公开(公告)号：US20130277205A1

公开(公告)日：2013-10-24

申请号：US13977314

申请日：2011-09-30

Applicant: Yangchao Li ,  Bo Geng ,  Xuewei Wu ,  Guoqing Qiu ,  Xu Liu

Inventor： Yangchao Li ,  Bo Geng ,  Xuewei Wu ,  Guoqing Qiu ,  Xu Liu

IPC: C23C14/35

CPC classification number: C23C14/35 ,  H01J37/3408 ,  H01J37/3455 ,  H01J37/347

Abstract: Provided is a magnetron source, which comprises a target material, a magnetron located thereabove and a scanning mechanism connected to the magnetron for controlling the movement of the magnetron above the target material. The scanning mechanism comprises a peach-shaped track, with the magnetron movably disposed thereon; a first driving shaft, with the bottom end thereof connected with the origin of the polar coordinates of the peach-shaped track, for driving the peach-shaped track to rotate about the axis of the first driving shaft; a first driver connected to the first driving shaft for driving the first driving shaft to rotate; and a second driver for driving the magnetron to move along the peach-shaped track via a transmission assembly. A magnetron sputtering device including the magnetron and a method for magnetron sputtering using the magnetron sputtering device are also provided.

Abstract translation: 提供了一种磁控管源，其包括目标材料，位于其上的磁控管和连接到磁控管的扫描机构，用于控制磁控管在目标材料上方的移动。 扫描机构包括桃形轨道，磁控管可移动地设置在其上; 第一驱动轴，其底端与桃形轨道的极坐标的原点连接，用于驱动桃形轨道围绕第一驱动轴的轴线旋转; 连接到第一驱动轴的第一驱动器，用于驱动第一驱动轴旋转; 以及第二驱动器，用于驱动磁控管经由传输组件沿着桃形轨迹移动。 还提供了包括磁控管的磁控溅射装置和使用磁控溅射装置的磁控溅射方法。

公开(公告)号：US09399817B2

公开(公告)日：2016-07-26

申请号：US13977314

申请日：2011-09-30

Applicant: Yangchao Li ,  Bo Geng ,  Xuewei Wu ,  Guoqing Qiu ,  Xu Liu

Inventor： Yangchao Li ,  Bo Geng ,  Xuewei Wu ,  Guoqing Qiu ,  Xu Liu

IPC: C23C14/35 ,  H01J37/34

CPC classification number: C23C14/35 ,  H01J37/3408 ,  H01J37/3455 ,  H01J37/347

Abstract: Provided is a magnetron source, which comprises a target material, a magnetron located thereabove and a scanning mechanism connected to the magnetron for controlling the movement of the magnetron above the target material. The scanning mechanism comprises a peach-shaped track, with the magnetron movably disposed thereon; a first driving shaft, with the bottom end thereof connected with the origin of the polar coordinates of the peach-shaped track, for driving the peach-shaped track to rotate about the axis of the first driving shaft; a first driver connected to the first driving shaft for driving the first driving shaft to rotate; and a second driver for driving the magnetron to move along the peach-shaped track via a transmission assembly. A magnetron sputtering device including the magnetron and a method for magnetron sputtering using the magnetron sputtering device are also provided.

Abstract translation: 提供了一种磁控管源，其包括目标材料，位于其上的磁控管和连接到磁控管的扫描机构，用于控制磁控管在目标材料上方的移动。 扫描机构包括桃形轨道，磁控管可移动地设置在其上; 第一驱动轴，其底端与桃形轨道的极坐标的原点连接，用于驱动桃形轨道围绕第一驱动轴的轴线旋转; 连接到第一驱动轴的第一驱动器，用于驱动第一驱动轴旋转; 以及第二驱动器，用于驱动磁控管经由传输组件沿着桃形轨迹移动。 还提供了包括磁控管的磁控溅射装置和使用磁控溅射装置的磁控溅射方法。

公开(公告)号：US20130013578A1

公开(公告)日：2013-01-10

申请号：US13176279

申请日：2011-07-05

Applicant: Linjun Yang ,  Bo Geng ,  Xian-Sheng Hua ,  Yang Cai

Inventor： Linjun Yang ,  Bo Geng ,  Xian-Sheng Hua ,  Yang Cai

IPC: G06F17/30

CPC classification number: G06F17/30277 ,  G06F17/30256

Abstract: Some implementations provide techniques and arrangements to perform image retrieval. For example, some implementations identify an object of interest and a visual context in a first image. In some implementations, a second image that includes a second object of interest and a second visual context may be compared to the object of interest and the visual content, respectively, to determine whether the second image matches the first image.

Abstract translation: 一些实现提供执行图像检索的技术和布置。 例如，一些实现在第一图像中识别感兴趣的对象和视觉上下文。 在一些实施方案中，可以分别将包括感兴趣的第二对象和第二视觉上下文的第二图像与感兴趣对象和视觉内容进行比较，以确定第二图像是否匹配第一图像。

公开(公告)号：US08560517B2

公开(公告)日：2013-10-15

申请号：US13176279

申请日：2011-07-05

Applicant: Linjun Yang ,  Bo Geng ,  Xian-Sheng Hua ,  Yang Cai

Inventor： Linjun Yang ,  Bo Geng ,  Xian-Sheng Hua ,  Yang Cai

IPC: G06F17/30

CPC classification number: G06F17/30277 ,  G06F17/30256

Abstract: Some implementations provide techniques and arrangements to perform image retrieval. For example, some implementations identify an object of interest and a visual context in a first image. In some implementations, a second image that includes a second object of interest and a second visual context may be compared to the object of interest and the visual content, respectively, to determine whether the second image matches the first image.

Abstract translation: 一些实现提供执行图像检索的技术和布置。 例如，一些实现在第一图像中识别感兴趣的对象和视觉上下文。 在一些实施方案中，可以分别将包括感兴趣的第二对象和第二视觉上下文的第二图像与感兴趣对象和视觉内容进行比较，以确定第二图像是否匹配第一图像。

公开(公告)号：US20100217732A1

公开(公告)日：2010-08-26

申请号：US12391511

申请日：2009-02-24

Applicant: Linjun Yang ,  Bo Geng ,  Xian-Sheng Hua

Inventor： Linjun Yang ,  Bo Geng ,  Xian-Sheng Hua

IPC: G06F15/18

CPC classification number: G06N99/005

Abstract: Techniques described herein create an accurate active-learning model that takes into account a sample selection bias of elements, such as images, selected for labeling by a user. These techniques select a first set of elements for labeling. Once a user labels these elements, the techniques calculate a sample selection bias of the selected elements and train a model that takes into account the sample selection bias. The techniques then select a second set of elements based, in part, on a sample selection bias of the elements. Again, once a user labels the second set of elements the techniques train the model while taking into account the calculated sample selection bias. Once the trained model satisfies a predefined stop condition, the techniques use the trained model to predict labels for the remaining unlabeled elements.

Abstract translation: 本文描述的技术创建了一种精确的主动学习模型，其考虑了由用户选择进行标签选择的元素（例如图像）的样本选择偏差。 这些技术选择用于标记的第一组元素。 一旦用户标记了这些元素，这些技术就会计算所选元素的样本选择偏差，并训练考虑样本选择偏倚的模型。 然后，技术部分地基于元素的样本选择偏差来选择第二组元素。 同样，一旦用户标记第二组元素，则该技术训练模型，同时考虑计算的样本选择偏差。 一旦训练的模型满足预定义的停止条件，该技术使用经过训练的模型来预测剩余的未标记元素的标签。

公开(公告)号：US08903166B2

公开(公告)日：2014-12-02

申请号：US12690817

申请日：2010-01-20

Applicant: Linjun Yang ,  Bo Geng ,  Xian-Sheng Hua

Inventor： Linjun Yang ,  Bo Geng ,  Xian-Sheng Hua

IPC: G06K9/62

CPC classification number: G06K9/6252

Abstract: This document describes techniques that utilize a learning method to generate a ranking model for use in image search systems. The techniques leverage textual information and visual information simultaneously when generating the ranking model. The tools are further configured to apply the ranking model responsive to receiving an image search query.

Abstract translation: 本文档描述了利用学习方法生成用于图像搜索系统的排名模型的技术。 该技术在生成排名模型时同时利用文本信息和视觉信息。 所述工具还被配置为响应于接收图像搜索查询来应用排序模型。

公开(公告)号：US20120102018A1

公开(公告)日：2012-04-26

申请号：US12911503

申请日：2010-10-25

Applicant: Linjun Yang ,  Bo Geng ,  Xian-Sheng Hua

Inventor： Linjun Yang ,  Bo Geng ,  Xian-Sheng Hua

IPC: G06F15/18 ,  G06F17/30

CPC classification number: G06F16/3347

Abstract: An adaptation process is described to adapt a ranking model constructed for a broad-based search engine for use with a domain-specific ranking model. An example process identifies a ranking model for use with a broad-based search engine and modifies that ranking model for use with a new (or “target”) domain containing information pertaining to a specific topic.

Abstract translation: 描述适应过程以适应为基于广泛的搜索引擎构建的排名模型，以便与域特定排名模型一起使用。 示例过程识别用于基于广泛的搜索引擎的排名模型，并修改与包含与特定主题相关的信息的新（或“目标”）域一起使用的排名模型。

公开(公告)号：US20140250110A1

公开(公告)日：2014-09-04

申请号：US13394425

申请日：2011-11-25

Applicant: Linjun Yang ,  Bo Geng ,  Xian-Sheng Hua ,  Shipeng Li

Inventor： Linjun Yang ,  Bo Geng ,  Xian-Sheng Hua ,  Shipeng Li

IPC: G06F17/30

CPC classification number: G06F16/5866 ,  G06F16/2228 ,  G06F16/248 ,  G06F16/583 ,  G06F16/9535

Abstract: Attractiveness of an image may be estimated by integrating extracted visual features with contextual cues pertaining to the image. Image attractiveness may be defined by the visual features (e.g., perceptual quality, aesthetic sensitivity, and/or affective tone) of elements contained within the image. Images may be indexed based on the estimated attractiveness, search results may be presented based on image attractiveness, and/or a user may elect, after receiving image search results, to re-rank the image search results by attractiveness.

Abstract translation: 图像的吸引力可以通过将提取的视觉特征与与图像有关的语境线索相结合来估计。 图像吸引力可以由包含在图像内的元素的视觉特征（例如感知质量，美学敏感度和/或情感色调）来定义。 可以基于估计的吸引力来索引图像，可以基于图像吸引力来呈现搜索结果，和/或用户可以在接收图像搜索结果之后选择通过吸引力重新排列图像搜索结果。

公开(公告)号：US08219511B2

公开(公告)日：2012-07-10

申请号：US12391511

申请日：2009-02-24

Applicant: Linjun Yang ,  Bo Geng ,  Xian-Sheng Hua

Inventor： Linjun Yang ,  Bo Geng ,  Xian-Sheng Hua

IPC: G06F15/18 ,  G06F17/10 ,  G06N3/08

CPC classification number: G06N99/005

Abstract: Techniques described herein create an accurate active-learning model that takes into account a sample selection bias of elements, such as images, selected for labeling by a user. These techniques select a first set of elements for labeling. Once a user labels these elements, the techniques calculate a sample selection bias of the selected elements and train a model that takes into account the sample selection bias. The techniques then select a second set of elements based, in part, on a sample selection bias of the elements. Again, once a user labels the second set of elements the techniques train the model while taking into account the calculated sample selection bias. Once the trained model satisfies a predefined stop condition, the techniques use the trained model to predict labels for the remaining unlabeled elements.

Abstract translation: 本文描述的技术创建了一种精确的主动学习模型，其考虑了由用户选择进行标签选择的元素（例如图像）的样本选择偏差。 这些技术选择用于标记的第一组元素。 一旦用户标记了这些元素，这些技术就会计算所选元素的样本选择偏差，并训练考虑样本选择偏倚的模型。 然后，技术部分地基于元素的样本选择偏差来选择第二组元素。 同样，一旦用户标记第二组元素，则该技术训练模型，同时考虑计算的样本选择偏差。 一旦训练的模型满足预定义的停止条件，该技术使用经过训练的模型来预测剩余的未标记元素的标签。

公开(公告)号：US20110176724A1

公开(公告)日：2011-07-21

申请号：US12690817

申请日：2010-01-20

Applicant: Linjun Yang ,  Bo Geng ,  Xian-Sheng Hua

Inventor： Linjun Yang ,  Bo Geng ,  Xian-Sheng Hua

IPC: G06K9/62

CPC classification number: G06K9/6252

Abstract: This document describes techniques that utilize a learning method to generate a ranking model for use in image search systems. The techniques leverage textual information and visual information simultaneously when generating the ranking model. The tools are further configured to apply the ranking model responsive to receiving an image search query.

Abstract translation: 本文档描述了利用学习方法生成用于图像搜索系统的排名模型的技术。 该技术在生成排名模型时同时利用文本信息和视觉信息。 所述工具还被配置为响应于接收图像搜索查询来应用排序模型。