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5th International Conference on Computer Science and Application engineering, CSAE 2021

作者： Liu, Zhenqi Liu, Changhua Chen, Jiaxi University of Sheffield Automatic Control and Systems Engineering South Yorkshire Sheffield United Kingdom School of Mathematics and Computer Science Wuhan Polytechnic University Hubei Wuhan China

ISBN: (纸本)9781450389853

Leaf is an important part of plants. A rapid and accurate detection of plant leaf area is an important guidance to reasonable fertilizer application and accurate sprinkler irrigation. In order to solve the problem of large error and incomplete target region in segmenting leaves when people using Otus's threshold method, a method based on watershed algorithm was proposed to segment the leaves of Brassica napus. Firstly, the images of relevant crop leaves are collected by the digital camera on site. Then these images are preprocessed by Sobel operator and morphological operation. Thirdly, labeled images are segmented by watershed algorithm to get target images. Finally the leaf area is calculated by counting the number of pixels in target area. The experimental results show that this method can solve the problem of incomplete segmentation of the Otus's threshold method and effectively reduce the measurement error to less than 2.2%.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Mathematical morphology

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Path planning with moving obstacles using stochastic optimal control

arXiv

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arXiv 2025年

作者： Jafari, Seyyed Reza Hansson, Anders Wahlberg, Bo Division of Automatic Control Department of Electrical Engineering Linköping University LinköpingSE-581 83 Sweden Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden

Navigating a collision-free, optimal path for a robot poses a perpetual challenge, particularly in the presence of moving objects such as humans. In this study, we formulate the problem of finding an optimal path as a stochastic optimal control problem. However, obtaining a solution to this problem is nontrivial. Therefore, we consider a simplified problem, which is more tractable. For this simplified formulation, we are able to solve the corresponding Bellman equation. However, the solution obtained from the simplified problem does not sufficiently address the original problem of interest. To address the full problem, we propose a numerical procedure where we solve an optimization problem at each sampling instant. The solution to the simplified problem is integrated into the online formulation as a final-state penalty. We illustrate the efficiency of the proposed method using a numerical example. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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Feedback-Optimizing Model Predictive control for Constrained Linear systems

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IFAC-PapersOnLine 2021年第6期54卷 43-49页

作者： Amba Asuk Paul Trodden Department of Automatic Control and Systems Engineering University of Sheffield UK

This paper proposes a feedback-optimizing model predictive control (FOMPC) algorithm to regulate a disturbed linear time-invariant system to an equilibrium point that is the solution to a steady-state optimization problem. This regulation is achieved without knowledge of the optimal steady-state set-points or explicit solution of the steady-state optimization problem. We develop the FOMPC algorithm by indirectly formulating the residuals of the Karush–Kuhn– Tucker (KKT) optimality conditions associated with the steady-state optimization into the transient performance objective of the MPC controller. By solving the steady-state optimization problem implicitly, via feedback, the proposed algorithm allows the optimal steady-states to be reached despite unknown disturbances and/or set-point changes, and in the presence of linear inequality constraints. We establish recursive feasibility and stability of the approach, and show that FOMPC is a generalization of conventional tracking model predictive control.

关键词： Optimization Model Predictive control Tracking Path Following Predictive control Economic Predictive control

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Stochastic Optimization Problem Solved Using SVM Based Prediction For Photovoltaic Plants

Stochastic Optimization Problem Solved Using SVM Based Predi...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Ştefania-Cristiana Colbu Daniel-Marian Băncilă Dumitru Popescu Severus-Constantin Olteanu Alina Petrescu - Niţă Department of Automatic Control and Systems Engineering University POLITEHNICA of Bucharest Bucharest Romania Department of Mathematical Methods and Models University POLITEHNICA of Bucharest Bucharest Romania

In the current paper, a strategy for handling a stochastic optimization problem based on metaheuristic techniques is presented. This optimization problem is defined based on the impact of environmental factors such as temperature and irradiance on photovoltaic panels. The objective aims to solve the defined problem by finding an SVM model of a photovoltaic panel. To enhance the efficiency of power generation, this model will be utilized for the purpose of predicting the voltage linked to the Maximum Power Point of the panel.

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Sharing the control of Robot Swarms Among Multiple Human Operators: A User Study

Sharing the Control of Robot Swarms Among Multiple Human Ope...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Genki Miyauchi Yuri K. Lopes Roderich Groß Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom Department of Computer Science Santa Catarina State University Joinville Brazil

Simultaneously controlling multiple robot swarms is challenging for a single human operator. When involving multiple operators, however, they can each focus on controlling a specific robot swarm, which helps distribute the cognitive workload. They could also exchange some robots with each other in response to the requirements of the tasks they discover. This paper investigates the ability of multiple operators to dynamically share the control of robot swarms and the effects of different communication types on performance and human factors. A total of 52 participants completed an experiment in which they were randomly paired to form a team. In a $2\times 2$ mixed factorial study, participants were split into two groups by communication type (direct vs. indirect). Both groups experienced different robot-sharing conditions (robot-sharing vs. no-robot-sharing). Results show that although the ability to share robots did not necessarily increase task scores, it allowed the operators to switch between working independently and collaboratively, reduced the total energy consumed by the swarm, and was considered useful by the participants.

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Efficient Centralised and Decentralised Gaussian Process Approaches for Online Tracking within Stone Soup

Efficient Centralised and Decentralised Gaussian Process App...

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International Conference on Information Fusion

作者： Chenyi Lyu Xingchi Liu James Wright Jordi Barr Alasdair Hunter Lyudmila Mihaylova Department of Automatic Control & Systems Engineering University of Sheffield UK Scientific Computing Department Rutherford Appleton Laboratory Science and Technology Facilities Council UK Defence Science and Technology Laboratory UK

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

This paper explores the application of centralised and distributed Gaussian process algorithms to real-time target tracking and compares their performance. By embedding the algorithms into the Stone Soup, the focus is on the innovative implementation of Gaussian process methods with learning hyperparameters and implementation with a factorised variance of the Gaussian kernel. The performance of the methods with different kernels was evaluated, not only with the Gaussian kernel. Extensive experiments with various kernel configurations demonstrate their importance in enhancing prediction accuracy and efficiency, especially in real-time tracking. The case studies with manoeuvring targets show significant advancements in tracking capabilities, particularly in wireless sensor networks, using optimised Gaussian process methods. This work advances Stone Soup’s capabilities and lays the groundwork for future investigations into adaptive Gaussian Process applications in tracking and sensor data analysis.

关键词： Wireless sensor networks Technological innovation Analytical models Target tracking Data analysis Gaussian processes Benchmark testing Real-time systems Data models Kernel

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Command filter-based adaptive backstepping controller for high-order uncertain nonlinear systems

Command filter-based adaptive backstepping controller for hi...

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Advances in Electrical and Communication Technologies (ICAECOT), International Conference on

作者： Yassine Soukkou Hicham Khebbache Ammar Soukkou Mohamed Tadjine Mokhtar Nibouche Research Center in Industrial Technologies CRTI Algiers Algeria Department of Automatic Control LAJ University of MSBY Jijel Algeria Department of Electronics Renewable Energy Laboratory University of MSBY Jijel Algeria Department of Automatic Control LCP National Polytechnic School of Algiers (ENP) Algiers Algeria School of Engineering University of the West of England Bristol UK

ISBN: (数字)9798350353754

ISBN: (纸本)9798350353761

This article proposes a command filtering-based adaptive backstepping strategy for a category of uncertain high-order nonlinear systems. The proposed method effectively addresses the explosion of complexity issue. The overall system variables are established to be uniformly ultimately bounded (UUB) based on Lyapunov analysis, and the tracking errors can convergence to a small region around zero. The results of simulation are displayed to demonstrate the presented scheme viability.

关键词： Adaptive systems Backstepping Filtering Adaptive filters control systems Explosions Complexity theory Reliability Nonlinear systems Convergence

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Toward Robust Visual Odometry Using Prior 2D Map Information and Multiple Hypothesis Particle Filtering 22nd

Toward Robust Visual Odometry Using Prior 2D Map Information...

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22th Annual Conference Towards Autonomous Robotic systems, TAROS 2021

作者： Edwards, Seamus Mihaylova, Lyudmila Aitken, Jonathan M. Anderson, Sean Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom

ISBN: (纸本)9783030891763

Visual odometry can be used to estimate the pose of a robot from current and recent video frames. A problem with these methods is that they drift over time due to the accumulation of estimation errors at each time-step. In this short paper we propose and briefly demonstrate the potential benefit of using prior 2D, top-down map information combined with multiple hypothesis particle filtering to correct visual odometry estimates. The results demonstrate a substantial improvement in robustness and accuracy over the sole use of visual odometry. © 2021, Springer Nature Switzerland AG.

关键词： Information filtering

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A Sparse Autoencoder Based Adversarial Open Set Domain Adaptation Model for Fault Diagnosis of Rotating Machinery 3

A Sparse Autoencoder Based Adversarial Open Set Domain Adapt...

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3rd International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Liu, Zhao-Hua Jiang, Lin-Bo Wei, Hua-Liang Wang, Chang-Tong Lv, Ming-Yang Chen, Lei School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan China The University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom Harbin Electric Corporation Wind Power Co. Ltd Xiangtan China

ISBN: (纸本)9781665492812

Rotating machinery is an integral part of many industrial systems. Domain adaptation technique provides a powerful tool to detect faults under different working conditions. However, there is still a challenge: conventional domain adaptation approach only works under the 'closed set' assumption that all test classes are known at training time. In practice, a more realistic situation is 'open set', i.e., knowledge is incomplete in the training process, resulting in unknown classes during the testing. In this paper, a sparse autoencoder based adversarial open set domain adaptation (SAOSDA) model is proposed for rotating machinery fault diagnosis under open set scenarios, which can recognize the unknown faults and detect the known faults under different working conditions. This model utilizes adversarial learning to reduce the discrepancies between source samples and known target samples and reject the unknown target samples simultaneously. Experimental results of the actual bearing dataset verify the superiority and effectiveness of this method. © 2022 IEEE.

关键词： adversarial learning deep learning domain adaptation fault diagnosis open set recognition rotating machinery sparse autoencoder unknown faults

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RGB-D Image-Based Deep Neural Network Body Shape Classifier

RGB-D Image-Based Deep Neural Network Body Shape Classifier

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Data Science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Meng Xu Yuanhao Gong Sanja Dogramadzi Department of Automatic Control and Systems Engineering University of Sheffield S10 2TN Sheffield Faculty of Electronics and Information Engineering Shenzhen University

Leveraging the untapped potential of depth information in RGB-D images, this study introduces a deep neural network classifier for advanced body shape classification. Going beyond traditional RGB image analysis, our method innovatively employs multi-task learning, simultaneously performing body shape classification, posture estimation, and body part segmentation to achieve superior accuracy. This approach promises to revolutionize personalization avenues in healthcare, fashion, and entertainment industries, establishing a new benchmark in body shape analysis.

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