The thermodynamics of strange quark matter with density dependent bag constant are studied self-consistently in the framework of the general ensemble theory and the MIT bag model.In our treatment,an additional term is...The thermodynamics of strange quark matter with density dependent bag constant are studied self-consistently in the framework of the general ensemble theory and the MIT bag model.In our treatment,an additional term is found in the expression of pressure.With the additional term,the zero pressure locates exactly at the lowest energy state,indicating that our treatment is a self-consistently thermodynamic treatment.The self-consistent equations of state of strange quark matter in both the normal and color-flavor-locked phase are derived.They are both softer than the inconsistent ones.Strange stars in both the normal and color-flavor locked phase have smaller masses and radii in our treatment.It is also interesting to find that the energy density at a star surface in our treatment is much higher than that in the inconsistent treatment for both phases.Consequently,the surface properties and the corresponding observational properties of strange stars in our treatment are different from those in the inconsistent treatment.展开更多
本文利用Python语言,对25 000条英文影评数据进行文本分类。首先利用词袋模型对文本数据进行分类。在此基础上加入Word2Vec建立新的词向量特征,通过精准率和召回率对比前后2种模型的分类效果;最后通过逻辑回归和朴素贝叶斯分类模型的分...本文利用Python语言,对25 000条英文影评数据进行文本分类。首先利用词袋模型对文本数据进行分类。在此基础上加入Word2Vec建立新的词向量特征,通过精准率和召回率对比前后2种模型的分类效果;最后通过逻辑回归和朴素贝叶斯分类模型的分类效果对照得出研究结论。结果表明:对于英文影评文本分类,在同等条件下,使用Word2Vec构建词向量模型的精准率和召回率比使用bag of Word词袋模型分别高出0.02个百分点和0.026个百分点;在使用Word2Vec的基础上,朴素贝叶斯分类器的精准率和召回率分别高出逻辑回归分类0.027个百分点和0.028个百分点。展开更多
针对室内环境中视觉同时定位与建图(simultaneous localization and mapping,SLAM)精度不高和实用性较差等问题,采用深度相机作为传感器,提出一种基于改进词袋模型的视觉SLAM算法。该算法通过增加节点距离的方式,对传统的词袋模型进行改...针对室内环境中视觉同时定位与建图(simultaneous localization and mapping,SLAM)精度不高和实用性较差等问题,采用深度相机作为传感器,提出一种基于改进词袋模型的视觉SLAM算法。该算法通过增加节点距离的方式,对传统的词袋模型进行改进,采用octree方法转化点云,生成可用于导航的八叉树图,并进行改进前后词袋模型对比实验、数据集精度实验和实验室实测。结果表明,改进后的词袋模型相似度计算能力和区分度更强,SLAM算法在环境有回环和相机运动较慢的情况下,效果较好,可满足室内同时定位与建图及后续导航需求。展开更多
基金Supported by the National Natural Science Foundation of China (Grant Nos.10275029 and 10675024)the National Fundamental Fund Project Subsidiary Funds of Personnel Training (Grant No.J0730311)
文摘The thermodynamics of strange quark matter with density dependent bag constant are studied self-consistently in the framework of the general ensemble theory and the MIT bag model.In our treatment,an additional term is found in the expression of pressure.With the additional term,the zero pressure locates exactly at the lowest energy state,indicating that our treatment is a self-consistently thermodynamic treatment.The self-consistent equations of state of strange quark matter in both the normal and color-flavor-locked phase are derived.They are both softer than the inconsistent ones.Strange stars in both the normal and color-flavor locked phase have smaller masses and radii in our treatment.It is also interesting to find that the energy density at a star surface in our treatment is much higher than that in the inconsistent treatment for both phases.Consequently,the surface properties and the corresponding observational properties of strange stars in our treatment are different from those in the inconsistent treatment.
文摘本文利用Python语言,对25 000条英文影评数据进行文本分类。首先利用词袋模型对文本数据进行分类。在此基础上加入Word2Vec建立新的词向量特征,通过精准率和召回率对比前后2种模型的分类效果;最后通过逻辑回归和朴素贝叶斯分类模型的分类效果对照得出研究结论。结果表明:对于英文影评文本分类,在同等条件下,使用Word2Vec构建词向量模型的精准率和召回率比使用bag of Word词袋模型分别高出0.02个百分点和0.026个百分点;在使用Word2Vec的基础上,朴素贝叶斯分类器的精准率和召回率分别高出逻辑回归分类0.027个百分点和0.028个百分点。
文摘针对室内环境中视觉同时定位与建图(simultaneous localization and mapping,SLAM)精度不高和实用性较差等问题,采用深度相机作为传感器,提出一种基于改进词袋模型的视觉SLAM算法。该算法通过增加节点距离的方式,对传统的词袋模型进行改进,采用octree方法转化点云,生成可用于导航的八叉树图,并进行改进前后词袋模型对比实验、数据集精度实验和实验室实测。结果表明,改进后的词袋模型相似度计算能力和区分度更强,SLAM算法在环境有回环和相机运动较慢的情况下,效果较好,可满足室内同时定位与建图及后续导航需求。
