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1. 发明授权

US07340328B2 Scheduling system and method 有权
标题翻译：调度系统和方法
公开(公告)号：US07340328B2
公开(公告)日：2008-03-04
申请号：US10438822
申请日：2003-05-16
申请人： William L. Matheson , Paul M. Julich , Michael S. Crone , Douglas A. Thomae , Thu V. Vu , M. Scott Wills
发明人： William L. Matheson , Paul M. Julich , Michael S. Crone , Douglas A. Thomae , Thu V. Vu , M. Scott Wills
IPC分类号： G06Q99/00
CPC分类号： G06Q10/04 , B61L27/0016 , B61L27/0022 , B61L27/0027 , B61L2205/04 , G06Q10/047 , G06Q10/06 , G06Q10/06311 , G06Q10/06312 , G06Q10/06314 , G06Q10/06315 , G06Q10/06316 , G06Q10/06375 , G06Q10/0639 , Y10S706/932
摘要： A scheduling system and method for moving plural objects through a multipath system described as a freight railway scheduling system. The scheduling system utilizes a cost reactive resource scheduler to minimize resource exception while at the same time minimizing the global costs associated with the solution. The achievable movement plan can be used to assist in the control of, or to automatically control, the movement of trains through the system.
摘要翻译：一种用于通过描述为货运铁路调度系统的多路径系统移动多个物体的调度系统和方法。调度系统利用成本反应性资源调度器来最小化资源异常，同时最小化与解决方案相关联的全局成本。可实现的运动计划可用于协助控制或自动控制列车通过系统的运动。

2. 发明授权

US4881175A Computer based system and method for determining and displaying possible chemical structures for converting double- or multiple-chain polypeptides to single-chain polypeptides 失效
标题翻译：用于确定和显示将双链或多链多肽转化为单链多肽的可能的化学结构的基于计算机的系统和方法
公开(公告)号：US4881175A
公开(公告)日：1989-11-14
申请号：US204940
申请日：1988-06-09
申请人： Robert C. Ladner
发明人： Robert C. Ladner
IPC分类号： C07K1/00 , G06F17/50
CPC分类号： C07K1/00 , G06F19/16 , G06F19/22 , Y10S706/932
摘要： A computer based system and method determines and displays possible chemical structures for converting two naturally aggregated but chemically separated polypeptide chains into a single polypeptide chain which will fold into a three dimensional structure very similar to the original structure made of the two polypeptide chains. A data base contains a large number of amino acid sequences for which the three dimensional structure is known. After plausible sites have been selected, this data base is examined to find which amino acid sequences (linkers) can bridge the gap between the plausible sites to create a plausible one-polypeptide structure. The testing of each possible linker proceeds in three steps. First, the span (a scalar quantity) of the candidate is compared to the span of the gap. If the span is close enough, step two is done which involves aligning the first peptides of the candidate with the initial peptide of the gap. The three dimensional vector from tail to head of the candidate is compared to the three dimensional vector from tail to head of the gap. If there is a sufficient match between the two vectors, step three is done, which involves fitting the termini of the candidate (using, for example, a least squares procedure) to the termini of the gap. If these two termini fit well enough, the candidate is enrolled for a ranking process.
摘要翻译：基于计算机的系统和方法确定并显示用于将两个天然聚集但化学分离的多肽链转化成单个多肽链的可能的化学结构，其将折叠成与由两条多肽链构成的原始结构非常相似的三维结构。数据库包含大量已知三维结构的氨基酸序列。在选择合适的位点后，检查该数据库以找出哪些氨基酸序列（接头）可以弥合可信位点之间的间隙，以产生合理的单多肽结构。每个可能的接头的测试分三步进行。首先，将候选者的跨度（标量）与间隙的跨度进行比较。如果跨度足够接近，则进行第二步，其包括将候选物的第一个肽与间隙的初始肽对齐。从候选人的尾部到头部的三维向量与从间隙的尾部到头部的三维向量进行比较。如果两个向量之间存在足够的匹配，则完成步骤3，这涉及将候选者的终端（例如使用最小二乘法）适配到间隙的终点。如果这两个终端足够好，候选人将被注册进行排名过程。

3. 发明申请

US20070185824A1 Heuristic method of classification 失效
标题翻译：启发式分类法
公开(公告)号：US20070185824A1
公开(公告)日：2007-08-09
申请号：US11735028
申请日：2007-04-13
申请人： Ben Hitt
发明人： Ben Hitt
IPC分类号： G06N3/08
CPC分类号： G06K9/6217 , G06K9/6228 , G06K9/6284 , G06N3/08 , Y10S706/90 , Y10S706/932
摘要： The invention concerns heuristic algorithms for the classification of Objects. A first learning algorithm comprises a genetic algorithm that is used to abstract a data stream associated with each Object and a pattern recognition algorithm that is used to classify the Objects and measure the fitness of the chromosomes of the genetic algorithm. The learning algorithm is applied to a training data set. The learning algorithm generates a classifying algorithm, which is used to classify or categorize unknown Objects. The invention is useful in the areas of classifying texts and medical samples, predicting the behavior of one financial market based on price changes in others and in monitoring the state of complex process facilities to detect impending failures.
摘要翻译：本发明涉及用于对象分类的启发式算法。第一学习算法包括用于抽取与每个对象相关联的数据流的遗传算法和用于对对象进行分类并测量遗传算法染色体的适应度的模式识别算法。学习算法应用于训练数据集。学习算法生成一个分类算法，用于对未知对象进行分类或分类。本发明对文本和医疗样本进行分类，根据其他金融市场价格变动预测一个金融市场的行为，监测复杂流程设施状况以检测即将发生的失败。

4. 发明授权

US07222083B2 Resource schedule for scheduling rail way train resources 有权
标题翻译：铁路列车资源调度资源调度
公开(公告)号：US07222083B2
公开(公告)日：2007-05-22
申请号：US10438900
申请日：2003-05-16
申请人： William L. Matheson , Paul M. Julich , Michael S. Crone , Douglas A. Thomae , Thu V. Vu , M. Scott Wills
发明人： William L. Matheson , Paul M. Julich , Michael S. Crone , Douglas A. Thomae , Thu V. Vu , M. Scott Wills
IPC分类号： G06Q10/00
CPC分类号： G06Q10/04 , B61L27/0016 , B61L27/0022 , B61L27/0027 , B61L2205/04 , G06Q10/047 , G06Q10/06 , G06Q10/06311 , G06Q10/06312 , G06Q10/06314 , G06Q10/06315 , G06Q10/06316 , G06Q10/06375 , G06Q10/0639 , Y10S706/932
摘要： A scheduling system and method for moving plural objects through a multipath system described as a freight railway scheduling system. The scheduling system utilizes a cost reactive resource scheduler to minimize resource exception while at the same time minimizing the global costs associated with the solution. The achievable movement plan can be used to assist in the control of, or to automatically control, the movement of trains through the system.
摘要翻译：一种用于通过描述为货运铁路调度系统的多路径系统移动多个物体的调度系统和方法。调度系统利用成本反应性资源调度器来最小化资源异常，同时最小化与解决方案相关联的全局成本。可实现的运动计划可用于协助控制或自动控制列车通过系统的运动。

5. 发明申请

US20050289089A1 Methods for multi-class cost-sensitive learning 审中-公开
标题翻译：多类成本敏感性学习方法
公开(公告)号：US20050289089A1
公开(公告)日：2005-12-29
申请号：US10876533
申请日：2004-06-28
申请人： Naoki Abe , Bianca Zadrozny
发明人： Naoki Abe , Bianca Zadrozny
IPC分类号： G06F9/44 , G06F15/18 , G06K9/62 , G06N3/08 , G06N7/02 , G06N7/06
CPC分类号： G06K9/6256 , G06N20/00 , Y10S706/932
摘要： Methods for multi-class cost-sensitive learning are based on iterative example weighting schemes and solve multi-class cost-sensitive learning problems using a binary classification algorithm. One of the methods works by iteratively applying weighted sampling from an expanded data set, which is obtained by enhancing each example in the original data set with as many data points as there are possible labels for any single instance, using a weighting scheme which gives each labeled example the weight specified as the difference between the average cost on that instance by the averaged hypotheses from the iterations so far and the misclassification cost associated with the label in the labeled example in question. It then calls the component classification algorithm on a modified binary classification problem in which each example is itself already a labeled pair, and its (meta) label is 1 or 0 depending on whether the example weight in the above weighting scheme is positive or negative, respectively. It then finally outputs a classifier hypothesis which is the average of all the hypotheses output in the respective iterations.
摘要翻译：多类成本敏感学习的方法基于迭代示例加权方案，并使用二进制分类算法解决多类成本敏感学习问题。其中一种方法通过迭代地应用来自扩展数据集的加权采样来工作，该扩展数据集通过使用给出每个实例的加权方案来增强原始数据集中具有尽可能多的数据点的数据点与任何单个实例的可能标签而获得的每个示例而获得标示的重量指定为该实例的平均成本与目前为止的迭代的平均假设之间的差异以及与所标记的示例中的标签相关联的错误分类成本。然后，对修改后的二进制分类问题调用组件分类算法，其中每个示例本身已经是一个标记对，根据上述加权方案中的示例权重是正还是负，其（元）标签为1或0，分别。然后，它最终输出一个分类器假设，它是相应迭代中输出的所有假设的平均值。

6. 发明申请

US20040093245A1 System and method for scheduling and train control 失效
标题翻译：用于调度和列车控制的系统和方法
公开(公告)号：US20040093245A1
公开(公告)日：2004-05-13
申请号：US10438912
申请日：2003-05-16
发明人： William L. Matheson , Paul M. Julich , Michael S. Crone , Douglas A. Thomae , Thu V. Vu , M. Scott Wills , Alan L. Polvika
IPC分类号： G06F017/60
CPC分类号： G06Q10/04 , B61L27/0016 , B61L27/0022 , B61L27/0027 , B61L2205/04 , G06Q10/047 , G06Q10/06 , G06Q10/06311 , G06Q10/06312 , G06Q10/06314 , G06Q10/06315 , G06Q10/06316 , G06Q10/06375 , G06Q10/0639 , Y10S706/932
摘要： A scheduling system and method for moving plural objects through a multipath system described as a freight railway scheduling system. The scheduling system utilizes a cost reactive resource scheduler to minimize resource exception while at the same time minimizing the global costs associated with the solution. The achievable movement plan can be used to assist in the control of, or to automatically control, the movement of trains through the system.
摘要翻译：一种用于通过描述为货运铁路调度系统的多路径系统移动多个物体的调度系统和方法。调度系统利用成本反应性资源调度器来最小化资源异常，同时最小化与解决方案相关联的全局成本。可实现的运动计划可用于协助控制或自动控制列车通过系统的运动。

7. 发明授权

US5794172A Scheduling system and method 失效
标题翻译：调度系统和方法
公开(公告)号：US5794172A
公开(公告)日：1998-08-11
申请号：US787168
申请日：1997-01-23
申请人： William L. Matheson , Paul M. Julich , Michael S. Crone , Douglas A. Thomae , Thu V. Vu , M. Scott Wills
发明人： William L. Matheson , Paul M. Julich , Michael S. Crone , Douglas A. Thomae , Thu V. Vu , M. Scott Wills
IPC分类号： B61L27/00 , G06Q10/00 , B61L27/04
CPC分类号： G06Q10/06 , B61L27/0016 , B61L27/0022 , B61L27/0027 , G06Q10/047 , G06Q10/08 , B61L2205/04 , Y10S706/932
摘要： A scheduling system and method for moving plural objects through a multipath system described as a freight railway scheduling system. The achievable movement plan can be used to assist in the control of, or to automatically control, the movement of trains through the system.
摘要翻译：一种用于通过描述为货运铁路调度系统的多路径系统移动多个物体的调度系统和方法。可实现的运动计划可用于协助控制或自动控制列车通过系统的运动。

8. 发明授权

US07499891B2 Heuristic method of classification 失效
公开(公告)号：US07499891B2
公开(公告)日：2009-03-03
申请号：US11735028
申请日：2007-04-13
申请人： Ben Hitt
发明人： Ben Hitt
IPC分类号： G06F15/18
CPC分类号： G06K9/6217 , G06K9/6228 , G06K9/6284 , G06N3/08 , Y10S706/90 , Y10S706/932
摘要： The invention concerns heuristic algorithms for the classification of Objects. A first learning algorithm comprises a genetic algorithm that is used to abstract a data stream associated with each Object and a pattern recognition algorithm that is used to classify the Objects and measure the fitness of the chromosomes of the genetic algorithm. The learning algorithm is applied to a training data set. The learning algorithm generates a classifying algorithm, which is used to classify or categorize unknown Objects. The invention is useful in the areas of classifying texts and medical samples, predicting the behavior of one financial market based on price changes in others and in monitoring the state of complex process facilities to detect impending failures.

9. 发明授权

US07240038B2 Heuristic method of classification 失效
公开(公告)号：US07240038B2
公开(公告)日：2007-07-03
申请号：US11273432
申请日：2005-11-15
申请人： Ben Hitt
发明人： Ben Hitt
IPC分类号： G06F15/18
CPC分类号： G06K9/6217 , G06K9/6228 , G06K9/6284 , G06N3/08 , Y10S706/90 , Y10S706/932
摘要： The invention concerns heuristic algorithms for the classification of Objects. A first learning algorithm comprises a genetic algorithm that is used to abstract a data stream associated with each Object and a pattern recognition algorithm that is used to classify the Objects and measure the fitness of the chromosomes of the genetic algorithm. The learning algorithm is applied to a training data set. The learning algorithm generates a classifying algorithm, which is used to classify or categorize unknown Objects. The invention is useful in the areas of classifying texts and medical samples, predicting the behavior of one financial market based on price changes in others and in monitoring the state of complex process facilities to detect impending failures.

10. 发明授权

US07096206B2 Heuristic method of classification 有权
公开(公告)号：US07096206B2
公开(公告)日：2006-08-22
申请号：US09883196
申请日：2001-06-19
申请人： Ben Hitt
发明人： Ben Hitt
IPC分类号： G06F15/18
CPC分类号： G06K9/6217 , G06K9/6228 , G06K9/6284 , G06N3/08 , Y10S706/90 , Y10S706/932
摘要： The invention concerns heuristic algorithms for the classification of Objects. A first learning algorithm comprises a genetic algorithm that is used to abstract a data stream associated with each Object and a pattern recognition algorithm that is used to classify the Objects and measure the fitness of the chromosomes of the genetic algorithm. The learning algorithm is applied to a training data set. The learning algorithm generates a classifying algorithm, which is used to classify or categorize unknown Objects. The invention is useful in the areas of classifying texts and medical samples, predicting the behavior of one financial market based on price changes in others and in monitoring the state of complex process facilities to detect impending failures.

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