MATLAB Modules Research Articles

Navigating urban day-ahead energy management considering climate change toward using IoT enabled machine learning technique: Toward future sustainable urban

The rapid evolution of technology and urban population growth has led to a significant increase in energy consumption, particularly in electricity production. However, the ability to meet energy demands, especially during peak periods, is constrained by various factors including environmental concerns, technological limitations, and economic constraints. Effective energy management strategies are crucial to mitigate these challenges and ensure a sustainable energy future. One promising approach is the implementation of demand response programs. In this context, we explore the potential of Home Energy Management Systems (HEMS) leveraging modern Internet of Things (IoT) technology. HEMS systems are at the forefront of research and development in the field of smart grids (SGs), offering capabilities in demand-side management and power availability control. These systems achieve efficient energy usage by optimizing the operation of home appliances (HAs). To further enhance the performance of HEMS, this study introduces a novel metaheuristic known as the Grey Wolf and Crow Search Optimization Algorithm (GWCSOA). This algorithm is employed to determine the optimal scheduling of HAs within the HEMS framework. Additionally, we utilize MATLAB and ThingSpeak modules to implement and test the HEMS design. The proposed HEMS, integrated with GWCSOA, demonstrates remarkable capabilities in reducing daily power costs, minimizing the peak-to-average ratio (PAR), and enhancing consumer satisfaction. The findings of this study indicate that the devised system holds the promise of substantially diminishing both power expenses and microgrid emissions. Through the optimization of Home Energy Management Systems (HEMS) operations, this methodology plays a vital role in realizing a sustainable urban energy ecosystem. This aligns seamlessly with the overarching objectives of combating climate change and securing the enduring sustainability of our cities. The proposed HEMS, integrated with GWCSOA, demonstrates remarkable capabilities in reducing daily power costs by an average of 25.98 %, minimizing the peak-to-average ratio (PAR) by 30 %, and enhancing consumer satisfaction. The outcomes of this research reveal that the developed system has the potential to significantly reduce both power costs and microgrid emissions. By optimizing HEMS operations, this approach contributes to achieving a sustainable urban energy ecosystem, aligning with the goals of addressing climate change and ensuring the future sustainability of our cities.

Control system and kinematic analysis of lower limb prosthetic knee joint

AbstractIn order to study the motion characteristics of the prosthesis, a three‐dimensional model of the test platform was established, and the corresponding control scheme was designed according to this model. For the lower limb prosthesis test platform, a fuzzy proportion‐integration‐differentiation (PID) control model was created. Second, the matlab modules were used to construct the virtual model, and the goal function‘s control research was conducted. Finally, a virtual model was created, and the outcomes were studied, in order to assess the differences between the control results of proportional‐integral‐differential controller and fuzzy proportional‐integral‐differential controller. The results verify that the test platform using fuzzy proportional‐integral‐differential controller is capable of greater stability. The lower limb prosthetic knee joint test platform was built, and the corresponding experimental research was carried out on the motion of the lower limb prosthetic knee joint. The experimental data showed that the angle error of the hip joint was within 2° when the normal human gait was walking on the ground. The error of the joint angle does not exceed 3°. The prosthesis can clearly better mimic the angles of the hip and knee joints.

A Novel Solution for Day-Ahead Scheduling Problems Using the IoT-Based Bald Eagle Search Optimization Algorithm

Advances in technology and population growth are two factors responsible for increasing electricity consumption, which directly increases the production of electrical energy. Additionally, due to environmental, technical and economic constraints, it is challenging to meet demand at certain hours, such as peak hours. Therefore, it is necessary to manage network consumption to modify the peak load and tackle power system constraints. One way to achieve this goal is to use a demand response program. The home energy management system (HEMS), based on advanced internet of things (IoT) technology, has attracted the special attention of engineers in the smart grid (SG) field and has the tasks of demand-side management (DSM) and helping to control equality between demand and electricity supply. The main performance of the HEMS is based on the optimal scheduling of home appliances because it manages power consumption by automatically controlling loads and transferring them from peak hours to off-peak hours. This paper presents a multi-objective version of a newly introduced metaheuristic called the bald eagle search optimization algorithm (BESOA) to discover the optimal scheduling of home appliances. Furthermore, the HEMS architecture is programmed based on MATLAB and ThingSpeak modules. The HEMS uses the BESOA algorithm to find the optimal schedule pattern to reduce daily electricity costs, reduce the PAR, and increase user comfort. The results show the suggested system’s ability to obtain optimal home energy management, decreasing the energy cost, microgrid emission cost, and PAR (peak to average ratio).

DETERMINATION OF RESS GENERATION USING ARTIFICIAL NEURAL NETWORKS

Paper considers the perspectives and potential of using renewable energy sources to decide the global warming problem. The World trend of increasing electricity generation by photovoltaic power stations according to the International Renewable Energy Agency and the trend of increasing the installed capacity of photovoltaic power stations in Ukraine, which supply the generated capacity at a "green" tariff according to the National Commission for State Regulation of Energy utilities of Ukraine. Opportunities and conditions of using artificial neural networks to defined the power generation of photovoltaic power stations on the example of the power plant "Tsekinivska-2" 4–5 turns are investigated. A platform developed by the European Commission – Photovoltaic Geographical Information System – was used to create a database for the creation and training of artificial neural networks. Regularities of change of meteorological satellite data and their influence on electricity generation of photovoltaic power stations are established. For this purpose, the software complex MATLAB was used, namely the module for the creation of artificial neural networks – Neural Networks Toolbox. The height of the sun is conditionally considered constant and its value is repeated from year to year or has a slight deviation, so it can be used as an indicator of the hour and can be considered known in advance, so determined by empirical formulas and changes only under certain astrophysical laws. Regarding the temperature at 2 m and the wind at 10 m, these meteorological data are known, as they are needed not only for forecasting the operation of renewable energy sources but also in agriculture. Therefore, data related to solar radiation are considered to be the most problematic, as this value is the most difficult to determine. Satellite data may have an error, the installation of weather stations, namely quality pyranometers is a costly procedure, but will help provide a training sample of quality data. To forecast with satisfactory accuracy, it is necessary to collect data for 1 year of operation of the weather station. The nntool and Anfis MATLAB modules were used to predict generation. But the obtained results can be used to assess the effectiveness of the photovoltaic power stations, but they are unsatisfactory for the operational balancing of the system.

Biomarkers of Seizure Activity in Patients With Intracranial Metastases and Gliomas: A Wide Range Study of Correlated Regions of Interest.

Introduction: Studies quantifying cortical metrics in brain tumor patients who present with seizures are limited. The current investigation assesses morphometric/volumetric differences across a wide range of anatomical regions, including temporal and extra-temporal, in patients with gliomas and intracranial metastases (IMs) presenting with seizures that could serve as a biomarker in the identification of seizure expression and serve as a neuronal target for mitigation.Methods: In a retrospective design, the MR sequences of ninety-two tumor patients [55% gliomas; 45% IM] and 34 controls were subjected to sophisticated morphometric and volumetric assessments using BrainSuite and MATLAB modules. We examined 103 regions of interests (ROIs) across eight distinct cortical categories of interests (COI) [gray matter, white matter; total volume, CSF; cortical areas: inner, mid, pial; cortical thickness]. The primary endpoint was quantifying and identifying ROIs with significant differences in z-scores based upon the presence of seizures. Feature selection employing neighborhood component analysis (NCA) determined the ROI within each COI having the highest significance/weight in the differentiation of seizure vs. non-seizure patients harboring brain tumor.Results: Overall, the mean age of the cohort was 58.0 ± 12.8 years, and 45% were women. The prevalence of seizures in tumor patients was 28%. Forty-two ROIs across the eight pre-defined COIs had significant differences in z-scores between tumor patients presenting with and without seizures. The NCA feature selection noted the volume of pars-orbitalis and right middle temporal gyrus to have the highest weight in differentiating tumor patients based on seizures for three distinct COIs [GM, total volume, and CSF volume] and white matter, respectively. Left-sided transverse temporal gyrus, left precuneus, left transverse temporal, and left supramarginal gyrus were associated with having the highest weight in the differentiation of seizure vs. non-seizure in tumor patients for morphometrics relating to cortical areas in the pial, inner and mid regions and cortical thickness, respectively.Conclusion: Our study elucidates potential biomarkers for seizure targeting in patients with gliomas and IMs based upon morphometric and volumetric assessments. Amongst the widespread brain regions examined in our cohort, pars orbitalis, supramarginal and temporal gyrus (middle, transverse), and the pre-cuneus contribute a maximal potential for differentiation of seizure patients from non-seizure.

Robust power spectral estimation for EEG data

BackgroundTypical electroencephalogram (EEG) recordings often contain substantial artifact. These artifacts, often large and intermittent, can interfere with quantification of the EEG via its power spectrum. To reduce the impact of artifact, EEG records are typically cleaned by a preprocessing stage that removes individual segments or components of the recording. However, such preprocessing can introduce bias, discard available signal, and be labor-intensive. With this motivation, we present a method that uses robust statistics to reduce dependence on preprocessing by minimizing the effect of large intermittent outliers on the spectral estimates. New methodUsing the multitaper method (Thomson, 1982) as a starting point, we replaced the final step of the standard power spectrum calculation with a quantile-based estimator, and the Jackknife approach to confidence intervals with a Bayesian approach. The method is implemented in provided MATLAB modules, which extend the widely used Chronux toolbox. ResultsUsing both simulated and human data, we show that in the presence of large intermittent outliers, the robust method produces improved estimates of the power spectrum, and that the Bayesian confidence intervals yield close-to-veridical coverage factors. Comparison to existing methodThe robust method, as compared to the standard method, is less affected by artifact: inclusion of outliers produces fewer changes in the shape of the power spectrum as well as in the coverage factor. ConclusionIn the presence of large intermittent outliers, the robust method can reduce dependence on data preprocessing as compared to standard methods of spectral estimation.

Computation of Sensitivity Indices with different routines for Latin Hypercube Designs

Complex mathematical simulations are performed to understand the various phenomena in physics, chemistry, etc. Usually these models consist of several input variables and sensitivity analysis explains the variation of computer code output due to the changes in the input values. We have used the software package, Gaussian Emulation Machine for sensitivity analysis (GEM-SA), (Kennedy and O’Hagan, 2006) for performing sensitivity analysis and chosen Rosenbrock function as a test problem. In this paper we have studied the effect of different available software routines for Latin hypercube designs (LHD) on values of the variance-based sensitive indices, total sensitive indices and roughness parameters for the test problem. We have used MATLAB, R software and GEM-SA modules for LHD.

The Kohonen and CP-ANN toolbox: A collection of MATLAB modules for Self Organizing Maps and Counterpropagation Artificial Neural Networks

Kohonen maps (or Self Organizing Maps, SOMs) and Counterpropagation Artificial Neural Networks are two of the most popular Neural Networks proposed in literature and are increasing in uses and applications related to several multivariate chemical issues, since they can handle both supervised and unsupervised problems. This work deals with the presentation of the Kohonen and CP-ANN toolbox, that is a collection of MATLAB modules freely available via the Internet ( http://www.disat.unimib.it/chm) for the calculation of the quoted models. A graphical user interface (GUI), which allows an easy model calculation and analysis of results, is also provided. The toolbox features are presented by reproducing the classification of a real multivariate dataset. This work is not an attempt to summarize the general applications of Self Organizing Maps, but to inform chemometricians and practitioners who are not skilled programmers of the existence of a user-friendly Matlab toolbox to develop unsupervised and supervised SOM models.

Algorithm 835

MultRoot is a collection of Matlab modules for accurate computation of polynomial roots, especially roots with non-trivial multiplicities. As a blackbox-type software, MultRoot requires the polynomial coefficients as the only input, and outputs the computed roots, multiplicities, backward error, estimated forward error, and the structure-preserving condition number. The most significant features of MultRoot are the multiplicity identification capability and high accuracy on multiple roots without using multiprecision arithmetic, even if the polynomial coefficients are inexact. A comprehensive test suite of polynomials that are collected from the literature is included for numerical experiments and performance comparison.

A Matlab package computing polynomial roots and multiplicities

MULTROOT is a collection of Matlab modules for accurate computation of polynomial roots, especially roots with high multiplicities, using standard machine precision. As a blackbox-type software, MULTROOT requires the polynomial coefficients as the only input, and outputs the computed roots, multiplicities, backward error, estimated forward error, as well as the pejorative condition number.There are two common limitations for standard numerical root-finding software when multiple roots are present. Namely, those methods suffer from severe loss of accuracy and lack the capacity of multiplicity identification. Symbolic polynomial factorization requires exact rational coefficients. In contrast, the most significant features of MULTROOT are the multiplicity identification capability and the remarkable accuracy on multiple roots without using the multiprecision arithmetic, even if the polynomial coefficients are inexact.There is a so-called "attainable accuracy" for conventional root-finders: the attainable number of corrected digits of a computed root is limited by the data/machine precision divided by the multiplicity. For roots with high multiplicities, this accuracy barrier suggests that multiprecision arithmetic is required in addition to exact coefficients. Using a novel approach based on the pejorative manifold theory, MULTROOT achieves high accuracy even if the polynomial is perturbed and the machine precision is not extended. A stable numerical polynomial GCD-finder is also developed as an essential component that determines the multiplicity structure.MULTROOT is a combination of two programs that can be used independently. GCDROOT calculates the multiplicity structure and initial root approximation. PEJROOT refines the root approximations by projecting the polynomial onto a prescribed pejorative manifold. A comprehensive test suit of polynomials that are collected from the literature is included for numerical experiments and performance comparison.The detailed algorithm and analysis is presented in this conference [1].

Algorithm 808

ARfit is a collection of Matlab modules for modeling and analyzing multivariate time series with autoregressive (AR) models. ARfit contains modules to given time series data, for analyzing eigen modes of a fitted model, and for simulating AR processes. ARfit estimates the parameters of AR models from given time series data with a stepwise least squares algorithm that is computationally efficient, in particular when the data are high-dimensional. ARfit modules construct approximate confidence intervals for the estimated parameters and compute statistics with which the adequacy of a fitted model can be assessed. Dynamical characteristics of the modeled time series can be examined by means of a decomposition of a fitted AR model into eigenmodes and associated oscillation periods, damping times, and excitations. The ARfit module that performs the eigendecomposition of a fitted model also constructs approximate confidence intervals for the eigenmodes and their oscillation periods and damping times.