Neural Network Alarm Detector Training Mode
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Solution Overview
Problem
Conventional alarm systems often trigger false alarms due to environmental factors, leading to delays in responding to genuine emergencies, increased costs, and potential vulnerability to intruders, as they are not adequately trained to distinguish between false and genuine disturbances in real-world settings.
Innovation Solution
An alarm system equipped with a neural network-based detector that can be trained to differentiate between false alarms and genuine disturbances through a user-instructed training mode, allowing for the labeling of disturbances as either false or genuine, and utilizing a remote training system to update the neural network parameters based on recorded data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional motion detectors are used to monitor areas, then the system can detect disturbances, but false alarms are triggered frequently due to environmental factors
Solution Approach 1:
The system changes the parameters of disturbance detection by analyzing multiple characteristics (amplitude, frequency, duration, pattern) rather than relying on a single threshold parameter. This multi-parameter analysis allows the system to distinguish between genuine intrusions and environmental disturbances, reducing false alarms while maintaining detection sensitivity
Solution Approach 2:
The system performs preliminary learning during a training period where it observes and records environmental disturbances specific to the installation location. By pre-learning the characteristic patterns of false alarm sources (pets, fans, air conditioning) before normal operation begins, the system can later distinguish these from genuine intrusions, thereby reducing false alarms while maintaining reliability
2Reliability
If the alarm system is trained to distinguish false alarms from genuine disturbances, then false alarm rate decreases, but the system complexity increases due to additional training requirements
Solution Approach 1:
The system performs self-learning by automatically observing and recording disturbances during the training period without requiring external intervention. The detector autonomously identifies patterns of environmental disturbances and adjusts its detection parameters accordingly, eliminating the need for complex external training equipment or expert configuration while improving alarm accuracy
Solution Approach 2:
The system performs preliminary learning during a training period where it observes and records environmental disturbances specific to the installation location. This advance training simplifies the overall system complexity by automating the adaptation process, allowing the system to learn location-specific characteristics before normal operation begins without requiring complex external training infrastructure
3Measurement precision
If extensive testing is performed in controlled environments, then the system achieves required accuracy levels, but the testing time and cost increase significantly
Solution Approach 1:
The system applies local quality by learning the specific characteristics of the installation location rather than relying on generic controlled environment testing. By adapting to the local environmental conditions (specific pets, appliances, room layout, disturbance patterns) through on-site training, the system achieves high detection accuracy for that specific location without requiring extensive universal testing
Solution Approach 2:
The system performs preliminary learning during a training period where it observes and records environmental disturbances specific to the installation location. This advance training at the actual installation site allows the system to achieve high detection accuracy for location-specific conditions without requiring extensive controlled environment testing, thereby reducing testing time and costs
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves improved accuracy in distinguishing between false and genuine alarms, reducing unnecessary alerts and enhancing the detection of intruders by learning the specific environmental factors of the installed location, thereby minimizing false alarms and ensuring timely responses to actual threats.
Implementation Method 1
the detector comprises a neural network defined by a topology and a set of parameters, the neural network having been trained to discriminate false alarms which should not result in an alarm being triggered by the alarm system in a normal mode of operation from disturbances for which an alarm should be triggered in the normal mode of operation
Data Source
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AI summary
A detector 10 for an alarm system 100 is operable to monitor a monitored area for disturbances. The detector comprises a first neural network which has been trained to discriminate false alarms which should not result in an alarm being triggered by the alarm system 100 in a normal mode of operation from disturbances for which an alarm should be triggered in the normal mode of operation. The detector 10 is configured to receive an input from a user instructing the detector 10 to enter a training mode. The input includes a categorization setting instructing the detector 10 to label subsequent disturbances recorded whilst the detector 10 is in the training mode as either disturbances indicative of a false alarm, or as disturbances for which the alarm should be triggered in the normal mode of operation.