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| 1 | Further results on cloud control systems显示文摘This paper is devoted to further investigating the cloud control systems(CCSs). The benefits and challenges of CCSs are provided. Both new research results of ours and some typical work made by other researchers are presented. It is believed that the CCSs can have huge and promising effects due to their potential advantages. | Yuanqing XIA Yongming QIN Di-Hua ZHAI Senchun CHAI | 2016 | Science China(Information Sciences)2016，59，7： | 6 |
| 2 | Deep Auto-encoded Clustering Algorithm for Community Detection in Complex Networks显示文摘The prevalence of deep learning has inspired innovations in numerous research fields including community detection, a cornerstone in the advancement of complex networks. We propose a novel community detection algorithm called the Deep auto-encoded clustering algorithm(DAC), in which unsupervised and sparse single autoencoders are trained and piled up one after another to embed key community information in a lowerdimensional representation, such that it can be handled easier by clustering strategies. Extensive comparison tests undertaken on synthetic and real world networks reveal two advantages of the proposed algorithm: on the one hand, DAC shows higher precision than the kmeans community detection method benefiting from the integration of sparsity constraints. On the other hand,DAC runs much faster than the spectral community detection algorithm based on the circumvention of the time-consuming eigenvalue decomposition procedure. | WANG Feifan ZHANG Baihai CHAI Senchun | 2019 | Chinese Journal of Electronics2019，28，3： | 5 |
| 3 | High-fidelity trajectory optimization for aeroassisted vehicles using variable order pseudospectral method显示文摘In this study,the problem of time-optimal reconnaissance trajectory design for the aeroassisted vehicle is considered.Different from most works reported previously,we explore the feasibility of applying a high-order aeroassisted vehicle dynamic model to plan the optimal flight trajectory such that the gap between the simulated model and the real system can be narrowed.A highly-constrained optimal control model containing six-degree-of-freedom vehicle dynamics is established.To solve the formulated high-order trajectory planning model,a pipelined optimization strategy is illustrated.This approach is based on the variable order Radau pseudospectral method,indicating that the mesh grid used for discretizing the continuous system experiences several adaption iterations.Utilization of such a strategy can potentially smooth the flight trajectory and improve the algorithm convergence ability.Numerical simulations are reported to demonstrate some key features of the optimized flight trajectory.A number of comparative studies are also provided to verify the effectiveness of the applied method as well as the high-order trajectory planning model. | Runqi CHAI Antonios TSOURDOS AI SAVVARIS Senchun CHAI Yuanqing XIA | 2021 | Chinese Journal of Aeronautics2021，34，1： | 3 |
| 4 | Design, Simulation and Implementation of Networked Predictive Control Systems显示文摘 | Liu Guoping Rees D Chai Senchun | 2005 | Measurement & Control2005，38，1： | 1 |
| 5 | Design and practical implementation of internet-based predictive control of a servo system显示文摘 | Chai Senchun Liu Guo-Ping Rees David Xia Yuanqing | 2008 | IEEE Transactions on Control Systems Technology2008，16，1： | 1 |
| 6 | Deep Multimodal Learning and Fusion Based Intelligent Fault Diagnosis Approach显示文摘Industrial Internet of Things(IoT)connecting society and industrial systems represents a tremendous and promising paradigm shift.With IoT,multimodal and heterogeneous data from industrial devices can be easily collected,and further analyzed to discover device maintenance and health related potential knowledge behind.IoT data-based fault diagnosis for industrial devices is very helpful to the sustainability and applicability of an IoT ecosystem.But how to efficiently use and fuse this multimodal heterogeneous data to realize intelligent fault diagnosis is still a challenge.In this paper,a novel Deep Multimodal Learning and Fusion(DMLF)based fault diagnosis method is proposed for addressing heterogeneous data from IoT environments where industrial devices coexist.First,a DMLF model is designed by combining a Convolution Neural Network(CNN)and Stacked Denoising Autoencoder(SDAE)together to capture more comprehensive fault knowledge and extract features from different modal data.Second,these multimodal features are seamlessly integrated at a fusion layer and the resulting fused features are further used to train a classifier for recognizing potential faults.Third,a two-stage training algorithm is proposed by combining supervised pre-training and fine-tuning to simplify the training process for deep structure models.A series of experiments are conducted over multimodal heterogeneous data from a gear device to verify our proposed fault diagnosis method.The experimental results show that our method outperforms the benchmarking ones in fault diagnosis accuracy. | Huifang Li Jianghang Huang Jingwei Huang Senchun Chai Leilei Zhao Yuanqing Xia | 2021 | Journal of Beijing Institute of Technology2021，30，2： | 0 |
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