Tarun Kumar

US Patent:

7329080, Feb 12, 2008

Filed:

Aug 23, 2005

Appl. No.:

11/208930

Inventors:

Tarun Kumar - South River NJ, US

International Classification:

B65G 67/02

US Classification:

414537

Abstract:

A sports equipment transporting apparatus is described. The apparatus is designed to be put into a vehicle with a mid- to long-size body length and is housed within a ramp housing. The ramp housing is two-tiered and includes a slide-out ramp located within the upper tier, with the ramp being mounted on a pair of guide tracks. The lower tier includes a storage compartment and a spare tire compartment. The rear entry of the vehicle is modified so that the top door panel becomes a roll-out door panel, with two swinging doors attached that pivot outwards ninety degrees. When the ramp is fully extended from the ramp housing, the ramp angles downward and touches an external ground surface. Once a piece of sports equipment has been placed on top of the ramp housing, a series of tie-downs are utilized to keep the sports equipment in place.

US Patent:

20200387119, Dec 10, 2020

Filed:

Mar 10, 2020

Appl. No.:

16/814388

Inventors:

- Valhalla NY, US
Younghun Kim - White Plains NY, US
Tarun Kumar - Mohegan Lake NY, US
Abhishek Raman - Santa Clara CA, US
Rui Zhang - Ossining NY, US

Assignee:

Utopus Insights, Inc. - Valhalla NY

International Classification:

G05B 13/02
G01F 22/02
H04L 12/26
H04L 29/08
G01M 3/28
G05D 7/06

Abstract:

Technical solutions are described for predicting linepack delays. An example method includes receiving temporal sensor measurements of a first fluid-delivery pipeline network and generating a causality graph of the first fluid-delivery pipeline network. The method also includes determining a topological network of the stations based on the causality graph, where the topological network identifies a temporal delay between a pair of stations. The method also includes generating a temporal delay prediction model based on the topological network and predicting the linepack delays of a second fluid-delivery pipeline network based on the temporal delay prediction model, where a compressor station of the second fluid-delivery pipeline network compresses fluid based on the predicted linepack delays to maintain a predetermined pressure.

US Patent:

20200210854, Jul 2, 2020

Filed:

Dec 27, 2018

Appl. No.:

16/234465

Inventors:

- Valhalla NY, US
Younghun Kim - Pleasantville NY, US
Tarun Kumar - Chappaqua NY, US

Assignee:

Utopus Insights, Inc. - Valhalla NY

International Classification:

G06N 5/02
G06N 20/00
G06N 3/08

Abstract:

An example method comprises receiving historical sensor data of a first time period, the historical data including sensor data of a renewable energy asset, extracting features, performing a unsupervised anomaly detection technique on the historical sensor data to generate first labels associated with the historical sensor data, performing at least one dimensionality reduction technique to generate second labels, combining the first labels and the second labels to generate combined labels, generating one or more models based on supervised machine learning and the combined labels, receiving current sensor data of a second time period, the current sensor data including sensor data of the renewable energy asset, extracting features, applying the one or more models to the extracted features of the current sensor data to create a prediction of a future fault in the renewable energy asset, and generating a report including the prediction of the future fault in the energy asset.

US Patent:

20200112488, Apr 9, 2020

Filed:

Aug 20, 2019

Appl. No.:

16/546276

Inventors:

- Valhalla NY, US
Younghun Kim - Pleasantville NY, US
Tarun Kumar - Chappaqua NY, US
Mark A. Lavin - Katonah NY, US
Giuliano Andrea Pagani - Groningen, NL
Abhishek Raman - Mahopac NY, US

International Classification:

H04L 12/24
G06N 7/00

Abstract:

A system and method of managing a network with assets are described. The method includes generating a directed graph with each of the assets represented as a node, determining individual failure probability of each node, computing downstream failure probability of each node according to an arrangement of the nodes in the directed graph, computing upstream failure probability of each node according to the arrangement of the nodes in directed graph, and computing network failure probability for each node based on the corresponding individual failure probability, the downstream failure probability, and the upstream failure probability. Managing the network is based on the network failure probability of the assets.

US Patent:

20180223804, Aug 9, 2018

Filed:

Feb 7, 2017

Appl. No.:

15/426544

Inventors:

- Armonk NY, US
Younghun Kim - White Plains NY, US
Tarun Kumar - Mohegan Lake NY, US
Wander S. Wadman - New York NY, US
Kevin W. Warren - Hopewell Junction NY, US

International Classification:

F03D 7/02
F03D 7/04
F03D 9/25
F03D 17/00
G05B 19/416
G05B 13/02

Abstract:

Historical power output measurements of a wind turbine for a time period immediately preceding a specified past time are received. Historical wind speed micro-forecasts for the wind turbine for a time period immediately preceding the specified past time and for a time period immediately following the specified past time are received. The historical wind speed micro-forecasts are converted to wind power values. Based on the historical power output measurements and the wind power output values, a machine learning model for predicting wind power output is trained. Real-time power output measurements of the wind turbine and real-time wind speed micro-forecasts for the wind turbine are received. The real-time wind speed micro-forecasts are converted to real-time wind power values. Using the machine learning model with the real-time power output measurements and the real-time wind power values, a wind power output forecast for the wind turbine at a future time is outputted.

US Patent:

20180223805, Aug 9, 2018

Filed:

Dec 13, 2017

Appl. No.:

15/840249

Inventors:

- ARMONK NY, US
Younghun Kim - White Plains NY, US
Tarun Kumar - Mohegan Lake NY, US
Wander S. Wadman - New York NY, US
Kevin W. Warren - Hopewell Junction NY, US

International Classification:

F03D 7/02
G05B 19/416
F03D 17/00
F03D 9/25
F03D 7/04
G05B 13/02

Abstract:

Historical power output measurements of a wind turbine for a time period immediately preceding a specified time are received. Historical wind speed micro-forecasts for the wind turbine for a time periods immediately preceding the specified past time and immediately following the specified past time are received. The historical wind speed micro-forecasts are converted to wind power values. Based on the historical power output measurements and the wind power output values, a machine learning model for predicting wind power output is trained. Real-time power output measurements of the wind turbine and real-time wind speed micro-forecasts for the wind turbine are received. The real-time wind speed micro-forecasts are converted to real-time wind power values. Using the machine learning model with the real-time power output measurements and the real-time wind power values, a wind power output forecast for the wind turbine at a future time is outputted.

US Patent:

20180223806, Aug 9, 2018

Filed:

Apr 27, 2018

Appl. No.:

15/964370

Inventors:

- Armonk NY, US
Younghun Kim - White Plains NY, US
Tarun Kumar - Mohegan Lake NY, US
Wander S. Wadman - New York NY, US
Kevin W. Warren - Hopewell Junction NY, US

International Classification:

F03D 7/02
F03D 7/04
F03D 9/25
F03D 17/00
G05B 13/02
G05B 19/416

Abstract:

Historical power output measurements of a wind turbine for a time period immediately preceding a specified time are received. Historical wind speed micro-forecasts for the wind turbine for a time periods immediately preceding the specified past time and immediately following the specified past time are received. The historical wind speed micro-forecasts are converted to wind power values. Based on the historical power output measurements and the wind power output values, a machine learning model for predicting wind power output is trained. Real-time power output measurements of the wind turbine and real-time wind speed micro-forecasts for the wind turbine are received. The real-time wind speed micro-forecasts are converted to real-time wind power values. Using the machine learning model with the real-time power output measurements and the real-time wind power values, a wind power output forecast for the wind turbine at a future time is outputted.

US Patent:

20180223807, Aug 9, 2018

Filed:

Apr 27, 2018

Appl. No.:

15/964434

Inventors:

- Armonk NY, US
Younghun Kim - White Plains NY, US
Tarun Kumar - Mohegan Lake NY, US
Wander S. Wadman - New York NY, US
Kevin W. Warren - Hopewell Junction NY, US

International Classification:

F03D 7/02
F03D 7/04
F03D 9/25
F03D 17/00
G05B 13/02
G05B 19/416

Abstract:

Historical power output measurements of a wind turbine for a time period immediately preceding a specified time are received. Historical wind speed micro-forecasts for the wind turbine for a time periods immediately preceding the specified past time and immediately following the specified past time are received. The historical wind speed micro-forecasts are converted to wind power values. Based on the historical power output measurements and the wind power output values, a machine learning model for predicting wind power output is trained. Real-time power output measurements of the wind turbine and real-time wind speed micro-forecasts for the wind turbine are received. The real-time wind speed micro-forecasts are converted to real-time wind power values. Using the machine learning model with the real-time power output measurements and the real-time wind power values, a wind power output forecast for the wind turbine at a future time is outputted.