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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Thiruvenkadam, T. Batra, Raman Kumar, Rakesh Department of Computer Science Bangalore India Noida Institute of Engineering and Technology Department of Mechanical Engineering Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350357769

This study highlights the concrete effects of worldwide warming on our environment while offering a brief introduction to green technology (GT) in the fourth industrial century (IR 4.0). The urgent need to move toward sustainable lifestyles and behaviors is clear, particularly in light of the currently catastrophic COVID-19 epidemic that has had a significant impact on social, economic, and environmental aspects. In order to get through this crisis, a shift to sustainable, green technologies is essential. This means implementing Internet of Things (IoT) approaches and Green Creative Technologies (GIT) to encourage the creation of long-lasting, biodegradable, and environmentally friendly products, which will help ensure a sustainable future. Green chemical engineering and environmental biotechnology are two particularly important pillars in the quest for sustainable development. The Sustainable Development Goals, set by the United Nations, (SDGs) are largely achieved thanks to the growing use of materials made from plants for pollution separation and improvements in product development for the creation of ecologically benign products. This coordinated effort supports the main objective of attaining IR 4.0, which is to create a more balanced and environmentally friendly environment for future generations. To sum up, in order to effectively address the issues presented by the effects of climate change and realize the goals of Industry 4.0, convergence of environmentally conscious technology, Internet of Things solutions, and cooperative methods between companies and governments is critical. The UN's Sustainable Development Goals (SDGs) continue to be a major component of our collective progress toward a fair and ecologically responsible future. © 2023 IEEE.

关键词： Environmental Biotechnology Green Chemical engineering Green Creative Technologies (GIT) Green Economy and Bioremediation Green Technology Industry 4.0 Internet of Things (IoT) Nanotechnology Renewable Energy Sustainable Development Goals (SDGs) Sustainable Future

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Efficient practical Byzantine Consensus-based reputation method for IoT based electronic waste tracking and tracing system using blockchain

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Multimedia Tools and Applications 2024年第37期83卷 84639-84672页

作者： Sharma, Lokesh Gupta, Ravi Kant Lamba, Chattar Singh Kumar, Ajay Lathar, Pankaj Department of IT Manipal University Jaipur Rajasthan303007 India Department of Mechanical Engineering Manipal University Jaipur Rajasthan303007 India Department of Computer Science & amp Engineering Manipal University Jaipur Rajasthan303007 India Department of Mechatronics Engineering Manipal University Jaipur Rajasthan303007 India Department of Computer Science and Applications Delhi Skill and Entrepreneurship University Dwarka New Delhi Delhi110077 India

The utilization of smart contracts enables the integration of blockchain technology to provide incentives for the gathering of electronic trash. Traditional blockchain-based Internet of Things (IoT)-E-Waste Tracking systems have challenges such as data privacy issues, significant delays, and low transaction per second (TPS) rates. In order to tackle these difficulties, the proposed approach utilizes a blockchain-based technology called IoT-enabled Efficient Practical Byzantine Consensus-based Reputation (EPBCR). This approach effectively monitors the post-production business processes of electronic devices by using hybrid consensus and reputation optimization algorithms. As a result, it enhances transparency, traceability, and privacy. The system efficiently regulates and oversees electronic gadgets throughout its supply chain by employing smart contracts on an unchangeable distributed ledger. The methodology is supported by analyzing the gas usage of Smart contracts on the Ethereum network. This results in the creation of, efficient, and high-throughput system that assists participants in conducting economic transactions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Smart contract

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YOLO-Based Design and Optimization of Weld Seam Detection Model 7

YOLO-Based Design and Optimization of Weld Seam Detection Mo...

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7th International Conference on computer Network, Electronic and Automation, ICCNEA 2024

作者： Zhou, Zhaoxuan Cao, Yan School of Mechanical and Electrical Engineering Xi'an Technological University Xi’an710021 China School of Computer Science and Engineering Xi'an Technological University Xi’an710021 China

The traditional welding seam inspection efficiency is low, the model possesses a significant amount of computational parameters, and it is not suitable for the application of small and medium-sized welding seam recognition machines. In view of the wide application and remarkable effect of deep learning in the domain of machine vision, a welding seam defect detection model designed by G-Efficientnet-CA neural network was proposed and optimized. EfficientNet was employed as the core architecture for extracting features to greatly reduce calculation parameters and model volume. The CA attention mechanism is incorporated to enhance the model's capacity for focusing attention on the weld image, thereby improving its ability to discern and analyze relevant features, and the accuracy is improved. The Generalized Intersection over Union (GIoU) loss function is changed from the original loss function to optimize the calculation of the coincidence degree between the real frame and the predicted frame. The efficient K-Means++ clustering algorithm is utilized to calculate the initial anchor frame which is more suitable for different weld data sets. The experimental outcomes demonstrate that the optimized model, when compared to the YOLOv3 model, exhibits superior effectiveness in detecting the VOC2007 dataset, as evidenced by a notable 12.7% increase in the average precision (mAP) and a significant reduction of 88% in the number of parameters. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： K-means clustering

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Impact on Efficiency of Reactors via Bubbles

Impact on Efficiency of Reactors via Bubbles

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kumar, Ajay Jamal, Ahmad Mahajan, Prerna Noida Institute of Engineering and Technology Department of Mechanical Engineering Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science Bangalore India

ISBN: (纸本)9798350357769

With an emphasis on battery-comparable network theories and the function of power control devices, the current study investigates the effect of bubble on the electrical energy transformation effectiveness of chemical reactors. A thorough knowledge of chemical conditions for storing is necessary because to the growing dependence on charging devices, particularly in electronics that can be carried. The models that now exist are divided into four categories by the literature review: Electrical, A number, Logical, and Point Input Systems. Each category has pros and cons of its own. The proposed approach describes in detail the electric modelling technique for lithium-polymer, lithium-ion, and nickel-metal hybrid power sources, emphasising certain characteristics for examination. The section on findings and discussion highlights the trade-off amongst reliability and system brevity, recognising the difficulties in striking a balance of computing viability and accuracy. Evaluation of batter comparable network models is now necessary due to the development of power controllers. The benefit of this kind of simulation is that it can precisely predict the kinetic properties of a charger. The goal of identifying circuit characteristics is to predict electrochemical storage conditions. The various circuit types presented in this article are taken from published books. The Metals Combination, Potassium, Lead-Acid, and Rechargeable battery varieties are examined. © 2023 IEEE.

关键词： Electromagnetic modeling techniques Lithium-ion (Li-ion) battery and Lithium polymer (LiPo) battery Nickel-metal hybrid (NiMH) battery

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Detecting Counterfeit Indian Currency A Comparative Analysis of Machine Learning Algorithms and Image Processing 15

Detecting Counterfeit Indian Currency A Comparative Analysis...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Vyshnavi, M.R.V. Varshitha, N.L. Harshith Kumar, Konda V. S. Singh, Tripty Sharma, Mansi Chatterjee, Suman Amrita School of Computing Department of Computer Science Engineering Bengaluru India Dehradun India Kongju National University Department of Mechanical Engineering Cheonan Korea Republic of

ISBN: (纸本)9798350370249

The degree of secured currency is one of the key areas that makes its contribution in solving the problem of economic insecurity, highlights the most important. Even as it can be quite strained to actually identify which precise time frame this fake process was prepared in, complex fakes render the use of validation techniques quite challenging. This paper has aimed at displaying and evaluating the overall outcome of the different strategies of image, and the acquired classifiers in the identification of the fake FI currency. The following are the subsequent of the other operation that was applied in order to achieve the objectives of the intended work;Image capture This is whereby the images to be used are taken by the appropriate camera or scanner. Others used include the Naïve Bayes, Support Vector Classifier and Gradient Descent and Artificial Neural Network in this paper. As in the conclusion, with the help of the mathematical formulation, it can be confirmed that in fact, within an experiment, Neural Networks do have a higher level of optimization, much more than an average image processing if it is provoked to identify fake notes. In other words if used and implemented with the best of the machine learning approach and best of the image processing method then solitaire for detection of the counterfeit currency can be made more dependable than all the combined methods stated above. The objective of this study is to build and compare the performance of different machine learning algorithms and image processing methodologies for the identification of fake Indian currency notes. © 2024 IEEE.

关键词： Counterfeit Detection Gradient Descent Image Processing Machine Learning Mathematical Formulation Naïve Bayes Neural Networks Support Vector Classifier

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Low Cost Wireless Data Acquisition System for CMOS MEMS Sensor 1

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19th International Conference of Intelligent Unmanned Systems, ICIUS 2023

作者： Athikary, Kunal G. Joseph, Vivek J. Yang, Lung J. Wang, Wei C. Department of Computer Science and Engineering School of Engineering Presidency University Karnataka Bangalore560064 India Department of Mechanical and Electromechanical Engineering Tamkang University Tamsui Taipei251301 Taiwan

ISBN: (数字)9789819765911

ISBN: (纸本)9789819765904

This present study is to focus and evaluate the performance of a complementary metal–oxide–semiconductor (CMOS) micro-electro-mechanical systems (MEMS) piezoresistive sensor with different pressure ranges under vacuum condition using a self-developed low-cost wireless data acquisition system (WDAQ). The pressure sensor was designed and fabricated using UMC 0.18 µm 1P6M standard CMOS process and MEMS technology. The sensing elements of the piezoresistors are polysilicon and they are integrated with CMOS circuits to complete the Wheatstone bridge and sensor system. The testing of pressure sensor was done with a vacuum pressure range of 0–100 kPa. The output signals were extracted using a commercial data acquisition system DAQ970A and compared with the self-developed ESP8266-based WDAQ. The results extracted using both the DAQ and WDAQ showed good linearity response over the pressure range with a sensitivity of 6.878 µV/V/psi, and the average efficiency of WDAQ was found to be 98% compared to the DAQ970A. The proposed WDAQ is a low-cost solution for remote data collection and transmission. The WDAQ system is built using ESP8266 WIFI module, coupled with an Arduino Nano. Java small object notation (JSON) is used to send data from Arduino nano to ESP8266 using the serial communication protocol, allowing for the transfer of data to Google Sheets using application programming interface (API). The system is enclosed in a package, making it suitable for wind turbines, flapping wings and other harsh environments. The WDAQ provides the required operating voltage to the CMOS MEMS sensor which makes it a self-reliant system. The design, implementation, and performance assessment of the WDAQ system are all thoroughly depicted herein. The work also concentrates on swarm networks to send data to servers in areas where there is limited network connectivity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： CMOS integrated circuits

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A Perspective Analysis of Optical Biosensors in Machine Learning Applications 18

A Perspective Analysis of Optical Biosensors in Machine Lear...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Kumar, B. Naveen Panduranga Rao, M.V. Sharan, Preeta Varun, K.R. Kallam, Suresh Kumaresan, M. Faculty of Engineering and Technology Department of Computer Science and Engineering Bengaluru India The Oxford College of Engineering Department of Electronics and Communication Engineering Bengaluru India The Oxford College of Engineering Department of Mechanical Engineering Bengaluru India

ISBN: (纸本)9789380544519

Optical biosensors, a novel invention at the intersection of biotechnology and optics, are revolutionizing the field of biomedical diagnostics. Optical biosensors act by identifying variations in light's wavelength, phase, polarization, or intensity caused by interactions between a sensing element and the target analyte. Optical biosensors outperform other sensor types in sensing applications due to their quick, accurate, and extremely sensitive results. When evaluating optical sensors, total power, core power, efficient area, and indices are the most crucial variables to consider. Recent years have seen notable advancements in machine learning (ML), a branch of artificial intelligence (AI). This review investigates the complementary nature of optical biosensors and machine learning methods. Here, the potential utility of ML for optical biosensors is explored in detail. The advantages and drawbacks of the most popular machine learning methods are listed based on an analysis of the optical biosensing data. It highlights the possible effects of machine learning on improving the sensitivity, selectivity, and adaptability of optical biosensing devices, and it summarizes current advancements in the field. Finally, the study considers the challenges and limitations that associated with current optical biosensing techniques. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Machine learning optical biosensor photonic crystal surface plasmon resonance surface-enhanced Raman scattering

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GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning

GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Po...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Keyi Luo, Yeqi Yang, Weidong Xu, Jingyi Li, Zhijun Chen, Wen-Ming Fei, Ben School of Computer Science Fudan University China School of Mechanical Engineering Tongji University China Academy for Engineering and Technology Fudan University China

ISBN: (纸本)9798350368741

Self-supervised learning of point cloud aims to leverage unlabeled 3D data to learn meaningful representations without reliance on manual annotations. However, current approaches face challenges such as limited data diversity and inadequate augmentation for effective feature learning. To address these challenges, we propose GS-PT, which integrates 3D Gaussian Splatting (3DGS) into point cloud self-supervised learning for the first time. Our pipeline utilizes transformers as the backbone for self-supervised pre-training and introduces novel contrastive learning tasks through 3DGS. Specifically, the transformers aim to reconstruct the masked point cloud. 3DGS utilizes multi-view rendered images as input to generate enhanced point cloud distributions and novel view images, facilitating data augmentation and cross-modal contrastive learning. Additionally, we incorporate features from depth maps. By optimizing these tasks collectively, our method enriches the tri-modal self-supervised learning process, enabling the model to leverage the correlation across 3D point clouds and 2D images from various modalities. We freeze the encoder after pre-training and test the model's performance on multiple downstream tasks. Experimental results indicate that GS-PT outperforms the off-the-shelf self-supervised learning methods on various downstream tasks including 3D object classification, real-world classifications, and few-shot learning and segmentation. Project page: https://***/Luoyeqi1/***. © 2025 IEEE.

关键词： 3D Gaussian Splatting 3D understanding point clouds pre-training self-supervised learning

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A computer vision based real-time warpage monitoring and detection system in fused deposition modeling

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Multimedia Tools and Applications 2024年 1-26页

作者： V S, Devesh Sundarrajan, Sridhar Venkatesan, M. Bhuvaneswari, S. School of Mechanical Engineering SASTRA Deemed University Tirumalaisamudram Tamilnadu Thanjavur613 401 India Department of Computer Science and Engineering Srinivasa Ramanujan Centre SASTRA Deemed University Tamilnadu Kumbakonam612 001 India

Additive Manufacturing (AM) manufactures a component by adding layers of material. Fused Deposition Modeling (FDM) is one such AM technique where polymer materials like ABS, and PLA are printed. The FDM method involves considerable time for printing the material. Any disruptions in the bed, power, or nozzle movements during printing will result in defective product. One of the reasons for a defective print arises due to a phenomenon called warping. These disruptions and errors pose challenges for early prevention as they are only visible gradually over time and cause warpage. In the present work, an attempt is made to predict the occurrence of warpage in FDM printed parts. In the present work, an attempt is made to avoid the adverse effect of warping by utilizing a computer vision model for real-time warpage detection. A real-time acquisition system is built to capture print layer-by-layer for a convolutional-based feature extraction process. This integration of a computer vision model aligned with continuous monitoring facilitates a timely shutdown process upon the occurrence of warpage. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Fused Deposition Modeling

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Common Problems in Software Requirement engineering Process: An Overview of Pakistani Software Industry 4

Common Problems in Software Requirement Engineering Process:...

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4th International Conference on Advancements in Computational sciences, ICACS 2023

作者： Khalid, Sadia Rasheed, Uzair Muneer, Mishal Butt, Wasi Haider Mehmood, Rabia Qamar, Usman College of Electrical and Mechanical Engineering NUST Department of Computer and Software Engineering Islamabad Pakistan University of Lahore Department of Computer Science and IT Lahore Pakistan Lahore College for Women University Department of Computer Science Lahore Pakistan

ISBN: (纸本)9781665463775

Requirement engineering is a major phase of software development process. A project's success mainly depends on an efficient and effective requirement engineering process. Practices have been defined to ensure successful requirement engineering of software projects. Yet the professionals face numerous issues during this phase. This paper explores the software requirement engineering practices from in the software industry of Pakistan. It highlights the common problems faced by the software professionals, as well as commonly deployed solutions and practices. © 2023 IEEE.

关键词： Requirements engineering

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