Mixed-Source Data Composition Analysis for Model Drift Diagnosis
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Solution Overview
Problem
Existing deep learning models struggle to accurately separate mixed data components due to changes in data characteristics over time, particularly when processing data from multiple sources, making it difficult to maintain performance and identify the causes of deterioration.
Innovation Solution
A data composition determination apparatus and method that utilizes a deep learning model to analyze mixed data by identifying and classifying data components into foreground and background sources, determining their presence and change in tendencies using multiple layers and evaluation scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a deep learning model is used to separate mixed data components, then the separation accuracy is improved, but the model performance deteriorates over time due to environmental changes
Solution Approach 1:
The patent implements dynamic adaptation by continuously monitoring data composition changes and updating the deep learning model accordingly. The system transitions from a static model to a dynamic one that can adapt to environmental changes over time, maintaining separation accuracy through ongoing adjustments to model parameters and architecture based on observed data characteristics.
Solution Approach 2:
The patent employs feedback mechanisms by evaluating the model's performance on incoming data and using this information to trigger model updates or retraining. The system monitors separation accuracy and other performance metrics, feeding this information back into the model management process to maintain optimal performance despite changing environmental conditions.
2Adaptability or versatility
If the deep learning model processes data from multiple sources, then the comprehensive analysis capability is improved, but it becomes difficult to identify the causes of performance deterioration
Solution Approach 1:
The patent segments the mixed data from multiple sources into distinct data components, allowing the system to analyze and track each component separately. This segmentation enables identification of which specific data source or component is causing performance deterioration, making diagnosis feasible even when processing complex multi-source data.
Solution Approach 2:
The patent introduces intermediate representations or feature vectors that mediate between the raw multi-source data and the final separation output. These intermediates serve as diagnostic checkpoints, allowing the system to identify where and how performance deterioration occurs in the processing pipeline, thereby simplifying cause analysis.
3Duration of action of stationary object
If the model operates for a long period of time, then the operational duration is improved, but the data characteristics change due to environmental factors
Solution Approach 1:
The patent performs preliminary actions by pre-processing data to normalize and stabilize its characteristics before feeding it to the deep learning model. This includes techniques such as feature standardization, outlier removal, and temporal smoothing that prevent environmental variations from directly affecting the model, thereby maintaining data characteristic consistency over long operational periods.
Solution Approach 2:
The patent dynamically adjusts model parameters and data preprocessing parameters based on observed environmental changes and data characteristic drift. By changing parameters adaptively rather than keeping them fixed, the system maintains stable data characteristics and model performance over extended operational durations despite varying environmental conditions.
Data Source
AI summary
According to one embodiment, a data composition determination apparatus includes a processor. The processor acquires mixed data to be determined in which data components from a plurality of data sources are included; acquires a model including a plurality of layers from an input layer to an output layer; applies the mixed data to the model to calculate a feature of the mixed data for each of some or all of the layers; and determines a composition of the data sources of the data components constituting the mixed data based on the calculated feature.