An antigen space density based real-value negative selection algorithm

The negative selection algorithm (NSA) is an important detector generation algorithm for artificial immune systems. In high-dimensional space, antigens (data samples) distribute sparsely and unevenly, and most of them reside in low-dimensional subspaces. Therefore, traditional NSAs, which randomly generate detectors without considering the distribution of the antigens, cannot effectively distinguish them. To overcome this limitation, the antigen space density based real-value NSA (ASD-RNSA) is proposed in this paper. The ASD-RNSA contains two new processes. First, in order to improve detection efficiency, ASD-RNSA utilizes the antigen space density to calculate the low-dimensional subspaces where antigens are densely gathered and directly generate detectors in these subspaces. Second, to eliminate redundant detectors and prevent the algorithm from prematurely converging in high-dimensional space, ASD-RNSA suppresses candidate detectors that are recognized by other mature detectors and adopts an “antibody suppression rate” to replace the “expected coverage” as the termination condition. Experimental results show that ASD-RNSA achieves a better detection rate and has better generation quality than classical real-value NSAs.     

EvoSeedRNSAII: An improved evolutionary algorithm for generating detectors in the real-valued Negative Selection Algorithms

In the previous work, a detector generation algorithm, named as the EvoSeedRNSA, is proposed. A genetic algorithm is adopted in the EvoSeedRNSA to evolve the random seeds to generate an approximately optimal detector set. This paper proposes an improved EvoSeedRNSA, named as the EvoSeedRNSAII, to generate a more efficient detector set. A multi-group random seed encoding scheme is designed to represent the individuals and different detector generation sequences are discussed. The experiments demonstrate that the EvoSeedRNSAII has a better performance than the EvoSeedRNSA.     

Test beam results of Current Injected Detectors (CID) irradiated up to 5×10 1 MeV neq/cm

Two full size strip detectors were investigated in this study: one with p⁺ strips (p⁺/n⁻/n⁺) and another with n⁺ strips (n⁺/p⁻/p⁺). Both detectors, are made of magnetic Czochralski silicon (MCz-Si) and irradiated to S-LHC fluencies, were tested with 225 GeV muon beam in the CERN H2 area. The Current Injected Detector (CID) sensors were operated in a cooling box capable of providing a −53 °C temperature. Results indicate a relative charge collection efficiency (CCE) at 5×10¹⁵ n_eq/cm² above 30% in irradiated p⁺/n⁻/n⁺ CID detector at 600 V bias voltage. The signal to noise ratio of this CID module was about eight and a forward current of 30 μA was needed for detector biasing. In standard reverse bias, the same detector could not provide a sufficiently large signal for particle tracking purposes. A p-type (n⁺/p⁻/p⁺) sensor was irradiated to a fluence of 2×10¹⁵ n_eq/cm² and measured under the same test beam conditions. According to the theory of CIDs developed by the CERN RD39 Collaboration, this detector module could be biased up to only 230 V due to the low irradiation fluence. The CCE at 230 V was 35% in CID operation and 20% when reverse biased.     

MEMS质谱仪研究综述

近年来,小体积、低功耗、价格低廉、使用方便的MEMS质谱仪成为了质谱领域的研究重点和热点。目前国外对MEMS质谱仪及其相关技术的研究已取得了一些成果,甚至研发出了较为完整MEMS质谱仪。但是整体而言,MEMS质谱仪的性能还很低,其研究主要还处于对其元件分别进行微型化的阶段。其中,MEMS离子源以对电喷雾离子源的研究为主;MEMS质量分析器以对离子阱的研究为主;MEMS离子检测器以对法拉第筒的研究为主。本文总结了MEMS质谱仪的最新成果,同时分析了可能面临的问题。     

基于级联和自适应子分类的目标检测方法

在基于机器学习的目标检测中,检测速度和检测准确性是主要考虑的问题。通过所设计的两级级联和自适应子分类的方法,有效提升了检测速度和检测的准确率。在实际检测时,可以通过一些简单的特征,快速否定绝大多数非检测目标的探测窗口,因此设计了两级级联的方法获得较高的检测速度:在两级级联的第一级采用一种快速简单的检测方法,快速地否定绝大多数错误的探测窗口,并使得几乎所有的正确的探测窗口通过第一级检测。在实际场景下,同一类目标常常有不同的表现形态,如不同姿势、颜色等,因此设计了自适应子分类的方法来获得较高的检测准确率:在两级级联的第二级通过对一类目标使用自动子分类的方法训练多个识别模型,在子分类过程中自动寻找最优分类方法,提升了识别的准确性。在利用著名的UIUC数据集以及一些高清图像的检测实验结果表明,该算法显著提升了检测速度和准确性。     

A method to modify coordinates of detectors in positron emission tomography systems

A new method to modify coordinates of detectors in any positron emission tomography (PET) system using a radioactive point source is developed. This method is based on selecting a centered detector in each detector block of PET and measuring coincidence counts between the two centered detectors in opposite detector blocks to find the coordinates of their LOR (Line of Response). Due to slight misalignment of detector positions, measured LORs may not intersect at a single point. Based on the proposed method, the coordinates of detectors can be measured with very high accuracy and the coordinate of the center of the gantry (which is normally the same as the center of field of view) can be defined correctly. The results of the application of our method to a small animal PET system (FinePET), which was recently developed at Tohoku University, Japan, are shown here. The method is expected to contribute to the design and development of PET systems which can realize a very high spatial resolution of less than 1 mm FWHM.     

提高火灾探测系统可靠性的探讨

分析了火灾探测系统常见的几种干扰，提出了科学选型、合理安装、规范布线等措施来减少干扰，并就主动式气体探测系统等新型探测系统的设计与安装应注意的问题进行探讨，以期提高火灾自动报警系统的可靠性。     

基于机器视觉的转辙机缺口检测方法研究

针对现有转辙机缺口检测方法所存在的问题,本文介绍了一种基于机器视觉技术的转辙机缺口检测基本方法及其原理和步骤。文章详细论述了LSD算法的工作原理,同时本文作者对该算法做出了改进。最后将改进的LSD算法应用在转辙机缺口检测当中,结果表明该检测算法具有良好的稳定性和快速性,并且能够很好的解决转辙机缺口检测的问题。     

KY120—1型紫外光栅分光光度计的研制

KY120-1型分光光度计系单光束紫外可见光分光光度计,它具有性能稳定,操作方便,数字直读,结构紧凑等特点,是实验室中常规的分析测试仪器。本文概述整机方案设计,各部分的简要工作原理及主要技术性能,并介绍仪器在现场的应用结果。