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检索条件"任意字段=Conference on Simulation-Driven Modeling and Optimization"

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Waveform optimization for Near-Field Wireless Powered Communication Using a Coil Array

Waveform Optimization for Near-Field Wireless Powered Commun...

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Asilomar conference on Signals, Systems & Computers

作者： Tomohiro Arakawa James V. Krogmeier David J. Love School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

driven by the growing number of Internet of Things (IoT) devices, there is a pressing need for powering devices wirelessly while communicating with them. This paper considers wireless powered communication (WPC) using near-field inductive coupling, a technique to communicate with remote devices that do not have batteries or a power supply. While near-field WPC has long been used for passive radio-frequency identification (RFID) systems, the performances of communication and power transfer have not been fully analyzed. This paper models near-field WPC using a coil array based on an abstract circuit model in which the characteristics of mutual coupling and other linear effects are captured by a small number of parameters. We investigate how the choices of load impedances and source voltages affect the performance of power transfer and communication. The improvement in power and information transfer are verified by simulation.

关键词： Wireless communication Performance evaluation Power supplies Pressing Internet of Things Integrated circuit modeling Passive RFID tags

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10th Annual Symposium on simulation for Architecture and Urban Design, SimAUD 2019

10th Annual Symposium on Simulation for Architecture and Urb...

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10th Annual Symposium on simulation for Architecture and Urban Design, SimAUD 2019

The proceedings contain 41 papers. The special focus in this conference is on simulation for Architecture and Urban Design. The topics include: Streets, parks & plazas: Analyzing daylight in the public realm;unleashing the diversity of conceptual building renovation design: Integrating high-fidelity simulation with rapid constraint-based scenario generation;application of surrogate modeling to multi-objective optimization for residential retrofit design;aerial thermography as a tool to inform building envelope simulation models;Towards assembly information modeling (AIM);influence of subjective impressions of a space on brightness satisfaction: An experimental study in virtual reality;immersive representation of urban data;a data-driven framework for urban building operational energy use modeling;an integrated urban planning and simulation method to enforce spatial resilience towards flooding hazards;A method for integrating an UBEM with GIS for spatiotemporal visualization and analysis;a technique for developing high-resolution residential occupancy schedules for urban energy models;environmental data and land use: Integration of site-specific ecology and urban design;how to generate a thousand master plans: A framework for computational urban design;cityfiction – scenarios for densification;exploring urban walkability models and pedestrian movement trends in a vancouver neighbourhood;a simulation-based design analysis for the assessment of indoor comfort under the effect of solar radiation;black globe free convection measurement error potentials;Assessing the performance of UFAD system in an office building located in various climate zones;evaluating the influence of three simplifications on natural ventilation rate simulation;adaptive occupancy scheduling: Exploiting microclimate variations in buildings;environmentally informed robotic-aided fabrication.

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Multiphysics Dynamic Model Validation Methodology for Laser-driven Microrobots 15

Multiphysics Dynamic Model Validation Methodology for Laser-...

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15th IEEE International conference on Automation Science and Engineering (IEEE CASE)

作者： Yang, Zhong Saadatzi, Mohammad N. Zhang, Ruoshi Sherehiy, Andriy Wei, Danming Harnett, Cindy K. Popa, Dan O. Univ Louisville Dept Elect & Comp Engn Next Generat Syst Grp Louisville KY 40292 USA

ISBN: (纸本)9781728103563

In this paper we discuss a comprehensive multiphysics modeling and simulation approach with experimental validation, aimed at understanding the behavior of laser-driven microrobot locomotion on two-dimensional (2D) substrates. The ChevBot is a novel class of light actuated sub-millimetric microrobots that can perform tasks equivalent to mobile robots in future microfactories for nano-bio-technology. The robot dynamics is driven by a complex Opto-Thermo-Mechanical conversion mechanism and stick-and-slip locomotion, which are difficult to simulate. Therefore, 1D and 2D reduced order models are proposed to approximate the microrobot dynamics. Models are then validated using a combination of Finite Element Analysis simulations and direct experimentation. The framework presented here automates the process of collecting motion tracking data from microrobots and uses it to validate dynamic models. In the future, our models can be used in subsequent closed-loop control and design optimization studies.

关键词： Dynamic models

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Deployment of Multiple Demand Response Programs Using Data-driven Multi-Step Method with Elasticity

Deployment of Multiple Demand Response Programs Using Data-D...

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Annual conference of Industrial Electronics Society

作者： Charles Ibrahim Imad Mougharbel Hadi Y. Kanaan Nivine Abou Daher Semaan Georges Maarouf Saad Faculty of Engineering – ESIB Saint-Joseph University of Beirut Lebanon Ecole de Technologie Supérieure Montreal Canada Institut de Recherche Hydro Québec Canada Computer and Communication Engineering Notre Dame University Beirut Lebanon

ISBN: (数字)9781728154145

ISBN: (纸本)9781728154152

Assessment and qualification of consumers' behavior play an important role in the selection of the suitable Demand Response Programs (DRPs) achieving their engagement and satisfaction. Targeting the assessed consumers' clusters with adequate and elastic pricing schemes ensures their participation. The model offers the full visibility on the dynamicity of pricing and demands for the various simultaneous clusters' DRPs operating in a synchronized manner. The originality of this paper resides in the classification of consumers' behaviors according to the criteria and the elasticity of the multiple dynamic offered pricing schemes for the clusters along with their impacts on each other. An optimal solution is reached through assessment, qualification, planning, benefits' visibility on various proposals and relocation. Intensive what if scenarios assist in the decision making process of the optimal selection in a planned phase. Thus, contracts' terms and conditions suiting the consumers and the electrical utility are arranged. The method is validated through a simulation on Matlab using clustering and multi-objective optimization.

关键词： Elasticity Pricing optimization Load modeling Real-time systems Mathematical model Heuristic algorithms

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Society of Petroleum Engineers - SPE Gas and Oil Technology Showcase and conference 2019, GOTS 2019

Society of Petroleum Engineers - SPE Gas and Oil Technology ...

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SPE Gas and Oil Technology Showcase and conference 2019, GOTS 2019

ISBN: (纸本)9781613997048

The proceedings contain 107 papers. The topics discussed include: accomplishing long-term sustainable well and reservoir health by employing a statistical methodology for choke size estimation in an integrated asset model;accurate prediction of CO2 minimum miscibility pressure using adaptive neuro-fuzzy inference systems;acid stimulation of gas wells with severely impaired gravel pack completion a case study of Mumbai offshore;acidic steam modeling and simulation for heavy oil and extra heavy oil reservoirs;advanced and integrated petrophysical evaluation for reservoir characterization of complex carbonate reservoir - case study;advanced logs and seismic driven geomechanics in complex sandstone reservoirs for drilling optimization;an artificial intelligence approach to predict molar compositions of reservoir fluid components;and automatic gas influxes detection in offshore drilling based on machine learning technology.

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Multi-Objective Simultaneous optimization for Linear Projects Scheduling

Multi-Objective Simultaneous Optimization for Linear Project...

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ASCE International conference on Computing in Civil Engineering (i3CE)

作者： Eid, Mohamed S. Elbeltagi, Emad E. El-Adaway, Islam H. Arab Acad Sci Technol & Maritime Construct & Bldg Engn Cairo 11799 Egypt Mansoura Univ Dept Struct Engn Mansoura 35516 Egypt Missouri Univ Sci & Technol Construct Engn & Management Dept Civil Architectural & Environm Engn 211 Butler Carlton Hall1401 N Pine St Rolla MO 65409 USA Missouri Univ Sci & Technol Dept Engn Management & Syst Engn 211 Butler Carlton Hall1401 N Pine St Rolla MO 65409 USA

ISBN: (数字)9780784482421

ISBN: (纸本)9780784482421

Scheduling linear projects requires an optimization tool that does not only minimizes project duration and cost, but also maximizes the utilization of crews, accounts for travelling distance between units, and meets the delivery dates of the project's units. This paper presents a multi-objective optimization model for scheduling linear projects through developing set of non-dominated optimal schedules. The proposed model consists of: ( 1) a resource driven scheduling module accounting for heterogeneity among construction crews, and ( 2) an evolutionary optimization module via genetics algorithms ( GAs) and Pareto front sorting ( PFS) that searches the solution space for optimal schedules. The model is tested on a case study drawn from the literature and provided significantly better results compared to some of the well-recognized scheduling models. The proposed model is coded using Visual Basics for Applications on a commercial scheduling tool and can be easily adopted by practitioners to provide a broad-spectrum of optimal schedules.

关键词： Solution space construction crews crews Optimal Travel distance Optimal scheduling scheduler module

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Gaussian Process Based Distributed Model Predictive Control for Multi-agent Systems using Sequential Convex Programming and ADMM

Gaussian Process Based Distributed Model Predictive Control ...

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Control Technology and Applications (CCTA),

作者： Viet-Anh Le Truong X. Nghiem School of Informatics Computing and Cyber Systems Northern Arizona University

ISBN: (数字)9781728171401

ISBN: (纸本)9781728171418

This paper develops a distributed algorithm for data-driven Distributed Model Predictive Control (DMPC) for multi-agent control systems, where the agents' dynamics are modeled by Gaussian Processes (GPs). A multi-agent control system with a coordinator is considered, in which computation and data must be distributed among the agents and the coordinator. We employ the linearized Gaussian Process (linGP) concept, proposed in our previous works, to sequentially approximate the stochastic latent processes of the GP models in a Sequential Convex Programming (SCP) framework, leading to a convex linGP-DMPC subproblem, which is solved cooperatively by the agents with the ADMM algorithm. The resulting distributed algorithm, called linGP-SCP-ADMM, can solve nonconvex GP-DMPC for multi-agent systems effectively since the data and computation are distributed among the agents. The effectiveness and advantages of the proposed algorithm are evaluated by simulation in a formation control example.

关键词： Control systems Prediction algorithms Mathematical model optimization Gaussian processes Multi-agent systems Computational modeling

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An Integrated Machine-Learning Approach to Shale-Gas Supply-Chain optimization and Refrac Candidate Identification

An Integrated Machine-Learning Approach to Shale-Gas Supply-...

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SPE Annual Technical conference and Exhibition

作者： Asala, Hope I. Chebeir, Jorge A. Manee, Vidhyadhar Gupta, Ipsita Dahi-Taleghani, Arash Romagnoli, Jose A. Louisiana State Univ Craft & Hawkins Dept Petr Engn Baton Rouge LA 70803 USA Louisiana State Univ Cain Dept Chem Engn Baton Rouge LA 70803 USA Penn State Univ Dept Energy & Mineral Engn Petr Engn University Pk PA 16802 USA

The unsteady recovery of oil and gas prices in early 2017 led to an increase in drilling and hydraulic-fracturing operations in liquid-rich shale plays in North America. As field-development strategies continue to evolve, refracturing and infill-well drilling must be carefully combined to optimize shale-project profitability. Moreover, operators must bear in mind the undulating natural-gas demands persisting in an oversupplied shale-gas environment. In this paper, we use data-driven approaches to predict successful refracturing candidates and local gas demand for the second-tier optimization of a shale-gas supply-chain network. A strategic-planning (SP) model is developed for optimizing the net present value (NPV) of a case-study shale-gas network in the Marcellus Play. This SP model uses a mixed-integer-nonlinear-programming (MINLP) formulation developed in the General Algebraic modeling System (GAMS, Release 27.1.0.2019). This model relies directly on input from reservoir simulation, local-gas-demand forecast, water-availability forecast, and natural-gas and West Texas Intermediate (WTI) crude-oil price forecasts. Before reservoir simulation, machine learning (ML) is used to predict successful refracturing candidates, using a feed-forward neural network (NN), random-forest (RF) classifier, and a t-distributed stochastic-neighbor-embedding (t-SNE) visualization technique. Using the obtained results, best-practice field-development strategies are implemented in the area of interest (AOI) using reservoir simulation. Local gas demand is forecasted using a long-short-term-memory (LSTM) recurrent NN (RNN) that uses a multivariate data set created from local and global variables affecting shale-gas demand. A water-management structure is also developed for the optimization framework. Using a 300-well data set (with 17 input features), successful refracturing candidates were proposed according to the joint outcome of an optimal 17/23/128/2 feed-forward NN, a t-SNE plot, a

关键词： Decision trees

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Recent Trends in Modelling and simulation with Machine Learning

Recent Trends in Modelling and Simulation with Machine Learn...

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Euromicro conference on Parallel, Distributed and Network-Based Processing

作者： Giuseppe A. Trunfio DADU University of Sassari Alghero Italy

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

In recent years, the increased accessibility of large computing power, together with the availability of big data and the relevant progress in algorithms, led to an exponential increment of Machine Learning (ML) applications to predictive tasks concerning complex systems. In general, ML automatically builds a model of the system under study by exploiting an available large dataset of input features coupled to the corresponding expected outputs. While automatically learning models from data is an extremely powerful approach, in the case of complex systems' dynamics the generalization ability of ML models can easily be limited, that is, the predictions can be inaccurate once the model is applied beyond the bounds of previously observed data. On the other hand, a traditional field that deals with predicting the dynamics of complex systems is represented by Modelling and simulation (M&S), in which, thanks to the knowledge of sufficient details concerning interactions and processes within the system under study, an abstract representation of it is developed and used for artificially reproducing its behaviour. Compared to ML models, when the M&S approach can be based on a sound theory about the functioning of the system, it can enable, in the line of principle, a more reliable forecasting of the system's dynamics beyond the bound defined by the observed historical behaviour. In spite of the outlined conceptual distance between the ML and M&S approaches, a recent and promising research trend encompasses their synergic combination for producing better data-driven simulation models. On the basis of an analysis of the relevant and recent scientific literature, the paper discusses new ideas and directions concerning M&S approaches based on advanced ML techniques.

关键词： Data models Computational modeling Predictive models Mathematical model Calibration optimization Complex systems

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Methods of simulation-driven design in the maritime industry 6

Methods of simulation-driven design in the maritime industry

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RINA, Royal Institution of Naval Architects - 6th International conference on Ship and Offshore Technology, ICSOT-INDIA 2019

作者： von Zadow, Heinrich Harries, Stefan Macedo, Paulo Saroch, Mike FRIENDSHIP SYSTEMS AG Potsdam14482 Germany KONGSBERG MARITIME Ålesund6009 Norway Strong Eagle Corp Bangkok10120 Thailand

ISBN: (纸本)9781909024922

A great effort is needed to keep up with an ever-advancing industry. This is especially true for the very complex field of maritime assets such as ships and marine structures. Whether it is a strive for higher performance or the necessity to keep up with increasing requirements, a design that is uncompromisingly safe yet the most economically attractive and environmentally friendly will always remain a key target of engineering efforts. This paper will show different methods of process integration and optimization strategies ranging from design space exploration to meta-modeling techniques. The design and optimization platform CAESES® is used to integrate all the necessary steps into an overarching system. In this manner, a time-consuming process, traditionally involving work by many specialists using various tools, can be wrapped into an automated, closed loop. As a result, the number of variants investigated before converging towards an optimized solution is greatly increased and significant improvements can be made in shortened lead times. An outlook towards holistic ship design from an all-encompassing viewpoint is given and different examples will be presented representative for the workflow. A comprehensive study of a RoPAX ferry, a complex geometry model of a tip rake propeller and its cavitation behavior and a SWATH hull, optimized towards the largest possible operational window will be presented as the main application cases. © 2019: The Royal Institution of Naval Architects

关键词： Offshore structures

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