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Minimization of construction waste in Chennai construction industry

Materials Today: Proceedings 2022年 52卷 1884-1890页

作者： R. Janani T. Ilango E. Meenakshi M.J. Rajesh kumar Department of Civil Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) India

Generally, solidified scrap is mostly produced by the construction field. There is a need to manage and reduce bulldozing scrap which has become a rising concern due to the adverse things of human’s happenings on the atmosphere. Wastage of construction materials, unsuitable management at the place and little alertness of waste reduction are common in Indian construction sites. With landfills becoming scarcer the need to reduce wastes generated at all stages and recycle construction and demolition wastes is becoming increasingly relevant. This research assessed the knowledge of construction professionals in Chennai about sustainable construction and waste management perception through a designed questionnaire survey that help to provide experiential evidence on levels which has become an evident to waste minimization methods for construction waste minimization. Study has shown that government legislature on non– availability of bulldozing scrap in the building area and the defendants measured various factors such as training of workers, company waste management policy, financial reward and promotion as more helpful incentives to reduce waste. Nearly half of the respondents says that, there is no bill available for minimizing scraps of resources that are used in construction. This article scrutinizes various factors affecting waste generation at all stages and gives remedial measures to be taken to curb this.

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A Synopsis of FAME 2024 Challenge: Associating Faces with Voices in Multilingual Environments 24

A Synopsis of FAME 2024 Challenge: Associating Faces with Vo...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Saeed, Muhammad Saad Nawaz, Shah Moscati, Marta Das, Rohan Kumar Tahir, Muhammad Salman Zaheer, Muhammad Zaigham Liaqat, Muhammad Irzam Khan, Muhammad Haris Nandakumar, Karthik Yousaf, Muhammad Haroon Schedl, Markus University of Engineering and Technology Taxila Pakistan Institute of Computational Perception Johannes Kepler University Linz Austria Fortemedia Singapore Singapore Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Imt School for Advanced Studies of Lucca Lucca Italy Human-centered Ai Group Ai Lab Linz Institute of Technology Linz Austria

ISBN: (纸本)9798400706868

Over half of the world's population is bilingual and people often communicate under multilingual scenarios. The Face-Voice Association in Multilingual Environments (FAME) 2024 Challenge, held at ACM Multimedia 2024, focuses on establishing face-voice association to analyze the impact of multiple languages on the verification process. This report provides a brief summary of the challenge. © 2024 Owner/Author.

关键词： Adversarial machine learning

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Optimized Energy Efficient Strategy for Data Reduction Between Edge Devices in Cloud-IoT

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Computers, Materials & Continua 2022年第7期72卷 125-140页

作者： Dibyendu Mukherjee Shivnath Ghosh Souvik Pal D.Akila N.Z.Jhanjhi Mehedi Masud Mohammed A.AlZain Department of Computer Science and Engineering Brainware UniversityBarasat700125India Department of Computer Science and Engineering Global Institute of Management and TechnologyKrishnanagar741102India Department of Information Technology School of Computing SciencesVels Institute of ScienceTechnology&Advanced StudiesChennai600117India School of Computer Science and Engineering SCE Taylor’s UniversitySubang Jaya47500SelangorMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Numerous Internet of Things(IoT)systems produce massive volumes of information that must be handled and answered in a quite short *** growing energy usage related to the migration of data into the cloud is one of the biggest *** computation helps users unload the workload again from cloud near the source of the information that must be handled to save time,increase security,and reduce the congestion of ***,in this paper,Optimized Energy Efficient Strategy(OEES)has been proposed for extracting,distributing,evaluating the data on the edge *** the initial stage of OEES,before the transmission state,the data gathered from edge devices are supported by a fast error like reduction that is regarded as the largest energy user of an IoT *** initial stage is followed by the reconstructing and the processing *** processed data is transmitted to the nodes through controlled deep learning *** entire stage of data collection,transmission and data reduction between edge devices uses less *** experimental results indicate that the volume of data transferred decreases and does not impact the professional data performance and predictive *** consumption of 7.38 KJ and energy conservation of 55.57 kJ was found in the proposed OEES *** accuracy is 97.5 percent,data performance rate was 97.65 percent,and execution time is 14.49 ms.

关键词： Energy efficient internet of things transmission performance cloud computing edge devices

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On Sparsity Measures in Deep Subspace Clustering 2

On Sparsity Measures in Deep Subspace Clustering

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2nd International Conference on Power, Control and Computing Technologies, ICPC2T 2022

作者： Das, Samiran Kyal, Chirag Pratiher, Sawon JIS University JIS Institute of Advanced Studies and Research Kolkata India Tata Consultancy Services Bangalore India Indian Institute of Technology Kharagpur Department of Electrical Engineering Kharagpur India

ISBN: (纸本)9781665458580

Traditional clustering methods groups data points according to attributes such as similarity, continuity, neighbor-hood information, etc. overlooks the structural properties of the data. Consequently, prevalent clustering approaches to below-par performance in real-world applications. Unlike traditional clustering approaches, subspace clustering methods attempt to group datapoints keeping the inherent structure and rank-related properties of the data into account. Despite the rapid growth in deep learning-based approaches, very few works have utilized deep learning for the subspace clustering task. This work introduced an auto-encoder-based deep learning architecture consisting of a self-expressive layer for the deep subspace clustering task. We use smoothed L2, L0.5 and Frobenius norms instead of the actual measures for ease of optimization task. We also explored the efficacy of sparsity measures that characterize the self-representation coefficient matrix of the self-expressive layer. The experiments conducted on standard datasets suggest that the application of efficient sparsity measures improves the performance of the subspace clustering approach and results in superior performance compared to the previous deep subspace clustering approaches. © 2022 IEEE.

关键词： Deep learning Computers Clustering methods Power control Computer architecture Indexes Task analysis

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Hyperspectral Images for Crop Prediction using Machine and Deep Learning Models

Hyperspectral Images for Crop Prediction using Machine and D...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： R. Mahalakshmi K. Kumutha M. Sakthivanitha R. Bagavathi Lakshmi Mohamed Sirajudeen Sankar Padmanabhan Department of Computer Applications-PG Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India Department of Computer Applications-PG Vels Institute of Science Technology Advanced Studies Chennai Tamil Nadu India Department of Computer Application Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India Department of Information Technology Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India PG and Research Department of Computer Science Nilgiri College of Arts and Science The Nilgiris Tami Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. Machine Learning (ML) and Deep Learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector Machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data.

关键词： Support vector machines Radio frequency Deep learning Recurrent neural networks Accuracy Crops Spatial databases Convolutional neural networks Reliability Hyperspectral imaging

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Localization through RGB-D Camera for the Quad-rotor

Localization through RGB-D Camera for the Quad-rotor

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International Conference on Computing and Networking technology (ICCNT)

作者： Vidit Datt Prabhakar Vijay Kumar Pandey Anuj Kumar Sharma Tushar Sandhan Department of Mechatronics Centre for Advanced Studies AKTU Lucknow India Department of Electrical Engineering Indian Institute of Technology Kanpur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper presents a thorough investigation into the implementation of a localization system using a RGB-D camera that prioritizes computational efficiency. The study focuses on utilizing the Oak D Pro depth camera, which includes an in-built Inertial Measurement Unit. Visual Odometry is used to estimate the camera pose which is lighter in computation than other algorithms like Simultaneous Localization and Mapping. The IMU is used to obtain the camera pose due to the presence of drift during the camera’s fast motion. The readings are fused together to get the correct camera pose by employing the Extended Kalman Filter for sensor fusion, achieving seamless integration of VO and IMU. This research explores the adaptation of Visual Inertial Odometry system for an autonomous Unmanned Aerial Vehicles applications, specifically for a quad-rotor/drone.

关键词： Location awareness Visualization Measurement units Simultaneous localization and mapping Inertial navigation Sensor fusion Cameras Kalman filters Odometry Visual odometry

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Securing Smart Grid IoT System using A Robust RNN based Cyberattack Detection Framework

Securing Smart Grid IoT System using A Robust RNN based Cybe...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Asha A Karthika S P.C. Saranya Deepak Arumugam St. Joseph’s Institute of Technology OMR Chennai KIT-Kalaignarkarunanidhi Institute of Technology Coimbatore School of Management Studies Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Cybersecurity in smart grid IoT systems is now a concern of high priority, as the integration of connected devices increases, and these systems become more vulnerable to sophisticated cyberattacks. This paper proposes a new RNN+GRU-based cyberattack detection system to meet such security challenges in smart grid IoT networks. The proposed model is based on Recurrent Neural Networks (RNN) and Gated Recurrent Units (GRU) to efficiently capture temporal and contextual patterns of the attack data for improving detection accuracy of different types of cyber-attacks. Comparing the performance of our proposed model with other models like Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Network (CNN), Deep Neural Network (DNN), and Long Short-Term Memory (LSTM) network is shown. Experimental results show that the RNN+GRU-based model outperforms these approaches in some key performance metrics: accuracy, precision, recall, F1-score, and ROC-AUC, better than other compared methods by achieving superior attack detection rates. Moreover, correlation analysis is also included for both the existing DNN model and the proposed approach; thus, the effectiveness of the RNN+GRU model in adapting to various scenarios of cyberattacks is noted. In the final analysis, the RNN+GRU-based cyberattack detection system shows great promise in securing smart grid IoT networks and shows better performance compared to some of the existing techniques.

关键词： Support vector machines Radio frequency Adaptation models Analytical models Accuracy Data models Smart grids Convolutional neural networks Internet of Things Long short term memory

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Detection of Irregular Shapes Resembling Non-Uniform Gaps for UAV Navigation

Detection of Irregular Shapes Resembling Non-Uniform Gaps fo...

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International Conference on Computing and Networking technology (ICCNT)

作者： Shashi Kumar Singh Anuj Kumar Sharma Tushar Sandhan Department of Mechatronics Centre For Advanced Studies AKTU Lucknow India Department of Electrical Engineering Indian Institute of Technology Kanpur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This research paper presents a novel approach for the detection and segmentation of non-uniform gaps to facilitate safe autonomous navigation of UAVs (Unmanned Aerial Vehicles) using the YOLOv8 deep learning model. The methodology comprises three key stages: data collection, annotation, and model training using the YOLOv8 architecture. Initially, the model is trained on regular shapes images such as lines, triangles, quadrilateral and hexagons which also resembles the many real-world scenarios to establish a foundational understanding before transitioning to non-uniform gaps. We solved the problem of data scarcity through synthetic to natural data training process. Non-uniform gaps, simulated to real-world, data is meticulously annotated to train the YOLOv8 model, ensuring robustness across diverse scenarios. A post-processing step is introduced to find the centroid of the segmentation mask of detected gaps, providing actionable guidance for UAV maneuvering with proper clearance. Experimental results demonstrate the efficacy of the proposed approach in accurately detecting and segmenting non-uniform gaps, showcasing its potential for real-world deployment in various applications. This research contributes to advancing UAVs navigation by providing a reliable solution for safe passage through complex environments.

关键词： Training Accuracy Navigation Shape Data collection Autonomous aerial vehicles Real-time systems Robustness Data models Drones

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Study of the growth mechanism of a self‑assembled and ordered multi‑dimensional heterojunction at atomic resolution

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Frontiers of Optoelectronics 2023年第4期16卷 37-48页

作者： Zunyu Liu Chaoyu Zhao Shuangfeng Jia Weiwei Meng Pei Li Shuwen Yan Yongfa Cheng Jinshui Miao Lei Zhang Yihua Gao Jianbo Wang Luying Li Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China Ministry-of-Education Key Laboratory for the Green Preparation and Application of Functional Materials Hubei Collaborative Innovation Center for Advanced Organic Chemical MaterialsSchool of Materials Science and EngineeringHubei UniversityWuhan 430061China Center for Electron Microscopy MOE Key Laboratory of Artificial Micro-and Nano-Structures and the Institute for Advanced StudiesSchool of Physics and TechnologyWuhan UniversityWuhan 430072China State Key Laboratory of Infrared Physics Shanghai Institute of Technical PhysicsChinese Academy of SciencesShanghai 200083China

Multi-dimensional heterojunction materials have attracted much attention due to their intriguing properties,such as high efciency,wide band gap regulation,low dimensional limitation,versatility and *** further improve the performance of materials,researchers have combined materials with various dimensions using a wide variety of ***,research on growth mechanism of such composite materials is still *** this paper,the growth mechanism of multidimensional heterojunction composite material is studied using quasi-two-dimensional(quasi-2D)antimonene and quasione-dimensional(quasi-1D)antimony sulfde as *** are synthesized by a simple thermal injection *** is observed that the consequent nanorods are oriented along six-fold symmetric directions on the nanoplate,forming ordered quasi-1D/quasi-2D *** transmission electron microscopy(TEM)characterizations confrm the chemical information and reveal orientational relationship between Sb2S3 nanorods and the Sb nanoplate as *** density functional theory calculations indicate that interfacial binding energy is the primary deciding factor for the self-assembly of ordered *** details may fll the gaps in the research on multi-dimensional composite materials with ordered structures,and promote their future versatile applications.

关键词： Multi-dimensional composite materials Ordered heterostructures-Self-assembly Growth mechanism

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Experimental investigation on sensitiveness WEDM parameters for12X18H10T – Stainless steel

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Materials Today: Proceedings 2023年 81卷 454-458页

作者： V.N. Aneesh K. Karunakaran Department of Mechanical Engineering School of Engineering Vels Institute of Science Technology and Advanced Studies (Deemed to be University) Vels University Chennai TN India

The unconventional machining processes are popular in machining the difficult to cut type complex contour/ profile. The key parameters are to be machined with care as they directly affects the function and non key dimensions are machined with other than consideration of functional aspect, like design, passage. That means accuracy is less important in those dimensions but finish is to be good. So the machining of engineering materials does not always require optimizing parameters based on multiple responses. This investigation focuses on the sensitiveness of Wire EDM of 12X18H10T - Stainless Steel and identifying the parameters contribution and establish mathematical model to predict values without trial. An austenitic alloy 12X18H10T - Stainless Steel exhibits its inherent mechanical properties even when it expose to extremely high temperature. Ithas potential application in aerospace engine components manufacturing. This investigation utilized the Taguchi L9 experiential Design with factors of Pulse off Time, Wire Tension, Pulse on Time, and Wire Feed Rate. This study aims to examine Maximize the rate of machining with Taguchi approach in the process of Wire EDM of 12X18H10T - Stainless Steel and establish a mathematical model to predict the highly influencing parameter(s) for desired rate of machining.

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