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2023 Advances in Science and engineering Technology International Conferences, ASET 2023

作者： Khattab, Omar Yasser, Ahmed Jaradat, Mohammad A. Romdhane, Lotfi American University of Sharjah Mechatronics Engineering Graduate Program Sharjah United Arab Emirates American University of Sharjah Mechanical Engineering Department Sharjah United Arab Emirates

ISBN: (纸本)9781665454742

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∗. © 2023 IEEE.

关键词： Motion planning

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An Intelligent Controller for an Assisted Electric Wheelchair based on Interval Type-2 Fuzzy Logic

An Intelligent Controller for an Assisted Electric Wheelchai...

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2023 Advances in Science and engineering Technology International Conferences, ASET 2023

作者： Al-Bashayreh, Qutaiba A. H. Kiftaro, Ammar Alsheikh, Mahmoud Jaradat, Mohammad A. American University of Sharjah Mechatronics Engineering Gradute Program Sharjah United Arab Emirates American University of Sharjah Mechanical Engineering Department Sharjah United Arab Emirates

ISBN: (纸本)9781665454742

This paper proposes an intelligent controller design for an assisted electric wheelchair using interval type-2 fuzzy logic controller. The controller design has been structured using cascaded fuzzy inference system with feedback loop to avoid obstacles. In addition, to control the dynamics of the wheelchair, a speed fuzzy inference system has been added to the proposed controller. The controller is designed so that the user input is firstly obtained from the wheelchair joystick. The surrounding obstacles, thereafter, are detected using the input of three sensors attached to the wheelchair. At each time step, and until reaching the target point, the performed controller output generates both the motion angle and the wheelchair speed commands. The performance of the controller is tested by simulating the wheelchair travelling in different navigation scenarios. The simulation results confirm the effectiveness of the presented controller. © 2023 IEEE.

关键词： Wheelchairs

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Self-Tuning Neural Network Controller Based on Fuzzy Logic for Multiple Positions Tracking of a Pneumatic Driven Soft Endoscope Actuator 10

Self-Tuning Neural Network Controller Based on Fuzzy Logic f...

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10th International Conference on Automation, Robotics, and Applications, ICARA 2024

作者： Acosta, Renzo Tafur, Julio Canahuire, Ruth Pontificia Universidad Catolica del Peru Control and Automation Engineering Master Degree Program Enginnering Department Lima Peru Universidad de Ingenieria y Tecnologia - UTEC Department of Electrical and Mechatronics Engineering Lima Peru

ISBN: (纸本)9798350394245

This paper presents the dynamic modelling and end effector position control of a soft endoscope. Soft endoscope system under study consists mainly of a pneumatic driven soft actuator (PDSA) with four independently chambers. Recurrent neural network (RNN) and dynamic back propagation (DBP) training algorithm are used to obtain PDSA dynamic model. PDSA position controller is based on a feedforward neural network (FNN) and it is trained using PDSA closed loop system (CLS) and DBP training algorithm. CLS stability analysis concludes that it is not possible control PDSA end effector position using one position controller for different end effector initial and desired positions. Fuzzy logic methodology is used to integrate several positions controllers in a one controller valid for all operation range. The controller implementation and close loop system simulation are performed in MATLAB for different end effector desired position to validate system performance in all range of operation. © 2024 IEEE.

关键词： Pneumatics

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DeepCat: A Deep Learning Approach to Understand Your Cat's Body Language 11

DeepCat: A Deep Learning Approach to Understand Your Cat's B...

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11th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Fawzy, Arwa Hassan, Ahmed Gad, Ahmed Zaky, Ahmed B. El-Hussieny, Haitham Alexandria Egypt program Alexandria Egypt Benha University Faculty of Engineering at Shoubra Electrical Engineering Department Egypt Egypt-Japan University of Science and Technology Department of Mechatronics and Robotics Engineering Alexandria Egypt

ISBN: (纸本)9798350369328

Pet owners experience difficulty in understanding their pets' body language and its implications for animal welfare, given that animals cannot utilize human speech to communicate their emotions and health conditions. However, previous experiments for analyzing cat behavior have demonstrated that cats are precisely expressive. DeepCat, a deep-learning approach developed in this study, translates cats' body language signals, enabling owners to discern their feline companions' intended messages and emotional states. Our DeepCat model was trained on a dataset comprising 10,000 cat images, implementing automatic labeling to track key features, including the tail, eyes, and mouth. Presented as a Flutter application, DeepCat can function everywhere, allowing owners to easily monitor their cats and make informed decisions in situations that require caution. This paper discusses the potential benefits and limitations of DeepCat and provides suggestions for future research in this domain. © 2023 IEEE.

关键词： Emotion Recognition

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WIND FARM SENSOR FAULT DIAGNOSIS USING ARTIFICIAL INTELLIGENCE AND WAVELET TRANSFORM

WIND FARM SENSOR FAULT DIAGNOSIS USING ARTIFICIAL INTELLIGEN...

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International Conference on Green Energy, Computing and Intelligent Technology 2024, GEn-CITy 2024

作者： Aly, Mahmoud Shaaba, Mahmoud Kasem, Tamer Shalaby, Raafat Mechatronics Program Nile University Giza Egypt Nile University Giza Egypt Dept. of Engineering Mathematics and Physics Cairo University Giza Egypt Automatic Control Dept. Faculty of Electronic Engineering Menofia University Egypt

ISBN: (纸本)9781837242689

A methodology for the diagnosis of sensor faults in wind farms is introduced. Simulations are performed using a multi-domain model that integrates mechanical, electromagnetic, electronic control, and power grid components. The prevalent issue of intermittent sensor failures is addressed. The discrete wavelet transform is employed for feature extraction. The transient signals are processed effectively. Various wavelet families are evaluated to identify optimal candidates for fault diagnosis. Additionally, the methodology integrates three machine learning classifiers, achieving accuracy rates consistently exceeding 99%. The outcomes demonstrate the advantages of this approach in terms of enhanced accuracy. This research represents a significant advancement in sensor fault diagnosis, contributing valuable insights for the operation and maintenance of renewable energy systems. © The Institution of engineering & Technology 2024.

关键词： Windmill

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Application of Recurrence Plots and VGG Deep Learning Model to the Study of Condition Monitoring of Robotic Grinding

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International Journal of Precision engineering and Manufacturing 2023年第9期24卷 1675-1683页

作者： Chung, Kuan-Jung Dai, Cheng-Han Chiang, Tung-Chun Xie, June-Jia Lin, Ming-Tzer Department of Mechatronics Engineering National Changhua University of Education Changhua500 Taiwan Graduate Institute of Precision Engineering National Chung Hsing University Taichung402 Taiwan Doctoral Program in Industrial and Smart Technology Academy of Circular Economy National Chung Hsing University Nantou540 Taiwan

This study adopted AI to identify the normal and abnormal vibration signals generated during robotic grinding. This study classified four fundamental factors affecting grinding into three levels to obtain a widely used result and designed an L9(3 4) orthogonal array for the grinding experiment. During experimentation, part of the grinding wheels was added weight to produce abnormal vibration signals, which an accelerometer would measure. The study transformed the collected vibration signals into recurrence plots and conducted model training with VGG16 CNN architecture. Finally, this study tested a model with 89.6% training accuracy. The results showed the model could identify whether the recurrence plots stand for normal or abnormal vibrations, with an accuracy of 85%. This means it could predict normal and abnormal grinding conditions and help avoid problems caused by abnormal vibrations. © 2023, The Author(s), under exclusive licence to Korean Society for Precision engineering.

关键词： Grinding (machining)

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Applying CNN Models with Stereo Camera for Object Detection and Distance Estimation in Automatic Micro Electric Vehicle (MEVi) 1

Applying CNN Models with Stereo Camera for Object Detection ...

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1st International Conference on Smart mechatronics, ICSMech 2024

作者： Harimanto, Farrell Putra Hasanah, Nurul Pradana, Muhammad Khristamto Aditya Puji, Muhammad Nurul Binus Aso School of Engineering Bina Nusantara University Automotive & Robotics Program Computer Engineering Department Jakarta11480 Indonesia National Research and Innovation Agency Kst Samaun Samadikun Research Center for Smart Mechatronics Bandung40135 Indonesia

ISBN: (纸本)9798350368918

Technological advancements in the automotive industry are currently focused on autonomous driving systems or driver assistance systems. Depth estimation is also an important feature of the autonomous driving system as it can accurately estimate the distance to the detected objects. In current developments technologies such as stereo cameras are more popular for a more accurate depth estimation. The process for object detection consists of a combination of classification, localization, and segmentation. Deep convolutional neural networks are the basis of object detection. The methods for object detection are divided into two categories namely the Region Proposal-based method such as RCNN, and the Classification-based method which provides for methods such as YOLO and MobileNet. As a part of the research conducted by the National Research and Innovation Agency (BRIN) on Automatic Micro Electric Vehicles or MEVi, this paper analyzed and compared the performance of several CNN models for object detection as well as the performance of depth sensing using ZED stereo camera which was used for the autonomous system in MEVi. Among several models that had been compared, YOLO v8 model demonstrates high accuracy and moderate speed as indicated by its high mAP (0.50) and FPS (38.46). Based on depth sensing results, the accuracy of distance measurement also depended on brightness and environment with the average of accuracy achieved indoors was 98.2% and outdoors was 96.3%. © 2024 IEEE.

关键词： Automotive industry

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High-Performance Chip Design With Parallel Architecture for Magnetic Field Imaging System

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IEEE Transactions on Instrumentation and Measurement 2024年 73卷 1-13页

作者： Lin, Ming-Yi Hsieh, Sheng-Hsien Chen, Ching-Han Lin, Chun-Hung Yuan Ze University Department of Computer Science and Engineering Taoyuan320315 Taiwan National Pingtung University of Science and Technology Program of Artificial Intelligence and Mechatronics Pingtung912301 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan32001 Taiwan International Games System Company Ltd. New Taipei City248 Taiwan

To address the limitations of typical coil detection systems and enhance the performance of traditional magnetic field imaging (MFI) systems, we propose a MFI system that uses a 4×4 array of anisotropic magnetoresistance (AMR) sensors. The core controller of the proposed system was designed using a pipelined parallelization algorithm and implemented on a high-speed field-programmable gate array (FPGA) in a fully hardware-based architecture. The designed MFI system can achieve parallel acquisition of magnetic field components from the 16-channel AMR sensor array, parallel data processing, parallel data transmission, and parallel computations in a single cycle of only 4 ms. Compared with typical detection systems, the proposed solution not only exhibits superior performance but can also provide dynamic visual representations of sensed information. © 1963-2012 IEEE.

关键词： Parallel architectures

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Implementation of a PID Controller using Online Tuning Applied to a Mobile Robot Obstacle Following/Avoidance

Implementation of a PID Controller using Online Tuning Appli...

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Argentine Conference on Electronics (CAE)

作者： M. A. Pastrana L. H. Oliveira D. A. Mendes Douglas. L. Silva Jose Mendoza-Peñaloza Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Graduate Program Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

The implementation of a bioinspired PID-based controller with online tuning capabilities is developed for obstacle following/avoidance of a nonholonomic mobile robot. An innovative mixture of the conventional PID controller and particle swarm optimization (PSO), which has a strong capability of adaptation across unknown scenarios, reduced objective error, brings us a novel online tuning methodology that can derive optimal controller parameters for a given scenario. It is appropriate for a kind of plant without prior knowledge or established mobile robot model. Computer simulation for obstacle following/avoidance is carried out in four different scenarios using the CoppeliaSim software and the EVA mobile robot platform. The effectiveness of the proposed control algorithm is demonstrated trough the simulation experiment, which shows its superior performance in the training scenario, including an overshoot of 20.1 %, a settling time of 10 seconds, and a steady-state error of 0.76%.

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Applying ant colony optimization algorithm to solve flying probe inspection problem

Applying ant colony optimization algorithm to solve flying p...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Chen, Chih-Yung Chang, Yu-Ming National Pingtung University of Science and Technology Program in Artificial Intelligence and Mechatronics Pingtung Taiwan Shu-Te University Department of Computer Science and Information Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350324174

This paper applies ant colony optimization (ACO) algorithm for the dual-pin flying probe circuit board inspection optimal path searching problem. First, the proposed approach creates a representation for circuit inspection path, which groups and encodes the measurement endpoints into a sequence. Moreover, the ant colony algorithm with the sequence can be used to solve the complex circuit optimal problem. The experimental results show that the best solutions of ACO are performed better than other algorithms. © 2023 IEEE.

关键词： Inspection

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