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Entropy Change in Reversible Processes01:10

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In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
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Task-Oriented Active Sensing via Action Entropy Minimization.

Tipakorn Greigarn1, Michael S Branicky2, M Cenk Ҫavuşoğlu1

  • 1Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH, USA.

IEEE Access : Practical Innovations, Open Solutions
|November 19, 2019
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Summary
This summary is machine-generated.

This study introduces a novel task-oriented active sensing method. It optimizes sensing actions by reducing uncertainty in future task-related actions, improving efficiency over traditional state uncertainty minimization.

Keywords:
Active SensingDecision MakingEntropyUncertainty

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Information Theory

Background:

  • Active sensing typically minimizes state uncertainty using information-theoretic measures.
  • This approach is insufficient when sensed information directly supports task performance.
  • Task-relevant state subspaces can have varying impacts on performance.

Purpose of the Study:

  • To develop a task-oriented active sensing scheme that prioritizes task utility over general state uncertainty.
  • To address the computational expense of traditional sequential decision-making models for active sensing.
  • To integrate task objectives directly into the selection of sensing actions.

Main Methods:

  • Proposed a novel active sensing strategy focusing on minimizing uncertainty in future task-related actions.
  • Shifted focus from minimizing overall state uncertainty to optimizing task-specific information gain.
  • Evaluated the method through extensive simulations.

Main Results:

  • The new task-oriented approach demonstrated effectiveness in simulations.
  • Optimizing for task-action uncertainty proved more beneficial than minimizing state uncertainty.
  • The method offers a computationally viable alternative for complex active sensing scenarios.

Conclusions:

  • Task-oriented active sensing can significantly outperform traditional methods when information is used for task execution.
  • Minimizing uncertainty in future task-related actions is a promising strategy for active sensing.
  • This research provides a foundation for more efficient and effective intelligent sensing systems.