Patents by Inventor Youngdeok HWANG
Youngdeok HWANG has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 10607145Abstract: Methods, systems, and computer program products for detection of an arbitrarily-shaped source of an abnormal event via use of a hierarchical reconstruction method are provided herein. A computer-implemented method includes detecting an abnormal event based on analysis of sensor data, wherein said analysis of the sensor data comprises comparing the sensor data to a user-defined threshold; generating a query based on the detected abnormal event; processing the query against one or more given data repositories; executing an inverse model using an output generated in relation to said processing to identify a source of the detected abnormal event, wherein the source comprises an arbitrary shape; and outputting the identified source of the detected abnormal event.Type: GrantFiled: November 23, 2015Date of Patent: March 31, 2020Assignee: International Business Machines CorporationInventors: Youngdeok Hwang, Jayant R. Kalagnanam, Xiao Liu, Kyong Min Yeo
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Patent number: 10599991Abstract: A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.Type: GrantFiled: July 14, 2015Date of Patent: March 24, 2020Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Hendrik F. Hamann, Youngdeok Hwang, Levente Klein, Jonathan Lenchner, Siyuan Lu, Fernando J. Marianno, Gerald J. Tesauro, Theodore G. van Kessel
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Patent number: 10592818Abstract: A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.Type: GrantFiled: July 14, 2015Date of Patent: March 17, 2020Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Hendrik F. Hamann, Youngdeok Hwang, Levente Klein, Jonathan Lenchner, Siyuan Lu, Fernando J. Marianno, Gerald J. Tesauro, Theodore G. van Kessel
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Patent number: 10592817Abstract: A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.Type: GrantFiled: July 13, 2015Date of Patent: March 17, 2020Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Hendrik F. Hamann, Youngdeok Hwang, Levente Klein, Jonathan Lenchner, Siyuan Lu, Fernando J. Marianno, Gerald J. Tesauro, Theodore G. van Kessel
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Publication number: 20170147927Abstract: Methods, systems, and computer program products for detection of an arbitrarily-shaped source of an abnormal event via use of a hierarchical reconstruction method are provided herein. A computer-implemented method includes detecting an abnormal event based on analysis of sensor data, wherein said analysis of the sensor data comprises comparing the sensor data to a user-defined threshold; generating a query based on the detected abnormal event; processing the query against one or more given data repositories; executing an inverse model using an output generated in relation to said processing to identify a source of the detected abnormal event, wherein the source to comprises an arbitrary shape; and outputting the identified source of the detected abnormal event.Type: ApplicationFiled: November 23, 2015Publication date: May 25, 2017Inventors: Youngdeok Hwang, Jayant R. Kalagnanam, Xiao Liu, Kyong Min Yeo
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Patent number: 9568519Abstract: A procedure for forecasting building energy consumption by evaluating performance of variable base degree and variable based enthalpy models. Dynamic weights are computed for the variable base degree and variable based enthalpy models and used in making future energy prediction based on weather forecast data. The weather forecast data may be corrected for bias. The variable base degree and variable based enthalpy models may be calibrated based on outlier removed historic energy consumption data and historic ambient air temperature data.Type: GrantFiled: May 15, 2014Date of Patent: February 14, 2017Assignee: International Business Machines CorporationInventors: Youngdeok Hwang, Young Min Lee, Yada Zhu
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Publication number: 20170017895Abstract: A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.Type: ApplicationFiled: July 14, 2015Publication date: January 19, 2017Inventors: Hendrik F. Hamann, Youngdeok Hwang, Levente Klein, Jonathan Lenchner, Siyuan Lu, Fernando J. Marianno, Gerald J. Tesauro, Theodore G. van Kessel
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Publication number: 20170017732Abstract: A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.Type: ApplicationFiled: July 13, 2015Publication date: January 19, 2017Inventors: Hendrik F. Hamann, Youngdeok Hwang, Levente Klein, Jonathan Lenchner, Siyuan Lu, Fernando J. Marianno, Gerald J. Tesauro, Theodore G. van Kessel
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Publication number: 20170017896Abstract: A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.Type: ApplicationFiled: July 14, 2015Publication date: January 19, 2017Inventors: Hendrik F. Hamann, Youngdeok Hwang, Levente Klein, Jonathan Lenchner, Siyuan Lu, Fernando J. Marianno, Gerald J. Tesauro, Theodore G. van Kessel
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Patent number: 9471884Abstract: A method and a system to perform multi-model blending are described. The method includes obtaining one or more sets of predictions of historical conditions, the historical conditions corresponding with a time T that is historical in reference to current time, and the one or more sets of predictions of the historical conditions being output by one or more models. The method also includes obtaining actual historical conditions, the actual historical conditions being measured conditions at the time T, assembling a training data set including designating the two or more set of predictions of historical conditions as predictor variables and the actual historical conditions as response variables, and training a machine learning algorithm based on the training data set. The method further includes obtaining a blended model based on the machine learning algorithm.Type: GrantFiled: May 30, 2014Date of Patent: October 18, 2016Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Hendrik F. Hamann, Youngdeok Hwang, Theodore G. van Kessel, Ildar K. Khabibrakhmanov, Siyuan Lu, Ramachandran Muralidhar
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Publication number: 20150347922Abstract: A method and a system to perform multi-model blending are described. The method includes obtaining one or more sets of predictions of historical conditions, the historical conditions corresponding with a time T that is historical in reference to current time, and the one or more sets of predictions of the historical conditions being output by one or more models. The method also includes obtaining actual historical conditions, the actual historical conditions being measured conditions at the time T, assembling a training data set including designating the two or more set of predictions of historical conditions as predictor variables and the actual historical conditions as response variables, and training a machine learning algorithm based on the training data set. The method further includes obtaining a blended model based on the machine learning algorithm.Type: ApplicationFiled: May 30, 2014Publication date: December 3, 2015Applicant: International Business Machines CorporationInventors: Hendrik F. Hamann, Youngdeok Hwang, Theodore G. van Kessel, Ildar K. Khabibrakhmanov, Siyuan Lu, Ramachandran Muralidhar
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Publication number: 20150331023Abstract: A procedure for forecasting building energy consumption by evaluating performance of variable base degree and variable based enthalpy models. Dynamic weights are computed for the variable base degree and variable based enthalpy models and used in making future energy prediction based on weather forecast data. The weather forecast data may be corrected for bias. The variable base degree and variable based enthalpy models may be calibrated based on outlier removed historic energy consumption data and historic ambient air temperature data.Type: ApplicationFiled: May 15, 2014Publication date: November 19, 2015Applicant: International Business Machines CorporationInventors: Youngdeok HWANG, Young Min LEE, Yada ZHU