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Flexible pressure sensors with ultrahigh stress tolerance enabled by periodic microslits

Microsystems & Nanoengineering 2024年第1期10卷 301-311页

作者： Song Wang Chenying Wang Yifan Zhao Yujing Zhang Yaxin Zhang Xiangyue Xu Qijing Lin Kai Yao Yuheng Wang Feng Han Yu Sun Zhuangde Jiang State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesSchool of Mechanical EngineeringXi’an Jiaotong UniversityXi’anChina State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesSchool of Instrument Science and TechnologyXi’an Jiaotong UniversityXi’anChina The Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingboChina Department of Mechanical and Industrial Engineering University of TorontoTorontoCanada

Stress tolerance plays a vital role in ensuring the effectiveness of piezoresistive sensing films used in flexible pressure ***,existing methods for enhancing stress tolerance employ dome-shaped,wrinkle-shaped,and pyramidal-shaped microstructures in intricate molding and demolding processes,which introduce significant fabrication challenges and limit the sensing *** address these shortcomings,this paper presents periodic microslits in a sensing film made of multiwalled carbon nanotubes and polydimethylsiloxane to realize ultrahigh stress tolerance with a theoretical maximum of 2.477 MPa and a sensitivity of 18.092 kPa−*** periodic microslits permit extensive deformation under high pressure(e.g.,400 kPa)to widen the detection ***,the periodic microslits also enhance the sensitivity based on simultaneously exhibiting multiple synapses within the sensing interface and between the periodic sensing *** proposed solution is verified by experiments using sensors based on the microslit strategy for wind direction detection,robot movement sensing,and human health *** these experiments,vehicle load detection is achieved for ultrahigh pressure sensing under an ultrahigh pressure of over 400 kPa and a ratio of the contact area to the total area of 32.74%.The results indicate that the proposed microslit strategy can achieve ultrahigh stress tolerance while simplifying the fabrication complexity of preparing microstructure sensing films.

关键词： robot shaped stress

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Design Consideration for Assembling of PV System for Small Enterprises 32

Design Consideration for Assembling of PV System for Small E...

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32nd National Conference with International Participation, TELECOM 2024

作者： Vacheva, Gergana Raynova, Kamelia Hinov, Nikolay Technical University of Sofia Faculty of Electronic Engineering and Technologies Departement of Power Electronics Sofia Bulgaria Technical University of Sofia Faculty of Computer Systems and Technologies Departement of Information Techonology in Industry Sofia Bulgaria

ISBN: (纸本)9798350355000

This paper examines the key aspects and challenges related to the design and assembly of photovoltaic (PV) systems for small enterprises. The main technical and economic considerations are discussed, including the selection of appropriate components, optimization of system capacity according to the enterprises' needs, and compliance with regulatory requirements. Attention is also given to factors such as panel positioning, the efficiency of different inverter solutions, and the necessity for adequate maintenance and monitoring mechanisms. The paper provides guidelines for the sustainable integration of PV systems in small businesses, aiming to optimize energy costs and minimize dependence on traditional energy sources. © 2024 IEEE.

关键词： design considerations energy efficiency energy independence photovoltaic systems small enterprises sustainable development

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LTSpice Based Stability Analysis of DC-DC Converter for LED Drivers 9

LTSpice Based Stability Analysis of DC-DC Converter for LED ...

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9th Junior Conference on Lighting, Lighting 2024

作者： Vacheva, Gergana Stanchev, Plamen Hinov, Nikolay Technical University of Sofia Faculty of Electronic Engineering and Technologies Departement of Power Electronics Sofia Bulgaria Technical University of Sofia Faculty of Computer Systems and Technologies Departement of Information Technology in Industry Sofia Bulgaria

ISBN: (纸本)9798350352757

This article presents a stability analysis of a boost DC-DC converter for powering an LED strip. A model of a DC converter is presented and the principle of operation is described. LTSpice software was used to implement the mathematical model. LED strip data provided by the manufacturer is used. Simulation results of the realized studies by Fourier transformation are presented. Investigating the influence of transient in LED drivers is useful in order to ensure the stability of the manufactured device under various external influences. © 2024 IEEE.

关键词： Light emitting diodes

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Model Agnostic Meta-Learning(MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks

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computers, Materials & Continua 2024年第5期79卷 2795-2811页

作者： Yasir Maqsood Syed Muhammad Usman Musaed Alhussein Khursheed Aurangzeb Shehzad Khalid Muhammad Zubair Department of Creative Technologies Faculty of Computing and Artificial IntelligenceAir UniversityIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Engineering Bahria School of Engineering and Applied SciencesBahria UniversityIslamabad44000Pakistan Laboratory for Neuro&Psychophysiology KU LeuvenLeuven3000Belgium

Wheat is a critical crop,extensively consumed worldwide,and its production enhancement is essential to meet escalating *** presence of diseases like stem rust,leaf rust,yellow rust,and tan spot significantly diminishes wheat yield,making the early and precise identification of these diseases vital for effective disease *** advancements in deep learning algorithms,researchers have proposed many methods for the automated detection of disease pathogens;however,accurately detectingmultiple disease pathogens simultaneously remains a *** challenge arises due to the scarcity of RGB images for multiple diseases,class imbalance in existing public datasets,and the difficulty in extracting features that discriminate between multiple classes of disease *** this research,a novel method is proposed based on Transfer Generative Adversarial Networks for augmenting existing data,thereby overcoming the problems of class imbalance and data *** study proposes a customized architecture of Vision Transformers(ViT),where the feature vector is obtained by concatenating features extracted from the custom ViT and Graph Neural *** paper also proposes a Model AgnosticMeta Learning(MAML)based ensemble classifier for accurate *** proposedmodel,validated on public datasets for wheat disease pathogen classification,achieved a test accuracy of 99.20%and an F1-score of 97.95%.Compared with existing state-of-the-art methods,this proposed model outperforms in terms of accuracy,F1-score,and the number of disease pathogens *** future,more diseases can be included for detection along with some other modalities like pests and weed.

关键词： Wheat disease detection deep learning vision transformer graph neural network model agnostic meta learning

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Application of IoT in the study of biogas release parameters 33

Application of IoT in the study of biogas release parameters

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33rd International Scientific Conference Electronics, ET 2024

作者： Nikolov, Neven K. Tzanova, Anelia I. Koparanov, Kiril A. Antonova, Elena V. Minkovska, Daniela V. Technical University of Sofia Faculty of Computer Systems and Technologies Department of Programming and Computer Technologies 8 Kliment Ohridski blvd. Sofia1000 Bulgaria Technical University of Sofia Faculty of Electronic Engineering and Technologies Department of Chemistry 8 Kliment Ohridski blvd. Sofia1000 Bulgaria

ISBN: (纸本)9798350376449

This paper describes the development of an IoT device prototype for measuring biogas release parameters. Through the device, measurements are made, and statistics are accumulated based on which calculations are performed. Two types of samples were tested, the obtained results were displayed graphically, and the data was analyzed. © 2024 IEEE.

关键词： biogas ESP32 IoT measurement MQTT

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Detection of Violent Elements in Digital Games Using Deep Learning

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SN computer Science 2023年第5期4卷 1-8页

作者： Yalçın, Nursel Çapanoğlu, Ahmet Edip Department of Computer and Instructional Technologies Education Gazi University Ankara Turkey Faculty of Engineering Gazi University Ankara Turkey

Detection of violence in digital games can be achieved by detecting elements of violence in digital game frames. For young, the detection of these elements can play an important role in the detection and prevention of violence by taking early actions. Violence detection from within the image is based on the analysis of images in periods. This means a large amount of computation based on image processing. In this study, an image detection algorithms based on CNN models are proposed using YOLOV4 Tiny advanced object detection algorithms. It has been shown that the proposed method can be used for real time games with detection speed and 93% accuracy performance. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep learning Violation in digital games YOLOv4 Tiny

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Machine Learning-Based Methods for Materials Inverse Design: A Review

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computers, Materials & Continua 2025年第2期82卷 1463-1492页

作者： Yingli Liu Yuting Cui Haihe Zhou Sheng Lei Haibin Yuan Tao Shen Jiancheng Yin Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650093China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and TechnologyKunming650500China Yunnan Tin Company Limited Tin Industry Branch Gejiu661000China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming650093China

Finding materials with specific properties is a hot topic in materials *** materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high *** the development of physics,statistics,computer science,and other fields,machine learning offers opportunities for systematically discovering new *** through machine learning-based inverse design,machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired *** paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse ***,three main inverse design methods—exploration-based,model-based,and optimization-based—are analyzed in the context of different application ***,the applications of inverse design methods in alloys,optical materials,and acoustic materials are elaborated on,and the prospects for materials inverse design are *** authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.

关键词： Materials inverse design machine learning target properties deep learning new materials discovery

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Automated Categorization of Turkish E-commerce Product Reviews Using BERTurk 8

Automated Categorization of Turkish E-commerce Product Revie...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Altintas, Volkan Kilinc, Murat Manisa Celal Bayar University Faculty of Engineering Department of Computer Engineering Manisa Turkey Manisa Celal Bayar University Department of Information Technologies Manisa Turkey

ISBN: (纸本)9798331531492

Thanks to rapid technological developments, users can now easily access and distribute information. While users have the opportunity to share content and information as they wish, they can also use the information shared to improve themselves in line with their own interests. Especially when buying a product, they make their final decision about the product by examining the content of the comments made about the product. Automatically classifying the comments made about the product by the system and displaying them in the relevant category is one of the issues that has been studied recently. In this study, comments on e-commerce sites were automatically categorized. The dataset was created by collecting comments in Turkish about phones, computers and headphones produced by the same company on ***, one of the world's largest e-commerce platforms, with the help of a Python script. User comments were automatically sorted into categories by machine learning algorithms such as Naive Bayes, Linear Support Vector Classifier and Random Forest algorithms and pre-trained and fine-tuned Bert Multilingual and BERTurk models based on transfomer architecture. The results obtained were compared with the help of the F1-score metric. When the results of different machine learning algorithms and BERT models were compared, it was seen that the BERTurk model gave more accurate results than other models. © 2024 IEEE.

关键词： Adversarial machine learning

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Numerical Modelling of The System of Doubly Nonlinear Diffusion Equations with Convective Transfer and Damping

Numerical Modelling of The System of Doubly Nonlinear Diffus...

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Sadullaeva, Shakhlo Azimbaevna Anarova, Shahzoda Amanbayevna Khaydarov, Abdugaffor Fayzullayeva, Zarnigor Nazirova, Dilorom Mirkhodzhaeva, Nazhiba departament Intellegent systems Joint Belarusian-Uzbek Intersectoral Institute of Applied Technical Qualifications Tashkent Uzbekistan Department of Convergence of Digital Technologies Computer Engineering Faculty Tashkent University of Information Technologies Tashkent Uzbekistan Department of Applied Mathematics and Computer Analysis National University of Uzbekistan Tashkent Uzbekistan Department of Informatics Computer Engineering Faculty Tashkent University of Information Technologies Tashkent Uzbekistan Department of Applied Mathematics and Computer Analysis NUUz Tashkent Uzbekistan

ISBN: (纸本)9798400716928

Currently, studies of double nonlinear equations and systems of double nonlinear equations representing many processes found in nature are considered relevant and necessary. These equations and systems, as a rule, are nonlinear, degenerate, and have properties that are absent in linear theory and therefore make them quite difficult for both analytical and numerical analysis of the solution. For this reason, great attention is paid to the research of nonlinear mathematical models, the construction of efficient numerical solution schemes and algorithms, and the creation of software using the capabilities of computer technologies. In this article, the mathematical model of the diffusion process in a two-component nonlinear medium under the influence of time-dependent convective transfer and damping is studied. The calculation scheme, algorithm, and software for nonlinear problems were developed. Based on a study of the qualitative properties of a nonlinear mathematical model of a reaction-diffusion system, computer modeling of reaction-diffusion processes was carried out. The developed computational schemes, algorithms, and software packages for solving systems of parabolic equations with double nonlinearity provide high productivity in studying the theory and processes of numerical solutions of such problems. © 2024 ACM.

关键词： Nonlinear equations

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The Impact of Weather Conditions on Renewable Energy Production: A Machine Learning Approach

The Impact of Weather Conditions on Renewable Energy Product...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Özbay, Recep Çelebi, Merve Yavanoǧlu, Uraz Gazi University Graduate School of Informatics Dept. Of Computer Forensics Ankara Turkey Hatay Mustafa Kemal University Faculty of Education Dept. Of Computer Education and Instructional Technologies Hatay Turkey Gazi University Faculty of Engineering Dept. of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350362480

In recent decades, obtaining energy from wind, solar, and hydro energy has brought hope for a sustainable future. Machine learning (ML) significantly impacts the field of energy systems. ML techniques have emerged as a powerful tool for analyzing and optimizing energy systems. This approach can potentially reduce the cost of analyzing a device by focusing solely on the data generated during its operation. This paper investigates the application of regression models to predict renewable energy production based on weather conditions, aiming to enhance the reliability and efficiency of renewable energy systems. Experiments are performed using Decision Tree (DT), k-nearest Neighbors (KNN), Multiple Linear (MLR), Polynomial (PR), Random Forest (RF), and Support Vector (SVR) Regression algorithms. The performances of ML models are compared based on the evaluations obtained through the Coefficient of Determination (R2), and error measures namely Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). RF is the most successful algorithm in predicting renewable energy production depending on weather conditions as it has the highest R2 value and the lowest error measures. © 2024 IEEE.

关键词： Clean energy

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