8 results on '"Ren, Kai"'
Search Results
2. An efficient data generation method for ANN-based surrogate models
- Author
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Tan, Ren Kai, Qian, Chao, Wang, Michael, and Ye, Wenjing
- Published
- 2022
- Full Text
- View/download PDF
3. A deep learning–based method for the design of microstructural materials
- Author
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Tan, Ren Kai, Zhang, Nevin L., and Ye, Wenjing
- Published
- 2020
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- View/download PDF
4. A deep learning–based method for the design of microstructural materials
- Author
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Wenjing Ye, Nevin L. Zhang, and Ren Kai Tan
- Subjects
Class (computer programming) ,Control and Optimization ,business.industry ,Computer science ,Deep learning ,Computer Graphics and Computer-Aided Design ,Convolutional neural network ,Computer Science Applications ,Surrogate model ,Computer engineering ,Control and Systems Engineering ,Tensor (intrinsic definition) ,Artificial intelligence ,Sensitivity (control systems) ,business ,Engineering design process ,Design methods ,Software - Abstract
Due to their designable properties, microstructural materials have emerged as an important class of materials that have the potential for used in a variety of applications. The design of such materials is challenged by the multifunctionality requirements and various constraints stemmed from manufacturing limitations and other practical considerations. Traditional design methods such as those based on topological optimization techniques rely heavily on high-dimensional physical simulations and can be inefficient. In addition, it is difficult to impose geometrical constraints in those methods. In this work, we propose a deep learning model based on deep convolutional generative adversarial network (DCGAN) and convolutional neural network (CNN) for the design of microstructural materials. The DCGAN is used to generate design candidates that satisfy geometrical constraints and the CNN is used as a surrogate model to link the microstructure to its properties. Once trained, the two networks are combined to form the design network which is utilized to for the inverse design. The advantages of the method include its high efficiency and the simplicity in handling geometrical constraints. In addition, no high-dimensional sensitivity simulations are required. The performance of the method is demonstrated on the design of microstructural materials with desired compliance tensor, subject to specified geometrical constraints.
- Published
- 2019
5. A Dual Global–Local Attention Network for Hyperspectral Band Selection.
- Author
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He, Ke, Sun, Weiwei, Yang, Gang, Meng, Xiangchao, Ren, Kai, Peng, Jiangtao, and Du, Qian
- Subjects
ARTIFICIAL neural networks ,DEEP learning - Abstract
This article proposes a dual global–local attention network (DGLAnet), which is an end-to-end unsupervised band selection (UBS) method that fully utilizes spatial and spectral information in both global and local aspects. The DGLAnet assumes that BS can be realized using the hyperspectral image (HSI) reconstruction process. First, the DGLAnet implements a dual attention module to obtain spatial–spectral and global–local features to reweight the HSI data. It adopts bi-directional relations to grasp spatial and spectral features from a global perspective. Meanwhile, the DGLAnet extracts local features through max-pooling and mean-pooling and then merges them via the convolution operation. Global–local features are utilized to learn attention to recalibrate the original data, and the reconstruction module is adopted to restore the original image from the reweighted HSI data. Finally, a proper band subset is selected by the constructed band evaluation index. Experiments on three hyperspectral data show that the DGLAnet outperforms other state-of-the-art methods and uses all bands with a lower computational cost. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
6. Generating high-resolution hyperspectral time series datasets based on unsupervised spatial-temporal-spectral fusion network incorporating a deep prior.
- Author
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Sun, Weiwei, Ren, Kai, Meng, Xiangchao, Yang, Gang, Liu, Qiang, Zhu, Lin, Peng, Jiangtao, and Li, Jiancheng
- Subjects
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TIME series analysis , *TRANSFORMER models , *GENERATIVE adversarial networks , *IMAGE fusion , *DEEP learning , *REMOTE sensing - Abstract
• The paper proposes a pioneering unsupervised deep learning method for spatial-temporal-spectral fusion of HSI and MSI. • TDGAN revolutionizes the generation of high-quality hyperspectral time-series datasets and for the first time. • TDGAN framework incorporates a deep prior, resulting in a significant enhancement of image fusion performance. • Swin transformer is firstly used for spectral super-resolution represents a groundbreaking application. • Three discriminators have been designed to minimize the loss of phenological information and spatial-spectral information. Over the past decade, image fusion has emerged as an indispensable tool for surface monitoring due to its capability to reconstruct high-quality surface reflectance. While satisfactory progress has been made in the spatial-spectral and spatial-temporal fusion of remote sensing images, the fusion of spatial-temporal-spectral data remains a challenging task. This challenge arises from the presence of multiple nonlinear relationships caused by inconsistent temporal, spatial, and spectral resolutions. In this paper, we propose a novel approach to address the spatial-temporal-spectral fusion of remote sensing images using a triple discriminator Generative Adversarial Network called TDGAN. Our approach represents the first attempt at spatial-temporal-spectral fusion based on deep learning techniques. Specifically, TDGAN achieves unsupervised learning by incorporating a deep spectral transformation prior, explores the application performance of the Swin Transformer in spectral super-resolution for the first time. Additionally, three discriminators are designed based on the prior consistencies: phenological consistency for data acquired at different times and spatial-spectral consistency for data acquired at the same time. These discriminators effectively enhance the prediction accuracy of missing hyperspectral images. Extensive simulations and real experiments were conducted to evaluate the effectiveness of our proposed TDGAN. The results demonstrate its robustness and comparable performance to other state-of-the-art methods. The proposed approach provides high-quality hyperspectral datasets with high spatial and temporal resolutions, facilitating continuous land cover observations. [ABSTRACT FROM AUTHOR]
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- 2024
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7. Q-DFN
- Author
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Mu Yi, Yin Yaguang, and Ren Kai
- Subjects
business.industry ,Minimum bounding box ,Computer science ,Feature (computer vision) ,Deep learning ,Q-learning ,Reinforcement learning ,Pattern recognition ,Artificial intelligence ,Function (mathematics) ,Pyramid (image processing) ,business ,Object detection - Abstract
Traditional approaches to each image simply depend on detectors of the classification network and the bounding box regression network. These approaches are, although working well, fail to exploit the satisfied accuracy for specific object detection or many methods sacrifice the accuracy for the speed of real-time detection. The reason for the unsatisfied results is that the relationship between the various objects is not effectively considered. In this paper, we propose a decisive fusion network for object detection. After the network extracts the features from the backbone first, the last five layers are treated as the feature pyramid structure. Each layer is responsible for the bounding box by the border regression algorithm, and the Deep Q-Learning (DQN) algorithm is responsible for the classification of the target. The fusion function connects the DQN algorithm with the object detection network, and considers the selection relationship between objects through the DQN algorithm to improve the overall accuracy. Through a lot of experiments, the parameters of DQN are better adjusted. The experiments show our method is better than tradition approaches.
- Published
- 2019
8. Deep learning for chemometric analysis of plastic spectral data from infrared and Raman databases.
- Author
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Neo, Edward Ren Kai, Low, Jonathan Sze Choong, Goodship, Vannessa, and Debattista, Kurt
- Subjects
DEEP learning ,PLASTIC analysis (Engineering) ,HIGH density polyethylene ,PLASTIC recycling ,POLYETHYLENE terephthalate ,CONVOLUTIONAL neural networks - Abstract
• Spectral database contains FTIR and Raman Data of over 20 different polymers. • Novel decision tree CNN architecture performed better than end-to-end networks. • Deep learning can distinguish between weathered and unaged plastic samples. Increasing plastic recycling rates is key to addressing plastic pollution. New technologies such as chemometric analysis of spectral data have shown great promises in improving the plastic sorting efficiency to boost recycling rates. In this work, a novel deep learning architecture, PolymerSpectraDecisionNet (PSDN) was developed, consisting of convolutional neural networks, residual networks and inception networks in a decision tree structure. To better represent the conditions in the plastic recycling industry, the models were built to identify the most widely recycled polymers – polyethylene, polypropylene and polyethylene terephthalate from open-sourced infrared and Raman spectral dataset containing over 20 different polymers. PSDN performed better than end-to-end neural networks, obtaining an accuracy of 0.949 and 0.967 with the Raman and infrared datasets respectively. The use of deep learning can also distinguish between weathered and unaged polymer samples, with accuracies of 0.954 for high density polyethylene and 0.906 for polyethylene terephthalate. [Display omitted] [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
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