A Linear Programming Framework for Outlier Detection

孤立点检测是数据挖掘中的重要问题,可以发现不具备一般特性的数据,进而发现潜在的有用信息。现有的孤立点检测算法对于孤立点组成小集群的情形,一般不能正确检出。针对这一问题,提出一种新的基于线性规划的孤立点检测方法,该方法基于一个简单的事实:紧邻的两个数据点,必然同时为孤立点或正常点。首先建立待检测数据点的图模型,通过构造顶点能量模型和边模型,建立孤立点检测问题的马尔科夫模型,之后通过求解线性规划问题,得到该模型的最优解,进而得到孤立点检测结果。最后,使用一个合成数据集和三个真实数据集进行实验,验证本文所提出的算法,实验结果表明,提出的算法对于普通数据集和含有孤立点组成小集群的数据集,都能够正确地检出,且具有较高的检测正确率

Human Action Trend Analyses Study Based on Depth and LLE

针对传统人体动作趋势预测方法存在的不足,提出一种基于深度图像和LLE(Locally Linear Embedding)相结合的人体动作分析方法。首先依据图像的颜色和深度信息,结合Hough森林法提取人体部位的关键点;再利用关键点的信息,将其转化为特征向量,将特征向量集合输入LLE算法,从而建立人体动作的低维流形,并对流形数据做相关分析,根据流形中的欧式距离判断相邻动作;最后,将当前人体动作映射到低维动作流形中,预测人体的动作趋势。实验结果表明:所采用的深度图像,明显提高人体动作识别率,对于人体动作趋势的判断有非常重要的意义;所提出的方法在人体动作趋势的预测中准确率较高,具有一定的可行性

国内8款常用植物识别软件的识别能力评价

随着智能手机和人工智能技术的发展,以手机app为载体的植物识别软件慢慢走进公众生活、科普活动和科研活动的各个方面。植物识别app的识别正确率是决定其使用价值和用户体验的关键因素。目前,国内应用市场上有许多植物识别app,它们的开发目的和应用范围各异,软件本身的关注点、数据库来源、算法、硬件要求也存在很大差异。对于不同人群,植物识别app有不同的意义,如对于科研人员来说,识别能力强的app是提高效率的一大工具;对植物爱好者来说,具一定准确率的识别app可以作为入门的工具。因此,对各app的识别能力进行分析与评价显得尤为重要。本文选取了8款常用的app,分别对400张已准确鉴定的植物图片进行识别,其中干旱半干旱区、温带、热带和亚热带4个区各选取100张。这些图片共计122科164属340种,涵盖了乔木、灌木、草本、草质藤本和木质藤本5种生长型,包含23种国家级保护植物。种、属、科准确识别正确分别计4分、2分、1分,以此标准对软件识别能力按总得分进行排序,正确率得分由高到低依次为花帮主、百度识图、花伴侣、形色、花卉识别、植物识别、发现识花、微软识花

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