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    • 1. 发明授权
    • Grid-based data clustering method
    • 基于网格的数据聚类方法
    • US08661040B2
    • 2014-02-25
    • US13468721
    • 2012-05-10
    • Cheng-Fa TsaiChun-Hao Chang
    • Cheng-Fa TsaiChun-Hao Chang
    • G06N5/00G06F17/30
    • G06F17/30598
    • A grid-based data clustering method performed by a computer system includes a setup step, a dividing step, a categorizing step and an expanding/clustering step. The setup step sets a grid quantity and a threshold value. The dividing step divides a space containing a data set having a plurality of data points into a two-dimensional matrix. The matrix has a plurality of grids G(i,j) comprising a plurality of target sequences and a plurality of non-target sequences interlaced with the plurality of target sequences. The indices “i” and “j” of each grid G(i,j) represents the coordinate thereof. The categorizing step determines whether each of the grids is valid based on the threshold value. The expanding/clustering step respectively retrieves each of the grids of the target sequences, performs an expansion operation on each of the grids retrieved and clusters the plurality grids G(i,j).
    • 由计算机系统执行的基于网格的数据聚类方法包括设置步骤,分割步骤,分类步骤和扩展/聚类步骤。 设置步骤设置网格数量和阈值。 分割步骤将包含具有多个数据点的数据集的空间划分成二维矩阵。 矩阵具有包含与多个目标序列隔行的多个目标序列和多个非目标序列的多个网格G(i,j)。 每个网格G(i,j)的索引“i”和“j”表示其坐标。 分类步骤基于阈值确定每个网格是否有效。 扩展/聚集步骤分别检索目标序列的每个网格,对所检索的每个网格执行扩展操作,并聚集多个网格G(i,j)。
    • 2. 发明申请
    • Data-Transmitting Method for Wireless Sensor Network
    • 无线传感器网络的数据传输方法
    • US20090046630A1
    • 2009-02-19
    • US12186583
    • 2008-08-06
    • Cheng-Fa TsaiShih-Yuan Chao
    • Cheng-Fa TsaiShih-Yuan Chao
    • H04Q7/00
    • H04W84/18H04W28/08H04W52/0219
    • A data-transmitting method for wireless sensor network comprises: constructing a wireless sensor network having a plurality of nodes for information sensing and a sink for quest raising and data collecting; clustering the nodes to form a plurality of groups, with one of the nodes in each group being identified as a kernel; identifying one of all the nodes as a summit dissemination node and the kernels in all the groups as first level dissemination nodes; and transmitting data between the quest-raising sink and one of the first level dissemination nodes or summit dissemination node to collect information sensed by a source that is one of the nodes.
    • 一种用于无线传感器网络的数据传输方法,包括:构建具有用于信息感测的多个节点的无线传感器网络和用于任务提升和数据采集的接收器; 将节点聚类以形成多个组,每个组中的一个节点被识别为内核; 将所有节点中的一个标识为峰顶传播节点,将所有组中的内核识别为第一级传播节点; 以及在所述寻求接收器和所述第一级传播节点或所述顶点传播节点之一之间传送数据,以收集由作为所述节点之一的源所感测的信息。
    • 3. 发明授权
    • Density-based data clustering method
    • 基于密度的数据聚类方法
    • US08429166B2
    • 2013-04-23
    • US13462460
    • 2012-05-02
    • Cheng-Fa TsaiTang-Wei Huang
    • Cheng-Fa TsaiTang-Wei Huang
    • G06F7/00G06F17/30G06F15/16
    • G06F17/30598
    • A density-based data clustering method executed by a computer system is disclosed. The method includes a setup step, a clustering step, an expansion step and a termination step. The setup step sets a radius and a threshold value. The clustering step defines a single cluster on a plurality of data points of a data set, and provides and adds a plurality of first boundary marks to a seed list as seeds. The expansion step expands the cluster from each seed of the seed list, and provides and adds at least one second boundary mark to the seed list as seeds. The termination step determines whether each of the data points is clustered, wherein the clustering step is re-performed if the determination is negative.
    • 公开了一种由计算机系统执行的基于密度的数据聚类方法。 该方法包括设置步骤,聚类步骤,扩展步骤和终止步骤。 设置步骤设置半径和阈值。 聚类步骤在数据集合的多个数据点上定义单个簇,并且将种子列表中的多个第一边界标记提供并添加到种子列表中。 扩展步骤从种子列表的每个种子扩展集群,并为种子提供并添加至少一个第二个边界标记到种子列表。 终止步骤确定每个数据点是否是聚类的,其中如果确定为负,则重新执行聚类步骤。
    • 4. 发明授权
    • Grid-based data clustering method
    • 基于网格的数据聚类方法
    • US08166035B2
    • 2012-04-24
    • US12652979
    • 2010-01-06
    • Cheng-Fa TsaiChien-Sheng Chiu
    • Cheng-Fa TsaiChien-Sheng Chiu
    • G06F17/30
    • G06F17/30705G06K9/6218
    • A grid-based data clustering method comprises: a parameter setting step, a partition step, a searching step, a seed-classifying step, an extension step, and a termination step. Through the above-mentioned steps, data in a data set are disposed in a plurality of grids, and the grids are classified into dense grids and uncrowded grids for a cluster to extend from one of the dense grid to gradually combine data in other dense grids nearby. Consequently, convenience in parameter setting, efficiency and accuracy in data clustering, and performance in noise filtering are achieved.
    • 基于网格的数据聚类方法包括:参数设置步骤,分区步骤,搜索步骤,种子分类步骤,扩展步骤和终止步骤。 通过上述步骤,将数据集中的数据设置在多个网格中,并且网格被分类为密集网格和未填充网格,用于从一个密集网格延伸到逐渐组合其他密集网格中的数据 附近。 因此,实现了参数设置的方便性,数据聚类的效率和精度以及噪声滤波中的性能。
    • 5. 发明授权
    • Grid-based data clustering method
    • 基于网格的数据聚类方法
    • US08666986B2
    • 2014-03-04
    • US13453408
    • 2012-04-23
    • Cheng-Fa TsaiYung-Ching Hu
    • Cheng-Fa TsaiYung-Ching Hu
    • G06N5/00G06F17/30
    • G06F17/30598
    • A grid-based data clustering method is disclosed. A parameter setting step sets a grid parameter and a threshold parameter. A diving step divides a space having a plurality of data points according to the grid parameter. A categorizing step determines whether a number of the data points contained in each grid is larger than or equal to a value of the threshold parameter. The grid is categorized as a valid grid if the number of the data points contained therein is larger than or equal to the value of the threshold parameter, and the grid is categorized as an invalid grid if the number of the data points contained therein is smaller than the value of the threshold parameter. The clustering step retrieves one of the valid grids. If the retrieved valid grid is not yet clustered, the clustering step performs horizontal and vertical searching/merging operations on the valid grid.
    • 公开了一种基于网格的数据聚类方法。 参数设置步骤设置网格参数和阈值参数。 潜水步骤根据网格参数划分具有多个数据点的空间。 分类步骤确定每个网格中包含的数据点的数量是否大于或等于阈值参数的值。 如果其中包含的数据点的数量大于或等于阈值参数的值,则网格被分类为有效网格,并且如果其中包含的数据点的数量较小,则网格被分类为无效网格 超过阈值参数的值。 聚类步骤检索一个有效的网格。 如果检索到的有效网格尚未聚类,则聚类步骤对有效网格执行水平和垂直搜索/合并操作。
    • 7. 发明申请
    • Detecting Method Over Network Intrusion
    • 网络入侵检测方法
    • US20080306715A1
    • 2008-12-11
    • US12021342
    • 2008-01-29
    • Cheng-Fa TsaiChia-Chen Yen
    • Cheng-Fa TsaiChia-Chen Yen
    • G06F17/10G06F21/00
    • H04L63/1425G06F21/55
    • A detecting method over network intrusion comprises: selecting a plurality of features contained within plural statistical data by a data-transforming module; normalizing a plurality of feature values of the selected features into the same scale to obtain a plurality of normalized feature data; creating at least one feature model by a data clustering technique incorporated with density-based and grid-based algorithms through a model-creating module; evaluating the at least one feature model through a model-identifying module to select a detecting model; and detecting whether a new packet datum belongs to an intrusion instance or not by a detecting module.
    • 网络入侵检测方法包括:通过数据转换模块选择多个统计数据中包含的多个特征; 将所选择的特征的多个特征值归一化为相同的比例以获得多个归一化特征数据; 通过模型创建模块通过结合基于密度和基于网格的算法的数据聚类技术创建至少一个特征模型; 通过模型识别模块来评估所述至少一个特征模型以选择检测模型; 以及检测模块检测新的分组数据是否属于入侵实例。
    • 8. 发明授权
    • Codebook generating method
    • 码本生成方法
    • US08407168B2
    • 2013-03-26
    • US12828617
    • 2010-07-01
    • Cheng-Fa TsaiYu-Chun Lin
    • Cheng-Fa TsaiYu-Chun Lin
    • G06F15/18G06F13/12
    • G06N3/08
    • A codebook generating method includes a dividing and transforming step dividing an original image into original blocks and transforming the original blocks into original vectors; a dividing step grouping the original vectors to obtain centroids; a first layer neuron training step selecting a portion of the centroids as first-level neurons; a grouping step assigning each of the original vectors to a closest first-level neuron so as to obtain groups; a second layer neuron assigning step assigning a number of second-level neurons in each of the groups, and selecting a portion of the original vectors in each of the groups as the second-level neurons; and a second layer neuron training step defining the original vectors in each of the groups as samples, training the second-level neurons in each of the groups to obtain final neurons, and storing vectors corresponding to the final neurons in a codebook.
    • 码本生成方法包括将原始图像分割成原始块并将原始块变换成原始向量的分割和变换步骤; 将原始矢量分组以获得质心的分割步骤; 选择一部分质心作为一级神经元的第一层神经元训练步骤; 分配步骤,将每个原始矢量分配给最接近的一级神经元,以便获得组; 第二层神经元分配步骤,在每个组中分配多个第二级神经元,并且将每组中的原始向量的一部分选择为第二级神经元; 以及第二层神经元训练步骤,将每组中的原始矢量定义为样本,训练每组中的第二级神经元以获得最终神经元,并将对应于最终神经元的载体存储在码本中。
    • 10. 发明授权
    • Detecting method for network intrusion
    • 网络入侵检测方法
    • US08037533B2
    • 2011-10-11
    • US12021342
    • 2008-01-29
    • Cheng-Fa TsaiChia-Chen Yen
    • Cheng-Fa TsaiChia-Chen Yen
    • G06F11/00G06F17/00
    • H04L63/1425G06F21/55
    • A detecting method for network intrusion includes: selecting a plurality of features contained within plural statistical data by a data-transforming module; normalizing a plurality of feature values of the selected features into the same scale to obtain a plurality of normalized feature data; creating at least one feature model by a data clustering technique incorporated with density-based and grid-based algorithms through a model-creating module; evaluating the at least one feature model through a model-identifying module to select a detecting model; and detecting whether a new packet datum belongs to an intrusion instance or not by a detecting module.
    • 一种用于网络入侵的检测方法包括:通过数据转换模块选择多个统计数据中包含的多个特征; 将所选择的特征的多个特征值归一化为相同的比例以获得多个归一化特征数据; 通过模型创建模块通过结合基于密度和基于网格的算法的数据聚类技术创建至少一个特征模型; 通过模型识别模块来评估所述至少一个特征模型以选择检测模型; 以及检测模块检测新的分组数据是否属于入侵实例。