A41 数学 标准查询与下载

KS A ISO 3:2012 优先数.优先数数列

이 표준은 표준수에 대하여 규정한다.

Preferred numbers-Series of preferred numbers

KS A ISO 497:2012 优先数列及舍入优先数列的选择指南

이 표준은 KS A ISO 17의 6.에서 언급된 것과 같이 더 끝맺음한 수열의 선택 및 가능한 사용에 대한보충적 지침으로 KS A ISO 17을 완성한다.이 표준은a) 더 큰 또는 더 작은 정도로 끝맺음한 2개의 수열 형식으로, 단지 허용가능한 더 끝맺음한 수열을제공한다.b) 더 끝맺음한 값이 사용되는, 그리고 그 사용결과에 대한 조건을 언급한다.c) 표준수와 여러 가지 더 끝맺음한 값의 선택에서의 불확정성을 피할 수 있게 하는 규칙을 제공한다.

Guide to the choice of series of preferred numbers and of series containing more rounded values of preferred numbers

BS EN ISO 5436-2:2012 几何产品技术规范 (GPS). 表面结构. 轮廓法. 测量标准. 软件测量标准

Geometrical product specifications (GPS). Surface texture. Profile method. Measurement standards. Software measurement standards

BS EN ISO 13102:2012 几何产品技术规范 (GPS). 尺寸测量设备. 电子数字指示器. 设计和计量特性

Geometrical product specifications (GPS). Dimensional measuring equipment. Electronic digital-indicator gauge. Design and metrological characteristics

BS ISO 22514-7:2012 过程管理的统计方法.能力和性能.测量过程能力

Statistical methods in process management. Capability and performance. Capability of measurement processes

ISO/TR 13519:2012 软件支持的ISO统计学出版物开发和使用导则

Guidance on the development and use of ISO statistical publications supported by software

ISO 22514-7:2012 过程管理的统计方法.能力和性能.第7部分:测量能力

Statistical methods in process management - Capability and performance - Part 7: Capability of measurement processes

BS ISO 7870-3:2012 控制图.验收控制图表

Control charts. Acceptance control charts

BS EN ISO 25178-3:2012 几何产品技术规范 (GPS). 表面结构: 平面式. 操作规范

Geometrical product specifications (GPS). Surface texture: Areal. Specification operators

ISO/TR 13587:2012 测量不确定性的解释和评定用三种统计方法

Three statistical approaches for the assessment and interpretation of measurement uncertainty

KS B ISO 8062-1:2012 产品几何技术规范(GPS).模制件尺寸和几何公差.第1部分:词汇

이 표준은 제품 형상 명세(GPS)에서 성형 부품에 대한 치수 및 기하 공차를 부여할 때

Geometrical product specifications(GPS)-Dimensional and geometrical tolerances for moulded parts-Part 1:Vocabulary

DIN EN ISO 14405-2:2012 产品几何量技术规范(GPS).尺寸公差.第2部分:除了线性尺寸之外的尺寸(ISO 14405-2-2011).德文版本 EN ISO 14405-2-2011

Geometrical product specifications (GPS) - Dimensional tolerancing - Part 2: Dimensions other than linear sizes (ISO 14405-2:2011); German version EN ISO 14405-2:2011

DIN ISO 18414:2012 按属性验收取样规程.控制出厂质量信用原则的验收零取样系统(ISO 18414-2006).德文和英文文本

Acceptance sampling procedures by attributes - Accept-zero sampling system based on credit principle for controlling outgoing quality (ISO 18414:2006); Text in German and English

DIN ISO/TR 22971:2012 测量方法和结果的准确性(真实性和精确性).设计,执行和统计分析实验室间可重复性和再现性结果中使用ISO 5725-2-1994的实用指南(ISO/TR 22971-2005)

Accuracy (trueness and precision) of measurement methods and results - Practical guidance for the use of ISO 5725-2:1994 in designing, implementing and statistically analysing interlaboratory repeatability and reproducibility results (ISO/TR 22971:2005);

ANSI/ASTM E1488:2012 试验方法开发和应用中使用的统计学程序指南

Guide for Statistical Procedures to Use in Developing and Applying Test Methods

ASTM E2587-12 统计过程控制中控制图使用的标准实施规程

4.1 This practice describes the use of control charts as a tool for use in statistical process control (SPC). Control charts were developed by Shewhart (1) in the 1920s and are still in wide use today. SPC is a branch of statistical quality control (2, 3), which also encompasses process capability analysis and acceptance sampling inspection. Process capability analysis, as described in Practice E2281, requires the use of SPC in some of its procedures. Acceptance sampling inspection, described in Practices E1994, E2234, and E2762, requires the use of SPC so as to minimize rejection of product. 4.2 Principles of SPC—A process may be defined as a set of interrelated activities that convert inputs into outputs. SPC uses various statistical methodologies to improve the quality of a process by reducing the variability of one or more of its outputs, for example, a quality characteristic of a product or service. 4.2.1 A certain amount of variability will exist in all process outputs regardless of how well the process is designed or maintained. A process operating with only this inherent variability is said to be in a state of statistical control, with its output variability subject only to chance, or common, causes. 4.2.2 Process upsets, said to be due to assignable, or special causes, are manifested by changes in the output level, such as a spike, shift, trend, or by changes in the variability of an output. The control chart is the basic analytical tool in SPC and is used to detect the occurrence of special causes operating on the process. 4.2.3 When the control chart signals the presence of a special cause, other SPC tools, such as flow charts, brainstorming, cause-and-effect diagrams, or Pareto analysis, described in various references (3-7), are used to identify the special cause. Special causes, when identified, are either eliminated or controlled. When special cause variation is eliminated, process variability is reduced to its inherent variability, and control charts then function as a process monitor. Further reduction in variation would require modification of the process itself. 4.3 The use of control charts to adjust one or more process inputs is not recommended, although a control chart may signal the need to do so. Process adjustment schemes are outside the scope of this practice and are discussed by Box and Luceño (

Standard Practice for Use of Control Charts in Statistical Process Control

ANSI/ASTM E2587:2012 统计学过程控制中控制图实施规程

Practice for Use of Control Charts in Statistical Process Control

ANSI/ASTM E2709:2012 符合成批验收程序的示范能力规程

Practice for demonstrating Capability to Comply with an Acceptance Procedure

ASTM E2862-12 击中/丢失数据检测分析的概率标准规程

The POD analysis method described herein is based on a well-known and well established statistical method. It shall be used to quantify the demonstrated POD for a specific set of examination parameters and known range of discontinuity sizes when the initial response from a nondestructive evaluation inspection system is ultimately binary in nature (that is, hit or miss). This method requires that a relationship between discontinuity size and POD exists and is best described by a generalized linear model with the appropriate link function for binary outcomes. Prior to performing the analysis it is assumed that the discontinuity of interest is clearly defined; the number and distribution of induced discontinuity sizes in the POD specimen set is known and well-documented; discontinuities in the POD specimen set are unobstructed; the POD examination administration procedure (including data collection method) is well-defined, under control, and unbiased; and the initial response is ultimately binary in nature (that is, hit or miss). The analysis results are only valid if convergence is achieved and the model adequately represents the data. The POD analysis method described herein is consistent with the analysis method for binary data described in MIL-HDBK-1823A, which is included in several widely utilized POD software packages to perform a POD analysis on hit/miss data. It is also found in statistical software packages that have generalized linear modeling capability. This practice requires that the analyst has access to either POD software or other software with generalized linear modeling capability.1.1 This practice defines the procedure for performing a statistical analysis on nondestructive testing hit/miss data to determine the demonstrated probability of detection (POD) for a specific set of examination parameters. Topics covered include the standard hit/miss POD curve formulation, validation techniques, and correct interpretation of results. 1.2 The values stated in inch-pound units are to be regarded as standard. The values given in parentheses are mathematical conversions to SI units that are provided for information only and are not considered standard. 1.3 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory limitations prior to use.

Standard Practice for Probability of Detection Analysis for Hit/Miss Data

ASTM C1210-12 制定核工业分析化学实验室用测量系统质量控制计划的标准指南

A laboratory quality assurance program is an essential program for laboratories within the nuclear industry. Guide C1009 provides guidance for establishing a quality assurance program for an analytical laboratory within the nuclear industry. The basic elements of a laboratory quality assurance program are organization, quality assurance program, training and qualification, procedures, laboratory records, control of records, control of procurement, control of measuring equipment and materials, control of measurements, and deficiencies and corrective actions. This guide deals with the control of measurements aspect of the laboratory quality assurance program. Fig. 1 shows the relationship of measurement control with other essential aspects of a laboratory quality assurance program. The fundamental purposes of a measurement control program are to provide the with use assurance (real-time control) that a measurement system is performing satisfactorily and to provide the data necessary to quantify measurement system performance. The with use assurance is usually provided through the satisfactory analysis of quality control samples (reference value either known or unknown to the analyst). The data necessary to quantify measurement system performance is usually provided through the analysis of quality control samples or the duplicate analysis of process samples, or both. In addition to the analyses of quality control samples, the laboratory quality control program should address (1) the preparation and verification of standards and reagents, (2) data analysis procedures and documentation, (3) calibration and calibration procedures, (4) measurement method qualification, (5) analyst qualification, and (6) other general program considerations. Other elements of laboratory quality assurance also impact the laboratory quality control program. These elements or requirements include (1) chemical analysis procedures and procedure control, (2) records storage and retrieval requirements, (3) internal audit requirements, (4) organizational considerations, and (5) training/qualification requirements. To the extent possible, this standard will deal primarily with quality control requirements rather than overall quality assurance requirements. Although the Standard Guide uses suggestive rather than prescriptive language (for example, “should” as opposed to “shall”), the elements being addressed should not be interpreted as optional. An effective and comprehensive laboratory quality control program should, at minimum, completely and adequately consider and include all elements listed in Section 1 and in the corresponding referenced sections of this guide. FIG. 1 Quality Assurance of Analytical Laboratory Data1.1 This standard provides guidance for establishing and maintaining a measurement system quality control program. Guidance is provided for general program considerations, preparation of quality control samples, analysis of quality control samples, quality control data analysis, analyst qualification, measurement system calibration, measurement method qualification, and measurement system maintenance. 1.2 This guidance is provided in the following sections:

Standard Guide for Establishing a Measurement System Quality Control Program for Analytical Chemistry Laboratories Within the Nuclear Industry