A framework for integrating diagnostic knowledge with nonlinear optimization for data reconciliation and parameter estimation in dynamic systems

Vachhani, Pramod; Rengaswamy, Raghunathan; Venkatasubramanian, Venkat

doi:10.1016/s0009-2509(00)00488-7

Cited by 33 publications

(11 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ACO is implemented in always all engineering applications like continuous casting of steel [38], data reconciliation and parameter estimation in dynamic systems [39], gaming theory [40], In-Core Fuel Management Optimization in Nuclear Engineering [41], target tracking problem in signal processing [42], design of automatic material handling devices [43], Mathematical and kinetic modeling of bio-film reactor [44], optimization of a rail vehicle floor sandwich panel [45], software design [46], Vehicle routing design [47], Quadratic Assignation problem [48], mutation problem [49]. The experimental of ACO shows [50] [51] that the ACO outperforms than the existing research methodologies.…”

Section: Resultsmentioning

confidence: 99%

Survey on Recent Research and Implementation of Ant Colony Optimization in Various Engineering Applications

B.¹,

Baskaran²

2011

IJCIS

View full text Add to dashboard Cite

Ant colony optimization (ACO) takes inspiration from the foraging behaviour of real ant species. This ACO exploits a similar mechanism for solving optimization problems for the various engineering field of study. Many successful implementations using ACO are now available in many applications. This paper reviewing varies systematic approach on recent research and implementation of ACO. Finally it presents the experimental result of ACO which is applied for routing problem and compared with existing algorithms.

show abstract

Section: Resultsmentioning

confidence: 99%

Survey on Recent Research and Implementation of Ant Colony Optimization in Various Engineering Applications

B.¹,

Baskaran²

2011

IJCIS

View full text Add to dashboard Cite

show abstract

“…For mobile data, gross errors are eliminated once samples are sniffed by sliding window technique which is verified in dynamic systems [28,29]. We normalize time variable into 10 s interval in each window.…”

Section: Sensor Calibrationmentioning

confidence: 99%

A Mobile Sensing System for UrbanPM2.5Monitoring with Adaptive Resolution

et al. 2016

View full text Add to dashboard Cite

This paper develops a mobile sensing system, the first system used in adaptive resolution urban air quality monitoring. In this system, we employ several taxis as sensor carries to collect original PM 2.5 data and collect a variety of datasets, including meteorological data, traffic status data, and geographical data in the city. This paper also presents a novel method AG-PCEM (Adaptive Grid-Probabilistic Concentration Estimation Method) to infer the PM 2.5 concentration for undetected grids using dynamic adaptive grids. We gradually collect the measurements throughout a year using a prototype system in Xiasha District of Hangzhou City, China. Experimental data has verified that the proposed system can achieve good performance in terms of computational cost and accuracy. The computational cost of AG-PCEM is reduced by about 40.2% compared with a static grid method PCEM under the condition of reaching the close accuracy, and the accuracy of AG-PCEM is far superior as widely used artificial neural network (ANN) and Gaussian process (GP), enhanced by 38.8% and 14.6%, respectively. The system can be expanded to wide-range air quality monitor by adjusting the initial grid resolution, and our findings can tell citizens actual air quality and help official management find pollution sources.

show abstract

“…In general, for practical implementation it is necessary to identify online the parameter or unmeasured disturbance variable that undergoes a change. A fault detection and identification strategy can be used for this purpose and integrated with the state and parameter estimator as described by Vachhani et al9 We would point out that the literature on state and parameter estimation rarely addresses the online implementation issues, although they are extremely important for deploying these methods. The scope of the present work, however, is limited to the development of the estimator, and online implementation issues will be taken up as part of future work.…”

Section: Issues and Techniques In State And Parameter Estimationmentioning

confidence: 99%