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Bending results of graphene origami reinforced doubly curved shell

Defence Technology（防务技术） 2024年第5期35卷 198-210页

作者： Nan Yang Yunhe Zou Mohammad Arefi School of Architecture and Civil Engineering Qiqihar UniversityQiqihar 161006HeilongjiangChina School of Mechanical Engineering Inner Mongolia University of TechnologyHohhot 010051Inner MongoliaChina Inner Mongolia Key Laboratory of Special Service Intelligent Robotics Hohhot 010051Inner MongoliaChina Faculty of Mechanical Engineering Department of Solid MechanicsUniversity of KashanKashan87317-51167Iran

The present work investigates higher order stress,strain and deformation analyses of a shear deformable doubly curved shell manufactures by a Copper(Cu)core reinforced with graphene origami auxetic metamaterial subjected to mechanical and thermal *** effective material properties of the graphene origami auxetic reinforced Cu matrix are developed using micromechanical models cooperate both material properties of graphene and Cu in terms of temperature,volume fraction and folding *** principle of virtual work is used to derive governing equations with accounting thermal *** numerical results are analytically obtained using Navier's technique to investigate impact of significant parameters such as thermal loading,graphene amount,folding degree and directional coordinate on the stress,strain and deformation responses of the *** graphene origami materials may be used in aerospace vehicles and structures and defence technology because of their low weight and high stiffness.A verification study is presented for approving the formulation,solution methodology and numerical results.

关键词： Graphene origami Copper matrix Doubly curved Shear deformable Auxetic metamaterial

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Classification of Cancer Cells from Mammogram Images Based on GoogLeNet and ResNet-50 Deep Learning Models 1

Classification of Cancer Cells from Mammogram Images Based o...

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1st International Conference on Smart Mechatronics, ICSMech 2024

作者： Jusman, Yessi Nur 'Aini, Masayu Alya Naufal, Zaidan Ahmad Ridho, Prajarino Rushartini, Hesti Wimala, Galuh Ananda, Indra Pradani, Nisha Alifa Universitas Muhammadiyah Yogyakarta Center of Artificial Intelligence and Robotics Studies Department of Electrical Engineering Bantul Indonesia Universiti Malaysia Perlis Faculty of Electrical Engineering and Technology Perlis Malaysia Universitas Muhammadiyah Yogyakarta Faculty of Engineering Department of Electrical Engineering Bantul Indonesia

ISBN: (纸本)9798350368918

Breast cancer remains one of the most common and deadly forms of cancer affecting women globally. Early detection through mammogram screening plays a vital role in improving survival rates by enabling timely diagnosis and treatment. This study explored the application of deep learning techniques, particularly GoogLeNet and ResNet-50 architectures, to classify mammogram images into benign and malignant categories. The dataset, sourced from Kaggle, consisted of 479 mammogram images. These images were preprocessed and divided into training and testing sets, with 90% allocated for training and 10% for testing. Both GoogLeNet and ResNet-50 models were trained and tested, yielding different performance metrics. GoogLeNet achieved the highest testing accuracy of 70.83%, while ResNet-50 attained 68.75% accuracy. GoogLeNet demonstrated better sensitivity at 92.85%, but ResNet-50 outperformed in terms of precision at 69.69%. Although both models displayed competitive results, GoogLeNet exhibited superior performance in classifying cancer cells based on sensitivity, making it more effective in identifying true positives. These findings demonstrated the potential of deep learning models to enhance breast cancer detection and classification using mammogram images, contributing to improved diagnostic accuracy and patient outcomes. © 2024 IEEE.

关键词： Lung cancer

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Foreign Object Debris detection at aerodromes using YOLOv5 27

Foreign Object Debris detection at aerodromes using YOLOv5

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27th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2024

作者： Suder, Jakub Marciniak, Tomasz Poznan University of Technology Faculty of Automatic Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Division of Electronic Systems and Signal Processing Jana Pawla II 24 Poznań60-965 Poland

ISBN: (纸本)9788362065486

The latest EASA recommendations from 2024 indicate the possibility of using machine learning techniques in aerodrome monitoring. The aim of the work was to analyze solutions and prepare FOD (Foreign Object Debris) object detection software on aerodromes. In order to implement the issue, a dataset of photos and video recordings for testing algorithms was developed. The dataset consists of 1480 photos showing FOD at aerodromes and photos of the runway itself, according to EASA and FAA recommendations. FOD detection was implemented using a classical, k-means method and effective YOLOv5. © 2024 Division of Signal Processing and Electronic Systems, Poznan University of Technology (DSPES PUT).

关键词： Video recording

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Parameter Design of Digital P-PI Controller for Motor Positioning Systems Using Limited Pole Placement Method 50

Parameter Design of Digital P-PI Controller for Motor Positi...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Urakawa, Yoshiyuki Sirichai, Ngamlamai Robotics Department Advanced Engineering Faculty Nippon Institute of Technology Saitama Japan Mechanical System Engineering Major Graduated School Nippon Institute of Technology Saitama Japan

ISBN: (纸本)9781665464543

Positioning systems with motors are widely used in industrial use. Many production systems, mechatronics systems or industrial robots use high speed and high precision positioning systems with motors. Generally, the controllers of the positioning systems are proportional-to-proportional-integral (P-PI) controllers. They are often used because they have small overshoots and easy to tune. However, it needs fine-tunings to make settling motions smooth. There may be two reasons why the fine-tunings are needed for P-PI controllers. One is the pole assignment constraint of P-PI controller. Three poles of the P-PI control systems for motor positioning cannot be assigned freely. One of the three poles always stay at a slow position. Another reason is the digital delay when the P-PI controllers are applied to the practical systems. The digital system with the delay becomes vibrational because of the phase delay of the calculation time. It makes the overshoot of the trajectory. In this paper, the influences and solutions of the pole assignment constraint and the digital delay are shown. The pole assignment constraint limits the performance of the closed loop. The feedforward controller is shown to be the solution of this constraint. The digital delay makes the overshoot, but it can be corrected by the limited pole placement (LPP) method proposed by the author. It enables accurate pole-zero assignment without overshoots under the conditions of the digital delay. © 2024 IEEE.

关键词： Industrial robots

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Reachability-Based Delayed Decision Guidance for Conventional Interceptors 63

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63rd Israel Annual Conference on Aerospace Sciences, IACAS 2024

作者： Shalumov, Vitaly Merkulov, Gleb Shima, Tal Technion – Israel Institute of Technology Haifa3200003 Israel The Technion Program for Autonomous Systems and Robotics Israel Faculty of Aerospace Engineering Israel

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Remote Automated Control and Monitoring Water Distribution System Utilizing A 3-Way Valve Logic on ThingsBoard Platform 10

Remote Automated Control and Monitoring Water Distribution S...

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10th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Sahlan, Shafishuhaza Nik Nazaruddin, Nik Muhammad Adib Hakimy Yunus, Mohd Amri Md. Rosli, Khairul Ijlal Jamali, Annisa Manaf, Mohamad Shukri Abdul Universiti Teknologi Malaysia Centre for Artificial Intelligence and Robotics Faculty of Electrical Engineering Johor Malaysia Universiti Teknologi Malaysia Faculty of Electrical Engineering Johor Malaysia ZEC Engineering Sdn. Bhd. Johor Bahru Malaysia Universiti Malaysia Sarawak Faculty of Engineering Sarawak Kota Samarahan Malaysia

ISBN: (纸本)9798350365283

In this paper, an automated Lab-scaled Valve control system for Water Distribution System (WDS) that mimics real-world conditions is presented. The system utilises ThingsBoard software as the Supervisory Control and Data Acquisition (SCADA) system and PANASONIC Programmable Logic Controller (PLC) FP7 CPS31 as the master controller. The lab-scaled system consists of reservoir tank, supply tank, variable speed drives (VSD), flow rate sensors, and level sensors. Meanwhile ThingsBoard software provides an intuitive monitoring purposes and by using the application of Node-RED, it can develop an alternative Human Machine Interface (HMI) for remote control. By modifying the motor speed, which is managed by the VSD, the automated algorithm in the PLC makes use of sensor relationships to regulate the water level in the distribution reservoir tank. For water flow rate in pipes, the incoming flow rate is analysed in relation to the height of the reservoir tank. As a result, an automated control system that is capable of remote monitoring, controlling, and gathering data for the lab-scale WDS is successfully developed. © 2024 IEEE.

关键词： Programmable logic controllers

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Multilingual Transliteration based Urdu Chatbot using Rasa Framework 17

Multilingual Transliteration based Urdu Chatbot using Rasa F...

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17th International Conference on Open Source Systems and Technologies, ICOSST 2023

作者： Mohiuddin, Hira Kasi, Bakhtiar Ullah, Anayat National Center of Robotics and Automation Control Automotive and Robotics Lab Rawalpindi Pakistan Balochistan University of IT Engineering and Management Sciences Faculty of Information and Communication Technology Quetta Pakistan

ISBN: (纸本)9798350381320

Chatbots, which emulate human conversation, are meeting the rising demand for efficient customer service by reducing the need for manual intervention and alleviating the challenges of timeconsuming human interactions. This paper introduces Ubot, a domain-specific chatbot fluent in both Urdu and English transliteration, designed to reduce the need for human intervention and lower service costs. We address the challenge of low-resource languages by creating a dataset comprising 750 utterances, 28 intents, and 45 entities, validated by 36 participants. Ubot, powered by the Rasa framework, achieves remarkable accuracy in intent detection 94.1% and entity extraction 95.4%, offering efficient multilingual conversational AI solutions. Our approach holds promise for similar script-sharing languages such as Persian and Arabic, contributing to advanced language applications and intelligent virtual assistants. © 2023 IEEE.

关键词： Chatbot Dialogue Management Entities Intents Rasa Ubot

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Texture Features-Based Machine Learning for Classification of Cervical Cell Microscopic Images 1

Texture Features-Based Machine Learning for Classification o...

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1st International Conference of Adisutjipto on Aerospace Electrical engineering and Informatics, ICAAEEI 2024

作者： Jusman, Yessi Rahmawati, Maryza Intan Sulaiman, Siti Noraini Universitas Muhammadiyah Yogyakarta Faculty of Engineering Center of Artificial Intelligence and Robotics Studies Department of Electrical Engineering Yogyakarta Indonesia Universiti Malaysia Perlis Faculty of Electrical Engineering and Technology Department of Mechatronic Engineering Perlis Malaysia College of Engineering Universiti Teknologi Mara Electrical Engineering Studies Penang Malaysia

ISBN: (纸本)9798331507930

Machine learning has been developed in biomedical science as a clinical decision-support technique. It can automatically recognize patterns in a given dataset to perform predictions and data classification. It has been widely applied to detect abnormalities in various diseases by leveraging texture-based features. This study utilized a Histogram of Oriented Gradients (HOG) as a feature extraction method and a Support Vector Machine (SVM) for classification in detecting the level of cancer abnormalities in cervical cells. The dataset of cervical cell microscopic images consisted of 972 images for training and 108 for testing. Among the models used, the Medium Gaussian SVM achieved the best performance, with a training accuracy of 73.3% and the fastest training time of 4.123 seconds. Whereas, the best performance matrix of testing results achieved 81.10% of accuracy. These results highlight the potential of the proposed method in improving cervical cancer detection through automated and efficient classification techniques. © 2024 IEEE.

关键词： Adversarial machine learning

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Magnetic Flux Servoing for Precise Localization Based on Gradient Tensor Contractions 19

Magnetic Flux Servoing for Precise Localization Based on Gra...

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19th IEEE International Conference on Automation Science and engineering, CASE 2023

作者： Martinovic, Dean Orsag, Matko University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Robotics and Intelligent Control Systems Zagreb10000 Croatia

ISBN: (纸本)9798350320695

This paper focuses on bridge inspections performed by intelligent unmanned aerial vehicles (UAV). For this, small data loggers are placed by the UAVs at the bridge, which have to be removed later. In our previous work we presented the magnetic localization method SRIOD, which not only detects the center of the data logger but also its orientation. In this work we proof SRIOD in a real application and let a robotic arm locate a magnetic target. The hardware involved is presented including the control strategy for the robot. The motions of the robot and the magnetic target are captured with 12 Optitrack cameras in order to validate the localization quality. © 2023 IEEE.

关键词： Antennas

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Cybersecurity advancements for medical image transmission: a hybrid optical-based cryptosystem harnessing chaos, DNA sequences, and mandelbrot keys

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Multimedia Tools and Applications 2025年 1-41页

作者： Alalwan, Nasser El-Shafai, Walid Amoon, Mohammed Benjdira, Bilel Department of Computer and Communication Engineering King Saud University Riyadh Saudi Arabia Prince Sultan University Riyadh11586 Saudi Arabia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt

In today's advanced technological age, characterized by innovations like big data processing, cloud computing, and the Internet of Things (IoT), there is a rising utilization of medical multimedia data, especially medical images. These images, integral to the Internet of Healthcare Things (IoHT), necessitate secure transmission due to the increasing risks of unauthorized breaches and tampering. Current security methods, especially for cloud and mobile platforms, often struggle with challenges related to processing capacity, memory use, data size, and energy, making them ill-suited for extensive medical data or resource-limited environments. To address these challenges, this study introduces a novel hybrid cryptosystem, drawing on the unique qualities of the optical Arnold chaotic map, DNA (DeoxyriboNucleic Acid) sequences, and Mandelbrot keys, providing a fortified approach to the secure streaming of medical images. The proposed framework operates via a precise and structured procedure. It begins by applying the optical Arnold chaotic map cipher to each of the three-color channels (R, G, and B) within a medical image. This is followed by overlaying DNA encoding sequences on the resultant encrypted image from the earlier ciphering phase. Leveraging this groundwork, we incorporate an advanced Mandelbrot set-driven shift mechanism specifically designed to create complex confusion patterns within the R, G, and B segments of the encrypted medical imagery. The efficacy of the proposed cryptosystem is rigorously substantiated through an extensive array of simulations supported by a comprehensive security analysis. The results highlight its unparalleled resilience and security capabilities in the realm of medical image encryption, marking a significant leap over previous systems in the literature. Essentially, our work pioneers a solution to a pressing challenge in medical image security, ensuring enhanced protection of delicate health data among the rapidly evolving advanc

关键词： Cybersecurity

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