Artificial Intelligence
Artificial Intelligence
-
Technical committeeTypeAcronymIEEE 3527.1-2020CommitteePublished year2020KeywordsDescription
In today’s digital age, technology has a firm grasp on practically every aspect of human life, consequently there is growing cross-sector demand to help individuals build digital competencies such as digital literacy, digital skills, and digital readiness. However, there is no universally accepted meaning of terms like “digital literacy,” “digital skills,” or “digital readiness,” which can lead to difficulty coordinating efforts to improve digital competencies worldwide. Digital Intelligence (DQ) was developed to encompass a comprehensive set of technical, cognitive, meta-cognitive, and socio-emotional competencies, which are grounded in universal moral values and enable individuals to face the challenges of digital life and adapt to its demands. The DQ Framework is comprised of 8 areas of digital life--identity, use, safety, security, emotional intelligence, literacy, communication, and rights--across 3 levels of experience--citizenship, creativity, and competitiveness. The objective of this standard is to establish a DQ global standard that encompasses a common framework to ensure that digital competency building efforts are coordinated globally. It includes a common set of definitions, language, and understanding of digital literacy, skills, and readiness that can be adopted by all stakeholders worldwide, including national governments, the educational industry, the technology industry, international agencies, private companies, and society as a whole.
-
Technical committeeTypeAcronymIEEE 2813-2020CommitteePublished year2020KeywordsDescription
This standard can be applied to internet-based business scenarios, and can also be served serve as a practical guide to achieve help assess business security risk control through the big data technology. This standard can be applied in other types of organization, including public or privately-owned or state-owned enterprises, associations, or organizations, or by individuals, to improve assessment of their protection capability against business security risks based on big data technology.
-
Technical committeeTypeAcronymIEEE 2755.2-2020CommitteePublished year2020KeywordsDescription
Utilizing terminology as established in IEEE 2755-2017 and technology taxonomy as established in IEEE 2755.1 – 2019, this Recommended Practice provides a comprehensive methodology for technology domain exploration, development of strategy, technology evaluation, Implementation, management, operations, program optimization and successful enterprise scaling for IPA programs. This Recommended Practice provides the reader a compilation of best practices from industry leaders on the proven methods from the initial discovery and exploration of the transformative capabilities of IPA technology through to developing and running an enterprise-wide program
Technology -
Technical committeeTypeAcronymIEEE 2755.1-2019CommitteePublished year2019KeywordsDescription
Utilizing terminology as established in IEEE Std 2755-2017, defined and classified in this guide are approximately 150 features and functions across five core areas of technology capability in the family of new technology products collectively referred to as Intelligent Process Automation. This guide is published to create clarity for individuals involved with Software-Based Intelligent Process Automation products so that industry participants may rely on a product manufacturer’s functionality claims and understand the underlying technological methods used to produce those functions and how one might approach evaluating the relative sophistication and importance of each function or feature. This guide represents the consensus of a diverse panel of industry participants.
-
Technical committeeTypeAcronymIEEE 2755-2017CommitteePublished year2017KeywordsDescription
An all new family of software based intelligent process automation technologies has emerged recently. Because of the newness of this kind of automation capability, there are no common definitions of concepts, capabilities, terms, technology, types, etc. This standard is published for the purpose of promoting clarity and consistency in the use of Software Based Intelligent Process Automation (SBIPA) terminology. The definitions represent the consensus of a diverse panel of industry participants.
-
Technical committeeTypeAcronymIEEE 269-2019CommitteePublished year2019KeywordsDescription
Practical methods for making electroacoustic laboratory measurements of analog and digital speech communications terminals and connected audio devices are contained in this standard. The methods are applicable to a wide variety of wired, wireless, cordless, and mobile communication terminals. Examples include Voice over Internet Protocol (VoIP), Wi-Fi®, and softphone devices. Tests applicable to connected audio devices such as wired, Bluetooth®, and universal serial bus (USB) handsets, headsets, and wearables are included. The standard contains objective metrics, subjective metrics, and subjective metric predictors. Application is in the frequency range from 20 Hz to 20 kHz.
-
Technical committeeTypeAcronymIEEE 1652-2016CommitteePublished year2016KeywordsDescription
The data and rationale for translating head and torso simulator measurements from eardrum to other acoustic reference points such as free field and diffuse field are provided in this standard.
-
Technical committeeTypeAcronymIEEE 1636.99-2013CommitteePublished year2013KeywordsDescription
This standard is intended to promote and facilitate interoperability between components of SIMICA. The standard defines EXPRESS information models and XML schemas that together define the common information elements supporting these interfaces.
-
Technical committeeTypeAcronymIEEE 1636.2-2018CommitteePublished year2018KeywordsDescription
Promoting and facilitating interoperability components of automatic test systems where actions taken during maintenance need to be shared is addressed in this standard. The standard thus facilitates the capture of maintenance action information data in storage devices and databases, facilitating online and offline analysis. The maintenance action information schema becomes a class of information that can be used within the SIMICA family of standards. The exchange format is expressed in both the OWL and XML formats.
-
Technical committeeTypeAcronymIEEE 1636-2018CommitteePublished year2018KeywordsDescription
Promoting and facilitating interoperability between components of automatic test systems where test results and/or maintenance actions need to be shared is addressed in this standard. The standard defines the common elements between both test results data and maintenance action data. The common schema becomes a class of information that shall be used within the SIMICA family of standards.