Back to results
US 12,649,133 B2Granted
Methods and systems for dynamic control of an acoustic field
AssigneeThe Trustees of Indiana University
InventorsFeng Guo, Hongwei Cai
Life of this patent
Estimating the term…
Abstract
Disclosed are methods and systems for implementing dynamic control of acoustic fields, which involves the use of machine learning through a Markov Decision Process. The machine learning algorithm monitors an attribute of a system and maintains the attribute within a range by altering a signal sent to an acoustic transducer. The acoustic transducer is configured to alter the attribute of the system. The attribute may be an attribute of the acoustic field produced by the acoustic transducer, or may be an attribute of another element of the system, such as a fluid, a rotor, or a cell mass.
Claims23
A non-transitory computer readable medium having a computer program stored thereon for controlling an acoustic field to rotate a rotor, the computer program comprising instructions for causing one or more processors to:
- transmit a signal to an acoustic transducer causing the acoustic transducer to generate an acoustic field for rotating a rotor, wherein the signal has a plurality of characteristics comprising amplitude, frequency and phase;
- set an attribute of the rotor, wherein the attribute comprises a range of rotor speed;
- and
- monitor the attribute of the rotor and execute a machine-learning algorithm to dynamically control the attribute of the rotor, wherein the machine-learning algorithm uses a Markov Decision Process to:
- (i) measure the attribute of the rotor using a sensor upon altering the attribute of the rotor by an amount by adjusting at least a first characteristic of the plurality of characteristics of the signal;
- (ii) calculate an output of the acoustic field associated with altering the attribute of the rotor by the amount;
- (iii) calculate a resulting reward based on (a) altering the attribute of the rotor by an amount and (b) a resulting output of the acoustic field associated with altering the attribute of the rotor by the amount;
- (iv) calculate a maximal reward value based upon an accumulation of the resulting reward of step (iii) and additional resulting rewards that are previously calculated;
- (v) continuously alter, based on a control policy and the maximal reward value, the attribute of the rotor by (a) further adjusting the first characteristic or (b) adjusting a second characteristic of the plurality of characteristics of the signal that is different from the first characteristic;
- and
- (vi) repeat steps (i) through (v) to maintain a stable rotor speed within the range.
8 more elements
A method for controlling a fluid characteristic within a medium through an acoustic field, the method comprising the steps of:
- transmitting a signal to an acoustic transducer causing the acoustic transducer to generate an acoustic field to rotate a rotor, wherein the signal has a plurality of characteristics comprising amplitude, frequency and phrase phase;
- calculating one or more parameters of the acoustic field upon transmitting the signal to the acoustic transducer;
- setting a desired attribute of the rotor upon being subject to the acoustic field, wherein the desired attribute comprises a range of fluid speed;
- and
- monitoring the attribute of the rotor and executing a machine-learning algorithm to dynamically control the attribute of the rotor, wherein the machine-learning algorithm uses a Markov Decision Process to:
- (i) measure the attribute of the rotor using a sensor upon altering the attribute of the rotor by an amount by adjusting at least a first characteristic of the plurality of characteristics of the signal;
- (ii) calculate an output of the acoustic field associated with altering the attribute of the rotor by the amount;
- (iii) calculate a resulting reward based on (a) altering the attribute of the rotor by an amount and (b) a resulting output of the acoustic field associated with altering the attribute of the rotor by the amount;
- (iv) calculate a maximal reward value based upon an accumulation of the resulting reward of step (iii) and additional resulting rewards that are previously calculated;
- (v) continuously alter, based on a control policy and the maximal reward value, the attribute of the rotor by (a) further adjusting the first characteristic or (b) adjusting a second characteristic of the plurality of characteristics of the signal that is different from the first characteristic;
- and
- (vi) repeat steps (i) through (v) to maintain a stable fluid speed within the range.
9 more elements
A system for controlling the behavior of a cell mass, the system comprising:
- a medium in which the cell mass is immersed;
- a rotor configured to alter a characteristic of the medium;
- an acoustic transducer configured to alter the characteristic of the medium through at least the rotor;
- a sensor configured to measure the characteristic;
- and
- a non-transitory computer readable medium having a computer program stored thereon, the computer program comprising instructions for causing one or more processors to:
- transmit a signal to the acoustic transducer, wherein the acoustic transducer is configured to generate an acoustic field to rotate a rotor, wherein the signal has a plurality of characteristics comprising amplitude, frequency and phase;
- set an attribute of the rotor upon being subject to the acoustic field, wherein the attribute comprises a range of cell mass rotation speed;
- and
- monitor the attribute of the rotor and execute a machine-learning algorithm to dynamically control the attribute of the rotor, wherein the machine-learning algorithm uses a Markov Decision Process to:
- (i) measure the attribute of the rotor using a sensor upon altering the attribute of the rotor by an amount by adjusting at least a first characteristic of the plurality of characteristics of the signal;
- (ii) calculate an output of the acoustic field associated with altering the attribute of the rotor by the amount;
- (iii) calculate a resulting reward based on (a) altering the attribute of the rotor by an amount and (b) a resulting output of the acoustic field associated with altering the attribute of the rotor by the amount;
- (iv) calculate a maximal reward value based upon an accumulation of the resulting reward of step (iii) and additional resulting rewards that are previously calculated;
- (v) continuously alter, based on a control policy and the maximal reward value, the attribute of the rotor by (a) further adjusting the first characteristic or (b) adjusting a second characteristic another one of the plurality of characteristics of the signal that is different from the first characteristic;
- and
- (vi) repeat steps (i) through (v) while maintaining a stable cell mass rotation speed within the range.
14 more elements
A non-transitory computer readable medium having a computer program stored thereon for controlling an acoustic field, the computer program comprising instructions for causing one or more processors to:
- transmit a signal to an acoustic transducer, wherein the acoustic transducer is configured to generate an acoustic field, wherein the signal has a plurality of characteristics comprising amplitude, frequency and phase;
- set at least two attributes of the acoustic field, wherein the at least two attributes comprise corresponding ranges of acoustic output;
- and
- monitor the two or more attributes of the acoustic field and execute a machine-learning algorithm to dynamically control one of the at least two attributes of the acoustic field, wherein the machine-learning algorithm uses a Markov Decision Process to:
- (i) measure the attribute of the one of the at least two attributes of the acoustic field using a sensor upon altering the one of the at least two attributes of the acoustic field by an amount;
- (ii) calculate an output of the other of the at least two attributes of the acoustic field associated with altering the one of the at least two attributes of the acoustic field by the amount;
- (iii) calculate a resulting reward based on (a) altering the one of the at least two attributes of the acoustic field by the amount and (b) the output of the other of the at least two attributes of the acoustic field associated with altering the one of the at least two attributes of the acoustic field by the amount;
- (iv) calculate a maximal reward value based upon an accumulation of the resulting reward of step (iii) and additional resulting rewards that are previously calculated;
- (v) continuously alter, based on a control policy and the maximal reward value, the one of the at least two attributes by adjusting at least one of the plurality of characteristics of the signal;
- and
- (vi) repeat steps (i) through (v) to maintain a stable acoustic output such that the two or more attributes of the acoustic field remain within the corresponding ranges.
8 more elements
Dependencies are inferred from the claim text.