公开(公告)号：US20170031867A1

公开(公告)日：2017-02-02

申请号：US15301481

申请日：2014-04-14

Applicant: Siemens Aktiengesellschaft

Inventor： Chao Yuan ,  Amit Chakraborty ,  Eberhard Ritzhaupt-Kleissl ,  Holger Hackstein

IPC: G06F17/18 ,  G06N99/00

Abstract: A method of predicting an amount of power that will be generated by a solar power plant at a future time includes: forecasting a value of a data variable at the future time that is likely to affect the ability of the solar power plant to produce electricity (S301); computing a plurality of features from prior observed amounts of power generated by the power plant during different previous durations (S302); determining a trending model from the computed features and the forecasted value (S303); and predicting the amount of power that will be generated by the power plant at the future time from the determined model (S304).

Abstract translation: 预测未来太阳能发电厂将产生的功率量的方法包括：预测未来可能影响太阳能发电厂发电的能力的数据变量的值（ S301）; 从先前在先前持续时间期间由发电厂产生的功率的电力计算多个特征（S302）; 根据计算出的特征和预测值确定趋势模型（S303）; 并且从所确定的模型预测未来发电厂将产生的功率量（S304）。

公开(公告)号：US20220292266A1

公开(公告)日：2022-09-15

申请号：US17690647

申请日：2022-03-09

Applicant: Siemens Aktiengesellschaft

Inventor： Biswadip Dey ,  Tongtao Zhang ,  Yaofeng Zhong ,  Amit Chakraborty

IPC: G06F40/40 ,  G06N3/04 ,  G06N3/08

Abstract: System and method for performing natural language processing are disclosed. An encoder includes a multi-head attention block for nonlinear transformation of inputs and a feed-forward network for learning parameters that result in best function approximation. Output of the multi-head attention block and the feed-forward network are coupled in parallel to produce a summed output. An ODE solver performs continuous depth integration of the summed output for reduced number of parameters.

公开(公告)号：US20200320456A1

公开(公告)日：2020-10-08

申请号：US16755905

申请日：2017-10-20

Applicant: Siemens Aktiengesellschaft

Inventor： Ioannis Akrotirianakis ,  Amit Chakraborty

IPC: G06Q10/06 ,  G06Q50/04 ,  G06Q10/04 ,  G06Q50/06

Abstract: A computer-implemented method of scheduling jobs for an industrial process includes receiving jobs to be executed on machines within a manufacturing facility. A job schedule is generated based on an optimization function that minimizes total energy cost for all the machines during a time horizon based on a summation of energy cost at each time step between a start time and an end time. The energy cost at each time step is a summation of (a) a first energy cost associated with each machine in sleeping mode during the time step, (b) a second energy cost associated with each machine in stand-by mode during the time step, and (c) a third energy cost associated with each machine in processing mode during the time step. The jobs are executed on the machines based on the job schedule.

公开(公告)号：US10713566B2

公开(公告)日：2020-07-14

申请号：US15290154

申请日：2016-10-11

Applicant: Siemens Aktiengesellschaft

Inventor： Xi He ,  Ioannis Akrotirianakis ,  Amit Chakraborty

IPC: G06N3/08

Abstract: A method for training a deep learning network includes defining a loss function corresponding to the network. Training samples are received and current parameter values are set to initial parameter values. Then, a computing platform is used to perform an optimization method which iteratively minimizes the loss function. Each iteration comprises the following steps. An eigCG solver is applied to determine a descent direction by minimizing a local approximated quadratic model of the loss function with respect to current parameter values and the training dataset. An approximate leftmost eigenvector and eigenvalue is determined while solving the Newton system. The approximate leftmost eigenvector is used as negative curvature direction to prevent the optimization method from converging to saddle points. Curvilinear and adaptive line-searches are used to guide the optimization method to a local minimum. At the end of the iteration, the current parameter values are updated based on the descent direction.

公开(公告)号：US10386544B2

公开(公告)日：2019-08-20

申请号：US15320899

申请日：2015-06-29

Applicant: Siemens Aktiengesellschaft

Inventor： Chao Yuan ,  Amit Chakraborty ,  Holger Hackstein

IPC: G01W1/10 ,  G06Q10/04 ,  G06N7/00 ,  G01W1/12 ,  G06F17/18 ,  G06Q50/06

Abstract: A method for solar forecasting includes receiving a plurality of solar energy data as a function of time of day at a first time, forecasting from the solar energy data a mode, where the mode is a sunny day, a cloudy day, or an overcast day, and the forecast predicts the mode for a next solar energy datum, receiving the next solar energy datum, updating a probability distribution function (pdf) of the next solar energy datum given the mode, updating a pdf of the mode for the next solar energy datum from the updated pdf of the new solar energy datum given the mode, forecasting a plurality of future unobserved solar energy data from the updated pdf of the mode, where the plurality of future unobserved solar energy data and the plurality of solar energy data have a Gaussian distribution for a given mode determined from training data.

公开(公告)号：US20170091150A1

公开(公告)日：2017-03-30

申请号：US14870380

申请日：2015-09-30

Applicant: Siemens Aktiengesellschaft

Inventor： Sridharan Palanivelu ,  Amit Chakraborty

IPC: G06F17/21 ,  G06F21/60 ,  G06F3/0484 ,  G06T19/00 ,  G06F17/22 ,  G06F17/24

CPC classification number: G06F17/211 ,  G06F17/2229 ,  G06F17/2247 ,  G06F17/227 ,  G06F17/243 ,  G06F17/248

Abstract: A method and system for generating a high-level language (i.e., PDF) report with embedded 3D objects. The report is prepared by using an XML template where selected 3D objects are imported into the template and enabled to be activated and manipulated by persons viewing the report, without the need to utilize vendor-specific 3D software. The XML template supports various types of 3D models from various data sources, such as engineering CAD models, medical volumetric data, etc. A specific XML fragment in the template is configured to allow for a 3D object (created using any type of software system) to be imported in “active” form to the document being created. Once the actual PDF report is generated, it may be distributed to various recipients who are then able to manipulate the 3D object(s).

公开(公告)号：US20160069776A1

公开(公告)日：2016-03-10

申请号：US14813244

申请日：2015-07-30

Applicant: Siemens Aktiengesellschaft

Inventor： Xinmin Cai ,  Chao Yuan ,  Amit Chakraborty

IPC: G01M15/14

CPC classification number: G01M15/14

Abstract: Reference data from sensors measuring characteristics of a gas turbine are analyzed to identify underperformance of the gas turbine, which may be a predictor of an unscheduled shutdown. Time series data from the sensors are compared to annotated query data using an open-begin-end dynamic time warping algorithm. Identified subsequences are examined as possible underperformance indicators. In a related technique, multiple time series from the sensors are pairwise compared using a dynamic time warping algorithm, and computed distances between the time series are used to group the time series using a hierarchical clustering algorithm. The clusters are examined to identify underperformance indicators.

Abstract translation: 分析来自测量燃气轮机特征的传感器的参考数据，以识别燃气轮机的性能不佳，这可能是非计划停机的预测因素。 来自传感器的时间序列数据使用开放式开始动态时间扭曲算法与注释查询数据进行比较。 检查确定的子序列作为可能的表现不佳指标。 在相关技术中，使用动态时间扭曲算法对来自传感器的多个时间序列进行成对比较，并且使用时间序列之间的计算距离来使用分层聚类算法对时间序列进行分组。 检查集群以识别绩效指标不佳。

公开(公告)号：US20150019181A1

公开(公告)日：2015-01-15

申请号：US14325862

申请日：2014-07-08

Applicant: Siemens Aktiengesellschaft

Inventor： Lu Wang ,  Zhixuan Duan ,  Chao Yuan ,  Yu Sun ,  Amit Chakraborty

IPC: G06F19/00

CPC classification number: G16C20/10 ,  F23N5/203 ,  F23N2023/40 ,  G16C20/70

Abstract: NOx generation in a coal burning furnace is estimating using a chemical reactor network model. The model is constructed with ideal chemical reactor modules, an input matrix and a tunable parameter matrix defining split ratios and flow rates among the ideal chemical reactor modules. Values in the tunable parameter matrix are learned by first measuring actual furnace outputs of the coal burning furnace for a known set of actual furnace inputs, and then applying the chemical reactor network, including an initially populated tunable parameter matrix, to a populated input matrix representing the known set of actual furnace inputs. The actual furnace outputs are compared with the output matrix, and the tunable parameter matrix is adjusted based on the comparison.

Abstract translation: 燃煤炉中的NOx生成是使用化学反应堆网络模型来估计的。 该模型由理想的化学反应器模块，输入矩阵和可调参数矩阵构成，定义理想化学反应堆模块之间的分流比和流量。 可调参数矩阵中的值通过首先测量煤燃烧炉的实际炉输出而获知，用于已知的一组实际炉输入，然后将化学反应堆网络（包括最初填充的可调参数矩阵）施加到表示 已知的一套实际炉子输入。 将实际炉输出与输出矩阵进行比较，并根据比较调整可调参数矩阵。

公开(公告)号：US20220245310A1

公开(公告)日：2022-08-04

申请号：US17591252

申请日：2022-02-02

Applicant: Siemens Aktiengesellschaft

Inventor： Yaofeng Zhong ,  Biswadip Dey ,  Amit Chakraborty

IPC: G06F30/27 ,  G06F30/23 ,  G06N3/08

Abstract: System and method for modeling motion and collision of rigid bodies in a dynamic system includes a collision detector that detects active contacts of the rigid bodies. A differentiable contact impulse solver applies constraints on contact forces related to a compression phase, applies coefficient of restitution on contact forces related to a restitution phase, solves for contact forces and velocity impulses associated with the active contacts in the compression phase and the restitution phase, and estimates trajectories of the rigid bodies while optimizing for maximum rate of energy dissipation.

公开(公告)号：US20210342703A1

公开(公告)日：2021-11-04

申请号：US17271646

申请日：2018-08-31

Applicant: Siemens Aktiengesellschaft

Inventor： Qi Wei ,  Chao Yuan ,  Amit Chakraborty

IPC: G06N3/08 ,  G06N3/04

Abstract: Systems, techniques, and computer-program products are provided to generate synthetic time series using a generative adversarial network. In some embodiment a technique includes configuring a first neural network having a first function representative of an output of the first neural network, and configuring a second neural network having a second function representative of an output of the second neural network. In addition, such a technique includes generating a generative adversarial network by solving an optimization problem with respect to an objective function based at least on the first function and the second function. The generative adversarial network includes a discriminator neural network and a generator neural network. A synthetic time series can be generated using at least the generator neural network.