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arXiv 2020年

作者： Poot, Maurice Portegies, Jim Oomen, Tom Control Systems Technology Group Dept. of Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands CASA Dept. of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands

Learning from data of past tasks can substantially improve the accuracy of mechatronic systems. Often, for fast and safe learning a model of the system is required. The aim of this paper is to develop a model-free approach for fast and safe learning for mechatronic systems. The developed actor-critic iterative learning control (ACILC) framework uses a feedforward parameterization with basis functions. These basis functions encode implicit model knowledge and the actor-critic algorithm learns the feedforward parameters without explicitly using a model. Experimental results on a printer setup demonstrate that the developed ACILC framework is capable of achieving the same feedforward signal as preexisting model-based methods without using explicit model knowledge. Copyright © 2020, The Authors. All rights reserved.

关键词： Learning algorithms

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Graph wavelet neural network 7

Graph wavelet neural network

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7th International Conference on Learning Representations, ICLR 2019

作者： Xu, Bingbing Shen, Huawei Cao, Qi Qiu, Yunqi Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China

We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform. Different from graph Fourier transform, graph wavelet transform can be obtained via a fast algorithm without requiring matrix eigendecomposition with high computational cost. Moreover, graph wavelets are sparse and localized in vertex domain, offering high efficiency and good interpretability for graph convolution. The proposed GWNN significantly outperforms previous spectral graph CNNs in the task of graph-based semi-supervised classification on three benchmark datasets: Cora, Citeseer and Pubmed. © 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved.

关键词： Wavelet transforms

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Temporal Logic Trees for Model Checking and control Synthesis of Uncertain Discrete-time Systems

arXiv

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arXiv 2020年

作者： Gao, Yulong Abate, Alessandro Jiang, Frank J. Giacobbe, Mirco Xie, Lihua Johansson, Karl H. Division of Decision and Control Systems Royal Institute of Technology Stockholm Sweden Department of Computer Science University of Oxford United Kingdom School of Electrical and Electronic Engineering Nanyang Technological University Singapore

We propose algorithms for performing model checking and control synthesis for discrete-time uncertain systems under linear temporal logic (LTL) specifications. We construct temporal logic trees (TLT) from LTL formulae via reachability analysis. In contrast to automaton-based methods, the construction of the TLT is abstraction-free for infinite systems, that is, we do not construct discrete abstractions of the infinite systems. Moreover, for a given transition system and an LTL formula, we prove that there exist both a universal TLT and an existential TLT via minimal and maximal reachability analysis, respectively. We show that the universal TLT is an underapproximation for the LTL formula and the existential TLT is an overapproximation. We provide sufficient conditions and necessary conditions to verify whether a transition system satisfies an LTL formula by using the TLT approximations. As a major contribution of this work, for a controlled transition system and an LTL formula, we prove that a controlled TLT can be constructed from the LTL formula via control-dependent reachability analysis. Based on the controlled TLT, we design an online control synthesis algorithm, under which a set of feasible control inputs can be generated at each time step. We also prove that this algorithm is recursively feasible. We illustrate the proposed methods for both finite and infinite systems and highlight the generality and online scalability with two simulated examples. Copyright © 2020, The Authors. All rights reserved.

关键词： Temporal logic

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Linear-time-varying feedforward control for position-dependent flexible structures

Linear-time-varying feedforward control for position-depende...

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European control Conference (ECC)

作者： Fatemeh Sharifi Hans Zwart Marcel Heertjes Nikolaos Kontaras Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands ASML Mechatronic System Development Veldhoven The Netherlands Dynamics and Control group Eindhoven University of Technology Eindhoven The Netherlands

ISBN: (数字)9783907144022

ISBN: (纸本)9781728188133

High performance motion systems in the semiconductor industry can achieve higher tracking accuracy when the structural dynamics of the system are taken into account. This work proposes an LTV feedforward control scheme which is based on the approximation of the system dynamics at low frequencies. The plant approximation contains both the low frequency contribution and resonance dynamics of the dominant modes. As an example of a flexible structure, the damped Euler-Bernoulli beam is investigated with considering the stable inversion of the approximated plant for feedforward control design. The results show a remarkable increase in positioning accuracy of the system being the consequence of the presence of feedforward control forces even when the reference setpoint accelerations equal zero.

关键词： Feedforward systems Resonant frequency control design Frequency control Acceleration Integrated circuits Mathematical model

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Dynamic event-triggered H∞ state estimation for discrete-time complex-valued memristive neural networks with mixed time delays

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Neural networks : the official journal of the International Neural Network Society 2025年 190卷 107631页

作者： Yufei Liu Bo Shen Hongjian Liu Tingwen Huang Hailong Tan Jie Sun School of Electrical Engineering Anhui Polytechnic University Wuhu 241000 China Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of Education Anhui Polytechnic University Wuhu 241000 China. College of Information Science and Technology Donghua University Shanghai 201620 China Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education Shanghai 201620 China. Electronic address: bo.shen@***. Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of Education Anhui Polytechnic University Wuhu 241000 China School of Mathematics-Physics and Finance Anhui Polytechnic University Wuhu 241000 China. Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen 518107 China. School of Electrical Engineering Shanghai Dianji University Shanghai 201306 China.

This paper explores the H state estimation problem for a category of discrete-time complex-valued memristive neural networks (CVMNNs). Regarding the studied CVMNNs, the phenomena of the distributed delay and time-varying delay are taken into account so as to describe the system more practically. Firstly, for further effective analysis, the examined CVMNNs are converted to an augmented system that integrates both the real and imaginary dynamics about the initial CVMNNs. To alleviate the communication burden, a representative dynamic event-triggered scheme is employed, for the first time, in the state estimator design of discrete-time CVMNNs. By establishing the Lyapunov functional, a sufficient condition is derived to assure the asymptotical stability of the estimation error system. Subsequently, the explicit expression of the desired estimator is obtained by resolving several matrix inequalities. Ultimately, the efficacy of the designed state estimator is substantiated through a simulation example.

关键词： Complex-valued memristive neural networks Discrete-time Dynamic event-triggered mechanism Mixed time delays State estimation

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Recycling qubits for the generation of Bell nonlocality between independent sequential observers

arXiv

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arXiv 2021年

作者： Cheng, Shuming Liu, Lijun Baker, Travis J. Hall, Michael J.W. The Department of Control Science and Engineering Tongji University Shanghai 201804 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai 201804 China Institute for Advanced Study Tongji University Shanghai 200092 China College of Mathematics and Computer Science Shanxi Normal University Linfen041000 China Centre for Quantum Dynamics Griffith University BrisbaneQLD4111 Australia Department of Theoretical Physics Research School of Physics Australian National University CanberraACT0200 Australia

There is currently much interest in the recycling of entangled systems, for use in quantum information protocols by sequential observers. In this work, we study the sequential generation of Bell nonlocality via recycling one or both components of two-qubit states. We first give a description of two-valued qubit measurements in terms of measurement bias, strength, and reversibility, and derive useful tradeoff relations between them. Then, we derive one-sided monogamy relations for unbiased observables, that strengthen the recent Conjecture in [S. Cheng et al., Phys. Rev. A 104, L060201 (2021) ] that if the first pair of observers violate Bell nonlocality then a subsequent independent pair cannot, and give semi-analytic results for the best possible monogamy relation. We also extend the construction in [P. J. Brown and R. Colbeck, Phys. Rev. Lett. 125, 090401 (2020)] to obtain (i) a broader class of two-qubit states that allow the recycling of one qubit by a given number of observers on one side, and (ii) a scheme for generating Bell nonlocality between arbitrarily many independent observers on each side, via the two-sided recycling of multiqubit states. Our results are based on a formalism that is applicable to more general problems in recycling entanglement, and hence is expected to aid progress in this field. Copyright © 2021, The Authors. All rights reserved.

关键词： Recycling

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Trading accuracy for power with a configurable approximate adder

Trading accuracy for power with a configurable approximate a...

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作者： Sato, Toshinori Yang, Tongxin Ukezono, Tomoaki Department of Electronics Engineering and Computer Science Fukuoka University Fukuoka-shi814-0180 Japan Graduate School of Information and Control Systems Fukuoka University Fukuoka-shi814-0180 Japan

Approximate computing is a promising paradigm to realize fast, small, and low power characteristics, which are essential for modern applications, such as Internet of Things (IoT) devices. This paper proposes the Carry-Predicting Adder (CPredA), an approximate adder that is scalable relative to accuracy and power consumption. The proposed CPredA improves the accuracy of a previously studied adder by performing carry prediction. Detailed simulations reveal that, compared to the existing approximate adder, accuracy is improved by approximately 50% with comparable energy efficiency. Two application-level evaluations demonstrate that the proposed approximate adder is sufficiently accurate for practical use. Copyright © 2019 The Institute of Electronics, Information and Communication Engineers.

关键词： Adders

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DPNet: Dynamic Pooling Network for Accurate and Efficient Size-Aware Tiny Object Detection

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IEEE Internet of Things Journal 2025年

作者： Gong, Luqi Chen, Haotian Chen, Yikun Yao, Tianliang Li, Chao Zhao, Shuai Han, Guangjie Beijing University of Posts and Telecommunications School of Computer Science Beijing China Zhejiang Lab Research Center for Space Computing System Hangzhou China Southwest Jiaotong University SWJTU-LEEDS Joint School Chengdu China LTD Guangdong Zhiyun City construction Technology Co Zhuhai China Tongji University College of Electronic and Information Engineering Department of Control Science and Engineering Shanghai China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

In unmanned aerial systems, especially in complex environments, accurately detecting tiny objects is crucial. Resizing images is a common strategy to improve detection accuracy, particularly for small objects. However, simply enlarging images significantly increases computational costs and the number of negative samples, severely degrading detection performance and limiting its applicability. This paper proposes a Dynamic Pooling Network (DPNet) for tiny object detection to mitigate these issues. DPNet employs a flexible down-sampling strategy by introducing a factor (df) to relax the fixed down-sampling process of the feature map to an adjustable one. Furthermore, we design a lightweight predictor to predict df for each input image, which will be used to decrease the resolution of feature map in backbone. Thus, we achieve input-aware down-sampling. We design an Adaptive Normalization Module (ANM) to make a unified detector well compatible with different dfs. At the same time, we also design a guidance loss to supervise the predictor’s training. DPNet realizes the dynamic allocation of computing resources to trade off detection accuracy and efficiency through this. Experiments on the TinyCOCO and TinyPerson datasets show that our DPNet can save over 35% and 25% GFLOPs, respectively, while maintaining comparable detection *** code will be made publicly available. © 2014 IEEE.

关键词： Object detection

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CBLNER: A Multi-models Biomedical Named Entity Recognition System Based on Machine Learning 1

引用

15th International Conference on Intelligent Computing, ICIC 2019

作者： Lejun, Gong Xiaolin, Liu Xuemin, Yang Lipeng, Zhang Yao, Jia Ronggen, Yang Jiangsu Key Lab of Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Faculty Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing211169 China

ISBN: (数字)9783030269692

ISBN: (纸本)9783030269685

Biomedical named entities is fundamental recognition task in biomedical text mining. This paper developed a system for identifying biomedical entities with four models including CRF, LSTM, Bi-LSTM and BiLSTM-CRF. The system achieved the following performance in test data Genia V3.02: CRF with an F score of 75.91%, LSTM with an F score of 71.69%, BiLSTM with a F score of 74.37%, BiLSTM-CRF with a F score of 76.81%. Experimental results show the performance of BiLSTM-CRF model is better than other three models. Compared with CRF model, Bi-LSTM-CRF model has better recognition effect for biological entities in long text and entities that modified by modifiers. Therefore, CBLNER system lays a foundation for further relationship and event extraction, and could also provide reference for entity recognition research in other fields. © 2019, Springer Nature Switzerland AG.

关键词： Character recognition

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SASRM: A Semantic and Attention Spatio-temporal Recurrent Model for Next Location Prediction

SASRM: A Semantic and Attention Spatio-temporal Recurrent Mo...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Xu Zhang Boming Li Chao Song Zhengwen Huang Yan Li Department of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data(PSPRC) China Academy of Electronics and Information Technology Beijing China Department of Electronic and Computer Engineering Brunel University London London United Kingdom Department of Computer Engineering Inha University Incheon South Korea

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Predicting user's next location is of great importance for a wide spectrum of location-based applications. However, most prediction methods do not take advantage of the rich semantic information contained in trajectory data. Meanwhile, the traditional LSTM-based model can not capture the spatio-temporal dependencies well. In this paper, we propose a Semantic and Attention Spatio-temporal Recurrent Model (SASRM) for next location prediction. Firstly, the SASRM put forward a method for encoding semantic vectors and concatenating vectors (location, time and semantic vectors) as input to the model. To capture the spatio-temporal dependencies, we design a variant recurrent unit based on LSTM. Further, an attention layer is used to weight hidden state to capture the influence of the historical locations on the next location prediction. We perform experiments on two real-life semantic trajectory datasets, and evaluation results demonstrate that our model outperforms several state-of-the-art models in accuracy.

关键词： Semantics Trajectory Hidden Markov models Predictive models Logic gates Analytical models Training

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