Scalability, testing, verification, and integration of neural network models into forecasting system are some of the major concerns today.
On the other hand a large number of parameters imposes new problems during IP qualification, verification and integration.
SRL has recently started development of model-based simulation, verification and integration environment to realize rapid and cost-effective development of reliable micro-satellites.
The approach has been experimented and validated through a detailed case study concerning the verification and the integration of the discrete and direct wavelet transform (DDWT) IP in a mixed hardware/software architecture.
Techniques/procedures/documentation requirements are a very much simplified requirement of that given in IEC 61508 and are more in-line with those given in IEC 61511 (application level requirements) and consist of, requirement specification for the SRP/CS and safety functions, design and integration, verification and validation, modification, and documentation.
Beside securing the engineering and anticipating the verification of design choices, one of the great benefits of model-based system engineering is the support to Integration, Verification and Validation (IVV) of the system (or more generally of the solution fulfilling the expressed needs).
The finalized architecture, commonly called physical architecture (subsequently abbreviated as PA), defines the solution at a sufficient level of detail to specify the developments and acquisitions of all subsystems (or components) to be implemented, and to define and orientate the system integration, verification and validation (IVV) phases.
The Space Robotics Laboratory of Tohoku University has been developing multiples of micro-satellites for years and has gathered experiences in their development, verification, integration, and operation.
There is a need for an integrated model-driven development environment that addresses all phases of application lifecycle including design, development, verification, analysis, integration, deployment, operation and maintenance, with supporting automation in every phase.
The conventional V-model of software development includes specification, design, implementation, integration, testing, and verification and validation stages.
Users and applications of ontologies benefit from the community agreement which ontologies can bring about and their resulting potential for ontology-based data annotation and integration, retrieval and querying, novel scientific analyses and in some cases consistency verification of data.
