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Calculation of Natural Vibration Frequencies of a Box Structure with Thermal Protection

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Calculation of Natural Vibration Frequencies of a Box Struct...

Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex (TIRVED)

作者： Ramil A. Nezhmetdinov Damir T. Bautdinov Alexey D. Chudnovsky Dilshod D. Yusupov Vladimir V. Bogdanov Computer Control Syatems Department Moscow State Technology University "STANKIN" Moscow Russia Institute of Information Systems State University of Management Moscow Russia Office for the Coordination of Scientific Research State University of Management Moscow Russia

ISBN: (数字)9798331518707

ISBN: (纸本)9798331518714

The article focuses on calculating the natural vibration frequencies of a box structure with thermal protection, a crucial component in many engineering fields such as mechanical engineering and construction. The thermal protective coating applied to the outer surfaces of the structure adds significant mass and requires consideration in static and dynamic analyses. The study employs nonlinear dynamic analysis to model the flexible, two-layered structure subjected to thermal and force loads. The authors derive a system of nonlinear differential equations for the deflection of the structure using the Bubnov-Galerkin method. They also account for the material properties, including the modulus of elasticity and density of both the box and thermal protection layers. Numerical integration techniques are employed to solve these equations, providing a relationship between dimensionless vibration amplitudes over time. The study demonstrates the applicability of the proposed methodology by calculating the lowest natural frequency for a specific plate configuration and extends this analysis to configurations with stiffening ribs. In conclusion, the approach offers a reliable means for dynamic analysis of similar structures, with potential applications in finite element modeling. The results serve as a reference for validating such models under geometrically nonlinear conditions.

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Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning

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arXiv 2024年

作者： Zeng, Runhao Zhou, Dingjie Liang, Qiwei Liu, Junlin Li, Hui Huang, Changxin Li, Jianqiang Hu, Xiping Sun, Fuchun Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Mechatronics and Control Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science and Technology Tsinghua University China

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running. Copyright © 2024, The Authors. All rights reserved.

关键词： Robots

Iterative regularized policy optimization with imperfect demonstrations 24

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Iterative regularized policy optimization with imperfect dem...

Proceedings of the 41st International Conference on Machine Learning

作者： Xudong Gong Dawei Feng Kele Xu Yuanzhao Zhai Chengkang Yao Weijia Wang Bo Ding Huaimin Wang College of Computer National University of Defense Technology Changsha Hunan China and State Key Laboratory of Complex & Critical Software Environment Changsha Hunan China Flight Automatic Control Research Institute AVIC Xian Shanxi China

Imitation learning heavily relies on the quality of provided demonstrations. In scenarios where demonstrations are imperfect and rare, a prevalent approach for refining policies is through online fine-tuning with reinforcement learning, in which a Kullback–Leibler (KL) regularization is often employed to stabilize the learning process. However, our investigation reveals that on the one hand, imperfect demonstrations can bias the online learning process, the KL regularization will further constrain the improvement of online policy exploration. To address the above issues, we propose Iterative Regularized Policy Optimization (IRPO), a framework that involves iterative offline imitation learning and online reinforcement exploration. Specifically, the policy learned online is used to serve as the demonstrator for successive learning iterations, with a demonstration boosting to consistently enhance the quality of demonstrations. Experimental validations conducted across widely used benchmarks and a novel fixed-wing UAV control task consistently demonstrate the effectiveness of IRPO in improving both the demonstration quality and the policy performance. Our code is available at https://***/GongXudong/IRPO.

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Learning Communities from Equilibria of Nonlinear Opinion Dynamics

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arXiv 2024年

作者： Xing, Yu Bizyaeva, Anastasia Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and with Digital Futures Stockholm Sweden The Sibley School of Mechanical and Aerospace Engineering Cornell University IthacaNY United States

This paper studies community detection for a nonlinear opinion dynamics model from its equilibria. It is assumed that the underlying network is generated from a stochastic block model with two communities, where agents are assigned with community labels and edges are added independently based on these labels. Agents update their opinions following a nonlinear rule that incorporates saturation effects on interactions. It is shown that clustering based on a single equilibrium can detect most community labels (i.e., achieving almost exact recovery), if the two communities differ in size and link probabilities. When the two communities are identical in size and link probabilities, and the inter-community connections are denser than intra-community ones, the algorithm can achieve almost exact recovery under negative influence weights but fails under positive influence weights. Utilizing fixed point equations and spectral methods, we also propose a detection algorithm based on multiple equilibria, which can detect communities with positive influence weights. Numerical experiments demonstrate the performance of the proposed algorithms. © 2024, CC BY.

关键词： Nash equilibrium

Shifting Spotlight for Co-supervision: A Simple yet Efficient Single-branch Network to See Through Camouflage

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arXiv 2024年

作者： Hu, Yang Zhang, Jinxia Zhang, Kaihua Yuan, Yin Huang, Jiale Zhan, Zechao Wang, Xin Key Laboratory of Measurement and Control of CSE School of Automation Southeast University China School of Computer Science Nanjing University of Information Science and Technology China Advanced Ocean Institute of Southeast University China Alibaba Group China

Camouflaged object detection (COD) remains a challenging task in computer vision. Existing methods often resort to additional branches for edge supervision, incurring substantial computational costs. To address this, we propose the Co-Supervised Spotlight Shifting Network (CS3Net), a compact single-branch framework inspired by how shifting light source exposes camouflage. Our spotlight shifting strategy replaces multi-branch designs by generating supervisory signals that highlight boundary cues. Within CS3Net, a Projection Aware Attention (PAA) module is devised to strengthen feature extraction, while the Extended Neighbor Connection Decoder (ENCD) enhances final predictions. Extensive experiments on public datasets demonstrate that CS3Net not only achieves superior performance, but also reduces Multiply-Accumulate operations (MACs) by 32.13% compared to state-of-the-art COD methods, striking an optimal balance between efficiency and effectiveness. Copyright © 2024, The Authors. All rights reserved.

关键词： Camouflage

Evaluation Metrics for Object Detection for Autonomous Systems

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arXiv 2022年

作者： Badithela, Apurva Wongpiromsarn, Tichakorn Murray, Richard M. Control and Dynamical Systems California Institute of Technology United States Department of Computer Science Iowa State University United States

This paper studies the evaluation of learning-based object detection models in conjunction with model-checking of formal specifications defined on an abstract model of an autonomous system and its environment. In particular, we define two metrics - proposition-labeled and class-labeled confusion matrices - for evaluating object detection, and we incorporate these metrics to compute the satisfaction probability of system-level safety requirements. While confusion matrices have been effective for comparative evaluation of classification and object detection models, our framework fills two key gaps. First, we relate the performance of object detection to formal requirements defined over downstream high-level planning tasks. In particular, we provide empirical results that show that the choice of a good object detection algorithm, with respect to formal requirements on the overall system, significantly depends on the downstream planning and control design. Secondly, unlike the traditional confusion matrix, our metrics account for variations in performance with respect to the distance between the ego and the object being detected. We demonstrate this framework on a car-pedestrian example by computing the satisfaction probabilities for safety requirements formalized in Linear Temporal Logic (LTL). © 2022, CC BY-NC-SA.

关键词： Object detection

DA-LMR: A Robust Lane Marking Representation for Data Association

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DA-LMR: A Robust Lane Marking Representation for Data Associ...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Miguel Á ngel Muñ oz-Bañ ó n Jan-Hendrik Pauls Haohao Hu Christoph Stiller Group of Automation Robotics and Computer Vision (AUROVA) University of Alicante Alicante Spain Institute of Measurement and Control Systems Karlsruhe Institute of Technology Karlsruhe Germany

While complete localization approaches are widely studied in the literature, their data association and data representation subprocesses usually go unnoticed. However, both are a key part of the final pose estimation. In this work, we present DA-LMR (Delta-Angle Lane Marking Representation), a robust data representation in the context of localization approaches. We propose a representation of lane markings that encodes how a curve changes in each point and includes this information in an additional dimension, thus providing a more detailed geometric structure description of the data. We also propose DC-SAC (Distance-Compatible Sample Consensus), a data association method. This is a heuristic version of RANSAC that dramatically reduces the hypothesis space by distance compatibility restrictions. We compare the presented methods with some state-of-the-art data representation and data association approaches in different noisy scenarios. The DA-LMR and DC-SAC produce the most promising combination among those compared, reaching 98.1 % in precision and 99.7% in recall for noisy data with 0.5 m of standard deviation.

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Green Routing Game: Strategic Logistical Planning using Mixed Fleets of ICEVs and EVs

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Green Routing Game: Strategic Logistical Planning using Mixe...

IEEE Conference on Decision and control

作者： Hampei Sasahara György Dán Saurabh Amin Henrik Sandberg Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan Division of Network and Systems Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper introduces a “green” routing game between multiple logistic operators (players), each owning a mixed fleet of internal combustion engine vehicle (ICEV) and electric vehicle (EV) trucks. Each player faces the cost of delayed delivery (due to charging requirements of EVs) and a pollution cost levied on the ICEVs. This cost structure models: 1) limited battery capacity of EVs and their charging requirement; 2) shared nature of charging facilities; 3) pollution cost levied by regulatory agency on the use of ICEVs. We characterize Nash equilibria of this game and derive a condition for its uniqueness. We also use the gradient projection method to compute this equilibrium in a distributed manner. Our equilibrium analysis is useful to analyze the trade-off faced by players in incurring higher delay due to congestion at charging locations when the share of EVs increases versus a higher pollution cost when the share of ICEVs increases. A numerical example suggests that to increase marginal pollution cost can reduce inefficiency of equilibria.

关键词： Costs Pollution Green products Internal combustion engines Games Routing Planning

NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds

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arXiv 2022年

作者： Feeney, Patrick Schneider, Sarah Lymperopoulos, Panagiotis Liu, Li-Ping Scheutz, Matthias Hughes, Michael C. Dept. of Computer Science Tufts University United States Center for Vision Automation and Control Austrian Institute of Technology Austria

In order for artificial agents to successfully perform tasks in changing environments, they must be able to both detect and adapt to novelty. However, visual novelty detection research often only evaluates on repurposed datasets such as CIFAR-10 originally intended for object classification, where images focus on one distinct, well-centered object. New benchmarks are needed to represent the challenges of navigating the complex scenes of an open world. Our new NovelCraft dataset contains multimodal episodic data of the images and symbolic world-states seen by an agent completing a pogo stick assembly task within a modified Minecraft environment. In some episodes, we insert novel objects of varying size within the complex 3D scene that may impact gameplay. Our visual novelty detection benchmark finds that methods that rank best on popular area-under-the-curve metrics may be outperformed by simpler alternatives when controlling false positives matters most. Further multimodal novelty detection experiments suggest that methods that fuse both visual and symbolic information can improve time until detection as well as overall discrimination. Finally, our evaluation of recent generalized category discovery methods suggests that adapting to new imbalanced categories in complex scenes remains an exciting open problem. © 2022, CC BY.

关键词： Classification (of information)