Centralized Expert System for Pattern Recognition
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Solution Overview
Problem
Pattern recognition systems face challenges in installation, configuration, and maintenance due to complexity and variability in environmental conditions, leading to inefficiencies and high costs, as they struggle to adapt to variations in sensor data and environmental noise without effective supervised or unsupervised learning approaches.
Innovation Solution
A collective learning pattern recognition system that uses a centralized expert system to collect and compile data from local image processing systems, allowing for automatic configuration and updates of similar systems, leveraging a low-complexity architecture with communication between local and centralized systems to adapt algorithms and improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pattern recognition systems use adaptive classification algorithms with supervised learning to adapt to environmental variations, then the system can accurately classify data under different conditions, but the installation and configuration complexity increases significantly requiring extensive manual supervision
Solution Approach 1:
The patent introduces a centralized server as an intermediary between multiple pattern recognition systems. The server collects training data from various systems, processes it centrally, and distributes optimized training sets back to the local systems. This mediator approach allows individual systems to adapt to their local environments without requiring complex manual configuration at each site, as the server handles the adaptive learning process centrally.
Solution Approach 2:
The patent merges multiple distributed pattern recognition systems into a coordinated network where training data and learning results are combined at a central server. By combining the training efforts of multiple systems and pooling their data, the system achieves adaptability across different environments while reducing the configuration burden on individual installations through shared learning resources.
2Ease of operation
If pattern recognition systems use unsupervised learning algorithms to adapt automatically, then the installation process is simplified, but the end state of the classification training set becomes unpredictable and individual sensors perform differently under identical conditions
Solution Approach 1:
The patent implements a feedback mechanism where the centralized server collects performance data and training results from multiple pattern recognition systems, analyzes the variations, and uses this feedback to adjust and optimize the training sets distributed to individual systems. This feedback loop ensures that while systems operate autonomously with simplified installation, their performance remains consistent and reliable across different installations.
Solution Approach 2:
The patent utilizes parameter changes in the training data distributed by the centralized server to individual systems. By dynamically adjusting training set parameters based on collective learning from multiple systems, the server ensures that each system achieves reliable and consistent performance in its specific environment without requiring complex manual configuration.
3Measurement precision
If high quality components and large training sets are used to minimize variations, then the system achieves better classification accuracy, but the cost and device complexity increase
Solution Approach 1:
The patent creates a universal centralized server that serves multiple pattern recognition systems simultaneously. This single multi-functional platform handles data collection, processing, and distribution for numerous systems, achieving high classification accuracy through shared resources rather than requiring each individual system to have complex, expensive components and large dedicated training sets.
Data Source
AI summary
A method for configuring a pattern recognition system begins by receiving object recognition data from at least one first local image processing system. The object recognition data is stored in at least one global database. Configuration data is determined for a second local image processing system based at least in part upon the received object recognition data from the at least one first image processing system, and then transmitted to the second local image processing system.