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1. 发明授权

US07761389B2 Method for anomaly prediction of battery parasitic load 失效
标题翻译：电池寄生负载异常预测方法
公开(公告)号：US07761389B2
公开(公告)日：2010-07-20
申请号：US11843876
申请日：2007-08-23
申请人： Swarup Medasani , Qin Jiang , Narayan Srinivasa , Yilu Zhang , Leandro G. Barajas , Nick S. Kapsokavathis
发明人： Swarup Medasani , Qin Jiang , Narayan Srinivasa , Yilu Zhang , Leandro G. Barajas , Nick S. Kapsokavathis
IPC分类号： G06F15/18 , G06N7/02
CPC分类号： G01R31/3606 , G01R31/3693
摘要： Anomaly prediction of battery parasitic load includes processing input data related to a state of charge for a battery and a durational factor utilizing a machine learning algorithm and generating a predicted start-up state of charge. Warnings are issued if the predicted start-up state of charge drops below a threshold level within an operational time.
摘要翻译：电池寄生负载的异常预测包括利用机器学习算法处理与电池的充电状态有关的输入数据和持续时间因子，并产生预测的充电状态。如果预计的启动状态在运行时间内下降到阈值以下，则发出警告。

2. 发明申请

US20090055330A1 METHOD FOR ANOMALY PREDICTION OF BATTERY PARASITIC LOAD 失效
标题翻译：电池仿真负载异常预测方法
公开(公告)号：US20090055330A1
公开(公告)日：2009-02-26
申请号：US11843876
申请日：2007-08-23
申请人： Swarup Medasani , Qin Jiang , Narayan Srinivasa , Yilu Zhang , Leandro G. Barajas , Nick S. Kapsokavathis
发明人： Swarup Medasani , Qin Jiang , Narayan Srinivasa , Yilu Zhang , Leandro G. Barajas , Nick S. Kapsokavathis
IPC分类号： G06F15/18 , G06N7/02
CPC分类号： G01R31/3606 , G01R31/3693
摘要： Anomaly prediction of battery parasitic load includes processing input data related to a state of charge for a battery and a durational factor utilizing a machine learning algorithm and generating a predicted start-up state of charge. Warnings are issued if the predicted start-up state of charge drops below a threshold level within an operational time.
摘要翻译：电池寄生负载的异常预测包括利用机器学习算法处理与电池的充电状态有关的输入数据和持续时间因子，并产生预测的充电状态。如果预计的启动状态在运行时间内下降到阈值以下，则发出警告。

3. 发明授权

US07292960B1 Method for characterization, detection and prediction for target events 有权
标题翻译：用于目标事件的表征，检测和预测的方法
公开(公告)号：US07292960B1
公开(公告)日：2007-11-06
申请号：US11427825
申请日：2006-06-30
申请人： Narayan Srinivasa , Qin Jiang , Leandro G. Barajas
发明人： Narayan Srinivasa , Qin Jiang , Leandro G. Barajas
IPC分类号： G06F17/40 , G06F17/00 , G06F19/00
CPC分类号： G06Q50/22 , G06Q10/06
摘要： A method for characterizing, detecting and predicting an event of interest, a target event, based on temporal patterns useful for predicting a probable occurrence of the target event is disclosed. Measurable events and their features are defined and quantized into event classes. Temporal series of the event classes are analyzed, and preliminary prediction rules established by analyzing temporal patterns of the event classes that precede an occurrence of the target event using a sliding time window. The quality of the preliminary prediction rules is evaluated and parameters thereof are optimized by using a defined fitness function, thereby defining finalized prediction rules. The finalized prediction rules are then made available for application on temporal series of the event classes to forecast a probable occurrence of the target event.
摘要翻译：公开了一种用于表征，检测和预测感兴趣事件的方法，基于用于预测目标事件的可能出现的时间模式的目标事件。可测量事件及其特征被定义和量化为事件类。分析事件类的时间序列，并通过使用滑动时间窗口分析在事件发生之前的事件类的时间模式来建立初步预测规则。评估初步预测规则的质量，并通过使用定义的适应度函数来优化其参数，从而定义最终预测规则。然后，最终确定的预测规则可用于事件类别的时间序列上的应用以预测目标事件的可能发生。

4. 发明申请

US20110099136A1 METHOD AND SYSTEM FOR CONCURRENT EVENT FORECASTING 有权
标题翻译：同步事件预测的方法和系统
公开(公告)号：US20110099136A1
公开(公告)日：2011-04-28
申请号：US12604606
申请日：2009-10-23
申请人： Leandro G. Barajas , Youngkwan Cho , Narayan Srinivasa
发明人： Leandro G. Barajas , Youngkwan Cho , Narayan Srinivasa
IPC分类号： G06N3/12
CPC分类号： G06N3/02 , G06F17/18 , G06K9/00496 , G06K9/6251
摘要： A method and system for characterizing, detecting, and predicting or forecasting multiple target events from a past history of these events includes compressing temporal data streams into self-organizing map (SOM) clusters, and determining trajectories of the temporal streams via the clusters to predict the multiple target events. The system includes an evolutionary multi-objective optimization (EMO) module for processing the temporal data streams, which are obtained from a plurality of heterogeneous domains; a SOM module for characterizing the temporal data streams into self-organizing map clusters; and a target event prediction (TEP) module for generating prediction models of the map clusters. The SOM module employs a vector quantization method that places a set of vectors on a low-dimensional grid in an ordered fashion. The prediction models each include trajectories of the temporal data streams, and the system predicts the multiple target events using the trajectories.
摘要翻译：用于从这些事件的过去历史表征，检测和预测或预测多个目标事件的方法和系统包括将时间数据流压缩为自组织映射（SOM）集群，以及通过集群确定时间流的轨迹以预测多个目标事件。该系统包括用于处理从多个异构域获得的时间数据流的进化多目标优化（EMO）模块; 用于将时间数据流表征为自组织映射簇的SOM模块; 以及用于生成地图簇的预测模型的目标事件预测（TEP）模块。 SOM模块采用矢量量化方法，其以有序的方式将一组向量放置在低维度网格上。预测模型各自包括时间数据流的轨迹，并且系统使用轨迹来预测多个目标事件。

5. 发明授权

US07526461B2 System and method for temporal data mining 有权
标题翻译：时间数据挖掘的系统和方法
公开(公告)号：US07526461B2
公开(公告)日：2009-04-28
申请号：US11199698
申请日：2005-08-09
申请人： Narayan Srinivasa , Leandro G. Barajas
发明人： Narayan Srinivasa , Leandro G. Barajas
IPC分类号： G06E1/00 , G06E3/00 , G06G7/00
CPC分类号： G06F17/30539 , G06F17/30551 , Y10S707/99943
摘要： A system, method, and apparatus for signal characterization, estimation, and prediction comprising an integrated search algorithm that cooperatively optimizes several data mining sub-tasks, the integrated search algorithm including a machine learning model, and the method comprising processing the data for data embedding, data embedding the processed data for searching for patterns, extracting time and frequency patterns, and training the model to represent learned patterns for signal characterization, estimation, and prediction.
摘要翻译：一种用于信号表征，估计和预测的系统，方法和装置，包括协同优化若干数据挖掘子任务的集成搜索算法，所述综合搜索算法包括机器学习模型，并且所述方法包括处理用于数据嵌入的数据，嵌入用于搜索模式的处理数据的数据，提取时间和频率模式，以及训练模型以表示用于信号表征，估计和预测的学习模式。

6. 发明授权

US08577815B2 Method and system for concurrent event forecasting 有权
标题翻译：并发事件预测方法与系统
公开(公告)号：US08577815B2
公开(公告)日：2013-11-05
申请号：US12604606
申请日：2009-10-23
申请人： Leandro G. Barajas , Youngkwan Cho , Narayan Srinivasa
发明人： Leandro G. Barajas , Youngkwan Cho , Narayan Srinivasa
IPC分类号： G06F15/18 , G06N3/00 , G06N3/12
CPC分类号： G06N3/02 , G06F17/18 , G06K9/00496 , G06K9/6251
摘要： A method and system for characterizing, detecting, and predicting or forecasting multiple target events from a past history of these events includes compressing temporal data streams into self-organizing map (SOM) clusters, and determining trajectories of the temporal streams via the clusters to predict the multiple target events. The system includes an evolutionary multi-objective optimization (EMO) module for processing the temporal data streams, which are obtained from a plurality of heterogeneous domains; a SOM module for characterizing the temporal data streams into self-organizing map clusters; and a target event prediction (TEP) module for generating prediction models of the map clusters. The SOM module employs a vector quantization method that places a set of vectors on a low-dimensional grid in an ordered fashion. The prediction models each include trajectories of the temporal data streams, and the system predicts the multiple target events using the trajectories.
摘要翻译：用于从这些事件的过去历史表征，检测和预测或预测多个目标事件的方法和系统包括将时间数据流压缩为自组织映射（SOM）集群，以及通过集群确定时间流的轨迹以预测多个目标事件。该系统包括用于处理从多个异构域获得的时间数据流的进化多目标优化（EMO）模块; 用于将时间数据流表征为自组织映射簇的SOM模块; 以及用于生成地图簇的预测模型的目标事件预测（TEP）模块。 SOM模块采用矢量量化方法，其以有序的方式将一组向量放置在低维度网格上。预测模型各自包括时间数据流的轨迹，并且系统使用轨迹来预测多个目标事件。

7. 发明授权

US07715591B2 High-performance sensor fusion architecture 有权
标题翻译：高性能传感器融合架构
公开(公告)号：US07715591B2
公开(公告)日：2010-05-11
申请号：US10132875
申请日：2002-04-24
申请人： Yuri Owechko , Narayan Srinivasa , Swarup Medasani , Riccardo Boscolo
发明人： Yuri Owechko , Narayan Srinivasa , Swarup Medasani , Riccardo Boscolo
IPC分类号： G06K9/00
CPC分类号： G06K9/6292 , G06K9/00201 , G06K9/00369 , G06K9/00832 , G06K9/209 , G06K9/522 , G06T7/97 , G06T2207/10021 , G06T2207/20064 , G06T2207/30268 , H04N2013/0081 , H04N2013/0092
摘要： A vision-based system for automatically detecting the type of object within a specified area, such as the type of occupant within a vehicle is presented. The type of occupant can then be used to determine whether an airbag deployment system should be enabled or not. The system extracts different features, including wavelet features and/or a disparity map from images captured by image sensors. These features are then processed by classification algorithms to produce class confidences for various occupant types. The occupant class confidences are fused and processed to determine occupant type. In a preferred embodiment, image features from image edges, wavelet features, and disparity are used. Various classification algorithms may be implemented to classify the object. Use of the disparity map and/or wavelet features provides greater computational efficiency.
摘要翻译：提出了一种基于视觉的系统，用于自动检测指定区域内物体的类型，例如车辆内乘客的类型。然后可以使用乘客的类型来确定是否应启用安全气囊展开系统。该系统从图像传感器捕获的图像中提取不同的特征，包括小波特征和/或视差图。然后通过分类算法对这些特征进行处理，以便为各种乘客类型生成类别信息。乘员班信心被融合和处理以确定乘客类型。在优选实施例中，使用来自图像边缘的图像特征，小波特征和视差。可以实现各种分类算法来对对象进行分类。使用视差图和/或小波特征提供更大的计算效率。

8. 发明申请

US20060129843A1 Method and apparatus for electronically extracting application specific multidimensional information from documents selected from a set of documents electronically extracted from a library of electronically searchable documents 审中-公开
公开(公告)号：US20060129843A1
公开(公告)日：2006-06-15
申请号：US11198798
申请日：2005-08-05
申请人： Narayan Srinivasa , Swarup Medasani , Yuri Owechko , Deepak Khosla
发明人： Narayan Srinivasa , Swarup Medasani , Yuri Owechko , Deepak Khosla
IPC分类号： G06F12/14
CPC分类号： G06F16/335 , Y10S707/99933 , Y10S707/99935 , Y10S707/99942 , Y10S707/99943 , Y10S707/99945
摘要： An apparatus and method is disclosed for providing application specific multi-dimensional information to an application running on a user computing device, wherein at least one dimension of the information is a category, from a plurality of member documents electronically extracted from a library of electronically searchable documents, which may comprise an application specific multidimensional information extractor adapted to extract occurrences of prospective representations of dimensions of application specific multidimensional information from the member documents, and to extract occurrences of non-application specific multidimensional information from the member documents; and, an encoder adapted to encode the occurrences of prospective dimensions of application specific multidimensional information and non-application specific multidimensional information contained in member documents according to a dimension specific coded representation of each dimension of application specific multidimensional information and a non-application specific coded representation of each non-application specific multidimensional information element. The apparatus and method may further comprise a member document identifier adapted to determine whether a member document contains coded formatting, and if not, whether the member document is a dense document, and if not, for rejecting the document from further processing, and the coded formatting may comprise network markup language coding. The apparatus and method may further comprise an application specific multidimensional information verification unit adapted verify the extraction of application specific multi-dimensional information from the member documents, and may further comprise a database for storing the application specific multi-dimensional information adapted to provide an application running on a user computing device access to the application specific multidimensional information. The application specific multidimensional information may be scheduled events having the dimensions of time, location and event identity, and the application running on the user computer can be an electronic calendar or other similar scheduling software program.

9. 发明授权

US07561732B1 Method and apparatus for three-dimensional shape estimation using constrained disparity propagation 有权
标题翻译：使用约束差异传播的三维形状估计的方法和装置
公开(公告)号：US07561732B1
公开(公告)日：2009-07-14
申请号：US11051592
申请日：2005-02-04
申请人： Yuri Owechko , Narayan Srinivasa , Swarup Medasani , Riccardo Boscolo
发明人： Yuri Owechko , Narayan Srinivasa , Swarup Medasani , Riccardo Boscolo
IPC分类号： G06K9/00
CPC分类号： G06T7/593 , G06T2207/10012 , G06T2207/20076
摘要： A method, an apparatus, and a computer program product for three-dimensional shape estimation using constrained disparity propagation are presented. An act of receiving a stereoscopic pair of images of an area occupied by at least one object is performed. Next, pattern regions and non-pattern regions are detected in the images. An initial estimate of śpatial disparities between the pattern regions in the images is generated. The initial estimate is used to generate a subsequent estimate of the spatial disparities between the non-pattern regions. The subsequent estimate is used to generate further subsequent estimates of the spatial disparities using the disparity constraints until there is no change between the results of subsequent iterations, generating a final estimate of the spatial disparities. A disparity map of the area occupied by at least one object is generated from the final estimate of the three-dimensional shape.
摘要翻译：提出了一种使用受限视差传播进行三维形状估计的方法，装置和计算机程序产品。执行接收由至少一个对象占据的区域的立体图像对的动作。接下来，在图像中检测图案区域和非图案区域。生成图像中的图案区域之间的空间差异的初始估计。初始估计用于产生非图案区域之间的空间差异的随后估计。随后的估计用于使用差异约束来生成空间差异的进一步后续估计，直到后续迭代的结果之间没有变化，产生空间差异的最终估计。从三维形状的最终估计中生成由至少一个对象占据的区域的视差图。

10. 发明授权

US07587064B2 Active learning system for object fingerprinting 有权
标题翻译：主体学习系统用于对象指纹识别
公开(公告)号：US07587064B2
公开(公告)日：2009-09-08
申请号：US11051860
申请日：2005-02-03
申请人： Yuri Owechko , Swarup Medasani , Narayan Srinivasa
发明人： Yuri Owechko , Swarup Medasani , Narayan Srinivasa
IPC分类号： G06K9/00 , G06K9/62
CPC分类号： G06K9/469 , G06K9/6254
摘要： Described is an active learning system for fingerprinting an object identified in an image frame. The active learning system comprises a flow-based object segmentation module for segmenting a potential object candidate from a video sequence, a fixed-basis function decomposition module using Haar wavelets to extract a relevant feature set from the potential object candidate, a static classifier for initial classification of the potential object candidate, an incremental learning module for predicting a general class of the potential object candidate, an oriented localized filter module to extract features from the potential object candidate, and a learning-feature graph-fingerprinting module configured to receive the features and build a fingerprint of the object for tracking the object.
摘要翻译：描述了一种用于对在图像帧中识别的对象进行指纹识别的主动学习系统。主动学习系统包括：基于流的对象分割模块，用于从视频序列分割潜在对象候选者;使用哈尔小波的固定基函数分解模块从潜在对象候选者提取相关特征集;初始化的静态分类器潜在对象候选者的分类，用于预测潜在候选对象的一般类别的增量学习模块，从潜在对象候选者提取特征的定向局部化过滤器模块，以及被配置为接收特征的学习特征图指纹模块并构建对象的指纹以跟踪对象。

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