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推求原子中各种电子组态的光谱项、光谱支项和光谱基项的计算机程序 被引量:2
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作者 徐顺 《郑州大学学报(自然科学版)》 1995年第2期77-79,共3页
本文报导的计算机程序,可推求原子中任一电子组态的光谱项和光谱支项,及每种谱项出现的次数,并选取基项。输出结果迅速、准确。
关键词 原子光谱 电子组态 原子结构 程序 光谱支
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原子基谱项和光谱支项的一种简捷推引法
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作者 周全法 《烟台师范学院学报(自然科学版)》 1990年第1期94-96,共3页
从原子的电子排布分析了原子的基谱项和光谱支项,得出了从电子排布推求基谱项的简捷方法。
关键词 原子 基谱项 光谱支 电子排布
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论同科电子P^2组态的光谱项及光谱支项
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作者 高文启 王英杰 《沈阳教育学院学报》 2001年第1期101-103,共3页
根据电子排布法按保里原理的要求 ,将给定的组态的电子分别排布在各可能的轨道上 ,然后在可能的电子排布的微观状态中找出MS 的最大值 (MS) max,以及与之相适应的 (ML) max,根据这两个值找出S和L值 ,得出光谱项 ,再将ML 和MS 偶合 。
关键词 同科电子 非同科电子 电子组态 光谱 光谱支 P2 电子排布法 保里原理
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Rf^2组态谱项与光谱支项能量计算
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作者 肖熙明 汤跃群 《长沙水电师院自然科学学报》 1992年第2期178-183,共6页
本文运用不可约张量法,对 f^2组态谱项与光谱支项及其相对能量进行了计算,从而确定了该组态各谱项以及光谱支项的能量高低顺序:~3H<~3F<~1G<~1D<~1I<~3P<~1S;
关键词 不可约张量法 光谱支 能量 计算
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双原子分子电子振动光谱的转动结构分析 被引量:8
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作者 余春日 潘康生 《原子与分子物理学报》 CAS CSCD 北大核心 2005年第4期713-717,共5页
本文分析了双原子分子电子振动光谱的转动结构规律,说明了如何对电子振转光谱进行标识,从而获得不同电子态的转动常数、转动惯量和核间距。
关键词 电子振动光谱 转动结构分析 Fortrat抛物线 光谱支 谱带头
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原子光谱项的意义和推求 被引量:1
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作者 姜心田 《陕西师范大学继续教育学报》 2004年第2期110-113,共4页
原子光谱项是反映原子内部轨-轨,轨-旋,旋-旋复杂相互作用能量效应的,是解释原子光谱的理论基础。本文就原子光谱项的意义,L-S耦合推求方法及Hund规则通过实例给出了说明。
关键词 原子状态 L-S耦合 量子数 Hund规则 光谱支 原子光谱 能量效应
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等价电子原子光谱项的简便推导
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作者 李仲辉 陈萌 《成都师范学院学报》 1998年第1期87-88,共2页
等价电子原子光谱项的简便推导李仲辉,陈萌目前的结构化学教材中有关注价电子原子光谱项和光谱支项的推导,一般都采用两种近似处理方法-L-S耦和j-j耦合,而且均从描述原子核外电子运动状态的四个量子数着手,按照一定原则进行... 等价电子原子光谱项的简便推导李仲辉,陈萌目前的结构化学教材中有关注价电子原子光谱项和光谱支项的推导,一般都采用两种近似处理方法-L-S耦和j-j耦合,而且均从描述原子核外电子运动状态的四个量子数着手,按照一定原则进行复杂的排列组合导出的。这种推导方法... 展开更多
关键词 原子光谱 等价电子 光谱支 耦合组 简便推导 结构化学 磁量子数 自旋态 加和法 错误和遗漏
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Classification of hyperspectral remote sensing images based on simulated annealing genetic algorithm and multiple instance learning 被引量:3
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作者 高红民 周惠 +1 位作者 徐立中 石爱业 《Journal of Central South University》 SCIE EI CAS 2014年第1期262-271,共10页
A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning (MIL). The band selection method was proposed from subspace decom... A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning (MIL). The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome. 展开更多
关键词 hyperspectral remote sensing images simulated annealing genetic algorithm support vector machine band selection multiple instance learning
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Wavelet De-noising of Speech Using Singular Spectrum Analysis for Decomposition Level Selection
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作者 蔡铁 朱杰 《Journal of Shanghai Jiaotong university(Science)》 EI 2007年第2期190-196,共7页
The problem of speech enhancement using threshold de-noising in wavelet domain was considered.The appropriate decomposition level is another key factor pertinent to de-noising performance.This paper proposed a new wav... The problem of speech enhancement using threshold de-noising in wavelet domain was considered.The appropriate decomposition level is another key factor pertinent to de-noising performance.This paper proposed a new wavelet-based de-noising scheme that can improve the enhancement performance significantly in the presence of additive white Gaussian noise.The proposed algorithm can adaptively select the optimal decomposition level of wavelet transformation according to the characteristics of noisy speech.The experimental results demonstrate that this proposed algorithm outperforms the classical wavelet-based de-noising method and effectively improves the practicability of this kind of techniques. 展开更多
关键词 speech enhancement wavelet de-noising singular spectrum analysis (SSA) support vector machine (SVM)
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Use of near-infrared spectroscopy and least-squares support vector machine to determine quality change of tomato juice 被引量:1
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作者 Li-juan XIE Yi-bin YING 《Journal of Zhejiang University-Science B(Biomedicine & Biotechnology)》 SCIE CAS CSCD 2009年第6期465-471,共7页
Near-infrared (NIR) transmittance spectroscopy combined with least-squares support vector machine (LS-SVM) was investigated to study the quality change of tomato juice during the storage. A total of 100 tomato juice s... Near-infrared (NIR) transmittance spectroscopy combined with least-squares support vector machine (LS-SVM) was investigated to study the quality change of tomato juice during the storage. A total of 100 tomato juice samples were used. The spectrum of each tomato juice was collected twice: the first measurement was taken when the tomato juice was fresh and had not undergone any changes, and the second measurement was taken after a month. Principal component analysis (PCA) was used to examine a potential capability of separating juice before and after the storage. The soluble solid content (SSC) and pH of the juice samples were determined. The results show that changes in certain compounds between tomato juice before and after the storage period were obvious. An excellent precision was achieved by LS-SVM model compared with discriminant partial least-squares (DPLS), soft independent modeling of class analogy (SIMCA), and discriminant analysis (DA) models, with 100% of a total accuracy. It can be found that NIR spectroscopy coupled with LS-SVM, DPLS, SIMCA, and DA can be used to control the quality change of tomato juice during the storage. 展开更多
关键词 Near-infrared (NIR) spectroscopy Least squares-support vector machine (LS-SVM) Quality change Tomato juice
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