TV Power-Signal State Detection Using a Cross-Device ML Model
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Solution Overview
Problem
Existing methods struggle to efficiently evaluate the performance of televisions due to varying power consumption patterns across different display technologies, leading to inaccurate state detection and inefficient resource utilization.
Innovation Solution
A machine learning model trained on data from multiple power sensors is used to detect television states agnostically, constructing arrays of power values and determining actions based on the detected state, regardless of television type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional power evaluation methods are used for televisions, then the evaluation process is simple, but the measurement precision is poor due to varying power consumption patterns across different display technologies
Solution Approach 1:
A machine learning model serves as an intermediary between raw power consumption data and television state determination. The model processes power value arrays and translates them into accurate state predictions, resolving the contradiction by providing high measurement precision through a sophisticated yet manageable computational intermediary.
Solution Approach 2:
The system transforms power consumption measurements into arrays of power values that are then processed by the machine learning model. By changing the parameter representation from single power readings to structured power value arrays, the system achieves higher detection accuracy while managing complexity through standardized data transformation.
2Measurement precision
If custom algorithms are developed for each television type, then the measurement precision improves, but the device complexity and loss of time increase due to multiple algorithms
Solution Approach 1:
The machine learning model is designed to be universal, handling multiple television types and display technologies through a single unified algorithm. This eliminates the need for developing and maintaining separate custom algorithms for each television type, reducing time loss while maintaining high detection accuracy across diverse devices.
Solution Approach 2:
Instead of creating custom algorithms for each television type, the system uses a single trained machine learning model that learns patterns from training data representing various television types. This copying approach allows the same model to accurately detect states across different television technologies without requiring separate algorithm development for each type.
3Device complexity
If a single power threshold is used for all televisions, then the device complexity is low, but the measurement precision deteriorates due to different power consumption characteristics
Solution Approach 1:
The system transitions from static fixed power thresholds to dynamic adaptive thresholds generated by the machine learning model. The model adapts its decision boundaries based on the specific television being evaluated and the observed power consumption patterns, enabling accurate state detection across different television types without requiring manual threshold configuration for each device.
4Measurement precision
If machine learning model is implemented, then the measurement precision improves for television state detection, but the use of energy and device complexity increase
Solution Approach 1:
The machine learning model is trained in advance using training data collected from multiple televisions. This preliminary action of offline training allows the model to learn complex power consumption patterns beforehand, enabling accurate real-time state detection with minimal computational resources during actual operation, thus reducing energy consumption while maintaining high precision.
Data Source
AI summary
Server to server direct integration is provided. A system constructs an array of power values from time series data collected by a power sensor coupled with a television. The system inputs the array of power values into a model trained with machine learning based on log files of data collected from a plurality of power sensors coupled with a plurality of televisions. The system determines, based on output from the model generated with the array of power values, the television is active. The system executes, responsive to the determination that the television is active, an action to evaluate a performance of content rendered by the television.