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GPIO Based Iterative Learning Control for Manipulators with Time-Varying Disturbances

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GPIO Based Iterative Learning Control for Manipulators with ...

第42届中国控制会议

作者： Huangyuwei Lu Zuo Wang Jiankun Sun Xi Xiao Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex systems of Engineering Ministry of Education Shenzhen Research Institute Southeast University School of Artificial Intelligence and Automation Huazhong University of Science and Technology Aviation Key Laboratory of Science and Technology on Aero Electromechanical System Integration

To improve the disturbance rejection ability and repetitive tracking accuracy of manipulators,a composite iterative learning control(ILC) scheme via generalized proportional integral observer(GPIO) is proposed.A high-order polynomial is first employed to model the time-varying disturbances that include unmodeled dynamics,parameter uncertainties and load fluctuations.A GPIO is then designed to estimate the disturbances and high-order time *** introducing estimations into a PD-type ILC at each iteration,a composite control scheme is obtained,such that the undesirable influence of time-varying disturbances can be effectively attenuated in the repetitive tracking process of *** this paper,the rigorous stability analysis of the closed-loop system is presented to guarantee that the tracking error exponentially converges to zero as the iteration number tends to *** effectiveness of the proposed control scheme is finally verified by simulation results.

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Coupling effect of wall slip and spanwise oscillation on drag reduction in turbulent channel flow

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Physical Review Fluids 2020年第12期5卷 124601-124601页

作者： Zexiang Li Songsong Ji Huiling Duan Shilong Lan Jinbai Zhang Pengyu Lv State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science BIC-ESAT College of Engineering Peking University Beijing 100871 China Key Laboratory of High Energy Density Physics Simulation Center for Applied Physics and Technology Peking University Beijing 100871 China National Laboratory of Computational Fluid Dynamics Beihang University Beijing 100191 China

The coupling effect of the isotropic wall slip and the spanwise oscillation boundary conditions on the drag reduction and turbulence properties are studied by direct numerical simulations. As the slip length increases, the drag reduction gradually changes from the oscillation dominated to the slip dominated. The increase of slip length will decrease the maximum spanwise velocity of the fluid on the wall, which is responsible for the decreased ability of the oscillatory wall motion to reduce the skin-friction drag. The drag reduction decomposition shows that the contribution from the modifications of turbulent dynamics will undergo a shift from drag increase to drag reduction as the isotropic slip length increases. Compared with the respective no-slip reference flow, the drag reduction of wall slip in laminar and turbulent channel flows can be expressed in a unified form versus the outer scale slip length. Furthermore, many aspects of the turbulence properties are influenced by the coupling effect. First, the wall slip condenses the envelope range of the phase fluctuations caused by the oscillatory wall motion, as well as the magnitude of the periodic fluctuation of the phase-averaged friction coefficient. Second, an unexpected property of the coupled boundary condition is found that the existence of the Stoke layer delays the relaminarization process caused by the large slip length. Third, the wall slip would narrow the periodic inclination of the streaks and then inhibit the energy transfer process in the horizontal direction. Fourth, in terms of phase, the Stokes strain and the shear angle have the same lag phase with the spanwise velocity, while the hysteresis of turbulent dynamics leads to the larger lag phase of the streaks and phase-averaged friction coefficient. These new features are valuable for increasing knowledge on this topic.

关键词： Slip boundary effects Turbulence Wall slip

Adaptive radar detection of subspace-based distributed target in power heterogeneous clutter

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arXiv 2024年

作者： Xiao, Daipeng Liu, Weijian Liu, Jun Dai, Lingyan Fang, Xueli Ge, Jianjun Wuhan Electronic Information Institute Wuhan430019 China Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China Key Laboratory of Complex Aviation Simulation System Beijing100076 China Information Science Academy China Electronics Technology Group Corporation Beijing100041 China

This paper investigates the problem of adaptive detection of distributed targets in power heterogeneous clutter. In the considered scenario, all the data share the identical structure of clutter covariance matrix, but with varying and unknown power mismatches. To address this problem, we iteratively estimate all the unknowns, including the coordinate matrix of the target, the clutter covariance matrix, and the corresponding power mismatches, and propose three detectors based on the generalized likelihood ratio test (GLRT), Rao and the Wald tests. The results from simulated and real data both illustrate that the detectors based on GLRT and Rao test have higher probabilities of detection (PDs) than the existing competitors. Among them, the Rao test-based detector exhibits the best overall detection performance. We also analyze the impact of the target extended dimensions, the signal subspace dimensions, and the number of training samples on the detection performance. Furthermore, simulation experiments also demonstrate that the proposed detectors have a constant false alarm rate (CFAR) property for the structure of clutter covariance matrix. © 2024, CC BY.

关键词： Radar clutter

Multimodal transformer with multi-view visual representation for image captioning

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arXiv 2019年

作者： Yu, Jun Li, Jing Yu, Zhou Huang, Qingming Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing101408 China

Image captioning aims to automatically generate a natural language description of a given image, and most state-ofthe-art models have adopted an encoder-decoder framework. The framework consists of a convolution neural network (CNN)-based image encoder that extracts region-based visual features from the input image, and an recurrent neural network (RNN)-based caption decoder that generates the output caption words based on the visual features with the attention mechanism. Despite the success of existing studies, current methods only model the co-attention that characterizes the inter-modal interactions while neglecting the self-attention that characterizes the intramodal interactions. Inspired by the success of the Transformer model in machine translation, here we extend it to a Multimodal Transformer (MT) model for image captioning. Compared to existing image captioning approaches, the MT model simultaneously captures intra- and inter-modal interactions in a unified attention block. Due to the in-depth modular composition of such attention blocks, the MT model can perform complex multimodal reasoning and output accurate captions. Moreover, to further improve the image captioning performance, multi-view visual features are seamlessly introduced into the MT model. We quantitatively and qualitatively evaluate our approach using the benchmark MSCOCO image captioning dataset and conduct extensive ablation studies to investigate the reasons behind its effectiveness. The experimental results show that our method significantly outperforms the previous state-of-the-art methods. With an ensemble of seven models, our solution ranks the 1st place on the real-time leaderboard of the MSCOCO image captioning challenge at the time of the writing of this paper. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering

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arXiv 2023年

作者： Yu, Zhou Ouyang, Xuecheng Shao, Zhenwei Wang, Meng Yu, Jun The Key Laboratory of Complex Systems Modeling and Simulation the School of Computer Science Hangzhou Dianzi University China The HDU-ITMO Joint Institute Hangzhou Dianzi University China The School of Computer Science and Information Engineering Hefei University of Technology China

Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. Early studies retrieve required knowledge from explicit knowledge bases (KBs), which often introduces irrelevant information to the question, hence restricting the performance of their models. Recent works have resorted to using a powerful large language model (LLM) as an implicit knowledge engine to acquire the necessary knowledge for answering. Despite the encouraging results achieved by these methods, we argue that they have not fully activated the capacity of the blind LLM as the provided textual input is insufficient to depict the required visual information to answer the question. In this paper, we present Prophet—a conceptually simple, flexible, and general framework designed to prompt LLM with answer heuristics for knowledge-based VQA. Specifically, we first train a vanilla VQA model on a specific knowledge-based VQA dataset without external knowledge. After that, we extract two types of complementary answer heuristics from the VQA model: answer candidates and answer-aware examples. Finally, the two types of answer heuristics are jointly encoded into a formatted prompt to facilitate the LLM’s understanding of both the image and question, thus generating a more accurate answer. By incorporating the state-of-the-art LLM GPT-3 [1], Prophet significantly outperforms existing state-of-the-art methods on four challenging knowledge-based VQA datasets. To demonstrate the generality of our approach, we instantiate Prophet with the combinations of different VQA models (i.e., both discriminative and generative ones) and different LLMs (i.e., both commercial and open-source ones). © 2023, CC BY.

关键词： Knowledge based systems

ActivityNet-QA: A dataset for understanding complex web videos via question answering

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arXiv 2019年

作者： Yu, Zhou Xu, Dejing Yu, Jun Yu, Ting Zhao, Zhou Zhuang, Yueting Tao, Dacheng Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China College of Computer Science Zhejiang University Hangzhou China UBTECH Sydney AI Centre SIT FEIT University of Sydney Australia

Recent developments in modeling language and vision have been successfully applied to image question answering. It is both crucial and natural to extend this research direction to the video domain for video question answering (VideoQA). Compared to the image domain where large scale and fully annotated benchmark datasets exists, VideoQA datasets are limited to small scale and are automatically generated, etc. These limitations restrict their applicability in practice. Here we introduce ActivityNet-QA, a fully annotated and large scale VideoQA dataset. The dataset consists of 58,000 QA pairs on 5,800 complex web videos derived from the popular ActivityNet dataset. We present a statistical analysis of our ActivityNet-QA dataset and conduct extensive experiments on it by comparing existing VideoQA baselines. Moreover, we explore various video representation strategies to improve VideoQA performance, especially for long videos. The dataset is available at https://***/MILVLG/ activitynet-qa. Copyright © 2019, The Authors. All rights reserved.

关键词： Modeling languages

Directed momentum current induced by the PT-symmetric driving

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Physical Review E 2019年第4期99卷 042201-042201页

作者： Wen-Lei Zhao Jiaozi Wang Xiaohui Wang Peiqing Tong School of Science Jiangxi University of Science and Technology Ganzhou 341000 China Department of Modern Physics University of Science and Technology of China Hefei 230026 China School of Information Engineering Jiangxi University of Science and Technology Ganzhou 341000 China Department of Physics and Institute of Theoretical Physics Nanjing Normal University Nanjing 210023 China Jiangsu Provincial Key Laboratory for Numerical Simulation of Large Scale Complex Systems Nanjing Normal University Nanjing Jiangsu 210023 China

We investigate the directed momentum current in the quantum kicked rotor model with PT-symmetric deriving potential. For the quantum nonresonance case, the values of quasienergy become complex when the strength of the imaginary part of the kicking potential exceeds a threshold value, which demonstrates the appearance of the spontaneous PT symmetry breaking. In the vicinity of the transition point, the momentum current exhibits a staircase growth with time. Each platform of the momentum current corresponds to the mean momentum of some eigenstates of the Floquet operator whose imaginary parts of the quasienergy are significantly large. Above the transition point, the momentum current increases linearly with time. Interestingly, its acceleration rate exhibits a kind of “quantized” increment with the kicking strength. We propose a modified classical acceleration mode of the kicked rotor model to explain such an intriguing phenomenon. Our theoretical prediction is in good agreement with numerical results.

关键词： Coherent control Cold atoms & matter waves Light-matter interaction Wave chaos

Insurance Strategy Can Promote Cooperation in the Presence of Antisocial Punishment in Public Goods Game

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Insurance Strategy Can Promote Cooperation in the Presence o...

International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)

作者： Hong Ding Yao Zhang Haiyang Hu Lei Shu Yizhi Ren Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Guangdong Petrochemical Equipment Fault Diagnosis Key Laboratory Guangdong University of Petrochemical Technology Maoming China

Previous researches have well demonstrated the importance of costly punishment for promoting the evolution of cooperation, while it remains unconsidered sides about the punishment. Recent evidences highlight that punishment cannot promote cooperation when punishment can be targeted at cooperators. In this paper, we study the evolutionary process of cooperation in the well-mixed population when the presence of antisocial punishment. Here cooperator can optionally adopt an insurance strategy which incurs an extra cost to resist the threat of antisocial punishment, and she will obtain compensation when being punished by antisocial punisher in the future. With this strategy, cooperators can opportunistically adjust their behavior, which makes cooperators evolve advantageously, and improve individuals' average payoff in population.

关键词： Sociology Statistics Insurance Integrated circuits Games Resists Investment

Multi-modal factorized bilinear pooling with co-attention learning for visual question answering

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arXiv 2017年

作者： Yu, Zhou Yu, Jun Fan, Jianping Tao, Dacheng Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University China Department of Computer Science University of North Carolina Charlotte United States UBTECH Sydney AI Centre School of IT FEIT University of Sydney Australia

Visual question answering (VQA) is challenging because it requires a simultaneous understanding of both the visual content of images and the textual content of questions. The approaches used to represent the images and questions in a fine-grained manner and questions and to fuse these multimodal features play key roles in performance. Bilinear pooling based models have been shown to outperform traditional linear models for VQA, but their high-dimensional representations and high computational complexity may seriously limit their applicability in practice. For multimodal feature fusion, here we develop a Multi-modal Factorized Bilinear (MFB) pooling approach to efficiently and effectively combine multi-modal features, which results in superior performance for VQA compared with other bilinear pooling approaches. For fine-grained image and question representation, we develop a 'co-attention' mechanism using an end-to-end deep network architecture to jointly learn both the image and question attentions. Combining the proposed MFB approach with co-attention learning in a new network architecture provides a unified model for VQA. Our experimental results demonstrate that the single MFB with co-attention model achieves new state-of-theart performance on the real-world VQA dataset. Code available at https://***/yuzcccc/mfb. Copyright © 2017, The Authors. All rights reserved.

关键词： Network architecture