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    Optimal buffer space configuration and scheduling for single-arm multi-cluster tools
    234.
    发明授权
    Optimal buffer space configuration and scheduling for single-arm multi-cluster tools 有权
    单臂多集群工具的最优缓冲空间配置和调度

    公开(公告)号：US09227318B1

    公开(公告)日：2016-01-05

    申请号：US14639137

    申请日：2015-03-05

    Applicant: Macau University of Science and Technology

    Inventor： Liping Bai ,  Naiqi Wu ,  Zhiwu Li ,  Mengchu Zhou

    IPC: G06F19/00 ,  B25J9/00 ,  G05B19/418 ,  G06F17/50

    CPC classification number: B25J9/009 ,  G05B19/41865 ,  G05B2219/45032 ,  G06F17/5013 ,  H01L21/67184 ,  H01L21/67276 ,  Y10S901/02 ,  Y10S901/41

    Abstract: A method for scheduling single-arm multi-cluster tools is provided. The present invention studies the scheduling problem of a single-arm multi-cluster tool with a linear topology and process-bound bottleneck individual tool. Its objective is to find a one-wafer cyclic schedule such that the lower bound of cycle time is reached by optimally configuring spaces in buffering modules that link individual cluster tools. A Petri net model is developed to describe the dynamic behavior of the system by extending resource-oriented Petri nets such that a schedule can be parameterized by robots' waiting time. Based on this model, conditions are presented under which a one-wafer cyclic schedule with lower bound of cycle time can be found. With the derived conditions, an algorithm is presented to find such a schedule and optimally configure the buffer spaces.

    Abstract translation: 提供了一种用于调度单臂多群集工具的方法。 本发明研究了具有线性拓扑和过程限制瓶颈的单个工具的单臂多群集工具的调度问题。 其目的是找到一个单晶圆循环调度，以便通过在链接各个集群工具的缓冲模块中最佳配置空间来达到循环时间的下限。 Petri网模型被开发用于通过扩展资源导向的Petri网来描述系统的动态行为，使得可以通过机器人的等待时间对时间表进行参数化。 基于该模型，提出了可以找到具有周期时间下限的单晶片循环调度的条件。 使用派生条件，提出了一种算法来查找这样的调度并最佳地配置缓冲区空间。

    Neferine and the use thereof in treating Huntington disease
    238.
    发明申请
    Neferine and the use thereof in treating Huntington disease 有权
    奈非炔及其在治疗亨廷顿疾病中的应用

    公开(公告)号：US20150164885A1

    公开(公告)日：2015-06-18

    申请号：US14562780

    申请日：2014-12-08

    Applicant: Macau University of Science and Technology

    Inventor： Yuen Kwan LAW ,  Kam Wai WONG ,  Liang LIU

    IPC: A61K31/4725 ,  A61K36/185

    CPC classification number: A61K31/4725 ,  A61K36/62

    Abstract: The present invention discloses a method of treating neurodegenerative disorder comprising administering an effective amount of an alkaloid which is a bisbenzylisoquinline alkaloid isolated from the traditional Chinese medicinal herb Nelumbo nucifera. The pharmaceutical composition thereof for treating Huntington's disease is also disclosed.

    Abstract translation: 本发明公开了一种治疗神经变性疾病的方法，包括给予有效量的生物碱，所述生物碱是从中草药Nelumbo Nucumera分离的双苄基异喹啉生物碱。 还公开了其用于治疗亨廷顿病的药物组合物。

    METHOD OF DETECTING INTRUSION BASED ON IMPROVED SUPPORT VECTOR MACHINE
    239.
    发明申请
    METHOD OF DETECTING INTRUSION BASED ON IMPROVED SUPPORT VECTOR MACHINE 有权
    基于改进的支持向量机检测入侵检测方法

    公开(公告)号：US20150135318A1

    公开(公告)日：2015-05-14

    申请号：US14201939

    申请日：2014-03-10

    Applicant: Macau University of Science and Technology

    Inventor： HON Chi Tin ,  XU Jia Hua

    IPC: G06F21/55 ,  H04L29/06

    CPC classification number: G06F21/55 ,  G06F21/554 ,  G06F2221/034 ,  H04L63/1408 ,  H04L63/1425

    Abstract: A method of detecting network intrusion based on improved support vector machine is disclosed. The method comprises the steps of identifying a plurality of features; computing information gain of each of the features; selecting a pre-determined number of features based on the computed information gain and augmenting that set of pre-determined number of features with special features to form a set of selected features; and classifying a network connection based on the selected features using support vector machine. In order to achieve better detection accuracy, cross-validation and grid-search are applied to select the radial basis function for the support vector machine.

    Abstract translation: 公开了一种基于改进的支持向量机检测网络入侵的方法。 该方法包括识别多个特征的步骤; 计算每个特征的信息增益; 基于所计算的信息增益来选择预定数量的特征，并且增加具有特殊特征的预定数量的特征的集合以形成所选特征的集合; 并使用支持向量机根据所选择的特征对网络连接进行分类。 为了实现更好的检测精度，应用交叉验证和网格搜索来选择支持向量机的径向基函数。

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