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Industrial IoT sensors
Connected devices collecting real-time data on industrial equipment, including vibration, temperature, pressure, and other operational parameters.
Heterogeneous data
Set of data of different natures (structured, unstructured, temporal, spatial) requiring specific methods for their coherent integration.
Fusion algorithms
Mathematical and computational methods allowing intelligent combination of multiple information sources into a unified and optimized output.
Temporal metadata
Time-related information associated with sensor data, including timestamps, sampling frequencies, and temporal relationships between events.
Cross-source validation
Technique for verifying data consistency and reliability by comparing information from different independent sources.
Dynamic weighting
Automatic adaptation of weights assigned to each data source based on their reliability and relevance for a given context.
Ensemble learning
Approach combining multiple machine learning models to improve predictive performance by aggregating their individual predictions.
Inter-source correlation analysis
Study of statistical relationships between different data sources to identify dependencies and synergies exploitable in fusion.
Multimodal preprocessing
Set of cleaning, normalization, and transformation techniques applied to different types of data before their integration into a unified model.
Centralized fusion architecture
Approach where all data sources are routed to a single central point to be processed and merged together.
Decentralized fusion architecture
Structure where data processing and partial fusion are performed locally before final aggregation, reducing bandwidth requirements.
Feature-level fusion
Combination of feature vectors extracted from different sources before applying the final classification or regression algorithm.
Decision-level fusion
Integration of individual predictions from multiple models trained on distinct sources to produce a final consensus decision.
Multi-sensor anomaly detection
Identification of abnormal behaviors by jointly analyzing data from multiple sensors to increase sensitivity and reduce false positives.
Missing data imputation
Statistical and AI techniques to estimate and replace missing values in multi-source time series while preserving correlations.
Bayesian probabilistic fusion
Method using Bayes' theorem to combine probabilities from different sources while accounting for their respective uncertainties.
Multimodal neural networks
Deep learning architectures specifically designed to process and simultaneously merge different types of data (images, text, time series).
Inter-source calibration
Process of adjusting measurements from different sensors to eliminate systematic biases and ensure measurement scale consistency.