this paper proposes a license plate recognition method based on YOLOv8-Pose and E-LPRNet for complex scenarios such as urban roadside and road inspection. E-LPRNet is a character recognition network formed by modifyin...
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For human health, medical diagnosis plays an irreplaceable role, conventional medical diagnosis methods cannot ensure the accuracy of diagnosis due to the interference of various external factors. therefore, this pape...
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Practical factors such as high labor cost of labelling defect samples and scarcity of defect samples make it difficult for supervised machine learning models to solve the problem of yam-dyed fabric defect detection. T...
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ISBN:
(纸本)9781728159225
Practical factors such as high labor cost of labelling defect samples and scarcity of defect samples make it difficult for supervised machine learning models to solve the problem of yam-dyed fabric defect detection. To solve this problem, this paper proposes an unsupervised yam-dyed fabric defect detection method based on U-shaped de-noising convolutional auto-encoder (UDCAE). Firstly, for tested samples of yam-dyed fabric, the training dataset was constructed by collecting the non-defect yarn-dyed fabric samples. then, the non-defect dataset is utilized to model and train the proposed UDCAE model. Finally, the defective area can be quickly detected by calculating the residual between the original tested yarn-dyed fabric image and its reconstructed item correspondingly. the experiment results show that the proposed method can accurately detect defects of yarn-dyed fabrics with different patterns.
For nonstationary processes working upon varying conditions, the conventional alarm thresholds such as 3sigma rules configured for one single operational zone often result in false and missed alarms. Besides, the inte...
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In this paper, a new model-free adaptive sliding mode load frequency control (LFC) scheme is designed for inter-connected power systems, where modeling is difficult and suffers from load change disturbances and denial...
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Reciprocating compressor is core equipment of petrochemical industry. Accurate state of health monitoring and faults early warning state are very important for the smooth running of a reciprocating compressor. Aiming ...
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f-CaO is a key factor affecting the quality of cement in production. In this paper, the cement clink production process is introduced and discussed in detail. the time delay between the variables leads to an inaccurat...
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ISBN:
(纸本)9781728159225
f-CaO is a key factor affecting the quality of cement in production. In this paper, the cement clink production process is introduced and discussed in detail. the time delay between the variables leads to an inaccurate matching relationship with each other and defects the performance of traditional soft sensors. To this end, a semi-supervised spatial-information-based soft sensor for f-CaO content is proposed. First, we analyzed the relationship between process variables and quality variable and then reconstruct the input of samples into data matrix by stitching unlabeled process data together. the semi-supervised structure helps retain process information in the data. then, an end-to-end soft sensor based on CNN is established: convolution and pooling operations are used to extract the features of two-dimensional data containing spatial information;a multi-layer perceptron models the extracted features regressively. Further, in order to solve the defect of insufficient generalization ability of the CNN-based model, a framework for spatial feature extracting and transferring is proposed. Compared withthe multilayer perceptron, strong regression models with spatial features get better prediction accuracy. An actual cement production case is used to verify the effectiveness of the proposed method.
this paper studies the output regulation problem for switched systems with an event-triggered model predictive control approach. First, an event-triggering strategy is adopted to configure data sampling and communicat...
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In this paper, a method fusing least squares support vector machine (LS-SVM) with Gaussian kernel function and zeroing neural network (ZNN) is proposed to forecast the continuous motion of lower limb. the surface elec...
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Modern industrial processes with increasing complexity not only contain nonlinear and multi-mode characteristics, but also are commonly the dynamic processes, which brought challenging problems to soft sensor modeling...
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