美利体育登录入口官网

ISO 14971 在人工智能和机器学习中的应用指南

Guidance on the Application of ISO 14971 to Artificial Intelligence and Machine Learning

ISO 14971 在人工智能机器学习中的应用 指南

Application of ISO 14971 to machine learning in artificial intelligence - Guide

机械安全 与ISO 12100的关系 第5部分：人工智能机器学习的影响（ISO/TR 22100-5:2021）（2022年6月西班牙标准化协会认可 ）

Safety of machinery - Relationship with ISO 12100 - Part 5: Implications of artificial intelligence machine learning (ISO/TR 22100-5:2021) (Endorsed by Asociación Espa?ola de Normalización in June 2022.)

基于海上管道服务数据库和机器学习算法的腐蚀风险预测

Corrosion Risk Prediction Based on Offshore Pipeline Service Database and Machine Learning Algorithms

信息技术 人工智能 AI 系统生命周期流程（ISO/IEC 5338:2023 IDT）

Information technology - Artificial intelligence - AI system life cycle processes (ISO/IEC 5338:2023, IDT)

人工智能 分析和机器学习 (ML) 的数据质量 第4部分：数据质量流程框架

Artificial intelligence - Data quality for analytics and machine learning (ML), Part 4: Data quality process framework

人工智能 分析和机器学习 (ML) 的数据质量 第5部分：数据质量治理框架

Artificial intelligence - Data quality for analytics and machine learning (ML), Part 5: Data quality governance framework

石油天然气行业大数据/数字化转型工作流程及应用指南

Guideline On Big Data/Digital Transformation Workflows and Applications for the Oil and Gas Industry

机械安全 与 ISO 12100 的关系 第5部分：人工智能机器学习的含义

Safety of machinery — Relationship with ISO 12100 — Part 5: Implications of artificial intelligence machine learning

人工智能中的机器学习 指南

machine learning in artificial intelligence ? Guide

人工智能中的机器学习 指南

machine learning in artificial intelligence ? Guide

使用机器学习（ML）的人工智能（AI）系统框架

Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)

信息技术 基于医学图像的 3D 打印建模 分割

Information technology. Medical image-based modelling for 3D printing - Segmentation

人工智能 分析和机器学习 (ML) 的数据质量 概述、术语和示例

Artificial intelligence. Data quality for analytics and machine learning (ML) - Overview, terminology, and examples

人工智能 分析和机器学习 (ML) 的数据质量 数据质量流程框架

Artificial intelligence. Data quality for analytics and machine learning (ML) - Data quality process framework

人工智能 分析和机器学习 (ML) 的数据质量 数据质量治理框架

Artificial intelligence. Data quality for analytics and machine learning (ML) - Data quality governance framework

信息技术词汇—第31部分：术语和定义

Information Technology - Vocabularies - Part 31: Terms and Definitions

机械安全 与 ISO 12100 的关系 第5部分：人工智能机器学习的含义 (ISO/TR 22100-5:2021)

Safety of machinery – Relationship with ISO 12100 – Part 5: Implications of artificial intelligence machine learning (ISO/TR 22100-5:2021)

人工智能 分析和机器学习（ML）的数据质量 第4部分：数据质量过程框架（ISO/IEC 5259-4:2024）

Artificial Intelligence – Data Quality for Analytics and Machine Learning (ML) – Part 4: Data Quality Process Framework (ISO/IEC 5259-4:2024)

使用机器学习 (ML) 的人工智能 (AI) 系统框架

Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)

人工智能 分析和机器学习 (ML) 的数据质量 第4部分：数据质量流程框架

Artificial intelligence – Data quality for analytics and machine learning (ML) – Part 4: Data quality process framework

人工智能 数据标注一般技术要求

General technical requirements for artificial intelligence data annotation

机械安全 与 ISO 12100 的关系 第5部分：人工智能机器学习的含义（ISO/TR 22100-5:2021）

Safety of machinery - Relationship with ISO 12100 - Part 5: Implications of artificial intelligence machine learning (ISO/TR 22100-5:2021); German version CEN ISO/TR 22100-5:2022

机械安全 与 ISO 12100 的关系 第5部分：人工智能机器学习的影响

Safety of machinery - Relationship with ISO 12100 - Part 5: Implications of artificial intelligence machine learning (ISO/TR 22100-5:2021); German version CEN ISO/TR 22100-5:2022

定义用于训练人工智能的数据成功方法 应用重点：问答 文本

Defining Data Success Methods for Training Artificial Intelligence Application Focus: Question and Answer Text

术语 铁路应用中的人工智能；英文文本

Terminology - AI in railway applications; Text in English

使用机器学习（ML）的人工智能（AI）系统框架（ISO/IEC 23053:2022）

Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML) (ISO/IEC 23053:2022); German and English version prEN ISO/IEC 23053:2023

人工智能 分析和机器学习（ML）的数据质量 第4部分：数据质量过程框架（ISO/IEC 5259-4:2024） 德语版本 EN ISO/IEC 5259-4:2025

Artificial intelligence - Data quality for analytics and machine learning (ML) - Part 4: Data quality process framework (ISO/IEC 5259-4:2024); German version EN ISO/IEC 5259-4:2025

语言模型知识图谱与知识图谱语言模型——符号化与亚符号化人工智能的混合应用

Knowledge Graphs for Language Models and Language Models for Knowledge Graphs – Hybrid Applications of symbolic and subsymbolic AI

人工情报 机器学习中的不确定性量化

Uncertainty Quantification in Artificial Intelligence Machine Learning

人工智能 质量要求和流程 整个生命周期内人工智能系统的风险识别和分析

Artificial intelligence – Quality requirements and processes – Risk identification and analysis for AI systems along the entire life cycle; Text in English

体验式网络智能（ENI） 模块化系统人工智能机制简介

Experiential Networked Interlligence (ENI); Introduction to Artificial Intelligence Mechanisms for Modular Systems

网络功能虚拟化 (NFV) 第 5 版 可靠性 关于可靠性运营数据认知使用的报告

Network Functions Virtualisation (NFV) Release 5; Reliability; Report on cognitive use of operations data for reliability

确保人工智能（SAI）安全 数据供应链安全

Securing Artificial Intelligence (SAI); Data Supply Chain Security

确保人工智能（SAI）的安全 问题陈述

Securing Artificial Intelligence (SAI); Problem Statement

零接触网络和服务管理 (ZSM) ZSM 中概念的术语

Zero-touch network and Service Management (ZSM); Terminology for concepts in ZSM

SmartM2M 物联网人工智能：概念验证

SmartM2M; AI for IoT: A Proof of Concept

确保人工智能

Securing Artificial Intelligence (SAI); Data Supply Chain Security

人工智能安全 AI安全测试

Securing Artificial Intelligence; Security Testing of AI

确保人工智能

Securing Artificial Intelligence (SAI); Problem Statement

为 oneM2M 提供 AI 支持（oneM2M TR-0068 版本 5.0.1）

AI enablement to oneM2M (oneM2M TR-0068 version 5.0.1)

AI赋能oneM2M（oneM2M TR-0068版本5.0.1）

AI enablement to oneM2M (oneM2M TR-0068 version 5.0.1)

5G 人工智能/机器学习（AI/ML）管理研究（3GPP TR 28.908 版本 18.1.0 Release 18）

5G; Study on Artificial Intelligence/Machine Learning (AI/ ML) management (3GPP TR 28.908 version 18.1.0 Release 18)

SmartM2M 物联网美利体育官网首页网址跨域数据可用性的要求和指南

SmartM2M; Requirements and Guidelines for cross-domain data usability of IoT devices

5G 管理和编排 人工智能/机器学习 (AI/ML) 管理（3GPP TS 28.105 版本 18.5.0 版本 18）

5G; Management and orchestration; Artificial Intelligence/ Machine Learning (AI/ML) management (3GPP TS 28.105 version 18.5.0 Release 18)

5G 管理和编排 人工智能/机器学习 (AI/ML) 管理

5G; Management and orchestration; Artificial Intelligence/ Machine Learning (AI/ML) management (3GPP TS 28.105 version 18.6.0 Release 18)

工业产品表面缺陷自动美利体育登录入口官网系统技术要求

Technical requirements for automatic detection system of surface defects of industrial products

信息技术.词汇.第31部分:人工智能.机器学习

Information technology - Vocabulary - Part 31: Artificial intelligence - Machine learning

人工智能 用于分析和机器学习的数据质量 部分1. 概览、术语和示例

Artificial intelligence. Data quality for analytics and machine learning. Part 1. Overview, terminology and examples

人工智能 用于分析和机器学习的数据质量 部分3. 数据质量管理的要求和建议

Artificial intelligence. Data quality for analytics and machine learning. Part 3. Data quality management requirements and guidelines

人工智能 数据分析和机器学习的数据质量 第4部分 数据质量管理过程的结构

Artificial intelligence. Data quality for analytics and machine learning. Part 4. Data quality process framework

信息技术 人工智能 人工智能系统生命周期流程

Information technology — Artificial intelligence — AI system life cycle processes

机械安全 与 ISO 12100 的关系 第5部分：人工智能机器学习的影响

Safety of machinery — Relationship with ISO 12100 — Part 5: Implications of artificial intelligence machine learning

地理信息 人工智能训练数据标注语言 第1部分：概念模型

Geographic information — Training data markup language for artificial intelligence — Part 1: Conceptual model

使用机器学习（ML）的人工智能（AI）系统框架

Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)

信息技术.词汇.第31部分:人工智能.机器学习

Information technology - Vocabulary - Part 31: Artificial intelligence - Machine learning

人工智能 分析和机器学习 (ML) 的数据质量 第5部分：数据质量治理框架

Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 5: Data quality governance framework

人工智能——用于数据分析和机器学习（ML）的数据质量 第2部分：数据质量度量

Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 2: Data quality measures

人工智能

Artificial intelligence

人工智能

Artificial intelligence

信息技术.人工智能（AI）.人工智能系统和人工智能辅助决策中的偏差

Information technology - Artificial intelligence (AI) - Bias in AI systems and AI aided decision making

道路车辆.安全和人工智能

Road vehicles — Safety and artificial intelligence

机械安全. 与ISO 12100的关系. 第5部分: 人工智能机器学习的意义

Safety of machinery - Relationship with ISO 12100 - Part 5: Implications of artificial intelligence machine learning

健康信息学.机器学习技术在成像和其他医学应用中的应用

Health informatics - Applications of machine learning technologies in imaging and other medical applications

基于人工智能的智慧课堂系统要求

Requirements for smart class systems based on artificial intelligence

大数据增强视觉监控服务的要求（第16研究组）

Requirements for big data-enhanced visual surveillance services (Study Group 16)

医学图像用于机器学习应用的质量控制框架

General framework of quality control of medical images for machine learning applications

人工智能/机器学习软件医疗器械质量评估要求

Quality assessment requirements for artificial intelligence/machine learning-based software as a medical device

供应链管理中机器学习流程的环境效率指南

Guidelines on the environmental efficiency of machine learning processes in supply chain management

电信场景中基于机器学习的 QoS/QoE 预测和网络性能管理解决方案的开发指南

Guidance for the development of machine-learning-based solutions for QoS/QoE prediction and network performance management in telecommunication scenarios

该建议书标题尚未译为中文。

ITU-T X.1231 Supplement on countering spam based on machine learning

ITU-T Y.3170系列 – IMT-2020等未来网络中的机器学习：用例

ITU-T Y.3170-series – Machine learning in future networks including IMT-2020: use cases

ITU-T Y.3800系列 – 量子密钥分发网络 – 机器学习的应用

ITU-T Y.3800-series – Quantum key distribution networks - Applications of machine learning

ITU-T Y.3000系列 人工智能标准化路线图

ITU-T Y.3000-series – Artificial intelligence standardization roadmap

自主网络 架构框架

Autonomous networks - Architecture framework

该建议书标题尚未译为中文。

Information-centric networking in networks beyond IMT-2020 Requirements and functional framework enhancement to support machine learning

基于人工智能方法的流量分类IMT-2020管理

Traffic typization IMT-2020 management based on an artificial intelligence approach

IMT-2020 等未来网络中基于 AI/ML 的网络设计优化要求和框架

Requirements and framework for AI/ML-based network design optimization in future networks including IMT-2020

IMT-2020网络中基于机器学习的服务质量保障要求

Requirements for machine learning-based quality of service assurance for the IMT-2020 network

未来网络（包括IMT-2020）中机器学习的架构框架

Architectural framework for machine learning in future networks including IMT-2020

用于在未来网络（包括 IMT-2020）中实现机器学习的数据处理框架

Framework for data handling to enable machine learning in future networks including IMT-2020

IMT-2020网络基于机器学习的服务质量保证的功能架构

Functional architecture of machine learningbased quality of service assurance for the IMT-2020 network

基于人工智能的网络自动化架构框架，用于未来网络（包括 IMT-2020）中的资源和故障管理

Architectural framework for artificial intelligence-based network automation for resource and fault management in future networks including IMT-2020

包括 IMT-2020 在内的未来网络中基于人工智能的网络服务供应的功能框架

Functional framework of artificial intelligence-based network service provisioning in future networks including IMT-2020

基于机器学习的应用描述符无关流量感知机制

Mechanism of traffic awareness for application-descriptor-agnostic traffic based on machine learning

利用垂直行业的体验质量反馈 通过机器学习辅助网络切片管理框架

Framework for network slicing management assisted by machine learning leveraging quality of experience feedback from verticals

分布式联合学习的要求和框架 以实现包括IMT-2020在内的未来网络中的机器学习

Requirements and framework for distributed joint learning to enable machine learning in future networks including IMT-2020

大数据驱动网络 – 机器学习机制

Big data driven networking Machine learning mechanism

量子密钥分发网络 基于机器学习的服务质量保证要求

Quantum key distribution networks - Requirements for machine learning based quality of service assurance

机器学习在碱性环境中钢腐蚀磨损建模

Machine learning to model steel corrosion and wear in alkaline environments

基于机器学习的地下管道腐蚀增长速率估算方法

Method for estimating corrosion growth rate of underground pipelines based on machine learning

基于实时传感器测量和机器学习方法的航空航天腐蚀速率测量与预测

Aerospace corrosion rate measurement and prediction based on real-time sensor measurement and machine learning methods

人工智能 分析和机器学习（ML）的数据质量 第4部分：数据质量过程框架

Artificial intelligence - Data quality for analytics and machine learning (ML) - Part 4 : data quality process framework

人工智能 分析和机器学习（ML）的数据质量 第4部分：数据质量过程框架

Artificial intelligence – Data quality for analytics and machine learning (ML) – Part 4: Data quality process framework

信息技术 人工智能 人工智能系统生命周期过程

Information technology – Artificial intelligence – AI system life cycle processes

机械安全 与 ISO 12100 的关系 人工智能机器学习的影响

Safety of machinery. Relationship with ISO 12100. Implications of artificial intelligence machine learning

核设施 仪器仪表和控制以及电力系统 人工智能应用

Nuclear facilities. Instrumentation and control, and electrical power systems. Artificial Intelligence applications

信息技术 人工智能 机器学习模型和人工智能系统的可解释性和可解释性目标与方法

Information technology — Artificial intelligence — Objectives and approaches for explainability and interpretability of machine learning (ML) models and artificial intelligence (AI) systems

系统和软件工程 系统和软件质量要求和评估（SQuaRE） 人工智能（AI）系统质量评估指南

Systems and software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Guidance for quality evaluation of artificial intelligence (AI) systems

航空系统中的人工智能：分类学

Artificial Intelligence in Aeronautical Systems: Taxonomy

航空系统中的人工智能：关注声明

Artificial Intelligence in Aeronautical Systems: Statement of Concerns

导航自动驾驶道路车辆机器学习安全标准的发展趋势

Navigating the Evolving Landscape of Safety Standards for Machine Learning-based Road Vehicle Functions

机械安全 与 ISO 12100 的关系 第5部分：人工智能机器学习的含义（ISO/TR 22100-5:2021）

Safety of machinery - Relationship with ISO 12100 - Part 5: Implications of artificial intelligence machine learning (ISO/TR 22100-5:2021)

信息技术 人工智能

Information technology — Artificial Intelligence (AI) — Use cases (ISO/IEC TR 24030:2021, IDT)

信息技术 人工智能 机器学习分类性能评估

Information technology — Artificial Intelligence — Assessment of machine learning classification performance (ISO/IEC TS 4213:2022, IDT)

道路车辆 安全和人工智能

Road vehicles — Safety and artificial intelligence (ISO/PAS 8800:2024, IDT)

机械安全 与 ISO 12100 的关系 第5部分：嵌入式人工智能 机器学习的含义 (ISO/TR 22100-5:2021)

Safety of machinery — Relationship with ISO 12100 — Part 5: Implications of embedded Artificial Intelligence-machine learning (ISO/TR 22100-5:2021)

人工智能从业人员能力要求

Competency requirements for artificial intelligence practitioners

人工智能（AI）系统使用机器学习（ML）框架（ISO/IEC 23053:2022）

Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML) (ISO/IEC 23053:2022)

人工智能 用于分析和机器学习（ML）的数据质量 第4部分：数据质量过程框架（ISO/IEC 5259-4:2024 IDT）

Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 4: Data quality process framework (ISO/IEC 5259-4:2024, IDT)

人工智能 分析和机器学习

Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 5: Data quality governance framework (ISO/IEC 5259-5:2025, IDT)

信息技术 人工智能 AI 系统生命周期流程（ISO/IEC 5338:2023 IDT）

Information technology — Artificial intelligence — AI system life cycle processes (ISO/IEC 5338:2023, IDT)

专科疾病标准数据集建设规范

Specifications for the construction of specialized disease standard datasets

人工智能芯片应用 面向汉盲翻译系统的技术要求

Application of artificial intelligence chip—Technical requirements to system of Chinese-Braille translation

机械安全 与 ISO 12100 的关系 第5部分：人工智能机器学习的影响

Safety of machinery - Relationship with ISO 12100 - Part 5: Implications of artificial intelligence machine learning (ISO/TR 22100-5:2021) (Endorsed by Asociación Espa?ola de Normalización in June of 2022.)

人工智能 数据质量用于分析和机器学习（ML） 第4部分：数据质量过程的参考框架

Artificial intelligence - Data quality for analytics and machine learning (ML) - Part 4: Data quality process framework

基于人工智能的接入网运维和业务智能化 场景与需求

Access network operation and maintenance and service intelligence scenarios and requirements based on artificial intelligence

面向机器学习的电信数据规范 数据模型

Telecom data specification data model for machine learning

医疗健康大数据平台 数据智能化处理要求和测试方法

Requirements and testing methods for intelligent data processing on medical and health big data platforms