Attention-Map Anomaly Detection for Low-Compute Edge Monitoring
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Solution Overview
Problem
Existing anomaly detection systems using artificial neural networks face challenges in edge computing due to limited computational resources and power, making it difficult to perform high-level calculations, and there is a need for a method that can accurately identify anomaly locations while minimizing computational cost.
Innovation Solution
Anomaly detection using an attention mechanism in an artificial neural network that generates an attention map to highlight relevant regions, allowing for accurate anomaly detection and minimizing computational cost through optimized network operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a high-performance discrimination device is used for anomaly detection, then detection accuracy is improved, but the system becomes vulnerable to single-point failure and requires expensive infrastructure
Solution Approach 1:
The patent divides the anomaly detection system into multiple independent edge discrimination devices, each responsible for specific monitoring targets. This segmentation eliminates single-point failure risks associated with centralized high-performance devices while maintaining detection accuracy through distributed intelligent analysis capabilities.
Solution Approach 2:
The patent transforms the detection network structure by applying attention mechanisms that dynamically adjust parameter importance weights. This allows edge devices to achieve high detection accuracy without requiring expensive high-performance hardware, as the attention mechanism optimizes computational resource allocation based on input data characteristics.
2Reliability
If multiple edge discrimination devices are deployed for redundancy and cost-effectiveness, then system reliability and cost-performance are improved, but computational resources and power consumption increase
Solution Approach 1:
The patent implements dynamic computation through attention mechanisms that adaptively adjust processing intensity based on input data characteristics. Edge discrimination devices only perform intensive calculations when anomalies are detected, reducing overall power consumption while maintaining system reliability through distributed architecture.
Solution Approach 2:
The attention mechanism dynamically changes computational parameters by assigning different weight values to different input features. This allows edge devices to optimize their computational resource usage, performing minimal necessary calculations while maintaining accurate anomaly detection capabilities across multiple distributed units.
3Measurement precision
If attention mechanisms are applied to identify anomaly locations, then detection precision is improved, but computational complexity increases
Solution Approach 1:
The attention mechanism implements partial action by focusing computational resources only on relevant input features and regions. Instead of processing all data uniformly, the mechanism selectively attends to suspicious areas identified by weight thresholds, reducing overall computational complexity while maintaining high anomaly location precision.
Solution Approach 2:
The patent applies local quality enhancement through attention maps that assign different importance weights to different spatial regions of the input data. This allows the network to concentrate computational effort on locally significant areas where anomalies are likely to occur, improving detection precision without proportionally increasing overall network complexity.
Data Source
AI summary
An anomaly detection method and apparatus are provided using an attention mechanism or based on an artificial neural network for minimizing computational cost. In an embodiment, the anomaly detection method may include entering, by a detection unit, input data into a detection network; generating, by the detection network, an attention map and output data through a plurality of operations in which a plurality of layer weights are applied to the input data; generating, by the detection unit, an attention map by overlapping the attention map with the input data when an attention region having an attention value greater than or equal to a predetermined threshold exists in the attention map; detecting, by the detection unit, whether the input data is normal or abnormal according to the output data; and outputting, by the detection unit, the detection map and an anomaly detection result indicating whether the input data is normal or abnormal.