Multiple-model fault tolerant control of terminal unites of HVAC Systems

In this work, a fault-tolerant control scheme is applied to a air handling unit of a heating, ventilation and air-conditioning system. Using the multiple-model approach it is possible to identify faults and to control the system under faulty and normal conditions in an effective way. Using well known techniques to model and control the process, this work focuses on the importance of the cost function in the fault detection and its influence on the reconfigurable controller. Experimental results show how the control of the terminal unit is affected in the presence a fault, and how the recuperation and reconfiguration of the control action is able to deal with the effects of faults.

Neural networks for power systems alarm handling

A multi-layered architecture of self-organizing neural networks is being developed as part of an intelligent alarm processor to analyse a stream of power grid fault messages and provide a suggested diagnosis of the fault location. Feedback concerning the accuracy of the diagnosis is provided by an object-oriented grid simulator which acts as an external supervisor to the learning system. The utilization of artificial neural networks within this environment should result in a powerful generic alarm processor which will not require extensive training by a human expert to produce accurate results.

Discriminative pattern mining in software fault detection

We present a method to enhance fault localization for software systems based on a frequent pattern mining algorithm. Our method is based on a large set of test cases for a given set of programs in which faults can be detected. The test executions are recorded as function call trees. Based on test oracles the tests can be classified into successful and failing tests. A frequent pattern mining algorithm is used to identify frequent subtrees in successful and failing test executions. This information is used to rank functions according to their likelihood of containing a fault. The ranking suggests an order in which to examine the functions during fault analysis. We validate our approach experimentally using a subset of Siemens benchmark programs.

Cigarette smokers differ in their handling of natural (RRR) and synthetic (all rac) alpha-tocopherol: a biokinetic study in apoE4 male subjects

We have compared the biokinetics of deuterated natural (RPR) and synthetic (all rac) alpha-tocopherol in male apoE4-carrying smokers and nonsmokers. In a randomized, crossover study subjects underwent two 4-week treatments (400 mg/day) with undeuterated RRR- and all rac-alpha-tocopheryl acetate around a 12-week washout. Before and after each supplementation period subjects underwent a biokinetic protocol (48 h) with 150 mg deuterated RRR- or all rac-alpha-tocopheryl acetate. During the biokinetic protocols, the elimination of endogenous plasma alpha-tocopherol was significantly faster in smokers (P < 0.05). However, smokers had a lower uptake of deuterated RRR than nonsmokers, but there was no difference in uptake of deuterated all rac. The supplementation regimes significantly raised plasma alpha-tocopherol (P < 0.001) with no differences in response between smokers and nonsmokers or between alpha-tocopherol forms. Smokers had significantly lower excretion of alpha-carboxyethyl-hydroxychroman than nonsmokers following supplementation (P < 0.05). Nonsmokers excreted more alpha-carboxyethyl-hydroxychroman following RRR than all rac; however, smokers did not differ in excretion between forms. At baseline, smokers had significantly lower ascorbate (P < 0.01) and higher F(2-)isoprostarres (P < 0.05). F-2-isoprostanes in smokers remained unchanged during the study, but increased in nonsmokers following alpha-tocopherol supplementation. These data suggest that apoE4-carrying smokers and nonsmokers differ in their handling of natural and synthetic alpha-tocopherol. (c) 2006 Elsevier Inc. All rights reserved.

Predicting fault inflow in iterative software development processes: an industrial evaluation

This paper addresses the need for accurate predictions on the fault inflow, i.e. the number of faults found in the consecutive project weeks, in highly iterative processes. In such processes, in contrast to waterfall-like processes, fault repair and development of new features run almost in parallel. Given accurate predictions on fault inflow, managers could dynamically re-allocate resources between these different tasks in a more adequate way. Furthermore, managers could react with process improvements when the expected fault inflow is higher than desired. This study suggests software reliability growth models (SRGMs) for predicting fault inflow. Originally developed for traditional processes, the performance of these models in highly iterative processes is investigated. Additionally, a simple linear model is developed and compared to the SRGMs. The paper provides results from applying these models on fault data from three different industrial projects. One of the key findings of this study is that some SRGMs are applicable for predicting fault inflow in highly iterative processes. Moreover, the results show that the simple linear model represents a valid alternative to the SRGMs, as it provides reasonably accurate predictions and performs better in many cases.

Hybrid fault detection and isolation in an heater bank of an HVAC system

In this work a hybrid technique that includes probabilistic and optimization based methods is presented. The method is applied, both in simulation and by means of real-time experiments, to the heating unit of a Heating, Ventilation Air Conditioning (HVAC) system. It is shown that the addition of the probabilistic approach improves the fault diagnosis accuracy.

Input constraints handling in an MPC/feedback linearization scheme

The combination of model predictive control based on linear models (MPC) with feedback linearization (FL) has attracted interest for a number of years, giving rise to MPC+FL control schemes. An important advantage of such schemes is that feedback linearizable plants can be controlled with a linear predictive controller with a fixed model. Handling input constraints within such schemes is difficult since simple bound contraints on the input become state dependent because of the nonlinear transformation introduced by feedback linearization. This paper introduces a technique for handling input constraints within a real time MPC/FL scheme, where the plant model employed is a class of dynamic neural networks. The technique is based on a simple affine transformation of the feasible area. A simulated case study is presented to illustrate the use and benefits of the technique.

Fault tolerance of Mobius cubes under two forbidden fault set models

An n-dimensional Mobius cube, 0MQ(n) or 1MQ(n), is a variation of n-dimensional cube Q(n) which possesses many attractive properties such as significantly smaller communication delay and stronger graph-embedding capabilities. In some practical situations, the fault tolerance of a distributed memory multiprocessor system can be measured more precisely by the connectivity of the underlying graph under forbidden fault set models. This article addresses the connectivity of 0MQ(n)/1MQ(n), under two typical forbidden fault set models. We first prove that the connectivity of 0MQ(n)/1MQ(n) is 2n - 2 when the fault set does not contain the neighborhood of any vertex as a subset. We then prove that the connectivity of 0MQ(n)/1MQ(n) is 3n - 5 provided that the neighborhood of any vertex as well as that of any edge cannot fail simultaneously These results demonstrate that 0MQ(n)/1MQ(n) has the same connectivity as Q(n) under either of the previous assumptions.

Learning and natal host influence host preference, handling time and sex allocation behaviour in a pupal parasitoid

The host choice and sex allocation decisions of a foraging female parasitoid will have an enormous influence on the life-history characteristics of her offspring. The pteromalid Pachycrepoideus vindemiae is a generalist idiobiont pupal parasitoid of many species of cyclorrhaphous Diptera. Wasps reared in Musca domestica were larger, had higher attack rates and greater male mating success than those reared in Drosophila melanogaster. In no-choice situations, naive female R vindemiae took significantly less time to accept hosts conspecific with their natal host. Parasitoids that emerged from M. domestica pupae spent similar amounts of time ovipositing in both D. melanogaster and M. domestica. Those parasitoids that had emerged from D. melanogaster spent significantly longer attacking M. domestica pupae. The host choice behaviour of female P. vindemiae was influenced by an interaction between natal host and experience. Female R vindemiae reared in M. domestica only showed a preference among hosts when allowed to gain experience attacking M. domestica, preferentially attacking that species. Similarly, female parasitoids reared on D. melanogaster only showed a preference among hosts when allowed to gain experience attacking D. melanogaster, again preferentially attacking that species. Wasp natal host also influenced sex allocation behaviour. While wasps from both hosts oviposited more females in the larger host, M. domestica, wasps that emerged from M. domestica had significantly more male-biased offspring sex ratios. These results indicate the importance of learning and natal host size in determining R vindemiae attack rates. mating success, host preference and sex allocation behaviour, all critical components of parasitoid fitness.

Effects of dietary fat modification on skeletal muscle fatty acid handling in the metabolic syndrome

Objective: In the metabolic syndrome (MetS), increased fat storage in ‘nonadipose’ tissues such as skeletal muscle may be related to insulin resistance (‘lipid overflow’ hypothesis). The objective of this study was to examine the effects of dietary fat modification on the capacity of skeletal muscle to handle dietary and endogenous fatty acids (FAs). Subjects and Methods: In total, 29 men with the MetS were randomly assigned to one of four diets for 12 weeks: a high-fat saturated fat diet (HSFA, n=6), a high-fat monounsaturated fat diet (HMUFA, n=7) and two low-fat high-complex carbohydrate diets supplemented with (LFHCCn−3, n=8) or without (LFHCC, n=8) 1.24 g per day docosahexaenoic and eicosapentaenoic acid. Fasting and postprandial skeletal muscle FA handling was examined by measuring arteriovenous concentration differences across the forearm muscle. [2H2]-palmitate was infused intravenously to label endogenous triacylglycerol (TAG) and free fatty acids in the circulation and subjects received a high-fat mixed meal (2.6 MJ, 61 energy% fat) containing [U-13C]-palmitate to label chylomicron-TAG. Results: Postprandial circulating TAG concentrations were significantly lower after dietary intervention in the LFHCCn−3 group compared to the HSFA group (ΔiAUC −139±67 vs 167±70 μmol l−1 min−1, P=0.009), together with decreased concentrations of [U-13C]-labeled TAG, representing dietary FA. Fasting TAG clearance across forearm muscle was decreased on the HSFA diet, whereas no differences were observed in postprandial forearm muscle FA handling between diets. Conclusion: Chronic manipulation of dietary fat quantity and quality did not affect forearm muscle FA handling in men with the MetS. Postprandial TAG concentrations decreased on the LFHCCn−3 diet, which could be (partly) explained by lower concentration of dietary FA in the circulation.

Handling single node failures using agents in computer clusters

The work reported in this paper is motivated towards handling single node failures for parallel summation algorithms in computer clusters. An agent based approach is proposed in which a task to be executed is decomposed to sub-tasks and mapped onto agents that traverse computing nodes. The agents intercommunicate across computing nodes to share information during the event of a predicted node failure. Two single node failure scenarios are considered. The Message Passing Interface is employed for implementing the proposed approach. Quantitative results obtained from experiments reveal that the agent based approach can handle failures more efficiently than traditional failure handling approaches.