Reaching Optimal Distributed Estimation Through Myopic Self-Confidence Adaptation

Adaptation to optimal cell growth through self-organized criticality.

A simple cell model consisting of a catalytic reaction network is studied to show that cellular states are self-organized in a critical state for achieving optimal growth; we consider the catalytic network dynamics over a wide range of environmental conditions, through the spontaneous regulation of nutrient transport into the cell. Furthermore, we find that the adaptability of cellular growth t...

On-line measurement validation through confidence level based optimal estimation of a process variable

In a continuous chemical process, the accuracy and reliability of process and analytical measurements creates the basis for control system performance and ultimately for product uniformity. Measurement results, whether from fast on-line measurement devices or from sample-based laboratory analyses, is the key for selecting the method for process control and analysis. Intelligent and advanced con...

Trust, self-confidence, and operators' adaptation to automation

Parameter Estimation in Distributed Systems: Optimal Design

We formulate a very general optimal design problem for the selection of best states to observe and optimal sampling times and locations (i.e., selections of what, when, and where to observe) for parameter estimation or inverse problems involving complex nonlinear partial differential systems. A theoretical framework along with a detailed iterative algorithm for implementation of the resulting m...

Efficient language model adaptation through MDI estimation

This paper presents a method for n-gram language model adaptation based on the principle of minimum discrimination information. A background language model is adapted to t constraints on its marginal distributions that are derived from new observed data. This work gives a di erent derivation of the model by Kneser et al. (1997) and extends its application to interpolated language models. The pr...