalization transmission scheme and linear block diagonalization. Moreover, the proposed systems are compared with state‐of‐the‐art beamforming techniques such as Tomlinson–Harashima precoding‐based MMSE. The simulation results demonstrated that the proposed beamforming schemes achieved better bit error rate performance than the block diagonalization and linear block diagonalization transmission schemes. Furthermore, the third beamforming scheme provides a gain of about 4dB compared to the cutting‐edge technology of beamforming Tomlinson–Harashima precoding‐based MMSE. Accordingly, more robust communications can be achieved for next‐generation wireless sensor networks. This research paper provides an efficient manner to meet the demanded reliability of next‐generation wireless sensor networks, which is a bit error rate of 10−9.

Performance Enhancement of a Solar Photovoltaic System with Differential Evolution-Optimized Quasi Sliding Mode Control

This paper presents a novel approach to enhancing the performance of a solar photovoltaic (PV) system by integrating a Differential Evolution (DE) optimization algorithm into the design of a Quasi Sliding Mode Controller (QSMC). The proposed method aims to address the challenges associated with Conventional Sliding Mode Control (CSMC), such as chattering and suboptimal tracking accuracy, which can significantly impact the stability and efficiency of PV systems. Simulation results show that the DE-optimized QSMC reduces tracking error to 0.05 V, while conventional SMC results in a tracking error of 0.15 V. Chattering amplitude is also significantly reduced, from 0.12 A to 0.03 A and the response time is improved from 0.8 seconds to 0.5 seconds. By leveraging the robustness of QSMC and the flexibility of DE, the DE-QSMC is fine-tuned to minimize tracking errors, reduce chattering, and maintain optimal performance under varying environmental conditions. The stability of the proposed technique is rigorously analyzed using the Lyapunov function theorem, ensuring robust system behavior. The effectiveness of the DE-optimized QSMC is validated through simulations conducted on the Matlab platform, demonstrating superior performance compared to conventional control techniques.

Development of lightweight self-compacting concrete incorporating waste-expanded polystyrene using hybrid approach

ABSTRACT Reusing industrial waste materials is vital for environmental conservation and sustainability, with concrete playing a significant role in recycling efforts and air purification. This paper proposes an efficient hybrid technique for developing LWSCC incorporating waste-expanded polystyrene (EPS). The proposed hybrid method is the joint execution of the war strategy optimisation (WSO) and tree hierarchical deep convolutional neural network (THDCNN). It is commonly referred to as the WSO-THDCNN approach. The primary aim is to enhance the hardening properties of LWSCC while minimising errors in material composition. WSO optimises the concrete mix by maximising the use of recycled EPS waste, while THDCNN predicts the material behaviour and structural characteristics. The method is implemented on the MATLAB platform, yielding superior outcomes compared to existing systems such as the Deep Neural Network (DNN) the Firefly Optimization Algorithm-Radial Basis Function Neural Network (FOA-RBFNN) and the Support Vector Machine (SVM). The accuracy of the existing methods is recorded at 0.780, 0.990 and 0.850, whereas the proposed technique achieves an impressive accuracy of 0.995. Furthermore, it displays a Root Mean Square Error (RMSE) of 3.549 and an Average Relative Error (ARE) of 5.43, demonstrating its effectiveness and reliability in improving LWSCC properties.

Experimental Study on the Damage Evolution Mechanism of Siltstone by Water Content and Confining Pressure

ABSTRACTProlonged exposure of deep coal mines to erosion from groundwater results in a gradual accumulation of rock mass damage, which can lead to geological hazards such as deformation and instability. These challenges significantly impact the safe operation of deep coal mines. To understand the mechanisms behind siltstone damage progression related to water content and confining pressure, this study explores the influence of these factors on the deformation and damage evolution of siltstone, employing a combination of rock mechanics testing, numerical simulation, and CT scanning techniques. Results demonstrate that increasing water content reduces the compressive strength of rock, leading to more complex failure modes. In contrast, higher confining pressure strengthens the compressive capacity, thereby suppressing the formation and growth of transverse cracks under compression. Using Avizo software, a three‐dimensional model of siltstone was developed to visualize the distribution of fractures in a three‐dimensional field. In the MATLAB platform, a box dimension algorithm based on three‐dimensional digital volume imaging was developed, employing box dimension theory and digital image storage methods. Fractal analysis reveals that the fractal dimension of internal fractures in loaded samples increases linearly with water content, indicating more extensive fracture development and greater specimen damage. Applying the box dimension from three‐dimensional digital volume images as a metric facilitates characterizing the damage evolution in siltstone under different water content conditions. This approach provides a new means to quantitatively evaluate the growth and complexity of internal fractures in siltstone, offering insights into rock damage progression under varying moisture conditions.

Determining the Location and Type of Faults in Electrical Cables Using an Artificial Neural Network

Detecting and locating faults in electrical cables has always been a challenge for power distribution systems. Over time, several methods have been developed to effectively manage these erroneous situations. This article develops a new approach to detecting, localizing, classifying, and predicting faults, especially in different types of short circuits in electrical cables, based on the artificial neural network method. The novelty of this approach lies in the ability of the method to predict the location and type of malfunction. The proposed method uses the Matlab and Simulink platform and includes four sequential stages. A virtual machine is used to reduce the time and resources of the modeling process. The study shows the effectiveness of the method and its ability to successfully predict faults in real power supply systems.

Some features satellite attitude control using quaternions

Algorithms using the kinematics equations in quaternions are widely used in the attitude control systems of modern spacecraft, primarily microsatellites. They ensure large-angle rotations and avoidance of singularity points characteristic of Euler-Krylov angles. However, the specificity of single quaternions as attitude representation parameters can create an "unwinding effect" in the control system, when turning the spacecraft to a desirable position is not performed by the shortest path and increases the electricity consumption, that is limited on board. This work, summarizing known publications, illustrates the causes of this effect and demonstrates a simple algorithm for its prevention. The effectiveness of this approach is shown on the MATLAB platform numerical simulation examples of the microsatellite control system with reaction wheels and a PD controller.

High order moment conserving method of classes in CFD code

Abstract Dispersed multiphase flows are widely present in the majority of chemical process industry equipment. For the purpose of design, optimization, and scale up of these industrial systems, robust, accurate and fast simulation methods are needed, especially when coupling computational fluid dynamics (CFD) with population balance models (PBM). In this work, the high-order moment conserving method of classes (HMMC) is implemented and tested within the commercial CFD software of ANSYS Fluent with two simple well-defined test cases and a realistic 3D simulation of rotating disc (RDC) extractor. Firstly, a zero-dimensional PBM with well-mixed assumption and no convection was solved for a single computational cell in Fluent to verify the implementation in comparison with a simple reactor model in MATLAB. Secondly, the convection was considered by solving a one-dimensional PBM for three computational cells side by side in Fluent and compared with an equivalent three staged reactor model in MATLAB. Eventually, realistic 3D RDC simulations were conducted to fully couple the HMMC and CFD in Fluent. The implementation of HMMC was compared to predictions with other state-of-the-art PBM solution methods, e.g., the quadrature method of moments (QMOM) and the fixed pivot technique (FPT) in Fluent and MATLAB platforms. The implementation of the HMMC in Fluent platform was straightforward with no additional challenges. The verification shows that the HMMC approach is robust and efficient for polydispersed multiphase CFD simulations.

Electrically controlled flexible and varifocal microlens array using liquid crystals on polymer

Abstract The microlens array (MLA) on the flexible substrates has a wide range of applications in advancing curved integral imaging and three-dimensional (3D) display systems with a large field of view. We developed an electrically controlled varifocal microlens array on flexible Polyethylene Terephthalate (PET) substrates by infiltrating the liquid crystals (LC) in polymeric concave lenses. The focusing and imaging characteristics of the flexible LC-MLA are studied experimentally. When voltage is applied across the LC layer, the refractive index of the LC changes, which causes a change in the effective focal length of MLA. The electrically tunable focal length and tunable focusing of the MLA are experimentally demonstrated. The mathematical analysis of the LC MLA is performed using the MATLAB platform. The presented flexible LC-MLA is developed using simple and cost-effective fabrication methods, which can potentially be used in future 3D displays and imaging solutions.

Investigation of the Effect of Diverse Dictionaries and Sparse Decomposition Techniques for Power Quality Disturbances

The quality of power signals is strongly influenced by nonlinear loads in Electrical Power systems. Representation of electrical signals using different Sparse techniques is an interesting area of research as it moderates the volume of data to be stored. The storage of signals in Sparse form will make data storage easier and more efficient. Earlier studies concentrated on blindly choosing Overcomplete Hybrid Dictionaries (OHDs) for Sparse representation. The effect of different dictionaries in representing electrical signals has also not been reviewed in them. This paper presents an investigation of the effect of various dictionaries and the sparsity constant on the representation of electrical signals. The validation for statements presented in this paper is carried out by representing power signals with diverse power line disturbances like Swell, DC offset, and random oscillation, with the help of various dictionaries in the simulation platform. The Sparse representation of the power signals was generated using the Orthogonal Matching Pursuit algorithm. The resultant Sparse representation was then compared with the original signal. The difference between them was found to be negligible with the help of different metrics. The ratio of the obtained signal from Sparse representation, the original signal (A/R ratio), and the Mean Squared Error were taken as the metrics. The MATLAB platform was used for performing the simulation study.

Research on learning and evaluation model of traditional sports skills of ethnic minorities using optimized spiking deep residual network

The athletic performance of competitors in sporting events is influenced by a range of parameters. It is challenging for coaches to develop specialized, scientific training programs for athletes’ problems because the standard sports performance prediction approach makes it impossible to obtain precise findings and the related data analysis promptly. A sports performance prediction using the Spiking Deep Residual Network based Coati Optimization Algorithm (SPP-SDRN-COA) is proposed in this manuscript. Initially, the input data was collected from the historical data of ethnic minority athletics. Afterward, data was fed to pre-processing. In pre-processing, the Domain Transform Filtering (DTF) method removed noise by using DTF. Next, the pre-processed data was given to the Spiking Deep Residual Network (SDRN) which classified the sports performance of an athlete’s strengths and weakness. In general, SDRN does not show any optimization adaption methods to determine the optimum parameter to offer an accurate prediction. The Coati Optimization Algorithm (COA) is proposed in this manuscript to optimize the SDRN classifier that detects the strengths and weaknesses of the sports performance in an accurate manner. The proposed SPP – SDRN – COA is implemented using the MATLAB platform. By using the proposed SPP – SDRN – COA based sports performance prediction, the method provides higher accuracy and better prediction. Additionally, coaches can analyze athletic performance and gain a more thorough understanding of athletes’ strength levels than using the conventional performance prediction method.

Multiobjective Optimization of Steel and Concrete Composite Slabs via NSGA algorithm

With the expansion of the civil construction sector, composite steel and concrete slabs with incorporated shapes have become increasingly common due to their ease of execution and the elimination of shoring. However, optimization studies focusing on this type of structural element are still limited. The objective of this study is to propose a formulation for the multi-objective optimization of composite steel and concrete slabs, considering both the phases before and after concrete curing. Objective functions will be established to minimize CO2 emissions and slab costs, as well as to maximize the load-bearing capacity of the slab for a given span. To address this optimization problem, the NSGA algorithm implemented in the Matlab platform will be used. Comparative analyses will be conducted between examples from the literature utilizing single-objective optimization and those employing multi-objective optimization to validate the proposed formulation. Additionally, new problem instances will be examined to identify the key factors that influence the final solution.

Altered Density of Resting-State Long- and Short-Range Functional Connectivity in Patients with Moderate-to-Severe Obstructive Sleep Apnea.

This study is to evaluate the altered number of functional connection (s) in patients with obstructive sleep apnea (OSA) by functional connectivity density (FCD), to investigate its relationship with cognitive function, and to explore whether these features could be used to distinguish OSA from healthy controls (HCs). Seventy-six OSA patients and 72HCs were included in the analysis. All participants underwent resting-state functional magnetic resonance imaging scan. Subsequently, intergroup differences between long-and short-range FCD groups were obtained in the Matlab platform by using the degree centrality option with a 75 mm cutoff. The partial correlation analysis were used to assess the relationship between the altered FCD value and clinical assessments in OSA patients. The FCD values of the different brain regions were used as classification features to distinguish the two groups by support vector machine (SVM). Compared to HCs, OSA patients had decreased long-range FCD in the right superior frontal gyrus (SFG), right precuneus, and left middle frontal gyrus (MFG). Simultaneously, increased long-range FCD in the right cingulate gyrus (CG). Meanwhile, the short-range FCD were decreased in the right postcentral gyrus (PoCG), right SFG, left MFG, and right CG. The short-range FCD values of the right PoCG were correlated with the Montreal Cognitive Assessment scores in OSA patients. SVM analysis showed that FCD in differential brain regions could differentiate OSA patients from HCs. Long- and short-range FCD values in different brain regions of OSA patients may be related to cognitive decline, and also be effective in distinguishing OSA patients from HCs. These findings provide new perspectives on neurocognition in OSA patients.

An Efficient Archerfish Hunting Optimizer (AHO)-Based Power Quality Improvement in Distribution System Connected UPQC

These days, because power quality (PQ) issues are costing industrial customers a lot of money, both electrical utilities and end-users are paying a lot of attention to them. The most significant and frequent PQ concerns for sensitive loads are voltage sag and swell. This manuscript proposes an optimization technique for enhancing the control system of a unified power quality conditioner (UPQC) connected solar photovoltaic system in the distributed generation system. The proposed strategy is the archerfish hunting optimizer (AHO). The objective of the AHO approach is to optimizing UPQC operation in distribution systems, focusing on targeted power quality enhancement. The optimal control pulses are produced by the series active power filter and the shunt active power filter, depending on the load-side and source circumstances. During the load variation states the AHO technique manages the power loss, total harmonic distortion (THD), and voltage instability issue respectively. The performance of the AHO methodology is carried out on the MATLAB platform and compared with existing methods, like particle swarm optimization (PSO), latent semantic analysis (LSA), and ant-lion optimizer (ALO). The existing methods THD are 6.35%, 7.85%, and 9.19%. The proposed method of THD is 4.22%. From the outcome, it concluded that the proposed method based on the THD is low contracted to present techniques.

Collision Avoidance for Unmanned Surface Vehicles in Multi-Ship Encounters Based on Analytic Hierarchy Process–Adaptive Differential Evolution Algorithm

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.

Heart disease prediction using ECG-based lightweight system in IoT based on meta-heuristic approach

Annually, the proportion of individuals suffering from cardiovascular disease rises significantly. Heart attacks are the most prevalent and unpleasant illness among them. Heart disease (HD) diagnosis can be complicated when there are multiple symptoms. The growing popularity of wearable smart devices has increased the likelihood of providing the Internet of Things (IoT). However, one of the biggest obstacles to overcome in implementing the system under IoT is developing a lightweight model for cardiac diagnosis and categorization. In this paper, we have presented a two-step heart disease classification method. This method includes demarcation of classes with the help of optimized non-linear support vector machine technique in the first step and determining the modified fuzzy class in the second step. Initially, pre-processing is accomplished using the ECG signals to eliminate noise and improve signal smoothness. Subsequently, features such as PQRS wave, linear characteristics, and reciprocal information are extracted from pre-processed signals. At the classification stage, the two-stage learning system is used to classify cardiac arrhythmias. First, using the wild horse optimization (WHO) technique (WHO-sigmoid-TH-NL-demarcation), each class is subjected to a binary classification based on feature demarcation, thresholding, and weighting of the sigmoid function. The information from the first stage will be transferred into the subsequent stage for an equal number of heart disease classifications. In the second step, a TS fuzzy logic system optimized by the Giza Pyramids Construction (GPC) approach (GPC-TS-Fuzzy) is utilized to classify each signal. The MIT-BIH arrhythmia dataset is used to assess the suggested approach. In a comprehensive evaluation of the suggested method, performance metrics including “accuracy, sensitivity, and specificity” yielded average values of 98.58 %, 98.13 %, and 96.47 %, respectively. The MATLAB platform is utilized to accomplish the proposed methodology.

Investigations on stability improvement by implementing SSSC optimally with X/R ratio

Series compensation is widely used to enhance the transmittable power through an existing transmission line. This is achieved by controlling series reactance of the transmission line by injecting a voltage in series with transmission line and is perpendicular to the line current. However, X/R ratio of the transmission line gets deteriorated with series compensation. X/R ratio plays an important role in maintaining stability and damping oscillation in power system. In the proposed scheme, X/R ratio of the line has been included as one of the constraints along with midpoint voltage and power balance equation, with the objective of minimizing power system losses while enhancing the power. It is found that by maintaining X/R ratio of the transmission line same as the original designed value, stability of the system can be improved while making an attempt to enhance the transmittable power capacity. Steady state and transient stability, voltage stability and damping oscillations are analyzed with proposed optimal control of statis synchronous series compensator (SSSC), and results are produced. Validation of proposed optimal control of SSSC is done by using MATLAB platform for enhancing transmittable power of a 400 kV, 400 km transmission line and results are presented here. The paper investigates the effectiveness of the proposed optimization technique on improvement of power system stability and oscillations damping as well.

A Comparative Analysis of Swarm Intelligence Algorithms for Event Detection in Wireless Sensor Networks

WSNs play an important role in monitoring and responding to dynamic events in applications such as disaster management, industrial automation, and environmental monitoring. Since event detection has the highest priority in a WSN, it is very crucial regarding timely response, energy efficiency, and system reliability. However, traditional threshold-based detection methods often lack flexibility and always consume large amounts of energy. SI algorithms, motivated by natural collective behaviors, offer robust and adaptive solutions for event detection in order to overcome the limitations of conventional methods. Performance analysis and comparison of three most powerful SI algorithms - DA, ACO, and PSO - for event detection in WSNs. Methods: The performance of these algorithms was analyzed for time efficiency, energy consumption, accuracy, and scalability using simulation-based experiments. The experiments simulate WSNs of different node densities and event characteristics on the widely used python and MATLAB platforms under identical conditions for each algorithm. Among them, the Dragonfly Algorithm outperformed ACO and PSO in time efficiency-66.48 seconds versus 85.10 and 77.03 seconds, respectively-energy consumption (0.000030 J vs. 0.000047 and 0.000035 J), and accuracy, 94.5% versus 92.1% and 93.2%, correspondingly. DA also has better scalability for different network sizes and performs consistently well for larger networks, up to 1024 nodes. ACO, though robust in path exploration, had the highest energy consumption and longest detection time, which seriously affects its scalability. PSO gave balanced performance, though it trailed DA significantly in key metrics. The Dragonfly Algorithm provides the most efficient and scalable solution for WSN event detection with high time efficiency, energy conservation, and accuracy. These results underline its applicability in real-world time-critical and energy-sensitive WSN applications.

Comparative analysis of two novel chaotic systems, and validation of hybrid function projective and complete synchronization using active-adaptive control

Abstract This study investigates the nonlinear characteristics of two novel 3D-chaotic models using phase portraits, bifurcation diagrams, Lyapunov exponents, time series analysis, and Poincaré maps. Here, we have examined hybrid-function projective and complete synchronization schemes via adaptive and active control methods. Moreover, the performance of hybrid-function projective synchronization, utilizing sine, cosine, and exponential terms, is compared to complete synchronization through two control strategies. Our designed controllers ensure asymptotic global chaotic synchronization based on Lyapunov stability principles. We have also compared our results with other competitive schemes and validated the theoretical findings through simulations on the MATLAB platform.

Development and Evaluation of User‐Friendly Modeled Approach for Sustainable Polymer Membranes for Advanced Hemodialysis

AbstractHemodialysis is crucial for patients with end‐stage renal disease, yet evaluating its operating parameters often requires complex mathematical models. To simplify this process, user‐friendly modules have been developed to accurately assess key parameters with minimal inputs, enabling users to track disease prognosis. These modules incorporate governing equations and allow straightforward analysis. Validation against experimental data from polymer membrane studies demonstrated that at a blood flow rate of 300 mL min−1, the model predicted a clearance of 262 mL min−1, showing 7% difference from the actual value of 281 mL min−1. At a dialysate flow of 400 mL min−1, the model's predicted clearance was 286.47 mL min−1, with only a 1% difference compared to previous model. The module also showed 40% higher clearance in counter‐current flow compared to co‐current, with a 47% difference at 400 mL min−1 dialysate flow. Increasing the hollow fibre length from 27 to 50 cm led to a 4% clearance increase. Additionally, increasing residual renal clearance by 0.5 mL min−1 doubled the standard Kt V−1 Kt/V, and similar effects were seen by increasing weekly hemodialysis sessions. The app allows simulations, plots, and comparisons with minimal inputs and can be integrated into MATLAB or other platforms, benefiting both patients and researchers in prognosis and treatment analysis.