Multi-source information fusion for smart health with artificial intelligence

The rapid developments in Artificial Intelligence present an opportunity for the research community to provide and advance Smart Health for the well-being of our society. By considering the availability of multi-source information and heterogeneous data in the era of Big Data, this Special Issue explores the theories, methodologies and possible breakthroughs that have designed and adopted information fusion for Smart Health powered by recent Artificial Intelligence advances. Specifically, this Special Issue focuses on three questions; How to achieve and realize human-level intelligence in Smart Health, How to achieve and benefit Smart Health from a multi-disciplinary balance, and How to utilize the power of Big Data for Smart Health. The Special Issue is a great success, with a small number of quality studies carefully selected from an overwhelming amount of contributions.     

Alloy design via additive manufacturing: Advantages,challenges, applications and perspectives

Additive manufacturing (AM) has rapidly changed both large- and small-scale production environments across many industries. By re-envisioning parts from the ground up, not limited to the challenges presented by traditional manufacturing techniques, researchers and engineers have developed new design strategies to solve large-scale materials and design problems worldwide. This is particularly true in the world of alloy design, where new metallic materials have historically been developed through tedious processes and procedures based primarily on casting methodologies. With the onset of directed energy deposition (DED) and powder bed fusion (PBF)-based AM, new alloys can be innovated and evaluated rapidly at a lower cost and considerably shorter lead time than has ever been achieved. This article details the advantages, challenges, applications, and perspectives of alloy design using primarily laser-based AM. It is envisioned that researchers in industry and academia can utilize this work to design new alloys leveraging metallic AM processes for various current and future applications.     

Presentation attack detection based on two-stream vision transformers with self-attention fusion

Aiming at the performance degradation of the existing presentation attack detection methods due to the illumination variation, a two-stream vision transformers framework (TSViT) based on transfer learning in two complementary spaces is proposed in this paper. The face images of RGB color space and multi-scale retinex with color restoration (MSRCR) space are fed to TSViT to learn the distinguishing features of presentation attack detection. To effectively fuse features from two sources (RGB color space images and MSRCR images), a feature fusion method based on self-attention is built, which can effectively capture the complementarity of two features. Experiments and analysis on Oulu-NPU, CASIA-MFSD, and Replay-Attack databases show that it outperforms most existing methods in intra-database testing and achieves good generalization performance in cross-database testing.     

Multimodal Attentive Fusion Network for audio-visual event recognition

Event classification is inherently sequential and multimodal. Therefore, deep neural models need to dynamically focus on the most relevant time window and/or modality of a video. In this study, we propose the Multimodal Attentive Fusion Network (MAFnet), an architecture that can dynamically fuse visual and audio information for event recognition. Inspired by prior studies in neuroscience, we couple both modalities at different levels of visual and audio paths. Furthermore, the network dynamically highlights a modality at a given time window relevant to classify events. Experimental results in AVE (Audio-Visual Event), UCF51, and Kinetics-Sounds datasets show that the approach can effectively improve the accuracy in audio-visual event classification. Code is available at: https://github.com/numediart/MAFnet     

直接熔样正热电离质谱法测定二硼化锆中硼同位素丰度

本工作选择m/z88(23 Na210BO2+)和m/z89(23 Na211BO2+)的Na2BO2+作为检测离子,研究了同位素干扰、涂样技术、ZrB2样品处理等对硼同位素测定的影响,建立了快速准确测定ZrB2中硼同位素丰度的正热电离质谱法。实验复现了硼同位素标准物质的标准值,对酸溶法分解试样后涂样和直接熔融法涂样测量结果的精密度进行了比较。结果表明,采用直接熔融法涂样可以快速准确地测定ZrB2中硼同位素丰度,δ10B同位素丰度测定结果精密度优于0.020%。     

RWO-Sampling: A random walk over-sampling approach to imbalanced data classification

This study investigates how to alleviate the class imbalance problems for constructing unbiased classifiers when instances in one class are more than that in another. Since keeping the data distribution unchanged and expanding class boundaries after synthetic samples have been added influence the classification performance greatly, we take into account the above two factors, and propose a Random Walk Over-Sampling approach (RWO-Sampling) to balancing different class samples by creating synthetic samples through randomly walking from the real data. When some conditions are satisfied, it can be proved that, both the expected average and the standard deviation of the generated samples equal to that of the original minority class data. RWO-Sampling also expands the minority class boundary after synthetic samples have been generated. In this work, we perform a broad experimental evaluation, and experimental results show that, RWO-Sampling statistically does much better than alternative methods on imbalanced data sets when implementing common baseline algorithms.     

电子陶瓷材料元素分析的前处理方法研究

陶瓷材料,由于其耐高温、耐腐蚀、耐磨等性能优异,在各行各业应用相当广泛。但也正是因为它的这些特性,在使用化学方法进行元素分析时,会遇到样品难以消解的问题,导致测试无法进行。文章重点研究电子元件中常用的三氧化二铝、氧化锆类陶瓷材料的消解方法,并介绍使用ICP-OES对消解完全的样品溶液进行元素分析的注意事项。     

高温下煤灰热膨胀行为研究

为了更好地了解硅铝比在煤炭燃烧和气化过程中对炉体结渣和堵渣产生的影响,选取硅铝比不同的A、B、C三种煤样,在弱还原性气氛下,利用自主研制高温熔融装置,考察三种煤灰样在高温熔融炉中的形变情况,探究煤灰热膨胀行为。针对A煤样,将其灰化后分别添加SiO2和Al2O3,添加量为煤基的1%~5%,研究其高温下XRD物相变化、熔渣流动现象和熔渣表观形貌。结果表明:不同硅铝比的煤灰热膨胀行为存在差异,B、C煤灰产生明显热膨胀现象,A煤灰未出现此现象;添加SiO2的A煤灰产生明显热膨胀现象,而添加Al2O3却未出现膨胀过程。添加SiO2后的煤灰在高温下灰粘度会增加,气体较难透过粘稠液体相,造成煤灰在高温下会出现明显膨胀现象,进而会导致煤炭在燃烧和气化过程中出现炉体内壁的结渣和出渣口堵渣等问题。通过考察弱还原性气氛下,研究不同硅铝比对煤灰高温熔融过程形变的影响,为解决实际生产中气化炉结渣和堵渣问题提供理论依据。