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Effects of barrier chemicals on flame retardant properties of inherently flame retardant fabric

Materials Today: Proceedings 2023年 80卷 439-442页

作者： Revaiah, R.G. Kotresh, T.M. Kandasubramanian, Balasubramanian Defence Bioengineering & Electromedical Laboratory Bangalore 560 093 India Defence Institue of Advanced Technology Girinagar Pune 411 025 India

Barrier chemicals are used for coating a substrate fabric surface when air-proofness or water-proofness is desired in military operations. The properties of the base fabric changes depending upon the barrier chemical employed for coating. In this study, authors selected inherently flame retardant Kevlar as base fabric and coating was done using four chemicals viz: Natural rubber (NR), Polyurethane (PU), Neoprene, and Hypalon. While NR and PU readily burn, Neoprene and Hypalon resist ignition due to the presence of chlorine atom. The resultant flame retardant properties were studied using UL-94 V and NFPA-701 methods. Although Kevlar is flame retardant in nature, it was observed that some coatings burned readily leaving behind charred base fabric. © 2022

关键词： Rubber

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Automatic Number Plate and Face Recognition System for Secure Gate Entry into Military Establishments

Automatic Number Plate and Face Recognition System for Secur...

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2024 International Conference on Smart Systems for Applications in Electrical Sciences, ICSSES 2024

作者： Iyer, Harini Umamaheshwaran Dhavale, Sunita Defence Institue of Advanced Technology School of Computer Engineering and Mathematical Sciences Pune India

ISBN: (纸本)9798350364040

There is a dire need for an automated, real-time mechanism for ensuring secure, authenticated gate entry of authorized personnel into high-security premises. Identification of the person as well as their vehicle is important for the security requirements of these premises. Considering these needs, we propose an integrated real-time system that employs both an Automatic Number Plate Recognition (ANPR) system and a Face Recognition system for secure gate entry. The designed YOLOv8 model for the ANPR system is trained on Indian number plate datasets assembled by gathering standard open datasets available from public resources. This improves the generalization capability of AI models to detect and recognize number plates accurately. Acknowledging the prevalence of nonstandard number plates in India, a custom dataset is curated and annotated. To avoid the risk of bypassing a single authentication mechanism, two-factor authentication using an additional security level based on Face Recognition is proposed. It uses the dlib face recognition CNN model to verify the identity of individuals. The proposed system registers vehicle and facial information for authentication in a secure database. Experimental results confirm that the proposed system using EasyOCR is capable of recognizing number plates in Devanagari text, achieving an accuracy of 88.14% for English and Marathi number plates with a face recognition accuracy of 98.34% with dlib. © 2024 IEEE.

关键词： Face recognition

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A Survey on Artificial Intelligence based Photovoltaic Cell Defect Detection Techniques 7

A Survey on Artificial Intelligence based Photovoltaic Cell ...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Pandey, Harshita Dhavale, Sunita Defence Institue of Advanced Technology Department of Computer Science and Engineering Pune411025 India

ISBN: (纸本)9798331527822

With the increase in demand for solar energy in the form of photovoltaic (PV) panels have become an important component in sustainable energy applications. The visible defects (cracks, black spots, corrosion, etc.) in PV cells can occur during manufacturing times or while sustaining under external environmental conditions like rain, extreme temperature, humidity, etc. As a result, timely fault analysis in solar panels is essential in order to enhance both the lifespan and energy efficiency performance. The Electrolumince (EL) imaging technique is one of the popular technique used to scan PV modules in order to identify these critical defects like cracks. However, traditional PV fault detection methods rely on manual inspection and hence tedious, prone to human errors. This survey paper discusses the different methodologies and techniques using artificial intelligence (AI), Deep learning (DL), and Computer vision (CV) for quick, reliable, and efficient crack detection in PV panels using EL imaging analysis. Convolutional Neural Networks (CNNs), Residual Neural Networks (ResNet), and Visual Geometry Group (VGG) have demonstrated significant improvements in defect detection accuracy. This survey paper also discusses the standard datasets available in this domain and a comparative analysis of proposed techniques in the literature. © 2024 IEEE.

关键词： Cracks

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Effects of barrier chemicals on flame retardant properties of inherently flame retardant fabric

Effects of barrier chemicals on flame retardant properties o...

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作者： Revaiah, R.G. Kotresh, T.M. Kandasubramanian, Balasubramanian Defence Bioengineering & Electromedical Laboratory Bangalore560 093 India Defence Institue of Advanced Technology Girinagar Pune411 025 India

关键词： Rubber

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Classification of Underwater Sea Species Using Convolutional Neural Network 6

Classification of Underwater Sea Species Using Convolutional...

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6th IEEE Pune Section International Conference, PuneCon 2023

作者： Waghumbare, Ajay Dhake, Khushi Singh, Upasna School of Computer Engg. & Mathematics Sciences Defence Institue of Advanced Technology Pune India International Institute of Information Technology Department of Information Technology Pune India

ISBN: (纸本)9798350324204

The importance of recognizing fish species has increased as a result of new marine science research. Classifying fish species manually requires a person who has knowledge about all the species and this is a time consuming process. The classification of fish species could be automated using technology, advancing marine science. Deep learning techniques have led to an increase in the number of image classification tasks. In this paper we have compared performance of proposed Convolutional Neural Network (CNN) model with existing CNN architectures for classification of LifeClef 2015 Fish (LCF-15) dataset. This dataset consist of 15 different classes of sea species and total number of images are 22444. It is observed that the proposed model achieves accuracy of 98.76%. The proposed model is light weight, having less number of parameters and flops compared to other existing models. The precision, recall and f1-score achieved for the proposed model are 98.78%,98.76% and 98.71 % respectively. © 2023 IEEE.

关键词： Image classification

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A Survey on Artificial Intelligence based Photovoltaic Cell Defect Detection Techniques

A Survey on Artificial Intelligence based Photovoltaic Cell ...

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IEEE Punecon

作者： Harshita Pandey Sunita Dhavale Department of Computer Science and Engineering Defence Institue of Advanced Technology Pune India

ISBN: (数字)9798331527822

ISBN: (纸本)9798331527839

关键词： Surveys Deep learning Computer vision Computer network reliability Imaging Solar energy Manuals Reliability Artificial intelligence Defect detection

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Automatic Number Plate and Face Recognition System for Secure Gate Entry into Military Establishments

Automatic Number Plate and Face Recognition System for Secur...

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Smart Systems for applications in Electrical Sciences (ICSSES), International Conference on

作者： Harini Umamaheshwaran Iyer Sunita Dhavale School of Computer Engineering and Mathematical Sciences Defence Institue of Advanced Technology Pune India

ISBN: (数字)9798350364040

ISBN: (纸本)9798350364057

关键词： Accuracy Text recognition Face recognition Authentication Logic gates Real-time systems Security

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NLP-based Auto Generation of Graph Database from Textual Requirements

NLP-based Auto Generation of Graph Database from Textual Req...

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Jagadish Shivamurthy Tarun Uppal Deepti Vidyarthi Aeronautical Development Establishment Defence R&D Organization Bangalore India School of Computer Engineering and Mathematical Sciences Defence Institue of Advanced Technology Pune India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

Natural Language (NL) continues to be the preferred medium of expression for requirements, especially for multi-disciplinary systems such as the Flight Control System (FCS) of an Aircraft. The importance of a precise and unambiguous understanding of requirements for the project's success is established beyond question. This aspect is further amplified in the case of a safety-critical system such as FCS. UML diagrams from use case to activity to sequence diagrams are used to understand and analyze the requirements. While these diagrams have immense value, Graph Databases add new benefits for analyzing and managing requirements. Graph databases are gaining popularity due to their simplicity, scalability, and benefits of visualization. In this paper, we propose an NLP-based method for auto-generating a graph database for textual requirements. We explore the benefits of representing requirements as a graph database that complements the other diagrams and fills a few gaps, specifically in requirements allocation, identification of interdependencies, and change management. Though we use the Neo4j graph database management system and Cypher query language, the concepts are generic. The proposed approach for auto-generation works on requirements expressed in the EARS (Easy Approach to RequirementsSyntax) template. However, it can be extended for other templates or free-flowing text.

关键词： Visualization Scalability Unified modeling language Natural languages Ear Database systems Visual databases

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Classification of Underwater Sea Species Using Convolutional Neural Network

Classification of Underwater Sea Species Using Convolutional...

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IEEE Punecon

作者： Ajay Waghumbare Khushi Dhake Upasna Singh School of Computer Engg. & Mathematics Sciences Defence Institue of Advanced Technology Pune India Department of Information Technology International Institute of Information Technology Pune India

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