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检索条件"任意字段=3rd International Conference on Computational and Experimental Methods in Mechanical Engineering"

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Solving Domain-Independent Dynamic Programming Problems with Anytime Heuristic Search 33

Solving Domain-Independent Dynamic Programming Problems with...

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33rd international conference on Automated Planning and Scheduling, ICAPS 2023

作者： Kuroiwa, Ryo Beck, J. Christopher Department of Mechanical and Industrial Engineering University of Toronto TorontoONM5S 3G8 Canada

ISBN: (纸本)1577358813

Domain-independent dynamic programming (DIDP) is a recently proposed model-based paradigm for combinatorial optimization where a problem is formulated as dynamic programming (DP) and solved by a generic solver. In this paper, we develop anytime heuristic search solvers for DIDP, which quickly find a feasible solution and continuously improve it to prove optimality. We implement six anytime heuristic search algorithms previously used as problem-specific methods and evaluate them on nine different problem classes. Our experimental results show that most of the anytime DIDP solvers outperform an existing A*-based solver, mixed-integer programming, and constraint programming in proving optimality, solution quality, and primal integral across multiple problem classes. In particular, complete anytime beam search (CABS) performs the best, improving on the best-known solution for one instance of traveling salesperson problem with time windows and closing five instances of one-to-one multi-commodity pick-and-delivery traveling salesperson problems. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Dynamic programming

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Semantic Segmentation of Dental Caries using Improved Deeplab V3Network

Semantic Segmentation of Dental Caries using Improved Deepla...

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international conference on Intelligent Communication and computational Techniques (ICCT)

作者： Manusha Kaki Suryanarayana Gunnam Sravani Dhanavath Prathap Kumar Gorantla Rakesh Saripineni Electronics and Communication Engineering Velagapudi RamaKrishna Siddhartha Engineering College Vijayawada India

Dental caries is a widespread health problem that can probably result to dental pulp as well as root apical infection. For patients to experience less pain, dental caries must be treated promptly and effectively. Conventional carious lesions diagnose techniques, such as naked-eye identification and panoramic radiograph evaluations, depends on qualified doctors, which can lead to misidentification and take a long time. This paper works on semantic segmentation using Improved Deeplab V3,a new deep learning design, to describe different carious lesions degrees from radiographic images. Semantic segmentation is analysis and classification of each pixel into multiple classes (labels). We begin by gathering a high-quality panoramic radiograph dataset of 2075 well-delineated caries, such as narrow, modest, and depth caries. The Deeplab V3network is then built with an additional channel and spatial attenuation module for segmenting these three types of caries from oral panoramic images. Hereafter, we make a comparison of improved DeeplabV3's semantic segmentation results with that of other previous methods. experimental studies demonstrate that our method can segment different stages of caries with a mean Dice coefficient of 94.7% and an accuracy of 94.01 %.

关键词： Deep learning Pain Semantic segmentation Semantics Neural networks Medical services Attenuation

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Approximation Model of the Method of Design Resistance of Reinforced Concrete for Bending Elements 3rd

Approximation Model of the Method of Design Resistance of Re...

引用

3rd international conference on Building Innovations, ICBI 2020

作者： Kosior-Kazberuk, Marta Kochkarev, Dmytro Azizov, Taliat Galinska, Tatiana Bialystok University of Technology Bialystok Poland National University of Water and Environmental Engineering Rivne Ukraine Pavlo Tychyna Uman State Pedagogical University Uman Ukraine National University "Yuri Kondratyuk Poltava Polytechnic" Poltava Ukraine

ISBN: (纸本)9783030850425

The paper considers approximation models of the method of design resistance of reinforced concrete for calculation of normal sections of bending reinforced concrete elements. It is proposed to use approximation dependences of two types—polynomial and linear. Both methods are based on the method of design resistance of reinforced concrete, which relies on universally accepted theory-based prerequisites and hypotheses. This method is based on the use of nonlinear deformation curves of concrete, Bernoulli hypothesis (the plane-sections hypothesis) is accepted as the correct one, and the extremum criterion for determining the bearing capacity (carrying force) based on a nonlinear deformation calculation model is used. The proposed techniques can massively simplify the calculation of bending reinforced concrete elements. They relieve from the necessity to use tables and perform complicated calculations with iteration methods, as it is intrinsic to the majority of existing methods. The possibility of determining on their basis both the bearing capacity (carrying force) and the relative height of the compressive zone of the concrete is shown in this article. The authors have conducted a check of the obtained approximation models of calculation on experimental samples of well-known researchers. The computational results indicate the computational accuracy, sufficient for practical calculations of the proposed methods. This paper presents the examples of determination of the carrying force and area of the effective reinforcement of normal sections of bending reinforced concrete elements by both offered methods. The proposed methods of calculating bending reinforced concrete elements can be widely used in design practice. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Reinforced concrete

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Network Coding for Scalable Genomic Data Transmission in Public Health Crises 3

Network Coding for Scalable Genomic Data Transmission in Pub...

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3rd international conference on Disruptive Technologies, ICDT 2025

作者： Alam, Sultan Kumar, Ram Sankat, Monica School of Computing Science and Engineering Vit Bhopal University Sehore466114 India

ISBN: (纸本)9798331519582

Quick genomic data transfer acts as an essential tool that public health teams need during emergencies like the COVID-19 outbreak. Network coding stands as a technology evaluated for improving large-scale genomic data transmissions in distributed networks. The experimental results show that network coding leads to substantial improvement of transmission throughput through reduces times by 47.2% relative to other standard approaches. Network coding technology displays superior error resilience capability because it recovers 92.3% of data at 10% error rate when compared to normal transmission methods which recover only 65.4%. The introduction of slight computational workloads provides high data integrity protection by maintaining reconstruction accuracy above 98.4% and achieving complete integrity for both small and large datasets. The utilization rate of networks can reach 89.5% across multiple server environments. The study demonstrates how network coding technology optimizes genetic data sharing processes by delivering quicker and more resilient and expandable information distribution for genomic research along with public health intervention programs. © 2025 IEEE.

关键词： Parallel processing systems

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AI-Powered VLSI Design Automation for Faster Chip Production in Emerging Technologies

AI-Powered VLSI Design Automation for Faster Chip Production...

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3rd IEEE international conference on Device Intelligence, Computing and Communication Technologies, DICCT 2025

作者： Maheswari, S. Gokilam, G.G. Navaneethan, S. Amer, Ayman Sunheriya, Neeraj Parthi, P. Sapthika Saveetha Institute of Medical and Technical Sciences Saveetha University Department of ECE Saveetha School of Engineering Chennai India Chennai India Saveetha Engineering College Department of ECE Tamil Nadu Chennai India Department of Energy Engineer Zarqa University zarqa Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Erode Sengunthar Engineering College Department of Electronics and Instrumentation Engineering Tamil Nadu Thudupathi India

ISBN: (纸本)9798331543358

The optimization techniques used in conventional VLSI design automation are rule-based and limited, which results in ineffective process execution and mistake detection. By using machine learning (ML) methods like Random Forest Regression (RFR), Convolutional Neural Networks (CNN), and Reinforcement Learning (RL), AI-powered VLSI design automation overcomes these constraints. Traditional design techniques are unable to keep up with next-generation semiconductor technology, which calls for AI-driven methods. The proposed approach increases accuracy and efficiency while decreasing human participation by automating many design processes. experimental findings show a 20-30% reduction in power usage and a 50% reduction in design time (e.g., 75 hours vs. 120 hours for high-complexity designs) as compared to traditional approaches. The approach significantly reduces error rates by more than 60% and increases answer generation quality, attaining 89% accuracy for high-complexity designs compared to 75% in previous techniques. These improvements in design accuracy, scalability, and power efficiency represent a major step toward more effective automation of semiconductor design. © 2025 IEEE.

关键词： Reinforcement learning

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Current Sensor Fault Detection and Isolation in Doubly Fed Induction Generator 3

Current Sensor Fault Detection and Isolation in Doubly Fed I...

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3rd international conference on computational and experimental methods in mechanical engineering, ICCEMME 2021

作者： Kumari, S. Anitha Priya, C. Department of Mechatronics Engg. Srm Institute of Science and Technology TamilNadu Chennai India Department of Electronics and Instrumentation Engg. Sri Sairam Engineering College TamilNadu Chennai India

Fault Detection and Isolation (FDI) is gaining interest as a means to increase the reliability and availability of efficient systems. Measuring sensors used for data acquisition are more prone to failures. The main objective of this paper is to provide model based FDI for current sensor fault closed-loop controlled Doubly Fed Induction Generator (DFIG) model connected to power grid. The sensor fault was detected and isolated by two methods including Unknown Input Observer (UIO) on reduced order DFIG model and fuzzy logic. The results show improvement in computational speed during online monitoring when the fault isolation is performed on reduced order models. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Fuzzy logic

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Students' Perception and Reconciliation: Decision Making in Project Groups Formation 3

Students' Perception and Reconciliation: Decision Making in ...

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3rd international conference on computational and experimental methods in mechanical engineering, ICCEMME 2021

作者： Rajeshwari, K. Hegde, Apurva Rohit Patil, Drishika Information Science and Engineering Bms College of Engineering Bangalore India

Academics is a huge repository of research avenue. Students tend to behave and adapt to the classroom based on their peer influences. Peers help in the increase of communication skills. Research shows group study is more effective than individual study. Group formation is influenced by several factors, like friendships, demographically and linguistically similar. Toppers are more considerate but again based on comfort level friends are chosen. In this paper the changes in preferences of group mates are analyzed, and inferences are drawn over their reconciliations. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Decision making

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Video Inference for Human Mesh Recovery with Vision Transformer 17

Video Inference for Human Mesh Recovery with Vision Transfor...

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17th IEEE international conference on Automatic Face and Gesture Recognition, FG 2023

作者： Cho, Hanbyel Ahn, Jaesung Cho, Yooshin Kim, Junmo School of Electrical Engineering Kaist Korea Republic of Kim Jaechul Graduate School of Ai Kaist Korea Republic of

ISBN: (纸本)9798350345445

Human Mesh Recovery (HMR) from an image is a challenging problem because of the inherent ambiguity of the task. Existing HMR methods utilized either temporal information or kinematic relationships to achieve higher accuracy, but there is no method using both. Hence, we propose 'Video Inference for Human Mesh Recovery with Vision Transformer (HMR-ViT)' that can take into account both temporal and kinematic information. In HMR-ViT, a Temporal-kinematic Feature Image is constructed using feature vectors obtained from video frames by an image encoder. When generating the feature image, we use a Channel Rearranging Matrix (CRM) so that similar kinematic features could be located spatially close together. The feature image is then further encoded using Vision Transformer, and the SMPL pose and shape parameters are finally inferred using a regression network. Extensive evaluation on the 3DPW and Human3.6M datasets indicates that our method achieves a competitive performance in HMR. © 2023 IEEE.

关键词： Recovery

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High Temperature Oxidation Behavior of 439 Ferritic Stainless Steel at Different Temperatures 3

High Temperature Oxidation Behavior of 439 Ferritic Stainles...

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2023 3rd international conference on Industrial Manufacturing and Structural Materials, IMSM 2023

作者： Zhu, E.N. Xia, Dianxiu Han, Qing Chen, Peidun Wang, Xuelin Liu, Zhiheng Jiang, Kun Department of Mechanical Engineering University of Jinan Shandong Jinan250022 China Technology R&D Center Taishan Steel Shandong Jinan271100 China School of Materials Science and Engineering University of Science and Technology Beijing Beijing100083 China

In recent years, due to the installation of selective catalytic reduction (Selective Catalytic Reduction, hereinafter referred to as SCR) devices, high temperature oxidation failure has occurred frequently in automobile exhaust systems. To explore its high temperature failure mechanism, this paper takes 439 ferritic stainless steel which is often used in automobile exhaust pipes as an example and uses Xrd, SEM, and EDS experimental methods to study its oxidation behavior at 700 °C, 800 °C and 900 °C. The results show that the oxide layer becomes thicker and the failure degree increases with the increase of the working temperature of 439 stainless steel. The oxidation products at different temperatures are different. When the oxidation temperature is 700 °C, the phase structure of the oxide layer on the surface of the sample is Cr2O3 and Fe2O3, and the oxide layer is dense and uniform. As the temperature rises to 800 °C, the surface oxidation products are sintered together, and the needle-like Fe2O3 structure appears on the oxide film. When the oxidation temperature rises to 900 °C, the oxide layer appears inner and outer layers, the outer layer is Fe2O3 shell structure, the inner layer is generated FeO, Fe2O3, and other iron oxides, and oxidation is very serious. At the junction of the oxide layer and the substrate, there are particles such as SiO2, TiO2, Fe2Nb in the substrate, which has a positive effect on the high temperature oxidation. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Thermooxidation

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DATA FUSION COGNITIVE COMPUTING FOR CHARACTERIZATION OF mechanical PROPERTY IN FRICTION STIR WELDING PROCESS

DATA FUSION COGNITIVE COMPUTING FOR CHARACTERIZATION OF MECH...

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ASME international Design engineering Technical conferences / 43rd Annual Computers and Information in engineering conference (IDETC-CIE)

作者： Hoang, Danny Chen, Ruimin Mishra, Debasish Pal, Surjya K. Imani, Farhad Univ Connecticut Dept Mech Engn Storrs CT 06269 USA Univ Connecticut UTC Inst Adv Syst Engn Storrs CT 06269 USA Indian Inst Technol Kharagpur Dept Mech Engn Kharagpur W Bengal India

ISBN: (纸本)9780791887295

High intricacy of the friction stir welding (FSW) process and severe plastic deformation typically result in serious part quality concerns, including thinning, groove, and kissing bond. These defects, in turn, impact mechanical properties (e.g., Young's modulus and yield strength), thereby deteriorating the performance of a joint part. Machine learning methods such as feature-based support vector machines and end-to-end deep neural networks are recently coupled with in-situ sensing to link process parameters with the final properties, yet the low accuracy and precision, poor sample efficiency, and lack of interpretable learning limit their capabilities for reliable characterization. This research introduces hyperdimensional cognitive computing (HCC) that mimics human brain functionalities to fuse power, torque, and force data to provide robust, sample-efficient, and explainable learning for process-property characterization in FSW. Data augmentation is integrated with HCC to tackle the imbalanced data problem. The experimental results show that HCC seamlessly fuses in-situ data to predict the ultimate tensile strength with an F1-score of 0.899 +/- 0.048, which is 21.579%, 47.779%, 1.933%, and 68.139% better than support vector machine, logistic regression, Naive-Bayes and multilayer perceptron, respectively. Besides the superior prediction capability, HCC symbolic representation reveals that measured force is the critical factor in the characterization of ultimate tensile strength. The proposed HCC is shown to be effective for data fusion and single-pass learning and eliminates the necessity of costly and long retraining in various manufacturing processes such as 3D printing and bio-fabrication.

关键词： Friction stir welding Cognitive computing Data fusion Property characterization

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