| 1. |
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- 2018
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Ingår i: Nuclear Fusion. - : IOP Publishing. - 1741-4326 .- 0029-5515. ; 58:1
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Forskningsöversikt (refereegranskat)
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| 2. |
- Bombarda, F., et al.
(författare)
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Runaway electron beam control
- 2019
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Ingår i: Plasma Physics and Controlled Fusion. - : IOP Publishing. - 1361-6587 .- 0741-3335. ; 61:1
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Tidskriftsartikel (refereegranskat)
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| 3. |
- Mika, Kevin, et al.
(författare)
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VEDLIoT: Next generation accelerated AIoT systems and applications
- 2023
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Ingår i: Proceedings of the 20th ACM International Conference on Computing Frontiers 2023, CF 2023. - 9798400701405
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Konferensbidrag (refereegranskat)abstract
- The VEDLIoT project aims to develop energy-efficient Deep Learning methodologies for distributed Artificial Intelligence of Things (AIoT) applications. During our project, we propose a holistic approach that focuses on optimizing algorithms while addressing safety and security challenges inherent to AIoT systems. The foundation of this approach lies in a modular and scalable cognitive IoT hardware platform, which leverages microserver technology to enable users to configure the hardware to meet the requirements of a diverse array of applications. Heterogeneous computing is used to boost performance and energy efficiency. In addition, the full spectrum of hardware accelerators is integrated, providing specialized ASICs as well as FPGAs for reconfigurable computing. The project's contributions span across trusted computing, remote attestation, and secure execution environments, with the ultimate goal of facilitating the design and deployment of robust and efficient AIoT systems. The overall architecture is validated on use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. Ten additional use cases are integrated via an open call, broadening the range of application areas.
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| 4. |
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- 2018
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Ingår i: Nuclear Fusion. - : IOP Publishing. - 1741-4326 .- 0029-5515. ; 58:9
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Tidskriftsartikel (refereegranskat)
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| 5. |
- Cassel, Anders, et al.
(författare)
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On Perception Safety Requirements and Multi Sensor Systems for Automated Driving Systems
- 2020
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Ingår i: SAE technical paper series. - 400 Commonwealth Drive, Warrendale, PA, United States : SAE International. - 0148-7191. ; 2020-April:April
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Tidskriftsartikel (refereegranskat)abstract
- One major challenge in designing SAE level 3-5 Automated Driving Systems (ADS) is to define requirements for the perception system that would enable argumentation for safe operation. The safety requirements on the perception system can only be fulfilled through redundancy in the sensor hardware. It is, however, a challenge to specify the redundancy that is required in the sensor system. Safe operation for an ADS is significantly more difficult compared to advanced driver assistance systems (ADAS). The safety argumentation for ADAS typically argues that in case of a failure in the sensor array a fail-silent behavior is acceptable because the human driver can take control of the vehicle back. This argumentation however is not possible when developing level 4 or higher automation. This paper investigates prerequisites for applying a systematic methodology for analyzing redundancy in a multi-sensor system and the relation to a conceptual ADS functional architecture. This analysis must address the complexity that comes with partially overlapping sensor data from different sensors and considers variations in performance and characteristics due to changes in the environmental conditions. The paper introduces the term incomplete redundancy and presents a systematic methodology for analyzing redundancy. The aim is to provide arguments for how several sensors in a system, when appropriately combined, meet an assigned safety requirement on a higher level. Each sensor will then be assigned a certain responsibility and contributes with a sub-set of information. A set of questions of importance to address as a foundation for such a methodology are defined and discussed. The definitions of redundancy and independence between sensors are discussed as well as contract-based functional safety to adapt to different environmental and operating conditions.
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| 6. |
- Gyllenhammar, Magnus, 1992-, et al.
(författare)
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Towards an Operational Design Domain That Supports the Safety Argumentation of an Automated Driving System
- 2020
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Ingår i: Proceeding of the 10th European Congress on Embedded Real Time Software and Systems. - Toulouse.
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Konferensbidrag (refereegranskat)abstract
- One of the biggest challenges for self-driving road vehicles is how to argue that their safety cases are complete. The operational design domain (ODD) of the automated driving system (ADS) can be used to restrict where the ADS is valid and thus confine the scope of the safety case as well as the verification. To complete the safety case there is a need to ensure that the ADS will not exit its ODD. We present four generic strategies to ensure this. Use cases (UCs) provide a convenient way providing such a strategy for a collection of operating conditions (OCs) and further ensures that the ODD allows for operation within the real world. A framework to categorise the OCs of a UC is presented and it is suggested that the ODD is written with this structure in mind to facilitate mapping towards potential UCs. The ODD defines the functional boundary of the system and modelling it with this structure makes it modular and generalisable across different potential UCs. Further, using the ODD to connect the ADS to the UC enables the continuous delivery of the ADS feature. Two examples of dimensions of the ODD are given and a strategy to avoid an ODD exit is proposed in the respective case.
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| 7. |
- Kaiser, M., et al.
(författare)
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VEDLIoT: Very Efficient Deep Learning in IoT
- 2022
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Ingår i: Proceedings of the 2022 Design, Automation and Test in Europe Conference and Exhibition, DATE 2022. - : IEEE. - 9783981926361
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Konferensbidrag (refereegranskat)abstract
- The VEDLIoT project targets the development of energy-efficient Deep Learning for distributed AIoT applications. A holistic approach is used to optimize algorithms while also dealing with safety and security challenges. The approach is based on a modular and scalable cognitive IoT hardware platform. Using modular microserver technology enables the user to configure the hardware to satisfy a wide range of applications. VEDLIoT offers a complete design flow for Next-Generation IoT devices required for collaboratively solving complex Deep Learning applications across distributed systems. The methods are tested on various use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. VEDLIoT is an H2020 EU project which started in November 2020. It is currently in an intermediate stage with the first results available.
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| 8. |
- Bencomo, N., et al.
(författare)
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The Secret to Better AI and Better Software (Is Requirements Engineering)
- 2022
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Ingår i: IEEE Software. - 0740-7459 .- 1937-4194. ; 39:1, s. 105-110
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Recently, practitioners and researchers met to discuss the role of requirements, and AI and SE. We offer here notes on that fascinating discussion. Also, have you considered writing for this column? This SE for AI column publishes commentaries on the growing field of SE for AI. Submissions are welcomed and encouraged (1,000-2,400 words, each figure and table counts as 250 words, try to use fewer than 12 references, and keep the discussion practitioner focused). Please submit your ideas to me at timm@ieee.org.-Tim Menzies © 1984-2012 IEEE.
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| 9. |
- Habibullah, Khan Mohammad, et al.
(författare)
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Requirements and software engineering for automotive perception systems: an interview study
- 2024
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Ingår i: REQUIREMENTS ENGINEERING. - 0947-3602 .- 1432-010X. ; 29:1, s. 25-48
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Tidskriftsartikel (refereegranskat)abstract
- Driving automation systems, including autonomous driving and advanced driver assistance, are an important safety-critical domain. Such systems often incorporate perception systems that use machine learning to analyze the vehicle environment. We explore new or differing topics and challenges experienced by practitioners in this domain, which relate to requirements engineering (RE), quality, and systems and software engineering. We have conducted a semi-structured interview study with 19 participants across five companies and performed thematic analysis of the transcriptions. Practitioners have difficulty specifying upfront requirements and often rely on scenarios and operational design domains (ODDs) as RE artifacts. RE challenges relate to ODD detection and ODD exit detection, realistic scenarios, edge case specification, breaking down requirements, traceability, creating specifications for data and annotations, and quantifying quality requirements. Practitioners consider performance, reliability, robustness, user comfort, and-most importantly-safety as important quality attributes. Quality is assessed using statistical analysis of key metrics, and quality assurance is complicated by the addition of ML, simulation realism, and evolving standards. Systems are developed using a mix of methods, but these methods may not be sufficient for the needs of ML. Data quality methods must be a part of development methods. ML also requires a data-intensive verification and validation process, introducing data, analysis, and simulation challenges. Our findings contribute to understanding RE, safety engineering, and development methodologies for perception systems. This understanding and the collected challenges can drive future research for driving automation and other ML systems.
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| 10. |
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