Missing data values and differing sampling rates, particularly for important parameters such as particle size and stream composition, are a common problem in minerals processing plants. Missing data imputation is used...
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Missing data values and differing sampling rates, particularly for important parameters such as particle size and stream composition, are a common problem in minerals processing plants. Missing data imputation is used to avoid information loss (due to downsampling or discarding incomplete records). A recent deep -learning technique, variational autoencoders (VAEs), has been used for missing data imputation in image data, and was compared here to imputation by mean replacement and by principal component analysis (PCA) imputation. The techniques were compared using a synthetic, nonlinear dataset, and a simulated milling circuit dataset, which included process disturbances, measurement noise, and feedback control. Each dataset was corrupted with missing values in 20% of records (lightly corrupted) and in 90% of records (heavily corrupted). For both lightly and heavily corrupted datasets, the root mean squared error of prediction for VAE imputation was lower than the traditional methods. Possibilities for the extension of missing data imputation to inferential sensing are discussed. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Machine learning techniques can help to represent and solve quantum systems. Learning measurement outcome distribution of quantum ansatz is useful for characterization of near-term quantum computing devices. In this w...
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Machine learning techniques can help to represent and solve quantum systems. Learning measurement outcome distribution of quantum ansatz is useful for characterization of near-term quantum computing devices. In this work, we use the popular unsupervised machine learning model, variational autoencoder (VAE), to reconstruct the measurement outcome distribution of quantum ansatz. The number of parameters in the VAE are compared with the number of measurement outcomes. The numerical results show that VAE can efficiently learn the measurement outcome distribution with few parameters. The influence of entanglement on the task is also revealed.
Here we report a method of finding multiple crystal structures similar to the known crystal structures of materials on database through machine learning. The radial distribution function is used to represent the gener...
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Here we report a method of finding multiple crystal structures similar to the known crystal structures of materials on database through machine learning. The radial distribution function is used to represent the general characteristics of the known crystal structures, and then the variational autoencoder is employed to generate a set of representative crystal replicas defined in a two-dimensional optimal continuous space. For given chemical compositions and crystal volume, we generate random crystal structures using constraints for crystal symmetry and atomic positions and directly compare their radial distribution functions with those of the known and/or replicated crystals. For selected crystal structures, energy minimization is subsequently performed through firstprinciples electronic structure calculations. This approach enables us to predict a set of new low-energy crystal structures using only the information on the radial distribution functions of the known structures.
Designers increasingly rely on parametric design studies to explore and improve structural concepts based on quantifiable metrics, generally either by generating design variations manually or using optimization method...
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Designers increasingly rely on parametric design studies to explore and improve structural concepts based on quantifiable metrics, generally either by generating design variations manually or using optimization methods. Unfortunately, both of these approaches have important shortcomings: effectively searching a large design space manually is infeasible, and design optimization overlooks qualitative aspects important in architectural and structural design. There is a need for methods that take advantage of computing intelligence to augment a designer's creativity while guiding-not forcing-their search for better-performing solutions. This research addresses this need by integrating conditional variational autoencoders in a performance-driven design exploration framework. First, a sampling algorithm generates a dataset of meaningful design options from an unwieldy design space. Second, a performance-conditioned variational autoencoder with a low-dimensional latent space is trained using the collected data. This latent space is intuitive to explore by designers even as it offers a diversity of high-performing design options.
Most existing image dehazing methods based learning are less able to perform well to real hazy *** important reason is that they are trained on synthetic hazy images whose distribution is different from real hazy *** ...
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Most existing image dehazing methods based learning are less able to perform well to real hazy *** important reason is that they are trained on synthetic hazy images whose distribution is different from real hazy *** relieve this issue, this paper proposes a new hazy scene generation model based on domain adaptation, which uses a variational autoencoder to encode the synthetic hazy image pairs and the real hazy images into the latent space to *** synthetic hazy image pairs guide the model to learn the mapping of clear images to hazy images, the real hazy images are used to adapt the synthetic hazy images' latent space to real hazy images through generative adversarial loss, so as to make the generative hazy images' distribution as close to the real hazy images' distribution as *** comparing the results of the model with traditional physical scattering models and Adobe Lightroom CC software, the hazy images generated in this paper is more *** end-to-end domain adaptation model is also very convenient to synthesize hazy images without depth *** traditional method to dehaze the synthetic hazy images generated by this paper, both SSIM and PSNR have been improved, proved that the effectiveness of our *** non-reference haze density evaluation algorithm and other quantitative evaluation also illustrate the advantages of our method in synthetic hazy images.
Constrained optimization problems can be difficult because their search spaces have properties not conducive to search, e.g., multimodality, discontinuities, or deception. To address such difficulties, considerable re...
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ISBN:
(纸本)9781450392686
Constrained optimization problems can be difficult because their search spaces have properties not conducive to search, e.g., multimodality, discontinuities, or deception. To address such difficulties, considerable research has been performed on creating novel evolutionary algorithms or specialized genetic operators. However, if the representation that defined the search space could be altered such that it only permitted valid solutions that satisfied the constraints, the task of finding the optimal would be made more feasible without any need for specialized optimization algorithms. We propose Constrained Optimization in Latent Space (COIL), which uses a VAE to generate a learned latent representation from a dataset comprising samples from the valid region of the search space according to a constraint, thus enabling the optimizer to find the objective in the new space defined by the learned representation. Preliminary experiments show promise: compared to an identical GA using a standard representation that cannot meet the constraints or find fit solutions, COIL with its learned latent representation can perfectly satisfy different types of constraints while finding high-fitness solutions.
Learning network representations is a fundamental task for many graph applications such as link prediction, node classification, graph clustering, and graph visualization. Many real-world networks are interpreted as d...
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ISBN:
(纸本)9783030438234;9783030438227
Learning network representations is a fundamental task for many graph applications such as link prediction, node classification, graph clustering, and graph visualization. Many real-world networks are interpreted as dynamic networks and evolve over time. Most existing graph embedding algorithms were developed for static graphs mainly and cannot capture the evolution of a large dynamic network. In this paper, we propose Dynamic joint variational Graph autoencoders (Dyn-VGAE) that can learn both local structures and temporal evolutionary patterns in a dynamic network. Dyn-VGAE provides a joint learning framework for computing temporal representations of all graph snapshots simultaneously. Each auto-encoder embeds a graph snapshot based on its local structure and can also learn temporal dependencies by collaborating with other autoencoders. We conduct experimental studies on dynamic real-world graph datasets and the results demonstrate the effectiveness of the proposed method.
In this work we propose a method based on geometric deep learning to predict the complete surface of the liver, given a partial point cloud of the organ obtained during the surgical laparoscopic procedure. We introduc...
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ISBN:
(纸本)9783030603649;9783030603656
In this work we propose a method based on geometric deep learning to predict the complete surface of the liver, given a partial point cloud of the organ obtained during the surgical laparoscopic procedure. We introduce a new data augmentation technique that randomly perturbs shapes in their frequency domain to compensate the limited size of our dataset. The core of our method is a variational autoencoder (VAE) that is trained to learn a latent space for complete shapes of the liver. At inference time, the generative part of the model is embedded in an optimisation procedure where the latent representation is iteratively updated to generate a model that matches the intraoperative partial point cloud. The effect of this optimisation is a progressive non-rigid deformation of the initially generated shape. Our method is qualitatively evaluated on real data and quantitatively evaluated on synthetic data. We compared with a state-of-the-art rigid registration algorithm, that our method outperformed in visible areas.
Quality variables are key indicators of the operating performance in industrial processes. Because they are difficult to measure, soft sensor models can be adopted to predict them timely. For accurate prediction, suff...
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Quality variables are key indicators of the operating performance in industrial processes. Because they are difficult to measure, soft sensor models can be adopted to predict them timely. For accurate prediction, sufficient training data are necessary to construct a good soft sensor model. In practical industrial processes, however, data labeled with quality variables are usually deficient in the desired region. Particularly, when the process is just switched to a new mode, available data in this new mode are initially quite a few. In this paper, a novel data synthesis method based on the regressor-embedded semi-supervised variational autoencoder (RSSVAE) model is proposed to generate synthetic labeled data when the original labeled data are inadequate. The proposed model utilizes not only the original data in the data-scarce region but also the data in other regions, which share some common information with the scarce data. Meanwhile, data synthesis and model correction mechanism are implemented iteratively to avoid model biases. Once the synthetic labeled data of the data-scarce region are acquired, they are combined with the original labeled data to establish a local soft sensor and predict the quality variables of the unlabeled data. Finally, a real ammonia synthesis process is introduced to demonstrate the effectiveness of the proposed method.
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