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第40届中国控制会议

作者： Shiye Pan Xinmei Wang 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

Determining the position and orientation of the camera is a crucial problem with the rapidly developing technology in visual Simultaneous Localization and Mapping(SLAM),augmented reality and 3 D *** the Pn P(perspective-n-point) problem is an effective method to calculate the pose of the camera and is also the most widely used method in many *** this paper,the methods for Pn P problem,including special Pn P problem and general Pn P problem are summarized ***,due to importance of performing Pn P methods in practical applications,ability to handle outliers for Pn P methods is ***,the main problems of the current researches on Pn P problem are presented.

关键词： Perspective-n-point Problem Camera Pose Estimation Robustness to Outliers

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Performance Improvement of TFRC Congestion control Algorithm in Wireless Network Streaming Media Transmission

Performance Improvement of TFRC Congestion Control Algorithm...

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第40届中国控制会议

作者： Kang Li Yan Yuan Yitao Chen Zhicheng Zhao 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

This paper studies a rate-based TCP-Friendly Rate control（TFRC） mechanism, which is widely used in multimedia real-time services, and analyzes its basic workflow, throughput model and calculation of key parameters. In order to meet the demand of real-time service for network transmission, the bandwidth-delay product（BDP） is applied to the congestion control of TFRC as the network congestion warning signal, and the improved TFRC algorithm is proposed. The simulation results show that this method can achieve good results in the network transmission of real-time services, and its friendliness and smoothness are improved to a certain extent.

关键词： Real-time multimedia application TFRC Bottleneck bandwidth and round-trip time（BBR） Wireless network

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Contextual Information Fusion for Small Object Detection

Contextual Information Fusion for Small Object Detection

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第40届中国控制会议

作者： Jun Chen Xiaoqiang Chen Linbo Luo Ganbei Wang 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

In view of the difficulty and low accuracy of small object detection for remote sensing images, this paper proposes a small object detection algorithm based on contextual information fusion to solve the problem of real-time detection accuracy of small object. In this paper, we use bottom-up VGG16 network to realize multi-scale feature extraction to deal with the problem of insufficient image feature extraction. To direct at the problem that the feature information of each feature layer is single, the shallow feature layer and the deep feature layer are fused through the feature fusion module, which achieves the purpose that some feature layers have more abundant fusion features in the structure level. Aiming at the problem that the detection objects in remote sensing images are mainly small and medium-sized objects, this paper proposes to use the multivariate information of four different scale feature layers for classification prediction and regression prediction, so as to reduce the complexity of network *** experimental results show that the proposed small object detection algorithm based on the fusion of four scale deep and shallow contextual information can obtain good accuracy and real-time performance on the NWPU VHR-10 dataset, improve the detection accuracy on the basis of ensuring the real-time detection, and perform well in the small object detection task of remote sensing images.

关键词： remote sensing deep learning small object detection SSD feature fusion

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Multi-condition recognition method based on LSTM＿GRU for heating processes

Multi-condition recognition method based on LSTM＿GRU for he...

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第40届中国控制会议

作者： Qingyi Zhu Weihua Cao Wenshuo Song 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

Aiming at the multi-condition problem of Continuous Annealing Processes(CAP), this paper proposes a new method based on Long Short-Term Memory(LSTM) and Gated Recurrent Unit(GRU) models to identify multiple conditions in ***, this work analyzes the parameters in CAP, selects the key variables that affect the working conditions, and then selects a piece of data in the CAP work process as the training data set to train the constructed LSTMRU neural network. This method realizes the recognition of different working conditions in CAP, which saves training time, simplifies internal *** with the traditional method, this method avoids the recognition error caused by personal experience factors, and the model accuracy has greatly improved.

关键词： Working condition recognition LSTMGRU Metallurgy Process industry CAP

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The Analysis of Muscle-Synergy Similarity between Walking and Pedaling Based on Variational Mode Decomposition and Non-Negative Matrix Factorization

The Analysis of Muscle-Synergy Similarity between Walking an...

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IEEE International Conference on Engineering, Technology and Innovation

作者： Wangyang Ge Jinhua She Shijie Guo Feng Wang Juan Zhao Seiichi Kawata 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 School of Engineering Tokyo University of Technology Hachioji Tokyo Japan Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies Hebei Key Laboratory of Robot Sensing and Human-Robot Interaction Tianjin China School of Mechanical Engineering Hebei University of Technology Tianjin China

ISBN: (数字)9798350362435

ISBN: (纸本)9798350362442

Pedaling lower-limb rehabilitation robots are widely used in various stages of lower-limb rehabilitation because of their high safety and low requirements for patients' lower-limb strength. researchers found that the activation sequence of the surface electromyographic (sEMG) signals of the muscles of the lower limb during pedaling is very similar to that of walking, which makes it possible to simulate the muscle force generation during walking by changing the pedal load during pedaling training. However, most pedaling lower-limb rehabilitation robots use constant loads, and it is of great importance to study the relationship between load and muscle synergy and to design load phase angle trajectories with higher muscle synergy consistency in the walking-pedaling motion to optimize the training effect of pedal-type rehabilitation robots. In this paper, we used variational mode decomposition (VMD) and nonnegative matrix factorization (NMF) to extract the muscle synergy features in each band of sEMG during walking and pedaling and to study the similarity of pedaling and walking and the influence of pedal load on muscle synergy consistency.

关键词： Legged locomotion Training Technological innovation Time series analysis Muscles Assistive robots Feature extraction Trajectory Safety Matrix decomposition

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Formation control of Nonlinear Multi-Agent systems with Actuator and Communication Attacks

Formation Control of Nonlinear Multi-Agent Systems with Actu...

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第40届中国控制会议

作者： Shihao Wang Shiqi Zheng Chenghao Zhao Haitao Jian Haiming Li 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

In this paper, we focus on the formation control problems of MAS over a directed graph with actuator and communication attacks. The considered system is composed of a leader, some followers and an attacked communication network. Firstly,a new distributed observer is proposed to estimate the leader information despite communication attacks. Then, for high-order nonlinear systems, we develop an adaptive control strategy to solve the actuator attack by using Nussbaum function and backstepping technique, so that the agent with actuator attacks can follow the leader’s trajectory. Finally, a simulation example is proposed to verify the results of this paper.

关键词： Multi-agent systems formation control distributed observer attack nussbaum function

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Detection of Sequential Alarm Patterns in Complex Industrial Facilities Using ClaSP and Top-K Algorithms

Detection of Sequential Alarm Patterns in Complex Industrial...

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第40届中国控制会议

作者： Zhuang Wang Wenkai Hu Weihua Cao Min Wu 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

In modern complex industrial processes,due to poor rationalization of alarm systems and the complexity of process interconnections,alarm floods are commonly *** floods are also identified as the main causes of many industrial *** valid approach to deal with alarm floods is to mine meaningful alarm sequential patterns from alarm *** identified patterns can help to analyze root causes or to configure dynamic alarming *** this paper,a method based on the combination of ClaSP and Top-K is proposed to mine interesting alarm sequential patterns from historical alarm *** contributions of this study are twofold:1) A pattern mining approach is adapted to mine interesting patterns from alarm flood sequences;2) A pattern compression strategy is proposed to reduce pattern redundancy.A case study is presented to demonstrate the effectiveness of the proposed method.

关键词： Alarm system alarm flood sequential pattern mining process monitoring

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A novel drilling rate of penetration(ROP) prediction method using data pre-processing techniques and T-S fuzzy inference

A novel drilling rate of penetration(ROP) prediction method ...

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第40届中国控制会议

作者： Xiang Wang Chao Gan Weihua Cao 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

Rate of penetration(ROP) prediction is crucial for the drilling optimization and cost-savings. In this paper, a novel drilling ROP prediction method is proposed and the prediction model can be divided into two stages(data pre-processing and T-S fuzzy inference modeling). In the first stage, four data pre-processing techniques(Reduction, re-sampling, wavelet filtering, and normalization) are used step by step to improve the quality of drilling data. In the second stage, T-S fuzzy inference method is introduced to establish the ROP prediction model. The experiment is executed by using the data from actual drilling process and the results demonstrate the effectiveness of proposed method in prediction accuracy compared with two conventional methods(response surface method and support vector regression).

关键词： Rate of penetration Drilling optimization Data pre-processing T-S fuzzy inference Drilling process

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Multi-omics graph convolutional networks for digestive system tumour classification and early-late stage diagnosis

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1572-1586页

作者： Lin Zhou Zhengzhi Zhu Hongbo Gao Chunyu Wang Muhammad Attique Khan Mati Ullah Siffat Ullah Khan School of Information Science and Technology University of Science and Technology of ChinaHefeiAnhuiChina Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot WuhuAnhuiChina Department of Breast Center West District of The Affiliated Hospital of University of Science and Technology of ChinaDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhuiChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiAnhuiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Biological and Environmental Engineering Chaohu UniversityChaohu Regional Collaborative Technology Service Center for Rural RevitalizationHefeiChina Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl-KhobarSaudi Arabia School of Automation Northwestern Polytechnical UniversityXi'anShaanxiChina Institute of Engineering and Computing Science University of Science and Technology of BannuKPKBannuPakistan

The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against *** neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related *** accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal *** this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of *** MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN *** outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST *** source code for this groundbreaking study is readily accessible for download at https://***/bigone1/MGTCN.

关键词： digestive system tumors early-late stage multi-omics tumor types

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Optimization of the Landslide Susceptibility Model based on Deep Forest and Twice-Sample

Optimization of the Landslide Susceptibility Model based on ...

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Chinese automation Congress (CAC)

作者： Yuelin Yang Wanke Yu Zepeng Liu Lingran Zhao Chengda Lu Min Wu Ruiqing Niu 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 Institute of Geophysics and Geomatics China University of Geosciences Wuhan China

In the analysis of regional landslide susceptibility, the geological data used usually have the characteristics of multiplicity and nonlinearity. At the same time, in the past researches, the selection of non-landslide units in the training set is usually random or subjective, and there is no guarantee sample accuracy. In this paper, the deep forest algorithm combined with the twice-sample method is used to evaluate the landslide susceptibility in the Zigui Badong area of the Three Gorges Reservoir area. First, 11 landslide impact factors were selected according to the geological data to initially construct a sample set, and the cleaned data set was obtained by twice-sample. After dividing the training set and the test set, the deep forest model was used to predict the landslide susceptibility and evaluate the accuracy. The results show that the AUC of the susceptibility prediction model built using the deep forest is 1%-10% higher than that of GBDT, MLP and other methods, while the Twice-Sample method improves the AUC by 2%.

关键词： Training Geology Predictive models Feature extraction Reservoirs Prediction algorithms Terrain factors

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