Advances in information and communication technologies have significantly transformed engineering education. Virtual laboratories are increasingly adopted to enhance student interaction with control system simulations...
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ISBN:
(数字)9798350391084
ISBN:
(纸本)9798350391091
Advances in information and communication technologies have significantly transformed engineering education. Virtual laboratories are increasingly adopted to enhance student interaction with control system simulations. The improved visualization and interaction capabilities of modern computers offer a more organic way to teach theoretical foundations. This paper describes the implementation of a methodology for teaching control theory that until now was entirely online in a face-to-face environment at the University of the Federal District in Brasilia, Brazil. This methodology utilizes virtual laboratories with CoppeliaSim, MATLAB, and the EVA mobile robot to teach control theory focused on single-input, single-output (SISO) systems for mobile robot tracking and obstacle avoidance applications. The results of the face-to-face implementation are compared to those of the fully online methodology, revealing that in-person teaching significantly enhances the quality of instruction.
A map is necessary for tasks such as path planning or localization, which are common to mobile robot navigation. However, a map may be unavailable if the environment in which a robot navigates is unknown. Creating a m...
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In this paper, we compare two measurement techniques to analyze the dynamic response of a MEMS resonator, made of a microcantilever beam, that is electrostatically actuated via a side electrode. The first method is ba...
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The Histogram of Oriented Gradients (HOG) algorithm is widely utilized in image processing for tasks such as detection, classification, and tracking. However, several challenges arise when implementing this algorithm ...
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Artificial neural networks (ANNs) are intricate mathematical models, drawing inspiration from the biological nervous system, and offering intelligence alongside nonparametric capabilities. The efficacy of ANNs heavily...
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ISBN:
(数字)9798350374575
ISBN:
(纸本)9798350374582
Artificial neural networks (ANNs) are intricate mathematical models, drawing inspiration from the biological nervous system, and offering intelligence alongside nonparametric capabilities. The efficacy of ANNs heavily relies on their learning process. Heuristic search algorithms have been proposed in literature as reliable options for optimizing neural networks utilizing the Perceptron architecture. This paper presents an suboptimal neural network Perceptron training method employing the Whale Optimization Algorithm for the application of farming watering mobile robots. To achieve this, a fitness function based on system responses such as overshoot, settling Time, and steady-state Error was developed. The sub-optimal Perceptron controller was then implemented using MATLAB, CoppeliaSim software and the EVA robot, resulting in a 66.17% overshoot, a 4.7% steady-state error, and a settling time of 3.05 seconds. Moreover, the sub-optimal Perceptron controller was mapped onto a System on Chip FPGA AMD-Xilinx Zynq 7020, resulting in a hardware resources consumption of 1,870 (3.52%) Lookup table, 2 (0.9%) Digital Signal Processing blocks, 1,019 (0.96%) Flip-Flops and a power consumtion of 0.005 W. Finally, Hardware-in-the-Loop validation was performed using CoppeliaSim, Python, Universal Direct Memory Access, and the EVA robot. The results showed an overshoot of 94.83%, a settling time of 2 seconds, and a steady-state error of 4.5%.
Low-cost navigation systems for ground vehicles often rely on the fusion with Global Positioning System (GPS) for improved state estimation. In this study a low-cost gyroscope is fused with a GPS for improved vehicle ...
Low-cost navigation systems for ground vehicles often rely on the fusion with Global Positioning System (GPS) for improved state estimation. In this study a low-cost gyroscope is fused with a GPS for improved vehicle heading angle estimation using several Artificial Intelligence (AI) based architectures that include Shallow Neural Networks (SNN), Multi-Layer Neural Networks (MLNN), and Adaptive Neuro- Fuzzy Inference Systems (ANFIS). The primary goal behind using AI based sensor fusion is to obtain a highly accurate vehicle heading estimation suitable for autonomous navigation applications. When available, the GPS signal is used to correct the vehicle's heading angle. The neural networks and ANFIS methods both use the difference between the GPS signal and the heading angle from the integrated gyroscope as inputs. The performance achieved is shown and analyzed. According to the results obtained, the MLNN provides the most accurate heading estimates.
In this paper a new variant of the widely used Rapidly exploring Random Tree (RRT*) algorithm is proposed. The main goal of this variant is to improve the efficiency of the generated path, in both computation time and...
In this paper a new variant of the widely used Rapidly exploring Random Tree (RRT*) algorithm is proposed. The main goal of this variant is to improve the efficiency of the generated path, in both computation time and the quality of the path. In a previous work by the authors, an improved version RRT*N was proposed, where a normal probability distribution was used to control the generation of the random nodes. The proposed variant utilizes an intelligent fuzzy logic system (FLS) to control the generation of the random nodes and commands the robot to the required target introducing an intelligent fuzzy adaptive RRT*N path planning approach (FA-RRT*N). The proposed approach not only reduced the time required about 29.5 % of the time required by the RRT*, but also generally resulted in shorter path about 63% of the path generated by the traditional RRT * .
Vibration analysis is crucial for predictive maintenance of bearing systems, aiming to reduce costs and prevent failures. This paper investigates signal processing techniques such as FFTs and Hilbert Transforms for fa...
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ISBN:
(数字)9798350374575
ISBN:
(纸本)9798350374582
Vibration analysis is crucial for predictive maintenance of bearing systems, aiming to reduce costs and prevent failures. This paper investigates signal processing techniques such as FFTs and Hilbert Transforms for fault diagnosis, alongside decision tree models, particularly the J48 algorithm within the WEKA environment, for fault classification. The study emphasizes the integration of signal processing and machine learning for effective fault diagnosis and maintenance. Notably, a classification model with low computational cost has been chosen, facilitating implementation in embedded systems. This model also offers interpretability of outputs, as it is based on decision trees of frequency components obtained through preprocessing.
In this work, a sliding detection algorithm was proposed for a previously developed robotic hand through the utilization of force sensors. Previous works have designed several hardware architectures to filter sensor d...
In this work, a sliding detection algorithm was proposed for a previously developed robotic hand through the utilization of force sensors. Previous works have designed several hardware architectures to filter sensor data, implement dynamic control, and machine learning models for controlling the robotic fingers using a single Field programmable Gate Arrays (FPGA) chip. In this regard, the slip detection algorithm was developed to comprise three stages, each with a low computational cost, including a moving average filter, a first-order derivative, and a peak detection algorithm. A reference model was constructed and validated through an experimental protocol employing daily use objects to create a database. Subsequently, the slip detection algorithm was mapped onto hardware utilizing previously developed floating-point arithmetic IP cores and implemented using a Zynq 7020 device. The FPGA implementation was characterized in terms of resource occupation, power consumption, execution time, and numerical error with the reference model.
Technological advancements are increasingly evident across various sectors, including automobiles, industry, and healthcare. In precision agriculture, significant progress has been made, with AgroTICs and Smart Agricu...
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ISBN:
(数字)9798350374575
ISBN:
(纸本)9798350374582
Technological advancements are increasingly evident across various sectors, including automobiles, industry, and healthcare. In precision agriculture, significant progress has been made, with AgroTICs and Smart Agriculture gaining substantial traction in the market. However, a gap remains between cutting-edge technology and family farming, presenting a challenge from both social and applied research perspectives. However, there is still a gap between cutting-edge technology and family farming, which creates a challenge from a social and applied research point of view. In this context, this paper proposes a monitoring model based on Fuzzy Logic and sensor automation applied to estimate the health of a corn crop. The proposed Fuzzy inference system involves calculating an indicator of nutrients as well as the average color and area of corn plants. The nutrient indicator is automatically computed by an ESP32 microcontroller using sensor readings, while the average color and area inputs are manually entered via a mobile application. Additionally, the Fuzzy inference is integrated into the ESP32. The model underwent experimental validation on the health of the plantation, and the results were evaluated in four areas: one was designated for testing, and three were for validation. The model achieved an accuracy of 97.5% in Scenario 3, categorized as ’Very Favorable’, and an accuracy of 65% in Scenarios 2 and 4, categorized as ‘Unfavorable’. The implications of this research contribute to the advancement of AgroTICs among small producers, with the potential to enhance and automate the monitoring of their harvest production.
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