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2021 China Automation Congress, CAC 2021

作者： Jiang, Zhuo Tian, Daiying Yang, Qingkai Peng, Zhihong The School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing China Peng Cheng Laboratory Guangdong Shenzhen China

ISBN: (纸本)9781665426473

This paper proposes a self-attention based temporal intrinsic reward model for reinforcement learning (RL), to synthesize the control policy for the agent constrained by the sparse reward in partially observable environments. This approach can solve the problem of temporal credit assignment to some extent and deal with the low efficiency of exploration. We first introduce a sequence-based self-attention mechanism to generate the temporary features, which can effectively capture the temporal property of the task for the agent. During the training process, the temporary features are employed in each sampled episode to elaborate the intrinsic rewards, which is combined with the extrinsic reward to help the agent learn a feasible policy. Then we use the meta-gradient to update this intrinsic reward model in order that the agent can achieve better performance. Experiments are given to demonstrate the superiority of the proposed method. © 2021 IEEE

关键词： Reinforcement learning

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Blast Furnace Gas Utilization Rate Prediction Model based on Northern Goshawk Optimization and Long Short-Term Memory in Massive Data Set

Blast Furnace Gas Utilization Rate Prediction Model based on...

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Industrial Cyber-Physical systems (ICPS)

作者： Yue Zhou Weihua Cao Zhuofu Zhang Yan Yuan School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

Affected by the global New Crown Pneumonia epidemic, energy prices in the global market continue to rise. The high quality production and energy saving of steel companies have become an important task. The blast furnace is the front-end core of the steel manufacturing process. The blast furnace gas utilization rate can effectively characterize the internal airflow distribution, blast furnace operation and energy consumption level of the blast furnace. The blast furnace Gas Utilization Rate (GUR) can effectively characterize the internal airflow distribution, blast furnace operation and energy consumption level. Most of the existing studies are based on data-driven models. The shallow neural network model is selected. But blast furnace iron making has complex uncertainty. Massive data samples under industrial information technology need to be processed. The robustness and generalization ability of the prediction model are not satisfactory. To address the above problems, this paper proposes a prediction model based on NGO-LSTM regression. The model parameter searchs can be intelligently. It achieves high-precision prediction results for massive data samples. Firstly, the selection of feature parameters is completed by maximal information coefficient. Secondly, the strong coupling of blast furnace ground needs to fully predict the relationship between the characteristic parameters. The Long Short-Term Memory (LSTM) neural network with certain memory capability is selected. And multiple parameters of this neural network model are optimized by the Northern Goshawk Optimization (NGO) algorithm. An NGO-LSTM regression prediction model is established. In this paper, experiments are carried out using actual production data. The experimental results show that the proposed method can accurately predict the blast furnace gas utilization rate. This can provide a reference for improving blast furnace product quality, reducing costs and increasing efficiency.

关键词： Energy consumption Neural networks Production Predictive models Blast furnaces Prediction algorithms Robustness

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Unsupervised Learning of Gaussian Mixture Model with Application to Image Segmentation

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Chinese Journal of Electronics 2023年第3期19卷 451-456页

作者： Bo Li Wenju Liu Lihua Dou School of Automation Beijing Institute of Technology Beijing China Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing Institute of Technology Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

Density estimation via Gaussian mixture modeling has been successfully applied to image segmentation, speech processing and other fields relevant to clustering analysis and Probability density function (PDF) modeling. Finite Gaussian mixture model is usually used in practice and the selection of number of mixture components is a significant problem in its application. For example, in image segmentation, it is the donation of the number of segmentation regions. The determination of the optimal model order therefore is a problem that achieves widely attention. This paper proposes a degenerating model algorithm that could simultaneously select the optimal number of mixture components and estimate the parameters for Gaussian mixture model. Unlike traditional model order selection method, it does not need to select the optimal number of components from a set of candidate models. Based on the investigation on the property of the elliptically contoured distributions of generalized multivariate analysis, it select the correct model order in a different way that needs less operation times and less sensitive to the initial value of EM. The experimental results show the effectiveness of the algorithm.

关键词： Training Image segmentation Analytical models Parameter estimation Estimation Probability density function Speech processing

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Analysis of OODA Loop based on Adversarial for complex Game Environments

arXiv

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arXiv 2022年

作者： Lu, Xiangri Ma, Hongbin Wang, Zhanqing State Key Laboratory of Intelligent Decision Control for Complex Systems Beijing Institute of Technology Beijing China Automated Institute Beijing Institute of Technology Beijing China

To address the problem of imperfect confrontation strategy caused by the lack of information of game environment in the simulation of non-complete information dynamic countermeasure modeling for intelligent game, the hierarchical analysis game strategy of confrontation model based on OODA ring (Observation, Orientation, decision, Action) theory is proposed. At the same time, taking into account the trend of unmanned future warfare, NetLogo software simulation is used to construct a dynamic derivation of the confrontation between two tanks. In the validation process, the OODA loop theory is used to describe the operation process of the complex system between red and blue sides, and the four-step cycle of observation, judgment, decision and execution is carried out according to the number of armor of both sides, and then the OODA loop system adjusts the judgment and decision time coefficients for the next confrontation cycle according to the results of the first cycle. Compared with traditional simulation methods that consider objective factors such as loss rate and support rate, the OODA-loop-based hierarchical game analysis can analyze the confrontation situation more comprehensively. © 2022, CC BY.

关键词： decision theory

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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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A Multi-Channel Adaptive Cancellation Algorithm for Active Suppression of Motion-Induced Noise in Magnetic Antennas

A Multi-Channel Adaptive Cancellation Algorithm for Active S...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Mingyi Hu Junlei Song Yi Yan Ying Liu Xiaoxue Li Kaifeng Dong Fang Jin Wenqin Mo Yajuan Hui School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798331540319

ISBN: (纸本)9798331540326

During super low frequency (SLF) magnetic induction communication, the sensitive antenna carried by autonomous underwater vehicles (AUVs) is highly susceptible to vibration, resulting in a large amount of noise in communication signals, which seriously affects communication performance. The type of motion-induced noise typically exhibits randomness and nonlinearity. Based on the strong correlation between the motion-induced noise and the antenna's vibration signal, we propose a multi-channel normalized least mean square (MCNLMS) algorithm. The algorithm utilizes the six degrees of freedom vibration acceleration information from both ends of the magnetic antenna as the reference signal input for the canceller, providing more comprehensive information related to motion-induced noise than the single-channel adaptive noise cancellation algorithm. Experimental results indicate that the MCNLMS algorithm demonstrates effective suppression of the motion-induced noise, achieving an average reduction of approximately 7 dB within the 160–170 Hz communication band. Additionally, it exhibits improved stability and faster convergence.

关键词： Vibrations Industrial electronics Correlation Adaptive arrays Magnetic noise Approximation algorithms Noise cancellation Antennas Magnetic susceptibility Convergence

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Multi-Agent Coverage control in Non-Convex Annulus Region with Conformal Mapping

arXiv

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arXiv 2025年

作者： Feng, Xun Zhai, Chao School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and Engineering Research Center of Intelligent Technology for Geo-exploration Ministry of Education Wuhan430074 China

Efficiently fulfilling coverage tasks in non-convex regions has long been a significant challenge for multi-agent systems (MASs). By leveraging conformal mapping, this paper introduces a novel sectorial coverage formulation to transform a non-convex annulus region into a topologically equivalent one. This approach enables the deployment of MASs in a non-star-shaped region while optimizing coverage performance and achieving load balance among sub-regions. It provides a unique perspective on the partitioned sub-regions to highlight the geodesic convex property of the non-star-shaped region. By utilizing the sectorial partition mechanism and the diffeomorphism property of conformal mapping, a decentralized control law is designed to drive MASs towards a desired configuration, which not only optimizes the global coverage cost but also ensures exponential convergence of equitable workload. Moreover, an iterative search algorithm is developed to identify the optimal approximation of multi-agent deployment in the non-star-shaped region. Theoretical analysis is conducted to confirm the asymptotic stability and global convergence with arbitrary small tolerance of the closed-loop system. Finally, numerical simulations demonstrate the practicality of the proposed coverage formulation with conformal mapping. Copyright © 2025, The Authors. All rights reserved.

关键词： Conformal mapping

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Fixed-/Preassigned-Time Stabilization of Memristive Neural Networks with Discontinuous Activations

Fixed-/Preassigned-Time Stabilization of Memristive Neural N...

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2021 International Conference on Neuromorphic Computing, ICNC 2021

作者： Li, Haoyu Wang, Leimin School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China

ISBN: (纸本)9781665412872

In this paper, the fixed-time stabilization (FTS) and preassigned-time stabilization (PTS) problems for memristive neural networks (MNNs) with discontinuous activation functions (DAF) is investigated by designing a unified controller. In addition, less conservative stabilization criteria are derived to ensure the FTS and PTS of MNNs. Besides, a more accurate estimate of settling time is obtained based on Lyapunov stability theory. Finally, a numerical simulation is illustrated to verify the validity of theoretical results. © 2021 IEEE.

关键词： Stabilization

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Application of Particle Swarm Optimization Algorithm Based on Adaptive Inertia Adjustment Strategy in Scheduling of the Coking Process

Application of Particle Swarm Optimization Algorithm Based o...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Yiheng Chen Fan Yin Xiaochong Chen Yi Ren Jie Hu Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

The sccheduling for pushing plan during the coking process critically affects the efficiency and stability of production. However, the complexity with mutiple-stage during production makes it difficult to design an efficient coke pushing plan. To address this issue, this paper proposes a scheduling method based on the particle swarm optimization algorithm. Firstly, the fuzzy c-means clustering is utilized to categorize actual operating conditions as either normal or abnormal, thereby facilitating the scheduling of pushing plan under disparate conditions. Subsequently, the scheduling problem for the pushing plan is transformed into a traveling salesman problem, and scheduling models under various conditions are established. Finally, to accelerate the convergence and enhance the algorithm's global search capability, an adaptive inertia adjustment strategy is employed to dynamically regulate the velocity and position of particles. The proposed method has been implemented in the coking process. Through the analysis of application results, the completion coefficient of pushing plan has been increased by 4.25%, demonstrating that the proposed has advantages in scheduling the pushing plan during the actual coking process.

关键词： Job shop scheduling Heuristic algorithms Clustering algorithms Production Traveling salesman problems Stability analysis Robustness complexity theory Particle swarm optimization Convergence

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Pipeline Anomaly Detection Based on KPCA and Cosine Distance Prototypical Network

Pipeline Anomaly Detection Based on KPCA and Cosine Distance...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Kang Yi Xin Liu Wenkai Hu Yupeng Li School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Accurately and promptly detecting the pipeline anomaly is crucial to the safe operation of pipeline systems, while a difficulty lies in that many existing methods require massive data for training models. However, pipelines are running under normal state for the most of the time, and labeled pipeline anomaly data is usually scarce. Among the commonly used sensors, vibration sensors are widely utilized in pipeline detection because of their advantages such as easy installation and high sensitivity. However, the vibration signal shows non-stationary characteristics when anomalies occur, and are contaminated by noises, making it difficult to represent the actual state with features extracted from either the time or frequency domain. Accordingly, this paper proposes a pipeline anomaly detection method based on the KPCA (kernel principal component analysis) and cosine distance prototypical network. First, features are extracted from original signals; then, the feature dimension is reduced by KPCA; last, the cosine distance is introduced to the prototypical network for anomaly detection. The effectiveness of the proposed method is demonstrated by case studies involving experimental data.

关键词： Vibrations Training Time-frequency analysis Sensitivity Pipelines Sensor phenomena and characterization Feature extraction Time-domain analysis Anomaly detection Principal component analysis

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