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    • 3. 发明授权
    • Methods of integrating real and virtual world using virtual sensor/actuator
    • 使用虚拟传感器/执行器整合真实世界和虚拟世界的方法
    • US07996113B2
    • 2011-08-09
    • US12237944
    • 2008-09-25
    • Chengyin YuanFangming GuStephan R. BillerLeandro G. Barajas
    • Chengyin YuanFangming GuStephan R. BillerLeandro G. Barajas
    • G05B15/00
    • G05B19/41885G05B2219/31078G05B2219/39014G05B2219/40288Y02P90/083Y02P90/26
    • An integrated real and virtual manufacturing automation system that employs a programmable logic controller that controls part flow between a real machine in the real world part of the system and a virtual machine in the virtual world part of the system using virtually coupled sensors and actuators. A real world sensor senses the position of the real world machine and a real world actuator actuates the real world machine. Likewise, a virtual world sensor senses the position of the virtual world machine and a virtual world actuator actuates the virtual world machine. An interface device transfers signals between the virtual world part of the system and the real world part of the system, and an input/output device processes signals sent to the programmable logic controller and signals sent from the programmable logic controller.
    • 一个集成的真实和虚拟制造自动化系统,其使用可编程逻辑控制器来控制系统的真实世界中的真实机器之间的部分流程,以及使用虚拟耦合的传感器和执行器的虚拟世界中的虚拟机部分。 一个现实世界的传感器感知现实世界的机器的位置,一个现实的执行器致动现实世界的机器。 同样,虚拟世界传感器感测虚拟世界机器的位置,虚拟世界执行器致动虚拟世界机器。 接口设备在系统的虚拟世界部分和系统的真实世界部分之间传送信号,并且输入/输出设备处理发送到可编程逻辑控制器的信号和从可编程逻辑控制器发送的信号。
    • 5. 发明授权
    • Method and system for training a robot using human-assisted task demonstration
    • 使用人为辅助任务演示训练机器人的方法和系统
    • US08843236B2
    • 2014-09-23
    • US13420677
    • 2012-03-15
    • Leandro G. BarajasEric MartinsonDavid W. PaytonRyan M. Uhlenbrock
    • Leandro G. BarajasEric MartinsonDavid W. PaytonRyan M. Uhlenbrock
    • B25J13/08
    • B25J9/1664G05B2219/40512
    • 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.
    • 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。
    • 6. 发明授权
    • Method and system for concurrent event forecasting
    • 并发事件预测方法与系统
    • US08577815B2
    • 2013-11-05
    • US12604606
    • 2009-10-23
    • Leandro G. BarajasYoungkwan ChoNarayan Srinivasa
    • Leandro G. BarajasYoungkwan ChoNarayan Srinivasa
    • G06F15/18G06N3/00G06N3/12
    • G06N3/02G06F17/18G06K9/00496G06K9/6251
    • 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.
    • 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
    • Behavior-based low fuel warning system
    • 基于行为的低燃油预警系统
    • US07999664B2
    • 2011-08-16
    • US12333422
    • 2008-12-12
    • Leandro G. Barajas
    • Leandro G. Barajas
    • B60Q1/00
    • B60R25/00
    • 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.
    • 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。
    • 8. 发明授权
    • System and method for signal prediction
    • 信号预测系统和方法
    • US07899761B2
    • 2011-03-01
    • US11113962
    • 2005-04-25
    • Shubha KadambeLeandro G. BarajasYoungkwan ChoPulak Bandyopadhyay
    • Shubha KadambeLeandro G. BarajasYoungkwan ChoPulak Bandyopadhyay
    • G06F15/18
    • G06K9/6297G05B23/0232G06K9/6219
    • 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.
    • 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。
    • 10. 发明申请
    • METHOD AND SYSTEM FOR TRAINING A ROBOT USING HUMAN-ASSISTED TASK DEMONSTRATION
    • 使用人为辅助任务演示来训练机器人的方法和系统
    • US20130245824A1
    • 2013-09-19
    • US13420677
    • 2012-03-15
    • Leandro G. BarajasEric MartinsonDavid W. PaytonRyan M. Uhlenbrock
    • Leandro G. BarajasEric MartinsonDavid W. PaytonRyan M. Uhlenbrock
    • B25J13/08
    • B25J9/1664G05B2219/40512
    • 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.
    • 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。