Probabilistic fault identification using vibration data and neural networks

Bayesian formulated neural networks are implemented using hybrid Monte Carlo method for probabilistic fault identification in cylindrical shells. Each of the 20 nominally identical cylindrical shells is divided into three substructures. Holes of (12±2) mm in diameter are introduced in each of the substructures and vibration data are measured. Modal properties and the Coordinate Modal Assurance Criterion (COMAC) are utilized to train the two modal-property-neural-networks. These COMAC are calculated by taking the natural-frequency-vector to be an additional mode. Modal energies are calculated by determining the integrals of the real and imaginary components of the frequency response functions over bandwidths of 12% of the natural frequencies. The modal energies and the Coordinate Modal Energy Assurance Criterion (COMEAC) are used to train the two frequency-response-function-neural-networks. The averages of the two sets of trained-networks (COMAC and COMEAC as well as modal properties and modal energies) form two committees of networks. The COMEAC and the COMAC are found to be better identification data than using modal properties and modal energies directly. The committee approach is observed to give lower standard deviations than the individual methods. The main advantage of the Bayesian formulation is that it gives identities of damage and their respective confidence intervals.

Spoken document representations for probabilistic retrieval

This paper presents some developments in query expansion and document representation of our spoken document retrieval system and shows how various retrieval techniques affect performance for different sets of transcriptions derived from a common speech source. Modifications of the document representation are used, which combine several techniques for query expansion, knowledge-based on one hand and statistics-based on the other. Taken together, these techniques can improve Average Precision by over 19% relative to a system similar to that which we presented at TREC-7. These new experiments have also confirmed that the degradation of Average Precision due to a word error rate (WER) of 25% is quite small (3.7% relative) and can be reduced to almost zero (0.2% relative). The overall improvement of the retrieval system can also be observed for seven different sets of transcriptions from different recognition engines with a WER ranging from 24.8% to 61.5%. We hope to repeat these experiments when larger document collections become available, in order to evaluate the scalability of these techniques.

The conceptual development of a simple scale-adaptive reactive pollutant dispersion model

The paper develops the basis for a self-consistent, operationally useful, reactive pollutant dispersion model, for application in urban environments. The model addresses the multi-scale nature of the physical and chemical processes and the interaction between the different scales. The methodology builds on existing techniques of source apportionment in pollutant dispersion and on reduction techniques of detailed chemical mechanisms. © 2005 Published by Elsevier Ltd.

Demodulation as probabilistic inference

Demodulation is an ill-posed problem whenever both carrier and envelope signals are broadband and unknown. Here, we approach this problem using the methods of probabilistic inference. The new approach, called Probabilistic Amplitude Demodulation (PAD), is computationally challenging but improves on existing methods in a number of ways. By contrast to previous approaches to demodulation, it satisfies five key desiderata: PAD has soft constraints because it is probabilistic; PAD is able to automatically adjust to the signal because it learns parameters; PAD is user-steerable because the solution can be shaped by user-specific prior information; PAD is robust to broad-band noise because this is modeled explicitly; and PAD's solution is self-consistent, empirically satisfying a Carrier Identity property. Furthermore, the probabilistic view naturally encompasses noise and uncertainty, allowing PAD to cope with missing data and return error bars on carrier and envelope estimates. Finally, we show that when PAD is applied to a bandpass-filtered signal, the stop-band energy of the inferred carrier is minimal, making PAD well-suited to sub-band demodulation. © 2006 IEEE.

Metal Retention Experiments for the Design of Soil-Mix Technology Permeable Reactive Barriers

Soil-mix technology is effective for the construction of permeable reactive barriers (PRBs) for in situ groundwater treatment. The objective of this study was to perform initial experiments for the design of soil-mix technology PRBs according to (i) sorption isotherm, (ii) reaction kinetics and (iii) mass balance of the contaminants. The four tested reactive systems were: (i) a granular zeolite (clinoptilolite-GZ), (ii) a granular organoclay (GO), (iii) a 1:1-mixture GZ and model sandy clayey soil and (iv) a 1:1:1-mixture of GZ, GO and model soil. The laboratory experiments consisted of batch tests (volume 900mL and sorbent mass 18g) with a multimetal solution of Pb, Cu, Zn, Cd and Ni. For the adsorption experiment, the initial concentrations ranged from 0.01 to 0.5mM (2.5 to 30mg/L). The maximum metal retention was measured in a batch test (300mg/L for each metal, volume 900mL, sorbent mass 90-4.5g). The reactive material efficiency order was found to be GZ>GZ-soil mix>GZ-soil-GO mix>GO. Langmuir isotherms modelled the adsorption, even in presence of a mixed cations solution. Adsorption was energetically favourable and spontaneous in all cases. Metals were removed according to the second order reaction kinetics; GZ and the 1:1-mix were very similar. The maximum retention capacity was 0.1-0.2mmol/g for Pb in the presence of clinoptilolite; for Cu, Zn, Cd and Ni, it was below 0.05mmol/g for the four reactive systems. Mixing granular zeolite, organoclay and model soil increased the chemisorption. Providing that GZ is reactive enough for the specific conditions, GZ can be mixed to obtain the required sorption. Granular clinoptilolite addition to soil is recommended for PRBs for metal contaminated groundwater. The laboratory experiments consisted of batch tests with a multimetal solution of Pb, Cu, Zn, Cd and Ni. The four reactive materials chosen were granular zeolite, clinoptilolite and model sandy clayey soil, granular organoclay and a mix of clinoptilolite, model soil and organoclay. The reactive material efficiency order was found to be granular clinoptilolite>clinoptilolite-soil mix>clinoptilolite-soil-organoclay mix>granular organoclay. © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Towards probabilistic performance metrics for climate change impact studies

This paper explores the current state-of-the-art in performance indicators and use of probabilistic approaches used in climate change impact studies. It presents a critical review of recent publications in this field, focussing on (1) metrics for energy use for heating and cooling, emissions, overheating and high-level performance aspects, and (2) uptake of uncertainty and risk analysis. This is followed by a case study, which is used to explore some of the contextual issues around the broader uptake of climate change impact studies in practice. The work concludes that probabilistic predictions of the impact of climate change are feasible, but only based on strict and explicitly stated assumptions. © 2011 Elsevier B.V. All rights reserved.

Thermal building simulation using the UKCP09 probabilistic climate projections

This article investigates how to use UK probabilistic climate-change projections (UKCP09) in rigorous building energy analysis. Two office buildings (deep plan and shallow plan) are used as case studies to demonstrate the application of UKCP09. Three different methods for reducing the computational demands are explored: statistical reduction (Finkelstein-Schafer [F-S] statistics), simplification using degree-day theory and the use of metamodels. The first method, which is based on an established technique, can be used as reference because it provides the most accurate information. However, it is necessary to automatically choose weather files based on F-S statistic by using computer programming language because thousands of weather files created from UKCP09 weather generator need to be processed. A combination of the second (degree-day theory) and third method (metamodels) requires only a relatively small number of simulation runs, but still provides valuable information to further implement the uncertainty and sensitivity analyses. The article also demonstrates how grid computing can be used to speed up the calculation for many independent EnergyPlus models by harnessing the processing power of idle desktop computers. © 2011 International Building Performance Simulation Association (IBPSA).