High-roubustness keystroke recognition method based on acoustic spatial gradient

For the fluctuation of CFCC caused by environmental noise is the main reason for the low accuracy of keystroke detection,the spatial characteristics of adjacent between CFCC were studied,and the spatial gradient structure of CFCC based on points was established.On this basis,the effect of CFCC spatial gradient on keystroke content recognition and the selection of precise neighborhood points were studied on training and testing.Finally,a high-robustness keystroke recognition algorithm based on acoustic signals was constructed.Extensive experiments in different environments demonstrate that the proposed CFCC spatial gradient sound feature achieves great performance and the recognition accuracy is 96.15%.     

惯性导航设备升沉数据测试系统研究

研制了一套基于STM32单片机与单轴摇摆台的测试系统,利用固定圆心、固定半径条件下的圆内三角形关系,通过拉线传感器采集单轴摇摆台复合运动中的升沉数据;同时,对惯性导航设备输出的升沉数据进行采集与处理。对测试系统输出的升沉数据进行了验证,实验结果表明:该测试系统的测试数据准确合理,可实现对惯性导航设备升沉参数的动态测试,弥补了现有同类测试系统的结构复杂、量程小等不足。     

金属有机框架化合物在荧光识别方面的研究进展

通过荧光识别化合物是一种简单可靠、方便快捷的一种分析手段。金属有机框架(metalorganic frameworks, MOFs)通过框架和客体分子之间的相互作用,在荧光识别和传感等领域有着广阔的应用前景而成为一个热点研究领域。主要综述了金属有机框架在荧光识别溶剂小分子、离子、芳香有机分子以及气体分子等领域的研究工作。     

选矿摇床智能巡检机器人开发与应用

摇床受处理量、给矿浓细度等条件变化影响,床面各条矿带的颜色、位置、宽度时常改变,需要人工及时调整接矿板的位置以达到合格的精矿品位,工人劳动强度大。为了实现摇床作业的自动巡检和操作,通过计算机视觉技术采集摇床矿带照片,研究了矿带图像识别算法,并在此基础上开发了摇床智能巡检机器人。工业试验表明,摇床智能巡检机器人实现了对一组多张摇床矿带的自动采图,图像识别算法实现了对矿带宽度、边界和颜色特征的数字化识别解析,实现了自动调节接矿板至目标位置,达到了替代人工、提升选矿指标的目的。     

热轧层流辊道内冷辊回水结构的优化改进

针对某热轧厂1 580 mm生产线精轧出口层流辊道内冷辊频繁发生操作侧轴承失效问题,对其原因进行了分析,同时核算了内冷辊受热轴向膨胀后操作侧轴承侧向间隙。结果表明:操作侧轴承侧向间隙满足使用要求,轴承失效和辊筒受热膨胀无关;操作侧轴承失效的直接原因是轴套制造精度低导致骨架油封失效、内冷辊高速旋转“吸水”效应,使冷却回水进入轴承座内部,进一步造成润滑不良进而造成轴承失效。为此,对内冷辊回水结构进行了优化改进:在操作侧轴承座端盖和水套之间增加法兰,延长轴套及安装轴套的轴头,在轴头和轴套之间增加O型密封圈。改进后两年的使用周期内没有发生过轴承失效故障。     

Shaking table study on seismic behavior of MSE wall with inclined backfill soils reinforced by polymeric geostrips

In this study, the seismic behavior of a mechanically stabilized earth (MSE) wall with inclined backfill is investigated under sinusoidal acceleration excitations using a series of 1-g shaking table tests performed on the MSE model of 150 cm in height reinforced with polymeric geostrips. The effects of the stiffness of the reinforcement and slope angles of the backfill soil on the acceleration amplification factor (RMSA), the lateral displacement of the wall, the surface displacement of the backfill, the distribution of dynamic earth pressure along the height of reinforced wall and the strain distributions on the surfaces of the polymeric geostrips in three planes of the wall are investigated. The experimental results show that the dynamic earth pressure determined by traditional pseudo-static approaches leads to overestimated values. In addition, increasing the inclination angle of backfill soil results in the increase of surface settlement, lateral wall displacements, soil dynamic earth pressures, acceleration amplification factors and strains on the polymeric geostrip materials. The stiffness of the polymeric geostrip material has a negligible effect on the displacement, dynamic earth pressures and failure surface geometry.     

Unconstrained Hand Dorsal Veins Image Database and Recognition System

Hand veins can be used effectively in biometric recognition since they are internal organs that, in contrast to fingerprints, are robust under external environment effects such as dirt and paper cuts. Moreover, they form a complex rich shape that is unique, even in identical twins, and allows a high degree of freedom. However, most currently employed hand-based biometric systems rely on hand-touch devices to capture images with the desired quality. Since the start of the COVID-19 pandemic, most hand-based biometric systems have become undesirable due to their possible impact on the spread of the pandemic. Consequently, new contactless hand-based biometric recognition systems and databases are desired to keep up with the rising hygiene awareness. One contribution of this research is the creation of a database for hand dorsal veins images obtained contact-free with a variation in capturing distance and rotation angle. This database consists of 1548 images collected from 86 participants whose ages ranged from 19 to 84 years. For the other research contribution, a novel geometrical feature extraction method has been developed based on the Curvelet Transform. This method is useful for extracting robust rotation invariance features from vein images. The database attributes and the veins recognition results are analyzed to demonstrate their efficacy.     

Subspace Clustering for Situation Assessment in Aquatic Drones: A Sensitivity Analysis for State-Model Improvement

Abstract

In this paper, we propose the use of subspace clustering to detect the states of dynamical systems from sequences of observations. In particular, we generate sparse and interpretable models that relate the states of aquatic drones involved in autonomous water monitoring to the properties (e.g., statistical distribution) of data collected by drone sensors. The subspace clustering algorithm used is called SubCMedians. A quantitative experimental analysis is performed to investigate the connections between i) learning parameters and performance, ii) noise in the data and performance. The clustering obtained with this analysis outperforms those generated by previous approaches.     

Molecular Recognition in C-Type Lectins: The Cases of DC-SIGN,Langerin, MGL,and L-Sectin

Carbohydrates play a pivotal role in intercellular communication processes. In particular, glycan antigens are key for sustaining homeostasis, helping leukocytes to distinguish damaged tissues and invading pathogens from healthy tissues. From a structural perspective, this cross-talk is fairly complex, and multiple membrane proteins guide these recognition processes, including lectins and Toll-like receptors. Since the beginning of this century, lectins have become potential targets for therapeutics for controlling and/or avoiding the progression of pathologies derived from an incorrect immune outcome, including infectious processes, cancer, or autoimmune diseases. Therefore, a detailed knowledge of these receptors is mandatory for the development of specific treatments. In this review, we summarize the current knowledge about four key C-type lectins whose importance has been steadily growing in recent years, focusing in particular on how glycan recognition takes place at the molecular level, but also looking at recent progresses in the quest for therapeutics.     

基于混合特征LLE融合与SVM的质量异常模式识别

针对生产过程中存在的异常模式识别的问题,提出基于LLE融合与支持向量机的质量异常模式识别方法。首先从动态数据流中提取其原始特征、统计特征、几何特征并将其进行混合,形成动态数据流的混合特征,然后利用LLE算法对混合特征进行降维,将降维后的特征集作为MSVM分类器的输入进行训练,同时采用粒子群算法对MSVM分类器进行参数寻优。最后用训练好的模型对动态数据流进行异常模式的识别。并将所提方法与单一类型特征方法、混合特征方法的识别模型进行比较,仿真结果和应用实例表明,所提方法的识别精度较高,可用于生产过程的质量异常模式识别中。