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2018 Chinese Automation Congress, CAC 2018

作者： Li, Xin Xie, Guo Liu, Han Wang, Wenqing Information Processing Xi 'An University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Xi'an China

ISBN: (纸本)9781728113128

Modern industrial equipment plays a vital role in the operation of large industrial systems. Accurate prediction of its remaining useful life will greatly reduce the possibility of unsafe accidents. The internal structure of modern industrial equipment is very complex, and a variety of state monitoring data affect the remaining useful life of the equipment, and the mutual influence between different monitoring data has greatly increased the difficulty of remaining useful life prediction. In this paper, the long short-term memory network in deep learning is used to solve this problem. The long short-term memory network has many advantages in time series prediction. The remaining useful life prediction model of industrial equipment based on long short-term memory network and multivariable monitoring data is established, and aiming at the characteristics of multivariable monitoring data, Mini-batch Gradient Descent algorithm (MBGD) is adopted to train the network. Finally, the engine monitoring data set provided by the NASA prediction center is used to verify the algorithm. The data set is divided into training set and test set, the training set is put into the long short-term memory network to get the prediction model, and then the remaining useful life prediction is carried out by the test set. The prediction accuracy and generalization ability of the algorithm are verified by predicting the remaining useful life of three different turbofan engines. © 2018 IEEE.

关键词： Long short-term memory

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Closer to pre-trained network transfer better 9

Closer to pre-trained network transfer better

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9th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2020

作者： Chen, Siyu Li, Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Image Processing and Intelligent Control Key Laboratory Education Ministry of China Wuhan430074 China

In recent years, Deep Neural Network (DNN) has been widely used in the domain of computer vision, but its further development is restricted because of the lack of train samples. Fine-tuning is one of deep transfer learning methods to solve this problem. Nowdays, some existing Fine-tuning approaches assume that if the fine-tuned network could be as close to the pre-trained network as possible, the source knowledge would be fully utilized to improve the study effect. Although these approaches work pretty well, the assumptions on which they are based have not been systematically proved. Due to the complexity of DNNs, offering a reasonable and accurate explanation of the performance of the network is significant for designing new algorithms. In this paper, we design a series of experiments with or without transfer learning respectively, to characterize the relationship between network differences and network performance. The results show that there is an obvious negative correlation between them under certain circumstances. This conclusion can not only provide a more reasonable explanation for the relevant transfer learning algorithm, but also provide support and in-spiration for the future work. © 2020 IEEE.

关键词： Deep neural networks

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Data Based Fault Diagnosis of Hot Axle for High-Speed Train

Data Based Fault Diagnosis of Hot Axle for High-Speed Train

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2018 Chinese Automation Congress, CAC 2018

作者： Sun, Lanlan Hei, Xinhong Xie, Guo Qian, Fueai Wang, Zhuxin Liu, Ran System Control and Intelligent Information Processing University of Technology Xi'An Shaanxi Key Laboratory of Complex Xi'an China

ISBN: (纸本)9781728113128

The axle temperature is an important performance index for high-speed train axle, and many types of high-precision detection device have been developed. However, because the existing hotbox detection system works by comparing the measured value with fixed and experiential temperature thresholds, without considering the dynamic influence of running environment, the rates of fault and failure alarm are still high. Regarding this problem, this paper is to realize high precision fault diagnosis. Firstly, the characteristics of the temperature rising rates of all axles, the axles on the same side of a carriage, the axles on the same side of a whole train are analyzed. Secondly, the support vector machine (SVM) is employed to establish a data-based fault diagnosis model, and the model is optimized using genetic algorithm. Finally, the proposed method is applied to an actual running data, the analysis results demonstrated the effectiveness and feasibility. © 2018 IEEE.

关键词： Genetic algorithms

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Learning Probabilistic Coordinate Fields for Robust Correspondences

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第10期45卷 12004-12021页

作者： Zhao, Weiyue Lu, Hao Ye, Xinyi Cao, Zhiguo Li, Xin Huazhong University of Science and Technology The Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Wuhan430074 China West Virginia University The Lane Department of Computer Science and Electrical Engineering MorgantownWV26506-6109 United States

We introduce Probabilistic Coordinate Fields (PCFs), a novel geometric-invariant coordinate representation for image correspondence problems. In contrast to standard Cartesian coordinates, PCFs encode coordinates in correspondence-specific barycentric coordinate systems (BCS) with affine invariance. To know when and where to trust the encoded coordinates, we implement PCFs in a probabilistic network termed PCF-Net, which parameterizes the distribution of coordinate fields as Gaussian mixture models. By jointly optimizing coordinate fields and their confidence conditioned on dense flows, PCF-Net can work with various feature descriptors when quantifying the reliability of PCFs by confidence maps. An interesting observation of this work is that the learned confidence map converges to geometrically coherent and semantically consistent regions, which facilitates robust coordinate representation. By delivering the confident coordinates to keypoint/feature descriptors, we show that PCF-Net can be used as a plug-in to existing correspondence-dependent approaches. Extensive experiments on both indoor and outdoor datasets suggest that accurate geometric invariant coordinates help to achieve the state of the art in several correspondence problems, such as sparse feature matching, dense image registration, camera pose estimation, and consistency filtering. Further, the interpretable confidence map predicted by PCF-Net can also be leveraged to other novel applications from texture transfer to multi-homography classification. © 1979-2012 IEEE.

关键词： image registration

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Contrast-modulated nonlinear diffusion for X-ray angiogram images

Contrast-modulated nonlinear diffusion for X-ray angiogram i...

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2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005

作者： Li, Baopu Sang, Nong Cao, Zhiguo Department of Electronic Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong Key Laboratory Ministry of Education for Image Processing and Intelligent Control Wuhan China

ISBN: (纸本)0780387406

In this paper, we present a new method for X-ray angiogram images enhancement using a contrast-modulated nonlinear diffusion. The original nonlinear diffusion is gradient driven, which leads into much dependence on the accurate estimation of the edge. However, it is very difficult to get the accurate estimation of edges for X-ray angiogram images, which are characterized with complex background. So it is necessary to do some improvements to this model. By designing a new concept of contrast space according to the characteristics of these images, we change the original nonlinear diffusion into contrast-modulated nonlinear diffusion. Compared with the traditional method, this new approach is proved to have a better performance of enhancement. © 2005 IEEE.

关键词： Medical imaging

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Bootstrapping Autonomous Lane Changes with Self-supervised Augmented Runs 17th

Bootstrapping Autonomous Lane Changes with Self-supervised ...

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17th European Conference on Computer Vision, ECCV 2022

作者： Xiang, Xiang Key Lab of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031250682

In this paper, we want to strengthen an autonomous vehicle’s lane-change ability with limited lane changes performed by the autonomous system. In other words, our task is bootstrapping the predictability of lane-change feasibility for the autonomous vehicle. Unfortunately, autonomous lane changes happen much less frequently in autonomous runs than in manual-driving runs. Augmented runs serve well in terms of data augmentation: the number of samples generated from augmented runs in a single one is comparable with that of samples retrieved from real runs in a month. In this paper, we formulate the Lane-Change Feasibility Prediction problem and also propose a data-driven learning approach to solve it. Experimental results are also presented to show the effectiveness of learned lane-change patterns for the decision making. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Autonomous vehicles

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Resilient Distributed Predefined Time Secondary control for Cyber-Physical Microgrids

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IET Renewable Power Generation 2025年第1期19卷

作者： Junfeng Tan Fan Zhang Yanlu Huang Shuai Zhao Hongyu Su China Southern Power Grid Digital Grid Research Institute Co. Ltd. China Southern Power Grid Artificial Intelligence Technology Co. Ltd. Guangzhou China School of Artificial Intelligence and Automation and Technology and also with the Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan China

This paper proposed a resilient distributed predefined-time sliding mode control for islanded AC microgrids with external disturbances caused by noisy circumstances or cyber-attacks. By utilizing the predefined-time convergence theory, the voltage regulation and frequency restoration as well as active power sharing can be achieved within a predefined time, which is directly equal to an adjustable parameter. Furthermore, based on the integral sliding mode control approach, the proposed method can completely compensate the external disturbance. Different from some voltage control methods based on complex second-order consensus, a novel secondary controller is designed by adopting the virtual control technique, such that the voltage regulation can be achieved under a first-order consensus with a corresponding tracking controller. In addition, the direct Lyapunov method is utilized to prove the stability of islanded AC microgrids under the proposed controller, and the analysis of predefined-time convergence is also given. Finally, case studies on a microgrid test system with four distributed generator is built in the MATLAB/SimPowerSystems software environment are conducted to demonstrate the effectiveness and superior performance of the proposed control scheme.

关键词： distributed power generation Microgrids robust control

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2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010

作者： Zhang, Zhujin Wang, Shuo Zhang, Xingyi Zhang, Zheng Department of Control Science and Engineering Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China School of Computer and Information Science University of South Australia Adelaide SA 5095 Australia Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei 230039 China

ISBN: (纸本)9781424464388

Randí et al. proposed a significant graphical representation for DNA sequences, which is very compact and avoids loss of information. In this paper, we build a fast algorithm for this graphical representation with time complexity O(n2), and find another important advantage in the representation: no degeneracy. Moreover, we propose a new method to do similarity analysis of DNA sequences based on the representation. The approach adopts four elements of covariance matrix as a descriptor, and is illustrated on the first exon of beta-globin genes from 11 different species. © 2010 IEEE.

关键词： DNA sequences

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Modelling Transcriptional Regulation with Fractional Order Differential Equation using Gaussian Process 35

Modelling Transcriptional Regulation with Fractional Order D...

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第35届中国控制会议

作者： JI Ruirui ZHANG Xinxin YAN Xiaomei School of Automation and Information Engineering Xi'an University of Technology Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing

ISBN: (纸本)9781509009107

A reasonable math model is fundamental to describing gene regulatory mechanism.A new method is proposed to model transcriptional regulation with transcription factor from gene expression profiles using fractional order differential *** Process is employed as a tool to model the latent transcription factor activity and particle swarm optimization algorithm is utilized to optimize the fractional order,kinetic parameters in the model and hyperparameters in kernel *** results of the experiment on real gene expression profiles indicate that the fractional order differential equation fits data better,also the proposed approach is feasible to model transcriptional regulation.

关键词： Transcriptional regulation Fractional order differential equation Gaussian Process Particle swarm optimization

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An End-to-End Network for Single image Dedusting 7

An End-to-End Network for Single Image Dedusting

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7th International Conference on Information Science and control Engineering, ICISCE 2020

作者： Huang, Jiayan Yu, Zhaochai Li, Zuoyong Zheng, Xiangpan Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China

ISBN: (纸本)9781728164069

Dust storm not only reduces visibility of natural scene, but also affects capture capability of outdoor monitoring equipment. image dedusting aims to remove dust storm in images and make image scenes clearer. However, most of existing image enhancement-based methods have focused on defogging and deraining, and have paid little attentions to image dedusting. Therefore, research of image dedusting is necessary. In this paper, we presented an end-to-end network for single image dedusting. First, the proposed method uses an encoder module to encode a dusty image into image feature maps. Second, it uses the latest smoothed dilated residual convolution module to alleviate gridding artifacts caused by a widely-used dilated convolution. Third, it adopts an attention-based module to fuse image features at different levels and extract more contextual image information. Finally, it utilizes a decoder module, which decodes the feature maps, to obtain a residue, and adds it to the input dusty image to obtain the final image dedusting result. Experiments demonstrate the effectiveness of the proposed method in image dedusting. © 2020 IEEE.

关键词： Convolution

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