Integrated approach for HP-LNG pump diagnosis and prognostics based on health state probability estimation

This paper presents an innovative prognostics model based on health state probability estimation embedded in the closed loop diagnostic and prognostic system. To employ an appropriate classifier for health state probability estimation in the proposed prognostic model, the comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault levels of three faults in HP-LNG pump. Two sets of impeller-rubbing data were employed for the prediction of pump remnant life based on estimation of discrete health state probability using an outstanding capability of SVM and a feature selection technique. The results obtained were very encouraging and showed that the proposed prognosis system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications.

Optimal coordination of overcurrent relays in distribution systems with distributed generators based on differential evolution algorithm

Distributed generators (DGs) are defined as generators that are connected to a distribution network. The direction of the power flow and short-circuit current in a network could be changed compared with one without DGs. The conventional protective relay scheme does not meet the requirement in this emerging situation. As the number and capacity of DGs in the distribution network increase, the problem of coordinating protective relays becomes more challenging. Given this background, the protective relay coordination problem in distribution systems is investigated, with directional overcurrent relays taken as an example, and formulated as a mixed integer nonlinear programming problem. A mathematical model describing this problem is first developed, and the well-developed differential evolution algorithm is then used to solve it. Finally, a sample system is used to demonstrate the feasiblity and efficiency of the developed method.

Reduced electron recombination of dye-sensitized solar cells based on TiO 2 spheres consisting of ultrathin nanosheets with [001] facet exposed

An anatase TiO 2 material with hierarchically structured spheres consisting of ultrathin nanosheets with 100% of the [001] facet exposed was employed to fabricate dye-sensitized solar cells (DSC s). Investigation of the electron transport and back reaction of the DSCs by electrochemical impedance spectroscopy showed that the spheres had a threefold lower electron recombination rate compared to the conventional TiO 2 nanoparticles. In contrast, the effective electron diffusion coefficient, D n, was not sensitive to the variation of the TiO 2 morphology. The TiO 2 spheres showed the same Dn as that of the nanoparticles. The influence of TiCl 4 post-treatment on the conduction band of the TiO 2 spheres and on the kinetics of electron transport and back reactions was also investigated. It was found that the TiCl 4 post-treatment caused a downward shift of the TiO 2 conduction band edge by 30 meV. Meanwhile, a fourfold increase of the effective electron lifetime of the DSC was also observed after TiCl4 treatment. The synergistic effect of the variation of the TiO 2 conduction band and the electron recombination determined the open-circuit voltage of the DSC. © 2012 Wang et al.

Molecular investigation of the mechanical properties of single actin filaments based on vibration analyses

The mechanical vibration properties of single actin filaments from 50 to 288 nm are investigated by the molecular dynamics simulation in this study. The natural frequencies obtained from the molecular simulations agree with those obtained from the analytical solution of the equivalent Euler–Bernoulli beam model. Through the convergence study of the mechanical properties with respect to the filament length, it was found that the Euler–Bernoulli beam model can only be reliably used when the single actin filament is of the order of hundreds of nanometre scale. This molecular investigation not only provides the evidence for the use of the continuum beam model in characterising the mechanical properties of single actin filaments, but also clarifies the criteria for the effective use of the Euler–Bernoulli beam model.

A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.

Leg Clubs - Beyond the ulcers : case studies based on participatory action research

Based on Participatory Action Research (PAR), the case studies in this paper examine the psychosocial benefits and outcomes for clients of community based Leg Clubs. The Leg Club model was developed in the United Kingdom (UK) to address the issue of social isolation and non-compliance to leg ulcer treatment. Principles underpinning the Leg Club are based on the Participatory Action Framework (PAR) where the input and involvement of participants is central. This study identifies the strengths of the Leg Club in enabling and empowering people to improve the social context in which they function. In addition it highlights the potential of expanding operations that are normally clinically based (particularly in relation to chronic conditions) but transferable to community settings in order that that they become “agents of change” for addressing such issues as social isolation and the accompanying challenges that these present, including no-compliance to treatment.