Construction and analysis based flow cytometry imaing technology about atlas database of red tide algae in the Fujian southern waters

赤潮藻类分析是一个包含多学科、多阶段、多任务的复杂过程，如何将各阶段任务自动、合理地组合起来，是完成整个赤潮藻类分析工作的关键性问题。 传统实验的各阶段存在大量主观和客观因素影响或者实验流程规模庞大时，单纯依赖传统的镜检（显微镜肉眼对经过特殊处理的水样进行分析计数）来分类识别耗时费力，且应对灾害突发状况时的时效性无法得到保证；同时，种类繁多形态复杂的赤潮藻类细胞对于海洋生物学专家背景知识的要求极大限制了普通实验人员的工作，阻碍了政府海洋决策部门面对赤潮灾害时进行针对性防治措施的实施。 因此，本文使用了国内外行业中最先进的图像识别处理系统——美国FluidImagingTechnologie...Harmful Algal analysis is a very inclusive multi-disciplinary, multi-stage, multi-tasking complex process, and how each phase of the task automatically, rational combination of red tide algae is a complete analysis of the key issues. Traditional experimental stages there are a lot of subjective and objective factors, or large-scale experimental procedure when relying solely on traditional microsc...学位：工程硕士院系专业：信息科学与技术学院_计算机技术学号：X200722102

FOURIER RECOGNITION ANALYSIS IN THE APPLICATION of AUTOMATIC RECOGNITION RED TIDE ORGANISMS

本文研究了赤潮优势种识别技术,并实际运用于厦门大学流式细胞技术的赤潮实时监控系统fCAM。使用傅里叶形状特征描述子分析结合SVM算法作分类计算,通过对2009年4~10月厦门海域6种最常见的赤潮优势种的3000个样本为专门研究,并结合另外2种描述子算法将实验结果的识别精度提高到95.8%,具备较好的代表性。The recognition technology for the preponderant algae in red tide was studied and was applied to FCAM of Xiamen University real time monitoring system that using flow cytometry technology.Analysis for Fourier shape and feature descriptors and SVM was used as classifier,the experiment for classifying recognition precision that uniting with other two algorithms could be achieved to 95.8% about specially research of 6 classes 3,000 samples of Xiamen sea area's red tide algae

Identification of Red Tide Algae Based on FlowCAM

采用流式细胞摄像系统(flOW CyTOMETEr And MICrOSCOPE,flOWCAM)技术,构建了福建南部海域的赤潮生物图谱数据库,并利用该数据库分别用VISuAlSPrEAd软件自动识别法和描述子分析算法(MATlAb)来鉴定样品,对两种方法的识别精确度进行了比较。VISuAlSPrEAdSHEET专业软件分析能力达到98.2%的平均精度,自主开发的特征描述子分类器达到94.1%的平均精度,拓展了实验设备的应用范围。An image database of red tide algae in the sea area south of Fujian is constructed by means of Flow Cytometer and Microscope(FlowCAM),with which the samples are identified by using two methods:the automatically identifying by applying VisualSpreadsheet software and the feature extracting by using a character descriptor classifier which was self-developed through MATLAB.The comparison between the two methods indicates that the application of the VisualSpreadsheet professional software can reach to an average accuracy of 98.2%,and the self-developed character descriptor classifier can achieve an average accuracy of 94.1%.These results have expanded the application scope of the experimental equipment.海洋赤潮灾害立体监测技术与应用国家海洋局重点实验室开放基金——福建省南部海域赤潮生物图谱数据库构建(MATHAB200901

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel

JUNO sensitivity on proton decay p → ν K + searches*

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the $p\to \bar{\nu} K^+$ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar{\nu} K^+$ is 36.9% ± 4.9% with a background level of $0.2\pm 0.05({\rm syst})\pm$$0.2({\rm stat})$ events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is $9.6 \times 10^{33}$ years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies