Tag: state

Data & Decision

Posted on by lechuck park

with a Claude’s Help
This diagram illustrates the process of converting real-world analog values into actionable decisions through digital systems:

  1. Input Data Characteristics
  • Metric Value: Represents real-world analog values that are continuous variables with high precision. While these can include very fine digital measurements, they are often too complex for direct system processing.
  • Examples: Temperature, velocity, pressure, and other physical measurements
  1. Data Transformation Process
  • Through ‘Sampling & Analysis’, continuous Metric Values are transformed into meaningful State Values.
  • This represents the process of simplifying and digitalizing complex analog signals.
  1. State Value Characteristics and Usage
  • Converts to discrete variables with high readability
  • Examples: Temperature becomes ‘High/Normal/Low’, speed becomes ‘Over/Normal/Under’
  • These State values are much more programmable and easier to process in systems
  1. Decision Making and Execution
  • The simplified State values enable clear decision-making (Easy to Decision)
  • These decisions can be readily implemented through Programmatic Works
  • Leads to automated execution (represented by “DO IT!”)

The key concept here is the transformation of complex real-world measurements into clear, discrete states that systems can understand and process. This conversion facilitates automated decision-making and execution. The diagram emphasizes that while Metric Values provide high precision, State Values are more practical for programmatic implementation and decision-making processes.

The flow shows how we bridge the gap between analog reality and digital decision-making by converting precise but complex measurements into actionable, programmable states. This transformation is essential for creating reliable and automated decision-making systems.

Event & Alarm

Posted on by lechuck park

From DALL-E with some prompting

The image illustrates the progressive stages of detecting alarm events through data analysis. Here’s a summary:

  1. Internal State: It shows a machine with an ‘ON/OFF’ state, indicating whether the equipment is currently operating.
  2. Numeric & Threshold: A numeric value is monitored against a set threshold, which can trigger an alert if exceeded.
  3. Delta (Changes) & Threshold: A representation of an alert triggered by significant changes or deviations in the equipment’s performance, as compared to a predefined threshold.
  4. Time Series & Analysis: This suggests that analyzing time-series data can identify trends and forecast potential issues.
  5. Machine Learning: Depicts the use of machine learning to interpret data and build predictive models.
  6. More Predictive: The final stage shows the use of machine learning insights to anticipate future events, leading to a more sophisticated alarm system.

Overall, the image conveys the evolution of alarm systems from basic monitoring to advanced prediction using machine learning.