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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381-390 订阅

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New Approaches in simulation-driven optimization 3

New Approaches in Simulation-driven Optimization

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3rd International conference on Applied Mathematics, modeling and simulation (AMMS)

作者： Tenne, Y. Ariel Univ Dept Mech & Mechatron Engn Ariel Israel

This paper presents a work regarding the integration of discriminant functions (classifiers) with search algorithms to tackle the problem of failed simulation runs. The discriminant output is used to guide the search towards better solutions while minimizing adverse effects. The search is managed with a trust-region approach for convergence in the presence of prediction inaccuracies. Numerical evaluations based on engineering problem show that the approach yielded better final results in the mean and median statistics when compared to reference algorithms.

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Knowledge-Guided Data-driven Decision-Making for Key Operational Variables in Sinering Processes

Knowledge-Guided Data-Driven Decision-Making for Key Operati...

引用

Annual conference of Industrial Electronics Society

作者： Yijing Fang Weihua Gui Zhaohui Jiang Jilin Zhu Dong Pan Central South University School of Automation Changsha China

Process modeling and intelligent decision-making play crucial roles in optimizing and controlling sintering processes. To tackle these tasks, we propose a novel approach that leverages a temporal convolutional network guided by working condition knowledge (WKGA-TCN) to predict the sintering condition. By incorporating sequential features, the model effectively captures the varying importance of different features. Subsequently, we simulate the state of the sintering process using our proposed method. To further enhance operational optimization, we introduce an intelligent decision-making strategy that combines dynamic matching of operating modes and a genetic algorithm. This strategy leverages historical data to learn from excellent operation cases while considering the inherent uncertainty in process data. Simultaneously, a model update strategy employing an event-triggered mechanism is introduced to guarantee the process model's comprehensive simulation of the current sintering process dynamics. Finally, we assess the effectiveness of the proposed method by applying it to actual industrial process data.

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A Multi-objective optimization Algorithm for Air-path System of Diesel Engines 6th

A Multi-objective Optimization Algorithm for Air-path System...

引用

6th IFAC conference on Engine Powertrain Control, simulation and modeling (E-COSM)

作者： Yu, Jingjiang Chen, Ran Li, Yuzhe Kang, Mingxin Yu, Shengping Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China

Modern engine is a typical multi-objective control system. This paper proposed a multi-objective Bayesian optimization strategy to deal with the performance optimization for diesel engines. Since the objective functions of diesel engines are complicated and computationally expensive, Gaussian processes(GPs) are constructed by using the data collected from the diesel engine to approximate the real objective functions. Non-dominated sorting genetic algorithm II(NSGAII) leverages the Gaussian process to generate the Pareto-optimal solutions. The Gaussian process will be updated iteratively by Bayesian posterior information, which increases the reliability of the models. The acquisition function Expected HyperVolume Improvement(EHVI), which can balance the trade-off between exploration and exploitation throughout the optimization process, is used to select the solutions for real computationally expensive multi-objective evaluation. The proposed algorithm is applied on a diesel engine, which shows its reliability and high efficiency. The metrics hypervolume(HV) and the control results demonstrate that the proposed algorithm has outstanding effects for performance optimization of diesel engine airpath. Copyright (C) 2021 The Authors.

关键词： data-driven optimization Gaussian process multi-objective Bayesian optimization diesel engine performance optimization

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Geology-driven EUR Forecasting in Unconventional Fields

Geology-Driven EUR Forecasting in Unconventional Fields

引用

2021 SPE Middle East Oil and Gas Show and conference, MEOS 2021

作者： Temizel, Cenk Canbaz, Celal Hakan Alsaheib, Hasanain Yanidis, Kirill Balaji, Karthik Alsulaiman, Nouf Basri, Mustafa Jama, Nayif Saudi Aramco Ege University Schlumberger University of North Dakota United States

ISBN: (纸本)9781613997727

EUR (Estimated Ultimate Recovery) forecasting in unconventional fields has been a tough process sourced by its physics involved in the production mechanism of such systems which makes it hard to model or forecast. Machine learning (ML) based EUR prediction becomes very challenging because of the operational issues and the quality of the data in historical production. Geology-driven EUR forecasting, once established, offers EUR forecasting solutions that is not affected by operational issues such as shut-ins. This study illustrates the overall methodology in intelligent fields with real-time data flow and model update that enables optimization of well placement in addition to EUR forecasting for individual wells. A synthetic but realistic model which demonstrates the physics is utilized to generate input data for training the ML model where the spatially-distributed geological parameters including but not limited to porosity, permeability, saturation have been used to describe the production values and ultimately the EUR. The completion is given where the formation characteristics vary in the field that lead to location-dependent production performance leading to well placement optimization based on EUR forecasting from the geological parameters. The algorithm not only predicts the EUR of an individual well and makes decision for the optimum well locations. As the training model includes data of interfering wells, the model is capable of capturing the pattern in the well interference. Even though a synthetic but realistic reservoir model is constructed to generate the data for the aim of assisting the ML model, in practice, it is not an easy task to (1) obtain the input parameters to build a robust reservoir simulation model and (2) understanding and modeling of physics of fluid flow and production in unconventionals is a complex and time-consuming task to build real models. Thus, data-driven approaches like this help to speed up reservoir management and development

关键词： Forecasting

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simulation and Experiment in Solving Inverse Kinematic for Human Upper Limb by Using optimization Algorithm 13th

Simulation and Experiment in Solving Inverse Kinematic for H...

引用

13th Asian conference on Intelligent Information and Database Systems (ACIIDS)

作者： Trung Nguyen Huy Nguyen Khai Dang Phuong Nguyen Ha Pham Anh Bui Hanoi Univ Sci & Technol 1 Dai Co Viet St Hanoi Vietnam Hanoi Univ Ind Hanoi Vietnam Minist Publ Secur Vietnam Hanoi Vietnam

ISBN: (纸本)9783030732790;9783030732806

The problem of inverse kinetics in the robotics field, especially for redundant driven robots, often requires the application of a lot of techniques. The redundancy in degree of freedom, the nonlinearity of the system lead to the inverse kinematics resolution more challenge. In this study, we proposed to apply the Differential Evolution optimization algorithm with the search space improvement to solve this problem for the human upper limb which is very typical redundancy model in nature. Firstly, the forward kinematic modeling and problem for the human arm was presented. Next, an Exoskeletal type Human Motion Capture (E-HMCS) device was shown. This device was used to measure the endpoint trajectories of Activities of Daily Living, together with desired joints' values corresponding. The measured endpoint trajectories then were put into the proposed optimization algorithm to solve the inverse kinematics to create a set of predicted joints value. The comparison results showed that the predicted joints' values are the same as the measured values. That demonstrates the ability to use the proposed algorithm to solve the problem of inverse kinematics for the natural movements of the human upper limbs.

关键词： Proposed differential evolution Inverse kinematics Degree of Freedom Exoskeleton type motion capture system Activities of Daily Living

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Data-driven exploration of lentic water bodies with ASVS guided by gradient-free optimization/contour detection algorithms 21

Data-driven exploration of lentic water bodies with ASVS gui...

引用

Proceedings of the Winter simulation conference

作者： Eva Besada-Portas José María Girón-Sierra Juan Jiménez José Antonio López-Orozco University Complutense of Madrid Madrid Spain

This paper presents a local-path planner for water quality monitoring involving an Autonomous Surface Vehicle (ASV). The planner determines new measuring waypoints based on the information collected so far, and on two gradient-free optimization and contour-detection algorithms. In particular, the optimization algorithm generates the locations where the variable/substance under study must be measured and use them as the waypoints of the external loop of the Guidance, Navigation and Control system of our ASV. Besides, the contour algorithm obtains useful waypoints to determine the water body locations where the variable/substance under study reaches a given value. The paper also analyzes how the approach works via progressive simulations over an ASV carefully modelled with a set of non-linear differential equations. Preliminary results suggest that the approach can be useful in real-world single-ASV water-quality monitoring missions where there is not previous knowledge of the state and location of the variable/substance under study.

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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...

引用

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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Eclares: Energy-Aware Clarity-driven Ergodic Search

Eclares: Energy-Aware Clarity-Driven Ergodic Search

引用

IEEE International conference on Robotics and Automation (ICRA)

作者： Kaleb Ben Naveed Devansh Agrawal Christopher Vermillion Dimitra Panagou Department of Robotics University of Michigan Ann Arbor MI USA Department of Aerospace Engineering University of Michigan Ann Arbor MI USA Department of Mechanical Engineering University of Michigan Ann Arbor MI USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Planning informative trajectories while considering the spatial distribution of the information over the environment, as well as constraints such as the robot’s limited battery capacity, makes the long-time horizon persistent coverage problem complex. Ergodic search methods consider the spatial distribution of environmental information while optimizing robot trajectories; however, current methods lack the ability to construct the target information spatial distribution for environments that vary stochastically across space and time. Moreover, current coverage methods dealing with battery capacity constraints either assume simple robot and battery models or are computationally expensive. To address these problems, we propose a framework called Eclares, in which our contribution is two-fold. 1) First, we propose a method to construct the target information spatial distribution for ergodic trajectory optimization using clarity, an information measure bounded between [0, 1]. The clarity dynamics allow us to capture information decay due to a lack of measurements and to quantify the maximum attainable information in stochastic spatiotemporal environments. 2) Second, instead of directly tracking the ergodic trajectory, we introduce the energy-aware (eware) filter, which iteratively validates the ergodic trajectory to ensure that the robot has enough energy to return to the charging station when needed. The proposed eware filter is applicable to nonlinear robot models and is computationally lightweight. We demonstrate the working of the framework through a simulation case study. [Code] a [Video] b

关键词： Graphical models Target tracking Computational modeling Stochastic processes Information filters Batteries Spatiotemporal phenomena

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Fair Communication Resource Scheduling in 6G and Digital Twin-driven Virtual Power Plants

Fair Communication Resource Scheduling in 6G and Digital Twi...

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IEEE International conference on Communications in China Workshops (ICCC)

作者： Xiao Liu Peng Yu Honglin Fang Can Tan Fanqin Zhou Wenjing Li State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

A virtual power plant (VPP) is a technology and management system that integrates distributed energy resources, offering significant advantages for the access and management of these distributed energy resources. With the development of distributed energy, higher requirements are being placed on the communication resource scheduling capabilities of virtual power plants. For dense source-load-storage devices, the high bandwidth, low latency, and high energy efficiency characteristics of 6G networks can meet the control requirements of virtual power plants for these devices. Digital twins are currently widely used in the industrial sector, providing positive effects such as real-time monitoring, prediction, and performance optimization. Applying digital twins to the communication resource scheduling of virtual power plants enables real-time perception of resource allocation and timely resource scheduling. This paper proposes a communication resource scheduling algorithm based on deep reinforcement learning, leveraging digital twin technology and the 6G distributed subnet architecture. This algorithm aims to efficiently utilize limited resources such as resource blocks, spectrum, and power to balance system throughput, fairness, and scheduling latency, thereby ensuring the QoS of source-load-storage device scheduling tasks. simulation results demonstrate that the proposed resource scheduling algorithm enhances system throughput and fairness while meeting scheduling latency requirements, thereby satisfying the QoS needs of scheduling tasks.

关键词： 6G mobile communication Job shop scheduling Scheduling algorithms Computational modeling Quality of service Virtual power plants Throughput Real-time systems Digital twins Resource management

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DeepSimHO: stable pose estimation for hand-object interaction via physics simulation 23

DeepSimHO: stable pose estimation for hand-object interactio...

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Proceedings of the 37th International conference on Neural Information Processing Systems

作者： Rong Wang Wei Mao Hongdong Li The Australian National University

This paper addresses the task of 3D pose estimation for a hand interacting with an object from a single image observation. When modeling hand-object interaction, previous works mainly exploit proximity cues, while overlooking the dynamical nature that the hand must stably grasp the object to counteract gravity and thus preventing the object from slipping or falling. These works fail to leverage dynamical constraints in the estimation and consequently often produce unstable results. Meanwhile, refining unstable configurations with physics-based reasoning remains challenging, both by the complexity of contact dynamics and by the lack of effective and efficient physics inference in the data-driven learning framework. To address both issues, we present DeepSimHO: a novel deep-learning pipeline that combines forward physics simulation and backward gradient approximation with a neural network. Specifically, for an initial hand-object pose estimated by a base network, we forward it to a physics simulator to evaluate its stability. However, due to non-smooth contact geometry and penetration, existing differentiable simulators can not provide reliable state gradient. To remedy this, we further introduce a deep network to learn the stability evaluation process from the simulator, while smoothly approximating its gradient and thus enabling effective back-propagation. Extensive experiments show that our method noticeably improves the stability of the estimation and achieves superior efficiency over test-time optimization. The code is available at https://***/rongakowang/DeepSimHO.

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