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

共 1171 条记录，以下是601-610 订阅

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modeling and optimization of Complex Building Energy Systems with Deep Neural Networks 51

Modeling and Optimization of Complex Building Energy Systems...

引用

51st IEEE Asilomar conference on Signals, Systems, and Computers

作者： Chen, Yize Shi, Yuanyuan Zhang, Baosen Univ Washington Dept Elect Engn Seattle WA 98195 USA

ISBN: (纸本)9781538618233

Modern buildings encompass complex dynamics of multiple electrical, mechanical, and control systems. One of the biggest hurdles in applying conventional model-based optimization and control methods to building energy management is the huge cost and effort of capturing diverse and temporally correlated dynamics. Here we propose an alternative approach which is model-free and data-driven. By utilizing high volume of data coming from advanced sensors, we train a deep Recurrent Neural Networks (RNN) which could accurately represent the operation's temporal dynamics of building complexes. The trained network is then directly fitted into a constrained optimization problem with finite horizons. By reformulating the constrained optimization as an unconstrained optimization problem, we use iterative gradient descents method with momentum to find optimal control inputs. simulation results demonstrate proposed method's improved performances over model-based approach on both building system modeling and control.

关键词： Building energy management deep learning gradient algorithms HVAC systems

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Soft-Sensing Development Using Adaptive PSO optimization Based Multi-Kernel ELM with Error Feedback

Soft-Sensing Development Using Adaptive PSO Optimization Bas...

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Data driven Control and Learning Systems (DDCLS)

作者： Yuan Xu Qiang Du Mingqing Zhang Qunxiong Zhu Yanlin He College of Information Science and Technology Beijing University of Chemical Technology Beijing China

ISBN: (纸本)9781538626191

It is very hard to measure some process variables directly in actual industrial processes, so a soft senor model using adaptive particle swarm optimization (PSO) optimization based multi-kernel ELM with error feedback is proposed in this paper. Firstly, multi-kernel ELM is constructed by adding gaussian and polynomial kernel function to ameliorate the overfitting problem in traditional ELM. Secondly, we propose an adaptive PSO (APSO) for ameliorating the low efficiency problem in the later period of PSO method by adding mutation operator. When given parameter reaches a threshold, the mutation operator adaptively adjusts the position of the particle. Also, the proportion of two kernel functions and the kernel parameters in training process are obtained by APSO. In each iteration, the training error is back propagated to the hidden layer as the co-outputs of hidden layer for further improving the accuracy and stability of the model. Finally, a simulation experiment on the purified terephthalic acid (PTA) solvent system is made to verify the modeling accuracy and optimized performances. The evaluation result demonstrates that the proposed method can provide higher accuracy and a more reliable soft senor model compared with other method.

关键词： Kernel Training Solvents optimization Poles and towers Sensors Artificial neural networks

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Agent-Based modeling and simulation of Hybrid Cyber-Physical Systems 3

Agent-Based Modeling and Simulation of Hybrid Cyber-Physical...

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3rd IEEE International conference on Cybernetics (CYBCONF)

作者： Novak, Petr Kadera, Petr Wimmer, Manuel Vienna Univ Technol Inst Software Technol & Interact Syst Business Informat Grp Christian Doppler Lab Model Integrated Smart Prod A-1040 Vienna Austria Czech Tech Univ CZ-16636 Prague Czech Republic Czech Tech Univ Czech Inst Informat Robot & Cybernet CZ-16636 Prague Czech Republic

ISBN: (纸本)9781538622018

Cyber-physical production systems are becoming more complex and heterogeneous in the frame of Internet of Cyber-Physical Things and Industry 4.0 environments. Behavior of such systems is difficult to analyze and control due to the system scale and emergency aspects. Multi-agent paradigm is a suitable formalism for modeling these kinds of systems and simulation modeling is important for getting an insight into the system, for synthesizing its control, and for analyzing performance and efficiency of systems under various circumstances. This paper is focused on simulation modeling for complex hybrid cyber-physical production systems. They combine continuous-time parts and discrete-event parts and the goal is to bring a methodology for addressing simulation for such complex hybrid systems. The paper presents two possible approaches how to unify access to both paradigms for system modeling and it explains how to combine them meaningfully. The proposed solution is based on the application of the traditional time-driven approach on the level of individual agents simulating cyber-physical components or sub-systems, and the dual value-driven approach frequently denoted as Quantized State Systems method in such a way that the benefits of both approaches are gained. The proposed approach simplifies synchronization and improves stability of coupled simulations by self-optimization of sampling periods for synchronizing the simulation agents for cyber-physical components.

关键词： Mathematical model Computational modeling Cyber-physical systems Testing Industries Analytical models Differential equations

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DATA driven STOCHASTIC APPROXIMATION FOR CHANGE DETECTION

DATA DRIVEN STOCHASTIC APPROXIMATION FOR CHANGE DETECTION

引用

Winter simulation conference (WSC)

作者： Flynn, Thomas Hadjiliadis, Olympia Stamos, Ioannis Vazquez-Abad, Felisa J. CUNY Grad Ctr Dept Comp Sci 365 5th Ave New York NY 10016 USA CUNY Hunter Coll Dept Math & Stat 695 Pk Ave New York NY 10065 USA CUNY Hunter Coll Dept Comp Sci 695 Pk Ave New York NY 10065 USA

ISBN: (纸本)9781538634288

Online change detection has many applications, ranging from finance and manufacturing, to security and computer vision. Designing a change detector for use in a given domain can be very time consuming, and model-based algorithms often require knowledge of the underlying stochastic model. To address these issues, in this work we explore a supervised learning approach to a change detector. We implement a gradient based procedure to find the optimal parameters for a change detector. We demonstrate the methodology on both synthetic and real world data for classifying 3D laser range image data in real-time.

关键词： Detectors Change detection algorithms Stochastic processes Three-dimensional displays optimization Delays Computational modeling

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Applied modelling, numerical simulation, computation and optimization

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2017年

作者： de Souza Neto, Eduardo Li, Chenfeng Emerald Publishing Limited

With the advancement of computer hardware providing more powerful computing environments, system modeling, numerical simulation and optimization researchers have been able to challenge solving larger and more complex problems. driven by such motivation, the innovative methodologies of system modeling, numerical simulation and optimization technologies are proposed not only in area of engineering but also in new paradigms in computer science. In addition, system modeling, numerical simulation and optimization researchers have applied the developed methods to various real world problems such as robotic system. This ebook includes the mathematical and physical theories of the nonlinear dynamics analysis, computation and optimization in physical, engineering, biological studies, and their various applications. It includes manuscripts of some of the presentations at the International conference on Computing and Precision Engineering (ICCPE-2015) held at Nantou, Taiwan. The topics covered in this ebook include: Mechanical and Electrical System modeling, Numerical simulation, Computation and Nonlinear Analysis for Real World System, optimization Schemes and Control Systems, Chaotic Systems, Other System simulation and Applications. It is hoped that it will be of great use for engineering specialists.

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Data-driven Approach to Simulating Realistic Human Joint Constraints

Data-Driven Approach to Simulating Realistic Human Joint Con...

引用

IEEE International conference on Robotics and Automation

作者： Yifeng Jiang C. Karen Liu School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA School of Interactive Computing Georgia Institute of Technology Atlanta GA USA

modeling realistic human joint limits is important for applications involving physical human-robot interaction. However, setting appropriate human joint limits is challenging because it is pose-dependent: the range of joint motion varies depending on the positions of other bones. The paper introduces a new technique to accurately simulate human joint limits in physics simulation. We propose to learn an implicit equation to represent the boundary of valid human joint configurations from real human data. The function in the implicit equation is represented by a fully connected neural network whose gradients can be efficiently computed via back-propagation. Using gradients, we can efficiently enforce realistic human joint limits through constraint forces in a physics engine or as constraints in an optimization problem.

关键词： Joints Mathematical model Physics Robots Neural networks Elbow Computational modeling

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AGENT-BASED simulation AND optimization OF HOSPITAL UTILIZATION IN A REGIONAL NETWORK

AGENT-BASED SIMULATION AND OPTIMIZATION OF HOSPITAL UTILIZAT...

引用

Winter simulation conference (WSC)

作者： Wang, Wei Li, Jun Jia, Yugang Santos, Marcelo Philips Res North Amer 2 Canal Pk3th Floor Cambridge MA 02141 USA

ISBN: (纸本)9781538634288

Healthcare resources allocation is inherently difficult due to the prescriptive "what-if nature of the question being asked. One kind of data driven method is building a statistical model for resource utilization and later leverage it for resource optimization. Another kind is agent based simulation, which is based on patient choice and service queue. In this paper, we applied both methods to an imaging device resource allocation problem with a public dataset from Parana, Brazil. Our results demonstrate that starting from diametric perspectives, the two approaches give consistent and interpretable results for finding the optimal allocation. Moreover, this can serve as the corroboration for the simulation model, which is notoriously difficult to validate.

关键词： Medical services Resource management

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Optimized Deep Learning Framework for water Distribution Data-driven modeling 18th

Optimized Deep Learning Framework for water Distribution Dat...

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18th International conference on Water Distribution System Analysis (WDSA)

作者： Wu, Zheng Yi Rahman, Atiqur Bentley Syst Inc Watertown CT 06795 USA

Deep Learning (DL), unlike conventional Artificial Neural network (ANN), is capable of self-learning data features layer by layer in unsupervised manner and creating a data-driven model with the given dataset. DL has been widely applied to big data analytics, graphics object detection, classification, voice recognition and many other problems. This paper presents an integrated data-driven modelling framework that couples DL with the well-developed evolutionary optimization tool in a scalable and heterogeneous high performance computing paradigm. The integrated framework enables modellers to effectively and efficiently construct a model with a given dataset. It is demonstrated that the framework has wide applicability including but not limited to the simulation, optimization and operation decision of water distribution systems. The paper elaborates the development of the deep learning framework with potential applications of facilitating the data fusion, system simulation and predictive analysis, anomaly detection from the time series data (pressures, flows and consumptions etc.), water usage prediction, construction of a meta-model as a surrogate to the physics-based models (hydraulic and water quality) for water distribution management. (C) 2016 The Authors. Published by Elsevier Ltd.

关键词： Deep learning optimization modelling data analysis water distribution Main text

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Advanced Computational Methods in Life System modeling and simulation 1st ed. 2017

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丛书名： Communications in Computer and Information Science

2017年

作者： Minrui Fei Shiwei Ma Xin Li Xin Sun Li Jia Zhou Su

ISBN: (数字)9789811063701

ISBN: (纸本)9789811063695

The three-volume set CCIS 761, CCIS 762, and CCIS 763 constitutes the thoroughly refereed proceedings of the International conference on Life System modeling and simulation, LSMS 2017, and of the International conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017, held in Nanjing, China, in September 2017. The 208 revised full papers presented were carefully reviewed and selected from over 625 submissions. The papers of this volume are organized in topical sections on: Biomedical Signal Processing; Computational Methods in Organism modeling; Medical Apparatus and Clinical Applications; Bionics Control Methods, Algorithms and Apparatus; modeling and simulation of Life Systems; Data driven Analysis; Image and Video Processing; Advanced Fuzzy and Neural Network Theory and Algorithms; Advanced Evolutionary Methods and Applications; Advanced Machine Learning Methods and Applications; Intelligent modeling, Monitoring, and Control of Complex Nonlinear Systems; Advanced Methods for Networked Systems; Control and Analysis of Transportation Systems; Advanced Sliding Mode Control and Applications; Advanced Analysis of New Materials and Devices; Computational Intelligence in Utilization of Clean and Renewable Energy Resources; Intelligent Methods for Energy Saving and Pollution Reduction; Intelligent Methods in Developing Electric Vehicles, Engines and Equipment; Intelligent Computing and Control in Power Systems; modeling, simulation and Control in Smart Grid and Microgrid; optimization Methods; Computational Methods for Sustainable Environment.

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Forecast-driven Power Planning Approach for Microgrids Incorporating Smart Loads Using Stochastic optimization

Forecast-Driven Power Planning Approach for Microgrids Incor...

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International conference on Smart Energy Systems and Technologies (SEST)

作者： Mohammed Hijjo Georg Frey Chair of Automation and Energy Systems Saarland University Saarbrücken (Germany)

The main focus of this work is on improving the resilience of the standalone microgrids using the available forecast of both energy sources and loads, provided that some smart loads are incorporated in the system. The proposed approach assumes that the smartness of the loads facilitates an accurate prediction of their consumption as well as the possible time span for their operation. The kernel of the proposed approach is to provide a greedy-based scheduling scheme of a group of non-preemptive loads in order to reduce the net deficit between the aggregate load and the low-price power profile, and therefore, the levelized cost of energy (LCoE) can be minimized. Based on the driven forecast, the power routings between the components of the microgrid are investigated considering the scheduling candidates of the incorporated smart loads. Thus, the optimal schedule is selected so as to ensure the maximum utilization of the low-price power. A stochastic optimization method based on the genetic algorithms (GAs) is used to cut-down the massive searching space and provide the optimal schedule within a reasonable time. An illustrative example is used to carry out this work using a group of synthetically created loads representing different facilities inside a hospital in Gaza city. simulation results show that the proposed algorithm can significantly reduce LCoE and meanwhile maximizing the utilization factor of the installed renewable energy sources.

关键词： Load modeling Microgrids Optimal scheduling Batteries Mathematical model Generators Smart buildings

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