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
The control strategy is very important for semiactive control or active control systems. An integrated intelligent control strategy for building structures incorpo rated with magnetorheological (MR) dampers subjected ...The control strategy is very important for semiactive control or active control systems. An integrated intelligent control strategy for building structures incorpo rated with magnetorheological (MR) dampers subjected to earthquake excitation is proposed. In this strategy, the time-delay problem is solved by a neural network and the control currents of the MR dampers are determined quickly by a fuzzy controller. Through a numerical example of a three-storey structure with one MR damper installed in the first floor, the seismic responses of the uncontrolled, the intelligently controlled, the passiveon controlled, and the passive-off controlled structures under different earthquake excitations are analyzed. Based on the numerical results, it can be found that the time domain and the frequency domain responses are reduced effectively when the MR damper is added in the structure, and the integrated intelligent control strategy has a better earthquake mitigation effect.展开更多
半自磨机具有多变量、非线性、强耦合、大滞后、时变性等特征,且很多过程参数难以检测,难以通过常规控制方法实现自动控制。为此,乌山选矿厂以人工经验为基础,找出半自磨机工作时给矿量、磨音、功率、轴压、磨矿浓度、给矿粒度比例之间...半自磨机具有多变量、非线性、强耦合、大滞后、时变性等特征,且很多过程参数难以检测,难以通过常规控制方法实现自动控制。为此,乌山选矿厂以人工经验为基础,找出半自磨机工作时给矿量、磨音、功率、轴压、磨矿浓度、给矿粒度比例之间的关系,并用计算机语言表述出来,得到一种定性的智能控制系统。实践表明:这种半自磨机智能控制系统可根据服务器设定的控制策略,实时采集半自磨机过程参数,自动调整至最优的半自磨机运行状态,在乌山选矿厂应用后较原人工控制可以提高处理量24.7 t/h、延长衬板使用寿命11.1 d、降低吨矿能耗0.49 k Wh/t,具有显著的经济效益,在金属矿山领域具有重要推广应用前景。展开更多
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.
基金the National Natural Science Foundation of China(Grant No.50508010),the Program for New Century Excellent in the Education Ministry of China,the Program for Jiangsu Province 333 Talents and the Scientific Research Foundation for the Returned Overseas Chinese Scholars,Education Ministry.of China
文摘The control strategy is very important for semiactive control or active control systems. An integrated intelligent control strategy for building structures incorpo rated with magnetorheological (MR) dampers subjected to earthquake excitation is proposed. In this strategy, the time-delay problem is solved by a neural network and the control currents of the MR dampers are determined quickly by a fuzzy controller. Through a numerical example of a three-storey structure with one MR damper installed in the first floor, the seismic responses of the uncontrolled, the intelligently controlled, the passiveon controlled, and the passive-off controlled structures under different earthquake excitations are analyzed. Based on the numerical results, it can be found that the time domain and the frequency domain responses are reduced effectively when the MR damper is added in the structure, and the integrated intelligent control strategy has a better earthquake mitigation effect.
文摘半自磨机具有多变量、非线性、强耦合、大滞后、时变性等特征,且很多过程参数难以检测,难以通过常规控制方法实现自动控制。为此,乌山选矿厂以人工经验为基础,找出半自磨机工作时给矿量、磨音、功率、轴压、磨矿浓度、给矿粒度比例之间的关系,并用计算机语言表述出来,得到一种定性的智能控制系统。实践表明:这种半自磨机智能控制系统可根据服务器设定的控制策略,实时采集半自磨机过程参数,自动调整至最优的半自磨机运行状态,在乌山选矿厂应用后较原人工控制可以提高处理量24.7 t/h、延长衬板使用寿命11.1 d、降低吨矿能耗0.49 k Wh/t,具有显著的经济效益,在金属矿山领域具有重要推广应用前景。
