Soft sensor, as an important paradigm for industrial intelligence, is widely used in production to achieve efficient monitoring and prediction of status including product quality. Data-driven soft sensor methods have attracted attention, which still challenges because complex data with diverse characteristics, nonlinear relationships, massive unlabeled samples. In this article, a data-driven se...

Modern interconnected power grids are a critical target of many kinds cyber-attacks, potentially affecting public safety and introducing significant economic damages. In such scenario, more effective detection early alerting tools needed. This study introduces novel anomaly architecture, empowered by modern machine learning techniques specifically targeted for control systems. It is based on st...

Computer-aided diagnosis (CAD) is a promising tool for accurate and consistent diagnosis and prognosis. Cell detection and segmentation are essential steps for CAD. These tasks are challenging due to variations in cell shapes, touching cells, and cluttered background. In this paper, we present a cell detection and segmentation algorithm using the sparse reconstruction with trivial templates and...

Stacked Denoising Autoencoders (SDAs) [4] have been used successfully in many learning scenarios and application domains. In short, denoising autoencoders (DAs) train one-layer neural networks to reconstruct input data from partial random corruption. The denoisers are then stacked into deep learning architectures where the weights are fine-tuned with back-propagation. Alternatively, the outputs...

For all the different alternatives, we use the adaptive gradient in the training of the autoencoder. Table 1 shows the classification accuracy for the task labeling problem achieved by each of the different choices and our Expert Gate autoencoder. It can be noticed that the linear gate (Linear Autoencoder) fails to recognize the examples from the Flowers dataset – the linearly learned subspace ...