Huang et al., 2024 - Google Patents
Neural Network-based Functional Degradation for Cyber-Physical SystemsHuang et al., 2024
- Document ID
- 16056797667157814717
- Author
- Huang Z
- Wu Y
- Lin Y
- Yu C
- Lee W
- Publication year
- Publication venue
- 2024 IEEE 24th International Conference on Software Quality, Reliability and Security (QRS)
External Links
Snippet
From gimmicky IoT devices to self-driving cars, cyber-physical systems have become increasingly accessible to the masses. As these systems interact intimately with the physical world, failures in the systems can lead to severe, potentially life-threatening damage …
- 238000013528 artificial neural network 0 title description 13
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Error detection; Error correction; Monitoring responding to the occurence of a fault, e.g. fault tolerance
- G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
- G06F11/0706—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
- G06F21/57—Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Koopman et al. | Credible autonomy safety argumentation | |
US8005657B2 (en) | Survivability mission modeler | |
EP3400676A2 (en) | A safety architecture for autonomous vehicles | |
US12248304B2 (en) | Functional safety with root of safety and chain of safety | |
Jenn et al. | Identifying challenges to the certification of machine learning for safety critical systems | |
Schumann et al. | Software health management with Bayesian networks | |
Serban | Designing Safety Critical Software Systems to Manage Inherent Uncertainty. | |
Cardoso et al. | A review of verification and validation for space autonomous systems | |
Ma et al. | Testing self-healing cyber-physical systems under uncertainty with reinforcement learning: an empirical study | |
Srivastava et al. | Software health management: a necessity for safety critical systems | |
Chaves et al. | DSVerifier-aided verification applied to attitude control software in unmanned aerial vehicles | |
Franchetti et al. | High-assurance SPIRAL: End-to-end guarantees for robot and car control | |
US10877471B2 (en) | Method and apparatus for generating a fault tree for a failure mode of a complex system | |
Gan et al. | Braum: Analyzing and protecting autonomous machine software stack | |
Goodloe | Challenges in high-assurance runtime verification | |
Huang et al. | Neural Network-based Functional Degradation for Cyber-Physical Systems | |
Abella et al. | Safexplain: Safe and explainable critical embedded systems based on ai | |
Coe et al. | Virtualized in situ software update verification: verification of over-the-air automotive software updates | |
Delmas et al. | Tiered model-based safety assessment | |
Wagner et al. | The open autonomy safety case framework | |
WO2013066809A1 (en) | System to establish trustworthiness of autonomous agent | |
Leach et al. | Start: A framework for trusted and resilient autonomous vehicles (practical experience report) | |
Durling et al. | Certification considerations for adaptive stress testing of airborne software | |
Nenchev | One Stack, Diverse Vehicles: Checking Safe Portability of Automated Driving Software | |
Carlan et al. | The Open Autonomy Safety Case Framework |