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2022 Chinese Automation Congress, CAC 2022

作者： Si, Zeze Zhao, Hongxia Tang, Haina Zhu, Fenghua Lv, Yisheng Lu, Hao Institute of Autamation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Qingdao Academy of Intelligent Industries Qingdao266000 China

ISBN: (纸本)9781665465335

In many driving situations, human mobility is an important topic in trajectory prediction. Considering the pedestrian trajectory as a sequence generative task, a prediction algorithm based on Social Long Short-Term Memory (Social LSTM) is implemented. In order to simulate the social interaction between pedestrians, Social Pooling (S-Pooling) is used to aggregate the hidden state of pedestrians, while the attention mechanism is utilized to aggregate information differently according to the importance of surrounding pedestrians. Furthermore, Convolutional Neural Networks (CNN) is introduced into Social LSTM model to consider both the interaction between people and the characteristic of scene scale in the prediction process. Experiments are carried out against baseline methods, and the results demonstrated that combining Social LSTM with attention mechanism or CNN can improve the performance of pedestrian trajectory prediction. © 2022 IEEE.

关键词： Long short-term memory

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Symbolic Verification of Fuzzy Logic Models

Symbolic Verification of Fuzzy Logic Models

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IEEE International Conference on Automated Software Engineering (ASE)

作者： Siang Zhao Zhongyang Li Zhenbang Chen Ji Wang College of Computer National University of Defense Technology China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology China State Key Laboratory of High Performance Computing National University of Defense Technology China

Fuzzy logic is widely applied in various applications. However, verifying the correctness of fuzzy logic models can be difficult. This extended abstract presents our ongoing work on verifying fuzzy logic models. We treat a fuzzy logic model as a program and propose a verification method based on symbolic execution for fuzzy logic models. We have developed and implemented the environment models for the common functions and the inference rules in fuzzy logic models. Our preliminary evaluation shows the potential of our verification method.

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Data-Driven Discovery of Nonlinear Dynamical systems from Noisy and Sparse Observations

Data-Driven Discovery of Nonlinear Dynamical Systems from No...

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New Trends in Computational Intelligence (NTCI), International Conference on

作者： Wei Zhu Chao Pei Yulan Liang Zhang Chen Jingsui Li School of Science Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798331517021

ISBN: (纸本)9798331517038

Sparse Identification of Nonlinear Dynamics (SINDy) is a data-driven algorithm for interpretability mod-eling. SINDy has a strong ability to learn dynamical systems from time series data. Sparse Identification of Nonlinear Dynamics (SINDy) can extract equations from data, but its performance will degrade when data has noise or sparse. In order to deal with this case, the idea of Monte Carlo Markov Chain (MCMC) is introduced, which is used to optimally select models. The SINDy with MCMC (MCMC-SINDy) can comprehensively discover model structures by employing random samples in data space. Simulation results show that MCMC-SINDy is more robust than SINDy with 1 % -15 % levels of noise or sparse 0.5. These results are promising for the discovery of governing equations for complex systems in neuroscience, power grids, epidemiology, finance or ecology, where governing equations have remained elusive. mportantly, compared to existing machine learning methods, the accuracy of modeling using MCMC SINDy is higher.

关键词： Neuroscience Biological system modeling Simulation Noise Time series analysis Mathematical models Power grids Nonlinear dynamical systems Noise measurement Periodic structures

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RGB-infrared fusion tracking algorithm based on Siamese network 2

RGB-infrared fusion tracking algorithm based on Siamese netw...

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2nd International Conference on Computer Vision, Image, and Deep Learning

作者： Li, Ruyou Cui, Wennan Wang, Zhiyong Zhang, Jingjing School of Automation China University of Geosciences Wuhan430074 China Key Laboratory of Intelligent Infrared Perception Chinese Academy of Sciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9781510646827

ISBN: (纸本)9781510646810

The target tracking method based on RGB image is affected by many factors such as light and haze weather, so it is difficult to distinguish the tracking target from the background, and it is easy to lead to the drift or even loss of the tracking target. Target tracking based on infrared images is not affected by light, haze and other illumination factors, but the target's color, texture and other characteristic information will be missing. Therefore, in order to obtain the target's color, texture and other characteristic information in a poorly illuminated environment, while achieving accurate and fast tracking of the object, this paper proposes a RGB and infrared image fusion tracking method based on a deep convolutional network. Firstly, the fusion method of RGB and infrared images is studied;secondly, a target tracking network based on the Siamese network is established to extract the image convolution features of the target template and the current target;finally, the response map is calculated by the deep cross-correlation module. At last, the performance test of the target tracking algorithm is carried out on the VOT2019RGBT data set. Experimental results show that the algorithm can effectively solve the problem of target tracking when the target is partially occluded, the tracking scene has no suitable illumination or the light changes strongly, and it is of great significance to improve the accuracy of target tracking under complex backgrounds. © 2021 SPIE.

关键词： Image fusion

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Quasiperiodic Symmetric Tilt FCC Grain Boundaries

arXiv

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arXiv 2024年

作者： Zou, Wenwen Zhang, Juan Xu, Jie Jiang, Kai Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China LSEC NCMIS Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

In this work, we investigate [110] symmetric tilt FCC grain boundaries (GBs) by a recently developed approach for quasiperiodic interfaces using the Landau-Brazovskii model. On special tilt angles associated with quadratic algebraic numbers, quasiperiodic GBs exhibit generalized Fibonacci substitution rules. The transition mechanism from quasiperiodic GBs to periodic GBs is explored through sphere offsets and spectral coalescence. We also propose an accurate method to calculate the GB energy for arbitrary tilt angle and analyze the factors affecting the GB energy. The revealing GB energy change is continue along with tilt angle except periodic GBs. © 2024, CC BY-NC-SA.

关键词： Grain boundaries

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Adaptive-triggered H∞ sliding-mode optimal control for nonlinear input-limited systems

SSRN

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SSRN 2024年

作者： Zhang, Hongxu Wang, Honggui Department of Applied Mathematics Harbin University of Science and Technology Harbin150080 China Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology Harbin150080 China

This paper concentrates on the H∞ sliding mode optimal control (SMOC) problem for nonlinear systems with mismatched inter ference and constrained input. Firstly, an adaptive-triggered strategy with respect to discontinuous control part is employed which can suppress input interference and the separated matched disturbances. Also, the system trajectories are guaranteed to converge to the sliding manifold. Moreover, the adaptive-triggered strategy is optimized to self-triggered strategy with aim to avoid the real-time measurement of triggered conditions, and the minimum time interval is obtained to keep away from Zeno phenomenon. Subsequently, the optimal H∞ control problem of sliding motion turns into a two-person ZS game issue. To derive the solutions of event-trigger HJI equation, an adaptive dynamic programming (ADP) algorithm with critic-only structure is implemented to obtain the optimal control online. Furthermore, by utilizing the Lyapunov technique, the uniformly ultimately bounded (UUB) conditions about system state and the weight estimation error are obtained. At last, simulation outcomes are analyzed to validate the effectiveness of the control strategy. © 2024, The Authors. All rights reserved.

关键词： Sliding mode control

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Valuing Equity-Linked Guaranteed Minimum Death Benefits with European-Style Asian Payoffs Under the Regime-Switching Diffusion Process with Jump

SSRN

引用

SSRN 2022年

作者： Wang, Yayun Liu, Shengda Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing400065 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

In this paper, we consider a novel and efficient method to price equity-linked guaranteed minimumdeath benefits (GMDB) with European-style geometric Asian and arithmetic Asian payoffs. In thesituation of continuous transition of market economy, we assume that the underlying asset price processfollows regime-switching Ĺevy model and use complex Fourier series (CFS) expansion method to derivethe approximate value of GMDB products. The error analysis shows an exponential convergence rateunder geometric Asian and arithmetic Asian payoffs. Furthermore, the CFS approach is comparedwith other Fourier transform methods and Monte Carlo simulation, the numerical experiment resultsillustrate our method has a distinct advantage over other methods in both efficiency and accuracy. © 2022, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Towards robust neural networks via a global and monotonically decreasing robustness training strategy

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Frontiers of Information Technology & Electronic Engineering 2023年第10期24卷 1375-1389页

作者： Zhen LIANG Taoran WU Wanwei LIU Bai XUE Wenjing YANG Ji WANG Zhengbin PANG Institute for Quantum Information&State Key Laboratory of High Performance Computing National University of Defense TechnologyChangsha 410073China State Key Laboratory of Computer Science Institute of Software Chinese Academy of SciencesBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100190China College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China Key Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China

Robustness of deep neural networks(DNNs)has caused great concerns in the academic and industrial communities,especially in safety-critical *** of verifying whether the robustness property holds or not in certain neural networks,this paper focuses on training robust neural networks with respect to given ***-of-the-art training methods,interval bound propagation(IBP)and CROWN-IBP,perform well with respect to small perturbations,but their performance declines significantly in large perturbation cases,which is termed“drawdown risk”in this ***,drawdown risk refers to the phenomenon that IBPfamily training methods cannot provide expected robust neural networks in larger perturbation cases,as in smaller perturbation *** alleviate the unexpected drawdown risk,we propose a global and monotonically decreasing robustness training strategy that takes multiple perturbations into account during each training epoch(global robustness training),and the corresponding robustness losses are combined with monotonically decreasing weights(monotonically decreasing robustness training).With experimental demonstrations,our presented strategy maintains performance on small perturbations and the drawdown risk on large perturbations is alleviated to a great *** is also noteworthy that our training method achieves higher model accuracy than the original training methods,which means that our presented training strategy gives more balanced consideration to robustness and accuracy.

关键词： Robust neural networks Training method Drawdown risk Global robustness training Monotonically decreasing robustness

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CoChain: High Concurrency Blockchain Sharding via Consensus on Consensus 42

CoChain: High Concurrency Blockchain Sharding via Consensus ...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Li, Mingzhe Lin, You Zhang, Jin Wang, Wei Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Research Institute of Trustworthy Autonomous Systems Computer Science and Engineering Department China Hong Kong University of Science and Technology Computer Science and Engineering Department Hong Kong Institute of High Performance Computing and Agency for Science Technology and Research Singapore

ISBN: (纸本)9798350334142

Sharding is an effective technique to improve the scalability of blockchain. It splits nodes into multiple groups so that they can process transactions in parallel. To achieve higher parallelism and concurrency at large scales, it is desirable to maintain a large number of small shards. However, simply configuring small shards easily results in a higher fraction of malicious nodes inside shards, causing shard corruption and compromising system security. Existing sharding techniques hence demand large shards, at the expense of limited concurrency. To address this limitation, we propose CoChain: a blockchain sharding system that can securely configure small shards for enhanced concurrency. CoChain allows some shards to be corrupted. For security, each shard is monitored by multiple other shards. The latter reach a cross-shard Consensus on the Consensus results of their monitored shard. Once a corrupted shard is found, its subsequent consensus will be taken over by another shard, hence recovering the system. Via Consensus on Consensus, CoChain allows the existence of shards with more fraction of malicious nodes ( © 2023 IEEE.

关键词： Blockchain

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A Gait Events Detection Algorithm Based on the Invariant Characteristic of Hip Joint Kinematics 33

A Gait Events Detection Algorithm Based on the Invariant Cha...

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第33届中国控制与决策会议

作者： Ningcun Xu Xiwei Peng Liang Peng Zeng-Guang Hou Institute of Intelligent Sensing & Motion Control School of AutomationBeijing Institute of Technology The State Key Laboratory for Management and Control of Complex Systems(SKL-MCCS) Institute of AutomationChinese academy of Science

ISBN: (纸本)9781665431293

In order to make up for the shortcomings of a single inertial sensor,which is easily disturbed and unable to directly describe the periodic characteristics of gait,a novel gait events detection algorithm is proposed which is based on the invariant characteristics of hip joint *** healthy volunteers conducted a walking experiment over the ground,who were equipped with motion capture device and insole with *** hip angle derived from motion capture device were applied to detect gait events,heel stride(HS) and toe off(TO).And the gait events detected by ground reactor force(GRF) were taken as the reference *** mean absolute difference of HS is 28±42 ms,and the mean absolute difference of TO is 27 ± 43 ms. And the confidence levels of the two gait events are 97.5% and 99.2%,*** results demonstrate that the proposed gait events detection algorithm is reliability and has potential clinical application value.

关键词： gait event inertial sensor hip joint kinematics statistical analysis

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