会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 6. 发明申请
    • THE TECHNICAL FEATURES MENTIONED IN THE ABSTRACT DO NOT INCLUDE A REFERENCE SIGN BETWEEN PARENTHESES
    • 摘要中提到的技术特征不包括父母之间的参考标志
    • WO2003092411A1
    • 2003-11-13
    • PCT/US2003/013242
    • 2003-04-29
    • FLORIDA DEPARTMENT OF CITRUS
    • DAVIS, Craig, L.THOMAS, MarkPAO, Shi-Chiang
    • A23L1/212
    • A23N1/003
    • A method of separating juice vesicles from a citrus fruit includes the step of forming a plurality of generally circumferential scores between a stem end and a stylar end of a citrus fruit (103). Preferably each score extends at least through a flavedo of a peel of the fruit. Next the fruit is cut into a plurality of slices in a direction normal to a longitudinal axis defined by the stem end and the stylar end (105). The slices are then frozen (108-110), and an impulsive force is applied to the slices to form a plurality of fruit components (112). These fruit components comprise juice vesicles and other fruit components, such as the peel, connective membranes, and seeds. The juice vesicles are then mechanically separated from the other fruit components (113).
    • 从柑橘类水果分离果汁的方法包括在柑橘类水果(103)的茎端和样式末端之间形成多个大致圆周的分数的步骤。 优选地,每个分数至少延伸通过水果的果皮的flavedo。 接下来,将水果沿垂直于由杆端和样式端(105)限定的纵向轴线的方向切成多个切片。 然后将切片冷冻(108-110℃),并将冲击力施加到切片上以形成多个果实成分(112)。 这些水果成分包括果汁囊泡和其他水果成分,例如果皮,结缔膜和种子。 然后将果汁泡囊与其他水果成分(113)机械分离。
    • 8. 发明公开
    • System and method for identifying the geographic origin of a fresh commodity
    • 系统和方法,用于识别新鲜消耗品的地理起源
    • EP1008952A2
    • 2000-06-14
    • EP99309933.2
    • 1999-12-10
    • Florida Department of Citrus
    • Anderson, Kim A.Smith, BrianMagnuson, Bernadene
    • G06N3/04
    • G06N3/0454G06K9/62G06K2209/17
    • The detection method includes generating a plurality of neural network models. Each model has as a training set a data set from a plurality of samples of a commodity of known origins. Each sample has been analyzed for a plurality of elemental concentrations. Each neural network model is presented for classification a test data set from a plurality of samples of a commodity of unknown origins. As with the training set, the samples have been analyzed for the same plurality of elemental concentrations. Next a bootstrap aggregating strategy is employed to combine the results of the classifications for each sample in the test data set made by each neural network model. Finally, a determination is made from the bootstrap aggregating strategy as to a final classification of each sample in the test data set. This final classification is indicative of the geographical origin of the commodity. The system includes software for generating the neural network models and a software routine for performing the bootstrap aggregating strategy.
    • 该检测方法包括:产生的神经网络模型的复数。 每个模型具有作为训练从已知的起点的商品样品的多个设定的数据集。 每个样品已分析元素浓度的多元性。 每个神经网络模型提出了一种用于分类测试数据从未知起源的商品的样本的多个设定。 与训练集,样品已分析元素浓度的相同的多个。 接着自举聚集的策略被用于分类的结果结合起来,在由每个神经网络模型进行的测试数据集合中的每个样本。 最后,确定从引导聚集的策略,以在测试数据集合中的每个样品的最终分类制成。 这最后的分类是表示商品的地理来源。 该系统包括用于生成神经网络模型和进行引导聚集策略的软件程序软件。