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arXiv 2022年

作者： Agand, Pedram Shoorehdeli, Mahdi Aliyari Department of Systems and Control K. N. Toosi University of Technology Tehran Iran Department of Mechatronics Engineering K. N. Toosi University of Technology Tehran Iran

Since batch algorithms suffer from lack of proficiency in confronting model mismatches and disturbances, this contribution proposes an adaptive scheme based on continuous Lyapunov function for online robot dynamic identification. This paper suggests stable updating rules to drive neural networks inspiring from model reference adaptive paradigm. Network structure consists of three parallel self-driving neural networks which aim to estimate robot dynamic terms individually. Lyapunov candidate is selected to construct energy surface for a convex optimization framework. Learning rules are driven directly from Lyapunov functions to make the derivative negative. Finally, experimental results on 3-DOF Phantom Omni Haptic device demonstrate efficiency of the proposed method. Copyright © 2022, The Authors. All rights reserved.

关键词： Lyapunov functions

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Automatic Model Creation Method of Hierarchical Parallel Network Model for Transfer Diagnosis of Rotating Machinery

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Jixie Gongcheng Xuebao/Journal of Mechanical engineering 2022年第22期58卷 115-128页

作者： Zhou, Jian Zheng, Lianyu Wang, Yiwei Wang, Yichuan School of Mechanical Engineering and Automation Beihang University Beijing100191 China MIIT Key Laboratory of Intelligent Manufacturing Technology for Aeronautics Advanced Equipments Ministry of Industry and Information Technology Beijing100191 China Beijing Institute of Precision Mechatronics Control Equipment Beijing100076 China Aerospace Servo Drive and Transmission Technology Laboratory Beijing100076 China

In view of the current deep learning-based fault diagnosis methods of rotating machinery relying on manual modeling experience, requiring manual parameter adjustment and continuous trial-and-error iteration, re-creation of diagnosis models for different diagnostic tasks, an automatic creation method of hierarchical parallel network model for transfer diagnosis of rotating machinery is proposed, which can quickly and automatically search high precision diagnosis models with heterogeneous transfer performance according to different diagnosis tasks. Based on neural architecture search (NAS) and modular design ideas, two types of foundation blocks of parallel structure containing multiple layers are designed, which is different from the traditional NAS method to search layer by layer, but searches based on the foundation blocks. The controller outputs decision sequence to determine the foundation blocks’ structure and stacks them to form a hierarchical parallel candidate model. Then according to the verification results of the candidate model on the diagnosis task, the controller is optimized using the strategy gradient algorithm, and the diagnosis accuracy of the candidate model is continuously improved by iterating the above process. The hierarchical parallel structure of the candidate model supports its good heterogeneous transfer performance. In addition, in order to solve the time-consuming bottleneck problem of NAS method, the weight sharing mechanism is set in the candidate model training process to improve the efficiency of automatic modeling. The proposed method is used to conduct automatic modeling and heterogeneous transfer diagnosis experiments for four different rotating machinery fault datasets, and the results show that the proposed method can efficiently create 100% accuracy diagnosis models for four different diagnosis tasks, consuming 313 s to 1 601 s, and the candidate model can achieve a transfer diagnosis accuracy of more than 95% for the target dia

关键词： Rotating machinery

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Onboard Plant Counting Framework for Early Crop Emergence Estimation from Aerial Images

SSRN

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SSRN 2023年

作者： Shahid, Ramsha Qureshi, Waqar Shahid Khan, Umar S. Munir, Arslan Zeb, Ayesha Moazzam, Syed Imran Department of Mechatronics Engineering National University of Sciences and Technology IslamabadH-12 Pakistan National University of Sciences and Technology IslamabadH-12 Pakistan School of Computer Science Technological University Dublin Dublin Ireland Kansas State University ManhattanKS United States

Crop emergence estimation at early crop growth stages is becoming increasingly important for the long-term sustainability of natural resources. It helps farmers and agricultural stakeholders in the efficient allocation of resources like water, pesticides, and fertilizers. It can be used to estimate the yield and seed quality, identify the region of potential yield losses, and make future agriculture plans. These future agriculture plans can play a crucial role in ensuring maximum crop population and yield while utilizing the same limited land and natural resources. Most of the existing plant counting frameworks require offline processing of images with computationally expensive algorithms including the structure for motion and multiview stereo to develop an orthomosaic. This study proposed a generalized plant counting framework capable of onboard processing and directly estimates counts from aerial images. It consists of three core modules: overlap detection, plant detection, and plant counting. The overlap detection module replaces the need for computationally expensive orthomosaic formation to avoid counting repetition by overlap masking based on only visual cues. Three different methods are evaluated as core modules for finding an optimal generalized solution for plant counting based on time complexity and accuracy. In the first method after overlap detection semantic segmentation with U-NET is employed as plant detection modules by counting connected pixels. Object detection with YOLOv7 is utilized as a plant detection module after overlap removal in the second method. The third method involves a counting framework based on multiple object tracking using YOLOv7 for object detection and SORT for object tracking as a replacement for the overlap detection module that has the potential for a real-time applicable system. The proposed algorithm is evaluated on two distinct tobacco fields. The high-resolution aerial data is collected from tobacco fields near Peshawar,

关键词： Object detection

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Employing Multi-Layer, Sensorised Kirigami Grippers for Single-Grasp Based Identification of Objects and Force Exertion Estimation

Employing Multi-Layer, Sensorised Kirigami Grippers for Sing...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Junbang Liang Joao Buzzatto Bryan Busby Ricardo V. Godoy Saori Matsunaga Rintaro Haraguchi Toshisada Mariyama Bruce A. MacDonald Minas Liarokapis Department of Mechanical and Mechatronics Engineering The New Dexterity research group The University of Auckland New Zealand The Information Technology R&D Center Mitsubishi Electric Corporation Japan The Advanced Technology R&D Center Mitsubishi Electric Corporation Japan Department of Electrical Computer and Soft-ware Engineering The Centre for Automation and Robotic Engineering Science The University of Auckland New Zealand

Soft robotic devices have been popular in handling intricate grasping and dexterous manipulation tasks, serving as an alternative to conventional, rigid end-effectors. These devices are relatively simple, lightweight, and cost-effective. Recently, kirigami based structures have been used to create low-cost and disposable soft robotic grippers and hands. These grippers undergo a complex post-contact reconfiguration and conform to an object's shape and size during grasping. In this paper, we explore this new class of soft robotic grippers by utilising them for single-grasp object classification and grasping force estimation. We install simplistic sensors on both the gripper and the actuation system to estimate the state of the kirigami gripper, and the collected data features are employed to train Random Forest models for identifying the grasped object. The classifier trained exhibits a high accuracy of 98 % in discriminating objects of various shapes. When handling food items, the classifier achieves an accuracy of 94 %, while in classifying transparent objects, the classifier obtained again a high accuracy of 97 %. Finally, object-specific force estimation models are triggered based on the classification decision of the Random Forest model to estimate the grasping force exerted by the gripper. These positive outcomes demonstrate the kirigami based robotic gripper's potential for object classification in a variety of circumstances, particularly where vision systems are not available or not reliable.

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In Situ Scattering Studies of Crystallization Kinetics in a Phase-Separated Zr-Cu-Fe-Al Bulk Metallic Glass

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Acta Metallurgica Sinica(English Letters) 2022年第1期35卷 103-114页

作者： Sinan Liu Jiacheng Ge Huiqaing Ying Chenyu Lu Dong Ma Xun-Li Wang Xiaobing Zuo Yang Ren Tao Feng Jun Shen Horst Hahn Si Lan Herbert Gleiter Institute of Nanoscience School of Materials Science and EngineeringNanjing University of Science and Technology200 Xiaolingwei AvenueNanjing210094China Department of Physics City University of Hong Kong83 Tat Chee AvenueKowloon999077Hong Kong SARChina Neutron Sciences Platform Songshan Lake Materials LaboratoryDongguan523000China Neutron Sciences Directorate Oak Ridge National Laboratory1 Bethel Valley RoadOak RidgeTN37830USA Center of Neutron Scattering City University of Hong Kong Shenzhen Research InstituteShenzhen518057China X-Ray Science Division Argonne National LaboratoryLemontIL60439USA College of Mechatronics and Control Engineering Shenzhen UniversityShenzhen518060China Institute for Nanotechnology Karlsruhe Institute of Technology76344Eggenstein-LeopoldshafenGermany

Thermal stability and the crystallization kinetics of a phase-separated Zr-Cu-Fe-Al bulk metallic glass were investigated using in situ high-energy synchrotron X-ray and neutron diffraction,as well as small-angle synchrotron X-ray *** was revealed that this glass with excellent glass-forming ability possesses a two-step crystallization *** crystalline products and their evolution sequence are more complicated than a homogeneous Zr-Cu-Al glass with average glass-forming *** experimental results indicate that a finely distributed nanometer-sized cubic Zr_(2)Cu phase forms first and then transforms to a tetragonal Zr_(2)Cu phase,while the matrix transforms to an orthorhombic Zr_(3)Fe *** strength of the Zr-Cu-Fe-Al composite containing cubic Zr_(2)Cu phase and glass matrix increases,and the plasticity also improves compared to the as-cast Zr-Cu-Fe-Al bulk metallic *** results suggest that the formation of multiple and complex crystalline products would be the characteristics of the Zr-Cu-Fe-Al glass with better glass-forming *** study may shed light on the synthesis of bulk-sized glass-nanocrystals composites of high strength and good plasticity.

关键词： Bulk metallic glass Crystallization kinetics Synchrotron and neutron scattering Phase separation

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An Expert System for Diagnosis of Cancer Diseases

An Expert System for Diagnosis of Cancer Diseases

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Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA), International Conference on

作者： Christabel Madzinga Tawanda Mushiri Talon Garikayi Charles Mbohwa Dept of Industrial and Mechatronics Engineering University of Zimbabwe Harare Zimbabwe Department of Biomedical Informatics and Biomedical Engineering Faculty of Medicine and Health Sciences University of Zimbabwe Harare Zimbabwe Senior Research Fellow Biomedical Engineering and Healthcare Technology (BEAHT) Research Centre and UJ Olympic Studies Centre (UJ-OSC) Faculty of Health Sciences University of Johannesburg South Africa Harare Institute of Technology Harare Zimbabwe Dept of Quality and Operations University of Johannesburg Johannesburg South Africa

Traditional ways of diagnosing patients can take a lot of time and are sometimes undependable thus computerized technologies could be employed for more accurate and faster analysis. The third most diagnosed cancer in the world is a cancer called colorectal cancer (CRC). CRC results from unrestrained cell growth in sections of the large intestine, particularly the colon or rectum. Early diagnosis of CRC is important for the survival of the patient since it can result from changes in lifestyle and increase in age of the patient. The MATLAB Image Processing toolbox together with the QUPATH software was utilized for image preprocessing, segmentation and feature extraction. Image were extracted for fine tuning the Visual Geometry Group-16 (VGG16) Convolutional Neural Network (CNN), which was pretrained on the ImageNet database thus enabling it to learn domain specific features necessary to classify the whole slide images. The concluding evaluation notes that the ground truth annotations are not ideal and thus the ability of deep learning to overcome issues in the quality of the data is verified. While due to the nature of the domain, deep learning techniques may never be suited to replace the expertise of practicing pathologists, they promise to aid in tasks which can be tedious, painstaking and subject to a degree of error and subjectivity."

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Modelling and finite element analysis of anti-roll bar using ANSYS software

Modelling and finite element analysis of anti-roll bar using...

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International Conference on Newer trends and Innovations in Mechanical engineering, ICONTIME 2020

作者： Mohanavel, V. Iyankumar, R. Sundar, M. Kumar, P. Kiran Pugazhendhi, L. Department of Mechatronics Engineering Chennai Institute of Technology Chennai Tamilnadu600069 India Department of Mechanical Engineering Chennai Institute of Technology Chennai Tamilnadu600069 India

This design and analysis study has been done for the effectiveness of torsion of anti roll bar to a passenger car and influence the handling of vehicle from body roll. Subsequently, the further study on stabilizer bars in the automotive vehicle, especially in traction and braking condition. Stabilizer bars (also known as Traction bars, Sway bars or Anti-roll bars) are used to stabilize a vehicle from excessive chassis roll when the vehicle is running in high traction conditions. Interestingly, the stabilizer bars are generally used on smooth traction conditions. In view of this work has been done to replace the forged Solid stabilizer rod by using a high strength tubular stabilizer bar thereby reducing the weight of the product and increasing the fuel efficiency of the vehicle. The project involves theoretical estimation of torsional stress in the stabilizer bar and validated with Finite Element Analysis (FEA) by using Solid works for modelling the design work and their experimental results were evaluated. Hence, the simulation has done to take part where the result can determined the effectiveness of the anti roll bar fitted to the vehicle. © 2020 Elsevier Ltd. All rights reserved.

关键词： Finite element method

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An Appraisal in to the Effects of Partial Shading on an Urban Photovoltaic System Using the Internet of Things

An Appraisal in to the Effects of Partial Shading on an Urba...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Lakshmana Phaneendra Maguluri S Farook Saravanan R Ashutosh Dixit K.P. Aishwarya JoshuvaArockia Dhanraj Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Electrical and Electronics Engineering School of Engineering Mohan Babu University Tirupathi Andhra Pradesh India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India Department of Electrical Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Information Technology Panimalar Engineering College Chennai Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Chennai Tamil Nadu India

Partial shadowing of a solar array is known to decrease the array's output power. Unfortunately, it is not always possible to calculate the exact degree of decrease in energy output from the darkened region alone. In this study, the process of partial PV shadowing on multiple PV cells by using IoT wired in series and/or parallel, with and without bypass diodes, is elucidated. A layperson interested in learning how a certain shading geometry affects a PV system may benefit from this study, which is provided in clear language. Commercial 100 W panel and 10 kW PV array data are used to show the study.

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RiceLeafClassifier-v1.0: A Quantized Deep Learning Model for Automated Rice Leaf Disease Detection and Edge Deployment

Engineering Reports

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engineering Reports 2025年第6期7卷

作者： Oluwaseun O. Martins Christiaan C. Oosthuizen Dawood A. Desai Department of Mechanical and Mechatronics Engineering Faculty of Engineering and the Built Environment Tshwane University of Technology Pretoria South Africa Department of Mechatronics Engineering Faculty of Engineering Federal University Oye-Ekiti Ikole Ekiti State Nigeria

Rice diseases critically threaten global food security, necessitating rapid, accurate detection methods. This study presents RiceLeafClassifier-v1.0, a lightweight quantized convolutional neural network (CNN) that classifies five rice leaf conditions: blast, bacterial blight, brown spot, healthy, and red stripe, with high accuracy and real-time performance. To improve generalization, the model was trained on 2807 images, 1144 field-collected and 1663 public. Training enhancements included data augmentation, dropout, dynamic learning rate scheduling, and early stopping. Unlike previous transfer learning approaches, RiceLeafClassifier-v1.0 was built from scratch to retain fine visual features while remaining efficient. Quantization reduced model size from 78.03 to 6.51 MB, enabling deployment on edge devices like the Raspberry Pi 4. Statistical validation ( p < 0.05) confirmed that RiceLeafClassifier-v1.0 outperforms VGG-16, VGG-19, and ResNet-50, achieving a classification accuracy of 92% compared to 49% (VGG-16), 48% (VGG-19), and 44% (ResNet-50). Post-training quantization further improved accuracy from 92% to 94% ( p = 0.0165) while reducing memory usage by 68% (from 82.14 to 26.24 MB, p < 0.0001). Additionally, inference time per image was significantly lower at 2.28 ± 0.35 s for the quantized model compared to 0.01 ± 0.01 s for the standard model ( p < 0.0001), demonstrating substantial gains in efficiency. Despite some limitations, including dataset bias and sensitivity to extreme conditions, the model shows very strong and highly promising potential for real-time disease monitoring in precision agriculture. Future work will expand the dataset, adopt advanced optimization techniques, and integrate IoT systems to support smallholder farmers in reducing crop losses and boosting food security.

关键词： convolutional neural networks deep learning edge AI rice leaf disease detection RiceLeafClassifier-v1.0 smart agriculture

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Tailoring the Mechanical and High-Temperature Tribological Properties of Si-Dlc Films by Controlling the Si Content

SSRN

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SSRN 2023年

作者： Yu, Weijie Mei, Qiuping Huang, Weijiu Su, Yongyao Wang, Junjun School of Intelligent Manufacturing and Transportation Chongqing Vocational Institute of Engineering Chongqing402260 China School of mechanical engineering Chongqing Vocational and Technical University of Mechatronics Chongqing402760 China College of Materials Science and Engineering Chongqing University of Arts and Sciences Chongqing402160 China College of Materials Science and Engineering Chongqing University of Technology Chongqing400054 China

The poor high-temperature tribological performance of diamond-like carbon (DLC) films severely limits their applications. The Si doped DLC films with Si content varied from 0 to 11.52 at.% are deposited by plasma-assisted reactive magnetron sputtering method. The effect of Si on film structure and mechanical properties are investigated by Raman, scanning electron microscope (SEM), X-ray photoelectron spectroscopy (XPS), nano-indentation, scratch techniques. The in-situ high-temperature test from room temperature to 500 °C are performed to evaluate the tribological properties of the Si-DLC films. The results show that different lubrication mechanisms exist for Si-DLC films with different Si content at different temperatures. As the test temperature and Si content increase, the lubrication mechanism of the film gradually transitions from high-temperature induced graphitization to a particle wear mechanism dominated by SiC and fomed SiO2 abrasive phases. The overall performance of the films reaches its optimum at a Si content of 4.72 at.%. © 2023, The Authors. All rights reserved.

关键词： Tribology

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