A learning-based approach for leaf detection in traffic surveillance video

Multidimensional Systems and Signal Processing - Traffic surveillance video is recorded in uncontrolled outdoor scenarios. If the camera view gets obstructed by the leaves, the video will fail to...     

Morphological segmenting and neighborhood pixel-based locality preserving projection on brain fMRI dataset for semantic feature extraction: an affective computing study

Two specific chemical receptive fields of brain, namely the amygdala and the orbital-frontal cortex, are related to valence and arousal in medical experiments. Functional magnetic resonance imaging (fMRI), which is a noninvasive, repeatable, and atomical tool for medical imaging in clinic system, was widely used in affective computing; however, it faces its dataset processing difficulty for dimensional reduction as well as for decreasing the computational complexity. In addition, features extraction from those de-dimensionality datasets is a challenging issue. The current work solved the de-dimensionality issue by using some preprocessing algorithms including clustering, morphological segmenting, and locality preserving projection. In order to keep useful information in fMRI dataset for reduction process, improved neighborhood pixel-based locality preserving projection (NP-LPP) algorithm was addressed and continuously for feature extraction operating using Otsu weighted sum of histogram. Furthermore, a modified covariance power spectral density (MC-PSD) separately in an fMRI Valence–Arousal experiments was measured. The results were analyzed and compared with affective norms English words system. The experiments established that the proposed methods of NP-LPP effectively simplified high complexity of fMRI, and Otsu weighted sum of histogram exhibited superior performance for features extraction compared to the MC-PSD through the calculation root mean standard error. The current proposed method provided a potential application and promising research direction on human semantic retrieval through medical imaging dataset.

     

红沿河核电厂主变在线色谱分析装置选用与评价

电力变压器在核动力发电厂可以被视为机组连接外电源(500k V电网)的咽喉,它承担着同电网之间传输电能的重要任务。在核电机组运行期间,如出现主变不可用的情况,机组将被迫停堆,由此可见主变其重要性。而目前变压器油样分析在业内被视作反映变压器内部健康状况的晴雨表,通过对油中气体含量进行取样分析,可以预判变压器内部是否出现局部放电、电流冲击、甚至短路等故障。而油色谱在线监测装置具有实时、快速、准确对油样气体含量进行分析的作用,采样周期比人工采样明显缩短,因此也为业界广为应用。     

微型中子源堆低浓化堆芯的探讨

通过对２３５Ｕ富集度为１９．９％的ＵＯ２和Ｕ３Ｓｉ２－Ａｌ的弥散体２种燃料进行物理计算，从中筛选出了优化的堆芯方案，并对其静态物理参数，诸如有效倍增因子、绝对中子通量密度、上铍反射层反应性价值、反应性温度系数、控制棒价值等进行了计算。     

低流量工况下燃料组件优化设计研究

为了探索适用于模块式小堆（SMR）的燃料组件优化设计,本文针对截短型CF2燃料组件在SMR参数范围下的热工-水力性能开展分析研究,获得搅混格架的间距及布置形式对于燃料组件热工-水力性能的影响规律。研究结果表明:①低流量工况下,搅混格架间距过长或过短都将降低燃料组件的热工性能,设计上应合理考虑搅混格架间距;② 燃料组件加热段上游区域的搅混格架布置对于提升SMR燃料组件的热工性能不明显,设计上在此区域应简化布置;③ SMR在正常运行与事故工况下,燃料组件的中下游区域的状态点参数相对恶劣,在中下游区域合理地设计搅混格架的间距与布置可以显著地提升燃料组件的热工性能,提高热工安全裕量。本文研究结果可为SMR燃料组件的设计优化提供参考。      

锆铀合金中痕量硼的同位素稀释质谱法测定

本文介绍了用同位素稀释技术测定锆铀合金试样中痕量硼的方法。选用富集~(10)B为同位素稀释剂,用硫酸-硫酸铵溶样,使硼形成挥发的硼酸甲酯与基样分离,用热电离质谱仪进行硼的同位素比值测量。方法的检测下限为1.5ng(硼)。对于含硼量小于1.0μg/g(样)的锆铀合金试样,相对标准偏差小于±1.0%。     

供锶-90和铯-137分析比对用的芦苇和茶叶样品的制备

本文报道了供实验室进行~(90)Sr 和~(137)Cs 分析比对用的芦苇和茶叶样品的制备和均匀性检查的方法。样品从核企业周围地区采集,经烘干和初步破碎后用超微粉碎机粉碎,在混样器上混合。装瓶后的样品随机抽取一部分进行灰化,测量其灰重,γ、β放射性和钾含量;用 F 检验和 t 检验的方法检查样品的均匀性。     

美国核安全管理体制与法律体系探究及启示

美国是重要的核能与核技术利用大国,在核安全监管方面,建立了独立的核安全监督管理部门,并通过建立较为有效的管理体制和完善的核安全法律体系对核能发展实现安全监管.日本福岛核事故后,如何实现核能发展与安全保障之间的平衡,提高我国核与辐射安全水平,成为社会公众关注的焦点.本文对美国现阶段核安全管理体制与法律体系进行了初步探究,...