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Fire Flame Detection in Tunnel Based on Halcon and ResNet50

Fire Flame Detection in Tunnel Based on Halcon and ResNet50

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Neural Networks, Information and Communication engineering (NNICE), international conference on

作者： Fengyuan Jia School of Mechanical Engineering Anhui University of Technology Maanshan Anhui China

Fires in tunnels have the problem of difficult detection and rescue. Thus, fire monitoring is crucial for prompt discovery of fires. Aiming at the problems of high false detection rate of traditional tunnel fire detection methods, a flame detection method based on Halcon and ResNet50 is designed. Gamma decoding is used for image preprocessing. The gamma decoding reduces the details of the dark part in the image and highlights the flame part. With the help of ResNet50 network, the flame after pretreatment is detected. The experimental result shows that the method has high recall and precision. This method provides an important reference value for the design of tunnel fire monitoring system.

关键词： Fires Decoding Monitoring Residual neural networks

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Comparative study between modified path matrix approach, modified gradient method and Bocher's technique to detect isomorphism in EGTs 3

Comparative study between modified path matrix approach, mod...

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3rd international conference on computational and experimental methods in mechanical engineering

作者： Mustafa, Jiyaul Hasan, Ali Department of Mechanical Engineering Jamia Millia Islamia New Delhi India

The Structural synthesis and topological analysis of Epicyclic gear trains (EGTs) have been great research area for checking isomorphic properties of mechanisms since few decades. In this paper, comparative analysis was done between three methods as Modified path matrix approach (MPM), Modified gradient method (MGM) and Bocher's Technique (BT) for checking isomorphism in EGTs and its inversions. These methods have computed on the basis of reliability of results, computational application & structural properties for theirs merits and demerits by using MATLAB software. The methods have been compared with examples of 4-link EGTs and 6-link EGTs One-DOF. All examples have found satisfactory results on the basis of literature. © 2020 Elsevier Ltd. All rights reserved.

关键词： MATLAB

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Preparation and Characterization of 3D Printed dECM/GelMA/NC/SA Tissue Engineered Hybrid Scaffold 30th

Preparation and Characterization of 3D Printed dECM/GelMA/NC...

引用

30th international conference on computational and experimental engineering and Sciences, ICCES 2024

作者： Shu, Yan Ye, Jinmeng Tang, Yuanyuan Fu, Tianlin Xing, Bao Liu, Ke Li, Jie Li, Xiangqin Song, Kedong State Key Laboratory of Fine Chemicals Dalian R&D Center for Stem Cell and Tissue Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9783031687747

At present, in vitro colorectal tumor model has received extensive attention. Among them, decellular matrix (dECM) is widely used in the preparation of tissue engineering scaffolds due to its excellent biocompatibility, low immunity and natural three-dimensional structure. Among them, there are abundant capillaries and lymphatic capillaries in the villi of the fold on the inner surface of the small intestine of pigs, which is conducive to the absorption of nutrients. In this experiment, physical, chemical and biological enzyme methods were combined to decellularize pig small intestine to obtain dECM. SEM and histochemical staining proved that the microstructure of the tissue was not destroyed and the cells were effectively removed after decellularization. Two kinds of hydrogel scaffolds were prepared by 3D extrusion bioprinting using dECM, methacrylated gelatin (GelMA), sodium alginate (SA) and nano-clay (NC). The mechanical properties, microstructure and biocompatibility of scaffolds were characterized. The results showed that dECM can reduce the pore size of the scaffold, improve the mechanical strength of the scaffold, and facilitate cell adhesion, growth, proliferation, migration and aggregation. dECM/GelMA/SA/NC scaffold is expected to provide a promising platform for the construction of colorectal tumor models in vitro. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Tumors

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MACHINE-LEARNING-BASED THERMAL CONDUCTIVITY PREDICTION IN TWO-DIMENSIONAL TIS2/MOS2 VAN DER WAALS HETEROSTRUCTURES 7

MACHINE-LEARNING-BASED THERMAL CONDUCTIVITY PREDICTION IN TW...

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7th ASME international conference on Micro/Nanoscale Heat and Mass Transfer (MNHMT)

作者： Nair, Akhil K. Da Silva, Carlos M. Amon, Cristina H. Univ Toronto Dept Mech & Ind Engn 5 Kings Coll Rd Toronto ON M5S 3G8 Canada Univ Toronto Dept Chem Engn & Appl Chem 5 Kings Coll Rd Toronto ON M5S 3G8 Canada

ISBN: (纸本)9780791888155

Two-dimensional (2D) materials and heterostructures display unique thermal characteristics compared to their bulk counterparts. However, the accurate estimation of the thermal conductivity of 2D materials, particularly of 2D van der Waals heterostructures, presents significant challenges for both computational and experimental methods. In this study, we propose a computationally efficient approach to investigate the thermal conductivity of 2D TiS2/MoS2 van der Waals heterostructures. Our approach utilizes machine-learning interatomic potentials (MLIPs) to predict the thermal conductivity of the heterostructure. This approach effectively incorporates intralayer interactions by utilizing moment tensor potentials (MTP) trained with computationally inexpensive density functional theory (DFT)-based datasets. These datasets are generated from ab-initio molecular dynamics (AIMD) trajectories over less than 1 ps, while the interlayer van der Waals interactions are calibrated using the D3-dispersion correction method. By explicitly incorporating the missing dispersion contribution into the MTP, this method provides greater accuracy in predicting interlayer interactions than the widely applied Lennard-Jones (LJ) potential. Finally, molecular dynamics (MD) simulations are conducted to determine the thermal conductivity of the TiS2/MoS2 heterostructures using the derived potential parameters. This study enhances our understanding of thermal transport in van der Waals (vdW) heterostructures, leveraging MLIPs to explore new nanostructured materials with superior thermal conductivity.

关键词： 2D materials van der Waals heterostructures machine-learning interatomic potentials thermal conductivity

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A critical review of aluminium based composite developed by various casting technique with different reinforcement particles to enhance tribo-mechanical behaviour 3

A critical review of aluminium based composite developed by ...

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3rd international conference on computational and experimental methods in mechanical engineering

作者： Bharti, Chitranshu Singh, Amit Rahul, Raja Sharma, Dusyant Dwivedi, Shashi Prakash G. L. Bajaj Institute of Technology and Management Gautam Buddha Nagar UP Greater Noida201310 India

The evolution of aluminium matrix composite (AMC) has been used for most advanced engineering applications. In spite of the advantageous properties of aluminium, the AMCs are useful in solving problems of selection of material. The institution of 2nd or 3rd stage particle into matrix of aluminium solves the most of the challenging problems. The AMCs fabricated using different casting technique has improved microstructural, corrosion and mechanical properties. Some conventional reinforcement agro-based waste, boride, carbide and nitride of certain particular metals. The inclusion of reinforcement to matrix of aluminium and processing technique used during addition decides the performance of AMCs. This paper gives review of Aluminium based composite developed by different casting technique to enhance Tribo-mechanical Behaviour. © 2020 Elsevier Ltd. All rights reserved.

关键词： Reinforcement

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Improved Deep Reinforcement Learning Based Network Slicing Method in 5G Networks 3

Improved Deep Reinforcement Learning Based Network Slicing M...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Abbas, Haydeer Mohamad Shashidhara, K.S. Balamurugan, D. Baraneetharan, E. Vigneshwaran, K.S. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Sona College of Technology Department of Computer Science And Engineering Salem India Dhanalakshmi Srinivasan College of Engineering Technology Department of Electrical and Electronics Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering chennai India

ISBN: (纸本)9798331508456

In recent scenario, 5G network is designed as a multi-service network supports the wide spread of services like enhanced Mobile Broad Band (eMBB), Ultra Reliable and Low Latency Communications (URLLC) and massive Machine-Type Communications (mMTC). Network slicing enables a single physical network to be divided into multiple virtual slices, each supporting independent services. In 5G systems, primary goal of network slicing is to employ physical Resource Blocks (RBs) based on Quality of Service (QoS) necessities of eMBB, URLLC and mMTC services. The aim of every service class is different and computational complexity caused by maximized number of time slots is large, it is challenging to employ the RBs correctly. In this manuscript, developed the improve Deep Reinforcement Learning (DRL) - based network slicing method in identifying resolution allocation policy which increases the high throughput when satisfying QoS requirements in 5G systems. The main aim of the developed method it to minimize the action space through removing decisions that can't satisfy the QoS requirements. The experimental results showed that the developed method obtained network throughput of 1754 for 60% and 2315 for 70% resource utilization while compared to conventional DRL. © 2025 IEEE.

关键词： Multicasting

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Revolutionizing COVID-19 Management: Block chain-Enabled Prediction and Secure Storage using Deep Learning Techniques 3

Revolutionizing COVID-19 Management: Block chain-Enabled Pre...

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3rd international conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Arulmozhi, B. Sheeba, J.I. Devaneyan, S. Pradeep Department of Computer Science and Engineering Puducherry Technological University Puducherry605014 India Department of Mechanical Engineering Sri Venkateshwaraa College of Engineering and Technology Puducherry605012 India

The proposed research presents a blockchain-based healthcare system that seeks to address the limitations of current data-sharing methods by providing secure and efficient data transfer, enhancing data privacy and security, enabling effective data sharing interoperability, and promoting data-driven healthcare decision-making. A blockchain platform, IPFS, and rule-based access control are among the technologies suggested for use in the proposed system. Implementing this system could result in more personalized and effective medication for patients, as well as lower healthcare costs and improved care quality. The suggested approach, known as the Homomorphic Zero-Knowledge Blockchain Algorithm (HZBA), employs a block chain technique linked with Proxy Re Encryption with Homomorphic with Zero proof knowledge. Finally, the Covid19-dataset has collected from Kaggle repository and HCDNN method has used to complete the classification. The proposed blockchain-based healthcare system has the potential to revolutionize healthcare by resolving data privacy and security concerns, increasing data accuracy, fostering more interoperability in data sharing, and enhancing data-driven healthcare decision-making. The proposed blockchain-based healthcare system provides potential answers to data sharing difficulties in the healthcare business. One potential disadvantage is the system's complexity and cost of installation. The experimental results and classification performance parameters have been thoroughly demonstrated © 2023 Elsevier B.V.. All rights reserved.

关键词： COVID-19

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An Investigation of Various methods for Evaluating the Measurement Uncertainty

An Investigation of Various Methods for Evaluating the Measu...

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international conference on Innovative Sustainable computational Technologies (CISCT)

作者： Harshvardhan Choudhary D. Vaithiyanathan Girija Moona Harish Kumar Department of Computing Science and Engineering Galgotias University Greater Noida India Department of Electronics and Communication Engineering National Institute of Technology Delhi Delhi India CSIR- National Physical Laboratory Dr K S Krishnan Marg New Delhi India Department of Mechanical Engineering National Institute of Technology Delhi Delhi India

This study presents an assessment of various techniques for evaluating the measurement uncertainty that are internationally accepted to fulfil the requirements of metrological and various industrial applications. A standard document, the Guide to the Expression of Uncertainty in Measurement (GUM) describes the elementary phases that are widely adapted for measurement uncertainty. Moreover, the prescribed technique is based on law of propagation of uncertainty (LPU). In this, repeated measurements are taken into account and the measurement procedure is performed using statistical model and sampling distribution. It gives basic model for estimating uncertainty of measurement requiring a fair amount of knowledge about measurand. Also, it provides best estimate of a quantity under measurement to calculate standard uncertainty related with it. An alternative method, named as Monte Carlo Method (MCM) has also been described that assign suitable probability density function (PDF) to input quantities and output quantity. The approach provides reduction in mathematical calculations and efforts. In Bayesian approach, prior distribution is calculated from the past information and posterior distribution is the integration of prior distribution and updated sample data. It uses different interpretations of probability as compared to other methods. Using Bayes' theorem, it is convenient to develop model for uncertainty of measurement as compared to other methods. Among different evaluation methods of Bayesian approach, maximum entropy prior gives more reliable and practical results.

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Photon-Counting Integral Imaging Using Bayesian Estimation with Depth Camera Information 23

Photon-Counting Integral Imaging Using Bayesian Estimation w...

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23rd international conference on Control, Automation and Systems, ICCAS 2023

作者： Uchino, Hideaki Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

In this paper, we propose a method for visualizing object information under low light conditions using photon-counting integral imaging and depth images as prior information. To visualize 3D objects under low-light conditions, computational photon-counting imaging may be used. It is possible to estimate photons using statistical methods such as the Poisson distribution. Therefore, photon-counting integral imaging can visualize 3D images under low-light conditions by maximum likelihood estimation (MLE). However, MLE does not consider prior information so it may not be an accurate estimation. In addition, it is difficult to obtain accurate depth information as the distance increases due to rounding errors caused by computational reconstruction. Therefore, we propose Bayesian estimation such as a maximum a posteriori estimation method that uses as a priori information images from a depth camera, which can be acquired even in low-light conditions, to improve the quality of 3D images. To evaluate image quality of the proposed method, optical experiments were conducted by calculating image quality metrics. This technology can be utilized for night vision applications, such as object recognition in security camera systems and autonomous driving, using RGB cameras and depth cameras. © 2023 ICROS.

关键词： Maximum likelihood estimation

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Comprehensive Review of Fatigue Life Prediction of Plain Concrete Using Machine Learning and Finite Element methods 30th

Comprehensive Review of Fatigue Life Prediction of Plain Con...

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30th international conference on computational and experimental engineering and Sciences, ICCES 2024

作者： Hairuddin, Haikhal Faeez Shah, Mohamad Shazwan Ahmad Noor, Norhazilan Md Yahaya, Nordin Tamin, Mohd Nasir Umar, Sarehati Faculty of Civil Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia Faculty of Mechanical Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia

ISBN: (纸本)9783031829062

This paper provides a systematic review of the fatigue life prediction of plain concrete, emphasizing the utilization of machine learning (ML) algorithms, finite element method (FEM) simulations, and comprehensive statistical analyzes. In fatigue assessment, various influential parameters such as frequency, stress conditions, and aggregate types can significantly impact the results. Therefore, it is crucial to thoroughly investigate these parameters to achieve a comprehensive understanding of the behavior and properties of concrete under fatigue conditions. Conventional fatigue testing methods often face challenges related to extensive testing time and high costs, which can be effectively mitigated through the implementation of FEM simulations. FEM simulations have proven to significantly reduce fatigue testing time while maintaining high accuracy and reliability of the results. Moreover, it is essential to analyze the probability distribution of fatigue data to gain insights into its underlying patterns and variability. Various probability distributions, such as Weibull and lognormal distributions, have been utilized to model fatigue data effectively. Studying different distributions is necessary, as each fitted distribution can yield distinct results based on the specific parameters under investigation. Fatigue life prediction can be enhanced using numerous ML models including artificial neural networks (ANN), random forests (RF), support vector regression (SVR), and decision trees (DT), which offer superior capabilities in handling the complex relationships between multiple influential parameters compared to traditional mathematical methods. This research aims to enhance the understanding of concrete behavior and properties under fatigue and to provide alternative, technologically advanced methods for predicting fatigue life. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Fatigue testing

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