MATLAB Environment Research Articles

INTEGRATING AI IN IDENTITY AND ACCESS MANAGEMENT FOR IMPROVED CYBERSECURITY POSTURE

Abstract—The rise of cybercrime has made fraud detection a critical focus for financial services, where even minor improvements in detection rates can translate into significant savings. Traditional rule- based detection systems are increasingly unable to keep up with the complexity and evolving nature of fraudulent schemes. This work explores the role of artificial intelligence (AI), particularly deep learning, in enhancing fraud detection capabilities. Here we propose a novel AI-driven fraud detection system that incorporates Single Sign-On (SSO) identity and access management (IAM) frameworks, leveraging a dual-layered approach with batch and real-time processing. The batch layer establishes a user trust identity by analysing historical behavioural patterns, which informs an access-granting mechanism that evaluates real-time transaction data for fraud indicators. A deep iterative convoluted memory classifier then assesses transactions, for detecting frauds. This architecture automates fraud detection, allowing analysts to focus on high-priority cases and ensuring that only authenticated users can access sensitive information. Our experiments are Conducted in the MATLAB environment, From the analysis it was revealed that the model's effectiveness, showing promise for scalable, efficient fraud prevention in financial services. This work underscores the transformative potential of AI in securing financial transactions against sophisticated cyber threats. Index Terms— cybercrime, Fraud, Financial Transactions, artificial intelligence, identity and access management, fraud detection,

Matlab implementation of pore morphology method for modeling liquid residue in porous media with heterogeneous wettabilities

An alternative to the popular volume-of-fluid (VOF) and lattice Boltzmann (LB) methods for simulating fluid flow in porous media is the purely geometric pore morphology method (PMM). The PMM does not require solving partial differential equations and is therefore, orders of magnitude faster than the VOF and LB methods in terms of CPU time (e.g., about a week on 100 cores for the VOF vs. about 30 mins on one core for the PMM in our case). The PMM employs voxel-based morphological operations to provide a quasi-static approximation for capillary flow in porous structures. This paper presents step-by-step instructions for how to implement the PMM in the MATLAB environment, and further to benchmark the method using the simulation results obtained from the VOF and other continuum-based numerical methods. More specifically, this paper presents PMM simulations for the intrusion of a non-wetting fluid in two- and three-dimensional porous media with heterogeneous contact angles. Detailed discussions are also presented for the pros and cons of the PMM and how its non-continuum nature can be used as an advantage to conduct two-phase flow simulations in geometries where a symmetry boundary condition cannot be applied.

Design and Optimization of a Half-Circular Ultra-Wideband Patch Antenna Using Genetic Algorithm

This research introduces an optimization design of an ultra-wideband (UWB) half-circular planar antenna using genetic algorithm. The optimization process is conducted using an Application Programming Interface (API) links two softwares; MATLAB environment and ANSYS HFSS software. The UWB antenna design includes a semi-circular patch element, the UWB behavior is obtained using truncated ground plane incorporating a rectangular slot cut out of the ground. Genetic algorithm is exploited to optimize the length of partial ground plane and the size and position of the rectangular slot. The overall size of the antenna is 28×29×1.6 mm3. Using the proposed fitness function, the ultra-wide band antenna configuration achieves an extensive impedance bandwidth spanning approximately 14.76 GHz (139.38%), covering frequencies from 3.21 GHz to 17.97 GHz for reflection loss S11 less than -10dB. The findings also indicate that the antenna exhibits a favorable peak gain of 3.7–6.24 dB in the desired band. Therefore, the proposed methodology proofs to be effective to acquire the best UWB behavior of the antenna.

Unlocking the 3D georeferencing accuracy potential of Göktürk-1 tri-stereo panchromatic level 2A images by sensor-dependent orientation model

The panchromatic tri-stereo images of Göktürk-1, the first sub-meter optical remote sensing satellite of Türkiye, was assessed with respect to 3D georeferencing accuracy using sensor-dependent orientation model. The investigated panchromatic images were acquired in tri-stereo mode with 50 cm ground sampling distance at nadir-view, covering Zonguldak (Türkiye) test site. The sensor-dependent orientation model developed for the Pléiades 1A/1B/Neo and SPOT 6/7 satellites were preferred since the characteristics of Göktürk-1’s imaging geometry was identical with them. Two GCP/ICP sets (50/9 and 44/15) were established by in-field GNSS survey, and adjusted georeferencing accuracy were estimated almost millimetre and ~ ± 50 cm (~ ± 1 GSD) at GCPs and ICPs, respectively. All process was carried out by Geo3o1 tool of GeoEtrim derived in MATLAB environment.

Fractional order PID controllers for collaborative energy management in IoT-Smart cities: Hybrid optimization algorithms for demand

Modern energy markets have undergone a revolution due to the development of smart grids, which allow users to take part in demand response (DR) initiatives and keep the balance between demand and power generation. This work presents a new method with a Fractional Order PID controller (FOPID) for optimizing the power consumption of appliances in smart homes. An improved Secretary Bird Optimization (ISBO) algorithm is presented in this research for better scheduling appliance power consumption under time-of-use (TOU) and critical peak pricing (CPP) schemes. This strategy uses TOU and CPP, two dynamic pricing schemes, to minimize peak demand and cut energy expenses. The proposed strategy creates an energy management system (EMS) that is more effective by integrating energy storage devices (ESD) and renewable energy sources (RES) with the electrical grid. Due to this integration with FOPID, the best possible scheduling of appliances is made possible by CPP and TOU pricing schemes, which are intended to shift load and lower the peak-to-average ratio (PAR). The FOPID had been incorporated with EMS to enhance the stability as well as precision. Reductions in energy consumption and electricity costs across a range of scenarios are evident in the simulation results conducted in the MATLAB environment.

An innovative finite-time leader-follower consensus control for a nonlinear mechanical disturbed system with actuation saturation

ABSTRACT This paper presents a novel leader-follower consensus control method for a special class of nonlinear multi-agent mechanical systems in the presence of control input saturation and external disturbances, and this class includes robot system dynamics with a wide range of potential applications in industry. In the innovative technique, one of the agents is selected as the leader and the other agents are guided in such a way that the entire states of the system can reach a consensus within the transient limits of the determined performance. Due to the presence of disturbance in most practical systems, the effect of limited disturbance has been evaluated in the consensus control method and actuator saturation is included in the design process of the control technique. Finally, a terminal sliding mode control method is adapted to ensure the stability of the overall system and fast finite-time leader-follower consensus control. The simulation results of the multi-agent nonlinear robot system in the MATLAB environment, in different scenarios by simultaneously considering the actuator saturation and external disturbance, show the effectiveness of the proposed control method.

Machine Learning Based Decision Support System for the Diagnosis of Breast Cancer

Breast cancer is among the most prevalent diseases encountered among women worldwide. Early diagnosis of breast cancer is crucial for the treatment of the disease. Detecting the disease at an early stage prevents deaths resulting from the condition. Recently, computer-aided systems have been developed to ensure early-stage diagnosis and accuracy of breast cancer. Computer-aided systems developed with machine learning approaches significantly contribute to the process of diagnosing breast cancer. The aim of this study is to propose a new classification system based on machine learning algorithms developed for the diagnosis of breast cancer. In this study, sub-data sets were created by reducing features, and data cleaning processes were applied. After these procedures, stages such as feature selection and feature extraction were applied. In this study, classification processes such as Ensemble, k- Nearest Neighbors (kNN), Support Vector Machines (SVMs), and Hybrid Artificial Intelligence were used in line with machine learning. With the obtained results, a Breast Cancer diagnosis algorithm was created. Performance evaluation criteria such as accuracy rate, specificity, sensitivity, kappa number and F-Measure were applied to the created algorithms. In the results obtained in this study, the highest accuracy rate was found to be 99.3% with the Ensemble method, the highest specificity rate was 98.7% with the Ensemble method, and the highest sensitivity rate was found to be 100% with many methods. In light of these results, it was observed that the machine learning algorithms used in this study, implemented in the Matlab environment, were effective. Consequently, it was proven that higher accuracy, specificity, and sensitivity rates can be found with different machine learning techniques. This also demonstrates that the study in our article is a reliable one in detecting diseased and healthy individuals in the diagnosis of breast cancer, showing that it is a more applicable and feasible study in the healthcare field.

Control of a BLDC Motor of an Electric Vehicle Powered by a Photovoltaic System based on Fuzzy Logic MPPT (FLC-MPPT)

Photovoltaic Systems (PV) represent a viable solution for converting solar energy into electricity without emitting harmful gases. This technology remains highly sought-after in the market, primarily due to its renewable characteristics and the absence of movable components, which renders it a low-maintenance option. This produced energy can be stored in batteries tobe used for example in operating electric vehicles. If the generated voltage is inadequate or requires regulation, a boost converter may be employed or a buck boost To manage energy during storage and discharge to provide the energy needed to run the induction motor. In this paper, we proposed the field-oriented control as an approach for controlling the speed of BLDC Motor drive electrical vehicles to improve the overall effectiveness of the proposed system, The BLDC Motor is powered by battery storage from a photovoltaic system. Furthermore, this work also addresses the specifics of modeling the Fuzzy logic control MPPT (FLC-MPPT) used for the energy management system (PV and batteries), as well as the proposed control approach for the electric vehicle motor, which are subsequently simulated in the MatLab environment.

Analysis of methods for reducing the signal PAPR under the influence of the Doppler effect in hybrid communication networks

Background. For further development of communication networks, it is planned to use hybrid satellite networks for traffic transmission. However, satellite communication channels have features such as distortion caused by the Doppler effect and increased energy efficiency requirements. Aim of this study is to analyze variants of orthogonal frequency multiplexing methods and modulation methods in order to choose the most stable technology, taking into account destabilizing factors. Method. Comparing different signal processing technologies and studying their resistance to bit errors is the imitation modeling of the communication channel in the Matlab environment. This approach allows creating a model of the communication network, taking into account the main parameters of communication channels, such as the Doppler effect, energy deficiency and destabilizing factors. Results. The distribution of the bit error coefficient for various signal processing technologies, depending on the signal-to-noise ratio, is compared. The method of frequency multiplexing is defined, providing the minimum peak factor and the most resistant to bit error. It is also noted that the effectiveness of all the studied technologies depends on the spacing and modulation constellations, and that it is necessary to adjust the characteristics of the system for each case. Conclusion. The results of this study can be used to improve the quality of communication in difficult interference conditions of hybrid mobile networks of 5 and 6 generations using the satellite segment.

Aeroelastic Structural Analysis to Calculate Symmetrical Divergence Modes of an Aircraft Wing Using Aerodynamic Lifting Line Theory

This work explores the use of numerical matrix iteration to determine an aircraft wing's divergence speed using the aerodynamic span effects approach. The present work begins with the construction of equilibrium equations in differential and integral forms. In this paper, integral formulas are used because it serves as a convenient basis for numerical solutions of complex practical problems. Second, the straight-tapered wing is divided into a number of Multhopp stations. Subsequently, the stations' torsional influence coefficient matrix has been calculated. Third, lifting line theory was used as a suitable choice of aerodynamic theory, and the governing equations were represented in matrix form. Finally, aerodynamic span effects are taken into consideration with induction effects according to Prandtl’s lifting line theory to calculate the symmetrical divergence speed from the lowest eigenvalue of the homogenous governing integral equation. To get the solution to converge, a matrix has been iterated using the MATLAB environment. The obtained results will aid modern aircraft designers in their understanding of wing instability in steady motion.

Detecting and evaluating surface defects in additive manufacturing composite materials via image processing technique

Purpose This study aims to offer an image-based robust edge detection system that can estimate, identify, locate and label surface flaws during manufacturing for real-time surface issue diagnostics. Of great concern, this methodology extrapolates surface defect information from scanning electron microscopy (SEM) images of composite fracture surfaces. This study predicts changes in topological intensity of composite fracture surfaces and display them as real-time surface intensity values for the first time. Design/methodology/approach This work, however, introduces a novel robust edge detection method – based image processing – as it is shown to be effective in locating defects, as measured by SEM images of composite fracture surfaces created using additive manufacturing (AM). SEM images, obtained in this study, are related to previous study considering the fracture surfaces of reinforced thermoset composites created via the AM method. These SEM images are of two types: fracture surface of AM of carbon fiber reinforced thermoset composites and fracture surface of AM of syntactic foam reinforced thermoset composites. Initially, MATLAB environment is used for analyzing the SEM images; the technique used, as well as the validity are explained more in the methodology section. Findings The robust surface defect inspection approach used herein is found to be capable of predicting, identifying, localizing and labeling surface defects during production, allowing for real-time surface issue diagnosis. Further, this work makes it possible to use image processing and analysis of these surfaces to anticipate fluctuations in the topological intensity of the fracture surfaces of composites and represent them as values of surface intensity in real time. Originality/value Rising worldwide company rivalry requires a fast, accurate component failure diagnostic method. To create an efficient feature set, a surface defect inspection system must identify product flaws in real time. Thus, this study proposed an image-based robust edge detection system – based on MATLAB environment – that is capable of estimating, identifying, locating and labeling surface faults during production. This paves the way for an extensive set of high-quality tools for dealing with a wide range of problems associated with digital image processing in composites. As a result, the ability to define methodologies and rapidly prototype prospective solutions typically minimizes the cost and time required to implement a successful system during the design phase of an image processing system.

Appraisal of partial anomalous pulmonary venous drainage through a lumped-parameter mathematical model: a new pathophysiological proof of concept.

Haemodynamic determinants of the ratio between pulmonary and systemic flow (Qp/Qs) in partial anomalous pulmonary venous return (PAPVR) are still not fully understood. Indeed, among patients with the same number of lung segments draining anomalously, a great variability is observed in terms of right ventricular overload. The aim of this study was to test the hypothesis that the anatomic site of drainage, affecting the total circuit impedance, independently influences the magnitude of shunt estimated by Qp/Qs. A zero-dimensional lumped parameter mathematical model was developed and validated on a sample of patients. We developed a zero-dimensional lumped parameter model, using time-varying elastances for heart chambers, RLC Windkessel circuits for the systemic and pulmonary circulations. Patients were categorized into vena cava (VC) type (including left drainage to anomalous vein) and right atrium (RA) type. The mathematical model is a system of ordinary differential equations that are numerically solved by means of the ode15s solver in the MATLAB environment. The model showed an increase of Qp/Qs with the increase of the number of anomalous veins. With the same number of anomalous veins, Qp/Qs was lower in patients with anomalous drainage to the VC as compared with RA. The validation sample consisted of 49 patients (27, 55% females). As predicted by the model, patients with PAPVR with VC type displayed a lower invasive and cardiac magnetic resonance Qp/Qs as compared with drainage to RA: 1.4 (1.2-1.7) and 1.45 (1.25-1.6) versus 2 (1.75-2.1) and 1.9 (1.6-2), P < 0.05. After stratifying for number of lung territories, a lower Qp/Qs was measured in patients with VC PAPVR as compared with RA. In patients with PAPVR, the site of anomalous drainage modulates the Qp/Qs. According to the model, this effect is mediated by the post-capillary impedance of the circuit and significantly decreases with the increase of pulmonary vascular resistances.

Improving Recognition of Road Users via Doppler Radar Data and Deep Learning Convolutional Networks

This research presents findings from laboratory experiments on a novel method for identifying and differentiating objects using radar signatures and a specialized convolutional neural network architecture. Previously introduced by the authors, this method has been validated through real-world measurements in an urban environment with a 24 GHz frequency-modulated continuous-wave radar. This study describes how radar signatures, generated in the MATLAB (R2023b) environment from I and Q signals captured by the uRAD USB v1.2 radar, were processed. A database of radar signatures for pedestrians, cyclists, and vehicles was created, and a tailored convolutional neural network was trained. The developed solution achieves an accuracy of over 95% in distinguishing between various objects. The simulation results and successful tests support the application of this system across various sectors. The key innovations include distinguishing multiple objects from a single radar signature, a custom architecture for the convolutional neural network, and an application that processes radar data to produce near-real-time recognition results.

Multi-objective optimization of FCC separation system based on NSGA-Ⅱ

Based on the industrial production data, the simulation of the separation process of a 2 million tons/year FCC unit including the fractionation and the absorption-stabilization systems were conducted on Aspen Plus software. To optimize the operation process, multi-objective optimization was performed integrating the Aspen Plus platform and MATLAB environment utilizing the improved non-dominated sorting genetic algorithm (NSGA-Ⅱ). The established multi-objective optimization model was used for the operating variable screening, the genetic algebra selection and the optimal solution determination. During the optimization, the total yield of LPG and stable gasoline was aimed as the first optimization objective while the energy consumption of the whole system as the second objective. Five expressions were applied as the constraint functions and nine operating variables were conducted as the decision variables. The results showed that the performances of FCC separation system have been greatly improved after optimization. Some optimization strategies are advised for the whole system. The total yield of LPG and stabilized gasoline increases by 2. 44 %, and the energy consumption of the separation system decreases by 41. 79 %. In addition, the CO2 emission is reduced by 23.59 t/h and the total annual cost (TAC) is reduced by $0.89 million/year. It has been revealed that the multi-objective optimization method based on NSGA-Ⅱ algorithm is useful for the guidance of the optimization of industrial petrochemical process.

Hunter–Prey Optimization Algorithm for Optimal Allocation of PV, DSTATCOM, and EVCS in Radial Distribution Systems

ABSTRACTThis research article instigates a seminal approach for optimizing reactive power in renewable energy sources (RES) and electric vehicles (EVs) coalescing distribution systems, using the innovative Hunter–Prey Optimization (HPO) algorithm in conjunction with DSTATCOM as a reactive power compensator. The proposed methodology aims to minimize losses, enhance voltage stability, and improve overall system performance by simultaneously optimizing reactive power flows in photovoltaic RES (PV_DG), EV charging stations (EVCS), and DSTATCOMs within the distribution system. Simulations carried on IEEE‐33, IEEE‐69, and IEEE‐118 test bus systems in MATLAB environment demonstrate that the HPO‐based approach achieves a 91.47% and 96.61% reduction in real power losses and an improvement in voltage profile with a minimum voltage value of 0.991 and 0.994 p.u. (respectively for IEEE‐33 and 69 bus systems), compared to traditional algorithms. These results highlight the lofty performance of the HPO method, effectively addressing the challenges posed by the integration of RES and EVs along with DSTATCOM.

Optimal sizing of battery energy storage system (BESS) for multiple applications using regression analysis and deep sleep optimizer algorithm

The multifunctional applications of battery energy storage system in a power system network will reduce the significant slack time of use as evident in many single-based applications. In order to deploy BESS for multiple applications, it is of utmost importance that the optimal size for the desired multiple functions, firstly be determined. This work proposes a novel methodology for the optimal sizing of battery energy storage system for frequency support, power loss minimization and voltage deviation mitigations. The suggested sizing methodology takes into account the level of penetration of the renewable energy sources in the power network. Regression analysis is used for mathematical formulations while Deep Sleep Heuristic algorithms in MATLAB environment is used for the optimization process for BESS optimal size. The robustness of the proposed method was tested by using IEEE modified 39-bus system. Simulation results show that with the BESS optimal size integrated into the network, voltage deviations were mitigated by about 20 % and power losses were reduced from 65.3 MW to 59.68 MW. Also, the system frequency nadir during the outage of the largest single generating, was sustained at 59.60 Hz whereas, without BESS it was 59.15 Hz. This is critical because such a frequency decline may activate the underfrequency load shedding relays.

Study on Second-order RC Model Charge State Estimation Method for Lithium Battery Based on EKF Algorithm

In order to improve the estimation accuracy of the state of charge (SOC) of lithium-ion batteries, this paper proposes a SOC estimation method based on the second-order RC equivalent circuit model and the extended Kalman filter (EKF) algorithm. Firstly, the second-order RC equivalent circuit model is selected as the research object in this paper, and the parameters of the second-order RC model are identified by combining the hybrid pulse charging and discharging test and the 1stopt software, and then the extended Kalman filtering algorithm is applied under the MATLAB environment to estimate the SOC based on the second-order RC equivalent circuit model. The simulation results show that the adopted estimation method can accurately track the SOC trend of the battery compared with the actual state-of-charge (SOC) reference value. Through experimental analysis, the estimation results of the EKF algorithm combined with the second-order RC equivalent circuit model have a small error with the actual value, and the estimation accuracy is high and ideal.

Comparative Approach to Performance Estimation of Pulsed Wave Doppler Equipment Based on Kiviat Diagram

Quality assessment of ultrasound medical systems is a demanding task due to the high number of parameters to quantify their performance: in the present study, a Kiviat diagram-based integrated approach was proposed to effectively combine the contribution of some experimental parameters and quantify the overall performance of pulsed wave Doppler (PWD) systems for clinical applications. Four test parameters were defined and assessed through custom-written measurement methods based on image analysis, implemented in the MATLAB environment, and applied to spectral images of a flow phantom, i.e., average maximum velocity sensitivity (AMVS), velocity measurements accuracy (VeMeA), lowest detectable signal (LDS), and the velocity profile discrepancy index (VPDI). The parameters above were scaled in a standard range to represent the four vertices of a Kiviat plot, whose area was considered the overall quality index of the ultrasound system in PWD mode. Five brand-new ultrasound diagnostic systems, equipped with linear array probes, were tested in two different working conditions using a commercial flow phantom as a reference. The promising results confirm the robustness of AMVS, VeMeA, and LDS parameters while suggesting further investigations on the VPDI.

Analysis of NR and LTE Target Signal Structure Based on Neural Network Approach and Deep Learning Methods

Purpose: the rapid development and consolidation of broadband wireless access networks of the fourth 4G LTE and fifth 5G NR generations determine the need to find ways to reuse frequencies. A well-known solution to this problem is the concept of cognitive radio. The use of artificial intelligence in radio networks in general and the use of a neural network approach with deep learning for recognizing signals of LTE and NR standards in particular have serious potential for the practical implementation of frequency resource reuse according to the concept of cognitive development. The aim of the work: is to analyze models, methods and algorithms that allow recognizing signals of the NR and LTE standards based on recording samples of radio signals obtained using pre-recorded NR and LTE signals in MatLab software. In this paper, the procedures for scanning and recognizing sections of the spectrum are considered. The operations of training a semantic segmentation network for the identification of these signals in a broadband spectrogram and the use of a trained network for subsequent spectrum sensing and recognition of signals of the NR and LTE standards are investigated. Methods: the method used to test the concept of cognitive radio is spectrum sensing, which results in information about the occupancy of frequency bands in a given location by primary users, and the identification of spectrum sections available for use by secondary users without interfering with the work of primary users in real time. The novelty of the work is the use of a neural network approach in the analysis of the target NR and LTE signal. Results: the model, trained on the basis of a neural network approach and deep learning methods, is able to distinguish between signals of the NR and LTE standards. Theoretical / Practical relevance: the software implementation of spectral sensing is implemented using an extension package in the MatLab environment and allows for experimental testing of a cognitive radio receiver when performing procedures for analyzing the structure of the target signal of the NR and LTE standards based on a neural network approach and deep learning methods.

Pressing Mechanism Design and Performance Analysis for Brake Pedal System at Low-Speed Driving

This article focuses on the modelling and analysis of an automatic braking mechanism for low-speed driving scenarios. The study aims to replicate the force exerted by a driver on the brake and accelerator pedals by strategically placing the suitable actuator within the pedal subsystem assembly. The primary objective is to model the pedal pressing mechanism for the braking subsystem and gain insights into driver ‘s behaviour during traffic congestion, thereby ensuring the effective operation of the automatic braking system. The actuator was modelled using 3D virtual environment, while the car body or dynamics was modelled using SimScape in MATLAB environment, and the simulation performance analysis was employed to evaluate the performance outcomes. It has shown that the mechanism designed mimics the actual manual pedal pressing mechanism of driver’s leg where the results show that the actuator managed to produce 150 N to reach speed at 8.5 km/h at 48 mm linear actuator stroke. Therefore, the research findings are anticipated to contribute significantly to the advancement of automatic braking systems, particularly during low speed in road traffic delay, which may help in reducing the fatigue among drivers, enhancing vehicle safety, and providing valuable insights for the development of more reliable and efficient systems within the automotive industry.