Integrating neural network technology and noise analysis

The integrated use of neural network and noise analysis technologies offers advantages not available by the use of either technology alone. The application of neural network technology to noise analysis offers an opportunity to expand the scope of problems where noise analysis is useful and unique ways in which the integration of these technologies can be used productively. The two-sensor technique, in which the responses of two sensors to an unknown driving source are related, is used to demonstration such integration. The relationship between power spectral densities (PSDs) of accelerometer signals is derived theoretically using noise analysis to demonstrate its uniqueness. This relationship is modeled from experimental data using a neural network when the system is working properly, and the actual PSD of one sensor is compared with the PSD of that sensor predicted by the neural network using the PSD of the other sensor as an input. A significant deviation between the actual and predicted PSDs indicate that system is changing (i.e., failing). Experiments carried out on check valves and bearings illustrate the usefulness of the methodology developed.     

On best practices: The Institute of Medicine scheme for developing,validating, and demonstrating clinical utility of omics-based diagnostic and predictive tests

The Institute of Medicine of the US National Academy of Sciences issued a major report in 2012 that provides important guidance for the proteomics community for credible and transparent development of all kinds of omics-based diagnostic and prognostic tests. The key elements are summarized here to provide a compass that can guide the studies emerging from many laboratories as reported in this special issue of Proteomics Clinical Applications on “Diagnostic Proteomics.”     

Deletion, replacement and mean-shift for diagnostics in linear mixed models

Deletion, replacement and mean-shift model are three approaches frequently used to detect influential observations and outliers. For general linear model with known covariance matrix, it is known that these three approaches lead to the same update formulae for the estimates of the regression coefficients. However if the covariance matrix is indexed by some unknown parameters which also need to be estimated, the situation is unclear. In this paper, we show under a common subclass of linear mixed models that the three approaches are no longer equivalent. For maximum likelihood estimation, replacement is equivalent to mean-shift model but both are not equivalent to case deletion. For restricted maximum likelihood estimation, mean-shift model is equivalent to case deletion but both are not equivalent to replacement. We also demonstrate with real data that misuse of replacement and mean-shift model in place of case deletion can lead to incorrect results.     

Process diagnostics using trace alignment: Opportunities, issues, and challenges

Business processes leave trails in a variety of data sources (e.g., audit trails, databases, and transaction logs). Hence, every process instance can be described by a trace, i.e., a sequence of events. Process mining techniques are able to extract knowledge from such traces and provide a welcome extension to the repertoire of business process analysis techniques. Recently, process mining techniques have been adopted in various commercial BPM systems (e.g., BPM|one, Futura Reflect, ARIS PPM, Fujitsu Interstage, Businesscape, Iontas PDF, and QPR PA). Unfortunately, traditional process discovery algorithms have problems dealing with less structured processes. The resulting models are difficult to comprehend or even misleading. Therefore, we propose a new approach based on trace alignment. The goal is to align traces in such a way that event logs can be explored easily. Trace alignment can be used to explore the process in the early stages of analysis and to answer specific questions in later stages of analysis. Hence, it complements existing process mining techniques focusing on discovery and conformance checking. The proposed techniques have been implemented as plugins in the ProM framework. We report the results of trace alignment on one synthetic and two real-life event logs, and show that trace alignment has significant promise in process diagnostic efforts.     

Using rapid compression machines for chemical kinetics studies

Rapid compression machines (RCMs) are used to simulate a single compression stroke of an internal combustion engine without some of the complicated swirl bowl geometry, cycle-to-cycle variation, residual gas, and other complications associated with engine operating conditions. RCMs are primarily used to measure ignition delay times as a function of temperature, pressure, and fuel/oxygen/diluent ratio; further they can be equipped with diagnostics to determine the temperature and flow fields inside the reaction chamber and to measure the concentrations of reactant, intermediate, and product species produced during combustion.This paper first discusses the operational principles and design features of RCMs, including the use of creviced pistons, which is an important feature in order to suppress the boundary layer, preventing it from becoming entrained into the reaction chamber via a roll-up vortex. The paper then discusses methods by which experiments performed in RCMs are interpreted and simulated. Furthermore, differences in measured ignition delays from RCMs and shock tube facilities are discussed, with the apparent initial gross disagreement being explained by facility effects in both types of experiments. Finally, future directions for using RCMs in chemical kinetics studies are also discussed.     

Evaluation of MOSA condition using leakage current method

This paper covers the evaluation of metal-oxide surge arrester (MOSA) condition using the method based on the analysis of leakage current at the operating voltage of the network. A comparison is made between the following variants of this method: (1) the method based on harmonic analysis of the total leakage current; (2) the method based on the third order harmonic of the resistive leakage current; (3) the method of power loss; (4) the capacitive current compensation method; and (5) the method based on direct measurement of the amplitude of the resistive leakage current. For these methods the appropriate indicators are introduced for evaluating the MOSA condition. The application of these methods is analyzed depending on fluctuation and the presence of higher harmonics of the MOSA operating voltage. Calculations were conducted using the program MATLAB on a simplified equivalent circuit of a MOSA with a non-linear element modeled using the degree function. Based on the calculation results, a gradation was made for the applicability of individual indicators for evaluating the MOSA condition which indicates a special advantage for using the fundamental harmonic of the resistive component of leakage current and the fundamental harmonic of power loss.     

Can proteomics-based diagnostics aid clinical psychiatry?

Despite major advances in infrastructure and instrumentation, proteomics-driven translational applications have not yet yielded the results that the scientific community has envisaged. In this viewpoint, the perspective of proteomics-based diagnostics in the field of clinical psychiatry is explored. The challenges that proteomics faces in the context of translational approaches are outlined and directions toward a successful clinical implementation are provided. Additional challenges that psychiatric disorders pose for clinical proteomics are highlighted and the potential of proteomics-based, blood tests for psychiatric disorders is being assessed. Proteomics offers a valuable toolkit for clinical translation that needs to be handled in a pragmatic manner and with realistic expectations.     

Local Influence of Predictors in Multiple Linear Regression

Local influence diagnostics can be used to assess the influence of predictor values in multiple linear regression. For n observations and k regressors, an eigenanalysis of an nk ×nk matrix is required to assess the influence on the estimated coefficients. We provide the analytic expressions for the eigenvectors and show that they are easily computed, describe influence on the parameter estimates of a principal components regression, and are related to leverage, outliers, and added-variables plots. The results indicate that multicollinearity and overfitting contribute to a fitted model's sensitivity, leading to strategies for model assessment and selection.     

Robust data reconciliation of combustion variables in multi-fuel fired industrial boilers

This paper introduces an application of simultaneous nonlinear data reconciliation and gross error detection for power plants utilizing a complex but computationally light first principle combustion model. Element and energy balances and robust techniques introduce nonlinearity and the consequent optimization problem is solved using nonlinear optimization. Data reconciliation improves estimation of process variables and enables improved sensor quality control and identification of process anomalies. The approach was applied to an industrial 200 MW_th fluidized bed boiler combusting wood, peat, bark, and slurry. The results indicate that the approach is valid and is able to perform in various process conditions. As the combustion model is generic, the method is applicable in any boiler environment.     

Impact of synchrotron radiation on fuel cell operation in imaging experiments

For the clarification of water transport mechanisms in operating fuel cells, synchrotron radiation computed tomography (SR-CT) was applied. A novel fuel cell has been designed that exposes the entire active area (7 mm²) to the SR beam while at the same time allowing for full fuel cell operation during the imaging experiment. This micro fuel cell has been qualified successfully prior to the SR imaging experiments. The cell voltage was 600 mV at 0.2 A cm⁻² (open circuit voltage, OCV > 950 mV) and the operation was stable for hours. However, under SR beam exposition for in situ imaging, severe cell performance degradation within minutes has been reproducibly observed. Even after the SR beam had been switched-off cell operation remained irreversibly degraded whilst OCV could be recovered. Preliminary results indicate a higher degradation sensitivity of the cathode side of the cell. Apparently, the unique setup of the experiment which exposes an operating fuel cell with the entire active area to the SR beam reveals otherwise unnoticeable degradation mechanisms. It may have to be concluded that the very same materials degrade heavily during beam exposure that are subject of the imaging investigation. Consequently, the applicability of SR imaging to study water transport in porous fuel cell materials has to be revisited critically. This publication describes the observations made during fuel cell operation under SR beam exposure and discuss potential mechanisms that may cause beam-induced performance degradation.