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第三十九届中国控制会议

作者： Xiaoqi Wang Weihua Cao Chaolong Yao Hang Yin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Feature extraction and matching of images is a key step in 3D reconstruction, and its accuracy directly affects the accuracy of 3D reconstruction. In this paper, aiming at the mismatch caused by the high similarity between screws, proposes a feature matching algorithm based on median filtering, Lowes algorithm and scale-invariant feature transform(SURF), called M-L-SURF algorithm. First, the median filtering is performed on the screw image to remove noise, then the SURF algorithm is used for feature extraction and matching, and finally, the Lowe's algorithm is used to filter the matching results. The results of experiments show that the M-L-SURF algorithm can achieve a 97.4% correct rate of screw image matching. The matching results obtained in this paper can be better applied to the subsequent work of 3D reconstruction.

关键词： Median filtering SURF Feature matching Lowes algorithm

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Distributed Resilient State Estimation for Time-Varying Fractional-Order Discrete complex Networks with Encoding-Decoding Scheme

Distributed Resilient State Estimation for Time-Varying Frac...

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Chinese control Conference (CCC)

作者： Yu Chen Jun Hu Jiaxing Li Aozhan Zhou Hui Yu Department of Applied Mathematics Harbin University of Science and Technology Harbin China Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology Harbin China School of Automation Harbin University of Science and Technology Harbin China

In this paper, the distributed state estimation method with resilient attenuation feature is proposed for time-varying fractional-order complex networks under encoding-decoding mechanism. The encoding-decoding-induced dynamic errors for distinct nodes are characterized by the truncated Gaussian distributions. In order to compensate the effects induced by encoding-decoding scheme, the variances of encoding-decoding-induced dynamic errors are considered in process of designing the resilient distributed estimation algorithm. In particular, the upper bounds of updated estimation error covariances are derived firstly. Subsequently, the upper bounds are minimized by constructing the gain matrices at each sampling instant. Besides, a sufficient condition is provided to guarantee the boundedness of estimation error dynamics in the mean-square sense. Finally, the validity of distributed resilient state estimation scheme is demonstrated by a simulation example.

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A Composite Cost-Sensitive Neural Network for Imbalanced Classification

A Composite Cost-Sensitive Neural Network for Imbalanced Cla...

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第三十九届中国控制会议

作者： Lei Chen Yuan Zhu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Imbalanced data with skewed class distributions and different misclassification costs is common in many real-world applications. Traditional classification approach does not work well for imbalanced data, because they assume equal costs for each class. To deal with this problem, cost-sensitive approaches assign different misclassification costs for different classes without disrupting the true original distributions of samples. However, due to lack of prior knowledge, the misclassification costs are usually unknown and hard to choose in practice. Whats more, even instances in the same class may have different misclassification costs. As an extension of class-dependent costs, this paper presents a composite cost-sensitive deep neural network(CCS-DNN) for imbalanced classification. A specifically-designed cost-sensitive matrix, which is composed of exampledependent costs and class-dependent costs, is embedded into the loss function to improve the classification performance. And the parameters of both the cost-sensitive matrix and the network are jointly optimized during training. The results of comparative experiments on some benchmark datasets indicate that the CCS-DNN performs better than other baseline methods.

关键词： Cost-sensitive Learning Composite Costs Imbalanced Classification Deep Learning

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ECBN: Ensemble Clustering based on Bayesian Network inference for Single-cell RNA-seq Data

ECBN: Ensemble Clustering based on Bayesian Network inferenc...

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第三十九届中国控制会议

作者： Dexin Zhang Yuan Zhu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

With the development of single-cell sequencing technology, it is a hot research topic to identify the cell types using single-cell sequencing data, and many single-cell clustering algorithms have been developed to study this issue. These methods capture partial information of single-cell sequencing data, and obtain the different performance on the same data set. Combining these different results into one can improve the accuracy and validity. Here, we proposed ECBN, Ensemble Clustering based on B ayesian Network. ECBN can ensemble several different results of state-of-the-art single cell clustering methods, such as Seurat, CIDR, SC3 and t-SNE+k-means, and generate a more optimal clustering result through Bayesian network. Experiments are carried on the 5 single cell data sets and compared with 4 individual single cell clustering methods and 3 integrative *** size of experiment data sets ranges from 822 to 3605 and the results show that our method can achieve good ***, ECBN can also use the graphical regularization to lighten the limitation which is generated by the different basis results.

关键词： Single-cell RNA-seq Ensemble clustering Bayesian network

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Forget less,count better:a domain-incremental self-distillation learning benchmark for lifelong crowd counting

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Frontiers of Information Technology & Electronic Engineering 2023年第2期24卷 187-202页

作者： Jiaqi GAO Jingqi LI Hongming SHAN Yanyun QU James ZWANG Fei-Yue WANG Junping ZHANG Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Institute of Science and Technology for Brain-inspired Intelligence Fudan UniversityShanghai 200433China Shanghai Center for Brain Science and Brain-inspired Technology Shanghai 201210China School of Information Science and Technology Xiamen UniversityXiamen 361005China College of Information Sciences and Technology the Pennsylvania State UniversityUniversity ParkPA 16802USA State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Crowd counting has important applications in public safety and pandemic control.A robust and practical crowd counting system has to be capable of continuously learning with the newly incoming domain data in real-world scenarios instead of fitting one domain ***-the-shelf methods have some drawbacks when handling multiple domains:(1)the models will achieve limited performance(even drop dramatically)among old domains after training images from new domains due to the discrepancies in intrinsic data distributions from various domains,which is called catastrophic forgetting;(2)the well-trained model in a specific domain achieves imperfect performance among other unseen domains because of domain shift;(3)it leads to linearly increasing storage overhead,either mixing all the data for training or simply training dozens of separate models for different domains when new ones are *** overcome these issues,we investigate a new crowd counting task in incremental domain training setting called lifelong crowd *** goal is to alleviate catastrophic forgetting and improve the generalization ability using a single model updated by the incremental ***,we propose a self-distillation learning framework as a benchmark(forget less,count better,or FLCB)for lifelong crowd counting,which helps the model leverage previous meaningful knowledge in a sustainable manner for better crowd counting to mitigate the forgetting when new data arrive.A new quantitative metric,normalized Backward Transfer(nBwT),is developed to evaluate the forgetting degree of the model in the lifelong learning *** experimental results demonstrate the superiority of our proposed benchmark in achieving a low catastrophic forgetting degree and strong generalization ability.

关键词： Crowd counting Knowledge distillation Lifelong learning

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An Improved Faster-RCNN Algorithm for Object Detection in Remote Sensing Images

An Improved Faster-RCNN Algorithm for Object Detection in Re...

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第三十九届中国控制会议

作者： Rui Liu Zhihua Yu Daili Mo Yunfei Cai School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Compared with conventional object detection, remote sensing images are taken from the air. The angle of view is not fixed and the object direction, scale which compared with conventional object detection algorithm are quite different. These factors lead to the object detection in remote sensing images difficult. To solve the above problems, this paper proposes an improved remote sensing object detection method based on Faster-RCNN algorithm. Using online difficult example mining technology,feature pyramid structure, Soft-NMS technology, and RoI-Align technology to enhance the capabilities of Faster-RCNN in small object detection task in remote sensing images. The algorithm in this paper was evaluated on the RSOD-Dataset, compared with the original Faster-RCNN algorithm, the proposed algorithm improves the detection accuracy and training convergence speed,which shows that these improvements are of great significance to the object detection algorithm of remote sensing images.

关键词： Remote Sensing Images Faster-RCNN Small Object Detection

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Recursive Filtering for Time-Varying Nonlinear Delayed systems with Stochastic Parameter Matrices and Censored Measurements

Recursive Filtering for Time-Varying Nonlinear Delayed Syste...

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Chinese control Conference (CCC)

作者： Shuo Yang Jun Hu Peixia Gao Ruijie Dong Zhihui Wu Department of Applied Mathematics Harbin University of Science and Technology Harbin China Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology Harbin China School of Automation Harbin University of Science and Technology Harbin China

This paper discusses the design problem of recursive filtering method for time-varying nonlinear delayed systems (NDSs) with stochastic parameter matrices (SPMs) and censored measurements. In particular, the Tobit Type I model provides a description of the censored measurements. The main objective of this paper is to construct a recursive filter for NDSs with both SPMs and censored measurements. The upper bound of the filtering error covariance is first calculated via mathematical induction, and the upper bound is then minimized by choosing proper filter gain. Besides, a sufficient condition is provided to guarantee that the filtering error is uniformly bounded in the mean-square sense. Finally, the viability and applicability of the proposed filtering method are demonstrated using a numerical simulation.

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Improved current-based droop control strategy for microgrids inverter

Improved current-based droop control strategy for microgrids...

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第三十九届中国控制会议

作者： Danyun Li Jiawei Li School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Inconsistent feeder impedances in microgrids easily lead to uneven reactive power output of the inverter. This paper proposes an improved current-based droop control strategy for this problem. The droop coefficient is adjusted by the inverter capacity ratio, and the reactive current measured at the point of common coupling（PCC） is used as a reference value for improved differential control to compensate the voltage and control its reactive power output. It is found that the improved droop control has good adaptive ability and stability. The simulation results also prove the correctness and feasibility of the proposed strategy.

关键词： microgrids inverter capacity ratio current-based droop control reactive power

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Improved-YOLOv3 network for object detection in simulated Space Solar Power systems images

Improved-YOLOv3 network for object detection in simulated Sp...

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第三十九届中国控制会议

作者： Guangjun Wang Nongwei Lei Huimin Liu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

In this paper, the object detection technology based on deep learning is applied to the assembly process of space power station simulation, which can provide assistance for the attitude adjustment and navigation of the aircraft through the detection of some components. Firstly, the 3 D modeling and rendering of the space power station are carried out, on which the image dataset is collected and established. Then, based on the YOLOv3 network, we improve the structure of feature *** fusing the information of shallow and deep features, we can improve the detection ability of the network for different scale *** and quantitative experimental results show that the improved YOLOv3 network can accurately and effectively detect the key components of the Space solar power station.

关键词： Space Solar Power systems object detection YOLOv3 multiscale features

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Stability Analysis of Milling Process With Time-Varying Delays Via Lyapunov-Krasovskii Functional

Stability Analysis of Milling Process With Time-Varying Dela...

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第三十九届中国控制会议

作者： Jiang He Chen Yan Hong-Zhang Wang Dan-Yun Li School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

The regenerative chatter during milling seriously affects the stability of the *** paper proposes a method based on Lyapunov-Krasovski functional analysis for the stability of the milling ***,the mechanism analysis of the milling process is performed,then the state-space equation of the time-varying delay system caused by the regeneration effect is ***,based on the model of a time-delay system,a stability criterion is developed by constructing an augmented LyapunovKrasovski functional(LKF) and using auxiliary function inequality with reciprocally convex combination ***,the validity of the method is verified through an example,and the milling stability domain lobe diagram with a parameter combination of spindle speed-cutting depth is obtained which provides operational guidelines to guarantee a stable vibration-free process.

关键词： Milling process time-varying delay Lyapunov reciprocally combination technology stability

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