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The impact of irrational behaviours in the optional prisoner’s dilemma with game-environment feedback

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arXiv 2021年

作者： Stella, Leonardo Bauso, Dario Department of Computing College of Science & Engineering University of Derby United Kingdom Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Netherlands Dipartimento di Ingegneria University of Palermo Italy

In the optional prisoner’s dilemma (OPD), players can choose to cooperate and defect as usual, but can also abstain as a third possible strategy. This strategy models the players’ participation in the game and is a relevant aspect in many settings, e.g. social networks or opinion dynamics where abstention is an option during an election. In this paper, we provide a formulation of the OPD where we consider irrational behaviours in the population inspired by prospect theory. Prospect theory has gained increasing popularity in recent times thanks to its ability to capture aspects such as reference dependence or loss aversion which are common in human behaviour. This element is original in our formulation of the game and is incorporated in our framework through pairwise comparison dynamics. Recently, the impact of the environment has been studied in the form of feedback on the population dynamics. Another element of novelty in our work is the extension of the game-environment feedback to the OPD in two forms of dynamics, the replicator and the pairwise comparison. The contribution of this paper is threefold. First, we propose a modelling framework where prospect theory is used to capture irrational behaviours in an evolutionary game with game-environment feedback. Second, we carry out the stability analysis of the system equilibria and discuss the oscillating behaviours arising from the game-environment feedback. Finally, we extend our previous results to the OPD and we discuss the main differences between the model resulting from the replicator dynamics and the one resulting from the pairwise comparison dynamics. © 2021, CC BY.

关键词： Game theory

Size-Invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection

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arXiv 2024年

作者： Li, Feiran Xu, Qianqian Bao, Shilong Yang, Zhiyong Cong, Runmin Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Sun Yat-Sen University Shenzhen Campus China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

Electrical and mechanical sensor-based mass flow rate measurement system: A comparative approach 4

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Electrical and mechanical sensor-based mass flow rate measur...

4th International Conference on Robotics and Automation Sciences, ICRAS 2020

作者： Abrar, Usama Shi, Liu Jaffri, Nasif Raza Short, Michael Hasham, Khawar North China Electric Power University School of Control and Computer Engineering Beijing102206 China Teesside University School of Computing Engineering and Digital Technologies Middlesbrough ClevelandTS1 3BA United Kingdom Interactive Group Islamabad Pakistan

ISBN: (纸本)9781728198576

Gas-Solid flow finds its application in various industries such as chemical and food processes, pharmaceuticals, automobile and power generation. The precise measurement and real-time monitoring of the flow process have garnered a lot of attraction in the modern industrial era. The effective utilization, with high efficiency, of the resources have compelled the researchers to investigate Gas-Solid flow monitoring and measurement process for the last two decades. Efforts are extended to design instruments for flow pattern visualization and quantification of mass flow rate. Generally, volumetric flow concentration and velocity profile of the solid particles are required to find the mass flow rate (MRF) in the case of non-invasive measurement techniques. This paper proposes a novel non-invasive mass flow rate measurement system for solid particles where electrical and mechanical sensors are simultaneously used to compensate each other's associated drawbacks. The solid particles are allowed to slide along an insulated inclined channel under the effect of gravity. The velocity profile and volumetric flow concentration are calculated using electrical sensors. Furthermore, load cell serves as a second sensor to calculate the mass flow rate. The experimental results from both the sensors are compared to investigate the accuracy and relative errors of both the sensors under different conditions. © 2020 IEEE.

关键词： Capacitive sensors

Hamiltonian Learning using Machine Learning Models Trained with Continuous Measurements

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arXiv 2024年

作者： Tucker, Kris Rege, Amit Kiran Smith, Conor Monteleoni, Claire Albash, Tameem Department of Applied Math University of Colorado Boulder United States Department of Computer Science University of Colorado Boulder United States Center for Quantum Information and Control University of New Mexico University of New Mexico AlbuquerqueNM United States Department of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM United States INRIA Centre de Recherche de Paris France Center for Computing Research Sandia National Laboratories AlbuquerqueNM87185 United States

We build upon recent work on using Machine Learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning where the weak measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised and unsupervised contexts. We demonstrate that the model benefits from larger training sets establishing that it is in fact "learning," and we show robustness to errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single particle relaxation. © 2024, CC BY.

关键词： Qubits

Parallel Systems Framework: A Safe and Efficient Testbed for Unmanned Ships

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Parallel Systems Framework: A Safe and Efficient Testbed for...

Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Long Fu Zhen Shen Xisong Dong Yunjun Han Shuguang Yang Gang Xiong Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Schoool of Rail Transportation Shandong Jiaotong University Jinan China State Key Laboratory for Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

In this study, unmanned ships test platform based on the parallel systems framework is proposed to improve the test efficiency and accuracy of unmanned ships in complex ocean environment. The parallel intelligence theory and ACP approach, the efficient interaction between virtual and real environment is achieved, which provides more comprehensive and accurate data for unmanned ships testing. Future research will further expand the scope of virtual environment simulation and improve systems accuracy and response speed to a wider range of unmanned ships test requirements.

关键词： Accuracy Oceans Virtual environments Digital twins Marine vehicles Testing

A Survey of Robot Manipulation in Contact

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arXiv 2021年

作者： Suomalainen, Markku Karayiannidis, Yiannis Kyrki, Ville Center of Ubiquitous Computing Faculty of Information Technology and Electrical Engineering University of Oulu Finland Systems and Control Department of Electrical Engineering Chalmers University of Technology Gothenburg Sweden Intelligent Robotics research group Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Helsinki Finland

In this survey, we present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly or explicitly control the contact force with the environment to complete the task. Robots can perform more and more manipulation tasks that are still done by humans, and there is a growing number of publications on the topics of 1) performing tasks that always require contact and 2) mitigating uncertainty by leveraging the environment in tasks that, under perfect information, could be performed without contact. The recent trends have seen robots perform tasks earlier left for humans, such as massage, and in the classical tasks, such as peg-in-hole, there is a more efficient generalization to other similar tasks, better error tolerance, and faster planning or learning of the tasks. Thus, in this survey we cover the current stage of robots performing such tasks, starting from surveying all the different in-contact tasks robots can perform, observing how these tasks are controlled and represented, and finally presenting the learning and planning of the skills required to complete these tasks. © 2021, CC BY.

关键词： Surveys

Volume control of Low-Cost Ventilator with Automatic Set-Point Adaptation

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Volume Control of Low-Cost Ventilator with Automatic Set-Poi...

European control Conference (ECC)

作者： Lukas Hewing Marcel Menner Nikolaos Tachatos Marianne Schmid Daners Cosima Du Pasquier Thomas S. Lumpe Kristina Shea Andrea Carron Melanie N. Zeilinger Institute for Dynamic Systems and Control ETH Zurich Zurich Switzerland Product Development Group Zurich ETH Zurich Zurich Switzerland Engineering Design and Computing Laboratory ETH Zurich Zurich Switzerland

This paper considers the control design for a low-cost ventilator that is based on a manual resuscitator bag (also known as AmbuBag) to pump air into the lungs of a patient who is physically unable to breathe. First, it experimentally shows that for accurately tracking tidal volumes, the controller needs to adjust to the individual patient and the different configurations, e.g., hardware or operation modes. Second, it proposes a set-point adaptation algorithm that uses sensor measurements of a flow meter to automatically adapt the controller to the setup at hand. Third, it shows in experiments on a mechanical lung simulator that such an adaptive solution improves the performance of the ventilator for various setups. One objective of this paper is to increase awareness of the need for feedback control using flow measurements in low-cost ventilator solutions in order to automatically adapt to the specific scenario.

关键词： Ventilators Adaptation models control design Volume measurement Lung Europe Manuals

Real-time In-situ Defect Detection in 3D Printing of Continuous Fiber-Reinforced Composites

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Real-time In-situ Defect Detection in 3D Printing of Continu...

Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Zhijian Yue Zhen Shen Xiangyang Dong Qihang Fang Weixing Wang Xisong Dong Gang Xiong Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China State Key Laboratory for Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

With the development of continuous fiber-reinforced composites (CFRCs) 3D printing technology, timely, efficient and accurate detection of fiber path defects is essential for ensuring product quality and performance. However, challenges still need to be addressed in this field, such as inadequate training data coverage and the complexity of fiber path structures. These factors hinder the capability of traditional methods to accurately assess the status of printed products. Therefore, developing accurate and robust fiber path defect detection techniques is crucial for CFRCs 3D printing technology. In this paper, we propose a precise detection method for fiber path defects in CFRCs 3D printing using the YOLOv7 object detection model combined with a Squeeze-and-Excitation (SE) attention mechanism. By introducing the SE attention mechanism, we enhance the perception capability of the YOLOv7 model for crucial features, thereby improving the accuracy of fiber path defect detection. The experimental results show that the YOLOv7 model, when augmented with an attention mechanism, achieves an average accuracy of 93.9% in identifying fiber path defects during the printing process. Compared to models without this attention mechanism, the results show that there is 10.7% increase, with a notably enhanced learning capacity during the training phase, which substantiates the efficacy and practicality of the proposed approach.

关键词： Training Solid modeling Accuracy Attention mechanisms Training data Three-dimensional printing Real-time systems Quality assessment Optical fiber testing Defect detection

Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration

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arXiv 2024年

作者： Zhang, Xiaoqing Nie, Qiushi Xiao, Zunjie Zhao, Jilu Wu, Xiao Guo, Pengxin Li, Runzhi Liu, Jin Wei, Yanjie Pan, Yi Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Actuarial Science The University of Hong Kong 999077 Hong Kong Cooperative Innovation Center of Internet Healthcare Zhengzhou University Zhengzhou450001 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830046 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Spatial pooling (SP) and cross-channel pooling (CCP) operators have been applied to aggregate spatial features and pixel-wise features from feature maps in deep neural networks (DNNs), respectively. Their main goal is to reduce computation and memory overhead without visibly weakening the performance of DNNs. However, SP often faces the problem of losing the subtle feature representations, while CCP has a high possibility of ignoring salient feature representations, which may lead to both miscalibration of confidence issues and suboptimal medical classification results. To address these problems, we propose a novel dual-view framework, the first to systematically investigate the relative roles of SP and CCP by analyzing the difference between spatial features and pixel-wise features. Based on this framework, we propose a new pooling method, termed dual-view pyramid pooling (DVPP), to aggregate multi-scale dual-view features. DVPP aims to boost both medical image classification and confidence calibration performance by fully leveraging the merits of SP and CCP operators from a dual-axis perspective. Additionally, we discuss how to fulfill DVPP with five parameter-free implementations. Extensive experiments on six 2D/3D medical image classification tasks show that our DVPP surpasses state-of-the-art pooling methods in terms of medical image classification results and confidence calibration across different DNNs. © 2024, CC BY.

关键词： Deep neural networks