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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Irfan Khan Shaik Anjimoon V Asha Atul Singla P. Akhil Nahed Mahmood Ahmed Alsultany Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Applications New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Dept. of CSE St. Martin’s Engineering College Secunderabad Telangana India Collage of Dentist National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

A comprehensive Wire Arc Additive Manufacturing (WAAM) approach that employs five algorithms to improve massive metal components is described in this research. The adaptive layer thickness control algorithm adjusts layer thickness based on real-time complexity. This ensures additive manufacturing accuracy throughout. Also, the Material Versatility Optimization Algorithm simplifies working with multiple materials, making casting more efficient. The Intelligent Path Planning Algorithm allocates the robotic arm’s movement to traverse the shortest distance and uniformly distribute heat during deposition. The MultiMaterial-Materialon Strategy Algorithm improves material amounts, making WAAM more usable for additional materials. The Real-Time Excellent Assurance Algorithm constantly examines and adjusts parameters to ensure excellent production. A complete analysis of research reveals how algorithms are related and how vital they are for accuracy, speed, and flexibility. Testing shows that the recommended technique controls layer thickness, material variety, deposition velocity, accuracy, energy efficiency, and real-time tracking better than typical WAAM methods. Figures 6, 7, and 8 indicate that the proposed method delivers superior results across several criteria. The recommended WAAM approach can create more massive metal components by fundamentally changing how things are done. This research provides a comprehensive additive manufacturing approach to solve numerous large-scale metal production issues.

关键词： Technological innovation Accuracy Metals Production Three-dimensional printing Real-time systems Path planning

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Pixel-Level Face Image Quality Assessment for Explainable Face Recognition

arXiv

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arXiv 2021年

作者： Terhörst, Philipp Huber, Marco Damer, Naser Kirchbuchner, Florian Raja, Kiran Kuijper, Arjan Norwegian University of Science and Technology Gjøvik Norway Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Technical University of Darmstadt Darmstadt Germany

An essential factor to achieve high performance in face recognition systems is the quality of its samples. Since these systems are involved in daily life there is a strong need of making face recognition processes understandable for humans. In this work, we introduce the concept of pixel-level face image quality that determines the utility of pixels in a face image for recognition. We propose a training-free approach to assess the pixel-level qualities of a face image given an arbitrary face recognition network. To achieve this, a model-specific quality value of the input image is estimated and used to build a sample-specific quality regression model. Based on this model, quality-based gradients are back-propagated and converted into pixel-level quality estimates. In the experiments, we qualitatively and quantitatively investigated the meaningfulness of our proposed pixel-level qualities based on real and artificial disturbances and by comparing the explanation maps on faces incompliant with the ICAO standards. In all scenarios, the results demonstrate that the proposed solution produces meaningful pixel-level qualities enhancing the interpretability of the complete face image quality. The code is publicly available. © 2021, CC BY-NC-ND.

关键词： Face recognition

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Sophisticated Neural Network Architectures for the Holistic Simulation and Performance Enhancement of Convective Boiling Processes in Industrial Applications

Sophisticated Neural Network Architectures for the Holistic ...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： V Asha Shaik Anjimoon Atul Singla Irfan Khan Haider Asaad Farhoud Alsharifi Ramya Maranan Department of Computer Applications New Horizon College of Engineering Bangalore Institute of Aeronautical Engineering Dundigal Hyderabad Lovely Professional University Phagwara Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh Collage of Dentist National University of Science and Technology Dhi Qar Iraq Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

In this research, neural network approaches for industrial convective boiling simulation are fully examined. Five strategies address feature extraction, temporal dynamics, memory retention, data enrichment, and knowledge sharing issues. Recurrent neural networks (RNNs) discover temporal changes, whereas CNNs find spatial patterns. Long ShortTerm Memory (LSTM) Networks assist in recalling things; Generative Adversarial Networks (GANs) add data; and Transfer Learning conveys information using taught models. These methods accurately simulate convective boiling $96.0 \%$ of the time. Compared to tried-and-true approaches, it’s faster, more extensible, and more resilient. Visualizations indicate how superior the technique is at accuracy, stability distribution, and multi-metric scores. Here’s a detailed design for modeling convective boiling, including how space and time change, how to protect memories, add data, and exchange information. Industrial usage is possible since the recommended technique mimics convective cooking processes more complexly and cost-effectively.

关键词： Training Technological innovation Accuracy Recurrent neural networks Transfer learning MIMICs Data models

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Inferring the 3D Standing Spine Posture from 2D Radiographs 23rd

Inferring the 3D Standing Spine Posture from 2D Radiographs

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Bayat, Amirhossein Sekuboyina, Anjany Paetzold, Johannes C. Payer, Christian Stern, Darko Urschler, Martin Kirschke, Jan S. Menze, Bjoern H. Department of Informatics Technical University of Munich Munich Germany Department of Neuroradiology Klinikum rechts der Isar Munich Germany Institute of Computer Graphics and Vision Graz University of Technology Graz Austria School of Computer Science University of Auckland Auckland New Zealand

ISBN: (纸本)9783030597245

The treatment of degenerative spinal disorders requires an understanding of the individual spinal anatomy and curvature in 3D. An upright spinal pose (i.e. standing) under natural weight bearing is crucial for such bio-mechanical analysis. 3D volumetric imaging modalities (e.g. CT and MRI) are performed in patients lying down. On the other hand, radiographs are captured in an upright pose, but result in 2D projections. This work aims to integrate the two realms, i.e. it combines the upright spinal curvature from radiographs with the 3D vertebral shape from CT imaging for synthesizing an upright 3D model of spine, loaded naturally. Specifically, we propose a novel neural network architecture working vertebra-wise, termed TransVert, which takes orthogonal 2D radiographs and infers the spines 3D posture. We validate our architecture on digitally reconstructed radiographs, achieving a 3D reconstruction Dice of 95.52 %, indicating an almost perfect 2D-to-3D domain translation. Deploying our model on clinical radiographs, we successfully synthesise full-3D, upright, patient-specific spine models for the first time. © 2020, Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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Intelligent Gates Exploring the Massiveness of Random Access Points in NGMA

Intelligent Gates Exploring the Massiveness of Random Access...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Sunil Prashanth Kumar P. Aswini H Pal Thethi K Smitha R. Akhilesh Reddy Ali Albawi Department of Mechanical Engineeering New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

With the rapid progress of Next-Generation Mobile Architecture come several obstacles in improving user experience and network performance. Active methods frequently face issues in allocating network resources correctly. Consequently this can lead to mismanagement or poor resource utilization affecting how users experience the network. As access requirements increase with the growth of architecture complexity protecting personal data is becoming more challenging. To tackle these issues the Dynamic Access Point Management (DAPM) framework shows great potential as it delivers a creative strategy for resource allocation and network optimization. This model modifies standard resource management practices to become sharper and more adaptable. Thorough examination is needed to confirm DAPM’s superiority over standard methods regarding the resolution of these problems comprehensively. This research will examine the gains in network efficiency and security outcomes from the sophisticated models of DAPM. The detailed methodology seeks to construct a network framework that is both dependable and secure and will boost NGMA’s operational effectiveness and durability.

关键词： Data privacy Adaptation models Aerodynamics Dynamic scheduling Throughput User experience Stability analysis Security Resource management Standards

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Implantable Neural Probe System for Patterned Photostimulation and Electrophysiology Recording

Implantable Neural Probe System for Patterned Photostimulati...

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CLEO: science and Innovations, S and I 2022

作者： Chen, Fu-Der Wahn, Hannes Xue, Tianyuan Jung, Youngho Straguzzi, John N. Azadeh, Saeed S. Stalmashonak, Andrei Chua, Hongyao Luo, Xianshu Shah, Prajay Chameh, Homeira Moradi Lo, Patrick Guo-Qiang Valiante, Taufik A. Sacher, Wesley D. Poon, Joyce K.S. Max Planck Institute for Microstructure Physics Weinberg 2 Halle06120 Germany Department of Electrical and Computer Engineering University of Toronto 10 King's College Rd. TorontoONM5S 3G4 Canada Advanced Micro Foundry Pte. Ltd. 11 Science Park Road Singapore Science Park II 117685 Singapore Krembil Brain Institute University Health Network TorontoON Canada Division of Neurosurgery Department of Surgery Toronto Western Hospital University of Toronto TorontoON Canada Institute of Biomedical Engineering University of Toronto TorontoON Canada

We demonstrate a system for implantable nanophotonic neural probes with custom packaging and peripherals. The probes, which were manufactured on 200mm Si wafers, monolithically integrate SiN waveguides with TiN electr... 详细信息

ISBN: (纸本)9781557528209

关键词： Electrophysiology

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Minimal Cases for Computing the Generalized Relative Pose using Affine Correspondences

Minimal Cases for Computing the Generalized Relative Pose us...

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International Conference on computer Vision (ICCV)

作者： Banglei Guan Ji Zhao Daniel Barath Friedrich Fraundorfer College of Aerospace Science and Engineering National University of Defense Technology China Computer Vision and Geometry Group ETH Zürich Institute for Computer Graphics and Vision Graz University of Technology Austria Remote Sensing Technology Institute German Aerospace Center Germany

ISBN: (纸本)9781665428132

We propose three novel solvers for estimating the relative pose of a multi-camera system from affine correspondences (ACs). A new constraint is derived interpreting the relationship of ACs and the generalized camera model. Using the constraint, we demonstrate efficient solvers for two types of motions assumed. Considering that the cameras undergo planar motion, we propose a minimal solution using a single AC and a solver with two ACs to overcome the degenerate case. Also, we propose a minimal solution using two ACs with known vertical direction, e.g., from an IMU. Since the proposed methods require significantly fewer correspondences than state-of-the-art algorithms, they can be efficiently used within RANSAC for outlier removal and initial motion estimation. The solvers are tested both on synthetic data and on real-world scenes from the KITTI odometry benchmark. It is shown that the accuracy of the estimated poses is superior to the state-of-the-art techniques.

关键词： computer vision Motion estimation Computational modeling Pose estimation Benchmark testing Cameras Image sequences

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Transformed ROIs for Capturing Visual Transformations in Videos

arXiv

引用

arXiv 2021年

作者： Rai, Abhinav Sener, Fadime Yao, Angela School of Computing National University of Singapore Singapore117417 Singapore Institute of Computer Science II University of Bonn Bonn53115 Germany

Modeling the visual changes that an action brings to a scene is critical for video understanding. Currently, CNNs process one local neighbourhood at a time, thus contextual relationships over longer ranges, while still learnable, are indirect. We present TROI, a plug-and-play module for CNNs to reason between mid-level feature representations that are otherwise separated in space and time. The module relates localized visual entities such as hands and interacting objects and transforms their corresponding regions of interest directly in the feature maps of convolutional layers. With TROI, we achieve state-of-the-art action recognition results on the large-scale datasets Something-Something-V2 and EPIC-Kitchens-100. Copyright © 2021, The Authors. All rights reserved.

关键词： Large dataset

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Strategic Decision Making in the AI Age: Examining Neural Network Models in Business Management

Strategic Decision Making in the AI Age: Examining Neural Ne...

引用

Communication, computer sciences and Engineering (IC3SE), International Conference on

作者： Bhavana Jamalpur Prithu Sarkar M Hari Krishna Melanie Lourens K. Shantha Lakshmi Navdeep Singh School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Grade II Amity School of Communication Amity University Kolkata Institute of Aeronautical Engineering Dundigal Hyderabad Deputy Dean Faculty of Management Sciences Durban University of Technology South Africa Department of Mathematics M. Kumarasamy College of Engineering Karur Lovely Professional University Phagwara

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Over the last 10 years, developments in machine learning techniques and artificial intelligence (AI) tools have garnered interest from corporations and contemporary literature. However, even if AI technology has a lot of promise to resolve problems, there are still issues with its practical use and a dearth of knowledge on how to use it strategically to generate commercial value. Businesses can quickly gather and analyse large quantities of data with the help of AI-powered systems, which facilitates speedier and better-informed decision-making. All things considered, incorporating AI into business decision-making has the power to propel organisational performance and influence how business is conducted in the future.

关键词： Ethics Navigation Decision making Neural networks Force Machine learning Propulsion

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The Future of Wire Arc Additive Manufacturing Comprehensive Strategies for Improved Close Loop Monitoring and Control

The Future of Wire Arc Additive Manufacturing Comprehensive ...

引用

Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Ginni Nijhawan G Lalitha V Asha V J Suresh Praveen Zahraa H. Abdulzahraa Lovely Professional University Phagwara Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Applications New Horizon College of Engineering Bangalore Dept. of CSE St. Martin’s Engineering College Secunderabad Telangana India Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh College of Health and Medical Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This article describes a series of approaches that improve wire are additive manufacturing (WAAM) for manufacturing many metal items. ATM, MCA 2, ALTC 2, and RSM are the five techniques in the framework. Ablation research examined each approach and how they work together to improve WAAM accuracy, distortion, and material compatibility. The thorough performance comparison, including photographs, indicates that WAAM is better in deposition rate, surface quality, clarity, material diversity, cost-effectiveness, and scalability. The integrated framework enables dynamic modifications, tracking, and iterative improvements. This makes WAAM the optimal additive manufacturing process for many applications. The data indicates how significant each strategy is and how they work together to improve WAAM. This study illuminates sophisticated additive manufacturing technologies and indicates that WAAM is a feasible and flexible alternative to large metal component manufacturing.

关键词： Accuracy Scalability Heuristic algorithms Metals Three-dimensional printing Distortion Wire

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