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公开(公告)号：US20110099136A1
公开(公告)日：2011-04-28
申请号：US12604606
申请日：2009-10-23
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。
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公开(公告)号：US08577815B2
公开(公告)日：2013-11-05
申请号：US12604606
申请日：2009-10-23
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。
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3.发明申请SPIKING DYNAMICAL NEURAL NETWORK FOR PARALLEL PREDICTION OF MULTIPLE TEMPORAL EVENTS 审中-公开SPIKING动态神经网络并行预测多个时间事件公开(公告)号：US20100179935A1
公开(公告)日：2010-07-15
申请号：US12353031
申请日：2009-01-13
CPC分类号： G06N3/049
摘要： A system and method for determining events in a system or process, such as predicting fault events. The method includes providing data from the process, pre-processing data and converting the data to one or more temporal spike trains having spike amplitudes and a spike train length. The spike trains are provided to a dynamical neural network operating as a liquid state machine that includes a plurality of neurons that analyze the spike trains. The dynamical neural network is trained by known data to identify events in the spike train, where the dynamical neural network then analyzes new data to identify events. Signals from the dynamical neural network are then provided to a readout network that decodes the states and predicts the future events.
摘要翻译： 用于确定系统或过程中的事件的系统和方法，例如预测故障事件。 该方法包括从该过程提供数据，预处理数据并将该数据转换成具有尖峰幅度和尖峰序列长度的一个或多个时间尖峰序列。 尖峰火车被提供到作为液态状态机操作的动力学神经网络，其包括分析尖峰火车的多个神经元。 动态神经网络由已知数据进行训练，以识别尖峰训练中的事件，其中动力神经网络分析新数据以识别事件。 然后将来自动态神经网络的信号提供给解码状态并预测未来事件的读出网络。
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公开(公告)号：US07899761B2
公开(公告)日：2011-03-01
申请号：US11113962
申请日：2005-04-25
IPC分类号： G06F15/18
CPC分类号： G06K9/6297 , G05B23/0232 , G06K9/6219
摘要： Disclosed herein are a system and method for trend prediction of signals in a time series using a Markov model. The method includes receiving a plurality of data series and input parameters, where the input parameters include a time step parameter, preprocessing the plurality of data series according to the input parameters, to form binned and classified data series, and processing the binned and classified data series. The processing includes initializing a Markov model for trend prediction, and training the Markov model for trend prediction of the binned and classified data series to form a trained Markov model. The method further includes deploying the trained Markov model for trend prediction, including outputting trend predictions. The method develops an architecture for the Markov model from the data series and the input parameters, and disposes the Markov model, having the architecture, for trend prediction.
摘要翻译： 这里公开了使用马尔可夫模型的时间序列中的信号趋势预测的系统和方法。 该方法包括接收多个数据序列和输入参数，其中输入参数包括时间步长参数，根据输入参数对多个数据序列进行预处理，以形成分类和分类数据序列，并处理分类和分类数据 系列。 该处理包括初始化用于趋势预测的马尔科夫模型，并训练马尔可夫模型用于仓位和分类数据序列的趋势预测，形成训练马尔可夫模型。 该方法还包括部署用于趋势预测的经过训练的马尔可夫模型，包括输出趋势预测。 该方法从数据系列和输入参数开发了Markov模型的架构，并将具有架构的马尔科夫模型用于趋势预测。
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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
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。
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6.发明授权公开(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
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。
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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
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。
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公开(公告)号：US07526461B2
公开(公告)日：2009-04-28
申请号：US11199698
申请日：2005-08-09
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。
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9.发明授权Method and system for training a robot using human-assisted task demonstration 有权使用人为辅助任务演示训练机器人的方法和系统公开(公告)号：US08843236B2
公开(公告)日：2014-09-23
申请号：US13420677
申请日：2012-03-15
IPC分类号： B25J13/08
CPC分类号： B25J9/1664 , G05B2219/40512
摘要： A method for training a robot to execute a robotic task in a work environment includes moving the robot across its configuration space through multiple states of the task and recording motor schema describing a sequence of behavior of the robot. Sensory data describing performance and state values of the robot is recorded while moving the robot. The method includes detecting perceptual features of objects located in the environment, assigning virtual deictic markers to the detected perceptual features, and using the assigned markers and the recorded motor schema to subsequently control the robot in an automated execution of another robotic task. Markers may be combined to produce a generalized marker. A system includes the robot, a sensor array for detecting the performance and state values, a perceptual sensor for imaging objects in the environment, and an electronic control unit that executes the present method.
摘要翻译： 用于训练机器人以在工作环境中执行机器人任务的方法包括：通过所述任务的多个状态和描述机器人行为序列的记录电机模式来移动所述机器人在其配置空间。 在移动机器人时记录描述机器人的性能和状态值的感官数据。 该方法包括检测位于环境中的对象的感知特征，将虚拟指示标记分配给所检测到的感知特征，以及使用所分配的标记和所记录的运动模式来随后在另一机器人任务的自动执行中控制机器人。 标记可以组合以产生广义标记。 系统包括机器人，用于检测性能和状态值的传感器阵列，用于在环境中成像对象的感知传感器，以及执行本方法的电子控制单元。
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公开(公告)号：US07999664B2
公开(公告)日：2011-08-16
申请号：US12333422
申请日：2008-12-12
申请人： Leandro G. Barajas
发明人： Leandro G. Barajas
IPC分类号： B60Q1/00
CPC分类号： B60R25/00
摘要： A method is provided for determining when to provide a refueling notification to a driver of a vehicle. A refueling behavior is determined for refueling the vehicle. The refueling behavior is associated at least in part to an amount of fuel customarily remaining in the vehicle when the vehicle is customarily refueled. A remaining amount of fuel in the vehicle and a fuel economy of the vehicle are determined. A distance the vehicle will travel to a next driving destination is estimated. An amount of fuel that will be used to travel to the next driving destination is estimated based on the estimated distance the vehicle will travel to the next driving destination and the fuel economy. A determination is made whether the amount of fuel that will be remaining in the vehicle after the vehicle travels to the next driving destination is less than the amount of a fuel customarily remaining in the vehicle when the vehicle is refueled. A refueling notification is actuated to a driver of a vehicle in response to the determination that the amount of fuel that will be remaining in the vehicle after the vehicle travels to the next driving destination will be less than the amount of fuel customarily remaining in the vehicle when the vehicle is refueled.
摘要翻译： 提供了一种用于确定何时向车辆的驾驶员提供加油通知的方法。 确定加油车辆的加油行为。 加油行为至少部分地与当车辆通常加油时通常保留在车辆中的燃料量相关联。 确定车辆中的剩余燃料量和车辆的燃料经济性。 估计车辆行驶到下一个行驶目的地的距离。 基于车辆将行驶到下一个驾驶目的地的估计距离和燃料经济性来估计用于行驶到下一个驾驶目的地的燃料量。 确定在车辆行驶到下一个驾驶目的地之后车辆中剩余的燃料量是否小于当车辆加油时通常保留在车辆中的燃料的量。 响应于车辆行驶到下一个驾驶目的地后剩余在车辆中的燃料量将小于车辆中常用的燃料量，确定车辆驾驶员的加油通知将被启动 当车辆加油时。
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