Artificial intelligence
Machine Learning: extracting knowledge from series of observations
Artificial intelligence algorithms surpass traditional approaches because they can autonomously interpret the operational scenario and phenomena of interest without requiring detailed descriptions as in model-based methods. Machine Learning extracts knowledge from data from various sources, such as sensors, automatically forming a model that addresses specific problems. This technology does not require explicit programming or complex modeling, making it highly advantageous for business value as it simulates human learning capabilities.
The main development phases used by INTECS
Study of the application domain
Analyze the state of the art and the various solutions available in the literature.
Targeted strategy.
Determine objectives and define a success criterion to achieve results.
Data acquisition
Define the different data acquisition campaigns.
Model creation
Determination of Machine Learning algorithms to be used for training.
Model validation
The execution process may include many cycles of running the routine to optimize and refine the results
Data Processing
Identify how to prepare and process the data for executing Machine Learning.
The approach presented has been applied in various domains, both for projects commissioned by clients and for product development.
The application areas of these products are diverse: acoustics, predictive maintenance, vibro-acoustic sensors, and road surface recognition.
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