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检索条件"任意字段=20th Conference on System Modeling and Optimization"

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2023 20th International Multi-conference on systems, Signals and Devices, SSD 2023

2023 20th International Multi-Conference on Systems, Signals...

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20th International Multi-conference on systems, Signals and Devices, SSD 2023

ISBN: (纸本)9798350332568

the proceedings contain 170 papers. the topics discussed include: design of a low-noise amplifier for ultra-low power wake-up receiver in 868 MHz;design and implementation of smart pill box for healthcare applications;solving inverse kinematics for 3R manipulator using artificial neural networks;a review of Alzheimer’s disease and emerging patient support systems;on the effect of optical power on quantum dot HEMT transistor;fast supercapacitor charging for electromagnetic converter systems by self-powered boost circuit;modeling and optimization of a hybrid energy harvesting system for IoT nodes through PSO-MPPT;distributed event-triggered scalable formation control for multiple autonomous vehicles;read and write circuit for memristive neural networks;and parameters optimization of convolutional neural network to synthesize 12-lead ECG from a 3-lead.

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Generalizing knowledge enabled fast-adaptive optimization for advanced machining systems 20

Generalizing knowledge enabled fast-adaptive optimization fo...

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IEEE 20th International conference on Automation Science and Engineering (CASE)

作者： Xiao, Qinge Wu, Chengke Yang, Zhile Guo, Yuanjun Guo, Hanping Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Shenzhen Tianhua Architectural Design Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9798350358513;9798350358520

the infusion of generalized knowledge into machining systems holds significant potential for enhancing optimization processes, rendering them fast-adaptive, flexible, and goal-oriented. To foster the autonomous evolution of central control systems and effectively address multi-objective requirements, this paper introduces a novel approach: a generative manifold-based policy-gradient method tailored for approximating the continuously distributed Pareto frontier in advanced machining system optimization. this method seamlessly integrates multi-pass operations into a multi-policy Markov Decision Process to adeptly respond to dynamic changes in machining configurations. Moreover, it leverages a multi-layered generator to effectively map the high-dimensional policy manifold from a simple Gaussian distribution, thereby simplifying intricate computations. Experimental findings in various cutting scenarios underscore the superior effectiveness of the proposed method compared to metaheuristics in tackling the challenges of advanced machining optimization.

关键词： Advanced machining knowledge learning Reinforcement learning Generative learning Policy search

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Ensemble-based Offline Data-driven Evolutionary optimization for Operation Parameter of Blast Furnace 20

Ensemble-based Offline Data-driven Evolutionary Optimization...

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20th International conference on Natural Computation, Fuzzy systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Ye, Qiucheng Cai, Hongxia Bian, Yunqi School of Mechatronic Engineering and Automation Shanghai University Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai China

ISBN: (纸本)9798350356328

Stable control of the iron-making process through the adjustment of operating parameters is essential to improve blast furnace productivity. However, due to the complex and dynamic nature of the reaction process, it is difficult to perform accurate modeling and simulation experiments for a blast furnace. this poses great difficulties in performing the evaluation of control strategies in real-time. Offline evolutionary optimization, as an optimization technique that relies only on historical data, enables the search for optimal operation parameters through the introduction of surrogates. Based on the characteristics of historical time-series data of the iron-making process, this study proposes an offline evolutionary optimization method based on advanced time-series surrogates. Ensemble strategy combining surrogates with different prediction time windows contributes robustness during the optimization process. Additionally, multiple sub-regions are maintained to enable extensive searching of the decision space with a multi-objective nature. the efficiency of the proposed pooling method is demonstrated through verification in actual production cases. © 2024 IEEE.

关键词： Blast furnaces

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Flexible Manufacturing system for Enhanced Industry 4.0 and Industry 5.0 Applications 20

Flexible Manufacturing System for Enhanced Industry 4.0 and ...

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20th Annual International conference on Distributed Computing in Smart Sensor systems and the Internet of things (DCOSS-IoT)

作者： Pavel, Mihai-Daniel Stamatescu, Grigore Asti Automat Res & Dev Bucharest Romania Univ Politehn Bucuresti Automat & Ind Informat Bucharest Romania

ISBN: (纸本)9798350369458;9798350369441

Flexible manufacturing systems (FMS) have the potential to increase efficiency and adaptability in manufacturing which results in both economic gains as well as support future environmental and social sustainability. However, the continuous optimization of such systems remains a challenging task due to the trade-offs between flexibility and production efficiency under increasing cost pressures. this work presents an enhanced design and development approach for flexible systems of fabrication that takes into account these trade-offs. Our approach is based on a combination of simulation and optimization techniques, and it has been validated through experiments on a real-world flexible system of fabrication with state-of-the-art components and tools for Industrial Internet of things (IIoT) and Industry 4.0/5.0 paradigm integration. the presented results demonstrate the effectiveness of our proposed approach in conjunction with reference performance metrics such as the Overall Equipment Effectiveness (OEE) and the power consumption.

关键词： flexible manufacturing system efficiency sustainability industry 4.0 industry 5.0

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Risk Adverse optimization on Transmission Expansion Planning Considering Climate Change and Extreme Weather Events - the Texas Case 20

Risk Adverse Optimization on Transmission Expansion Planning...

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20th International conference on the European Energy Market (EEM)

作者： de Oliveira, Luiz Eduardo Saraiva, Joao Tome Vilaca Gomes, Phillipe FEUP Dept Elect & Compt Engn Porto Portugal Univ Pontificia Comillas Inst Res Technol Madrid Spain

ISBN: (纸本)9798350381757;9798350381740

the global push for environmental sustainability is driving substantial changes in power systems, prompting extensive grid upgrades. Policies and initiatives worldwide aim to reduce CO2 emissions, with a focus on increasing reliance on Renewable Energy Sources (RESs) and electrifying transportation. However, the geographical variability and uncertainties of RESs directly impact power generation and distribution, necessitating adjustments in transmission system planning and operation. this paper presents a Transmission Expansion Planning (TEP) model using the 2021 Texas snowstorm as a benchmark scenario, incorporating wind and solar energy penetration while addressing associated uncertainties. Climate Change (CC) and Extreme Weather Events (EWE) are integrated into the set of scenarios aiming at evaluating the proposed method's effectiveness. Comparisons in extreme operative conditions highlight the importance of network reliability and security, emphasizing the significance of merged grids. All simulations are conducted using the ACTIVSg2000 synthetic test system, which emulates the ERCOT grid, with comparisons made between TEP scenarios considering and disregarding CC and EWEs, supporting the concept of umbrella protection.

关键词： Transmission Expansion Planning Climate Change Extreme Weather Events Low-Carbon Energy Transition Uncertainties

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Adaptive responses of cancer cells to tumor microenvironment changes: an evolutionary model perspective 20

Adaptive responses of cancer cells to tumor microenvironment...

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IEEE 20th International conference on Automation Science and Engineering (CASE)

作者： Romano, Chiara Borri, Alessandro Di Benedetto, Maria Domenica Univ Aquila Ctr Excellence Res DEWS Dept Informat Engn Comp Sci & Math Laquila Italy Natl Res Council Italy CNR IASI Biomath Lab Rome Italy

ISBN: (纸本)9798350358513;9798350358520

Evolution has always driven species change. the most interesting manifestations of evolution arise from natural selection, that favors the settling of more suitable species at the expense of others. this also occurs in cancer, where the hostile environment drives cancer cells to evolve to acquire favorable traits, phenotypes, in order to enlarge their species. A therapy can render the environment hostile, thus the physician's role in limiting cell growth while considering their evolution is crucial. In this paper, we focus on the formalization and analysis of a novel eco-evolutionary model of cancer cells, enriched by biological considerations. Simulations validate the model and show how the role of the treatment is crucial in driving cellular dynamics.

关键词： Modelling simulation and optimization in healthcare tumour modeling and treatment

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A learning-based method for performance optimization of timing analysis 20

A learning-based method for performance optimization of timi...

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20th International conference on Synthesis, modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2024

作者： Vagenas, Anastasis Garyfallou, Dimitrios Evmorfopoulos, Nestor Stamoulis, George University of Thessaly Department of Electrical and Computer Engineering Volos Greece

ISBN: (纸本)9798350351927

Timing analysis is a crucial verification method employed throughout the entire design cycle of integrated circuits. However, the increasing complexity of modern designs, driven by aggressive technology scaling, necessitates more efficient timing engines. In this paper, we present a learning-based approach to accelerate timing analysis. We propose XGBoost models to assess the complexity of each gate-level stage and choose the most suitable timing analysis method. By incorporating less computationally expensive methods, particularly in low-complexity stages, we can significantly improve the overall performance of timing analysis without sacrificing accuracy. Unlike previous studies, we extract representative electrical and topological features from each stage, avoiding additional computations such as effective capacitance or moments. Experimental evaluation on one million stages indicates that our learning-driven approach reduces the runtime of timing analysis by 3.36 × while achieving accuracy comparable to our best available timing analysis method. thus, the proposed models can be used during signoff or iterative optimization flows to expedite timing closure. © 2024 IEEE.

关键词： Adversarial machine learning

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Battery State-of-Charge Estimator Design based on the Least-Squares Support Vector Machine 20

Battery State-of-Charge Estimator Design based on The Least-...

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IEEE 20th International conference on Automation Science and Engineering (CASE)

作者： Cavanini, Luca Majecki, Pawel Grimble, Mike J. van der Molen, Gerrit M. Ind Syst & Control Ltd Glasgow Lanark Scotland Univ Politecn Marche Ancona Italy Univ Strathclyde Glasgow Lanark Scotland

ISBN: (纸本)9798350358513;9798350358520

the design and development of a data-driven algorithm for battery State-of-Charge estimation is presented. the estimation of battery SoC is important in the development of Battery Management systems. the proposed approach exploits the Least-Squares Support Vector Machine data-driven estimation paradigm and statistical methods. the algorithm's computational complexity is reduced by using a data pruning procedure. the optimization of the SVM-based estimator is performed by using a Particle Swarm optimization method. the design approach proposed to develop to estimator is validated using a simulation model of the battery and an Estimator Design Tool in MATLAB software which provides a user-friendly interface for the different algorithms that may be used in the estimator design. the approach is applicable to a wide range of applications including automotive systems.

关键词： Battery Management system Least-Squares Support Vector Machine State-of-Charge Estimator

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optimization and evaluation of the control framework for brain-machine interfaces 20

Optimization and evaluation of the control framework for bra...

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IEEE 20th International conference on Automation Science and Engineering (CASE)

作者： Forin, Paolo Beraldo, Gloria Tortora, Stefano Tonin, Luca Univ Padua Dept Informat Engn Padua Italy CNR Rome Italy Padova Neurosci Ctr Padua Italy

ISBN: (纸本)9798350358513;9798350358520

this work presents and evaluates a method for reducing the number of hyper-parameters in the continuous control system used by a 2-class motor imagery (MI) brainmachine interface (BMI). the work focuses on two parameters (. and.) used within a dynamical control system that considers the nature and temporal evolution of the BMI decoder output and that it has been already validated in the past. To identify the optimal values for the parameters, we analysed a dataset of 12 subjects performing 2-class MI tasks. For each subject, we defined a new metric to investigate the existence of a relationship between the hyper-parameters. the study reveals a quadratic relationship with coefficient of determination (R-2) of 81.67%. Finally, the established relationship was evaluated through an closed-loop experiment involving three healthy subjects. Results demonstrated the potential use of the discovered quadratic relationship to reduce the number of parameters for the dynamical control system and, thus, to simplify the BMI operations.

关键词： Closed loop control systems

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Automated optimization for FPGA-Based Sonar Object Recognition systems 20

Automated Optimization for FPGA-Based Sonar Object Recogniti...

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20th Asia-Pacific conference on Circuits and systems

作者： Tung, Pin-Hao Jen, Geng-Shi Lai, Bo -Chem Hung, Yu -Xiang Natl Yang Ming Chiao Tung Univ Inst Artificial Intelligence Innovat Hsinchu Taiwan Natl Yang Ming Chiao Tung Univ Dept Elect Hsinchu Taiwan

ISBN: (纸本)9798350378788;9798350378771

the sonar system has been widely adopted in underwater environment exploration. the system generally needs to first reconstruct sonar images and then perform object recognition of sonar images. Current designs often focus on either sonar image reconstruction, or object recognition separately, lacking an integrated approach. the changing conditions and diverse tasks have necessitated rapid trade-offs among enormous design factors to attain short processing latency and low power consumption. In this paper, we proposes an automated methodology to effectively explore the design space and optimize the sonar system. the generated design is synthesized on FPGAs. Our experiments have shown that the proposed design achieves 3.24x performance improvement over manual designs.

关键词： underwater ultrasound detection FPGA software & hardware co-design low latency USV

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