Physics Symbolic Learner for Discovering Ground-Motion Models Via NGA-West2 Database

Ground-motion model (GMM) is the basis of many earthquake engineering studies. In this study, a novel physics-informed symbolic learner (PISL) method based on the Nest Generation Attenuation-West2 database is proposed to automatically discover mathematical equation operators as symbols. The sequential threshold ridge regression algorithm is utilized to distill a concise and interpretable explicit characterization of complex systems of ground motions. In addition to the basic variables retrieved from previous GMMs, the current PISL incorporates two a priori physical conditions, namely, distance and amplitude saturation. GMMs developed using the PISL, an empirical regression method (ERM), and an artificial neural network (ANN) are compared in terms of residuals and extrapolation based on obtained data of peak ground acceleration and velocity. The results show that the inter- and intra-event standard deviations of the three methods are similar. The functional form of the PISL is more concise than that of the ERM and ANN. The extrapolation capability of the PISL is more accurate than that of the ANN. The PISL-GMM used in this study provide a new paradigm of regression that considers both physical and data-driven machine learning and can be used to identify the implied physical relationships and prediction equations of ground motion variables in different regions.

Battery Cloud with Advanced Algorithms

A Battery Cloud or cloud battery management system leverages the cloud computational power and data storage to improve battery safety, performance, and economy. This work will present the Battery Cloud that collects measured battery data from electric vehicles and energy storage systems. Advanced algorithms are applied to improve battery performance. Using remote vehicle data, we train and validate an artificial neural network to estimate pack SOC during vehicle charging. The strategy is then tested on vehicles. Furthermore, high accuracy and onboard battery state of health estimation methods for electric vehicles are developed based on the differential voltage (DVA) and incremental capacity analysis (ICA). Using cycling data from battery cells at various temperatures, we extract the charging cycles and calculate the DVA and ICA curves, from which multiple features are extracted, analyzed, and eventually used to estimate the state of health. For battery safety, a data-driven thermal anomaly detection method is developed. The method can detect unforeseen anomalies such as thermal runaways at the very early stage. With the further development of the internet of things, more and more battery data will be available. Potential applications of battery cloud also include areas such as battery manufacture, recycling, and electric vehicle battery swap.

Data-driven Thermal Anomaly Detection for Batteries using Unsupervised Shape Clustering

For electric vehicles (EV) and energy storage (ES) batteries, thermal runaway is a critical issue as it can lead to uncontrollable fires or even explosions. Thermal anomaly detection can identify problematic battery packs that may eventually undergo thermal runaway. However, there are common challenges like data unavailability, environment variations, and battery aging. We propose a data-driven method to detect battery thermal anomaly based on comparing shape-similarity between thermal measurements. Based on their shapes, the measurements are continuously being grouped into different clusters. Anomaly is detected by monitoring deviations within the clusters. Unlike model-based or other data-driven methods, the proposed method is robust to data loss and requires minimal reference data for different pack configurations. As the initial experimental results show, the method not only can be more accurate than the onboard BMS, but also can detect unforeseen anomalies at the early stage.

Image Super-Resolution via Residual Blended Attention Generative Adversarial Network with Dual Discriminators

When reviewing codes, it seems that there's something wrong with the training codes that we did not notice, this article is temporarily withdrawn and will be modified and resubmitted after codes reviewing.

Single Image Super-resolution via Dense Blended Attention Generative Adversarial Network for Clinical Diagnosis

In clinical diagnosis, doctors are able to see biological tissues and early lesions more clearly with the assistance of high-resolution(HR) medical images, which is of vital significance for improving diagnosis accuracy. In order to address the issue that medical images would suffer from severe blurring caused by lack of high-frequency details, this paper develops a novel image super-resolution(SR) algorithm called SR-DBAN via dense neural network and blended attention mechanism. Specifically, a novel blended attention block is proposed and introduced to dense neural network(DenseNet), so that the neural network can concentrate more attention to the regions and channels with sufficient high-frequency details adaptively. In the framework of SR-DBAN, batch normalization layers in the original DenseNet are removed to avoid loss of high-frequency texture details, final HR images are obtained by deconvolution at the very end of the network. Furthermore, inspired by the impressive performance of generative adversarial network, this paper develops a novel image SR algorithm called SR-DBAGAN via dense blended attention generative adversarial network. SR-DBAGAN consists a generator and a discriminator, the generator uses our proposed SR-DBAN to generate HR images and try to fool the discriminator while the discriminator is designed based on Wasserstein GAN(WGAN) to discriminate. We deployed our algorithms on blurry prostate MRI images, and experimental results showed that our proposed algorithms have generated considerable sharpness and texture details and have a significant improvement on the peak signal-to-noise ratio(PSNR) and structural similarity index(SSIM), respectively, compared with mainstream interpolation-based and deep learning-based image SR algorithms, which fully proves the effectiveness and superiority of our proposed algorithms.