Neural networks for topology optimization

Abstract In this research, we propose a deep learning based approach for speeding up the topology optimization methods. The problem we seek to solve is the layout problem. The main novelty of this work is to state the problem as an image segmentation task. We leverage the power of deep learning methods as the efficient pixel-wise image labeling technique to perform the topology optimization. We introduce convolutional encoder-decoder architecture and the overall approach of solving the above-described problem with high performance. The conducted experiments demonstrate the significant acceleration of the optimization process. The proposed approach has excellent generalization properties. We demonstrate the ability of the application of the proposed model to other problems. The successful results, as well as the drawbacks of the current method, are discussed.

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Multi-Task Model for Esophageal Lesion Analysis Using Endoscopic Images: Classification with Image Retrieval and Segmentation with Attention

Sensors ◽

10.3390/s22010283 ◽

2021 ◽

Vol 22 (1) ◽

pp. 283

Author(s):

Xiaoyuan Yu ◽

Suigu Tang ◽

Chak Fong Cheang ◽

Hon Ho Yu ◽

I Cheong Choi

Keyword(s):

Deep Learning ◽

Image Retrieval ◽

Learning Model ◽

Endoscopic Images ◽

Proposed Model ◽

Esophageal Lesion ◽

Lesion Analysis ◽

Diagnosis Accuracy ◽

Segmentation Task ◽

Deep Learning Model

The automatic analysis of endoscopic images to assist endoscopists in accurately identifying the types and locations of esophageal lesions remains a challenge. In this paper, we propose a novel multi-task deep learning model for automatic diagnosis, which does not simply replace the role of endoscopists in decision making, because endoscopists are expected to correct the false results predicted by the diagnosis system if more supporting information is provided. In order to help endoscopists improve the diagnosis accuracy in identifying the types of lesions, an image retrieval module is added in the classification task to provide an additional confidence level of the predicted types of esophageal lesions. In addition, a mutual attention module is added in the segmentation task to improve its performance in determining the locations of esophageal lesions. The proposed model is evaluated and compared with other deep learning models using a dataset of 1003 endoscopic images, including 290 esophageal cancer, 473 esophagitis, and 240 normal. The experimental results show the promising performance of our model with a high accuracy of 96.76% for the classification and a Dice coefficient of 82.47% for the segmentation. Consequently, the proposed multi-task deep learning model can be an effective tool to help endoscopists in judging esophageal lesions.

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Topology Optimization Design of Bars Structure Based on SKO Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.394.515 ◽

2013 ◽

Vol 394 ◽

pp. 515-520 ◽

Cited By ~ 1

Author(s):

Wen Jun Li ◽

Qi Cai Zhou ◽

Xu Hui Zhang ◽

Xiao Lei Xiong ◽

Jiong Zhao

Keyword(s):

Global Optimization ◽

Topology Optimization ◽

Optimization Design ◽

Optimization Problem ◽

Optimization Problems ◽

Optimization Methods ◽

Topology Optimization Problem ◽

Proposed Model ◽

Continuum Structure ◽

The Impact

There are less topology optimization methods for bars structure than those for continuum structure. Bionic intelligent method is a powerful way to solve the topology optimization problems of bars structure since it is of good global optimization capacity and convenient for numerical calculation. This article presents a SKO topology optimization model for bars structure based on SKO (Soft Kill Option) method derived from adaptive growth rules of trees, bones, etc. The model has been applied to solve the topology optimization problem of a space frame. It uses three optimization strategies, which are constant, decreasing and increasing material removed rate. The impact on the optimization processes and results of different strategies are discussed, and the validity of the proposed model is proved.

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Deep learning approaches for challenging species and gender identification of mosquito vectors

Scientific Reports ◽

10.1038/s41598-021-84219-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Veerayuth Kittichai ◽

Theerakamol Pengsakul ◽

Kemmapon Chumchuen ◽

Yudthana Samung ◽

Patchara Sriwichai ◽

...

Keyword(s):

Deep Learning ◽

High Performance ◽

Mosquito Species ◽

Characteristic Curve ◽

Confusion Matrix ◽

Morphological Identification ◽

Learning Approaches ◽

Mosquito Vectors ◽

Detection Rates ◽

Proposed Model

AbstractMicroscopic observation of mosquito species, which is the basis of morphological identification, is a time-consuming and challenging process, particularly owing to the different skills and experience of public health personnel. We present deep learning models based on the well-known you-only-look-once (YOLO) algorithm. This model can be used to simultaneously classify and localize the images to identify the species of the gender of field-caught mosquitoes. The results indicated that the concatenated two YOLO v3 model exhibited the optimal performance in identifying the mosquitoes, as the mosquitoes were relatively small objects compared with the large proportional environment image. The robustness testing of the proposed model yielded a mean average precision and sensitivity of 99% and 92.4%, respectively. The model exhibited high performance in terms of the specificity and accuracy, with an extremely low rate of misclassification. The area under the receiver operating characteristic curve (AUC) was 0.958 ± 0.011, which further demonstrated the model accuracy. Thirteen classes were detected with an accuracy of 100% based on a confusion matrix. Nevertheless, the relatively low detection rates for the two species were likely a result of the limited number of wild-caught biological samples available. The proposed model can help establish the population densities of mosquito vectors in remote areas to predict disease outbreaks in advance.

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Two-stage convolutional encoder-decoder network to improve the performance and reliability of deep learning models for topology optimization

Structural and Multidisciplinary Optimization ◽

10.1007/s00158-020-02788-w ◽

2021 ◽

Author(s):

Gorkem Can Ates ◽

Recep M. Gorguluarslan

Keyword(s):

Deep Learning ◽

Topology Optimization ◽

Learning Models ◽

Two Stage ◽

Convolutional Encoder

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Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement

Sensors ◽

10.3390/s21206839 ◽

2021 ◽

Vol 21 (20) ◽

pp. 6839

Author(s):

Aisha Al-Mohannadi ◽

Somaya Al-Maadeed ◽

Omar Elharrouss ◽

Kishor Kumar Sadasivuni

Keyword(s):

Deep Learning ◽

Early Diagnosis ◽

Large Scale ◽

Intima Media Thickness ◽

Semantic Segmentation ◽

Learning Techniques ◽

Proposed Model ◽

Decoder Architecture ◽

Huge Impact ◽

Good Learning

Cardiovascular diseases (CVDs) have shown a huge impact on the number of deaths in the world. Thus, common carotid artery (CCA) segmentation and intima-media thickness (IMT) measurements have been significantly implemented to perform early diagnosis of CVDs by analyzing IMT features. Using computer vision algorithms on CCA images is not widely used for this type of diagnosis, due to the complexity and the lack of dataset to do it. The advancement of deep learning techniques has made accurate early diagnosis from images possible. In this paper, a deep-learning-based approach is proposed to apply semantic segmentation for intima-media complex (IMC) and to calculate the cIMT measurement. In order to overcome the lack of large-scale datasets, an encoder-decoder-based model is proposed using multi-image inputs that can help achieve good learning for the model using different features. The obtained results were evaluated using different image segmentation metrics which demonstrate the effectiveness of the proposed architecture. In addition, IMT thickness is computed, and the experiment showed that the proposed model is robust and fully automated compared to the state-of-the-art work.

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Review methods for breast cancer detection using artificial intelligence and deep learning methods

System research and information technologies ◽

10.20535/srit.2308-8893.2021.1.08 ◽

2021 ◽

pp. 98-102

Author(s):

Maryam Naderan

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Cancer Detection ◽

High Performance ◽

False Negative ◽

Main Idea ◽

New Approach ◽

Training Time ◽

Proposed Model ◽

Convolutional Autoencoder

Nowadays, there are many related works and methods that use Neural Networks to detect the breast cancer. However, usually they do not take into account the training time and the result of False Negative (FN) while training the model. The main idea of this paper is to compare already existing methods for detecting the breast cancer using Deep Learning Algorithms. Moreover, since the breast cancer is one of the most common lethal cancers and early detection helps prevent complications, we propose a new approach and the use of the convolutional autoencoder. This proposed model has shown high performance with sensitivity, precision, and accuracy of 93,50%, 91,60% and 93% respectively.

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Enhanced Deep Learning Approach Based on the Deep Convolutional Encoder–Decoder Architecture for Electromagnetic Inverse Scattering Problems

IEEE Antennas and Wireless Propagation Letters ◽

10.1109/lawp.2020.2995455 ◽

2020 ◽

Vol 19 (7) ◽

pp. 1211-1215

Author(s):

He Ming Yao ◽

Lijun Jiang ◽

Wei E. I. Sha

Keyword(s):

Deep Learning ◽

Inverse Scattering ◽

Learning Approach ◽

Scattering Problems ◽

Inverse Scattering Problems ◽

Decoder Architecture ◽

Convolutional Encoder ◽

Electromagnetic Inverse Scattering

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Topology and Response Surface Optimization of a Bicycle Crank Arm with Multiple Load Cases

Applied Sciences ◽

10.3390/app10062201 ◽

2020 ◽

Vol 10 (6) ◽

pp. 2201 ◽

Cited By ~ 3

Author(s):

Ahmad Yusuf Ismail ◽

Gangta Na ◽

Bonyong Koo

Keyword(s):

Topology Optimization ◽

Response Surface ◽

Response Surface Method ◽

High Performance ◽

Experimental Validation ◽

Three Dimensional ◽

Optimization Methods ◽

Stress Constraint ◽

Element Analysis ◽

Surface Method

This paper presents an application of topology optimization and response surface method to optimize the geometry of a bicycle crank arm and the experimental validation of it. This is purposely to reduce the crank arm mass and create a preliminary design of a lightweight structure necessary for the high-performance bicycle development. A three-dimensional bike crank arm model was made in the SpaceClaim software followed by a static finite element analysis using ANSYS Workbench 2019 R1. A multiple cycling load was applied simultaneously in seven crank angles of 30, 45, 60, 90, 120, 135, and 150° relative to the horizontal position to create the multiple loads to the crank. From there, topology optimization was then conducted to investigate the effect of mass constraint, stress constraint, angle of cycling, and crank materials on the topological pattern result. To minimize stress concentration at corners, a shape optimization using the response surface method was conducted and obtained the final geometry. From the result, it is shown that both optimization methods not only successfully reduce the crank arm mass and provide several optimum design options but also are able to reduce the maximum stress in the crank arm up to 20% after the optimization process. The experimental validation using a newly developed wireless measurement system shows a considerable agreement to the numerical results.

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