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    • 3. 发明申请
    • MICROFLUIDIC VALVE MODULE AND SYSTEM FOR IMPLEMENTATION
    • 微流控阀模块和实现系统
    • US20140346378A1
    • 2014-11-27
    • US13977480
    • 2011-12-21
    • Chin Hock KuaZhenfeng WangWei FanCong Zhi Leon ChanZhiping Wang
    • Chin Hock KuaZhenfeng WangWei FanCong Zhi Leon ChanZhiping Wang
    • F16K99/00
    • F16K99/0015F16K99/0061F16K2099/0084
    • An improved microfluidic system with an improved microfluidic valve module is disclosed. The microfluidic system includes a microfluidic chip and one or more valve modules. The microfluidic chip has microfluidic channels and one or more cavities formed in the chip, each of the one or more cavities designed to receive one of the one or more valve modules. Each of the one or more valve modules includes a first layer, a control layer and one or more second layers. The first layer includes a deformable material. The control layer has a microfluidic control chamber formed in a portion of it. The control layer is also located adjoining the first layer and the deformable material of the first layer forms a deformable surface of the control chamber. The one or more second layers include an input microfluidic channel and an output microfluidic channel. The input microfluidic channel and the output microfluidic channel are fluidically coupled to the microfluidic control chamber, and fluid flow through the input microfluidic channel, the microfluidic control chamber and the output microfluidic channel is controlled in response to a force deforming the deformable material of the first layer at least a predetermined amount.
    • 公开了具有改进的微流体阀模块的改进的微流体系统。 微流体系统包括微流体芯片和一个或多个阀模块。 微流体芯片具有微流体通道和在芯片中形成的一个或多个空腔,所述一个或多个空腔中的每一个被设计成容纳一个或多个阀模块中的一个。 一个或多个阀模块中的每一个包括第一层,控制层和一个或多个第二层。 第一层包括可变形材料。 控制层具有形成在其一部分中的微流控制室。 控制层也邻接第一层并且第一层的可变形材料形成控制室的可变形表面。 一个或多个第二层包括输入微流体通道和输出微流体通道。 输入微流体通道和输出微流体通道流体耦合到微流控制室,并且通过输入微流体通道,微流控制室和输出微流体通道的流体流动被响应于使第一 层至少预定量。
    • 6. 发明授权
    • System and method for scalable cost-sensitive learning
    • 可扩展成本敏感学习的系统和方法
    • US07904397B2
    • 2011-03-08
    • US12690502
    • 2010-01-20
    • Wei FanHaixun WangPhilip S. Yu
    • Wei FanHaixun WangPhilip S. Yu
    • G06F15/18G06N3/00G06N3/12
    • G06N99/005
    • A method (and structure) for processing an inductive learning model for a dataset of examples, includes dividing the dataset of examples into a plurality of subsets of data and generating, using a processor on a computer, a learning model using examples of a first subset of data of the plurality of subsets of data. The learning model being generated for the first subset comprises an initial stage of an evolving aggregate learning model (ensemble model) for an entirety of the dataset, the ensemble model thereby providing an evolving estimated learning model for the entirety of the dataset if all the subsets were to be processed. The generating of the learning model using data from a subset includes calculating a value for at least one parameter that provides an objective indication of an adequacy of a current stage of the ensemble model.
    • 一种用于处理实例的数据集的感应学习模型的方法(和结构),包括将示例的数据集划分成多个数据子集,并使用计算机上的处理器生成使用第一子集的示例的学习模型 的多个数据子集的数据。 为第一子集生成的学习模型包括用于整个数据集的演进聚合学习模型(集合模型)的初始阶段,从而为整个数据集提供演进的估计学习模型,如果所有子集 被处理。 使用来自子集的数据生成学习模型包括计算至少一个参数的值,所述参数提供对所述集合模型的当前阶段的充分性的客观指示。
    • 7. 发明授权
    • System and method for sequence-based subspace pattern clustering
    • 基于序列的子空间模式聚类的系统和方法
    • US07565346B2
    • 2009-07-21
    • US10858541
    • 2004-05-31
    • Wei FanHaixun WangPhilip S. Yu
    • Wei FanHaixun WangPhilip S. Yu
    • G06F17/30
    • G06K9/6215Y10S707/99936
    • Unlike traditional clustering methods that focus on grouping objects with similar values on a set of dimensions, clustering by pattern similarity finds objects that exhibit a coherent pattern of rise and fall in subspaces. Pattern-based clustering extends the concept of traditional clustering and benefits a wide range of applications, including e-Commerce target marketing, bioinformatics (large scale scientific data analysis), and automatic computing (web usage analysis), etc. However, state-of-the-art pattern-based clustering methods (e.g., the pCluster algorithm) can only handle datasets of thousands of records, which makes them inappropriate for many real-life applications. Furthermore, besides the huge data volume, many data sets are also characterized by their sequentiality, for instance, customer purchase records and network event logs are usually modeled as data sequences. Hence, it becomes important to enable pattern-based clustering methods i) to handle large datasets, and ii) to discover pattern similarity embedded in data sequences. There is presented herein a novel method that offers this capability.
    • 与传统的集群方法不同,传统的集群方法集中在对一组维度上具有类似值的对象进行分组,通过模式相似性进行聚类可以找到在子空间中呈现一致的上升和下降模式的对象。 基于模式的群集扩展了传统群集的概念,受益于广泛的应用,包括电子商务目标营销,生物信息学(大规模科学数据分析)和自动计算(Web使用分析)等。然而,状态 基于图案的聚类方法(例如,pCluster算法)只能处理数千条记录的数据集,这使得它们不适合许多现实生活中的应用。 此外,除了巨大的数据量之外,许多数据集的特征还在于它们的顺序性,例如,客户购买记录和网络事件日志通常被建模为数据序列。 因此,重要的是启用基于图案的聚类方法i)处理大数据集,以及ii)发现嵌入在数据序列中的模式相似性。 这里提供了一种提供这种能力的新颖方法。