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1.发明授权High accuracy optical character recognition using neural networks with centroid dithering 失效使用具有重心抖动的神经网络的高精度光学字符识别
公开(公告)号：US5475768A
公开(公告)日：1995-12-12
申请号：US55523
申请日：1993-04-29
CPC分类号： G06K9/6217 , G06K9/36
摘要： Pattern recognition, for instance optical character recognition, is achieved by training a neural network, scanning an image, segmenting the image to detect a pattern, preprocessing the detected pattern, and applying the preprocessed detected pattern to the trained neural network. The preprocessing includes determining a centroid of the pattern and centrally positioning the centroid in a frame containing the pattern. The training of the neural network includes randomly displacing template patterns within frames before applying the template patterns to the neural network.
摘要翻译： 模式识别，例如光学字符识别，通过训练神经网络，扫描图像，分割图像以检测图案，预处理检测到的图案，以及将经过预处理的检测图案应用于经过训练的神经网络来实现。 预处理包括确定图案的质心并且在包含图案的框中心定位质心。 神经网络的训练包括在将模板图案应用于神经网络之前随机移位框架内的模板模式。
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公开(公告)号：US07987151B2
公开(公告)日：2011-07-26
申请号：US11066332
申请日：2005-02-25
申请人： Wade F. Schott , Thanh A. Diep
发明人： Wade F. Schott , Thanh A. Diep
IPC分类号： G06N5/00
CPC分类号： G06N5/043
摘要： The present invention relates to a system and method for problem solving using intelligent agents. The intelligent agents may be embodied as processor-readable software code stored on a processor-readable medium. The intelligent agents may include a brain agent to parse the input and direct the parsed input query to other intelligent agents within the system. The apparatus and method may use, for example, a personality agent, a language agent, a knowledge agent, a mood agent, a visual agent, sound agent, a tactile agent, and a smell/taste agent and various connectors to external data sources or other intelligent systems to interpret questions and provide responses back to the user. The apparatus and method may further parse questions in a conceptual manner. The apparatus and method may further optimize its system performance by evolving with and reacting to specific user interactions. Thus, the present invention may be configured to receive a human question and to output a human answer.
摘要翻译： 本发明涉及一种使用智能代理解决问题的系统和方法。 智能代理可以被实现为存储在处理器可读介质上的处理器可读软件代码。 智能代理可以包括大脑代理来解析输入并将解析的输入查询引导到系统内的其他智能代理。 该装置和方法可以使用例如个性代理，语言代理，知识代理，情绪代理，视觉代理，声音代理，触觉代理和气味/味觉代理以及与外部数据源的各种连接器 或其他智能系统来解释问题并向用户提供响应。 该装置和方法可以进一步以概念的方式解析问题。 该装置和方法可以通过与具体的用户交互进行演变和响应来进一步优化其系统性能。 因此，本发明可以被配置为接收人的问题并输出人的答案。
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公开(公告)号：US20100185566A1
公开(公告)日：2010-07-22
申请号：US11066332
申请日：2005-02-25
申请人： Wade F. Schott , Thanh A. Diep
发明人： Wade F. Schott , Thanh A. Diep
IPC分类号： G06F15/00
CPC分类号： G06N5/043
摘要： The present invention relates to a system and method for problem solving using intelligent agents. The intelligent agents may be embodied as processor-readable software code stored on a processor-readable medium. The intelligent agents may include a brain agent to parse the input and direct the parsed input query to other intelligent agents within the system. The apparatus and method may use, for example, a personality agent, a language agent, a knowledge agent, a mood agent, a visual agent, sound agent, a tactile agent, and a smell/taste agent and various connectors to external data sources or other intelligent systems to interpret questions and provide responses back to the user. The apparatus and method may further parse questions in a conceptual manner. The apparatus and method may further optimize its system performance by evolving with and reacting to specific user interactions. Thus, the present invention may be configured to receive a human question and to output a human answer.
摘要翻译： 本发明涉及一种使用智能代理解决问题的系统和方法。 智能代理可以被实现为存储在处理器可读介质上的处理器可读软件代码。 智能代理可以包括大脑代理来解析输入并将解析的输入查询引导到系统内的其他智能代理。 该装置和方法可以使用例如个性代理，语言代理，知识代理，情绪代理，视觉代理，声音代理，触觉代理和气味/味觉代理以及与外部数据源的各种连接器 或其他智能系统来解释问题并向用户提供响应。 该装置和方法可以进一步以概念的方式解析问题。 该装置和方法可以通过与具体的用户交互进行演变和响应来进一步优化其系统性能。 因此，本发明可以被配置为接收人的问题并输出人的答案。
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4.发明授权Method and apparatus for training a neural network model for use in computer network intrusion detection 有权用于训练用于计算机网络入侵检测的神经网络模型的方法和装置公开(公告)号：US06769066B1
公开(公告)日：2004-07-27
申请号：US09427147
申请日：1999-10-25
IPC分类号： G06F1300
CPC分类号： H04L63/1416 , G06F21/55 , G06F2221/2101
摘要： Detecting harmful or illegal intrusions into a computer network or into restricted portions of a computer network uses a process of synthesizing anomalous data to be used in training a neural network-based model for use in a computer network intrusion detection system. Anomalous data for artificially creating a set of features reflecting anomalous behavior for a particular activity is performed. This is done in conjunction with the creation of normal-behavior feature values. A distribution of users of normal feature values and an expected distribution of users of anomalous feature values are then defined in the form of histograms. The anomalous-feature histogram is then sampled to produce anomalous-behavior feature values. These values are then used to train a model having a neural network training algorithm where the model is used in the computer network intrusion detection system. The model is trained such that it can efficiently recognize anomalous behavior by users in a dynamic computing environment where user behavior can change frequently.
摘要翻译： 检测计算机网络或计算机网络的有限部分的有害或非法入侵使用合成用于训练基于神经网络的模型以用于计算机网络入侵检测系统中的异常数据的过程。 执行用于人为地创建反映特定活动的异常行为的一组特征的异常数据。 这与创建正常行为特征值一起完成。 然后以直方图的形式定义正常特征值的用户分布和异常特征值的用户的预期分布。 然后对异常特征直方图进行采样以产生异常行为特征值。 然后将这些值用于训练具有神经网络训练算法的模型，其中该模型用于计算机网络入侵检测系统。 该模型进行了训练，使其能够有效地识别用户在动态计算环境中的异常行为，其中用户行为可能会频繁更改。
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公开(公告)号：US06671811B1
公开(公告)日：2003-12-30
申请号：US09427176
申请日：1999-10-25
IPC分类号： G06F900
CPC分类号： G06F21/552
摘要： Detecting harmful or illegal intrusions into a computer network or into restricted portions of a computer network uses a features generator or builder to generate a feature reflecting changes in user and user group behavior over time. User and user group historical means and standard deviations are used to generate a feature that is not dependent on rigid or static rule sets. These statistical and historical values are calculated by accessing user activity data listing activities performed by users on the computer system. Historical information is then calculated based on the activities performed by users on the computer system. The feature is calculated using the historical information based on the user or group of users activities. The feature is then utilized by a model to obtain a value or score which indicates the likelihood of an intrusion into the computer network. The historical values are adjusted according to shifts in normal behavior of users of the computer system. This allows for calculation of the feature to reflect changing characteristics of the users on the computer system.
摘要翻译： 将有害或非法入侵检测到计算机网络或计算机网络的限制部分使用特征生成器或构建器来生成反映用户和用户组行为随时间变化的特征。 用户和用户组的历史平均值和标准偏差用于生成不依赖于刚性或静态规则集的特征。 这些统计和历史值通过访问用户在计算机系统上执行的用户活动数据列出活动来计算。 然后根据用户在计算机系统上执行的活动来计算历史信息。 该功能是使用基于用户或用户组活动的历史信息计算的。 该特征然后被模型利用以获得指示侵入计算机网络的可能性的值或分数。 历史价值根据计算机系统用户的正常行为的变化进行调整。 这允许计算功能以反映计算机系统上用户的变化特征。
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6.发明授权Synthesis of anomalous data to create artificial feature sets and use of same in computer network intrusion detection systems 有权综合异常数据创建人工特征集并在计算机网络入侵检测系统中使用公开(公告)号：US08527776B2
公开(公告)日：2013-09-03
申请号：US10866405
申请日：2004-06-10
IPC分类号： G06F21/00
CPC分类号： H04L63/1416 , G06F21/55 , G06F2221/2101
摘要： Detecting harmful or illegal intrusions into a computer network or into restricted portions of a computer network uses a process of synthesizing anomalous data to be used in training a neural network-based model for use in a computer network intrusion detection system. Anomalous data for artificially creating a set of features reflecting anomalous behavior for a particular activity is performed. This is done in conjunction with the creation of normal-behavior feature values. A distribution of users of normal feature values and an expected distribution of users of anomalous feature values are then defined in the form of histograms. The anomalous-feature histogram is then sampled to produce anomalous-behavior feature values. These values are then used to train a model having a neural network training algorithm where the model is used in the computer network intrusion detection system. The model is trained such that it can efficiently recognize anomalous behavior by users in a dynamic computing environment where user behavior can change frequently.
摘要翻译： 检测计算机网络或计算机网络的有限部分的有害或非法入侵使用合成用于训练基于神经网络的模型以用于计算机网络入侵检测系统中的异常数据的过程。 执行用于人为地创建反映特定活动的异常行为的一组特征的异常数据。 这与创建正常行为特征值一起完成。 然后以直方图的形式定义正常特征值的用户分布和异常特征值的用户的预期分布。 然后对异常特征直方图进行采样以产生异常行为特征值。 然后将这些值用于训练具有神经网络训练算法的模型，其中该模型用于计算机网络入侵检测系统。 该模型进行了训练，使其能够有效地识别用户在动态计算环境中的异常行为，其中用户行为可能会频繁更改。
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公开(公告)号：US06370648B1
公开(公告)日：2002-04-09
申请号：US09208617
申请日：1998-12-08
申请人： Thanh A. Diep
发明人： Thanh A. Diep
IPC分类号： G06F1100
CPC分类号： G06F21/55 , H04L63/123 , H04L63/1408
摘要： Detecting harmful or illegal intrusions into a computer network or into restricted portions of a computer network uses statistical analysis to match user commands and program names with a template sequence. Discrete correlation matching and permutation matching are used to match sequences. The result of the match is input to a feature builder and then a modeler to produce a score. The score indicates possible intrusion. A sequence of user commands and program names and a template sequence of known harmful commands and program names from a set of such templates are retrieved. A closeness factor indicative of the similarity between the user command sequence and a template sequence is derived from comparing the two sequences. The user command sequence is compared to each template sequence in the set of templates thereby creating multiple closeness or similarity measurements. These measurements are examined to determine which sequence template is most similar to the user command sequence. A frequency feature associated with the user command sequence and the most similar template sequence is calculated. It is determined whether the user command sequence is a potential intrusion into restricted portions of the computer network by examining output from a modeler using the frequency feature as one input.
摘要翻译： 将有害或非法入侵检测到计算机网络或计算机网络的限制部分使用统计分析来将用户命令和程序名称与模板序列进行匹配。 离散相关匹配和置换匹配用于匹配序列。 匹配的结果输入到特征构建器，然后输入到建模者以产生分数。 得分表示可能入侵。 检索一系列用户命令和程序名称以及来自一组这样的模板的已知有害命令和程序名称的模板序列。 通过比较两个序列，得出表示用户命令序列与模板序列之间的相似性的紧密度因子。 将用户命令序列与模板集中的每个模板序列进行比较，从而创建多个亲密度或相似性度量。 检查这些测量以确定哪个序列模板与用户命令序列最相似。 计算与用户命令序列和最相似的模板序列相关联的频率特征。 通过使用频率特征作为一个输入来检查来自建模者的输出来确定用户命令序列是否潜在地侵入计算机网络的受限部分。
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8.发明授权Training a neural network using centroid dithering by randomly displacing a template 失效通过随机取代模板来训练使用质心抖动的神经网络
公开(公告)号：US5625707A
公开(公告)日：1997-04-29
申请号：US445470
申请日：1995-05-22
CPC分类号： G06K9/6217 , G06K9/36
摘要： Pattern recognition, for instance optical character recognition, is achieved by training a neural network, scanning an image, segmenting the image to detect a pattern, preprocessing the detected pattern, and applying the preprocessed detected pattern to the trained neural network. The preprocessing includes determining a centroid of the pattern and centrally positioning the centroid in a frame containing the pattern. The training of the neural network includes randomly displacing template patterns within frames before applying the template patterns to the neural network.
摘要翻译： 模式识别，例如光学字符识别，通过训练神经网络，扫描图像，分割图像以检测图案，预处理检测到的图案，以及将经过预处理的检测图案应用于经过训练的神经网络来实现。 预处理包括确定图案的质心并且在包含图案的框中心定位质心。 神经网络的训练包括在将模板图案应用于神经网络之前随机移位框架内的模板模式。
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