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42nd Chinese control Conference, CCC 2023

作者： Zhang, Feng-Yu Yu, Gang He, Shu-Han He, Wang-Yong School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Co. Ltd. Wuhan430074 China

ISBN: (纸本)9789887581543

Given the dual robots play a key role for collaborative work. In this paper, an improved method is proposed to find the kinematic positive solution based on its D-H parameters, model the dual SCARA robot in MATLAB environment, and solve the collaborative workspace point cloud map by Monte Carlo method. The proposed method extracts and plots the boundary curves of the collaboration space by improved the α-Shapes algorithm, and obtains the shape of the common region of the collaboration space of the dual SCARA robots and the limit position parameters in the collaboration space. It provides a theoretical basis for the research of path planning, trajectory planning, dynamics analysis, mechanism parameter optimization, self-avoidance, and anti-collision problems of the dual-robot collaborative system. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Monte Carlo methods

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Blind Image Despeckling Using a Multiscale Attention-Guided Neural Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 205-216页

作者： Guo, Yu Lu, Yuxu Liu, Ryan Wen Zhu, Fenghua Wuhan University of Technology School of Navigation Wuhan430063 China Wuhan University of Technology Chongqing Research Institute Chongqing401151 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China

Coherent imaging systems have been applied in the detection of target of interest, natural resource exploration, ailment diagnosis, etc. However, it is easy to generate speckle-degraded images due to the coherent interference of reflected echoes, restricting these practical applications. Speckle noise is a granular interference that affects the observed reflectivity. It is often modeled as multiplicative noise with a negative exponential distribution. This nonlinear property makes despeckling of imaging data an intractable problem. To enhance the despeckling performance, we propose to blindly remove speckle noise using an intelligent computing-enabled multiscale attention-guided neural network (termed MSANN). In particular, we first introduce the logarithmic transformation to convert the multiplicative speckle noise model to an additive version. Our MSANN, essentially a feature pyramid network, is then exploited to restore degraded images in the logarithmic domain. To enhance the generalization ability of the MSANN, a multiscale feature enhancement attention module is incorporated into the MSANN to extract multiscale features for improving the imaging quality. A multiscale mixed loss function is further presented to increase the network robustness during training. The final despeckled images are naturally equivalent to the exponential versions of the output of the MSANN. Experimental results have shown that the MSANN has the capacity of effectively removing the speckle noise while preserving essential structures. It can achieve superior despeckling results in terms of visual quality and quantitative measures. © 2020 IEEE.

关键词： Additives

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A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction

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Energy and AI 2024年第4期18卷 368-379页

作者： Weipeng Li Yuting Chong Xin Guo Jun Liu School of Automation and Information Engineering Xi’an University of TechnologyNo.5 Jinhua South RoadXi’an710048ShaanxiChina Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing No.5 Jinhua South RoadXi’an710048ShaanxiChina

Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power *** data-driven forecasting methods are regarded as an effective ***,the inherent randomness and nonlinearity of wind power systems,along with the abundance of redundant information in measurement data,present challenges to forecasting *** integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced driven data-forecasting *** on the seasonal variation characteristics of wind energy,a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is *** effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.

关键词： Wind power prediction Data-driven Hybrid model Seasonal feature decomposition Gated recurrent networks Attention mechanism

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Unlocking the Future: Exploring Look-Ahead Planning Mechanistic Interpretability in Large Language Models

Unlocking the Future: Exploring Look-Ahead Planning Mechanis...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Men, Tianyi Cao, Pengfei Jin, Zhuoran Chen, Yubo Liu, Kang Zhao, Jun The Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Shanghai Artificial Intelligence Laboratory China

ISBN: (纸本)9798891761643

Planning, as the core module of agents, is crucial in various fields such as embodied agents, web navigation, and tool using. With the development of large language models (LLMs), some researchers treat large language models as intelligent agents to stimulate and evaluate their planning capabilities. However, the planning mechanism is still unclear. In this work, we focus on exploring the look-ahead planning mechanism in large language models from the perspectives of information flow and internal representations. First, we study how planning is done internally by analyzing the multi-layer perception (MLP) and multi-head self-attention (MHSA) components at the last token. We find that the output of MHSA in the middle layers at the last token can directly decode the decision to some extent. Based on this discovery, we further trace the source of MHSA by information flow, and we reveal that MHSA mainly extracts information from spans of the goal states and recent steps. According to information flow, we continue to study what information is encoded within it. Specifically, we explore whether future decisions have been encoded in advance in the representation of flow. We demonstrate that the middle and upper layers encode a few short-term future decisions to some extent when planning is successful. Overall, our research analyzes the look-ahead planning mechanisms of LLMs, facilitating future research on LLMs performing planning tasks. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Model Reconstruction Based on Physical-Informed Polynomial Neural ODEs 13

Model Reconstruction Based on Physical-Informed Polynomial N...

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13th IEEE Data Driven control and Learning systems Conference, DDCLS 2024

作者： Chen, Zhang Zhu, Wei Li, Jingsui Bian, Hanlin Pei, Chao School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing400065 China

ISBN: (纸本)9798350361674

Neural networks are capable function approximators for data, but their lack of interpretability poses challenges. Physical-informed Polynomial Neural Ordinary Differential Equations (PIPNODEs) are proposed. They combine the constraints of prior knowledge of the data with interpretable Polynomial Neural Ordinary Differential Equations (PolyNODEs) to discover or approximate the governing equations. In this work, Neural Ordinary Differential Equations (NODEs) are categorized into two layers: a physical-informed term, which represents the priori knowledge of the data, and a discrepancy term, which is derived from the approximation of PolyNODEs. Pruning strategies are also used to remove redundant terms with coefficients lower than a certain threshold. We demonstrate the effectiveness and flexibility of this framework in a series of numerical algorithms with varying levels of prior knowledge. We found that having correct but incomplete physical information accelerates training and approaching thresholds compared to having incorrect physical information. © 2024 IEEE.

关键词： data modeling discprepancy modeling interpretability Neural Ordinary Differential Equations physics-informed machine learning polynomial neural network

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Parameter Inversion of High-Dimensional Chaotic systems Using Neural Ordinary Differential Equations 13

Parameter Inversion of High-Dimensional Chaotic Systems Usin...

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13th IEEE Data Driven control and Learning systems Conference, DDCLS 2024

作者： Bian, Hanlin Zhu, Wei Chen, Zhang Jingsui, L. Pei, Chao Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing400065 China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9798350361674

High-dimensional chaotic systems possess complex structural characteristics, which have potential research value in fields like encrypted communication and chaos control. The inverse problem of parameters in high-dimensional chaotic systems is of significant importance for a profound understanding of the dynamic properties of such systems. This article takes a departure from neural ordinary differential equations and embeds the structural dynamics of the dynamical systems to the parameter inversion of high-dimensional chaotic systems. Numerical experiments on different high-dimensional chaotic systems indicate that neural ordinary differential equations perform exceptionally well in solving these inversion problems, with low errors and strong robustness. © 2024 IEEE.

关键词： Inverse problems

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Joint Local Contrast and First-Order Directional Derivative Methods for Detecting Infrared Small Targets in complex Backgrounds

IEEE Journal on Miniaturization for Air and Space Systems

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IEEE Journal on Miniaturization for Air and Space systems 2025年

作者： Zheng, Hanjia Zong, Xiaofeng China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Infrared target detection technology is one of the most key technologies of current detection systems, which is not only applied to automatic navigation, security protection and surveillance devices in daily life, but also widely used in the military. However, due to the influence of complex background, infrared detection system is often difficult to achieve accurate and fast detection of small targets, so the current small target detection methods still need to be improved. In this paper, an effective algorithm is proposed by complementally combining local contrast and first-order directional derivative algorithms, incorporating human visual characteristics for enhancement. Then according to the different characteristics of the background and small targets in infrared images, for the problem of excessive redundant information in infrared weak small target images, the high real-time and low leakage rate of LCM is used to find out the information-rich suspicious blocks, and then the low false alarm rate of FODD is used to find out the targets in the suspicious area. Through a lot number of experiments, and comparing with a variety of other algorithms. The results show that the detection algorithm proposed in this paper is significantly better than other algorithms in terms of low false alarm rate and can maintain good real-time performance. © 2019 IEEE.

关键词： Infrared imaging

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Compound Fault Separation and Diagnosis Method Based on FSA-CNN and DAN

Compound Fault Separation and Diagnosis Method Based on FSA-...

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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021

作者： Dong, Jiechao Yan, Liping Xia, Yuanqing Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing China

ISBN: (纸本)9781665401159

With the industry becoming larger and more complicated, the technology of fault diagnosis becomes more and more important. Due to the strong coupling and non-stationarity of the compound fault, the existing fault diagnosis methods cannot accurately identify all single faults' detailed information contained in the compound fault. This paper proposes a compound fault separation and diagnosis method based on FSACNN and DAN. Firstly, in order to highlight certain frequency segments, frequency segment attention module is added to CNN. Secondly, a compound fault feature separation framework based on DAN is proposed, which can separate compound fault to two fault components accurately. Thiredly, a signature matrix is introduced into ELM to improve the performance of the classifier. Finally, ablation experiments are designed to prove the advantage of the proposed method. © 2021 IEEE.

关键词： Failure analysis

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Adaptive control of Discrete-time Nonlinear systems Using ITF-ORVFL

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 556-563页

作者： Xiaofei Zhang Hongbin Ma Wenchao Zuo Man Luo School of Automation Beijing Institute of TechnologyBeijing 100081 School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China School of Automation Beijing Institute of Technologyand also with the State Key Laboratory of Intelligent Control and Decision of Complex Systems(Beijing Institute of Technology)Beijing 100081China School of Automation Beijing Institute of TechnologyBeijing 100081and he is with Beijing Institute of Electronic System EngineeringBeijing 100854China School of Automation Beijing Institute of TechnologyBeijing 100081and she is with Ant GroupBeijing 310013China

Random vector functional ink(RVFL)networks belong to a class of single hidden layer neural networks in which some parameters are randomly *** network structure in which contains the direct links between inputs and outputs is unique,and stability analysis and real-time performance are two difficulties of the control systems based on neural *** this paper,combining the advantages of RVFL and the ideas of online sequential extreme learning machine(OS-ELM)and initial-training-free online extreme learning machine(ITFOELM),a novel online learning algorithm which is named as initial-training-free online random vector functional link algo rithm(ITF-ORVFL)is investigated for training *** link vector of RVFL network can be analytically determined based on sequentially arriving data by ITF-ORVFL with a high learning speed,and the stability for nonlinear systems based on this learning algorithm is *** experiment results indicate that the proposed ITF-ORVFL is effective in coping with nonparametric uncertainty.

关键词： Adaptive control initial-training-free online learning algorithm random vector functional link networks

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ResFFont: Few-Shot Font Generation based on Reversible Network 43

ResFFont: Few-Shot Font Generation based on Reversible Netwo...

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43rd Chinese control Conference, CCC 2024

作者： Ju, Chuanying Zhang, Ziying Chen, Xin China University of Geosciences School of Automation Wuhan430074 China 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 Wuhan430074 China China University of Geosciences School of Arts and Communication Wuhan430073 China

ISBN: (纸本)9789887581581

The technique of generating fonts with few-shot learning has become a popular research topic. The goal of this technique is to transform the source-to-target domain by using a small number of sample images to generate characters for new fonts. However, current research is limited by model frameworks and migration algorithms, which pose challenges for maintaining content integrity and stroke-level style consistency when generating Chinese characters. This paper proposes ResFFont, a reversible network-based model that uses a unique reversible framework and Global and Local Feature Fusion Module (GaLSFM) to ensure complete delivery of content information and stroke-level style consistency. The experimental results confirm the effectiveness and superiority of the ResFFont model, providing a new solution to the font generation problem. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Few-Shot Font Generation Reversible Network Style Transfer

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