作者：
Lew, Andrew J.Buehler, Markus J.MIT
Lab Atomist & Mol Mech LAMM 77 Massachusetts Ave Cambridge MA 02139 USA MIT
Dept Chem 77 Massachusetts Ave Cambridge MA 02139 USA MIT
Schwarzman Coll Comp Ctr Computat Sci & Engn 77 Massachusetts Ave Cambridge MA 02139 USA
Variational autoencoders (VAE) are machine learning models that can extract low dimensional representations of data from datasets of high complexity and volume. Importantly, they can be used for generative purposes to...
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Variational autoencoders (VAE) are machine learning models that can extract low dimensional representations of data from datasets of high complexity and volume. Importantly, they can be used for generative purposes to reconstruct complex data, such as images, from a low dimensional encoding of only a few variables. Long shortterm memory (LSTM) neural networks are well suited to learning logical trajectory relationships within datasets. Using these two models in concert, we develop a VAE-LSTM approach to learn a classic mechanical materials design problem. Here, we focus on the compliance optimization of cantilever design, using a VAE to encode cantilever structures into a 2D latent space and a LSTM to learn trajectories in that latent space corresponding to the optimization process. Ultimately, we are able to clearly visualize the space of cantilever design, generate new design with extremely low density beyond the original dataset, and obtain optimal cantilever structures inspired by nature. We also demonstrate how the resulting designs can be manufactured using 3D printing, offering a rapid pathway from concept to prototype. The method we developed here can be generalized to other imagebased datasets encapsulating changes from multiple factors. The ability offered by our approach to interpret complex behavior, via representations in simplified space, has great potential for application in the intelligent design and manufacturing of materials structure problems.
Medical imaging has been widely used to diagnose diseases over the past two decades. The lack of information in this field makes it difficult for medical experts to diagnose diseases with a single modality. The combin...
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In the 21st-century, data is as valuable as gold. Many data-centric applications are generating a vast amount of data. Businesses can use this generated data to pinpoint the various sources of problems, if any. In add...
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Mining the large volume textual data produced by microblogging services has attracted much attention in recent years. An important preprocessing step of microblog text mining is to convert natural language texts into ...
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ISBN:
(纸本)9781479975921
Mining the large volume textual data produced by microblogging services has attracted much attention in recent years. An important preprocessing step of microblog text mining is to convert natural language texts into proper numerical representations. Due to the short-length characteristic, finding proper representations of microblog texts is nontrivial. In this paper, we propose to build deep network-based models to learn low-dimensional representations of microblog texts. The proposed models take advantage of the semantic relatedness derived from two types of microblog-specific information, namely the retweet relationship and hashtags. Experiment results show that the deep models perform better than traditional dimensionality reduction methods such as latent semantic analysis and latent Dirichlet allocation topic model, and the use of microblog-specific information can help to learn better representations.
Batik holds profound cultural significance within Indonesia, serving as a tangible expression of the nation's rich heritage and intricate philosophical narratives. This paper introduces the Batik Nitik Sarimbit 12...
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Batik holds profound cultural significance within Indonesia, serving as a tangible expression of the nation's rich heritage and intricate philosophical narratives. This paper introduces the Batik Nitik Sarimbit 120 dataset, originating from Yogyakarta, Indonesia, as a pivotal resource for researchers and enthusiasts alike. Comprising images of 60 Nitik patterns meticulously sourced from fabric samples, this dataset represents a curated selection of batik motifs emblematic of the region's artistic tradition. The Batik Nitik Sarimbit 120 dataset offers a comprehensive collection of 120 motif pairs distributed across 60 distinct categories. By providing a comprehensive repository of batik motifs, the Batik Nitik Sarimbit 120 dataset facilitates the training and validation of machine learning algorithms, particularly through the utilization of generative method. This enables researchers to explore and innovate in the realm of batik pattern generation, fostering new avenues for creativity and expression within this venerable art form. In essence, the Batik Nitik Sarimbit 120 dataset stands as a testament to the collaborative efforts of cultural institutions and academia in preserving and promoting Indonesia's rich batik heritage. Its accessibility and richness make it a valuable resource for scholars, artists, and enthusiasts seeking to delve deeper into the intricate world of Indonesian batik. (c) 2024 The Author(s). Published by Elsevier Inc.
Insufficient and imbalance data samples often prevent the development of accurate deep learning models for manufacturing defect detection. By applying data augmentation methods - including VAE latent space oversamplin...
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Insufficient and imbalance data samples often prevent the development of accurate deep learning models for manufacturing defect detection. By applying data augmentation methods - including VAE latent space oversampling and random data generation, and GAN multi-modal complementary data generation, we overcome the dataset limitations and achieve Pass/No-Pass accuracies of over 90%.
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