Sensitivity analysis of initial value problems with mixed odes and algebraic equations

An efficient method is described for sensitivity analysis of nonlinear initial value problems, which may include algebraic equations as well as ordinary differential equations.The linearity of the sensitivity equations is utilized to solve them directly via the local Jacobian of the state equations. The method is implemented with the implicit integrator DASSL and is demonstrated on a stiff industrial reaction model.     

Optimal tool replacement with asymmetric quadratic loss

In this paper, an analysis of a tool replacement problem with asymmetric quadratic loss function is presented. The expected average cost per unit time is derived and it is shown that the optimal initial setting and the optimal replacement time can be found by solving two nonlinear equations. The paper generalizes and improves on previous work in the area.     

The E3 architecture: enabling future cellular networks with cognitive and self‐x capabilities

Future mobile networks are expected to be complex heterogeneous systems. On the one hand this will enable users to take advantage of a number of different access technologies. On the other hand it will seriously affect network management procedures since more extensive operations and decisions will have to be dealt with. To tackle these challenges a number of new dynamic mechanisms need to be designed. It is imperative that certain network management tasks have to be performed without human intervention to reduce the OPEX costs and achieve faster responses in different events. To achieve this goal, the introduction of self‐x functionalities, combined with cognitive mechanisms and the ability to reconfigure network entities and terminals, is required. Moreover, the introduction of a new pilot channel needs to be considered to assist the terminals in selecting the most suitable radio access technology according to their requirements. We present the functional architecture of an evolved network that was designed in the context of the EU‐funded IP project ‘E³: End‐to‐End Efficiency’. This architecture aims to enhance existing procedures usually performed in traditional operation and maintenance systems (e.g. spectrum management, network planning, configuration actions). We explain the rationale of our design and provide specific examples to illustrate the role of the different functional entities and their interfaces. A considerable part of this architecture has recently been approved as a feasibility study by the ETSI Committee Reconfigurable Radio System. Copyright © 2010 John Wiley & Sons, Ltd.     

Monte Carlo simulation of the Greek Research Reactor neutron irradiation facilities

A Monte Carlo simulation of the Greek Research Reactor was carried out using MCNP-4C2 code and continuous energy cross-section data from ENDF/B-VI library. A detailed model of the reactor core was employed including standard and control fuel assemblies, reflectors and irradiation devices. The model predicted neutron flux distributions within the core in good agreement with calculations performed using the deterministic code CITATION and measurements using activation foils. The model is used for the prediction of the neutron field characteristics at the reactor irradiation devices and enables the design and evaluation of experiments involving material irradiations.     

Personal neutron dosimetry at a research reactor facility

Individual neutron monitoring presents several difficulties due to the differences in energy response of the dosemeters. In the present study, an individual dosemeter (TLD) calibration approach is attempted for the personnel of a research reactor facility. The neutron energy response function of the dosemeter was derived using the MCNP code. The results were verified by measurements to three different neutron spectra and were found to be in good agreement. Three different calibration curves were defined for thermal, intermediate and fast neutrons. At the different working positions around the reactor, neutron spectra were defined using the Monte Carlo technique and ambient dose rate measurements were performed. An estimation of the neutrons energy is provided by the ratio of the different TLD pellets of each dosemeter in combination with the information concerning the worker's position; then the dose equivalent is deduced according to the appropriate calibration curve.     

Optimal Bayesian maintenance policy for a gearbox subject to two dependent failure modes

Most maintenance optimization models of gear systems have considered single failure mode. There have been very few papers dealing with multiple failure modes, considering mostly independent failure modes. In this paper, we present an optimal Bayesian control scheme for early fault detection of the gear system with dependent competing risks. The system failures include degradation failure and catastrophic failure. A three‐state continuous‐time–homogeneous hidden Markov model (HMM), namely the model with unobservable healthy and unhealthy states, and an observable failure state, describes the deterioration process of the gear system. The condition monitoring information as well as the age of the system are considered in the proposed optimal Bayesian maintenance policy. The objective is to maximize the long‐run expected average system availability per unit time. The maintenance optimization model is formulated and solved in a semi‐Markov decision process (SMDP) framework. The posterior probability that the system is in the warning state is used for the residual life estimation and Bayesian control chart development. The prediction results show that the mean residual lives obtained in this paper are much closer to the actual values than previously published results. A comparison with the Bayesian control chart based on the previously published HMM and the age‐based replacement policy is given to illustrate the superiority of the proposed approach. The results demonstrate that the Bayesian control scheme with two dependent failure modes can detect the gear fault earlier and improve the availability of the system.     

Maximum Likelihood Estimation for a Hidden Semi‐Markov Model with Multivariate Observations

In this paper, a parameter estimation procedure for a condition‐based maintenance model under partial observations is presented. The deterioration process of the partially observable system is modeled as a continuous‐time homogeneous semi‐Markov process. The system can be in a healthy or unhealthy operational state, or in a failure state, and the sojourn time in the operational state follows a phase‐type distribution. Only the failure state is observable, whereas operational states are unobservable. Vector observations that are stochastically related to the system state are collected at equidistant sampling times. The objective is to determine maximum likelihood estimates of the model parameters using the Expectation–Maximization (EM) algorithm. We derive explicit formulae for both the pseudo likelihood function and the parameter updates in each iteration of the EM algorithm. A numerical example is developed to illustrate the efficiency of the estimation procedure. Copyright © 2012 John Wiley & Sons, Ltd.     

Availability maximization under partial observations

In this paper, we propose a new model for availability maximization under partial observations for maintenance applications. Compared with the widely studied cost minimization models, few structural results are known about the form of the optimal control policy for availability maximization models. We consider a failing system with unobservable operational states. Only the failure state is observable. System deterioration is driven by an unobservable, continuous-time homogeneous Markov process. Multivariate condition monitoring data which is stochastically related to the unobservable state of the system is collected at equidistant sampling epochs and is used to update the posterior state distribution for decision making. Preventive maintenance can be carried out at any sampling epoch, and corrective maintenance is carried out upon system failure. The objective is to determine the form of the optimal control policy that maximizes the long-run expected average availability per unit time. We formulate the problem as an optimal stopping problem with partial information. Under standard assumptions, we prove that a control limit policy is optimal. A computational algorithm is developed, illustrated by numerical results.     

On the accuracy of protein determination in large biological samples by prompt gamma neutron activation analysis

A prompt gamma neutron activation analysis (PGNAA) facility has been developed for the determination of nitrogen and thus total protein in large volume biological samples or the whole body of small animals. In the present work, the accuracy of nitrogen determination by PGNAA in phantoms of known composition as well as in four raw ground meat samples of about 1 kg mass was examined. Dumas combustion and Kjeldahl techniques were also used for the assessment of nitrogen concentration in the meat samples. No statistically significant differences were found between the concentrations assessed by the three techniques. The results of this work demonstrate the applicability of PGNAA for the assessment of total protein in biological samples of 0.25-1.5 kg mass, such as a meat sample or the body of small animal even in vivo with an equivalent radiation dose of about 40 mSv.