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13th International Conference on Bioinformatics and Biomedical Technology, ICBBT 2021

作者： Liu, Chenliang Zhu, Yuan Zhang, Houwang School of Automation China University of Geosciences Wuhan430074 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 China

ISBN: (纸本)9781450389655

Single cell RNA sequencing (scRNA-seq) technology can study gene expression in single cell resolution and solve cell heterogeneity that cannot be solved by the traditional RNA sequencing (Bulk RNA-seq) technology. It provides a powerful analytical tool for in-depth study of the immune, regulatory, replication, and other activities of cells and explanations of life activities rules. However, single cell sequencing experiments are hindered by several technical issues, which cause output scRNA-seq with a high percentage of zeros (dropout events), impacting the reliability of downstream analyses. Therefore, a great number of computational methods have been proposed to address the increased sparsity observed in scRNA-seq data. In this paper, motivated by the image denoising method, we propose an imputation algorithm CSI based on the similarity between cells. The cell similarity is considered as the weight to average the gene expression values to impute the missing values in the data. CSI achieves better accuracy than other the 7 publicly notable scRNA-seq imputation methods on some real scRNA-seq data, especially gains better performance in some downstream analysis such as cluster analysis and pseudo trajectory inference. © 2021 ACM.

关键词： RNA

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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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2021 China Automation Congress, CAC 2021

作者： Zhu, Tijian Zhu, Yuan Zhang, Chuan-Ke School of Automation China University of Geosciences Wuhan430074 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 China

ISBN: (纸本)9781665426473

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. © 2021 IEEE

关键词： RNA

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Adaptive Backward Stepwise Selection-Based Sparse Identification of Nonlinear Dynamics

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SSRN 2024年

作者： Jiang, Feng Du, Lin Xue, Qing Grebogi, Celso Deng, Zichen MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi’an710072 China School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University Xi’an710072 China School of Mathematics and Statics Northwestern Polytechnical University Xi’an710072 China MOE Key Laboratory for Complexity Science in Aerospace Northwestern Polytechnical University Xi’an710072 China Institute for Complex Systems and Mathematical Biology University of Aberdeen AberdeenAB24 3UE United Kingdom

Sparse identification of nonlinear dynamics (SINDy) has seen significant advancements in recent years, whereas determining hyperparameters within the SINDy remains challenging. In this study, we propose the Adaptive Backward Stepwise Selection of SINDy (ABSS-SINDy) to directly address the above issue, offering insight into the conditions under which SINDy excels and potential areas of failure from the perspective of backward stepwise selection. We validate the improved approach through a series of numerical experiments encompassing planar dynamic and three-dimensional chaotic systems, thereby illustrating the efficacy of the ABSS-SINDy. The findings underscore the capability of ABSS-SINDy to autonomously determine optimal hyperparameters within the SINDy framework, transcending a previously implicit and limiting assumption, thus extending the application capabilities of SINDy. © 2024, The Authors. All rights reserved.

关键词： Dynamics

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Dynamic Maintenance Document Management Based on BOM 7

Dynamic Maintenance Document Management Based on BOM

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7th International Conference on systems and Informatics, ICSAI 2021

作者： Zhou, Chunliu Ye, Shan Wang, Hongjun Gao, Zhenhua Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Key Laboratory of Multidisciplinary Management Ma'anshan China School of Management Science and Engineering Anhui University of Technology Ma'anshan China

ISBN: (纸本)9781665426244

To realize the efficient management of high-speed train maintenance documents, a document management method based on the Bill of Material (BOM) was proposed. Since the maintenance documents were dynamically changed as the design updated, and the change process was complicated and time-consuming via handwork, the relationship between the engineering BOM (EBOM) and the Documental BOM (DBOM) was established, which delivered the design change, updated the maintenance documents in real time, and provided the latest documents for on-site maintenance business. Technologies of fuzzy matching, version management and XML were used together to realized the dynamic management of maintenance documents, and an application was displayed with system interfaces to verify the DBOM method. © 2021 IEEE.

关键词： Maintenance

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Origami-Inspired Metamaterial with Cpmpression-Twist Coupling Effect For Low-Frequency Vibration Isolation

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SSRN 2023年

作者： Liu, Xiangbing Zhang, Kai Shi, Haizhou Hong, Fang Liu, Hong Deng, Zichen School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University Xi’an710072 China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi’an710072 China State Key Laboratory of Structural Analysis for Industrial Equipment Dalian University of Technology Dalian116024 China

Through unique folding methods and material selection, origami structures enable optimum structural design within a restricted space and provide strong force support. In aerospace engineering, origami-inspired structures are used in designing satellites, probes and other space devices for lightweight, compact and adjustable design requirements. A compression-twist coupled metamaterial inspired by Kresling origami structure consisting of beams and plates is presented in this study, and the vibration response of the structure is investigated. The bandgaps of the structure along longitudinal and transverse directions are studied separately due to the application of the polarization factor. The vibrational modes of the structure are further investigated to elucidate the mechanism of bandgap generation. Parametric studies make the optimal design of bandgap in graded structures possible. Finally, experiments are carried out to verify the correctness of the structural dynamic response. This research provides a new perspective on the design of origami-inspired metamaterial structures in terms of dynamics control and bandgap tuning. © 2023, The Authors. All rights reserved.

关键词： Energy gap

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A Feature Difference Network for Defect Detection on the Surface with complex Pattern of Ceramic Tile

A Feature Difference Network for Defect Detection on the Sur...

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Chinese control Conference (CCC)

作者： Jianru Xiong Jianqi An Xing-ao Wang Huimin Ou Xin Chen 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

The surface defects of ceramic tile greatly affect the service life of ceramic tile. At present, many detection methods of ceramic tile surface defects are mostly used for ceramic tiles with monochrome background or simple texture. However, many tiles with complex and irregular surface patterns are used in practical applications, but many methods cannot effectively detect surface defects in such tiles. This paper presents a double input feature difference network structure to overcome the limitation. First, a double input channel is constructed to extract features from the template image and the defect image respectively. Next, a method of feature difference is performed at different depths to suppress the background interference and prevent misclassification between different defect categories. Then a parameter-free attention module is embedded in the backbone to improve the ability of feature extraction. Experimental results show that this model effectively improves the mean average accuracy of 8.3% and the recall rate of 11.7%.

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Gradient Projection Differential Neural Solution for Quadratic Optimization with Quadratic Constraints: An ACP Perspective

Gradient Projection Differential Neural Solution for Quadrat...

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IEEE International Conference on systems, Man and Cybernetics

作者： Ying Liufu Mei Liu Long Jin Feiyue Wang School of Information Science and Engineering Lanzhou University Lanzhou China College of Computer Qinghai Normal University Xining China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

In recent years, quadratic optimizations have become increasingly popular in engineering. However, conventional methods that investigate this problem from the perspective of a canonical form with linear constraints are not effective in dealing with the significant challenges posed by quadratic constraints in practice. This paper proposes a solution framework for the quadratic optimization with quadratic constraints (QOQC) based on innovative artificial societies, computational experiments, and parallel execution (ACP) framework. Then, a gradient projection differential neural solution (GPDNS) is proposed to address this. To illustrate the effectiveness of the GPDNS model in solving the QOQC system, numerical simulations are provided. Overall, this paper presents the potential of innovative approaches like the ACP framework to enhance our capabilities in addressing challenging optimization systems.

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Energy-Aware Coverage control of Multi-Agent systems with control Barrier Function

Energy-Aware Coverage Control of Multi-Agent Systems with Co...

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Chinese control Conference (CCC)

作者： Chengkai Qu Chao Zhai 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

Geo-hazards have become one of the main disasters endangering the safety of people's lives and property in the world. In order to improve the early warning of disasters, a persistent monitoring method of multi-agent systems is proposed in this work. To ensure that the agent's energy is never exhausted, the set invariance constraint is included in the optimization problem. The goal is to minimize the difference between the actual control input of the robot and the nominal control input corresponding to the task to be performed. Moreover, the control barrier function (CBF) is used to transform the forward invariance of a subset of the robot state space into a control input constraint. The coverage control method in an uncertain environment is verified by numerical simulation. This work provides new insights into effective monitoring and early warning of geo-hazards.

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A StyleGAN3-Based Data Augmentation Method for Ceramic Defect Detection

A StyleGAN3-Based Data Augmentation Method for Ceramic Defec...

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Industrial Cyber-Physical systems (ICPS)

作者： Huimin Ou Jianqi An Xing-ao Wang Jianru Xiong Xin Chen Qingyi Wang 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

Deep learning is currently the mainstream method for ceramic defect detection, and it requires a large number of defect samples to train the network. However, collecting these defect samples is very time-consuming and deep learning suffers from few-shot learning problems. In this study, a StyleGAN3-based data augmentation method for ceramic defect detection was proposed which can generate ceramic defect samples and thus reduce the data collection work. Experiments show that our method uses less training time, has a more stable training process, and can improve the accuracy of the detection network.

关键词： Training Deep learning Image synthesis Data collection Cyber-physical systems Data models Ceramics

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Fault Diagnosis for Industrial Alarm Floods Based on Discrete Hidden Markov Model

Fault Diagnosis for Industrial Alarm Floods Based on Discret...

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Chinese control Conference (CCC)

作者： Xiwen Huang Wenkai Hu Weihua Cao 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

A fault diagnosis method based on Discrete Hidden Markov Models is proposed in this paper to identify the fault causing alarm flood sequences. The proposed method consists of the following steps: First, the alarm flood data is pre-processed to ensure that all sequences are of uniform length, and a separate Discrete Hidden Markov model is trained for each fault to capture the relationship between the fault and the alarm sequences. Second, given an observation sequence, the log-likelihood probability values under different Discrete Hidden Markov models are calculated and the maximum probability is selected to determine the type of corresponding fault. Last, the feasibility of the proposed method is verified by simulation data obtained from a public industrial model. The results show that the method can effectively identify the faults that trigger alarm floods.

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