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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...

引用

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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Feature Matching Algorithm Based on SURF and Lowes Algorithm

Feature Matching Algorithm Based on SURF and Lowes Algorithm

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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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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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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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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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Design of Ultrasonic Ranging System Based on Cross-correlation Method

Design of Ultrasonic Ranging System Based on Cross-correlati...

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第三十九届中国控制会议

作者： Han Lu Zhihua Li Pei Gao School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

In order to solve the problem that ultrasonic ranging is difficult to obtain the first wave, an ultrasonic ranging system based on cross-correlation method is designed. In this system, the signal generating unit is mainly responsible for transmitting high frequency sound waves, and the data acquisition unit is mainly responsible for collecting and storing the sound waves. Then,according to the time delay between the collected acoustic signals, the time difference between the signals received by the signal acquisition unit is obtained, so as to measure the distance.

关键词： Ultrasonic Cross-correlation Distance

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A State Feedback Voltage controller for Inverter-based Islanded Microgrid

A State Feedback Voltage Controller for Inverter-based Islan...

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第三十九届中国控制会议

作者： Yuqiao Jiao Min Ding Danyun Li Qingyi Wang Zhijian Fang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System

Since there is no external power supply in islanded microgrid, the waveform of output power is extremely vulnerable to negative effects of voltage fluctuation. In this paper, a state feedback voltage controller is proposed to track the reference voltage of the point of common coupling（PCC） for distributed generation unit in an islanded microgrid. It employs the internal model principle and optimal control approach to deal with the changes in the reference voltage. The state feedback gains of the controller are tuned by using the multi-objective genetic algorithm NSGA-II to improve the rapidity and accuracy of voltage tracking performance. Simulations in Matlab/Simulink show the effectiveness of voltage tracking performance with zero steadystate error in changing scenarios.

关键词： Islanded Microgrid Inverter State Feedback controller Optimal control

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Dissipativity analysis of time-delayed neural networks using a vector-related zero-value-term method and generalized reciprocally convex combination techniques

Dissipativity analysis of time-delayed neural networks using...

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第三十九届中国控制会议

作者： WANG Chen-Rui HE Yong School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper studies the dissipative problem of neural networks with time-varying delay and external disturbance. A suitable augmented Lyapunov-Krasovskii functional(LKF) is constructed by taking full advantage of the delay information of the system and the conditions of the excitation function. Then by employing auxiliary function inequalities, the reciprocally convex combination and a vector zero-value method to deal with the derivative of the LKF, a less conservative delay-dependent dissipative criterion is obtained. Finally, a numerical example is given to show the effectiveness of this criterion.

关键词： Neural networks Time delay Dissipativeness Lyapunov-Krasovskii functional

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Multiagent-based Sampled-data Frequency control of Microgrid with Additive Noise

Multiagent-based Sampled-data Frequency Control of Microgrid...

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第三十九届中国控制会议

作者： Shuqin He Xiaofeng Zong School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper presents a sampled-data frequency consensus control of microgrid with additive noise based on multiagent system. The distributed generations in microgrid are regarded as the agents and form the multiagent communication network. For a continuous-time multiagent system, due to the low efficiency and high cost, we apply the sampled-data approach to analyze the frequency consensus under additive noise. Here, based on primary droop control of microgrid, Alternating Direction Multiplier Method is applied to get the optimal frequency reference set points, and the frequency consensus based on leader-following multiagent system is obtained by the mean square consensus theory. The efficiency of the proposed method for control of the multiagent-based microgrid system under the additive noise is simulated in MATLAB.

关键词： multiagent system microgrid distributed control noise sampled-data analyze

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Positioning Method of Insulator Sheds Based on Depth Information

Positioning Method of Insulator Sheds Based on Depth Informa...

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第三十九届中国控制会议

作者： Xuan Gong Xin Chen Xu Jian Wenpeng He School of Automation China University of Geoscience Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

The identification and positioning of insulator shed feature is of vital significance for insulator cleaning. However,there exist few texture features the insulator and its surface is reflective, which makes it difficult to identify and locate the convex points on the surface of the insulator shed. In order to identify and locate the insulator shed, we propose a method to extract the convex points of the insulator skirt based on the binary image of the insulator and the position of the central axis. Binocular vision technology is applied to obtain the depth change curve of the insulator on its axis, with the help of the depth threshold method and principal component analysis（PCA） method. At the same time, the pixel coordinates on the central axis are used to traverse the depth on the central axis to extract the shed bumps, thereby achieving the spatial positioning of the insulator shed bumps. The experiments show that the bumps on the insulator’s central axis can be well marked and used in the insulator cleaning robot system.

关键词： Insulator cleaning Binocular vision Principal component analysis Shed bump Cleaning robot system

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