This standard provides an architectural framework for the Internet of Food (IoF) system applications. The framework addresses interoperability, scalability, and security requirements to help ensure cross-domain interaction, aid system efficiency and functional compatibility.

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This standard specifies requirements for the simulation of renewable, nuclear, and fossil-based energy sources. These simulations aid the financing and management of green energy businesses. The simulation scope, simulation participants relationship parameters, simulation tools, simulation data type, simulation data format, and simulation outcome verification are specified.

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This standard defines an architecture framework description for the Internet of Things (IoT). The architecture ontology and methodology of the framework architecture conforms to the international standard ISO/IEC/IEEE 42010:2011. The architecture framework description is motivated by concerns commonly shared by IoT system stakeholders across multiple domains (transportation, healthcare, Smart Grid, etc.). This standard provides a conceptual basis for the notion of things in the IoT and then… read more elaborates the shared concerns as a collection of architecture viewpoints that form the body of the framework description. read less

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This standard is intended to provide a standard framework for evaluating the quality of digital humans that look and act like actual humans. The quality of digital humans is related to the human factor for immersive content service that defines metrics for evaluating the realism of digital humans. The evaluation needs to define a framework that handles the digital human content as test data, define test methods and test cases, and provide a evaluation report of the test results. Therefore, the… read more framework for evaluating the quality of digital humans includes the following: - A set of cognitive-psychological factors that define how users feel the realism of digital humans. - Definitions on methods and metadata that describe the tests for the quality of digital humans. - A procedure that allows the quality evaluation of digital humans. read less

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This document provides a reference framework for trustworthy federated machine learning, including the principles of trustworthy federated machine learning, requirements for different roles and principles of trustworthy federated machine learning, and several technologies to realize trustworthy federated machine learning. It also lists some scenarios where trustworthy federated machine learning can be applied.

定价： 76元 / 折扣价： 65 元

The scope of this family of standards is to provide for open systems communications in healthcare applications, primarily between bedside medical devices and patient care information systems, optimized for the acute care setting. The scope of this document is to provide the overall definition of the family of standards. It does so by defining a conceptual model, an information model, and a communications model for medical device communications and by specifying constraints for conformance to… read more the set of standards. read less

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This recommended practice contains a conceptual model and a standard terminology for ad hoc network communication at the nanoscale. More specifically, this recommended practice contains: a) the definition of nanoscale communication networking; b) the conceptual model for ad hoc nanoscale communication networking; c) the common terminology for nanoscale communication networking, including: 1) the definition of a nanoscale communication channel highlighting the fundamental differences from a… read more macroscale channel; 2) abstract nanoscale communication channel interfaces with nanoscale systems; 3) performance metrics common to ad hoc nanoscale communication networks; 4) the mapping between nanoscale and traditional communication networks, including necessary high-level components such as a map of major components: coding and packets, addressing, routing, localization, layering, and reliability; read less

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This guide provides a technological framework that aims to increase trustworthiness of AI systems using explainable artificial intelligence (XAI) technologies and methods. The document also provides measurable solutions to evaluate AI systems in terms of explainability. Specifically, the document illustrates the following aspects of XAI systems: a) The requirements of providing human-understandable explanations for AI systems in different use-cases, for example, healthcare and financial… read more applications b) Approaches to offer a series of available tools for giving an AI model tenable explanations c) A set of measurable solutions to evaluate AI systems and corresponding performance, such as the availability, resiliency, accuracy, safety, security, and privacy of the AI system under such status read less

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This standard defines the requirements of an identity framework for metaverse. This standard provides an identity framework for use across different metaverse systems. Furthermore, the standard helps to recognize the relevance between real world and virtual world entities. The standard covers business logic, operational procedures, and authentication programs. Also, the standard defines terminologies, a basic architectural framework, and key indicators.

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This recommended practice contains a common framework of IEEE SA location services for healthcare (LS-H). LS-H includes hardware and software that provides location information for clinical and non-clinical healthcare use cases. The framework includes common terminology and a conceptual information model.

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This standard provides definitions, terminologies and key technological requirements for smart lock systems. Architectural framework and requirements for the overall functionality including requirements to achieve security are addressed.

定价： 58元 / 折扣价： 50 元

This standard provides an architectural framework for the Internet of Food (IoF) system applications. The framework addresses interoperability, scalability, and security requirements to ensure cross-domain interaction, aid system efficiency and functional compatibility.

定价： 56元 / 折扣价： 48 元

This guide provides a reference framework for trustworthy Federated Machine Learning. The document provides guidance with respect to provable security for data and models, optimized model utility, controllable communication and computational complexity, explainable decision making and supervised processes. The guide describes three main aspects: 1) principles for trustworthy Federated Machine Learning, 2) requirements for different roles in trustworthy Federated Machine Learning, and 3)… read more techniques to realize trustworthy Federated Machine Learning. read less

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