Intelligent manufacturing raises higher requirements for tool condition monitoring (TCM) in terms of accuracy, robustness, and adaptability. At present, direct methods based on image processing and deep learning have ...
详细信息
Intelligent manufacturing raises higher requirements for tool condition monitoring (TCM) in terms of accuracy, robustness, and adaptability. At present, direct methods based on image processing and deep learning have made breakthroughs in TCM. However, some issues, such as image quality, model parameters, and dataset scale in the abovementioned methods, restrict industrial applications of TCM. Regarding the abovementioned issue, the purpose of this article is to propose a lightweight network model based on multiple activation functions to promote the intelligent industrial application of TCM. First, the image quality mechanism caused by complex working conditions is analyzed in industrial environments. Correspondingly, data augmentation is adopted to solve the problem of data scale under the premise of ensuring data quality and richness. Then, the adaptive activation function and the hard version of swish are introduced at the front and second half of the network to avoid information loss and reduce the activation function cost. Finally, a lightweight network based on cloud-edge collaboration for TCM is constructed. The model is iteratively optimized in the cloud and inferenced on the edge embedded device. The accuracy and adaptability of the proposed network are verified by accelerating milling cutter life under multiple working conditions.
In this paper, an improved deep echo state network is proposed, named as multiple activation functions deep echo state network (MAF-DESN), where states are activated by multiple activation functions. A sufficient cond...
详细信息
In this paper, an improved deep echo state network is proposed, named as multiple activation functions deep echo state network (MAF-DESN), where states are activated by multiple activation functions. A sufficient condition for MAF-DESN is given to guarantee that MAF-DESN possesses the echo state property. Finally, the MAF-DESN is applied to chaotic time-series predictions and compared to other ESN deformation models and popular LSTM. Simulation results show that under same network size condition, MAF-DESN possesses stronger explanatory power in chaotic far-infrared laser predictions (R-square=0.9537, others0.6487), and better fitting ability in daily foreign exchange rates (MAE=0.0040, others0.0047) and chaotic far-infrared laser (MAE=3.4042, others4.9021). In high-dimension-input task, MAF-DESN improved the performance when the results were compared (R-square=0.4274, others0.3975 and MAE=5.2221, others7.6876), while the train time of MAF-DESN did not increase when compared to DESN.
For industry 4.0, intelligent modeling is very important. Modelling plays a very important role in making control strategies and production plans. Nevertheless, establishing an accurate and robust model becomes more d...
详细信息
ISBN:
(纸本)9781728100845
For industry 4.0, intelligent modeling is very important. Modelling plays a very important role in making control strategies and production plans. Nevertheless, establishing an accurate and robust model becomes more difficult because of the increasing complexity of modelling data. To solve this problem, a novel feature extraction based multiple activation functions extreme learning machine (LV-MAFELM) is presented. The LV-MAFELM model is easy to construct: firstly, generate the input weights at random;secondly, select several different nonlinear activationfunctions and compute the hidden layer outputs;thirdly, extract principal components from the hidden layer outputs;finally, compute the output weights analytically. For verifying the model performance, the LV-MAFELM model is applied in one petrochemical industry process - the Purified Terephthalic Acid (PTA) process. Simulation results demonstrate that the presented LV-MAFELM achieves good performance, which indicates that accuracy and stability of energy prediction models can be improved.
This study introduces a novel stereo matching algorithm designed for agricultural applications in greenhouses. Addressing the limitations of existing convolutional neural network (CNN)-based stereo matching models, th...
详细信息
This study introduces a novel stereo matching algorithm designed for agricultural applications in greenhouses. Addressing the limitations of existing convolutional neural network (CNN)-based stereo matching models, the proposed architecture enhances efficiency by converting RGB images to grayscale and applying histogram equalization. This method preserves essential visual information while reducing data size and computational complexity. Rectangle filter kernels are also used to prioritize horizontal information, aligning with the typical arrangements of stereo camera pairs. The disparity prediction model is initially trained on a subset of 29,204 image pairs from the Scene Flow dataset and subsequently retrained and evaluated using 2,470 tomato image pairs and 486 napa cabbage image pairs from Greenhouse. This further refines its performance in real agricultural settings. The proposed model surpasses existing models such as geometry and context network (GC-Net), pyramid stereo matching network (PSMNet), 2D-Mobilestereonet and 3D-Mobilestereonet in terms of disparity prediction accuracy and computational speed, consuming less than one-third of the memory. Given the unique challenges of greenhouse environments, this approach demonstrates a robust method for developing stereo matching algorithms suited for stereo vision applications in such settings.
暂无评论