证据推理的组合规则研究

对不精确、不完整信息系统的决策规则,证据理论提供了一种卓有成效的处理方法.从应用的角度出发,人们提出了修正的证据推理组合规则.为了能对各种证据推理组合规则有深刻理解,以证据理论为基础,对各种组合规则进行了深入的研究,分析了不同情况下各证据组合规则解决冲突问题的能力.接着给出了一种基于证据的条件概率指派合成方法,并证明了其有效性.最后,对各种证据组合规则的适用范围进行了比较.     

一种改进的证据冲突衡量因子

为解决证据融合时证据间冲突衡量不准确问题,提出了一种新的证据冲突衡量方法。对当前冲突衡量方法国内外研究现状进行总结,结合典型算例分析,指出了当前方法存在的主要问题。基于前人研究成果,提出了一种改进冲突因子用于衡量证据间冲突,主要基于修正证据距离与合取冲突,对两者采用加性组合。通过一般类型算例与特殊类型算例对所提方法进行验证,结果表明改进的冲突衡量因子能够准确衡量证据间冲突,并具有普适性。     

Bright Aggregation‐Induced Emission Dots for Dynamic Tracking and Grading of Patient‐Derived Xenografts in Zebrafish

Cancer prognosis will benefit from a scoring system that could grade malignant traits of patient‐derived cells by assessing their growth and metastasis in a living system. Specific tracking of patient‐derived cells requires labeling by contrast agents with good signal‐to‐noise ratio and no specific stain of host tissues. Towards this aim, aggregation‐induced emission (AIE) dots are developed for in vivo cancer tracking with emphasis on reproducible optimized formulation and specific fluorescent labeling of cells that enable enhanced spatial temporal resolution in vivo. The importance of energy‐dependent AIE dots uptake for patient‐derived cell labeling is emphasized to reveal their specific uptake by viable cancer cells. Using optically transparent zebrafish embryo, the ability is demonstrated to follow the engraftment of transplanted AIE dot labeled cells in zebrafish brains over one week. Cells detected outside the brain after 7 d are quantified as metastatic cells. Results from seven clinical samples demonstrate the utility of this methodology to differentiate low engraftment level of benign neoplasms from higher engraftment level and metastasis detected in malignant ovarian cancer specimens. Achieving clinically validated results supports the use of AIE dot labeled patient derived cells in zebrafish xenografts for future cancer prognosis.     

Helical‐Like 3D Ultrathin Piezoelectric Element for Complicated Ultrasonic Field

The developments of personalized medicine, ultrasound imaging, and contactless “microscopic handle” techniques are pushing ultrasonic transducers toward features of high frequency, device miniaturization, and even novel function. However, the conventional ultrasonic transducer has severely limited the development of novel ideas for applications due to its ordinary ultrasonic field. Although transducer arrays and monolithic acoustic holograms are capable of producing the complicated ultrasonic field, it is still difficult to achieve high frequency, device miniaturization, and novel function simultaneously. Here, a simple but effective approach is introduced that aims at reconstructing the complicated and high‐frequency ultrasonic field via a compact single‐element ultrasonic transducer. The 3D ultrathin piezoelectric element with a complex configuration is demonstrated theoretically and experimentally to produce the desired complicated ultrasonic field. With helical‐like configuration, the single‐element ultrasonic transducer offers efficient noncontact trapping and manipulation of suspended microparticles and biological cells. Moreover, its strong trapping capability leads to the 3D stacking of microparticles, which is a novel and interesting phenomenon achieved by a single‐element ultrasonic transducer. This work brings the possibility of a complicated ultrasonic field for achieving novel high‐frequency ultrasound applications through the design of smart structure ultrathin piezoelectric materials.     

Semiconducting Nanocomposite with AIEgen‐Triggered Enhanced Photoluminescence and Photodegradation for Dual‐Modality Tumor Imaging and Therapy

Semiconducting polymer nanoparticles (SPNs) have potential in biological applications. While some SPNs have significant photothermal conversion efficiencies (PCEs) as photothermal and photoacoustic agents, other SPNs offer high fluorescence yields as photoluminescent agents. However, the energy balance distribution in SPNs inhibits their successful applications in photoluminescence/photoacoustic (PL/PA) dual‐modality imaging. Additionally, the ultrastability of SPNs in vivo may cause damage to organisms. This work reports nanocomposite semiconducting polymer and tetraphenylethene nanoparticles (STNPs) constructed by semiconducting polymers (SPs) and tetraphenylethene aggregation‐induced emission luminogens (TPE AIEgens). The SP SPC10 endows good photothermal conversion ability, and the AIEgen TPBM supports enhanced photoluminescence of the STNPs. The results show that the STNPs can act as PL/PA dual‐modality imaging agents. The signal‐to‐noise (S/N) ratio in the PL modality reaches 8.7, and the imaging depth in the PA modality is 5.8 mm. The SPC10 in the STNPs can be decomposed under 90 mW cm^?2 white light irradiation in 6 h without any other additional agents. Furthermore, the STNPs are sufficient for the treatment of xenograft 4T1 tumor‐bearing mice based on photothermal therapy. The nanocomposite STNPs achieve optimized dual‐modality PL/PA imaging and the AIEgen‐triggered in situ photodegradation of SPNs. These properties indicate the significant potential of STNPs in clinical diagnosis and noninvasive therapy.     

一种SSDF攻击检测的证据理论合作频谱感知算法

现有的基于证据理论的合作频谱感知认为所有认知用户都是诚实的,没有考虑恶意用户的存在。当恶意用户篡改本地感知的结果,发送错误的数据到数据融合中心,将会降低合作频谱感知的性能,这种攻击称为频谱感知数据篡改（spectrum sensing data falsification,SSDF）攻击。由于恶意用户发送的证据与其它认知用户的证据存在差别,本文使用Jousselme距离来衡量证据的可信度,提出一种SSDF攻击检测算法。数据融合中心接收所有认知用户的证据,让可信度低的证据不参与融合判决,可信度高的证据根据可信度进行加权融合。仿真结果表明,所提出的SSDF攻击检测算法在恶意用户发起SSDF攻击时可以很好的改善频谱感知性能。      

基于Haenni方法的证据冲突原因分析

由于Dempster组合规则所存在的不足,在组合冲突信息时,会得出与直觉相反的结论。针对Zadeh悖论,Haenni给出了反驳,但冲突产生的原因分析不够全面。鉴于此,本文在他们的基础上,总结出冲突产生的原因,它不仅与传感器受干扰有关,与识别框架不完整也有关系,其中识别框架不完整还包括框架中的元素不详尽,出现了新目标和在两两证据进行推理过程中目标丢失导致的潜在冲突。     

计算机取证技术研究

随着信息技术、网络技术的发展,计算机越来越多的出现在犯罪活动当中,计算机取证正逐渐成为人们研究与关注的焦点。本文首先介绍了计算机取证的概念、特点、取证的原则和取证的步骤,然后探讨了计算机取证的相关技术和软件。     

一种新的基于改进型D-S证据理论的P2P信任模型

在大规模对等网(P2P)文件共享环境中,节点之间信任问题亟需解决。该文提出一种基于改进型D-S证据理论的P2P信任模型,给出了该模型的数学分析和抑制各类恶意节点攻击的措施。实验证明该算法较已有的一些信任机制在系统成功交易率、模型的安全性等问题上有较大改进。     

YOLSO: You Only Look Small Object

Small object detection is challenging and far from satisfactory. Most general object detectors suffer from two critical issues with small objects: (1) Feature extractor based on classification network cannot express the characteristics of small objects reasonably due to insufficient appearance information of targets and a large amount of background interference around them. (2) The detector requires a much higher location accuracy for small objects than for general objects. This paper proposes an effective and efficient small object detector YOLSO to address the above problems. For feature representation, we analyze the drawbacks in previous backbones and present a Half-Space Shortcut(HSSC) module to build a background-aware backbone. Furthermore, a coarse-to-fine Feature Pyramid Enhancement(FPE) module is introduced for layer-wise aggregation at a granular level to enhance the semantic discriminability. For loss function, we propose an exponential L1 loss to promote the convergence of regression, and a focal IOU loss to focus on prime samples with high classification confidence and high IOU. Both of them significantly improves the location accuracy of small objects. The proposed YOLSO sets state-of-the-art results on two typical small object datasets, MOCOD and VeDAI, at a speed of over 200 FPS. In the meantime, it also outperforms the baseline YOLOv3 by a wide margin on the common COCO dataset.