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.     

一种基于证据推理的视频运动目标融合检测算法

为了解决复杂场景下传统的运动目标检测问题, 利用证据推理—–谨慎有序加权平均方法(COWA-ER), 提出一种综合使用混合高斯、均值滤波和码本的多方法融合的检测方法. 该融合检测算法以上述3 种检测方法为准则建立一个多准则决策框架, 通过双阈值检测法来表征检测过程中的不确定性, 最终利用COWA-ER 方法进行决策级融合, 实现多种方法的优势互补. 实验表明, 所提出的融合检测算法具有更理想的目标检测效果, 能有效应对诸如阴影及光照突变等问题对检测性能的影响.

     

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。     

碳酸钠熔融法提取粉煤灰中铝和铁的研究

研究了粉煤灰的前期预处理以及酸浸活化后的粉煤灰中铁离子与铝离子浸出的影响因素.试验表明：煅烧能将粉煤灰中的有机质除去,并不能起到活化作用.粉煤灰中未燃烧碳在800℃焙烧2.5h可完全去除,用3 mol/L的HCl,固液比为1 g固体、10 mL液体,常温下,600 r/min反应2h后Fe、Al的浸出率最大.     

A multi-model approach for soft sensor development based on feature extraction using weighted kernel fisher criterion简

Multi-model approach can significantly improve the prediction performance of soft sensors in the proc- ess with multiple operational conditions. However, traditional clustering algorithms may result in overlapping phe- nomenon in subclasses, so that edge classes and outliers cannot be effectively dealt with and the modeling result is not satisfactory. In order to solve these problems, a new feature extraction method based on weighted kernel Fisher criterion is presented to improve the clustering accuracy, in which feature mapping is adopted to bring the edge classes and outliers closer to other normal subclasses. Furthermore, the classified data are used to develop a multiple model based on support vector machine. The proposed method is applied to a bisphenol A production process for prediction of the quality index. The simulation results demonstrate its ability in improving the data classification and the prediction performance of the soft sensor.