Engineering Change (EC) management for a Product Lifecycle Management (PLM) system, including EC data model, EC evaluation and EC implementation is presented. A method of dynamic and parallel EC evaluation is proposed...Engineering Change (EC) management for a Product Lifecycle Management (PLM) system, including EC data model, EC evaluation and EC implementation is presented. A method of dynamic and parallel EC evaluation is proposed, which breaks an EC evaluation task into several subtasks. Such subtasks can be dynamically and simultaneously proceeded, disassembled and communicated by different specialists, and at the same time a set of EC objects is created. This approach provides flexible and effective evaluation of the EC. The EC implementation followed will further ensure the completeness and consistency of EC data.展开更多
The brokering approach can be successfully used to overcome the crucial question of searching among enormous amount of data （raw and/or processed） produced and stored in different information systems. In this paper,...The brokering approach can be successfully used to overcome the crucial question of searching among enormous amount of data （raw and/or processed） produced and stored in different information systems. In this paper, authors describe the Data Management System the DMS （Data Management System） developed by INGV （Istituto Nazionale di Geofisica e Vulcanologia） to support the brokering system GEOSS （Global Earth Observation System of Systems） adopted for the ARCA （Arctic Present Climate Change and Past Extreme Events） project. This DMS includes heterogeneous data that contributes to the ARCA objective （www.arcaproject.it） focusing on multi-parametric and multi-disciplinary studies on the mechanism （s） behind the release of large volumes of cold and fresh water from melting of ice caps. The DMS is accessible directly at the www.arca.rm.ingv.it, or through the IADC （Italian Arctic Data Center） at http：//arcticnode.dta.cnr.it/iadc/gi-portal/index.jsp that interoperates with the GEOSS brokering system （http：//www.geoportal.org0 making easy and fast the search of specific data set and its URL.展开更多
We advance here a novel methodology for robust intelligent biometric information management with inferences and predictions made using randomness and complexity concepts. Intelligence refers to learning, adap- tation,...We advance here a novel methodology for robust intelligent biometric information management with inferences and predictions made using randomness and complexity concepts. Intelligence refers to learning, adap- tation, and functionality, and robustness refers to the ability to handle incomplete and/or corrupt adversarial information, on one side, and image and or device variability, on the other side. The proposed methodology is model-free and non-parametric. It draws support from discriminative methods using likelihood ratios to link at the conceptual level biometrics and forensics. It further links, at the modeling and implementation level, the Bayesian framework, statistical learning theory (SLT) using transduction and semi-supervised lea- rning, and Information Theory (IY) using mutual information. The key concepts supporting the proposed methodology are a) local estimation to facilitate learning and prediction using both labeled and unlabeled data;b) similarity metrics using regularity of patterns, randomness deficiency, and Kolmogorov complexity (similar to MDL) using strangeness/typicality and ranking p-values;and c) the Cover – Hart theorem on the asymptotical performance of k-nearest neighbors approaching the optimal Bayes error. Several topics on biometric inference and prediction related to 1) multi-level and multi-layer data fusion including quality and multi-modal biometrics;2) score normalization and revision theory;3) face selection and tracking;and 4) identity management, are described here using an integrated approach that includes transduction and boosting for ranking and sequential fusion/aggregation, respectively, on one side, and active learning and change/ outlier/intrusion detection realized using information gain and martingale, respectively, on the other side. The methodology proposed can be mapped to additional types of information beyond biometrics.展开更多
基金Supported by Fujian Province Natural Science Foundation (A04400066)Xiamen Science and Technology Program(3502Z20055028)
文摘Engineering Change (EC) management for a Product Lifecycle Management (PLM) system, including EC data model, EC evaluation and EC implementation is presented. A method of dynamic and parallel EC evaluation is proposed, which breaks an EC evaluation task into several subtasks. Such subtasks can be dynamically and simultaneously proceeded, disassembled and communicated by different specialists, and at the same time a set of EC objects is created. This approach provides flexible and effective evaluation of the EC. The EC implementation followed will further ensure the completeness and consistency of EC data.
文摘The brokering approach can be successfully used to overcome the crucial question of searching among enormous amount of data （raw and/or processed） produced and stored in different information systems. In this paper, authors describe the Data Management System the DMS （Data Management System） developed by INGV （Istituto Nazionale di Geofisica e Vulcanologia） to support the brokering system GEOSS （Global Earth Observation System of Systems） adopted for the ARCA （Arctic Present Climate Change and Past Extreme Events） project. This DMS includes heterogeneous data that contributes to the ARCA objective （www.arcaproject.it） focusing on multi-parametric and multi-disciplinary studies on the mechanism （s） behind the release of large volumes of cold and fresh water from melting of ice caps. The DMS is accessible directly at the www.arca.rm.ingv.it, or through the IADC （Italian Arctic Data Center） at http：//arcticnode.dta.cnr.it/iadc/gi-portal/index.jsp that interoperates with the GEOSS brokering system （http：//www.geoportal.org0 making easy and fast the search of specific data set and its URL.
文摘We advance here a novel methodology for robust intelligent biometric information management with inferences and predictions made using randomness and complexity concepts. Intelligence refers to learning, adap- tation, and functionality, and robustness refers to the ability to handle incomplete and/or corrupt adversarial information, on one side, and image and or device variability, on the other side. The proposed methodology is model-free and non-parametric. It draws support from discriminative methods using likelihood ratios to link at the conceptual level biometrics and forensics. It further links, at the modeling and implementation level, the Bayesian framework, statistical learning theory (SLT) using transduction and semi-supervised lea- rning, and Information Theory (IY) using mutual information. The key concepts supporting the proposed methodology are a) local estimation to facilitate learning and prediction using both labeled and unlabeled data;b) similarity metrics using regularity of patterns, randomness deficiency, and Kolmogorov complexity (similar to MDL) using strangeness/typicality and ranking p-values;and c) the Cover – Hart theorem on the asymptotical performance of k-nearest neighbors approaching the optimal Bayes error. Several topics on biometric inference and prediction related to 1) multi-level and multi-layer data fusion including quality and multi-modal biometrics;2) score normalization and revision theory;3) face selection and tracking;and 4) identity management, are described here using an integrated approach that includes transduction and boosting for ranking and sequential fusion/aggregation, respectively, on one side, and active learning and change/ outlier/intrusion detection realized using information gain and martingale, respectively, on the other side. The methodology proposed can be mapped to additional types of information beyond biometrics.
