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检索条件"机构=China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex System"

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Survival prediction of LGG patients based on deep learning of MRI data and genomic data integration

Survival prediction of LGG patients based on deep learning o...

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Chinese automation Congress (CAC)

作者： Lingzhi Sun Yong He School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China

ISBN: (纸本)9781665426480

Low-grade gliomas (LGG) is the most common primary intracranial tumor, with high incidence rate, high recurrence rate, high mortality rate and low cure rate. Therefore, it is necessary to predict the survival of LGG patients in diagnosis. Considering the complementarity of information, the new proposed algorithm in this study is designed to integrate Magnetic Resonance Image (MRI) data and gene expression data using deep learning method to predict the Disease Specific Survival (DSS) of LGG patients. Firstly, MRI data of 44 patients is screened from TCIA database, and then the corresponding gene expression data of 44 patients is searched from TCGA database. Then, 724 image feature data extracted from MRI data are filtered and extracted by deep learning method, and DSS tags are used to train the model; deep learning method is used to extract 20530 features of gene expression data, and DSS tags are also used for training. As a contrast, the deep learning method is used to integrate the two features to train the model. Experiments are evaluated on MRI data, gene expression data and the integration data of MRI and gene expression data, respectively. The results show that by using the integration of MRI data and gene expression data performs better than using single data in terms of the time-dependent receiver operating characteristic(ROC) and the area under the curve (AUC) of the ROC curve criteria.

关键词： Deep learning Training Databases Magnetic resonance imaging Feature extraction Data models Gene expression

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Semi-supervised Anomaly Detection and Location Based on Generative Adversarial Network

Semi-supervised Anomaly Detection and Location Based on Gene...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Wei Liu Mingqiang Gao Shuaidong Duan Longsheng Wei School of Automation China University of Geosciences 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: (纸本)9781665446006

With the development of artificial intelligence, the anomaly detection plays more and more important role in security monitoring field. Because it is difficult to label abnormal data, most of the supervised methods consumed a lot of manpower and obtained low performance and generality. Inspired by this motivation, this paper proposes a semi-supervised method for anomaly detection in video frames based on GAN (Generative Adversarial Network), in which only normal data was used as the training sample. The quality gap between the predicted frame and the ground truth is used as the basis to determine whether it is abnormal. Moreover, the mathematical morphology approach was adopted to locate the anomaly area in the frames. Experiments show that our method can successfully detect abnormal frames in video and can also locate the area where abnormal behavior occurs in frames.

关键词： Training Conferences Morphology Generative adversarial networks Feature extraction Security Artificial intelligence

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Multi-objective Evolutionary Algorithm Based Graph Neural Network Architecture Search 11th

Multi-objective Evolutionary Algorithm Based Graph Neural Ne...

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11th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2024

作者： He, Lianyi Liu, Xiaobo Xiang, Hongbo Wang, Guangjun School of Automation China University of Geosciences 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 Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of Geosciences Wuhan430074 China

ISBN: (纸本)9789819647521

Graph Neural Networks (GNN) has become a powerful graph data processing method, which has been widely used in node classification, link prediction, and other graph analysis tasks. Due to the diversity and complexity of graph structures and information propagation, as well as the handling of heterogeneous graphs, etc., the design of GNNs presents many challenges. The existing methods have high classification accuracy, but their structures are very complex. In this paper, a graph neural network architecture search framework named MO-GNN, which is based on multi-objective evolutionary algorithm is proposed. Furthermore, a weight sharing strategy MO-GNN-WS is proposed to reduce the resource consumption caused by weight training of different architectures. To verify the performance of the proposed algorithm, experiments on four popular graph datasets are used for transduction and induction tasks. The experimental results show that the MO-GNN-WS algorithm outperforms the most advanced neural network architecture search methods in terms of classification accuracy and resource consumption. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data Sharing

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Incomplete Multi-view Clustering for Single cell RNA Sequencing Data

Incomplete Multi-view Clustering for Single cell RNA Sequenc...

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Chinese automation Congress (CAC)

作者： Tijian Zhu Yuan Zhu Chuan-Ke Zhang 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

ISBN: (纸本)9781665426480

With the rapid development of sequencing technology, researchers can obtain a large number of single cell RNA sequencing (scRNA-seq) data which is useful for analysis of cell fate decision and growth process at individual cell resolution. But due to the limitations of sequencing technology, the data acquired has dropouts which may affect the results of down-steam analysis. Therefore, many algorithms have been proposed to impute the data before clustering, here in, imputation and clustering are considered as two separate processing stage. In this paper, we adopt a clustering algorithm—Incomplete Multiple Kernel k-means Clustering with Mutual Kernel Completion (MKKM-IK-MKC) to analyze scRNA-seq data. It unifies imputation and clustering into a process. Comparing with some existing "two stage" (imputation +clustering) algorithms, the experimental results on five scRNA-seq datasets from various species demonstrate the effective performance of our new proposed method.

关键词： Dimensionality reduction Sequential analysis automation RNA Clustering algorithms Optimization methods Iterative methods

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Anomaly Detection Based on Principle of Justifiable Granularity and Probability Density Estimation

Anomaly Detection Based on Principle of Justifiable Granular...

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Chinese automation Congress (CAC)

作者： Sheng Du Xian Ma Xiang Li Min Wu Weihua Cao Witold Pedrycz 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 Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Sariyer/Istanbul Turkiye

Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes.

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High-Precision Admittance control of Mechanical Arm Based on Force Sensor Compensation

High-Precision Admittance Control of Mechanical Arm Based on...

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Chinese control and Decision Conference, CCDC

作者： Yaohui Wu Jiangbo Zhao Junzheng Wang Xin Zhang Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing Institute of Technology Beijing China Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information Technology School of Automation Beijing Institute of Technology Beijing China Technical Center Xinxing Cathay International Group Co. Ltd. Beijing China

ISBN: (纸本)9781665478977

Admittance control is one of the important methods of compliance control. It is performed by inputting force signals and outputting position signals. Position control can achieve high accuracy, but the force signals have large errors, which is an important factor affecting its accuracy. When the force sensor collects force data, there are non-negligible initial deviations due to gravity, installation, and other factors, resulting in poor admittance control accuracy. In order to solve this problem, this paper takes the six-degree-of-freedom manipulator admittance control as the research object. According to the mapping relationship between the end attitude and the sensor errors, we propose a method of obtaining the initial offset value of the sensor through data interpolation. This method traverses the initial values of the sensor in each working posture when the sensor has no external force input, and then performs secondary interpolation to obtain the offset values of the sensor in any posture, corrects the output values of the force/torque sensor, and then improve the accuracy of admittance control. The experimental results show that this method can quickly and effectively improve the calculation accuracy of the sensor’s initial deviation values and the admittance control accuracy.

关键词： Interpolation Attitude control Force Dynamics Position control Robot sensing systems Real-time systems

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Disturbance Rejection Based on Equivalent-Input-Disturbance Approach Using High-Order Filter

Disturbance Rejection Based on Equivalent-Input-Disturbance ...

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International Conference on Mechatronics (ICM)

作者： Qicheng Mei Jinhua She Zhentao Liu Yonghua Xiong Wangyong He Danyun Li Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9781728144429

ISBN: (纸本)9781728144436

This paper presents an improved equivalent-input-disturbance (EID) approach to deal with exogenous disturbances. In this approach, an EID estimator that contains a high-order filter is used to estimate and compensate for the disturbances. The system design is based on the results of stability analysis. Simulation results of a position control system demonstrate the validity of the approach and its superiority over the conventional one.

关键词： Mechatronics Simulation Position control Transfer functions Observers Stability analysis system analysis and design

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An improved ant colony optimization for path planning with multiple UAVs

An improved ant colony optimization for path planning with m...

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International Conference on Mechatronics (ICM)

作者： Jing Li Yonghua Xiong Jinhua She Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9781728144429

ISBN: (纸本)9781728144436

As exploiting unmanned aerial vehicles (UAVs) as mobile elements is a new research trend recently, approximation algorithms to solve path planning problems for UAVs are promising approaches. This paper present a solution for the problem of minimum mission time to cover a set of target points in the surveillance area with multiple UAVs. In this methodology, we propose an improved ant colony optimization (ACO) combining ACO with greedy strategy. The main purpose is to find the optimal number of UAVs and to plan the paths of the minimum mission time. Simulation results demonstrate the validity and the superiority of the proposed algorithm.

关键词： Ant colony optimization Mechatronics Surveillance Simulation Market research Path planning Unmanned aerial vehicles

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An Adaptive Model Management Strategy: Balancing Exploration and Exploitation

An Adaptive Model Management Strategy: Balancing Exploration...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Caie Hu Sanyou Zeng Changhe Li Mechanical Engineering and Electronic Information China University of Geoscience Wuhan China Automation and Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geoscience Wuhan China

ISBN: (纸本)9781728190495

Model management is a critical component in data-driven surrogate-assisted evolutionary optimization. It is mainly used for selecting candidate samples to be evaluated using exact functions to balance the exploration and exploitation of algorithms. However, there are some potential limitations for typical infill sampling criteria. An adaptive model management strategy is proposed to overcome the limitations. It adaptively adjusts the selection probability of candidate samples, and avoids potentially unreliability and the burden of selecting parameters of typical infill sampling criteria. The comparison results on benchmark problems demonstrate the competitiveness of the strategy in solving data-driven optimization problems.

关键词： Adaptation models Toy manufacturing industry Reinforcement learning Evolutionary computation Benchmark testing Linear programming Optimization

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Locating,Mapping and Motion Planning of Smart Wheelchair

Locating,Mapping and Motion Planning of Smart Wheelchair

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第40届中国控制会议

作者： Mengyang Yao Yi Liu Chi Xu School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

Smart wheelchair plays an important role in rehabilitation training and daily movement of people with high mobility *** ensure that the smart wheelchair can operate stably in an unfamiliar environment,it is necessary to solve the problems of locating,mapping and motion *** the complex indoor environment of rehabilitation training and the wheelchair model,we propose a new smart wheelchair system *** on Simultaneous Localization and Mapping（SLAM） technology,location and mapping can be obtained through the Rao-Blackwellized Particle Filters（RBPF） *** integrate the sensor data into the proposal distribution to solve the particle degradation problem of RBPF and use selective resampling to improve the algorithm *** to the wheelchair’s own location and the surrounding environment map,global path planning and local real-time path planning are used to complete the motion *** show that the smart wheelchair system we proposed can operate robustly and quickly in a complex indoor environment.

关键词： Smart wheelchair Rehabilitation training SLAM Motion planning

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