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    • 142. 发明申请
    • Depth-First Search For Target Value Problems
    • 深度优先搜索目标值问题
    • US20110004581A1
    • 2011-01-06
    • US12497326
    • 2009-07-02
    • Tim SchmidtRong ZhouLukas D. KuhnRobert PriceJohan de Kleer
    • Tim SchmidtRong ZhouLukas D. KuhnRobert PriceJohan de Kleer
    • G06N7/04G06F17/30G06N7/02
    • G06F17/11
    • A method for determining a target path for a model-based control system. The model-based control system includes a directed acyclic graph, where the directed acyclic graph includes a plurality of vertices interconnected by a plurality of edges. The method includes the steps of performing a depth-first search of the directed acyclic graph for the target path. The depth-first search is operative to return an explicit solution or an implicit solution, wherein the implicit solution is determined using a heuristic. The method further includes determining if the depth-first search returned an explicit solution or an implicit solution, and if the depth-first search returned an implicit solution, constructing the target path from the implicit solution. The method may further include constructing a pattern database.
    • 一种用于确定基于模型的控制系统的目标路径的方法。 基于模型的控制系统包括有向非循环图,其中有向非循环图包括由多个边缘互连的多个顶点。 该方法包括以下步骤:针对目标路径执行有向无环图的深度优先搜索。 深度优先搜索可操作以返回显式解或隐式解,其中使用启发式确定隐式解。 该方法还包括确定深度优先搜索是否返回显式解或隐式解,以及如果深度优先搜索返回隐式解,则从隐式解构成目标路径。 该方法还可以包括构造模式数据库。
    • 144. 发明申请
    • SPLIT VARIATIONAL INFERENCE
    • 分散变化影响
    • US20100318490A1
    • 2010-12-16
    • US12481802
    • 2009-06-10
    • Guillaume M. BouchardOnno Zoeter
    • Guillaume M. BouchardOnno Zoeter
    • G06N7/02
    • G06F17/10G06K9/6221
    • A method comprises: partitioning a region of interest into a plurality of soft bin regions that span the region of interest; estimating an integral over each soft bin region of a function defined over the region of interest; and outputting a value equal to or derived from the sum of the estimated integrals over the soft bin regions spanning the region of interest. The method may further comprise: integrating a Bayesian theorem function using the partitioning, estimating, and outputting operations, and classifying an object to be classified using a classifier trained using the Bayesian machine learning. The method may further comprise performing optimal control by iteratively minimizing a controlled system cost function to determine optimized control inputs using the partitioning, estimating, and outputting with the function equal to the controlled system cost function having the selected control inputs, and controlling the controlled system using the optimized control inputs.
    • 一种方法包括:将感兴趣区域划分成跨越感兴趣区域的多个软仓区域; 估计在感兴趣区域上定义的函数的每个软仓区域上的积分; 并且在跨越感兴趣区域的软仓区域上输出等于或从所估计的积分的总和导出的值。 该方法还可以包括:使用分区,估计和输出操作来整合贝叶斯定理函数,并且使用使用贝叶斯机器学习训练的分类器对要分类的对象进行分类。 该方法还可以包括通过迭代地最小化受控系统成本函数来执行最优控制,以使用等于具有所选择的控制输入的受控系统成本函数的功能进行分区,估计和输出来确定优化的控制输入,并且控制受控系统 使用优化的控制输入。
    • 146. 发明申请
    • Generating and determining bicycle configurations conforming to constraints
    • 生成并确定符合约束条件的自行车配置
    • US20100306160A1
    • 2010-12-02
    • US12474646
    • 2009-05-29
    • Clifford Simms
    • Clifford Simms
    • G06N7/02
    • G06Q30/0603
    • Systems and methods are described for determining a subset of conforming descriptions of a set of descriptions of bicycle configurations, which are combinations of candidate components, such as frames, forks, stems, handlebars, seat posts, and saddles. For determining whether a candidate description conforms, (1) a set of candidate components with a physical specification of each candidate component is accessed, (2) at least one biomechanical constraint is input, and (3) optionally a non-biomechanical constraint is input, such as weight, material, or price. An embodiment may generate a biomechanical constraint from a physical measurement taken from a particular bicycle and/or from a physical measurement taken from a particular cyclist.
    • 描述了用于确定自行车配置的一组描述的一致的描述的子集的系统和方法,所述自行车配置是候选组件(例如框架,叉,杆,车把,座椅柱和鞍座)的组合。 为了确定候选描述是否符合,(1)访问具有每个候选部件的物理规格的一组候选部件,(2)输入至少一个生物力学约束,并且(3)可选地输入非生物力学约束 ,如重量,材料或价格。 实施例可以从从特定自行车获得的物理测量和/或从特定骑自行车者进行的物理测量产生生物力学约束。
    • 148. 发明申请
    • INVENTORY MANAGEMENT SYSTEM IN A PRINT- PRODUCTION ENVIRONMENT
    • 印刷生产环境中的库存管理系统
    • US20100268572A1
    • 2010-10-21
    • US12425545
    • 2009-04-17
    • John C. HandleyYasin Alan
    • John C. HandleyYasin Alan
    • G06Q10/00G06N5/02G06N7/02G06Q50/00
    • G06Q10/00G06Q10/087
    • An inventory management system for forecasting demand in a print production environment may include a computing device and a computer-readable storage medium in communication with the computing device. The computer-readable storage medium may include programming instructions for updating a predictive model with intervention information comprising an anticipated demand value and a confidence value associated with the anticipated demand value. The predictive model may be associated with a demand distribution of a print-related service. The computer-readable storage medium may include programming instructions for generating a demand forecast associated with the print-related service by using the updated predictive model, using the generated demand forecast to compare a current inventory level associated with the print-related service to an anticipated inventory level associated with the demand forecast of the print-related service, and ordering additional inventory in response to the current inventory level being less than the anticipated inventory level.
    • 用于在打印生产环境中预测需求的库存管理系统可以包括与计算设备通信的计算设备和计算机可读存储介质。 计算机可读存储介质可以包括用于使用包括预期需求值和与预期需求值相关联的置信度的干预信息来更新预测模型的程序指令。 预测模型可以与打印相关服务的需求分布相关联。 计算机可读存储介质可以包括用于通过使用更新的预测模型来生成与打印相关服务相关联的需求预测的编程指令,使用所生成的需求预测来将与打印相关服务相关联的当前库存水平与预期的 与打印相关服务的需求预测相关联的库存水平,以及响应于当前库存水平低于预期库存水平的订单额外库存。