Existing battery State of Health (SOH) prediction approaches often struggle to provide both accurate predictions and reliable uncertainty estimates. This paper presents a novel Multi-Task Learning (MTL) framework that jointly tackles SOH prediction and provides a proxy metric for uncertainty through a unified architecture. The framework combines a Physics-Informed Neural Network (PINN) for SOH prediction with a deep autoencoding Gaussian mixture model for uncertainty modeling. Particularly, the energy score from the Gaussian mixture model serves as a proxy metric for uncertainty, where a higher score indicates potential prediction unreliability. Moreover, to enhance task-specific learning, we employ a multi-head attention mechanism that adaptively captures distinct feature relationships. Our experiments show improvements in prediction performance compared to the state-of-the-art baseline. A comprehensive evaluation on six XJTU battery benchmark datasets demonstrates that our framework achieves a prediction accuracy of 99.50% (MAPE: 0.0050) while providing reliable uncertainty quantification through the proxy metric.

In image annotation refinement, word correlations among candidate annotations are used to reserve high relevant words and remove irrelevant words. Existing methods build word correlations on textual annotations of images. In this paper, visual contents of images are utilized to explore better word correlations by using multi-graph similarity reinforcement method. Firstly, image visual similarity graph and word correlations graph are built respectively. Secondly, the two graphs are iteratively reinforced by each other through image-word transfer matrix. Once the two graphs converge to steady states, the new word correlations graph is used to refine the candidate annotations. The experiments show that our method performs better than method not considering visual content of images.

Traditional image annotation approaches are only applicable for datasets with small and limited lexicon. Besides, annotation words are treated equally without considering the importance of each word in the real world. To address these problems, we propose TagRank, a method to model the relative importance of every candidate word. By exploiting tag clusters on Flickr, TagRank could be modeled as random walk with restarts, which incorporates both word frequency and word correlation information. As a result, a ranked annotation vocabulary could be built. By utilizing the tag importance in a real image annotation experiment, we show that TagRank is helpful for improving the performance of image annotation.

In this paper, we proposed an image ranking model called SocialRank for web image search engine, which leverages rich metadata and structures of virtual communities in online photo-sharing sites in Web 2.0. In SocialRank, three levels of static rankings for sites, users and images are computed, respectively. To achieve combined image rankings in a site, a graph model is proposed to integrate social relations between users and images to reinforce their static rankings. To obtain the unified image rankings of different sites, image rankings are normalized and combined by considering duplicate images and static site rankings. The diversity of search results is also an important factor of image search engines. So we proposed an offline static diversity ranking of images. The experimental results show that the proposed SocialRank performs comparably with Google Image Search.

Nowadays personal albums are becoming more and more popular due to the explosive growth of digital image capturing devices. An effective automatic annotation system for personal albums is desired for both efficient browsing and search. Existing research on image annotation evolves through two stages: learning-based methods and web-based methods. Learning-based methods attempt to learn classifiers or joint probabilities between images and concepts, which are difficult to handle large-scale concept sets due to the lack of training data. Web-based methods leverage web image data to learn relevant annotations, which greatly expand the scale of concepts. However, they still suffer two problems: the query image lacks prior knowledge and the annotations are often noisy and incoherent. To address the above issues, we propose a web-based annotation approach to annotate a collection of photos simultaneously, instead of annotating them independently, by leveraging the abundant correlations among the photos. A multi-graph similarity propagation based semi-supervised learning (MGSP-SSL) algorithm is proposed to suppress the noises in the initial annotations from the Web. Experiments on real personal albums show that the proposed approach outperforms existing annotation methods.