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Spherical modification of tungsten powder by particle composite system

rare Metals 2022年第6期41卷 1972-1976页

作者： Cong-Cong Wang Cheng-Chang Jia Peng Gao Guo-Sheng Gai Yu-Fen Yang School of Materials Science and Engineering University of Science and Technology BeijingBeijing 100083China Powder Technology R&D Group Department of Material Science and EngineeringTsinghua UniversityBeijing 100084China

Owing to contradiction between increasing demand of spherical tungsten powder and limitation of traditional manufacturing technology,a novel preparation method was developed to sphericize the polygonal tungsten powder by means of modification of particle composite *** powder particles were modified by par-ticle composite system,and detailed characterization by scanning electron microscopy(SEM)was *** size distribution and function mechanism were analyzed,and the internal relationship between average diameter and processing time was *** results show that the spherical tungsten powder with an average diameter of 6.41μm is obtained from polyhedral tungsten powder with an average diameter of 7.50μ*** spherical effect could be achieved(sharp edge angles of particles are rounded off and reshaped)when the processing time is over 30 *** relationship between average diameter(d)and pro-cessing time can be described by the exponential decay model,which provides a good interpretation for the process of *** relationship between them can be expressed by the equation d=1.87406exp(-x/8.92718)+*** proposed method could readily enable large-scale production of spherical tungsten powder.

关键词： Tungsten powder Modification Average diameter Powder metallurgy

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Precision in Audio: CNN+LSTM-Based 3d Sound Event Localization and detection in real-world Environments 2

Precision in Audio: CNN+LSTM-Based 3D Sound Event Localizati...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Shriya Varnita, Lakkavaram S. M. Subramanyam, Kandlagunta Ananya, Madireddy Mathilakath, Pranav Krishnaraj, N. Mukesh Krishnan, M.B. Gaurav Tiwari, Sourabh Shankarappa, rashmi T. College of Engineering and Technology SRMIST Department of Networking and Communication Chennai India Samsung R&D Institute Voice Intelligence R&D Bangalore India

ISBN: (纸本)9798350365269

The task of Sound event localization and detection (SELd) is one of the emerging and key areas that will help shape the machines of the future. It amalgamates the problems of Sound event detection (SEd) and direction of arrival (dOA). The paper proposes a convolution recurrent neural network (CrNN) with Long Short-Term Memory (LSTM) based solution for spatial localization and classification of the sound source. The convolutional layers help in extracting spatial features while the LSTM layers capture the long-range temporal dependencies of the audio data. The inclusion of techniques like attention mechanism, normalization, pooling, and dropout-regularization results in an effective SELd model. The model is then evaluated on SELd score, which is a function of SEd and dOA scores. The results suggest the promising ability of proposed architecture on localizing and distinguishing from spatially and semantically different sound sources in the environment. © 2024 IEEE.

关键词： direction of arrival

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A Study to Automate Workload Assessment Procedure using Computer Vision 9

A Study to Automate Workload Assessment Procedure using Comp...

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Joint 9th International Conference on digital Arts, Media and technology with 7th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI dAMT and NCON 2024

作者： Moon, Jaekyeong Kim, Jong Hyun Yoo, Youngjun Tae, Hyunchul Korea Institute of Industrial Technology Digital Healthcare R&d Department Cheonan Korea Republic of Korea Institute of Industrial Technology Smart Manufacturing System R&d Department Cheonan Korea Republic of

ISBN: (纸本)9798350318241

In the field of occupational health, accurate and efficient ergonomic risk assessment is of paramount importance. This study presents an innovative computer vision-based approach to automate workload assessment with the goal of preventing work-related musculoskeletal disorders (WMSds). Our methodology, which uses a two-step algorithm, captures 3d skeletal representations from video footage of workers performing tasks such as lifting, lowering, and carrying. We conducted experiments stratified by object weight and task height to simulate different workplace scenarios. Our system uses AlphaPose for human pose estimation and MotionBErT for 3d skeleton extraction to provide a dynamic and accurate assessment of workload. By analyzing spatial and contextual data, this method provides real-time ergonomic risk assessment, which is critical in today's variable work environments. © 2024 IEEE.

关键词： Ergonomics

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Optimal Energy Forecasting Using Hybrid recurrent Neural Networks

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Intelligent Automation & Soft Computing 2023年第4期36卷 249-265页

作者： Elumalaivasan Poongavanam Padmanathan Kasinathan Kulothungan Kanagasabai Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Electrical and Electronics Engineering Agni College of TechnologyChennai600130India Department of Information Science and Technology Anna UniversityChennai600025India

The nation deserves to learn what India’s future energy demand will be in order to plan and implement an energy *** energy demand will have to be fulﬁlled by an adequate mix of existing energy sources,considering the con-straints imposed by future economic and social changes in the direction of a more sustainable *** energy demand,on the other hand,is a tricky task because it is inﬂuenced by numerous *** a result,an macro model with only a few factors that may be predicted globally,rather than a detailed ana-lysis for each of these variables,is *** this work,a hybrid approach is proposed for identifying the optimal generation-based mix of electricity systems in *** approach is developed by combing recalling-Enhanced recurrent Neural Network(rErNN)and Giza Pyramids Construction(GPC).rErNN pos-sesses selective memory features whereas GPC is a meta-heuristic algorithm that deals with different sets of *** goal of this approach is to assess the present load requirements and production proﬁle to understand the current requirement and its production *** on regards to electricity load proﬁle involving the generation of all power plants,capacity factors and transmission limits were gathered from different sources including the Indian National Load dispatch Centre,Central Electricity Authority database,annual reports of India and regional load dispatching *** proposal has introduced the rErNN technique to analyze the optimal time series whereas the GPC technique is involved in the optimization of rErNN *** present work is simu-lated in MATLAB and the performance assessed with different *** present technique gives an effective results in terms of accuracy,computational efﬁciency and feasibility.

关键词： Electricity generation demand rErNN GPC time series prediction

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The Impact of Heat Treatment and Surface Treatment on Thermal Conductivity of Heat Sinks Made of Aluminium die Casting Alloys

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Journal of Harbin Institute of technology(New Series) 2024年第4期31卷 34-41页

作者： rajesh Kumar Manickam dilip raja Narayana Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600001Tamil NaduIndia

This research contributes to understand the thermal management capabilities of Plate Fin Heat Sinks(PFHS)fabricated from *** uniqueness in this study is that the heat sinks were exposed to abrasive blasting,heat treatment,and graphene coating,and a full evaluation of the influence of the aforementioned treatments on the thermal management capacities of PFHS was *** PFHS is compared with 1)abrasive blasted and graphene coated heat sink,and 2)heat treated and graphene coated heat *** assess the thermal efficiency of the PFHS variants,a dedicated experimental set up was meticulously *** is noteworthy that a junction temperature of 60℃was assumed as the reference point for the *** results revealed that the charging cycle time which denotes the time required attaining the junction temperature,increased 1.3 times for the sample being abrasive-blasted at 0.5 MPa pressure and graphene-coated for 0.5 mm when the maximum heat input of 45 W is *** low heat input of 15 W is evaluated,the results revealed that there is no significant difference in charging cycle when compared to the untreated heat *** charging cycle time increased 2 times for the sample which is heat-treated at 450℃and graphene-coated for 0.5 mm at heat input of 15 *** finding unequivocally underscores the heightened capacity of the heat treated and graphene coated PFHS made of AlSi10Mg to withstand elevated junction temperatures.

关键词： PFHS abrasive blasting heat treatment graphene

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Free Vibration-Based delamination detection in Fiber Metal Laminates Composite Beam

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International Journal of Acoustics and Vibrations 2023年第3期28卷 258-269页

作者： Jarali, O.A. Logesh, K. Khalkar, Vikas Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Mechanical Engineering Gharda Institute of Technology Lavel Ratnagiri Khed India

delamination is a typical flaw in fiber-metal laminated composite structures that, when hidden from view, can significantly lower structural stiffness and alter a structure's ability to respond dynamically to forces like natural frequencies. delamination is undoubtedly an important topic as it causes the performance of the Fiber Metal Laminate structures in the service to worsen. delamination detection and severity analysis are essential in the aerospace industry for both safety and cost reasons. Although natural frequencies do not directly reveal the location or extent of damage, they are the most dependable metrics for damage detection. Frequency shifts in various modes are used to solve the inverse problem to pinpoint the damage location and extent. The bending natural frequencies shift brought on by delamination is utilized as an input to predict the delamination parameters. This study used an approach based on machine learning and a regression model to find the delamination parameters, i.e., locations and severity, in fiber metal laminated cantilever beams. FEA simulations using ANSYS were used to get the dataset pertaining to the position, severity, and eigen values (or bending natural frequencies) of the delamination. The results of this study indicate that the delamination locations and severity predictions developed using machine learning and regression models are reasonably accurate and demonstrate good agreement with the observed data. © 2023 International Institute of Acoustics and Vibrations. All rights reserved.

关键词： Laminated composites

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A Circular Slotted Wide Band Antenna for Sub 6 GHz Wireless Applications 3

A Circular Slotted Wide Band Antenna for Sub 6 GHz Wireless ...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Chinnathambi, Murugan Thandapani, Kavitha Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Electronics and Communication Department Chennai India

ISBN: (纸本)9798350369083

This paper introduces a circular slotted antenna tailored for 5G applications, resonating within the 4.4 to 7.25 GHz frequency band. Fabricated on a 1.6 mm Fr4 substrate with a dielectric constant of 4.4 and a tangential loss of 0.02, the antenna boasts dimensions of 22 mm x 15 mm. Performance analysis reveals gains of up to 4 dBi and remarkable efficiency levels of up to 85% across the operational spectrum. The compact size and impressive performance metrics make it a compelling choice for modern wireless communication systems. The antenna's broadband characteristics enable it to cover a significant portion of the 5G frequency range, ensuring compatibility with emerging communication standards. Moreover, its efficient design and high gain capabilities contribute to improved signal transmission and reception, enhancing overall network performance. Through extensive analysis, this research underscores the antenna's suitability and efficacy for 5G applications. The results affirm the circular slotted antenna as a promising solution to address the evolving demands of 5G technology. © 2024 IEEE.

关键词： 5G mobile communication systems

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Improving the Autonomous Vehicle Vision with Synthetic data using Gen AI 3

Improving the Autonomous Vehicle Vision with Synthetic Data ...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IdCIoT 2025

作者： Kadiyapu, durga Chandra Mouli Mangali, Venkata Sai Thummuru, Jaideep reddy Sattula, Sriya Annapureddy, Sasivardhan reddy Arunkumar, M.S. Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

This research focuses on addressing the challenges associated with training object detection and segmentation models for autonomous vehicles using real-world data, which is often difficult to collect, limited in diversity, and expensive to annotate. To overcome these limitations, we propose a novel framework for generating synthetic datasets using deep learning techniques and generative AI. Specifically, we utilize the Stable diffusion models to generate synthetic images, and annotate them using a combination of the Segment Anything Model (SAM) and the CVAT tool. The annotations are further refined to produce label files, and the dataset is split into training and validation sets. For evaluation, the YOLOv8 is applied for object detection and segmentation tasks using the synthetic dataset. Additionally, the Salesforce/Blip image-captioning model is integrated to provide descriptive captions for the detected objects. Experimental results demonstrate that synthetic datasets generated by this approach not only provide coverage of complex and rare scenarios but also achieve comparable or superior performance compared to real-world datasets in object detection and segmentation tasks. These findings underscore the potential of synthetic data to serve as a cost-effective and scalable alternative to real-world data for autonomous vehicle training. © 2025 IEEE.

关键词： Adversarial machine learning

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NMF based Speech Enhancement using CNN 15

NMF based Speech Enhancement using CNN

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Jessintha, d. Palas, P. Bini Berakhah, F. Stanley Kumar, r. Jeen retna Easwari Engineering College Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331300579

Convolutional Neural Networks (CNNs), are adept at learning complex patterns in data and can effectively denoise speech signals by mapping noisy spectrograms to clean ones. NMF, on the other hand, is a signal processing technique that can decompose a spectrogram into additive components, including a noise profile. By estimating the noise profile from the noisy speech spectrogram, NMF enables the separation of noise from the desired signal. Combining deep learning with NMF leverages the strengths of both approaches, allowing for robust and efficient speech enhancement even in challenging noisy environments. First, noisy and clean speech samples are loaded and pre-processed, including trimming or padding to a fixed length and computing magnitude spectrograms. A CNN-based deep learning model is then defined and compiled, with two convolutional layers for feature extraction from input spectrograms followed by dropout layers for regularization. This model is trained using the noisy spectrograms as input and their corresponding clean spectrograms as target output. Meanwhile, NMF is applied to estimate the noise profile from the magnitude spectrogram of the noisy speech signal, separating noise from the signal. Finally, the trained model is used to denoise the spectrogram, and the enhanced audio is reconstructed and saved. This approach combines the strengths of CNNs for learning complex mappings and NMF for noise estimation, resulting in improved speech enhancement performance. © Grenze Scientific Society, 2024.

关键词： Non-negative matrix factorization

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Low-voltage soft robots based on carbon nanotube/polymer electrothermal composites

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Chinese Physics B 2022年第12期31卷 703-709页

作者： Qi Wang Ying-Qiong Yong Zhi-Ming Bai School of Civil and Resource Engineering University of Science and Technology BeijingBeijing 100083China R&D Department China Academy of Launch Vehicle TechnologyBeijing 100076China

Nowadays,soft robots have become a research hot spot due to high degree of freedom,adaptability to the environment and safer interaction with *** carbon nanotube(CNT)/polydimethylsiloxane(PdMS)electrothermal composites have attracted wide attention in the field of flexible actuations due to large deformation at low ***,the preparation process of CNT/PdMS composites was designed and optimized,and electrothermal actuators(ETAs)were fabricated by cutting the CNT/PdMS composite films into a“U”shape and coating conductive *** deformation performance of the ETAs with different thicknesses at different voltages was *** a low voltage of about 7 V,the ETA has a deformation rate of up to 93%.Finally,two kinds of electrothermal soft robots(ETSrs)with four-legged and three-legged structures were fabricated,and their inchworm-like motion characteristics were *** ETSr2 has the best motion performance due to the moderate thickness and three-legged electrode structure.

关键词： carbon nanotube(CNT) polydimethylsiloxane(PdMS) electrothermal actuator soft robot

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