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Adaptive Robust Optimization Based on Data-driven Uncertainties of Source and Load for Microgrid Operation 1

Adaptive Robust Optimization Based on Data-driven Uncertaint...

引用

1st international conference on Cyber-Energy Systems and Intelligent Energy, ICCSIE 2022

作者： Li, Zibin Tan, Mao Ren, Yuling Jiang, Hongwei Xiangtan University School of Automation and Electronic Information Xiangtan China Xiangtan University Hunan Engineering Research Center of Multi-energy Cooperative Control Technology Xiangtan China Xiangtan University Hunan National Center for Applied Mathematics Xiangtan China

ISBN: (纸本)9781665452151

The strong uncertainty of renewable energy output and load demand makes the stable operation of microgrids a challenging and important issue. However, the scheduling methods based on deterministic models cannot accurately describe the influence of uncertainties on operation of microgrids. To address this problem, this paper proposes a two-stage adaptive robust optimal scheduling model (TSARO) that considers both source and load uncertainties. The model first adopts the dirichlet process mixture model (DPMM) to perform cluster analysis and parameter estimation on massive historical data, and constructs a data-driven uncertainty set of source and load. Then, based on the uncertainty set, the TSARO model aiming at minimizing the microgrid operation cost is developed under the worst-case scenario. Finally, this paper solves the optimization model using column constraint generation algorithm (C&CG) to obtain the day-ahead power dispatching plan. Simulation results show that the proposed model in this paper has better economic benefits compared with several classical optimization models. © 2023 IEEE.

关键词： Cluster analysis

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Research on Steel Plate Linear Heating Robot Based on Deep Q Network of Gradient Strategy Method

Research on Steel Plate Linear Heating Robot Based on Deep Q...

引用

IEEE international conference on robotics and Biomimetics

作者： Zhiliang Zhang Ryojun Ikeura Zheng Wang Department of Engineering Mie University Tsu Mie Japan

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

The heating and forming process of hull steel plates not only affects the hydrodynamic performance of ships, but also affects the cycle and cost of hull construction. In the process of 3D curved surface linear heating and forming of hull steel plates, the heating line planning difficulties, as well as the low forming efficiency and precision are solved. ANSYS is used to simulate the linear heating and forming process of steel plates to obtain data, and a linear heating model of a deep Q network(DQN) based on the gradient strategy method is established. This model can not only plan the heating line, but also optimize the heating line in real time, and realize the linear planning of steel plate heating for auxiliary workers. The results show that this research method can effectively realize the robot to plan the linear heating line of the target steel plate and heat the steel plate, providing a new solution for the optimization of the 3D curved surface forming process of steel plates and the realization of semi-automation.

关键词： Heating systems Deformable models Three-dimensional displays Costs Deformation Data models Planning Steel Robots Surface treatment

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COVID-19 Classification with CT Scan and Advanced Deep Learning Technologies 3

COVID-19 Classification with CT Scan and Advanced Deep Learn...

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3rd international conference on Computer Vision, Image and Deep Learning and international conference on Computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Li, Ziyi Nanjing Normal University School of Electrical and Automation Engineering Nanjing China

ISBN: (纸本)9781665459112

The COVID-19 epidemic is still very serious, because the United States and other countries have relaxed prevention and control, and the vaccine is ineffective against the mutant virus, resulting in a large number of new cases. The existing epidemic detection methods are still insufficient, and some detection methods are relatively expensive and complicated, resulting in the supply not keeping up with the demand for detection. The purpose of this study is to use relatively convenient, fast and low-cost computer vision technology for epidemic detection. We tried the VGG, ResNet and DenseNet models on an open Kaggle dataset, and found that DenseNet achieved the best results, achieving 95% accuracy, and there is hope for further applications in the future. © 2022 IEEE.

关键词： Computerized tomography

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Autonomous Navigation and Mapping of Snake Robot for Urban Search and Rescue

Autonomous Navigation and Mapping of Snake Robot for Urban S...

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international conference on robotics and Artificial Intelligence (ICRAI)

作者： Syed Izzat Ullah Tallat Mahmood Anayatullah Department of Computing Sciences Texas A&M University-Corpus Christi Control Automotive and Robotics Lab National Centre of Robotics and Automation Rawalpindi Pakistan

In an Urban Search and Rescue situation, under extreme time pressure, rescue workers have to locate and extract the trapped people in collapsed structures. Due to the lack of medical treatment, food, and water, the victim's mortality rate dramatically increases over time. Rescue operations for both rescue workers and victims might be as dangerous as the initial event. For such situations, snake robots which are inspired by their biological counterparts, are shown to be a good option in the literature, to help the rescue workers in positioning the victims or delivering life-saving drugs to extend the life of the victims for some time. However, current research mainly focuses on mechanical design, control mechanisms, and gait generation. To alleviate this concern, we have integrated state-of-the-art methods to develop an autonomous snake robot that can navigate in an unknown environment while also generating a 3D map, to provide a better idea of the environment to the rescue workers. A simulated maze environment is implemented and demonstrated by using the CoppeliaSim simulation, running on Robot Operation System (ROS) and Linux OS. The simulation result shows the effectiveness of the proposed autonomous navigation system for the snake robot to plan an obstacle-free path from the robot's current position to the goal position without an apriori knowledge of the environment.

关键词： Three-dimensional displays Simultaneous localization and mapping Navigation Service robots Linux Simulation Snake robots

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Enhancing Depth Image with Richer Convolution Feature for Improved Perception

Enhancing Depth Image with Richer Convolution Feature for Im...

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international conference on Control, automation and Systems ( ICCAS)

作者： Donggil You Jihoon Jung Jusang Jeong Junghyun Oh Department of Robotics Kwangwoon University Seoul Korea

Depth cameras are widely used in various fields, particularly in robotics, for tasks such as robot path planning, object avoidance, target tracking, and autonomous driving. However, the incomplete depth sensing technology of depth cameras results in depth images with significant noise, including errors in depth values and detection failures due to environmental instability. This noise adversely affects the performance and stability of robotic applications. To address this issue, various filtering methods have been proposed, but the corrected depth images still contain residual noise. This paper utilize a deep learning-based edge detection method, Richer Convolution Feature, to accurately estimate planar regions in depth images. The RANSAC algorithm is used to enhance the planes of objects. Experimental results demonstrate the effectiveness of the proposed approach in reducing noise and improving depth image quality.

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Efficient-Net ASPP Deep Network for Malignant Ultrasound Breast Cancer Segmentation

Efficient-Net ASPP Deep Network for Malignant Ultrasound Bre...

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2023 IEEE international conference on Emerging Trends in Engineering, Sciences and Technology, ICES and T 2023

作者： Azam, Komal M.A., Azam M.A., Qureshi K.B., Khan M.A., Azam College Women University The Government Sadiq Department of Computer Science Bahawalpur Pakistan The Islamia University of Bahawalpur The Faculty of Engineering Bahawalpur Pakistan Istituto Italiano di Tecnologia Department of Advanced Robotics Genoa Italy

ISBN: (纸本)9781665455602

Worldwide, breast cancer is one of the main causes of mortality among women. Only through early recognition of symptoms is it possible to limit the incidence of premature deaths. Utilizing standard deep-learning semantic segmentation, tumors are automatically segmented. It requires minimal human contact. The standard methods are less efficient and precise in accuracy. In this paper, we propose an Efficient-Net based Atrous Spatial Pyramid Pooling (Efficient-Net ASPP) model for efficient and improved segmentation. The proposed model is comprised of an EfficientNetV2B3 backbone with dilated convolutional, skip connection, max pooling, and an ASSP block. A publicly available Malignant Breast Cancer Ultrasound (MBCU) subset image dataset was investigated in our study. Several baseline segmentation methods were compared with our proposed method. The quantitative benchmark analysis shows the proposed model for segmenting MBCU images achieved 70% intersection over union (IOU) and a 62% score on the Dice Similarity Coefficient (DSC) metrics. The proposed network improves MBCU image segmentation by 14% on DSC and 18% on IOU when compared to baseline models. © 2023 IEEE.

关键词： Ultrasonics

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Quantitative models for automation rate and situation awareness response: A case study of levels of driving automation

Quantitative models for automation rate and situation awaren...

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2nd IEEE international conference on Human-Machine Systems (ICHMS)

作者： Liu, Yunmei Kaber, David Univ Florida Dept Ind & Syst Engn Gainesville FL USA

ISBN: (纸本)9781665401708

Many taxonomies of levels of automation have been presented in the literature;however, the discrete and ordinal nature of these methods may limit reliable prediction of operator performance. This study defined an "automation rate" to quantify the level of automation in systems. To calculate the automation rate it is necessary to: classify all functions in the automation system according to stages of information processing, calculate the automation rate for each stage, set weights for these automation rates, and finally obtain the overall automation rate for the system. The practicality and feasibility of this model are verified through a case study analysis. In addition, this paper proposes a new relationship between the automation rate and operator situation awareness response, based on existing empirical research findings. Through case analysis and mathematical proof, the rationality of the form is demonstrated. This work lays the foundation for subsequent operator performance optimization analysis.

关键词： automation rate information processing stages situation awareness response quantitative model

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Data-Driven Robust Model Predictive Control for Pose Tracking of Redundant Manipulators

Data-Driven Robust Model Predictive Control for Pose Trackin...

引用

IEEE international conference on robotics and Biomimetics

作者： Zhaoyang Chen Fenglei Ni Kang Min Hong Liu State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin Heilongjiang Province China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

In this paper, we present a data-driven robust model predictive control (DD-RMPC) method for pose tracking of redundant manipulators. Firstly, a MPC-based trajectory tracking framework is established, in which the joint limits at three different levels (angle, velocity, and acceleration) are satisfied. Secondly, to deal with the system model's uncertainty, accelerate the error convergence speed, and reduce tracking errors, a novel data-driven RMPC is proposed, in which the system's input-output data are used to compensate for the conservatism of the system. Finally, the simulation results on a seven degrees of freedoms (DOFs) redundant manipulator show that DD-RMPC provides a faster error convergence rate and achieves smaller pose tracking errors than that of the comparison methods.

关键词： Jacobian matrices Uncertainty Trajectory tracking Simulation Biomimetics Predictive models Manipulators Data models Convergence Predictive control

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Chance-Constrained SMPC for Autonomous Vehicles Approaching Unsignalized Crosswalks

Chance-Constrained SMPC for Autonomous Vehicles Approaching ...

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international conference on Autonomous Robots and Agents, ICARA

作者： Lea Pavelko Branimir Škugor Joško Deur Faculty of Mechanical Engineering and Naval Architecture University of Zagreb Zagreb Croatia

ISBN: (数字)9798350394245

ISBN: (纸本)9798350394252

The paper proposes a chance-constrained stochastic model predictive control strategy to handle autonomous vehicle interactions with pedestrians near unsignalized crosswalks-in a safe and efficient manner. The strategy is designed to account for inherent uncertainties of pedestrian crossing behavior, and the effect of the vehicle state on pedestrian decisions. Probabilistic constraints are introduced within the optimal control problem to facilitate pedestrians' understanding of vehicle intentions and thus to avoid safety-critical situations. Finally, the proposed strategy is verified against a baseline control strategy via simulations of pedestrian model developed by using experimental data, for a variety of initial conditions and levels of pedestrian behavior uncertainty.

关键词： Pedestrians Uncertainty Stochastic processes Optimal control Predictive models Probabilistic logic Data models

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Accessibility Framework for Determining Collisions and Coverage for Radiation Scanning 25th

Accessibility Framework for Determining Collisions and Cov...

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25th Annual conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Bettles, Joshua West, Andrew Andrew, Jeremy Darby, Iain Lennox, Barry Manchester Centre for Robotics and AI The University of Manchester ManchesterM13 9PL United Kingdom Nuclear Restoration Services Ltd. Dounreay Thurso CaithnessKW14 7TZ United Kingdom National Nuclear Laboratory Chadwick House Birchwood Park Warrington CheshireWA3 6AE United Kingdom School of Physics and Astronomy University of Glasgow GlasgowG12 8QQ United Kingdom

ISBN: (纸本)9783031720581

Radioactive contamination monitoring is an important part of radiological protection. automation of surface monitoring poses difficulties, with a major challenge being determining the coverage of a radiation probe over an object in close proximity without collision. We propose a new accessibility framework to determine if radiation probes, modelled as convex hulls, collide with 3D point clouds representing the objects. We explain how to structure and analyze point clouds to extract properties such as the surface normal for each point. Our method for approximating radiation probes is demonstrated using the BP4 probe. This approach models both the probe and the sensor’s effective scan volume with geometric primitives, providing a computationally efficient way to detect collisions with flat surfaces. The accessibility assessment builds on common methods within computer science for determining intersections. A laptop in various positions was used to demonstrate that the framework can efficiently categorise points as accessible or inaccessible, identifying unscannable regions. The output of this framework can then be used to plan collision-free paths over objects and will be the foundation of a robotic survey assistant. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Probes

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