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。     

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.     

SDN‐based dynamic multipath forwarding for inter–data center networking

Equal‐cost multipath (ECMP)–based traffic engineering (TE) methods are commonly used in intra–data center (DC) networks to improve the transmission performance for east‐west traffic (ie, traffic from server to server within a DC). However, applying ECMP on inter‐DC wide area network (WAN) offers limited performance enhancement as a result of irregular network topology. Since TE can be intelligently and efficiently realized with software‐defined networking (SDN), SDN‐based multipath becomes a popular option. However, SDN suffers from scalability issue caused by limited ternary content‐addressable memory (TCAM) size. In this paper, we propose an SDN‐based TE method called dynamic flow‐entry‐saving multipath (DFSM) for inter‐DC traffic forwarding. DFSM adopts source‐destination–based multipath forwarding and latency‐aware traffic splitting to reduce the consumption of flow entries and achieve load balancing. The evaluation results indicate that DFSM saves 15% to 30% of system flow entries in practical topologies and reduces the standard deviation of path latencies from 10% to 7% than do label‐switched tunneling, and also reduces average latency by 10% to 48% by consuming 6% to 20% more flow entries than do ECMP in less‐interconnected topologies. Note that the performance gain may not always be proportional to flow entry investment, with the interconnectivity between nodes being an important factor. The evaluation also indicates that per‐flow provision consumes several times the flow entries consumed by DFSM but reduces latency by 10% at most. Besides, DFSM reduces the standard deviation of path latencies from 14% to 7% than do even traffic splitting.     

Throughput-based fair bandwidth allocation in OBS networks

Fair bandwidth allocation (FBA) has been studied in optical burst switching (OBS) networks, with the main idea being to map the max-min fairness in traditional IP networks to the fair-loss probability in OBS networks. This approach has proven to be fair in terms of the bandwidth allocation for differential connections, but the use of the ErlangB formula to calculate the theoretical loss probability has made this approach applicable only to Poisson flows. Furthermore, it is necessary to have a reasonable fairness measure to evaluate FBA models. This article proposes an approach involving throughput-based-FBA, called TFBA, and recommends a new fairness measure that is based on the ratio of the actual throughput to the allocated bandwidth. An analytical model for the performance of the output link with TFBA is also proposed.     

Motion control of underactuated linkage robot based on gymnastic skill

In this paper, a reproduction of a swing‐up and a giant swing motion of underactuated robots based on technique of the horizontal bar gymnast is discussed with focusing on an equivalent center of mass (ECM) of underactuated robots and the gymnast. At first, the behaviors of the ECM of the gymnast (ECMG) are analyzed by using a motion capturing technique and an efficient motion of the ECMG for the swing‐up and for the giant swing motion are identified from analysis results, respectively. Next, a partial linearization method, which can realize that the ECM of the Acrobot (ECMA) replicates this efficient motion, is designed and reproduces the underactuated robots the swing‐up and giant swing motion like the gymnast. Finally, an effectiveness of the proposed controller is shown by numerical simulations.     

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.     

基于双层修正无迹卡尔曼的水下节点定位算法* .txt

摘要：针对水下无线传感网络中运动节点定位精度低的问题，提出了一种新的基于双层修正无迹卡尔曼的水下节点定位算法(DLMUKF)。该算法利用下层无迹卡尔曼滤波算法对节点状态进行预测，根据各信标节点的测距传播时延对预测的节点状态进行修正。运用上层无迹卡尔曼滤波算法对修正后的状态进行新的预测与修正。仿真实验中，DLMUKF算法的平均定位误差约为传统多边定位算法的15%，约为基于无迹卡尔曼滤波（UKF）定位算法的16%，受节点运动时间与速度的影响最小。通过实验证明DLMUKF算法能更充分利用实际距离值，可以有效减小运动节点的定位误差。 .txt     

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。     

基于敏感度分析的机床关键几何误差元素辨识与评定

为了正确识别和判定机床关键几何误差元素对机床精度设计的影响,以PCV-620立式加工中心为研究对象,采用多体系统理论建立机床空间误差模型,从而得到机床几何误差元素与机床精度之间的关联函数。对空间误差模型进行灵敏度分析,获得机床各运动方向的局部灵敏度系数,完成机床关键几何误差元素的初步辨识。以局部灵敏度系数为基础,提出一种与局部灵敏度系数和工作空间中任意位置处的几何误差元素值相关的全局灵敏度系数计算方法,将其作为机床关键几何误差元素的辨识和评定标准,分析得到PCV-620立式加工中心的关键几何误差元素包含3项定位误差、3项垂直度误差和5项直线度误差。