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1.发明授权公开(公告)号：US08051018B1
公开(公告)日：2011-11-01
申请号：US11999427
申请日：2007-12-04
CPC分类号： G06N3/126
摘要： The present invention relates to a method for optimizing the design of a shape morphing structure using a genetic algorithm. The method includes defining design parameters of a surface having variable properties into a chromosome. The variable properties of the chromosome are the actual properties of the chromosome. The chromosome has a total of Nmax genes, where each gene corresponds to a variable property element in the surface. Additionally, each gene has n design parameters, wherein the design parameters are incremental changes to the actual properties of the chromosome. A genetic algorithm is employed to optimize the genes until a fitness level for at least one chromosome has been exceeded. When the fitness value for any chromosome in the population is above a predetermined threshold, then the design process is terminated and the final design solution[s] are the design parameters of the chromosome[s] that exceed the predetermined threshold value.
摘要翻译： 本发明涉及使用遗传算法优化形状变形结构的设计的方法。 该方法包括将具有可变特性的表面的设计参数定义到染色体中。 染色体的可变性质是染色体的实际性质。 染色体共有Nmax基因，其中每个基因对应于表面中的可变性质元素。 此外，每个基因具有n个设计参数，其中设计参数是对染色体的实际性质的增量变化。 使用遗传算法来优化基因，直到超过至少一个染色体的适应度水平为止。 当群体中任何染色体的适合度值高于预定阈值时，终止设计过程，并且最终设计解决方案是超过预定阈值的染色体的设计参数。
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公开(公告)号：US20210327376A1
公开(公告)日：2021-10-21
申请号：US17359520
申请日：2021-06-26
申请人： Narayan Srinivasa
发明人： Narayan Srinivasa
摘要： Apparatuses, methods, and articles of manufacture are disclosed. An example apparatus includes processor circuitry to assign a location value hyperdimensional vector (HDV) to a location in an image of a first patch of one or more pixels, assign at least a first channel HDV to the first patch, determine at least one pixel intensity value HDV for each of the one or more pixels in the first patch, bind together each of the pixel intensity value HDVs into at least one patch intensity value HDV, bind together the at least first channel HDV and the at least one patch intensity value HDV to produce a patch consensus intensity HDV, and generate a first hyperdimensional representation patch value HDV of the first patch by binding together at least a combination of the patch consensus intensity HDV and the location value HDV.
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公开(公告)号：US20180308200A1
公开(公告)日：2018-10-25
申请号：US15494886
申请日：2017-04-24
申请人： Prasoonkumar Surti , Narayan Srinivasa , Feng Chen , Joydeep Ray , Ben J. Ashbaugh , Nicolas C. Galoppo Von Borries , Eriko Nurvitadhi , Balaji Vembu , Tsung-Han Lin , Kamal Sinha , Rajkishore Barik , Sara S. Baghsorkhi , Justin E. Gottschlich , Altug Koker , Nadathur Rajagopalan Satish , Farshad Akhbari , Dukhwan Kim , Wenyin Fu , Travis T. Schluessler , Josh B. Mastronarde , Linda L. Hurd , John H. Feit , Jeffery S. Boles , Adam T. Lake , Karthik Vaidyanathan , Devan Burke , Subramaniam Maiyuran , Abhishek R. Appu
发明人： Prasoonkumar Surti , Narayan Srinivasa , Feng Chen , Joydeep Ray , Ben J. Ashbaugh , Nicolas C. Galoppo Von Borries , Eriko Nurvitadhi , Balaji Vembu , Tsung-Han Lin , Kamal Sinha , Rajkishore Barik , Sara S. Baghsorkhi , Justin E. Gottschlich , Altug Koker , Nadathur Rajagopalan Satish , Farshad Akhbari , Dukhwan Kim , Wenyin Fu , Travis T. Schluessler , Josh B. Mastronarde , Linda L. Hurd , John H. Feit , Jeffery S. Boles , Adam T. Lake , Karthik Vaidyanathan , Devan Burke , Subramaniam Maiyuran , Abhishek R. Appu
CPC分类号： G06T1/20 , G06F8/41 , G06F9/45533 , G06F9/5061 , G06F9/5094 , G06F2009/45583 , G06N3/0445 , G06N3/0454 , G06N3/063 , G06N3/084
摘要： An apparatus to facilitate compute optimization is disclosed. The apparatus includes a plurality of processing units each comprising a plurality of execution units (EUs), wherein the plurality of EUs comprise a first EU type and a second EU type
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公开(公告)号：US09697462B1
公开(公告)日：2017-07-04
申请号：US14588929
申请日：2015-01-03
CPC分类号： G06N3/04
摘要： A synaptic time-multiplexed (STM) neuromorphic network includes a neural fabric that includes nodes and switches to define inter-nodal connections between selected nodes of the neural fabric. The STM neuromorphic network further includes a neuromorphic controller to form subsets of a set of the inter-nodal connections representing a fully connected neural network. Each subset is formed during a different time slot of a plurality of time slots of a time multiplexing cycle of the STM neuromorphic network. In combination, the inter-nodal connection subsets implement the fully connected neural network. A method of synaptic time multiplexing a neuromorphic network includes providing the neural fabric and forming the subsets of the set of inter-nodal connections.
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公开(公告)号：US20110099136A1
公开(公告)日：2011-04-28
申请号：US12604606
申请日：2009-10-23
IPC分类号： G06N3/12
CPC分类号： G06N3/02 , G06F17/18 , G06K9/00496 , G06K9/6251
摘要： A method and system for characterizing, detecting, and predicting or forecasting multiple target events from a past history of these events includes compressing temporal data streams into self-organizing map (SOM) clusters, and determining trajectories of the temporal streams via the clusters to predict the multiple target events. The system includes an evolutionary multi-objective optimization (EMO) module for processing the temporal data streams, which are obtained from a plurality of heterogeneous domains; a SOM module for characterizing the temporal data streams into self-organizing map clusters; and a target event prediction (TEP) module for generating prediction models of the map clusters. The SOM module employs a vector quantization method that places a set of vectors on a low-dimensional grid in an ordered fashion. The prediction models each include trajectories of the temporal data streams, and the system predicts the multiple target events using the trajectories.
摘要翻译： 用于从这些事件的过去历史表征，检测和预测或预测多个目标事件的方法和系统包括将时间数据流压缩为自组织映射（SOM）集群，以及通过集群确定时间流的轨迹以预测 多个目标事件。 该系统包括用于处理从多个异构域获得的时间数据流的进化多目标优化（EMO）模块; 用于将时间数据流表征为自组织映射簇的SOM模块; 以及用于生成地图簇的预测模型的目标事件预测（TEP）模块。 SOM模块采用矢量量化方法，其以有序的方式将一组向量放置在低维度网格上。 预测模型各自包括时间数据流的轨迹，并且系统使用轨迹来预测多个目标事件。
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公开(公告)号：US07797259B2
公开(公告)日：2010-09-14
申请号：US11786949
申请日：2007-04-12
申请人： Qin Jiang , Narayan Srinivasa
发明人： Qin Jiang , Narayan Srinivasa
IPC分类号： G06E1/00
CPC分类号： G06N3/0436
摘要： Described is a system for temporal prediction. The system includes an extraction module, a mapping module, and a prediction module. The extraction module is configured to receive X(1), . . . X(n) historical samples of a time series and utilize a genetic algorithm to extract deterministic features in the time series. The mapping module is configured to receive the deterministic features and utilize a learning algorithm to map the deterministic features to a predicted {circumflex over (x)}(n+1) sample of the time series. Finally, the prediction module is configured to utilize a cascaded computing structure having k levels of prediction to generate a predicted {circumflex over (x)}(n+k) sample. The predicted {circumflex over (x)}(n+k) sample is a final temporal prediction for k future samples.
摘要翻译： 描述了一种用于时间预测的系统。 该系统包括提取模块，映射模块和预测模块。 提取模块被配置为接收X（1），。 。 。 X（n）时间序列的历史样本，并利用遗传算法提取时间序列中的确定性特征。 映射模块被配置为接收确定性特征并利用学习算法来将确定性特征映射到时间序列的预测{（x）}（n + 1）样本。 最后，预测模块被配置为利用具有k个预测级别的级联计算结构来生成预测的（x（x）}（n + k）个样本。 预测的（{x}}（n + k）样本的回归是k个未来样本的最终时间预测。
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公开(公告)号：US07787709B2
公开(公告)日：2010-08-31
申请号：US11541711
申请日：2006-09-29
申请人： Narayan Srinivasa
发明人： Narayan Srinivasa
CPC分类号： G06T5/40 , G06T5/008 , G06T2207/20012 , G06T2207/30252
摘要： A method for enhancing the quality of a digital image by using a single user-defined parameter. A virtual image is created based on the single user-defined parameter and the original digital image. An adaptive contrast enhancement algorithm operates on a logarithmically compressed version of the virtual image to produce adaptive contrast values for each pixel in the virtual image. A dynamic range adjustment algorithm is used to generate logarithmic enhanced pixels based on the adaptive contrast values and the pixels of the logarithmically compressed version of the virtual image. The logarithmic enhanced pixels are exponentially expanded and scaled to produce a compensated digital image.
摘要翻译： 一种通过使用单个用户定义的参数来增强数字图像的质量的方法。 基于单个用户定义的参数和原始数字图像创建虚拟图像。 自适应对比度增强算法对虚拟图像的对数压缩版本进行操作，以为虚拟图像中的每个像素产生自适应对比度值。 动态范围调整算法用于基于自适应对比度值和虚拟图像的对数压缩版本的像素来生成对数增强像素。 对数增强像素以指数方式扩展和缩放以产生补偿数字图像。
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公开(公告)号：US20090055330A1
公开(公告)日：2009-02-26
申请号：US11843876
申请日：2007-08-23
申请人： Swarup Medasani , Qin Jiang , Narayan Srinivasa , Yilu Zhang , Leandro G. Barajas , Nick S. Kapsokavathis
发明人： Swarup Medasani , Qin Jiang , Narayan Srinivasa , Yilu Zhang , Leandro G. Barajas , Nick S. Kapsokavathis
CPC分类号： G01R31/3606 , G01R31/3693
摘要： Anomaly prediction of battery parasitic load includes processing input data related to a state of charge for a battery and a durational factor utilizing a machine learning algorithm and generating a predicted start-up state of charge. Warnings are issued if the predicted start-up state of charge drops below a threshold level within an operational time.
摘要翻译： 电池寄生负载的异常预测包括利用机器学习算法处理与电池的充电状态有关的输入数据和持续时间因子，并产生预测的充电状态。 如果预计的启动状态在运行时间内下降到阈值以下，则发出警告。
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9.发明授权公开(公告)号：US07292960B1
公开(公告)日：2007-11-06
申请号：US11427825
申请日：2006-06-30
申请人： Narayan Srinivasa , Qin Jiang , Leandro G. Barajas
发明人： Narayan Srinivasa , Qin Jiang , Leandro G. Barajas
摘要： A method for characterizing, detecting and predicting an event of interest, a target event, based on temporal patterns useful for predicting a probable occurrence of the target event is disclosed. Measurable events and their features are defined and quantized into event classes. Temporal series of the event classes are analyzed, and preliminary prediction rules established by analyzing temporal patterns of the event classes that precede an occurrence of the target event using a sliding time window. The quality of the preliminary prediction rules is evaluated and parameters thereof are optimized by using a defined fitness function, thereby defining finalized prediction rules. The finalized prediction rules are then made available for application on temporal series of the event classes to forecast a probable occurrence of the target event.
摘要翻译： 公开了一种用于表征，检测和预测感兴趣事件的方法，基于用于预测目标事件的可能出现的时间模式的目标事件。 可测量事件及其特征被定义和量化为事件类。 分析事件类的时间序列，并通过使用滑动时间窗口分析在事件发生之前的事件类的时间模式来建立初步预测规则。 评估初步预测规则的质量，并通过使用定义的适应度函数来优化其参数，从而定义最终预测规则。 然后，最终确定的预测规则可用于事件类别的时间序列上的应用以预测目标事件的可能发生。
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公开(公告)号：US20070025633A1
公开(公告)日：2007-02-01
申请号：US11541711
申请日：2006-09-29
申请人： Narayan Srinivasa
发明人： Narayan Srinivasa
IPC分类号： G06K9/40
CPC分类号： G06T5/40 , G06T5/008 , G06T2207/20012 , G06T2207/30252
摘要： A method for enhancing the quality of a digital image by using a single user-defined parameter. A virtual image is created based on the single user-defined parameter and the original digital image. An adaptive contrast enhancement algorithm operates on a logarithmically compressed version of the virtual image to produce adaptive contrast values for each pixel in the virtual image. A dynamic range adjustment algorithm is used to generate logarithmic enhanced pixels based on the adaptive contrast values and the pixels of the logarithmically compressed version of the virtual image. The logarithmic enhanced pixels are exponentially expanded and scaled to produce a compensated digital image.
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