the rapid development and applications of precision motion systems pose a great challenge on tracking performance improvement to complete various industrial or scientific tasks. In this paper, an iterative learning en...
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In recent years, Chinese Liquor is widely loved by consumers in the world. therefore, the quality of Chinese Liquor has also attracted much attention. In order to establish a fast and accurate method for identifying t...
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Withthe improvement of living standards, heart diseases have become one of the common diseases that threaten health of human beings. Electrocardiogram (ECG) is an important basis for diagnosing heart diseases. In thi...
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ISBN:
(纸本)9781728159225
Withthe improvement of living standards, heart diseases have become one of the common diseases that threaten health of human beings. Electrocardiogram (ECG) is an important basis for diagnosing heart diseases. In this paper, we propose a model to predict 55 classes of heart diseases simultaneously, that is, to solve a multi-label classification task. In order to make full use of the characteristics of the ECG, we propose a network structure combining residual neural network (ResNet) and gated recurrent unit neural network (GRU). On this basis, in order to solve the problem of imbalanced data set, the loss function is a improved focal loss. the results of experiments show the effectiveness of our method. More specifically, the method improves F1 score, while the hamming loss is reduced. Observing the classify result of each single class, we improve F1 score and average area under the receiver operating characteristic curve (AUC) for most classes.
the article studies the cluster synchronization for a kind of nonlinear coupled complex network with time-varying delay. Considering the networks may subject to certain uncertainties, the model of complex networks con...
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this paper presents a novel adaptive optimal setpoint tracking control method for unknown linear continuous-time systems with constant disturbances using adaptive dynamic programming (ADP). Compared withthe existing ...
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Elevators are gradually becoming an indispensable part of people's daily life. At the same time, various safety problems caused by elevators are also seriously threatening people's life and property security. ...
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ISBN:
(纸本)9781728159225
Elevators are gradually becoming an indispensable part of people's daily life. At the same time, various safety problems caused by elevators are also seriously threatening people's life and property security. Elevator traction wire rope damage is a common type of potential safety hazard. At present, the defect detection method for elevator traction wire rope damage generally has high complexity and labor cost. this paper presents a method to detect the abrasion of elevator traction wire rope by taking full use of the texture feature of the traction wire rope images. First, the interference factors in the process of image acquisition are removed by the pretreatment methods of graying and denoising. then, the abrasion area of the traction wire rope is determined according to the similarity measurement criteria by combining edge detection and template matching. Finally, the image is finely segmented and the abrasion rates of different areas are calculated. this method is convenient and intuitive to detect the changes of elevator wire rope abrasion. On one hand, the complexity of the detection and the loss of the human labor can be reduced;on the other hand, valuable reference can be provided for elevator maintenance. Experiment on actual wire rope images validate the effectiveness of the proposed method.
PID controller is a common control regulator for industrial processes and is widely used in chemical processes. However, conventional proportional-integral-derivative controller has been found insufficient for nonline...
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Withthe increasing complexity of industrial production, data-driven based monitoring methods attract more attention. However, the conventional static process monitoring methods may show poor performance for the time-...
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ISBN:
(纸本)9781728159225
Withthe increasing complexity of industrial production, data-driven based monitoring methods attract more attention. However, the conventional static process monitoring methods may show poor performance for the time-varying processes since they fail to track the time-varying characteristics. As Gaussian Mixture Model (GMM) has been widely used for process monitoring, this paper presents a new incremental GMM model for monitoring time-varying processes. First, an incremental GMM (IGMM) model is proposed, which can recursively update model parameters, adaptively add new Gaussian components and discard the irrelevant component based on the shifting samples online. then the Bayesian Inference Probability (BIP) is introduced for monitoring statistics and a two-level partition strategy that can separate normal shifting samples from fault samples is proposed, which reduces the possibility of adding fault samples to the model. On the basis of IGMM model, an adaptive monitoring scheme is developed, which can track the time-varying characteristics of processes. Finally, a time-varying numerical example and the Tennessee Eastman process are adopted to validate the feasibility of the proposed monitoring model. Experimental results clearly demonstrate the adaptiveness of the monitoring model to time-varying processes and the ability to avoid false updates.
In this paper, a leader following consensus problem for a first-order multi-agent system without priori knowledge of the control direction is studied by using adaptive iterative learningcontrol. For the uncertainty o...
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High-speed trains always operate from the same departure station to the same terminal station and hence iterative learningcontrol (ILC) is an appropriate approach for automatic train control. However, due to complex ...
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ISBN:
(纸本)9781538626184
High-speed trains always operate from the same departure station to the same terminal station and hence iterative learningcontrol (ILC) is an appropriate approach for automatic train control. However, due to complex environment and unknown uncertainties, the train may not arrive at the terminal station on time, or earlier and later than the schedule time in each operation. To address this problem, a modified proportional-type (P-type) ILC is presented where the trial length in each operation can be randomly varying. Moreover, the convergence condition in 2-norm is also derived through rigorous analysis. the effectiveness of the modified P-type ILC is further verified through simulations.
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