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检索条件"任意字段=International Conference on Modeling, Simulation and Applied Optimization"

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2021 27th international conference on Mechatronics and Machine Vision in Practice, M2VIP 2021

2021 27th International Conference on Mechatronics and Machi...

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2021 27th international conference on Mechatronics and Machine Vision in Practice, M2VIP 2021

ISBN: (纸本)9781665431538

The proceedings contain 137 papers. The topics discussed include: sliding-mode control of a dielectric elastomer actuator featuring non-invertible dynamics;linked dynamic graph CNN: learning through point cloud by linking hierarchical features;modeling and robust adaptive control for tendon-driven with bending tip systems;design and simulation of a robotic knee exoskeleton with a variable stiffness actuator for gait rehabilitation;a real-time double emulsion droplets detection system using Hough circle transform and color detection;a lightweight, integrated and portable force-controlled ankle exoskeleton for daily walking assistance;online change-point detection of force signal at the tip of surgical instrument with morphological wavelet;application optimization of fine-grained vehicle classification based on backbone network;and multi-slip conditions acquisition of planetary rovers with application to terrain parameter identification.

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Leveraging Hybrid Classical-Quantum Methods for Efficient Load Rebalancing in HPC

Leveraging Hybrid Classical-Quantum Methods for Efficient Lo...

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High Performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the international conference for

作者： Justyna Zawalska Minh Chung Katarzyna Rycerz Laura Schulz Martin Schulz Dieter Kranzlmüller Institute of Computer Science AGH University of Krakow Krakow Poland Academic Computer Center CYFRONET AGH Krakow Poland Leibniz Supercomputing Centre (LRZ) Garching bei München Germany MNM-Team Ludwig-Maximilians-Universität München (LMU) Germany Chair for Computer Architecture and Parallel Systems Technical University of Munich (TUM) Germany

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

Load imbalance is a challenge for parallel applications in High Performance Computing (HPC). It is caused by processes having different execution times or load values, leading to idle or wait times at synchronization points, where faster processes must wait for the slowest process to catch up. To mitigate this issue, applications can employ load balancing (LB) strategies, which migrate load between processes to even out load. This is often referred to as the Load Rebalancing Problem (LRP). While many approaches solving the LRP exist, they can only be heuristics and hence further optimization potential exists. In our work, we turn to a novel approach by using hybrid classical-quantum approaches and present two versions of the constrained quadratic model for solving the LRP; the two differ in how they balance the number of qubits required with the types of applied constraints. We compare the quantum-based methods with classical methods using heuristic algorithms Greedy, Karmarkar–Karp, and ProactLB. We evaluate our approaches using imbalance ratio and speedup as metrics, as well as the number of migrated tasks to indicate overhead caused by migrations. Our results show that the quantum-based methods outperform the classic methods. For example, we need only 1/4 of the number of migrated tasks in a realistic use case compared with classical methods, particularly Greedy and KK, to balance the load.

关键词： Measurement Upper bound NP-hard problem High performance computing Heuristic algorithms Qubit Load management Synchronization optimization Load modeling

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Machine learning-based process simulation approach for real-time optimization and active control of composites autoclave processing

Machine learning-based process simulation approach for real-...

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SAMPE 2021 conference and Exhibition

作者： Humfeld, Keith D. Zobeiry, Navid Boeing Research and Technology SeattleWA United States Materials Science & Engineering University of Washington SeattleWA United States

ISBN: (纸本)9781934551394

For manufacturing of composites, several parts may be processed simultaneously in an autoclave or oven. Depending on the equipment design, tool/part geometries, and tool nesting, convective heat transfer Boundary Conditions (BCs) may vary around parts in each load. As a result, temperature histories in some of the parts may not conform to specifications such as limits on maximum part temperature, or part temperature rate. To mitigate risk, in addition to conducting finite element simulations prior to fabrication based on assumed BCs, leading and lagging thermocouples, embedded in parts or placed in proxy locations, are used to monitor temperature history during processing. In this study, a recently developed machine learning framework, CompML (Composites Machine Learning) is used for active control of the autoclave. CompML uses TC data at the start of the autoclave processing for real-time inverse modeling of the thermochemical problem, and to identify BCs for all parts in each load. The results are then used for realtime optimization of autoclave cure recipe to the shortest cycle that satisfies specifications in all parts. A successful virtual demonstration of the approach for HEXCEL AS4/8552 parts processed on Invar tools is discussed in the paper. © 2021 Soc. for the Advancement of Material and Process Engineering. All rights reserved.

关键词： optimization

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Improvising Integrated Gas Planning Through Network optimization for End-To-End Value Chain Enhancement

Improvising Integrated Gas Planning Through Network Optimiza...

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2021 international Petroleum Technology conference, IPTC 2021

作者： Kulkarni, Sukrut Shridhar Mohammad, Marliana Bt. Petroliam Nasional Berhad PETRONAS

ISBN: (纸本)9781613997314

This paper describes a suggestion to improvise an integrated gas planning process through network optimization. As a prudent operator it is imperative to formulate long-term gas supply outlook and scenarios to ensure efficient and effective resource management with due considerations of growth strategies while maximizing value for purpose of production-focused conversations, technical assessment of forthcoming developments, commercial arrangement policy and strategic expansions. Also, it necessitates to develop and implement resolution plan arising from supply planning areas i.e. shortfall mitigation, facilities ullage, constraint and complying specifications commitment. It thereby imposes to implement robust network optimization workflow in place to improvise the integrated gas management cycle to manage the current existing gas supply and to also regulate strategy in terms of line-ups, evacuation path of forthcoming fields economically. Precedingly integrated gas planning exercise was executed via stacking up list of production sources with forecasted demands. The precedent approach was emphasized purely on mathematical and statistical method of capitalizing the production profile and geographical traits of the production sources. Notwithstanding, the approach usually linked with identified challenges and pain points throughout the planning cycle. Challenges and pain points in integrated gas planning were briefly outlined to understand limitations of existing work process as well as the need of improvising the same by embedding network optimization by simulation modeling. It was observed that multiple challenges did occur during planning preparation until post planning implementation. Workflow for strategic integrated gas planning was established to include step by step process to illustrate the ideal case otherwise known as base case scenario. The work process for constructing a mathematical model for integrated gas planning was demonstrated to reflect the comple

关键词： Gasoline

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Graph Low-Rank Non-Negative Matrix Factorization with Auto-Encoders for Fault Detection

Graph Low-Rank Non-Negative Matrix Factorization with Auto-E...

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international conference on Information Science and Technology (ICIST)

作者： Yabing Liu Shanshan Yu Wei Guo Man-Fai Leung School of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Cyber-Physical Fusion Intelligent Computing South-Central Minzu University State Ethnic Affairs Commission Wuhan China Faculty of Science and Engineering School of Computing and Information Science Anglia Ruskin University Cambridge United Kingdom

ISBN: (数字)9798350353334

ISBN: (纸本)9798350353341

Fault detection is the process of detecting and diagnosing faults or abnormalities in a system by analyzing its operational data. However, with the complexity of modern industrial processes, some faults are difficult to be detected in a timely manner due to various factors such as noise and data nonlinearity. Therefore, data-driven Fault Detection (FD) has become a widely used method to detect abnormal events in functional modules. Non-negative Matrix Factorization (NMF), as an efficient dimensionality reduction technique, has not had potential applications in fault detection (FD) thoroughly explored. In order to improve the FD methods based on NMF, we have developed a new approach, named graph low-rank non-negative matrix Factorization with auto-encoders (GLNMFA). GLN-MFA integrates the Laplacian operator effectively identifies t he l ocal s tructure a mong d ata p oints, enhancing the performance of dimensionality reduction algorithms. It also introduces the nuclear norm to find a low-rank approximation to the original matrix, thereby constraining sparsity. Additionally, auto-encoders are incorporated to learn a low-dimensional representation of the data and extract key features, which are subsequently applied for fault detection purposes. We employ an optimization algorithm based on Alternating Direction Method of Multipliers (ADMM) to optimize this model. Two test statistics $T^{2}$ (Hotelling's T-squared), SPE (Squared Prediction Error) are used to evaluate detection efficiency. K ernel Density Estimation (KDE) are used to estimate control limits for fault detection. The effectiveness of GLNMFA is validated on two benchmark datasets.

关键词： Dimensionality reduction Accuracy Fault detection Computational modeling Noise Feature extraction Approximation algorithms Robustness optimization Tuning

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Spline Global MPPT with PID Controller Based on Levy Invasive Weed Technique for Renewable Energy Resources of Smart Homes

Spline Global MPPT with PID Controller Based on Levy Invasiv...

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IEEE international conference on Power and Power and Renewable Energy (ICPRE)

作者： Aziza I. Hussein Mady Mohammad Passent Elkafrawy Rabab Hamed M. Aly College of Engineering Energy and Technology Research Center Effat University Jeddah Saudi Arabia College of Architecture Effat University Jeddah Saudi Arabia The Higher Institute for Management and Information Technology Minya Egypt Computer Science Department Faculty of Computer Science Nahda University Beni Suef Egypt

ISBN: (数字)9798350377460

ISBN: (纸本)9798350377477

Renewable energy systems, particularly photovoltaic (PV) systems, have been played important role in the reducing carbon emissions. A primary concern in the field of photovoltaics (PV) based on the design of the maximum power point tracking (MPPT) is the capacity to accurately monitor power across many parameters in addition to ascertain the power production of solar cells or wind turbines and adjust the load to maximise power efficiency under varying weather conditions. On another hands, the hybrid smart system uses a wind catcher to reduce the amount of energy consumed by buildings from the grid, which is a historically significant architectural component for cross ventilation and passive cooling. This paper presents an updating of model that proposes improvements to the regulation of Spline MPPT with tuning PID controller by using Levy Invasive Weed optimization (LIWO)technique. A rapid, accurate, and straightforward approach for determining the (MPPT) of PV systems under consistent irradiation and partial shade, as well as wind turbines, is presented using the Spline- MPPT technique. The model of this paper employs LIWO in conjunction with a PID controller to improve the selection of PID gains. Moreover, is applied to generate the optimal values of duty cycle of the model. The comprehensive system design depicted has been simulated using MATLAB Simulink to verify the functionality of the system. The model has attained an accuracy of 93%. The outcomes of this model contribute greatly to our understanding of the suitability and efficacy of both AI and traditional MPPT controllers. This modeling will be beneficial to the renewable energy industry.

关键词： Maximum power point trackers Photovoltaic systems Renewable energy sources Accuracy Software packages Mathematical models Wind turbines Splines (mathematics) optimization Tuning

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Robust Extreme Learning Machine based Sentiment Analysis and Classification

Robust Extreme Learning Machine based Sentiment Analysis and...

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international conference on Smart Systems and Inventive Technology (ICSSIT)

作者： R. Gnanakumaran Divya Rohatgi A K Sampath Nidhi Nagar D. Amuthaguka Raj Kumar Gupta Computer Science Annai Vailankanni Arts and Science College Thanjavur Tamilnadu India Department of CSE Amity University Maharashtra New Panvel India Department of CSE Presidency University Bengaluru Karnataka India Department of Statistics D.A.-V. College Kanpur Department of Computer Applications Kalasalingam Academy of Research and Education Krishnan Koil Physics Department Sardar Vallabbhai Patel College Bhabua (V.K.S.U Ara)

In recent times, S entimentalAnalysis (SA) acquires important attention in the process of decision making, primarily implied for the classification and extraction of the sentiments exist in the online reviews posted by the user. SA could be assumed as a sentiment classification (SC)issue where the online reviews experiences classification into negative and positive polarities based on the words available in the online reviews. This study focuses on the design of Robust Extreme Learning Machine Based Sentiment Analysis and Classification (RLM-SAC) model. The presented REIM-SAC model majorly aims to determine the nature of sentiments exist in the input data. Primarily, the input data is thoroughly pre-processed to get rid of unwanted data, which helps in enhancing the classification accuracy and minimizing the computational complexity. In addition, the presented REIM-SAC model applies ELM model to allocate proper class labels to it. To adjust the parameters of the ELM model, comprehensive learning particle swarm optimization (CLPSO) technique was used. The performance assessment of the RELM-SAC model is experimented with using benchmark database and the outcomes are scrutinized under numerous aspects. The simulation outcomes pointed out that the RELM-SAC method has obtained improved outcomes than other models.

关键词： Adaptation models Sentiment analysis Analytical models Extreme learning machines Databases Computational modeling optimization methods

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Hilbert-encoded Cooperative Coevolutionary Genetic Algorithm for Few-shot Learning

Hilbert-encoded Cooperative Coevolutionary Genetic Algorithm...

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Machine Intelligence Theory and Applications (MiTA), international conference on

作者： Xinyuan Zhang Boda Ou Yang Yue Hu School of Intelligent Systems Sciences and Engineering Jinan University Guang Zhou China School of Humanities Jinan University Guang Zhou China

ISBN: (数字)9798350385670

ISBN: (纸本)9798350385687

In recent years, the advancement of artificial intelligence algorithms, particularly in the domain of semantic segmentation within computer vision, has been noteworthy. This progress is attributed to the availability of diverse large-scale datasets and the continual improvement of computational resources, with deep learning serving as a representative paradigm. Consequently, numerous sophisticated segmentation models have emerged for visual tasks. However, the efficacy of these models hinges on the availability of substantial annotated datasets for training. A notable challenge arises in scenarios where few-shot datasets are prevalent due to inherent limitations. The sparse data volume in these instances often stems from difficulties in obtaining a sufficiently large and high-quality dataset. The process of image annotation is resource-intensive, demanding considerable time and human effort. Addressing the consequential shortage of sample size in few-shot datasets becomes a paramount concern. This paper tackles the aforementioned issues related to few-shot datasets and introduces the Hilbert encoded cooperative coevolutionary genetic algorithm (HCCGA), a novel image enhancement algorithm designed for the automated generation of 3D-Lookup Tables (3D LUTs). HCCGA leverages 3D LUTs generated automatically through the genetic algorithm to map the original few-shot dataset, thereby obtaining a more extensive set of high-quality samples. The methodology employed in this research encompasses the generation and optimization of 3D LUTs using a divide-and-conquer strategy based on genetic algorithms. Additionally, it involves the mapping of new images using 3D LUTs in conjunction with sampling and interpolation. The evaluation of the proposed approach encompasses comparisons based on various image binary classification evaluation metrics applied to images before and after mapping through the results of model inference. Experimental results obtained using HCCGA on diverse publ

关键词： Measurement Training Visualization Three-dimensional displays Semantic segmentation Computational modeling Table lookup optimization Standards Genetic algorithms

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optimization of temperature regimes of the hotend for devices of additive manufacturing

Optimization of temperature regimes of the hotend for device...

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international conference on Intelligent Manufacturing and Materials, IMM 2021

作者： Sinev, Andrey Yu. Panasenko, Roman A. Petrenko, Valeria V. Berestov, Aleksandr V. Bozhko, Yuri V. IP Sinev Dukhovskoy lane 17 Moscow Russia National Research Nuclear University "MEPhI" Kashirskoe hw 31 Moscow Russia

ISBN: (纸本)9783035718348

In this work, the authors tried to solve a typical problem of inexpensive 3D printers using FDM technology: material clogging of the hotend. To cope with the problem, it is necessary to solve the problem of optimization the temperature regimes of the hotend. For this, the temperature fields of the heating device were simulated taking into account the physical properties of the printing materials. modeling of the temperature distribution in the print head has been carried out using the SolidWorks simulation software package, the verification of theoretical calculations was carried out in a full-scale experiment. The data obtained has used to upgrade the 3D printer hotend. © 2021 Trans Tech Publications Ltd, Switzerland.

关键词： Verification

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Design and optimization of Antenna for Short Range Applications in Ultra Wide Band

Design and Optimization of Antenna for Short Range Applicati...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： Srividhya R M Anto Bennet Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science & Technology Chennai India

This study presents the design and analysis of an Ultra-Wide Band (UWB) antenna specifically tailored for short-range communication applications. UWB technology enables high-speed data transmission and precise positioning capabilities, making it suitable for a wide range of applications such as wireless sensor networks, radar systems, and indoor localization. The proposed UWB antenna is designed to operate over a frequency range of 3.1 GHz to 10.6 GHz, covering the entire UWB spectrum allocated by regulatory bodies. The antenna is based on a compact planar structure, allowing for easy integration into small devices. It consists of a modified printed monopole radiator with a tapered shape and a ground plane with specific slots for impedance matching and radiation pattern optimization. simulation tools such as ADS (Advanced Design System) and electromagnetic modeling techniques are employed to analyze the antenna's performance parameters, including return loss, radiation pattern, and gain. The antenna design is optimized to achieve a low return loss and high radiation efficiency across the UWB frequency range. Additionally, specific techniques such as impedance matching and radiation pattern shaping are implemented to enhance the antenna's overall performance. The fabricated prototype of the UWB antenna is tested and evaluated using experimental measurements. The results demonstrate that the antenna achieves excellent performance characteristics, including a wide bandwidth, omni-directional radiation pattern, and good impedance matching. These features make it suitable for short-range communication applications, where high data rates and precise localization are required. In conclusion, this research presents a compact and efficient UWB antenna design for short-range applications. The proposed antenna offers excellent performance characteristics, making it a promising candidate for various wireless communication systems and localization applications in indoor environm

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