Artificial Intelligence
Artificial Intelligence
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Technical committeeTypeAcronymIEEE 1546-2000CommitteePublished year2000KeywordsDescription
An aid in the understanding and use of digital test interchange format (DTIF) files is provided in this guide. This information will be an aid to users in developing tools such as pre-processors and postprocessors of DTIF data and other utilities.
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Technical committeeTypeAcronymIEEE 1445-2016CommitteePublished year2016KeywordsDescription
The information content and the data formats for the interchange of digital test program data between digital automated test program generators (DATPGs) and automatic test equipment (ATE) for board-level printed circuit assemblies are defined. This information can be broadly grouped into data that defines the following: user under test (UUT) model, stimulus and response, fault dictionary, and probe.
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Technical committeeTypeAcronymIEEE 1329-2010CommitteePublished year2010KeywordsDescription
This standard provides techniques for objective measurement of electroacoustic and voice-switching characteristics of speakerphones that connect directly or indirectly to an analog or digital telephone network. Due to the various characteristics of speakerphones and the environments in which they operate, not all of the test procedures in this standard are applicable to all speakerphones. Application of the test procedures to atypical speakerphones should be determined on an individual basis.
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Technical committeeTypeAcronymIEEE 1232.3-2014CommitteePublished year2014KeywordsDescription
Guidance to developers of IEEE Std 1232-conformant applications is provided in this guide. A simple doorbell is used as an example system under test to illustrate how the static model constructs of Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE) are used to form a diagnostic reasoner knowledge base. Each of AI-ESTATE’s knowledge base types is discussed in conceptual terms, and how those concepts are represented in exchange files is shown. Also, some of the nuanced aspects of diagnostic knowledge bases in AI-ESTATE are clarified. An example reasoner session is provided to illustrate the use of AI-ESTATE services.
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Technical committeeTypeAcronymIEEE 1232-2010CommitteePublished year2010KeywordsDescription
Data interchange and standard software services for test and diagnostic environments are defined by Artificial Intelligence Exchange and Service Tie to All Test Environments (AIESTATE). The purpose of AI-ESTATE is to standardize interfaces for functional elements of an intelligent diagnostic reasoner and representations of diagnostic knowledge and data for use by such diagnostic reasoners. Formal information models are defined to form the basis for a format to facilitate exchange of persistent diagnostic information between two reasoners and also to provide a formal typing system for diagnostic services. The services to control a diagnostic reasoned are defined by this standard.
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Technical committeeTypeAcronymIEC White Paper Safety in the future:2020CommitteePublished year2020KeywordsDescription
Advanced robotics, artificial intelligence, the Internet of Things are transforming how humans and electrotechnical systems interconnect. With the introduction of new technologies, it is critically important to ensure that human safety remains at the centre of the human-machine relationship.
Each year, several million workers are injured on the job. Aside from the economic cost, this is the source of immeasurable suffering that is largely preventable.
Using real-life examples, this white paper addresses safety in the future by exploring current social trends and initiatives as well as projects that are pioneering innovative safety solutions. All of them are based on the concept that safety will be integral to systems in which humans and machines closely interface. The paper also introduces a collaborative framework – the tripartite system for safety – which offers a systematic approach to examining key safety elements.
Bringing these safety concepts to fruition will require significant standardization efforts to mitigate challenges related to decision-making involving machines and humans.
The white paper formulates recommendations both of a general nature as well as to the IEC community.
The white paper was developed by the IEC Market Strategy Board (MSB) safety in the future project team, directed by Dr Kazuhiko Tsutsumi, MSB Convenor, Mitsubishi Electric Corporation, with major contributions from the lead project partner, Dr Coen van Gulijk, TNO, the Netherlands.
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Technical committeeTypeAcronymIEC White Paper AI:2018CommitteePublished year2018KeywordsDescription
Artificial intelligence (AI) is continuously making inroads into domains previously reserved to humans. Robots support workers in the manufacturing sector; digital assistants automate office tasks; intelligent appliances order food based on owners’ preferences or control lighting and temperature in the home in preparation of their arrival. Increasingly sophisticated algorithms have the potential to help address some of humanity’s biggest challenges. They also bring about a number of risks and threats that businesses, governments and policy makers need to understand and tackle carefully.
This white paper sets the scene for understanding where AI stands today and the outlook for the next 5 to 10 years. Taking an industrial perspective, it discusses in more detail: smart homes, intelligent manufacturing, smart transportation/self-driving vehicles, and the energy sector.
It covers current technological capabilities and provides a detailed description of some of the major existing and future challenges related to safety, security, privacy, trust and ethics that AI will have to address at the international level. AI will become one of the core technologies across many different industries and standardization will play a critical role in shaping its future.
The white paper was developed by the IEC Market Strategy Board (MSB) with major contributions from Haier Group and project partner the German Research Centre for Artificial Intelligence (DFKI). Supporting project team members included SAP, Huawei, NSW Data Analytics Centre (DAC), China Electronic Standardization Institute (CESI), LG Electronics, and Korea Electric Power Corporation (KEPCO).
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Technical committeeTypeAcronymIEC 62243:2012CommitteePublished year2012KeywordsDescription
IEC 62243:2012(E) defines formal specifications for supporting system diagnosis. These specifications support the exchange and processing of diagnostic information and the control of diagnostic processes. Diagnostic processes include, but are not limited to, testability analysis, diagnosability assessment, diagnostic reasoning, maintenance support, and diagnostic maturation.
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Technical committeeTypeAcronymETSI TR 103 674CommitteePublished year2021KeywordsDescription
Detailed description of selected use cases and identification of architectural evolutions (components, required mappings, etc.) to the oneM2M framework.