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arXiv 2023年

作者： Thalhammer, Stefan Weibel, Jean-Baptiste Vincze, Markus Garcia-Rodriguez Automation and Control Institute TU Wien Vienna Austria Department of Computer Technology University of Alicante San Vicente del Raspeig Spain

Object pose estimation is important for object manipulation and scene understanding. In order to improve the general applicability of pose estimators, recent research focuses on providing estimates for novel objects, that is objects unseen during training. Such works use deep template matching strategies to retrieve the closest template connected to a query image. This template retrieval implicitly provides object class and pose. Despite the recent success and improvements of Vision Transformers over CNNs for many vision tasks, the state of the art uses CNN-based approaches for novel object pose estimation. This work evaluates and demonstrates the differences between self-supervised CNNs and Vision Transformers for deep template matching. In detail, both types of approaches are trained using contrastive learning to match training images against rendered templates of isolated objects. At test time, such templates are matched against query images of known and novel objects under challenging settings, such as clutter, occlusion and object symmetries, using masked cosine similarity. The presented results not only demonstrate that Vision Transformers improve in matching accuracy over CNNs, but also that for some cases pre-trained Vision Transformers do not need fine-tuning to do so. Furthermore, we highlight the differences in optimization and network architecture when comparing these two types of network for deep template matching. © 2023, CC BY.

关键词： Template matching

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Adaptive Finite Time Prescribed Performance Fault Tolerant control for Spacecraft Attitude Maneuver

Adaptive Finite Time Prescribed Performance Fault Tolerant C...

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IEEE Conference on Decision and control

作者： Ze Yang Baoqing Yang Ruihang Ji Jie Ma Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Electrical and Computer Engineering National University of Singapore Singapore

In this paper, an active fault tolerant control method for spacecraft against actuator faults, uncertainties and disturbances is investigated. First, an adaptive iterative learning observer with improved adaptive law is proposed, which greatly improves the accuracy and speed of fault estimation. Then, a novel adaptive finite time prescribed performance fault tolerant controller is proposed, which has flexible performance constraints according to faults and control references, with better robustness and lower conservatism, breaking the limitation of fixed performance constraint. Next, an online optimal control allocation strategy is designed to achieve high-performance actuator allocation under saturation and fault constraints. Finally, through numerical simulation, the effectiveness and robustness of the proposed scheme are illustrated by comparing with existing methods.

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What is the Expected Transient Behavior of Opinion Evolution for Two Communities?

arXiv

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arXiv 2023年

作者： Xing, Yu Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Digital Futures Stockholm Sweden

We study the transient behavior of a gossip model, in which agents randomly interact pairwise over a weighted graph with two communities. Edges within each community have identical weights, different from the weights between communities. It is shown that, at the early stage of the opinion evolution, the expected agent states in the same community have identical sign, despite influence of stubborn agents. Moreover, it is shown that the expected states of the agents in the same community concentrate around the initial average opinion of that community, if the weights within communities are larger than between. In contrast, if the edge weights between communities are larger, then the expected states of all agents concentrate around everyone’s initial average opinion. Different from the traditional asymptotic analysis in the opinion dynamics literature, these results focus on the initial phase of opinion evolution and establish a correspondence between community structure and transient behavior of the gossip model. The results are illustrated by numerical examples. © 2023, CC BY.

关键词： Asymptotic analysis

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Real Time Nanoscale Cleaning Phenomenon Observation During Enforcing MHz Wave By Evanescent Field 241

Real Time Nanoscale Cleaning Phenomenon Observation During E...

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241st ECST Meeting

作者： Terayama, Yutaka Khajornrungruang, Panart Suzuki, Keisuke Mori, Ryotaro Hamada, Satomi Wada, Yutaka Hiyama, Hirokuni Mechanical Science and Technology Division Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka iizuka820-8502 Japan Mechanical Science and Technology Division Department of Intelligent and Control Systems Ebara Corporation Ltd. Kanagawa fujisawa144-8510 Japan

ISBN: (纸本)9781607685395

Post-CMP Cleaning phenomenon is considered as the detachment of the nanoparticle from the substrate surface to be cleaned, and the occasional reattachment of the nanoparticle to surface in nanoscale. However, residual contamination on the substrate has been usually inspected only after the cleaning process. Therefore, the cleaning mechanism of nanoparticles has not already clearly known. Hence, we have established dynamically observation of cleaning phenomena near the surface by applying the evanescent light which occurred a few hundred nm from the surface. In this article, detachments of φ30, 50, 70 nm silica particles and the acoustic cavitation effect were observed during enforcing 1.1 MHz waves in 60 frames per second. The main phenomenon was described that some of each silica nanoparticles were randomly intermittently detached from the surface when the cavitation effect repeatedly irregularly occurs and decays near the surface or remained on the surface during enforcing MHz wave. © 2022 ECS - The Electrochemical Society.

关键词： Silica nanoparticles

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Adaptive Sampling of Algal Blooms Using an Autonomous Underwater Vehicle and Satellite Imagery

Adaptive Sampling of Algal Blooms Using an Autonomous Underw...

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control technology and Applications (CCTA),

作者： Joana Fonseca Alexandre Rocha Miguel Aguiar Karl H. Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden

This paper proposes a method that uses satellite data to improve adaptive sampling missions. We find and track algal bloom fronts using an autonomous underwater vehicle (AUV) equipped with a sensor that measures the concentration of chlorophyll a. Chlorophyll a concentration indicates the presence of algal blooms. The proposed method learns the kernel parameters of a Gaussian process model using satellite images of chlorophyll a from previous days. The AUV estimates the chlorophyll a concentration online using locally collected data. The algal bloom front estimate is fed to the motion control algorithm. The performance of this method is evaluated through simulations using a real dataset of an algal bloom front in the Baltic. We consider a real-world scenario with sensor and localization noise and with a detailed AUV model.

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Convergent Approaches to AI Explainability for HEP Muonic Particles Pattern Recognition

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Computing and Software for Big Science 2023年第1期7卷 1-18页

作者： Maglianella, Leandro Nicoletti, Lorenzo Giagu, Stefano Napoli, Christian Scardapane, Simone Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Rome Italy Department of Physics Sapienza University of Rome Rome Italy Institute for Systems Analysis and Computer Science Italian National Research Council Rome Italy Department of Computational Intelligence Electronics and Telecommunications Czestochowa University of Technology Czestochowa Poland Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Rome Italy

Neural networks are commonly defined as ‘black-box’ models, meaning that the mechanism describing how they give predictions and perform decisions is not immediately clear or even understandable by humans. Therefore, Explainable Artificial Intelligence (xAI) aims at overcoming such limitation by providing explanations to Machine Learning (ML) algorithms and, consequently, making their outcomes reliable for users. However, different xAI methods may provide different explanations, both from a quantitative and a qualitative point of view, and the heterogeneity of approaches makes it difficult for a domain expert to select and interpret their result. In this work, we consider this issue in the context of a high-energy physics (HEP) use-case concerning muonic motion. In particular, we explored an array of xAI methods based on different approaches, and we tested their capabilities in our use-case. As a result, we obtained an array of potentially easy-to-understand and human-readable explanations of models’ predictions, and for each of them we describe strengths and drawbacks in this particular scenario, providing an interesting atlas on the convergent application of multiple xAI algorithms in a realistic context. © 2023, The Author(s).

关键词： Explainable Artificial Intelligence High-energy physics Intrinsically interpretable Decision Trees Saliency maps methods Tracing gradient descent

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Multiplayer Pursuit-Evasion Games with Distributed Nash Equilibrium Solution

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IEEE Transactions on Network Science and Engineering 2025年

作者： Xu, Wenqi Wang, Tong Qiu, Jianbin Liu, Xiaoping Harbin Institute of Technology Research Institute of Intelligent Control and Systems Harbin150080 China Lakehead University Electrical and Computer Engineering Thunder BayONP7B 5E1 Canada

This paper concentrates on solving the multiplayer pursuit-evasion (MPE) game issue. In the existing MPE game framework, the fact that the Nash equilibrium and distribution are two contradicting properties which can not be achieved simultaneously. To tackle this challenge, novel cost functions that combine the best response approach and min-max scheme, are introduced such that the coupling terms in the existing MPE game formulations are removed. Consequently, the corresponding Nash and distributed solutions are obtained. Furthermore, a more general situation that the pursuers are not aware of the global information of the communication topology is discussed. In this framework, the adaptive coupling gains are incorporated into the improved cost functions to further realize the Nash equilibrium and distributed control strategies without the necessity of the information of topology graph. The sufficient conditions in two scenarios are given while the stability of adaptive coupling gains are provided as well. Besides, the Nash equilibrium properties of the solutions in two scenarios are analyzed, respectively. Finally, a simulation example is displayed to validate the theoretical results. © 2013 IEEE.

关键词： Cost functions

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FPGA-based Real-time High-resolution Image Processing for rocket on-board processing

FPGA-based Real-time High-resolution Image Processing for ro...

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International Conference on computer Research and Development (ICCRD)

作者： Hui Zhou Xiaofeng Wang YuJia Xie Yongxiang Cao Yu Tang Yongchun Huo Huan Wen Xiongbo Zhao Chaoran Li Beijing Aerospace Automatic Control Institute National key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing China Computer Science and Engineering Beihang University Beijing China National Superior College for Engineers Beihang University Beijing China

ISBN: (数字)9798331531881

ISBN: (纸本)9798331531898

The structure of the rocket-borne model is inherently complex, with processed images exhibiting high resolution and generating substantial amounts of data and calculations. Achieving robust real-time computing on an embedded platform poses significant challenges due to strictly limited resources, power consumption constraints, and size limitations. Our review of rocket-borne applications reveals considerable variability in the design resources of different devices, indicating a need for expanded design approaches. Upon evaluating existing methods, we identified two primary drawbacks. First, certain operators within the high-resolution target detection model are difficult to parallelize, resulting in significant inference delays that hinder the ability to meet task requirements. Although existing methods have been extended, there remains significant potential for performance enhancement in core scheduling for poor acceleration. This paper proposes an optimized architecture for the target detection algorithm accelerator designed for high-resolution images, along with a novel highly parallel data pre-processing and post-processing module implemented on FPGA to address these issues. Compared to the ARM implementation, this architecture demonstrates an improved performance of 24.64x. Furthermore, to ensure flexible application across various rocket launch scenarios, we introduce an optimization structure for convolution, pooling, and fusion operators and a multi-core expansion optimization method. This approach yields a 1.29x improvement in computing unit utilization compared to state-of-the-art multi-core scaling efforts. Finally, we assessed the accelerator architecture across multiple FPGA platforms, achieving a peak processing element utilization rate of 99.71% for a single core and layer. The overall computing efficiency, excluding the first layer, exceeded 90%. The peak computing power for the four cores reached 1638.4 GOPS, and the end-to-end computation time for

关键词： YOLO Rockets Power demand Convolution Multicore processing Reviews Scalability Accelerator architectures Real-time systems Field programmable gate arrays

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Research on the Flux-weakening Capability of the Reverse-salient Permanent Magnet Synchronous Motor

Research on the Flux-weakening Capability of the Reverse-sal...

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International Conference on Electrical Machines and Systems (ICEMS)

作者： Xiaokun Zhao Cheng Zhang Weinan Wang Baoquan Kou Liangkuan Zhu College of Computer and Control Engineering Northeast Forestry University Harbin China Department of Electrical and Automation Harbin Institute of Technology Harbin China

Permanent magnet synchronous motors (PMSMs) offer the benefits of high torque density and a superior power factor. Nevertheless, the challenge lies in the inherent difficulty of adjusting the permanent magnet flux. To address this issue, this paper analyzes the influence of electromagnetic parameters on the constant-power speed range (CPSR). First, the analysis focuses on the influence of d-axis inductance and saliency ratio, aiming to establish a design method for achieving a wide CPSP in PMSMs. Second, based on the electromagnetic parameter requirements, a reverse-salient PMSM is proposed and its working principle is explained. Finally, the electromagnetic characteristics, including torque and the CPSP, are determined through the application of the finite element method (FEM). The obtained results confirm that the reverse-salient PMSM achieves a CPSP of 7:1, thus validating the accuracy of the theoretical analysis.

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Classification algorithm of streaming media big data fusion under multi-Task information filtering

Classification algorithm of streaming media big data fusion ...

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2022 International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2022

作者： Ma, Miantao Chen, Zeting Fang, Zhou Xu, Haoyu Institute of Computer Control Technology Guangzhou Huali College Guangzhou China School of Media and Art Design Guangzhou Huali College Guangzhou China School of Data Science Guangzhou Huashang College Guangzhou China

ISBN: (纸本)9781665462877

In order to improve the recognition ability of streaming media big data on the communication platform of information fusion terminal, a streaming media big data fusion algorithm based on multi-Task information filtering is proposed. This paper analyzes the storage structure model of streaming media big data of information fusion terminal communication platform, uses association matching filtering fusion detection method to cluster the fusion of streaming media big data of information fusion terminal communication platform, mines fuzzy correlation feature quantity of streaming media big data of information fusion terminal communication platform, and uses support vector machine learning model to classify features of streaming media big data of information fusion terminal communication platform. According to the classification attribute of streaming media big data of information fusion terminal communication platform, multi-Task information screening and fusion clustering processing are carried out, and the feature reconstruction and optimized clustering of streaming media big data of information fusion terminal communication platform are realized by using statistical sequence analysis method. The simulation results show that this method has good adaptability in streaming media big data clustering on the communication platform of information fusion terminal, and the recognition degree of big data fusion is high and the error rate is low. © 2022 IEEE.

关键词： Big data

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