The building information model/modeling （BIM） technology is currently applied in a broad range of applications and research for facility management, while the BIM-based mobile FM is difficult owing to various factor...The building information model/modeling （BIM） technology is currently applied in a broad range of applications and research for facility management, while the BIM-based mobile FM is difficult owing to various factors and environments. For example, the mobile applications usually require frequent cross-equipment compatibility. This paper proposes a reasonable BIM-based FM cross-platform framework and develops a mobile application on the basis of an existing BIM-based FM system. The developed mobile application is applied in a case study of a metro station project in Guangzhou to verify its effectiveness in FM practice. It helps maintenance staff in viewing BIMs, accessing related information, and updating maintenance records in a unique platform. The test results demonstrate that the proposed BIM-based cross-platform framework meet the FM application requirements and supports the extension of FM functions.展开更多
Objective To study how to ensure the quality of listed drugs by using quality risk management to control the risks in the drug collinear production because some pharmaceutical companies produce drugs with multiple dos...Objective To study how to ensure the quality of listed drugs by using quality risk management to control the risks in the drug collinear production because some pharmaceutical companies produce drugs with multiple dosage forms and specifications on the same line to save costs in China.Methods The application status of quality risk management in the production of drugs was analyzed by consulting literature,field investigation and research re-lated policies.Results and Conclusion When introducing co-line products,we should focus on combining prod-uct reality,establish a cross-professional team in comprehensive quality,R&D,equipment,and production.Then we can coordinate quality risk management to integrate with each other to avoid“shortcomings”in different links,which will affect the effectiveness of risk management.When a company conducts co-line production,it should rely on quality risk management theories to establish a scientific quality management system,reasonably use qual-ity risk assessment tools to evaluate the types of co-line production,and control key risk factors effectively.These methods can ensure the risk of co-production within an acceptable range.展开更多
Modern mobile devices have several network interfaces and can run various network applications. In order to remain always best connected, events need to be communicated through the entire protocol stack in an efficien...Modern mobile devices have several network interfaces and can run various network applications. In order to remain always best connected, events need to be communicated through the entire protocol stack in an efficient manner. Current implementations can handle only a handful of low level events that may trigger actions for mobility management, such as signal strength indicators and cell load. In this paper, we present a framework for managing mobility triggers that can deal with a greater variety of triggering events, which may originate from any component of the node’s protocol stack as well as mobility management entities within the network. We explain the main concepts that govern our trigger management framework and discuss its architecture which aims at operating in a richer mobility management framework, enabling the deployment of new applications and services. We address several implementation issues, such as, event collection and processing, storage, and trigger dissemination, and introduce a real implementation for commodity mobile devices. We review our testbed environment and provide experimental results showcasing a lossless streaming video session handover between a laptop and a PDA using mobility and sensor-driven orientation triggers. Moreover, we empirically evaluate and analyze the performance of our prototype. We position our work and implementation within the Ambient Networks architecture and common prototype, centring in particular on the use of policies to steer operation. Finally, we outline current and future work items.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 the National High-tech Research and Development Program of China(2013AA041307)the National Natural Science Foundation of China(51478249)the Tsinghua University-Glodon Joint Research Centre for Building Information Model
文摘The building information model/modeling （BIM） technology is currently applied in a broad range of applications and research for facility management, while the BIM-based mobile FM is difficult owing to various factors and environments. For example, the mobile applications usually require frequent cross-equipment compatibility. This paper proposes a reasonable BIM-based FM cross-platform framework and develops a mobile application on the basis of an existing BIM-based FM system. The developed mobile application is applied in a case study of a metro station project in Guangzhou to verify its effectiveness in FM practice. It helps maintenance staff in viewing BIMs, accessing related information, and updating maintenance records in a unique platform. The test results demonstrate that the proposed BIM-based cross-platform framework meet the FM application requirements and supports the extension of FM functions.
文摘Objective To study how to ensure the quality of listed drugs by using quality risk management to control the risks in the drug collinear production because some pharmaceutical companies produce drugs with multiple dosage forms and specifications on the same line to save costs in China.Methods The application status of quality risk management in the production of drugs was analyzed by consulting literature,field investigation and research re-lated policies.Results and Conclusion When introducing co-line products,we should focus on combining prod-uct reality,establish a cross-professional team in comprehensive quality,R&D,equipment,and production.Then we can coordinate quality risk management to integrate with each other to avoid“shortcomings”in different links,which will affect the effectiveness of risk management.When a company conducts co-line production,it should rely on quality risk management theories to establish a scientific quality management system,reasonably use qual-ity risk assessment tools to evaluate the types of co-line production,and control key risk factors effectively.These methods can ensure the risk of co-production within an acceptable range.
文摘Modern mobile devices have several network interfaces and can run various network applications. In order to remain always best connected, events need to be communicated through the entire protocol stack in an efficient manner. Current implementations can handle only a handful of low level events that may trigger actions for mobility management, such as signal strength indicators and cell load. In this paper, we present a framework for managing mobility triggers that can deal with a greater variety of triggering events, which may originate from any component of the node’s protocol stack as well as mobility management entities within the network. We explain the main concepts that govern our trigger management framework and discuss its architecture which aims at operating in a richer mobility management framework, enabling the deployment of new applications and services. We address several implementation issues, such as, event collection and processing, storage, and trigger dissemination, and introduce a real implementation for commodity mobile devices. We review our testbed environment and provide experimental results showcasing a lossless streaming video session handover between a laptop and a PDA using mobility and sensor-driven orientation triggers. Moreover, we empirically evaluate and analyze the performance of our prototype. We position our work and implementation within the Ambient Networks architecture and common prototype, centring in particular on the use of policies to steer operation. Finally, we outline current and future work items.
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.
