Monitoring of Assembly Process Using Deep Learning Technology
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Monitoring of Assembly Process Using Deep Learning Technology
Monitoring the assembly process is a challenge in manual assembly production mass customization, where the operator needs to change the assembly process in accordance with different products. If the assembly is not immediately detected errors during the assembly process of products, can lead to errors and loss of time and money in the subsequent assembly process, and will affect the quality of the product.
To monitor the assembly process, this paper explored two methods: recognizes actions assembly and recognize the parts of the assembled complex product. In recognition assembly action, improvement of three-dimensional model of the convolutional neural network (CNN 3D) with a batch normalization proposed to detect the missing assembly action. In recognition part, fully convolutional network (FCN) is used for the segment, identify the different parts of the assembled complex product assembly sequence for checking missing or parts align. An action assembly and assembly of data sets segmentation data sets created.
The experimental results show that the action of the assembly recognition CNN 3D models with batch normalization that reduces the computational complexity, increasing the speed of training and accelerate the convergence of the model, while maintaining accuracy. FCN experimental results show that the FCN-2S provides a pixel higher recognition accuracy than other FCNs. Depression and anxiety co-occur with chronic pain, and the three allegedly caused by dysregulation in brain systems alongside associated with emotional processing associated with bodily sensations.
Understanding the relationship between emotional states, pain, and bodily sensations may help understand chronic pain conditions. We developed a mobile platform to measure pain, emotions and body sensations associated in patients with chronic pain in conditions of their daily lives. Sixty-five patients with chronic back pain were reported intensity of their pain, 11 emotional state, and the corresponding location of the body. This variable is used to predict the pain 2 weeks later.
Evaluation of technology-based learning by dental students during the pandemic outbreak of Corona virus disease 2019
[Learning curve in the acquisition of experience to the new technology]
Objective: Analysis of national and foreign trials investigating the accumulation of experience in using innovative technology learning curve
Materials and methods. s: earching for Russian language manuscript done in reference of the article and in the eLibrary database. foreign trials were selected from PubMed database in accordance with the keyword «learning curve in surgical practice». publications found in accordance with the purpose studied for this research.
Results: The accumulated experience in using the new technology a valuable learning curve to improve the training, determine the duration of the development of new technologies and the factors that influence its characteristics.
Applying machine learning, we developed two models predicted future pain, emphasizes interpretability. One of the models excluded features associated pain as a predictor of future pain and pain-related including other predictions. The best predictor of future pain is the interactive effects of (a) a body map fatigue with negative affect and (b) positively affect the pain of the past.
Description: CRK, also known as p38, is a protein that in humans is encoded by the CRK gene. This gene is a member of an adapter protein family that binds to several tyrosine-phosphorylated proteins. It is mapped to 17p13.3. The protein participates in the Reelin signaling cascade downstream of DAB1. The product of this gene has several SH2 and SH3 domains (src-homology domains) and is involved in several signaling pathways, recruiting cytoplasmic proteins in the vicinity of tyrosine kinase through SH2-phosphotyrosine interaction. The N-terminal SH2 domain of Crk functions as a positive regulator of transformation whereas the C-terminal SH3 domain functions as a negative regulator of transformation. Two alternative transcripts encoding different isoforms with distinct biological activity have been described.
Our findings emphasize the contribution of emotions, especially emotional experiences felt in the body, chronic pain above and beyond the understanding of mere tracking pain levels. The results may contribute to a new generation of artificial intelligence framework to assist in the development of diagnostic and therapeutic approaches are better for chronic pain.
