检索结果-内蒙古大学图书馆

Prior area searching for energy-based sound source localization

Science China(Information Sciences) 2022年第12期65卷 220-231页

作者： Feng GAO Yeyun CAI Fang DENG Chengpu YU Jie CHEN Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center Guohao Academy Tongji University

Traditional energy-based sound source localization methods have the problems of the large solution space and time-consuming calculation. Accordingly, this paper proposes to use the data collected by each acoustic sensor and their corresponding weights to adaptively initialize the prior area of a target. In this way, the potential existence range of the target is reduced and the location estimate can be determined in a small area. Specifically, we first determine the initial search point based on the current sound data and the set rules. Then, the prior location of the target is iteratively searched according to different sound energy circles' weights. Next, the prior area of the target is determined around the prior location. Finally, the precise location of the target is further traversed to minimize the objective function, which is constructed by the weighted nonlinear least squares location(WNLS) algorithm. A series of indoor experiments are *** results show that our method can effectively improve the positioning accuracy by approximately 13%and greatly reduce the calculation time.

关键词： prior area searching sound source localization adaptively initialization iterative search indoor environment

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Relevance Search over Knowledge Graph with User-entity Interaction Data

Relevance Search over Knowledge Graph with User-entity Inter...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Jianyu Li Kun Yue School of Information Science and Engineering Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

Relevance search is to find a list of entities in a knowledge graph (KG), which are associative to a query entity. However, many entities are not linked in KG but are actually associated by user interactions. To this end, we propose a joint weighting function to evaluate the entity associations from both KG and user-entity interaction data simultaneously. Upon the subgraph extracted from KG w.r.t the query entity, we obtain the associative entities by calculating their association degrees. Experimental results show that the effectiveness of our method outperforms other competitors.

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Coordinated Motion Control for a Six-wheel-drive Skid-steering Mobile Robot

Coordinated Motion Control for a Six-wheel-drive Skid-steeri...

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2021 China Automation Congress, CAC 2021

作者： Xu, Yongkang Wang, Shoukun Zhao, Hongqiang Chen, Zhihua State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China State Key Laboratory of High Performance and Complex Manufacturing Central South University Changsha China

ISBN: (纸本)9781665426473

A speed consensus control strategy for a six-wheel-drive skid-steering mobile robot was proposed based on a hierarchical controller in a complex environment with unknown disturbances. The effective method of reliable speed consensus control strategy should consider some disturbances, such as the internal-robot assembly error, which including instability of motor control system and external physical interference in robot dynamic system. In this paper, a driving anti-slip control system with PID and the stability control system with sliding mode by variable structure is designed and integrated to elevate the stability of the robot. And, a G-vector control algorithm based on sliding mode control is proposed to reduce the yaw angle as much as possible during the process of differential steering of robots. The effectiveness of the proposed control strategy is verified by an example of a *** results show that the method has good control performance in control accuracy and stability. © 2021 IEEE

关键词： Motion control

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A Weighting Factors Autotuning Strategy for Model Predictive Torque Control of Permanent Magnet Synchronous Motor Based on Adaptive Multi-objective Black Hole Algorithm

A Weighting Factors Autotuning Strategy for Model Predictive...

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第42届中国控制会议

作者： Zixiang Zhou Tao Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems System Integration and Industrial Robotics Laboratory

The rational design of weighting factors in the cost function for finite control set model predictive torque control(FCS-MPTC) has been a matter of great interest in power electronics and electrical drives. In order to solve this problem, a weighting factors autotuning strategy for FCS-MPTC of permanent magnet synchronous motor(PMSM) based on the adaptive multi-objective black hole algorithm(AMOBH) is proposed. In this paper, the design process of the FCS-MPTC algorithm is first analyzed in detail. Then, an AMOBH algorithm that can take into account both population convergence and population diversity is introduced, and based on this algorithm, the design problem of the weighting factors is successfully transformed into a multiobjective optimization problem by means of reconstructing the cost function and designing the motor operation information collected in real time as the objective functions of the multi-objective optimization algorithm. Simulation results show that the proposed method can find a set of weighting factor combinations suitable for different working condition requirements, and these weighting factors can effectively improve the operation performance of the PMSM system.

关键词： Model Predictive Torque Control Weighting Factors Multi-objective Optimization Algorithm Permanent Magnet Synchronous Motor

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Temperature Rising Model of Tubular Permanent Magnet Linear Synchronous Motor Based on Transfer Learning-Deep Neural Network

Temperature Rising Model of Tubular Permanent Magnet Linear ...

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第42届中国控制会议

作者： YiFei Wang Tao Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems System Integration and Industrial Robotics Laboratory

High temperature rise of permanent magnet linear synchronous motor can lead to irreversible demagnetization of the motor permanent magnet, which can negatively affect the motor performance. To address this problem, a thermal modeling analysis method based on Transfer learning-Deep neural network(TL-DNN) was proposed in this paper. Its specific implementation steps include(1) corresponding to different heat source inputs, the equivalent thermal circuit method and the finite element analysis was adopted based on the structure and main parameters of PMLSM including overall average temperature rise, coil temperature rise and permanent magnet temperature rise from the data sets of the motor;(2) TL-DNN was used to fit the functional relationship between the input source features and the output targets based on the characteristics of the sample data sets. In order to verify the accuracy of the prediction model with small sample data sets, this paper divided the proportion of the data set and compared the results with other classic nonparametric models(random forest, support vector machine, and deep neural network). The results show that the TL-DNN model outperforms other machine learning models and has better robustness and generalization ability when the training datasets are insufficient, and achieves an organic combination of physical field path model and data-driven model, which provides a feasible solution for PMLSM modeling in the case of small samples.

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Structural Entropy Based Graph Node Classification

Structural Entropy Based Graph Node Classification

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Jun Zhang Kun Yue School of Information Science and Engineering Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

Node classification is to predict the labels of the unlabeled nodes in a graph, which is useful for various applications of social network and biological information analysis. To measure the uncertainty of structural information, we propose the structural entropy theory based method for graph node classification. First, we calculate the structural entropy of different graph structures, since the smaller the structural entropy, the less the uncertainty of the structural relationship in the graph. Then, we use the minimal structural entropy to determine the uncertainty of graphical structures. Finally, the nodes with similar structural relationships in the graph are classified. Experimental results show that our method outperforms some state-of-the-art competitors.

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Predicting microbe-disease associations via attention guided graph convolutional networks 4

Predicting microbe-disease associations via attention guided...

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4th International Conference on Biomedicine and Bioinformatics Engineering, ICBBE 2024

作者： Gong, Shiyun Zhu, Yuan School of Automation China University of Geosciences Lumo Road Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Lumo Road Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Lumo Road Hongshan District Hubei Wuhan430074 China School of Mathematics and Physics China University of Geosciences Lumo Road Wuhan430074 China

ISBN: (纸本)9781510682443

It is important to predict microbe-disease associations, as it helps to understand the cause of diseases episodes, the prevention of diseases, among other roles. Traditionally, the study of microbe-disease association is mainly based on laboratory purified culture for research and observation, which is highly scientific but has some limitations, such as some microbes needing to be cultured in a laboratory environment and costs heavily. This study proposes an attention guided graph convolutional network d approach (AGGCN) to predict possible potential associations between microbes and diseases. In the first place, AGGCN constructs four different similarity networks of microbes and diseases, which are combined with association networks to form a microbe-disease heterogeneous network. Subsequently, it is used as an input to the graph convolutional neural network to obtain each node embedding of this heterogeneous network. In this study, a three-layer network is used, so three different types of node embeddings can be obtained, and these three different types of node embedding representations are integrated via the multichannel graph attention mechanism to obtain the final node embeddings. Finally, the node embeddings obtained above are used for association prediction of microbes-diseases in heterogeneous networks. Experimentally verified, the AUC of AGGCN is 0.8972, and the AUPR is 0.5387. Comparative experimental results indicated that AGGCN shows better than the other five mainstream methods. It provides a new insight to explore network potential feature representation and predict associations between microbe and disease. © 2024 SPIE.

关键词： Graph neural networks

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Estimation of Bottom-Hole Pressure Based on Bayesian Optimization and Bidirectional Long Short-Term Memory Neural Network 43

Estimation of Bottom-Hole Pressure Based on Bayesian Optimiz...

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43rd Chinese Control Conference, CCC 2024

作者： Zhang, Zihan Lu, Chengda Wang, Yawu Zhou, Yang Wu, Min She, Jinhua China University of Geosciences School of Future Technology Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China China University of Geosciences School of Automation Wuhan430074 China Tokyo University of Technology School of Engineering Hachioji192-0982 Japan

ISBN: (纸本)9789887581581

Bottom-hole pressure (BHP) plays a crucial role in a drilling process. The accurate estimation of BHP ensures safe and efficient drilling operations. Artificial neural networks can predict BHP indirectly by analyzing the non-linear relationship with wellhead variables. This paper introduces a BHP estimation model that leverages Bayesian optimization and a bidirectional long short-term memory neural network (BO-BiLSTM). The model employs Bayesian optimization to determine optimal hyperparameters, including the number of hidden layers, initial learning rate, and L2 regularization parameter. The BO-BiLSTM model achieves a mean absolute percentage error of 2.056% and an R-squared score of 0.94596. The results demonstrate that the presented model enhances estimation precision and outperforms other methods examined in this study. This method facilitates indirect measurement of BHP using wellhead data, and contributes to the exploration of deep-sea resources in complex environments. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Bottom hole pressure

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Multiscale Bilateral Attention Fusion Network for Pansharpening

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： Data mining

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Stable Control and Disturbance Rejection Strategy for Planar 2R Underactuated Robot via intelligent Algorithm 48

Stable Control and Disturbance Rejection Strategy for Planar...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Huang, Zixin Wan, Xiao Zhou, Yaosheng Wang, Lejun Wang, Liheng School of Electrical and Information Engineering Wuhan Institute of Technology Hubei Key Laboratory of Digital Textile Equipment Wuhan Textile University 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 Institute of Technology School of Electrical and Information Engineering Wuhan China Wuhan Institute of Technology School of Innovation and Entrepreneurship Wuhan China Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Stable control and active disturbance rejection strategy is proposed for planar 2R underactuated robot via intelligent algorithm in this paper. At first, we build the dynamic model and describe the control characteristic. After that, we design PD controller for the active link. According to the underactuated constraint, we then optimize the parameters of controller by using the differential evolution algorithm (DEA), so that we can ensure the target states of all links could eventually be reached at the identical time. And the simulation results indicate the strategy is effective. © 2022 IEEE.

关键词： Disturbance rejection

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