可编程序控制器梯形图编程的一种通用方法

本文针对可编程序控制器用户程序的编制问题,给出了一种用阶段过渡控制图作为桥梁来编制梯形图的通用方法,并对若干实例在PLC-2/30可编程控制器上全部验证通过,说明这种方法正确无误。该方法严密清晰,简单实用,有规可循,因此是解决PC用户编程很有实用价值的一种通用方法。     

Carcinogenic Risk Assessment with Time-Dependent Exposure Patterns

Previous applications of carcinogenic risk assessment using mathematical models of carcinogenesis have focused largely on the case where the level of exposure remains constant over time. In many situations, however, the dose of the carcinogen varies with time. In this paper, we discuss both the classical Armitage-Doll multistage model and the Moolgavkar-Venzon-Knudson two-stage birth-death-mutation model with time-dependent dosing regimens. Bounds on the degree of underestimation of risk that can occur through the use of a simple time-weighted average dose are derived by means of comparison with an equivalent constant dose corresponding to the actual risk under the time-dependent dosing regimen.     

普朗克黑体辐射理论的建立——普朗克常数h的发现

本文叙述了德国伟大物理学家普朗克是如何从熵和几率的关系出发,提出能量子假说,最终建立起黑体辐射理论的过程。     

浅论一维透射光栅的性能

本文对一维透射光栅话线的衍射角、强度、谱线宽度、色散率、分辩本领等方面的决定因素和性能特点进行了论证。     

微型可编程序控制器对万能液压机的控制

本文介绍用ＩＰ—１６１２微型可编程序控制器控制节能液压系统驱动的ＹＡ３２—３１５四柱式万能液压机在煤气罐拉深工艺过程中的成功应用。显示出用微型可编程序控制器控制中、小型机械设备以及对老式设备的技术改造的优越性。     

命题形式与命题的符号化

命题形式是用符号来表示的,但命题形式是不是就是命题的符号化？传统逻辑对这个问题的回答似乎是不言而喻的,但数理逻辑却使这个问题变得复杂起来。数理逻辑的谓词逻辑对非逻辑常项的引入,导致了命题的符号化和命题形式之间的相对分离,使命题形式已不能简单地等同于命题的符号化。命题的符号化可以借助非逻辑常项实现,但纯命题形式不包含非逻辑常项,在纯形式推演中也没有真正意义上的非逻辑常项。     

论刑事评议表决规则

刑事评议表决规则主要包括迅速集中评议、秘密评议和有序评议等。迅速集中评议规则要求合议庭应当在庭审结束之后迅速、及时且不间断地进行案件的评议工作。秘密评议和有序评议规则旨在保证合议庭成员独立自由地根据理性和良心依法评议与表决。     

关于马克思"不变资本节约"理论的现实思考

马克思关于“不变资本使用上的节约”的论述 ,阐述了资本家通过不变资本节约获得更多剩余价值的具体方式。不变资本的节约 ,实际上提高了企业生产要素的协同水平 ,这些方法中有些对于我们今天提高企业经济效益仍然可以直接运用     

Calibrated path sampling and stepwise bridge sampling

A computational problem in many fields is to evaluate multiple integrals and expectations simultaneously. Consider probability distributions with unnormalized density functions indexed by parameters on a 2-dimensional grid, and assume that samples are simulated from distributions on a subgrid. Examples of such unnormalized density functions include the observed-data likelihoods in the presence of missing data and the prior times the likelihood in Bayesian inference. There are various methods using a single sample only or multiple samples jointly to compute each integral. Path sampling seems a compromise, using samples along a 1-dimensional path to compute each integral. However, different choices of the path lead to different estimators, which should ideally be identical. We propose calibrated estimators by the method of control variates to exploit such constraints for variance reduction. We also propose biquadratic interpolation to approximate integrals with parameters outside the subgrid, consistently with the calibrated estimators on the subgrid. These methods can be extended to compute differences of expectations through an auxiliary identity for path sampling. Furthermore, we develop stepwise bridge-sampling methods in parallel but complementary to path sampling. In three simulation studies, the proposed methods lead to substantially reduced mean squared errors compared with existing methods.     

Additive Risk Model Using Piecewise Constant Hazard Function

We study a weighted least squares estimator for Aalen's additive risk model with right-censored survival data which allows for a very flexible handling of covariates. We divide the follow-up period into intervals and assume a constant hazard rate in each interval. The model is motivated as a piecewise approximation of a hazard function composed of three parts: arbitrary nonparametric functions for some covariate effects, smoothly varying functions for others, and known (or constant) functions for yet others. The proposed estimator is an extension of the grouped data version of the Huffer and McKeague (1991 Huffer , F. W. , McKeague , I. W. ( 1991 ). Weighted least squares estimation for Aalen's additive risk model . Journal of the American Statistical Association 86 : 114 – 129 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) estimator. For our model, since the number of parameters is finite (although large), conventional approaches (such as maximum likelihood) are easy to formulate and implement. The approach is illustrated by simulations, and is compared to the previous studies. The method is also applied to the Framingham study data.