scholarly journals Intelligent Generation Method of Innovative Structures Based on Topology Optimization and Deep Learning

Materials ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 7680
Author(s):  
Yingqi Wang ◽  
Wenfeng Du ◽  
Hui Wang ◽  
Yannan Zhao
Keyword(s):  
Deep Learning ◽  
Topology Optimization ◽  
Computer Aided Design ◽  
Mechanical Performance ◽  
Learning Algorithm ◽  
Structural Models ◽  
Generative Adversarial Networks ◽  
Application Process ◽  
Training Set ◽  
Deep Learning Algorithm

Computer-aided design has been widely used in structural calculation and analysis, but there are still challenges in generating innovative structures intelligently. Aiming at this issue, a new method was proposed to realize the intelligent generation of innovative structures based on topology optimization and deep learning. Firstly, a large number of structural models obtained from topology optimization under different optimization parameters were extracted to produce the training set images, and the training set labels were defined as the corresponding load cases. Then, the boundary equilibrium generative adversarial networks (BEGAN) deep learning algorithm was applied to generate numerous innovative structures. Finally, the generated structures were evaluated by a series of evaluation indexes, including innovation, aesthetics, machinability, and mechanical performance. Combined with two engineering cases, the application process of the above method is described here in detail. Furthermore, the 3D reconstruction and additive manufacturing techniques were applied to manufacture the structural models. The research results showed that the proposed approach of structural generation based on topology optimization and deep learning is feasible, and can not only generate innovative structures but also optimize the material consumption and mechanical performance further.

scholarly journals Multimedia Teaching of College Musical Education Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wei Li
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Research Work ◽  
Auxiliary Function ◽  
Multimedia Technology ◽  
Application Process ◽  
Musical Education ◽  
Deep Learning Algorithm ◽  
Multimedia Teaching ◽  
College Music

In view of the current situation of musical education and the need for reform in China, we adopt two different methods, i.e., literature method and interview method in this research work. From these methods, we read a lot of musical education, multimedia technology, and modern teaching and reform. This research work is divided into two main phases. Firstly, the article mainly discusses the characteristics of college musical education compared with other cultural courses and the feasibility of multimedia technology and the auxiliary function of musical education that is applied in school’s musical education. Secondly, brain computing attempts to analyze things by simulating the structure and information processing of biological neural networks. The intelligent learning characteristic of a deep learning algorithm is proposed to monitor the process of musical education teaching and analyze the process quality. Finally, we introduced the design and production of network multimedia courseware which will help in theoretical guidance and reference to the application of multimedia technology in college musical education in China. Moreover, the outcome of the proposed model can play a role in solving and answering questions in the current multimedia application process and Chinese college music workers will apply multimedia technology more effectively and skillfully.

scholarly journals Identifying Periampullary Regions in MRI Images Using Deep Learning

2020 ◽  
Author(s):  
Yong Tang ◽  
Xinpei Chen ◽  
Weijia Wang ◽  
Jiali Wu ◽  
Yingjun Zheng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Learning Method ◽  
Training Set ◽  
Deep Learning Algorithm ◽  
Invasive Method ◽  
T1 And T2 ◽  
Magnetic Resonance Imaging Mri ◽  
Validation Set ◽  
Development And Validation

Abstract Background: Development and validation of a deep learning method to automatically segment the peri-ampullary (PA) region in magnetic resonance imaging (MRI) images. Methods: A group of patients with or without periampullary carcinoma (PAC) was included. The PA regions were manually annotated in MRI images by experts. Patients were randomly divided into one training set and one validation set. A deep learning method to automatically segment the PA region in MRI images was developed using the training set. The segmentation performance of the method was evaluated in the validation set. Results: The deep learning algorithm achieved optimal accuracies in the segmentation of the PA regions in both T1 and T2 MRI images. The value of the intersection over union (IoU) was 0.67 and 0.68 for T1 and T2 images, respectively. Conclusions: Deep learning algorithm is promising with accuracies of concordance with manual human assessment in segmentation of the PA region in MRI images. This automated non-invasive method helps clinicians to identify and locate the PA region using preoperative MRI scanning.

scholarly journals Study on Radar Echo-Filling in an Occlusion Area by a Deep Learning Algorithm

2021 ◽  
Vol 13 (9) ◽  
pp. 1779
Author(s):  
Xiaoyan Yin ◽  
Zhiqun Hu ◽  
Jiafeng Zheng ◽  
Boyong Li ◽  
Yuanyuan Zuo
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Learning Algorithm ◽  
Weather Radar ◽  
Loss Functions ◽  
Training Dataset ◽  
Echo Intensity ◽  
Common Mean ◽  
Deep Learning Algorithm ◽  
Radar Beam

Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5° elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5° elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.

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