Patents by Inventor Ti-Chiun Chang

Ti-Chiun Chang 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).

  • Publication number: 20220171907
    Abstract: A method includes receiving, via a first component in a production environment, a sensor measurement corresponding to a second component in the production environment. A first digital twin corresponding to the first component is identified, and a perception algorithm is applied to identify a component type associated with the second component. A second digital twin is selected based on the component type, and a third digital twin is selected that models interactions between the first digital twin and the second digital twin. The third digital twin is used to generate instructions for the first component that allow the first component to interact with the second component. The instructions may then be delivered to the first component.
    Type: Application
    Filed: March 18, 2019
    Publication date: June 2, 2022
    Inventors: Ti-chiun Chang, Pranav Srinivas Kumar, Reed Williams, Arun Innanje, Janani Venugopalan, Edward Slavin, III, Lucia Mirabella
  • Patent number: 11321938
    Abstract: Systems and methods are provided for adapting images from different cameras so that a single trained classifier or an analyzer may be used. The classifier or analyzer operates on images that include a particular color distribution or characteristic. A generative network is used to adapt images from other cameras to have a similar color distribution or characteristic for use by the classifier or analyzer. A generative adversarial process is used to train the generative network.
    Type: Grant
    Filed: December 21, 2017
    Date of Patent: May 3, 2022
    Assignee: Siemens Aktiengesellschaft
    Inventors: Ti-chiun Chang, Jan Ernst, Patrick Reeb, Joachim Bamberger
  • Publication number: 20210165130
    Abstract: A method of predicting the intensity of sun light irradiating the ground. At least two input images are provided of a time series of images captured from the sky; a plurality of image features are extracted from the at least two input images; a set of meta data associated with the at least two input images are determined; the image features and the meta data are supplied as input data to a neural network; and neural network operations predict the future intensity of the sun light as a function of the input data. Further, a data processing unit and a computer program for controlling or carrying out the described method are described, as well as an electric power system with such a data processing unit.
    Type: Application
    Filed: June 14, 2018
    Publication date: June 3, 2021
    Inventors: TI-CHIUN CHANG, PATRICK REEB, JOACHIM BAMBERGER
  • Publication number: 20210166065
    Abstract: A method of classifying a near sun sky image includes at least one of the following steps: providing a recurrent neural network in the shape of a long short-term memory cell, the memory cell having at least an input gate, a neuron with a self-recurrent connection, a forget gate, and an output gate, and using a convolutional neural network, which includes, in the cited order, at least an input layer, one or more convolutional layers, an average pooling layer, and an output layer.
    Type: Application
    Filed: June 14, 2018
    Publication date: June 3, 2021
    Inventors: Ti-chiun Chang, Patrick Reeb, Andrei Szabo, Joachim Bamberger
  • Publication number: 20210166403
    Abstract: Pixels are classified within a time series of first and second images for the first image, a first probability map is provided with a first probability for a cloud for each first pixel and, for the second image, a second probability map with a second probability for a cloud for each second pixel; first and second mean intensity values are calculated for the pixels; local zero mean images are calculated by subtracting the mean intensity value from the intensity value of the respective pixel; a maximum difference map is generated by calculating, for spatially corresponding pixels, an absolute difference value between a first and second zero mean value; a weighting map is produced by multiplying each absolute difference value with a non-linear function; and a classifying map is computed based on the first probability map, the second probability map, and the weighting map.
    Type: Application
    Filed: June 14, 2018
    Publication date: June 3, 2021
    Inventors: Ti-chiun Chang, Patrick Reeb, Andrei Szabo, Joachim Bamberger
  • Publication number: 20210158010
    Abstract: Deep learning is used to train a neural network for end-to-end prediction of short term (e.g., 20 minutes or less) solar irradiation based on camera images and metadata. The architecture of the neural network includes a recurrent network for temporal considerations. The images and metadata are input at different locations in the neural network. The resulting machine-learned neural network predicts solar irradiation based on camera images and metadata so that a solar plant and back-up power source may be controlled to minimize output power variation.
    Type: Application
    Filed: May 31, 2018
    Publication date: May 27, 2021
    Inventors: Ti-chiun Chang, Patrick Reeb, Joachim Bamberger, Kuan-Chuan Peng, Jan Ernst
  • Publication number: 20210135623
    Abstract: A system and method for correcting short term irradiance prediction for control of a photovoltaic power station includes an irradiance data manager that generates an observed irradiance curve based on irradiance measurements received from an irradiation sensor, and a reference curve generation engine that generates a reference curve representative of irradiance values for a clear sky day. An irradiance prediction engine generates an irradiance prediction curve based on image segmentation and motion filtering of cloud pixels in sky images. A prediction correction engine corrects the irradiance prediction curve within a short term future time interval based on probabilistic analysis of time segmented intervals of the observed irradiance curve, the reference curve, and the irradiance prediction curve.
    Type: Application
    Filed: October 29, 2020
    Publication date: May 6, 2021
    Inventors: Aditi Roy, Ti-chiun Chang, Patrick Reeb, Joachim Bamberger
  • Patent number: 10914798
    Abstract: A method for estimating a coil sensitivity map for a magnetic resonance (MR) image includes providing a matrix A of sliding blocks of a 3D image of coil calibration data, calculating a left singular matrix V? from a singular value decomposition of A corresponding to ? leading singular values, calculating P=V?V?H, calculating a matrix that is an inverse Fourier transform of a zero-padded matrix P, and solving MHcr=(Sr)Hcr for cr, where cr is a vector of coil sensitivity maps for all coils at spatial location r, and M = ( ( 1 1 … 1 0 0 … 0 … … … 0 0 … 0 ) ? ( 0 0 … 0 1 1 … 1 … … … 0 0 … 0 ) ? ? … ? ? ( 0 0 … 0 0 0 … 0 … … … 1 1 … 1 ) ) .
    Type: Grant
    Filed: September 27, 2013
    Date of Patent: February 9, 2021
    Assignee: Siemens Healthcare GmbH
    Inventors: Jun Liu, Hui Xue, Marcel Dominik Nickel, Ti-chiun Chang, Mariappan S. Nadar, Alban Lefebvre, Edgar Mueller, Qiu Wang, Zhili Yang, Nirmal Janardhanan, Michael Zenge
  • Publication number: 20200372282
    Abstract: Systems and methods are provided for adapting images from different cameras so that a single trained classifier or an analyzer may be used. The classifier or analyzer operates on images that include a particular color distribution or characteristic. A generative network is used to adapt images from other cameras to have a similar color distribution or characteristic for use by the classifier or analyzer. A generative adversarial process is used to train the generative network.
    Type: Application
    Filed: December 21, 2017
    Publication date: November 26, 2020
    Inventors: Ti-chiun Chang, Jan Ernst, Patrick Reeb, Joachim Bamberger
  • Patent number: 10672115
    Abstract: Systems and methods are disclosed for processing an image to detect anomalous pixels. An image classification is received from a trained convolutional neural network (CNN) for an input image with a positive classification being defined to represent detection of an anomaly in the image and a negative classification being defined to represent absence of an anomaly. A backward propagation analysis of the input image for each layer of the CNN generates an attention mapping that includes a positive attention map and a negative attention map. A positive mask is generated based on intensity thresholds of the positive attention map and a negative mask is generated based on intensity thresholds of the negative attention map. An image of segmented anomalous pixels is generated based on an aggregation of the positive mask and the negative mask.
    Type: Grant
    Filed: December 6, 2017
    Date of Patent: June 2, 2020
    Assignee: Siemens Corporation
    Inventors: Rameswar Panda, Ziyan Wu, Arun Innanje, Ramesh Nair, Ti-chiun Chang, Jan Ernst
  • Publication number: 20190287234
    Abstract: Systems and methods are disclosed for processing an image to detect anomalous pixels. An image classification is received from a trained convolutional neural network (CNN) for an input image with a positive classification being defined to represent detection of an anomaly in the image and a negative classification being defined to represent absence of an anomaly. A backward propagation analysis of the input image for each layer of the CNN generates an attention mapping that includes a positive attention map and a negative attention map. A positive mask is generated based on intensity thresholds of the positive attention map and a negative mask is generated based on intensity thresholds of the negative attention map. An image of segmented anomalous pixels is generated based on an aggregation of the positive mask and the negative mask.
    Type: Application
    Filed: December 6, 2017
    Publication date: September 19, 2019
    Inventors: Rameswar Panda, Ziyan Wu, Arun Innanje, Ramesh Nair, Ti-chiun Chang, Jan Ernst
  • Patent number: 10303942
    Abstract: A short-term cloud forecasting system includes a cloud segmentation processor that receives image data from images captured by an all sky camera. The cloud segmentation processor calculates a probability for each pixel in an image that the pixel is representative of a cloud. A cloud motion estimation processor calculates a motion vector representing estimated cloud motion calculates weights representing the likelihood that the cloud motion will cause a cloud to occlude the sun at a time in the near future. An uncertainty processor calculates one or more uncertainty indexes that quantify the confidence that a cloud forecast is accurate. Combining the cloud probabilities, the global motion vector and the at least one uncertainty index, in a sun occlusion prediction processor produces a short-term cloud forecast based on the image data that may be used as input to control systems for controlling a power grid.
    Type: Grant
    Filed: February 16, 2017
    Date of Patent: May 28, 2019
    Assignee: Siemens Aktiengesellschaft
    Inventors: Ti-chiun Chang, Jan Ernst, Jeremy-Ralph Wiles, Joachim Bamberger, Andrei Szabo
  • Publication number: 20180232557
    Abstract: A short-term cloud forecasting system includes a cloud segmentation processor that receives image data from images captured by an all sky camera. The cloud segmentation processor calculates a probability for each pixel in an image that the pixel is representative of a cloud. A cloud motion estimation processor calculates a motion vector representing estimated cloud motion calculates weights representing the likelihood that the cloud motion will cause a cloud to occlude the sun at a time in the near future. An uncertainty processor calculates one or more uncertainty indexes that quantify the confidence that a cloud forecast is accurate. Combining the cloud probabilities, the global motion vector and the at least one uncertainty index, in a sun occlusion prediction processor produces a short-term cloud forecast based on the image data that may be used as input to control systems for controlling a power grid.
    Type: Application
    Filed: February 16, 2017
    Publication date: August 16, 2018
    Inventors: Ti-chiun Chang, Jan Ernst, Jeremy-Ralph Wiles, Joachim Bamberger, Andrei Szabo
  • Patent number: 9684982
    Abstract: A computer-implemented method for performing isotropic reconstruction of Magnetic Resonance Imaging (MRI) data includes receiving a stack of slices acquired by an MRI device in two or more directions and reslicing the stack of slices into (i) an acquired view stack comprising high-resolution slices acquired in-plane, and (ii) a reslice stack comprising degraded slices acquired out of plane. An estimated slice profile is generated based on the stack of slices and the acquired view stack is convolved with the estimated slice profile to yield a simulated distorted slice stack. The simulated distorted slice stack is subtracted from the acquired view stack to yield a high-frequency band estimate and the high-frequency band estimate is combined with the reslice stack to yield isotropic reconstruction results.
    Type: Grant
    Filed: September 1, 2015
    Date of Patent: June 20, 2017
    Assignee: Siemens Healthcare GmbH
    Inventors: Ti-chiun Chang, Xiaoguang Lu, Peter Speier
  • Patent number: 9632156
    Abstract: A method for parallel magnetic resonance imaging (MRI) reconstruction of digital images includes providing a set of acquired k-space MR image data v, a redundant Haar wavelet matrix W satisfying WTW=I, wherein I is an identity matrix, a regularization parameter ??0, and a counter limit k, initializing a variable z0=Wv, and intermediate quantities p0=q0=0, calculating yi=arg minz½?z?(pi+zi)?22+??z?1 for 0?i?k, wherein z denotes values of an MR image sought to be reconstructed, updating pi+1=(pi+zi)?yi, updating zi+1=arg minz½?z?(qi+zi)?22+g(z), wherein g ? ( z ) = { 0 , z = WW T ? z , + ? , otherwise ; and updating qi+1=(qi+yi)?zi?l, wherein x=WTz is a solution of min x ? 1 2 ? ? Wx - Wv ? 2 2 + ? ? ? Wx ? 1 that specifies a reconstruction of the MR image.
    Type: Grant
    Filed: December 18, 2012
    Date of Patent: April 25, 2017
    Assignee: Siemens Healthcare GmbH
    Inventors: Jun Liu, Jeremy Rapin, Alban Lefebvre, Mariappan S. Nadar, Ti-chiun Chang
  • Patent number: 9602781
    Abstract: A system and methods to deblend seismic data from a plurality of sources and received by a plurality of sensors as shot gathers are disclosed. The deblending is performed by a Mutual Interdependence Analysis Method to separate contributions of different shots. Deblending is also performed by applying a measure of coherence in parallel data domains such as Common Shot Gather and Common Midpoint. Deblending is also achieved by using the hyperbolic nature of seismic data in the common midpoint domain. Deblended signals are estimated and are applied to create a seismic image. Also, Bergman iteration based migration is applied directly on the blended seismic shot gathers without first deblending as an alternative method. The methods are applied in seismic imaging for exploration of natural resources.
    Type: Grant
    Filed: March 21, 2012
    Date of Patent: March 21, 2017
    Assignee: Siemens Aktiengesellschaft
    Inventors: Heiko Claussen, Ti-chiun Chang, Justinian Rosca
  • Publication number: 20170061652
    Abstract: A computer-implemented method for performing isotropic reconstruction of Magnetic Resonance Imaging (MRI) data includes receiving a stack of slices acquired by an MRI device in two or more directions and reslicing the stack of slices into (i) an acquired view stack comprising high-resolution slices acquired in-plane, and (ii) a reslice stack comprising degraded slices acquired out of plane. An estimated slice profile is generated based on the stack of slices and the acquired view stack is convolved with the estimated slice profile to yield a simulated distorted slice stack. The simulated distorted slice stack is subtracted from the acquired view stack to yield a high-frequency band estimate and the high-frequency band estimate is combined with the reslice stack to yield isotropic reconstruction results.
    Type: Application
    Filed: September 1, 2015
    Publication date: March 2, 2017
    Inventors: Ti-chiun Chang, Xiaoguang Lu, Peter Speier
  • Patent number: 8948480
    Abstract: A method for image reconstruction includes receiving under-sampled k-space data, determining a data fidelity term of a first image of the under-sampled k-space data in view of a second image of the under-sampled k-space data, wherein a time component separated the first image and the second image, determining a spatial penalization on redundant Haar wavelet coefficients of the first image in view of the second image, and optimizing the first image according the data fidelity term and the spatial penalization, wherein the spatial penalization selectively penalizes temporal coefficients and an optimized image of the first image is output.
    Type: Grant
    Filed: September 14, 2012
    Date of Patent: February 3, 2015
    Assignee: Siemens Aktiengesellschaft
    Inventors: Jun Liu, Jeremy Rapin, Alban Lefebvre, Mariappan S. Nadar, Ti-chiun Chang, Michael Zenge, Edgar Müller
  • Patent number: 8879852
    Abstract: A reconstructed image is rendered of a patient by a processor from a set of undersampled MRI data by first subtracting two repetitions of the acquired data in k-space to create a third dataset. The processor reconstructs the image by minimizing an objective function under a constraint related to the third dataset, wherein the objective function includes applying a Karhunen-Loeve Transform (KLT) to a temporal dimension of data. The objective function under the constraint is expressed as arg minf{??(f)?1 subject to ?Af?y?2??}. The reconstructed image is an angiogram which may be a 4D angiogram. The angiogram is used to diagnose a vascular disease.
    Type: Grant
    Filed: October 28, 2011
    Date of Patent: November 4, 2014
    Assignee: Siemens Aktiengesellschaft
    Inventors: Ti-chiun Chang, Mariappan S. Nadar, Jens Gühring, Michael Zenge, Kai Tobias Block, Peter Schmitt, Edgar Mueller
  • Publication number: 20140088899
    Abstract: A method for estimating a coil sensitivity map for a magnetic resonance (MR) image includes providing a matrix A of sliding blocks of a 3D image of coil calibration data, calculating a left singular matrix V? from a singular value decomposition of A corresponding to ? leading singular values, calculating P=V?V?H, calculating a matrix S that is an inverse Fourier transform of a zero-padded matrix P, and solving MHcr=(Sr)Hcr for cr, where cr is a vector of coil sensitivity maps for all coils at spatial location r, and M = ( ( 1 1 … 1 0 0 … 0 … … … 0 0 … 0 ) ? ( 0 0 … 0 1 1 … 1 … … … 0 0 … 0 ) ? ? … ? ? ( 0 0 … 0 0 0 … 0 … … … 1 1 … 1 ) ) .
    Type: Application
    Filed: September 27, 2013
    Publication date: March 27, 2014
    Applicants: SIEMENS AKTIENGESELLSCHAFT, SIEMENS CORPORATION
    Inventors: Jun Liu, Hui Xue, Marcel Dominik Nickel, Ti-chiun Chang, Mariappan S. Nadar, Alban Lefebvre, Edgar Mueller, Qiu Wang, Zhili Yang, Nirmal Janardhanan, Michael Zenge