MATLAB Environment Research Articles

Fast and accurate Zero-defect manufacturing using collaborative real-time photogrammetry

Abstract. For the machining of industrial components, their accurate alignment on operative machines is a crucial step with significant impacts on the whole process, especially when dealing with large-scale elements. To achieve this alignment, a trained operator is needed along with expensive equipment, such as laser trackers, whose correct use requires the measurements of many significant points and is time-consuming. The lack of a precise alignment comes with possible incorrect parts machining, resulting in quality concerns and potentially expensive rework. To face this problem, collaborative real-time photogrammetry is an effective solution for guiding untrained users in correctly positioning various measured and codified components to generate the best solution for machining high-volume parts. The solution proposed in this contribution is part of the TACCO project, co-financed by EIT Manufacturing (co-funded by the EU). It takes advantage of the TACCO system, where auxiliary components (scale bars, dome-shaped components, coded targets and a cross-reference system) are placed on the object to be measured for their correct positioning in order to obtain an optimal photogrammetric reconstruction. This allows the reduction of the error ellipsoids on several uncoded targets, which are calculated through an iterative simulation carried out in a MATLAB environment. Unskilled operators are then guided through an Augmented Reality headset for their correct placement on the real-world object and for defining the positions and orientations from which to acquire images. This methodology will lead to an accurate measurement of the target points to be machined and, thus, a precise alignment process for the subsequent machining operations.

Drone path planning and dynamic arena-target allocation for crowd surveillance

The paper addresses the problem of surveillance of dynamic crowds in a bounded arena using multiple drones. The strategy proposes a prediction-based path planning approach for drones, where the target’s future position is predicted and used to generate a traverse path. The procedure for grouping numerous human beings in order to deal with a dense crowd scenario is also described. An algorithm to dynamically divide and allocate the arena to drones is also proposed to ensure equal workload distribution among all drones in the framework. The paper also discusses relevant concepts like detecting anomalous crowd activity and searching for any missing target in the arena. The proposed algorithms are validated on MATLAB environment numerically and ROS/Gazebo physics engine as a part of simulation-in-the-loop (SITL).

Computational study of the performance of a solar dryer for improvement in the shelf life of the food materials.

In this study, the thermal and drying characteristics of a thin layer food sample were investigated. An indirect type, simple, efficient, and economically feasible solar dryer was fabricated and used for food preservation. However, a dynamic model of a fabricated solar dryer was also presented to gain a better insight into the drying and thermal actions. This model consists of thermal modeling of the drying chamber, solar collector, and solar-dried food sample. The law of conservation of energy was applied to evaluate the temperature at different sections of the solar dryer with respect to drying time. All listed model equations were solved in the MATLAB environment. This study helps to examine the influence of solar radiation on the collector plate temperature, drying chamber temperature, food sample temperature, and performance parameters such as thermal efficiency with respect to drying time. Model data was found in good agreement with experimental data within a 4% error. It is concluded that the drying of food material is affected by air temperature, the collector temperature, mode of heat transfer, and material characteristics such as dimension and mass of the food sample.

A novel design and analysis of hybrid fuzzy logic MPPT controller for solar PV system under partial shading conditions

Renewable energy resources are more useful when associated with the thermal power generation network because of their high accessibility in the environment, good system response, easy manufacturing, plus high scalable. So, the present research is going on solar power to reduce consumer grid dependency. The running of the PV network is quite easier, plus less human sources are involved. However, the solar modules’ power generation is nonlinear fashion. So, the collection of peak power from the sunlight-dependent systems is a highly challenging task. In this article, a Modified Differential Step Grey Wolf Optimization with Adaptive Fuzzy Logic Controller (MDSGWO with FLC) is developed for collecting the maximum power from renewable energy resources under diverse Partial Shading Conditions (PSCs). The introduced method comprehensive analysis has been done along with the other recently existing MPPT methods in terms of convergence speed, MPP tracking accuracy, operating efficiency of the introduced method, functioning duty value of the DC–DC boost power converter, dependence of MPPT on sunlight system, total number of sensing devices are needed, plus peak power extraction from the proposed system. Here, the sunlight power generation cost is more to limit this issue, a power converter is selected in the second objective to develop the voltage source capability of the PV network. The overall PV-interfaced power converter network is examined by utilizing the MATLAB environment.

AIR POLLUTION SOURCES AND THEIR IMPACT ON THE ENVIRONMENT

This paper provides an overview of the various sources of air pollution and their impact on the environment and human health, distinguishing between natural and anthropogenic (man-made) sources. The study assessed the environmental impact of these sources – the overall level of air pollution, taking into account the predominant anthropo-genic source, such as emissions from vehicles and industrial activities. The important role of geographic information systems (GIS) in monitoring air pollution, performing spatial data analysis, and identifying pollution hotspots is emphasized. This method proposes to analyze the impact of the air quality result on public health and the environment. The main focus of the study is on the predominant pollutants in the atmosphere such as particulate matter (PM), nitrogen oxides (NOx), and sulfur dioxide (SO2). The object of the study was the Old Town of the city of Vilnius. A calculation model was created in the MatLab environment to determine the pollution. The results reveal trends in environmental indicators. In the Old Town, decreasing pollution levels of PM10 and CO are observed, and NO2 remained relatively constant from 2020 the month of January until 2022 December. The primary importance of environmental monitoring depends on government regulation, technological advancements, and public participation to effectively reduce air pollution and protect the environment.

Meshing principle and numerical study of classic conic worm drive

To explore the meshing mechanism of classic conic worm drive and provide a theoretical basis for creating other types of worm drive, a complete meshing theory on the proposed worm pair and its meshing performance are investigated. A new mathematical model containing a full set of geometric parameters on this drive is established using differential geometry. The formulae for calculating the reference point coordinates in different coordinate systems are attained, and then the blank size of the conical worm wheel is given. To build tooth surface boundaries, the meshing theory based on reference point is applied, which ensures the location of this point on the tooth surface. By solving multivariate nonlinear equation system in MATLAB environment, these conjugate regions and instantaneous contact lines are plotted within the axial section of the conic worm pair, at the same time, these values of sliding angle and induced principal curvature at meshing points are calculated. Finally, a numerical study is performed and the results verify that the conic worm pair has a relatively high tooth surface utilization ratio and favorable conditions for contact stress and lubrication, which is consistent with the practical application.

Single-Cell Model of Orexin Neuron and Adaptive Glucose-sensing Mechanism

Orexin neurons are critical for regulating sleep-wake behavior. Their intrinsic firing activity promotes wakefulness. Orexin neurons are also specialized sensor of the body’s glucose level. Small rises in glucose level inhibit intrinsic action potential firing of orexin neuron. A distinct group of orexin neuron exhibits only transient inhibitory responses to sustained glucose stimulation. This adaptation involves recovery from inhibition via closure of leak-like potassium channels. In this project, we simulated the baseline behavior and adapting/nonadapting orexin response to sustained glucose stimulation in MatLab environment.

3D Modelling and Simulation Based on Kinematic Analysis for Hippotherapy

The engineering perspective on the physiology and anatomy of living things has led to the emergence of the science of biomechanics. Developing computer technologies have made it possible to examine the behaviour of living things in more detail and to develop technologies that enable kinetic and kinematic analysis of movements to imitate real limbs. In this study, the natural gaits of horses (Walk, Kenter and Trot) were analysed with image processing techniques, kinematic analyses were made and three-dimensional solid models were created. Giving movement ability to the extracted solid models was carried out with the simulation software prepared on the Matlab-SimMechanic platform. By comparing the findings obtained as a result of image processing and simulation, the success of transferring the movements to a developed system was evaluated. After mathematical equations were created in the MATLAB environment, the equations were tested on the developed system and their performance rates were determined. With the developed system, joint angles, ability to reach the correct position and movement ability were analysed and it was shown how the mathematical equations required to perform the movements could be derived. As a result, a design process model for movement analysis, simulation and control of different living groups is presented with the equations obtained by formulating the information obtained from the developed system.

Performance comparison of linear and nonlinear compliant liquid dampers-inerter in controlling across-wind response of benchmark tall building

The potential of compliant liquid dampers-inerter (CLDI) for response control of tall buildings, capitalizing on the mass amplification property of the inerter, has been explored. However, the influence of the compliance connection on the damper displacement, energy dissipation, and inerter force is never addressed. The present work introduces a nonlinear compliant liquid damper-inerter (NCLDI), in which the compliance connection is achieved by joining the tank with the building through nonlinear spring and linear dashpot elements. The nonlinear mechanism (restoring force) is generated by connecting two linear springs in series. The effectiveness of NCLDI in reducing the across-wind response of tall buildings is examined and compared with that of a CLDI. For numerical demonstration, the 76-storey benchmark tall building is considered and subjected to across-wind loading. With the building's available mass, damping, and stiffness matrices, the uncontrolled and controlled structural responses are obtained using the Newmark–Beta method in the MATLAB environment. Iterations are executed at each time step to address the nonlinear stiffness term incorporating the Newton-Raphson method. Optimization is performed to estimate the optimum damper parameters utilizing MATLAB's multi-objective genetic algorithm solver, ‘gamultiobj’. The four selected objective functions are the peak and root-mean-square values of the roof displacement and acceleration. The performance of both the dampers is investigated for different inerter topologies with variable inertance ratios. The CLDI is found to be efficient when the inertance ratio and topology are less. However, an enhanced mitigation effect of the NCLDI compared to the CLDI is observed for higher inertance with a large topology. In addition, the stroke length of the damper is reduced significantly in case of NCLDI, which is a notable advantage for any passive damping device.

An integrated method for compensating and correcting nonlinear error in five-axis machining utilizing cutter contacting point data

In current five-axis computer numerical control (CNC) machining, the use of minute linear path segments as an approximation for the ideal cutter contacting (CC) point trajectory is still prevalent. However, introducing rotation axes leads to a deviation of the actual CC point trajectory from the ideal, resulting in nonlinear errors. An integrated method is proposed in this paper for compensating and correcting both the contour error, associated with the approximation of the part surface by the ideal CC point trajectory and the nonlinear error of the CC point trajectory based on the information in the CC point data. By analyzing the spatial relationship between the tool posture and the CC point path during the five-axis linear interpolation process, two adjacent machining tool positions containing CC point data information are selected as the starting and ending points of the five-axis linear interpolation machining. The ideal tool center point and the actual CC point are calculated during the interpolation process, as well as the distance and the unit vector in the perpendicular direction between the actual CC point and the ideal CC point trajectory segment. In the comprehensive error compensation and correction phase, the obtained unit vectors are used as direction vectors for error compensation, and the tool center point during interpolation is first compensated and corrected. This ensures the actual CC point and the contour curve are on the same plane. The compensation direction for contour error is calculated using the start/end tool axis vectors and the ideal CC point trajectory vectors. The size of the contour error approximating the contour curve is calculated through the chord error. A second compensation and correction are applied to the tool center point for interpolation, ultimately achieving comprehensive compensation and correction of nonlinear errors. The data calculations were conducted in the MATLAB environment using actual machining data. After compensation and correction, the contour error was reduced by 76%, the nonlinear error of the CC point trajectory decreased to below 0.88 μm, and the comprehensive nonlinear error of the CC point trajectory was reduced from 19 to 1.5 μm, a reduction of 93%. This demonstrates significant practical value in enhancing the accuracy of five-axis CNC machining. Through actual machining verification, after using the method described in this paper, the average surface roughness decreased from 1.133 to 0.220 μm, and the maximum surface roughness decreased from 6.667 to 1.240 μm. This significantly demonstrates that the compensation and correction method proposed in this paper can significantly improve the surface quality of machined parts.

Experimental analysis of a modified scheme for supplying single-phase remote loads from micro-hydro based three-phase self-excited induction generator

Induction machine (IM) plays significant role in rural electrification, acting as a generator for small kilowatt to large megawatt applications. These IMs are therefore used for power generation, gaining a huge acceptance within the researchers because of its brushless system, robust design and cheap market rate. In off-grid operation, the induction generator (IG) also known as self-excited IG (SEIG), requires supply of external reactive power (RP) for building up its terminal voltage and satisfying the electrical load demand. A small kilowatt level renewable-driven three-phase SEIG is generally deployed for generating single-phase power in off-grid mode for remote areas. Generation of single-phase power and supplying the load from a three-phase SEIG in off-grid operation, results in the imbalance of the SEIG's stator current and phase voltage. In this regard, various capacitor-based excitation (CBE) schemes have been reported for supplying the required RP to the three-phase SEIG and generating single-phase power, while reducing the mentioned difficulties. The current paper emphasizes on the steady-state mathematical formulation of a proposed modified CBE scheme for generating single-phase power and supplying remote hilly load from a hydro-driven three-phase SEIG under the fixed speed of the generator. Further, a state-of-the-art experimental test-bench of the proposed scheme, where a hydro-driven three-phase SEIG supplying single-phase resistive-inductive (RL) load has been showcased with the same being simulated in the MATLAB environment. The result indicates that the proposed CBE scheme can effectively supply single-phase RL load of different power factors (p.f.), having promising voltage regulation with balanced SEIG operation.

Survey on the quality of automatic parameter detection of PID controller for DC motor using Genetic Algorithm and Particle Swarm Optimization

This article presents the results of a study on selecting optimal PID parameters tuned by Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) used for a DC motor. The simulating controller response results show that the PID - GA and PID - PSO combination algorithms are superior to traditional methods. The result also allows for the selection of the optimal algorithm - combining the PSO - PID to design a controller that has smaller settling error but larger overshoot and settling time compared to GA-PID method. The simulation was taken in Matlab environments

Impact of Weight Functions on Performance of Three-Input Integrated Dc-Dc Converter in H ∞ Control

Many power electronic systems applications namely locomotives, hybrid electric vehicles, and all renewable energy sourced systems are shifting towards Multi-Input Multi-Output (MIMO) integrated DC-DC converters due to their reliability and flexible nature. Designing controllers for MIMO integrated DC-DC converter is complicated due to its integrated structure, presence of common elements, and interactions between the input and output variables of the converter. In this work, a Three-Input Integrated DC-DC (TIID) converter is modeled using state-space analysis, and a Transfer Function Matrix (TFM) is acquired from the small signal continuous time model. A robust controller based on the loop shaping method is designed for the TIID converter. In this loop-shaping method, the desired robustness and the performance of the controller are represented with weight functions i.e., loop-shaping filters. These weight functions are designed using TFM and are frequency-dependent. The robustness of the controller depends on the weight function parameters. The effect of varying the parameters of the weight functions on system dynamics, robustness, and performance are studied and plotted. TIID converter of 288 W, 24V-30V-36V to 48 V is considered and the impact of weight function on closed-loop system dynamics and sensitivity characteristics under varying parameter conditions are analyzed in MATLAB Environment.

Power Signal Analysis for Early Fault Detection in Brushless DC Motor Drivers Based on the Hilbert–Huang Transform

Brushless DC machines have demonstrated significant advantages in electrical engineering by eliminating commutators and brushes. Every year, these machines increase their presence in transportation applications. In this sense, early fault identification in these systems, specifically in the electronic speed controllers, is relevant for correct device operation. In this context, the techniques reported in the literature for fault identification based on the Hilbert–Huang transform have shown efficiency in electrical systems. This manuscript proposes a novel technique for early fault identification in electronic speed controllers based on the Hilbert–Huang transform algorithm. Initially, currents from the device are captured with non-invasive sensors in a time window during motor operation. Subsequently, the signals are processed to obtain pertinent information about amplitudes and frequencies using the Hilbert–Huang transform, focusing on fundamental components. Then, estimated parameters are evaluated by computing the error between signals. The existing electrical norms of a balanced system are used to identify a healthy or damaged driver. Through amplitude and frequency error analysis between three-phase signals, early faults caused by system imbalances such as current increasing, torque reduction, and speed reduction are detected. The proposed technique is implemented through data acquisition devices at different voltage conditions and then physical signals are evaluated offline through several simulations in the Matlab environment. The method’s robustness against signal variations is highlighted, as each intrinsic mode function serves as a component representation of the signal and instantaneous frequency computation provides resilience against these variations. Two study cases are conducted in different conditions to validate this technique. The experimental results demonstrate the effectiveness of the proposed method in identifying early faults in brushless DC motor drivers. This study provides data from each power line within the electronic speed controller to detect early faults and extend different approaches, contributing to addressing early failures in speed controllers while expanding beyond the conventional focus on motor failure analysis.

Numerical Analysis of New PCM Thermal Storage Systems

In this paper, a thermal storage system based on a phase change material is proposed and investigated. The system is composed of several tubes that cross a phase change material mass. A fluid flowing in the tubes charges and discharges the heat storage system. A mathematical model of the system has been developed, which provides the time and space distribution of velocity, temperature, and liquid phase-changing material concentration in a non-stationary regime. A hybrid solution method based on finite volumes and finite differences techniques has been employed for the model equations in the MATLAB environment. To the tubes, a rectangular cross section has been assigned. The performance of the system in terms of accumulated energy density and accumulated power density has been investigated by varying some geometric parameters. The considered geometric parameters influence the number of tubes per unit of system width, the tube hydraulic resistance, the amount of phase change material around each tube, the heat transfer surface of the tube, and the heat storage velocity. In the parametric analysis, peaks have been evidenced in the investigated performance parameters at different instants after the beginning of the heat storage.

On Combining Shape Memory Alloy Actuators and Pneumatic Actuators for Performance Enhancement in Soft Robotics

Through soft robotics, flexible structures confer an elevated degree of protection and safety in usage, as well as precision and reliability. Using theoretical models while combining different types of soft components opens a wide variety of possibilities for the development of new and better alternatives to rigid robots. Modeling and controlling soft robotic structures is still a challenge and is presented in different ways by the scientific community. The present scientific work aims to combine two of the most popular types of soft actuators, specifically shape memory alloy and pneumatic actuators. The purpose is to observe the interaction between individual entities and the resulting combined dynamics, highlighting the distinctive effects and influences observed in the combined system. An evaluation is conducted from a numerical simulation perspective in the MATLAB environment using representative mathematical models. The tests prove that a structure combining these particular actuators benefits from the advantages of both components and even compensates for individual downsides.

Comparison of Different MATLAB Environments for Designing DC Motor Main Dimensions

Abstract: DC motor is an important device for various industries due to its variable speed control, efficiency, reliability and versatility. The Design calculations of DC Motor are long and tedious process if done manually. A program can be made in any programming language, but again the program length is large, and it is difficult to understand and summarize the design steps in short. In this paper an effort has been made to understand the Design Procedure of a DC Motor using different MATLAB environments. Design of main dimensions of DC Motor was done in three MATLAB environments. First by writing program in editor file, second using Simulink block sets and lastly by forming MATLAB GUI interface. Comparison between each method is discussed in short.

Time of Flight Distance Sensor–Based Construction Equipment Activity Detection Method

In this study, we delve into a novel approach by employing a sensor-based pattern recognition model to address the automation of construction equipment activity analysis. The model integrates time of flight (ToF) sensors with deep convolutional neural networks (DCNNs) to accurately classify the operational activities of construction equipment, focusing on piston movements. The research utilized a one-twelfth-scale excavator model, processing the displacement ratios of its pistons into a unified dataset for analysis. Methodologically, the study outlines the setup of the sensor modules and their integration with a controller, emphasizing the precision in capturing equipment dynamics. The DCNN model, characterized by its four-layered convolutional blocks, was meticulously tuned within the MATLAB environment, demonstrating the model’s learning capabilities through hyperparameter optimization. An analysis of 2070 samples representing six distinct excavator activities yielded an impressive average precision of 95.51% and a recall of 95.31%, with an overall model accuracy of 95.19%. When compared against other vision-based and accelerometer-based methods, the proposed model showcases enhanced performance and reliability under controlled experimental conditions. This substantiates its potential for practical application in real-world construction scenarios, marking a significant advancement in the field of construction equipment monitoring.

Mathematical Modelling and Simulations of Active Direct Methanol Fuel Cell

A one dimensional isothermal model is proposed by modelling the kinetics of methanol transport at anode flow channel (AFC), membrane and cathode catalyst layer of direct methanol fuel cell (DMFC). Analytical model is proposed to predict methanol cross-over rate through the electrolyte membrane and cell performance. The model presented in this paper considered methanol diffusion and electrochemical oxidation at the anode and cathode channels. The analytical solution of the proposed model was simulated in a MATLAB environment to obtain the polarization curve and leakage current. The effect of methanol concentration on cell voltage and leakage current is studied. The methanol cross-over has the significant impact on cell performance. The presented model predicts higher leakage current with the increase of methanol feed concentration. The cell performance was predicted at 70°C and various methanol feed concentration. The proposed model was validated with the experimental polarization curve of active DMFC.

On usage of the neural network technologies in the it- structure components’ diagnosing.

The idea of using neural network technologes to prove electrophysical diagnostic methods based on the integral physical effects of IT structure components is considered. It is proposed to transform the received information using a discrete Karhunen-Loeve expansion, which gives the minimum root mean square error of packing a priory vectors in multidimensional space. The use of neural networks: MLP, self-organizing (Kohonen Maps) and RBF in MATLAB environment is verified. The best result for microcircuits was obtained using probabilistic RBF-neural networks. A new neural network approach to diagnostics made it possible to perform individual sorting of elements and ststistical evaluation of the IT structure components batch.