Jin U. Kang - Ellicott City MD, US Seth D. Billings - Pellston MD, US Peter L. Gehlbach - Monkton MD, US James T. Handa - Baltimore MD, US Yong Huang - Baltimore MD, US Russell H. Taylor - Severna Park MD, US Yi Yang - Greensboro NC, US
Assignee:
The Johns Hopkins University - Baltimore MD
International Classification:
F21V 8/00 G02B 6/04 G02B 6/30
US Classification:
362554, 385 49, 362558, 362555
Abstract:
An illumination system includes a light source, an optical waveguide that has a proximal end and a distal end such that the proximal end is arranged to receive light from the light source and the distal end is suitable to illuminate an object of interest; and an optical coupler constructed and arranged to couple light from the light source into the optical waveguide. The optical coupler includes a reflective surface that reflects at least some light diverging from the light source to be coupled into the optical waveguide.
System And Method Employing A Self-Organizing Map Load Feature Database To Identify Electric Load Types Of Different Electric Loads
BIN LU - Shanghai, CN Ronald G. Harley - Lawrenceville GA, US Liang Du - Atlanta GA, US Yi Yang - Milwaukee WI, US Santosh K. Sharma - Pune, IN Prachi Zambare - Pune, IN Mayura A. Madane - Pune, IN
International Classification:
G06F 17/30
US Classification:
707737, 707E17046
Abstract:
A method identifies electric load types of a plurality of different electric loads. The method includes providing a self-organizing map load feature database of a plurality of different electric load types and a plurality of neurons, each of the load types corresponding to a number of the neurons; employing a weight vector for each of the neurons; sensing a voltage signal and a current signal for each of the loads; determining a load feature vector including at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the loads; and identifying by a processor one of the load types by relating the load feature vector to the neurons of the database by identifying the weight vector of one of the neurons corresponding to the one of the load types that is a minimal distance to the load feature vector.
System And Method Employing A Minimum Distance And A Load Feature Database To Identify Electric Load Types Of Different Electric Loads
BIN LU - Shanghai, CN Yi Yang - Milwaukee WI, US Santosh K. Sharma - Pune, IN Prachi Zambare - Pune, IN Mayura A. Madane - Pune, IN
International Classification:
G06F 17/30
US Classification:
707748, 707758, 707E17014, 707E17044
Abstract:
A method identifies electric load types of a plurality of different electric loads. The method includes providing a load feature database of a plurality of different electric load types, each of the different electric load types including a first load feature vector having at least four different load features; sensing a voltage signal and a current signal for each of the different electric loads; determining a second load feature vector comprising at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the different electric loads; and identifying by a processor one of the different electric load types by determining a minimum distance of the second load feature vector to the first load feature vector of the different electric load types of the load feature database.
System And Method Employing A Hierarchical Load Feature Database To Identify Electric Load Types Of Different Electric Loads
BIN LU - Shanghai, CN Yi Yang - Milwaukee WI, US Santosh K. Sharma - Pune, IN Prachi Zambare - Pune, IN Mayura A. Madane - Pune, IN
International Classification:
G06F 17/30
US Classification:
707758, 707E17044, 707E17014
Abstract:
A method identifies electric load types of a plurality of different electric loads. The method includes providing a hierarchical load feature database having a plurality of layers; including with each of a plurality of the layers a corresponding load feature set, the corresponding load feature set of at least one of the layers being different from the corresponding load feature set of at least another one of the layers; including with one of the layers a plurality of different electric load types; sensing a voltage signal and a current signal for each of the different electric loads; determining at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the different electric loads; and identifying by a processor one of the different electric load types by relating the different load features to the hierarchical load feature database.
System And Method For Electric Load Identification And Classification Employing Support Vector Machine
BIN LU - Shanghai, CN Ronald G. Harley - Lawrenceville GA, US Liang Du - Atlanta GA, US Yi Yang - Milwaukee WI, US Santosh K. Sharma - Pune, IN Prachi S. Zambare - Pune, IN Mayura A. Madane - Pune, IN
A method identifies electric load types of a plurality of different electric loads. The method includes providing a support vector machine load feature database of a plurality of different electric load types; sensing a voltage signal and a current signal for each of the different electric loads; determining a load feature vector including at least six steady-state features with a processor from the sensed voltage signal and the sensed current signal; and identifying one of the different electric load types by relating the load feature vector including the at least six steady-state features to the support vector machine load feature database.
- Dublin, IE Souvik Chandra - Lakewood CO, US Abrez Mondal - Lakewood CO, US Yi Yang - Arvada CO, US
International Classification:
H02J 3/42 H02J 3/44 G01R 19/25
Abstract:
Electrically connecting a first node of a first power grid to a second node of a second power grid includes: determining a phase angle of at least one phase of an AC voltage at the first node in the first power grid; determining a phase angle of at least one phase of an AC voltage at the second node in the second power grid; determining a phase angle metric based on comparing the phase angle of the AC voltage in the first power grid to the phase angle of the AC voltage in the second power grid; comparing the phase angle metric to a phase angle threshold; and if the phase angle metric is equal to or exceeds the phase angle threshold, controlling a dispatchable energy source in the first power grid in a P-Q control mode to adjust the phase angle of at least one phase of the AC voltage at the first node.
Arc Fault Circuit Interrupter Apparatus And Methods Using Symmetrical Component Harmonic Detection
Methods include sensing phase currents through a circuit interruption device, generating at least one symmetrical component harmonic current signal for at least one harmonic of the phase currents responsive to the sensed phase currents, and interrupting the phase currents responsive to the at least one symmetrical component harmonic current signal. The phase currents may be interrupted responsive to a phase and/or a magnitude of the at least one symmetrical component harmonic current signal meeting a criterion. The at least one harmonic may include at least one odd harmonic, such as at least one of a third harmonic and a fifth harmonic. The at least one symmetrical component harmonic current signal may include a positive sequence harmonic current signal and/or a negative sequence harmonic current signal.
- CLEVELAND OH, US Yi Yang - Milwaukee WI, US Charles John Luebke - Hartland WI, US Xin Zhou - Wexford PA, US
Assignee:
EATON CORPORATION - CLEVELAND OH
International Classification:
H02H 3/093
Abstract:
A method of protecting a power distribution system from a fault on a feed conductor thereof, wherein an HFAC signal is provided to the feed conductor from a location downstream of the feed conductor in the power distribution system. The method includes, in a module located upstream of the feed conductor in the power distribution system, determining an HFAC signal magnitude in the module, the module including a circuit interrupter, and controlling operation of the circuit interrupter based on the determined magnitude.
Georgia Institute of Technology Atlanta, GA Aug 2010 to Feb 2011 Research Assistant, School of EconomicsOffice of International Education
May 2010 to Jul 2010 Graduate AssistantSchool of Economics
May 2009 to May 2010 Graduate Research AssistantSchool of Economics
Jan 2009 to May 2009 Graduate Teaching Assistant
Education:
Georgia Institute of Technology Atlanta, GA Jan 2010 to Jan 2010 Master of Science in StatisticsGeorgia Institute of Technology Atlanta, GA Jan 2008 to Jan 2010 Master of Science in EconomicsNankai University Jan 2004 to Jan 2006 Bachelor of Economics in FinanceTianjin University Jan 2002 to Jan 2006 Bachelor of Arts in English