检索结果-内蒙古大学图书馆

第34届中国控制与决策会议

作者： Wei Wei You Lv Yizhuo Liu Qing Li Shisen Xu Jun Li School of Control and Computer Engineering North China Electric Power University Key Laboratory of Power Station Energy Transfer Conversion and System North China Electric Power University Beijing Key Laboratory of New Technology and System on Measuring and Control for Industrial Process North China Electric Power University Huaneng Technology Innovation Center Xian Thermal Power Research Institute Company Limited

The induced draft(ID) fan is a significant piece of auxiliary equipment in thermal power *** early detection of possible faults in the ID fan may be used for effective predictive maintenance,thereby reducing the overall maintenance cost and improving the reliability and availability of thermal power *** paper proposed a genetic algorithm and long-short term memory network(GA-LSTM)-based condition monitoring approach using normal operation data of the ID ***,the LSTM network and the sliding window technique were introduced to construct double thresholds,which contains a dynamic threshold and a static *** GA-LSTM-double thresholds method was applied to a coal-fired power plant to achieve early fault detection and *** results demonstrates that the proposed method is capable of detecting minor anomalies in advance.

关键词： Early Fault Detection Long-Short Term Memory(LSTM) Genetic Algorithm(GA)

来源：评论

学校读者我要写书评

暂无评论

An Efficient Implementation for Bayesian Manifold Regularization Method

An Efficient Implementation for Bayesian Manifold Regulariza...

引用

IEEE Conference on Decision and control

作者： Junpeng Zhang Yue Ju Bo Wahlberg Biqiang Mu Tianshi Chen School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Instituite of Technology Stockholm Sweden Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and System Science Chinese Academy of Sciences Beijing China

When applying the Bayesian manifold regularization method to function estimation problem with manifold constraints, the direct implementation has computational complexity $\mathcal{O}(N^{3})$ , where $N$ is the number of input-output data measurements. This becomes particularly costly when $N$ is large. In this paper, we propose a more efficient implementation based on the Kalman filter and smoother using a state-space model realization of the underlying Gaussian process. Moreover, we explore the sequentially semi-separable structure of the Laplacian matrix and the posterior covariance matrix. Our proposed implementation has computational complexity $\mathcal{O}(N)$ and thus can be applied to large data problems. We exemplify the effectiveness of our proposed implementation through numerical simulations.

关键词：

来源：评论

学校读者我要写书评

暂无评论

HiCRISP: An LLM-based Hierarchical Closed-Loop Robotic Intelligent Self-Correction Planner

arXiv

引用

arXiv 2023年

作者： Ming, Chenlin Lin, Jiacheng Fong, Pangkit Wang, Han Duan, Xiaoming He, Jianping The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China The Department of Computer Science University of Illinois Urbana-Champaign IL United States The Department of Engineering Science University of Oxford Oxford United Kingdom

The integration of Large Language Models (LLMs) into robotics has revolutionized human-robot interactions and autonomous task planning. However, these systems are often unable to self-correct during the task execution, which hinders their adaptability in dynamic real-world environments. To address this issue, we present an LLM-based Hierarchical Closed-loop Robotic Intelligent Self-correction Planner (HiCRISP), an innovative framework that enables robots to correct errors within individual steps during the task execution. HiCRISP actively monitors and adapts the task execution process, addressing both high-level planning and low-level action errors. Extensive benchmark experiments, encompassing virtual and real-world scenarios, showcase HiCRISP’s exceptional performance, positioning it as a promising solution for robotic task planning with LLMs. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

An Automatic Ridge Estimation Method for Tacholess Order Tracking of Wind Turbine Gearbox Under Varying-Speed Operation

SSRN

引用

SSRN 2023年

作者： Liu, Changliang Liu, Shaokang Wu, Yingjie Liu, Weiliang Liu, Shuai Control and Computer Engineering Department University of North China Electric Power Beijing102200 China Baoding key Laboratory of State Detection and Optimization Regulation for Integrated Energy System Baoding071000 China Automation Engineering Department University of Northeast Electric Power Jinlin132000 China

The instantaneous shaft speed estimation method of wind turbine gearbox based on time-frequency ridge estimation is the most widely used method, but it requires parameter adjustment and expert knowledge to ensure the accuracy of ridge estimation, which significantly reduces the effectiveness of such methods in practice. In this paper, an end-to-end automated ridge estimating (ARE) method is proposed. ARE uses the inherent harmonic relationship in the vibration signal of wind turbine (WT) gearbox to construct a selection index for automatically selecting the target ridge with clear physical meaning. Then, a sub-region division strategy is proposed to avoid the interference of other components and improve computational efficiency. Finally, a parameter-free objective function considering both the amplitude and smoothness of the ridge is proposed to achieve target ridge estimation. The vibration signals of different WT gearboxes collected on-site were applied to verify the performance of ARE. The result shows that ARE realizes the automatic estimation of a ridge. It outperforms other methods in terms of accuracy and computational efficiency. © 2023, The Authors. All rights reserved.

关键词： Computational efficiency

来源：评论

学校读者我要写书评

暂无评论

Research on Fault Early Warning of Wind Turbine Pitch system based on Long Short-Term Memory Neural Network

Research on Fault Early Warning of Wind Turbine Pitch System...

引用

2021 China Automation Congress, CAC 2021

作者： Li, Xiongwei Lv, Guanghua Zhu, Runze Li, Gengda Lin, Zhongwei Wang, Wenbin Cui, Qingru China Energy New Energy Technology Reserch Institute Co. Ltd Beijing China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control Computer and Engineering North China Electric Power University Beijing China

ISBN: (纸本)9781665426473

To reduce the downtime of wind turbine caused by the fault of pitch system that usually has a high failure rate, a fault early warning method based on long short-term memory (LSTM) neural network is proposed. According to the operation characteristics of the pitch system, monitoring parameters recorded by SCADA, such as wind speed, generator speed, rotor speed, wind direction, pitch angle and output power, are utilized to establish the fault early warning model. Compared with the model based on BP neural network, the prediction accuracy of the LSTM model is effectively improved. The weights and thresholds of the LSTM neural network are optimized, and a reasonable threshold is set to determine the fault early warning criterion for the pitch system. To verify the effectiveness of the proposed method, the LSTM model is applied to the fault early warning for a 1.5MW wind turbine pitch system. The results show that the LSTM model can give early warning about two and a half days before the fault occurs, verifying the feasibility of the proposed method. © 2021 IEEE

关键词： Wind turbines

来源：评论

学校读者我要写书评

暂无评论

MFGB: Molecular Properties Prediction leveraging Self-supervised Morgan Fingerprint Representation Learning

MFGB: Molecular Properties Prediction leveraging Self-superv...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuyan Fang Yu Liu Song Liu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Provincial Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China Hubei Provincial Key Laboratory of Occupational Hazard Identification and Control Hubei Province Wuhan University of Science and Technology Wuhan China

Simple molecular graphs or molecular line notations are insufficient for molecular representation learning models that automatically learn molecule representations to acquire deep semantic features about chemistry. Therefore, we proposed the Morgan Fingerprint Graph, a novel molecular graph model constructed with the nodes of the Morgan Fingerprint Identifier, whose nodes incorporate various properties of the atom neighborhood. Based on our Morgan Fingerprint Graph, we trained a BERT-based molecular representation model MFGB which employs a local rather than global attention module to adapt the model to the Morgan Fingerprint Graph. The attention module of MFGB regulates the exchange of information in the attention layer based on the connection information of the atoms in the molecules. To verify the effectiveness of MFGB, we selected several mainstream datasets for evaluation. The results demonstrated that our method improve the performance of molecular properties prediction. In addition, we analyzed MFGB from both the over-smoothing and the attention perspectives. The analysis indicates that the node vector representations learned from the Morgan Fingerprint Graph are generally less smoothing, and the attention distributions of MFGB match the partial charge density distribution of the molecules.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Modelling of SFR for Wind-Thermal Power systems via Improved RBF Neural Networks

Modelling of SFR for Wind-Thermal Power Systems via Improved...

引用

Chinese Intelligent systems Conference, CISC 2020

作者： Zhang, Jianhua Li, Hongrui Hu, Bo Min, Yong Chen, Qun Hou, Guolian Huang, Congzhi State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China State Grid Liaoning Electric Power Supply Co. Ltd. Shenyang China School of Control and Computer Engineering Tsinghua University Beijing100089 China

ISBN: (纸本)9789811584572

In this paper, one kind of system identification algorithm based on an improved radial basis function (RBF) neural networks is presented for nonlinear and non-Gaussian systems. Survival information potential (SIP) of identification errors is employed to constructed the performance index to train the RBF neural networks. The data driven system identification algorithm implemented by the proposed RBF neural networks is applied to obtain the equivalent model of wind-thermal integrated power systems. Compared with the traditional RBF neural networks and back-propagation (BP) neural networks based on mean square error (MSE) criteria, simulation results demonstrate that the proposed system identification algorithm can obtain better SFR models for wind-thermal integrated power systems. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Functions

来源：评论

学校读者我要写书评

暂无评论

Path planning of mobile robot based on improved DDQN 2

Path planning of mobile robot based on improved DDQN

引用

2nd International Conference on computer Vision and Data Mining, ICVDM 2021

作者： Yang, Yunxiao Wangjun Zhang, Hualiang Dai, Shilong School of Computer Science Shenyang University of Chemical Technology Shenyang110142 China Industrial Control Network and System Laboratory Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institudes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

Aiming at the problem of overestimation and sparse rewards of deep Q network algorithm in mobile robot path planning in reinforcement learning, an improved algorithm HERDDQN is proposed. Through the deep convolutional neural network model, the original RGB image is used as input, and it is trained through an end-to-end method. The improved deep reinforcement learning algorithm and the deep Q network algorithm are simulated in the same two-dimensional environment. The experimental results show that the HERDDQN algorithm solves the problem of overestimation and sparse reward better than the DQN algorithm in terms of success rate and reward convergence speed, Which shows that the improved algorithm finds a better strategy than the DQN algorithm. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Mobile robots

来源：评论

学校读者我要写书评

暂无评论

Local Topology Inference of Mobile Robotic Networks under Formation control

arXiv

引用

arXiv 2022年

作者： Li, Yushan He, Jianping Cai, Lin Guan, Xinping The Dept. of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China The Dept. of Electrical and Computer Engineering University of Victoria BC Canada

The interaction topology is critical for efficient cooperation of mobile robotic networks (MRNs). We focus on the local topology inference problem of MRNs under formation control, where an inference robot with limited observation range can manoeuvre among the formation robots. This problem faces new challenges brought by the highly coupled influence of unobservable formation robots, inaccessible formation inputs, and unknown interaction range. The novel idea here is to advocate a range-shrink strategy to perfectly avoid the influence of unobservable robots while filtering the input. To that end, we develop consecutive algorithms to determine a feasible constant robot subset from the changing robot set within the observation range, and estimate the formation input and the interaction range. Then, an ordinary least squares based local topology estimator is designed with the previously inferred information. Resorting to the concentration measure, we prove the convergence rate and accuracy of the proposed estimator, taking the estimation errors of previous steps into account. Extensions on nonidentical observation slots and more complicated scenarios are also analyzed. Comprehensive simulation tests and method comparisons corroborate the theoretical findings. Copyright © 2022, The Authors. All rights reserved.

关键词： Topology

来源：评论

学校读者我要写书评

暂无评论

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

引用

IEEE International Conference on Industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：