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1.发明申请公开(公告)号：US20200293910A1
公开(公告)日：2020-09-17
申请号：US16354883
申请日：2019-03-15
IPC分类号： G06N5/02 , G06F16/901 , G06F16/23
摘要： A sub-process sequence is identified from a temporal dataset. Based on time information, predictors are categorized as being available or not available during time periods. The predictors are used to make predictions of quantities that will occur in a future time period. The predictors are grouped into groups of a sequence of sub-processes, each including a grouping of one or more of the predictors. Information is output that allows a human being to modify the groups. The groups are finalized, responsive to any modifications. Prediction models are extracted based on dependencies between groups and sub-processes. A final predication model is determined based on a prediction model from the prediction models that best meets criteria. A dependency graph is generated based on the final prediction model. Information is output to display the final dependency graph for use by a user to adjust or not adjust elements of the sequential process.
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公开(公告)号：US12099941B2
公开(公告)日：2024-09-24
申请号：US16925013
申请日：2020-07-09
发明人： Arun Kwangil Iyengar , Jeffrey Owen Kephart , Dhavalkumar C. Patel , Dung Tien Phan , Chandrasekhara K. Reddy
IPC分类号： G06Q10/00 , G06F18/214 , G06F18/22 , G06N20/20 , G06Q10/04
CPC分类号： G06Q10/04 , G06F18/214 , G06F18/22 , G06N20/20
摘要： Techniques for generating model ensembles are provided. A plurality of models trained to generate predictions at each of a plurality of intervals is received. A respective prediction accuracy of each respective model of the plurality of models is determined for a first interval of the plurality of intervals by processing labeled evaluation data using the respective model. Additionally, a model ensemble specifying one or more of the plurality of models for each of the plurality of intervals is generated, comprising selecting, for the first interval, a first model of the plurality of models based on (i) the respective prediction accuracies and (ii) at least one non-error metric.
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公开(公告)号：US20230267339A1
公开(公告)日：2023-08-24
申请号：US17675202
申请日：2022-02-18
发明人： Dzung Tien Phan , Connor Aram Lawless , Jayant R. Kalagnanam , Lam Minh Nguyen , Chandrasekhara K. Reddy
IPC分类号： G06N5/02
CPC分类号： G06N5/022
摘要： In unsupervised interpretable machine learning, one or more datasets having multiple features can be received. A machine can be trained to jointly cluster and interpret resulting clusters of the dataset by at least jointly clustering the dataset into clusters and generating hyperplanes in a multi-dimensional feature space of the dataset, where the hyperplanes separate pairs of the clusters, where a hyperplane separates a pair of clusters. Jointly clustering the dataset into clusters and generating hyperplanes can repeat until convergence, where the clustering in a subsequent iteration uses the generated hyperplanes from a previous iteration to optimize performance of the clustering. The hyperplanes can be adjusted to further improve the performance of the clustering. The clusters and interpretation of the clusters can be provided, where a cluster's interpretation is provided based on hyperplanes that construct a polytope containing the cluster.
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公开(公告)号：US10572819B2
公开(公告)日：2020-02-25
申请号：US14812344
申请日：2015-07-29
发明人： Tamir Klinger , Chandrasekhara K. Reddy , Ashish Sabharwal , Horst C. Samulowitz , Gerald J. Tesauro , Deepak S. Turaga
摘要： A system, method, and computer program product for automatically selecting from a plurality of analytic algorithms a best performing analytic algorithm to apply to a dataset is provided. The automatically selecting from the plurality of analytic algorithms the best performing analytic algorithm to apply to the dataset enables a training a plurality of analytic algorithms on a plurality of subsets of the dataset. Then, a corresponding prediction accuracy trend is estimated across the subsets for each of the plurality of analytic algorithms to produce a plurality of accuracy trends. Next, the best performing analytic algorithm is selected and outputted from the plurality of analytic algorithms based on the corresponding prediction accuracy trend with a highest value from the plurality of accuracy trends.
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公开(公告)号：US20140330609A1
公开(公告)日：2014-11-06
申请号：US13874615
申请日：2013-05-01
IPC分类号： G06Q10/06
CPC分类号： G06Q10/0631 , G06Q10/06313
摘要： Embodiments of the invention relate to a method for providing performance driven municipal asset needs and sustainability analysis. The method includes calculating asset health scores for a plurality of assets in an infrastructure. The asset health scores change as a function of time. The method also includes identifying prescription options for the assets. The identifying is based on the asset health scores. The prescription options include cost, value, and time for execution. A multi-objective optimization is applied based on the asset health scores and prescription options to identify at least a subset of the prescription options that may be implemented within a provided budget to maintain a sustainability threshold for an overall infrastructure health score.
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公开(公告)号：US12073152B2
公开(公告)日：2024-08-27
申请号：US16933977
申请日：2020-07-20
发明人： Elham Khabiri , Anuradha Bhamidipaty , Robert Jeffrey Baseman , Chandrasekhara K. Reddy , Srideepika Jayaraman
摘要： A computing device includes a processor and a storage device. A vehicle asset modeling module is stored in the storage device and is executed by the processor to configure the computing device to perform acts of identifying and clustering a plurality of assets based on static properties of a vehicle asset using a first module of the vehicle asset modeling module. The clustered plurality of assets is determined based on dynamic properties of the vehicle asset using a second module. Event prediction is performed by converting a numeric data of the clustered plurality of assets to a natural language processing (NLP) domain by a third module. One or more sequence-to-sequence methods are performed to predict a malfunction of a component of the vehicle asset and/or an event based on past patterns. Prediction information is stored in the storage device.
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公开(公告)号：US20220019710A1
公开(公告)日：2022-01-20
申请号：US16933972
申请日：2020-07-20
发明人： Elham Khabiri , Anuradha Bhamidipaty , Robert Jeffrey Baseman , Chandrasekhara K. Reddy , Srideepika Jayaraman
摘要： A computing device includes a processor and a storage device. A wafer asset modeling module is stored in the storage device and is executed by the processor to configure the computing device to perform acts identifying and clustering a plurality of assets based on static properties of a wafer asset using a first module of the wafer asset modeling module. The clustered plurality of assets is determined based on dynamic properties of the wafer asset using a second module of the wafer asset modeling module. Event prediction is performed by converting a numeric data of the clustered plurality of assets to a natural language processing (NLP) domain by a third module of the wafer asset modeling module. One or more sequence-to-sequence methods are performed to predict a malfunction of a component of the wafer asset and/or an event based on past patterns. Prediction information is stored in the storage device.
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公开(公告)号：US20220019708A1
公开(公告)日：2022-01-20
申请号：US16933977
申请日：2020-07-20
发明人： Elham Khabiri , Anuradha Bhamidipaty , Robert Jeffrey Baseman , Chandrasekhara K. Reddy , Srideepika Jayaraman
摘要： A computing device includes a processor and a storage device. A vehicle asset modeling module is stored in the storage device and is executed by the processor to configure the computing device to perform acts of identifying and clustering a plurality of assets based on static properties of a vehicle asset using a first module of the vehicle asset modeling module. The clustered plurality of assets is determined based on dynamic properties of the vehicle asset using a second module. Event prediction is performed by converting a numeric data of the clustered plurality of assets to a natural language processing (NLP) domain by a third module. One or more sequence-to-sequence methods are performed to predict a malfunction of a component of the vehicle asset and/or an event based on past patterns. Prediction information is stored in the storage device.
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9.发明申请公开(公告)号：US20140330600A1
公开(公告)日：2014-11-06
申请号：US13967431
申请日：2013-08-15
IPC分类号： G06Q10/06
CPC分类号： G06Q10/0631 , G06Q10/06313
摘要： Embodiments of the invention relate to a method for providing performance driven municipal asset needs and sustainability analysis. The method includes calculating asset health scores for a plurality of assets in an infrastructure. The asset health scores change as a function of time. The method also includes identifying prescription options for the assets. The identifying is based on the asset health scores. The prescription options include cost, value, and time for execution. A multi-objective optimization is applied based on the asset health scores and prescription options to identify at least a subset of the prescription options that may be implemented within a provided budget to maintain a sustainability threshold for an overall infrastructure health score.
摘要翻译： 本发明的实施例涉及一种用于提供性能驱动的市政资产需求和可持续性分析的方法。 该方法包括计算基础设施中多个资产的资产健康分数。 资产健康分数随时间变化而变化。 该方法还包括识别资产的处方选项。 识别是基于资产健康评分。 处方选项包括成本，价值和执行时间。 基于资产健康评分和处方选项来应用多目标优化，以鉴定可在所提供的预算内实施的处方选项的至少一个子集，以维持整体基础设施健康评分的可持续性阈值。
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公开(公告)号：US20230128821A1
公开(公告)日：2023-04-27
申请号：US17491494
申请日：2021-09-30
发明人： Dzung Tien Phan , Lam Minh Nguyen , Jayant R. Kalagnanam , Chandrasekhara K. Reddy , Srideepika Jayaraman
IPC分类号： G06N20/10
摘要： A computer implemented method of generating a classifier engine for machine learning includes receiving a set of data points. A semi-supervised k-means process is applied to the set of data points from each class. The set of data points in a class is clustered into multiple clusters of data points, using the semi-supervised k-means process. Multi-polytopes are constructed for one or more of the clusters from all classes. A support vector machine (SVM) process is run on every pair of clusters from all classes. Separation hyperplanes are determined for the clustered classes. Labels are determined for each cluster based on the separation by hyperplanes.
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