对超重、失重演示实验的改进

对传统的超重、失重演示实验进行了改进,使弹簧秤的指针能静止在超重(或失重)的位置上,从而能准确地读出读数,提高演示效果.     

西藏区域经济系统的灰色关联分析

区域经济的发展受许多不同因素的影响，本结合西藏社会经济发展的特点，选取了总产值指标、第一产业指标、第二产业指标和第三产业指标等四大类指标体系，采用灰色关联分析法对西藏的区域经济系统进行了简要的分析评价，以研究西藏区域经济的发展状况，从社会经济的角度探讨西藏经济发展落后的原因，以期能为西藏经济发展政策提供参考依据。     

期货经纪公司保证金的一种确定方法

本文基于马尔可夫链在存储论中的应用，结合Ergodic定理，得到确定期货经纪公司保证金的Ergodic模型，即一个双目标规划问题，然后应用乘积最大化准则，将该模型转化为单目标规划问题来求解。该方法考虑期货经纪公司承担的风险和对投资者的吸引程度，为保证金的确定提供新的思路。     

F／Cl比与Eu~（2＋）：BaFCl中F色心的浓度和光激励截面的关系

研究了Ｅｕ（２＋）：ＢａＦｘＣｌ２在紫外线辐照下的光激励发光。通过改变激励方式及激励光的扫描方向，给出了Ｅｕ（２＋）：ＢａＦｘＣｌ２－ｘ光激励发光过程中，两种Ｆ色心的浓度比值与光激励截面比值的测定方法。利用这种测定方法，进一步研究了两种Ｆ色心的浓度比值和光激励截面比值与Ｆ／ＣＩ比值的关系。     

对光磁共振实验中测量gF值的方法的评述和改进

对文献中测量gF因子的方法进行了评述，阐述了如何基于三角波扫场，用抽运信号确定计算和测量gF的方法。     

Synthesis, Crystal Structure, Fluorescent and Thermal Properties of Sodium Tridecafluorodizirconate Na_5Zr_2F_(13) Compound

1 INTRODUCTION Considerable research effort on the heavy metal fluoride glasses like barium, zirconium fluoride and a series of rare-earth fluorozirconate compounds du- ring the last two decades was initiated by the broad optical transmission window of these glasses and thereby the potential for ultralow-loss optical fi- bers[1~10]. The fluorozirconate of alkali metals was first reported in 1938[11], and most of the sodium and potassium fluorozirconates were reported amongthe 1940s and 19…     

粒子分辨CCT方法中的δ电子效应

提出了切伦科夫相关时间测量(CCT)中初始粒子在辐射体里产生的δ电子的干扰问题,以及降低干扰的方法. 通过模拟计算给出了北京τ-C工厂探测器初步设计中CCT的π/K分辨本领.     

Average Atom Model in Hot Plasmas

Ａｖｅｒａｇｅ　Ａｔｏｍ　Ｍｏｄｅｌ　ｉｎ　Ｈｏｔ　ＰｌａｓｍａｓＡｖｅｒａｇｅＡｔｏｍＭｏｄｅｌｉｎＨｏｔＰｌａｓｍａｓ￥ＹｕａｎＪｉａｎｈｕｉ；ＳｕｎＹｏｎｇｓｈｅｎｇ；ＺｈｅｎｇＳｈａｏｔａｎｇ（ＩｎｓｔｉｔｕｔｅｏｆＡｐｐｌｉｅｄＰｈｙｓｉｃ...     

紫外分光光度法测定甲磺酸帕珠沙星原料药含量

采用紫外分光光度法测定了甲磺酸帕珠沙星原料药的含量,并与高效液相色谱法和酸碱滴定法的测定结果进行了比较.该法简便、快速、结果准确.为甲磺酸帕珠沙星在生产过程中的质量控制提供了可靠依据.     

A new type SVM-projected SVM

Support vector machine (SVM), developed by Vapnik et al., is a new and promising technique for classification and regression and has been proved to be competitive with the best available learning machines in many applications. However, the classification speed of SVM is substantially slower than that of other techniques with similar generalization ability. A new type SVM named projected SVM (PSVM), which is a combination of feature vector selection (FVS) method and linear SVM (LSVM), is proposed in present paper. In PSVM, the FVS method is first used to select a relevant subset (feature vectors, FVs) from the training data, and then both the training data and the test data are projected into the subspace constructed by FVs, and finally linear SVM(LSVM) is applied to classify the projected data. The time required by PSVM to calculate the class of new samples is proportional to the count of FVs. In most cases, the count of FVs is smaller than that of support vectors (SVs), and therefore PSVM is faster than SVM in running. Compared with other speeding-up techniques of SVM, PSVM is proved to possess not only speeding-up ability but also de-noising ability for high-noised data, and is found to be of potential use in mechanical fault pattern recognition.