Expert systems for fault tree synthesis

Hazard and operability (HAZOP) studies on chemical process plants are very time consuming, and often tedious, tasks. The requirement for HAZOP studies is that a team of experts systematically analyse every conceivable process deviation, identifying possible causes and any hazards that may result. The systematic nature of the task, and the fact that some team members may be unoccupied for much of the time, can lead to tedium, which in turn may lead to serious errors or omissions. An aid to HAZOP are fault trees, which present the system failure logic graphically such that the study team can readily assimilate their findings. Fault trees are also useful to the identification of design weaknesses, and may additionally be used to estimate the likelihood of hazardous events occurring. The one drawback of fault trees is that they are difficult to generate by hand. This is because of the sheer size and complexity of modern process plants. The work in this thesis proposed a computer-based method to aid the development of fault trees for chemical process plants. The aim is to produce concise, structured fault trees that are easy for analysts to understand. Standard plant input-output equation models for major process units are modified such that they include ancillary units and pipework. This results in a reduction in the nodes required to represent a plant. Control loops and protective systems are modelled as operators which act on process variables. This modelling maintains the functionality of loops, making fault tree generation easier and improving the structure of the fault trees produced. A method, called event ordering, is proposed which allows the magnitude of deviations of controlled or measured variables to be defined in terms of the control loops and protective systems with which they are associated.

Exploiting uncertainty in nonlinear stochastic control problem

This work introduces a novel inversion-based neurocontroller for solving control problems involving uncertain nonlinear systems which could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. Based on importance sampling from these distributions a novel robust inverse control approach is obtained. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider. Convergence of the output error for the proposed control method is verified by using a Lyapunov function. Several simulation examples are provided to demonstrate the efficiency of the developed control method. The manner in which such a method is extended to nonlinear multi-variable systems with different delays between the input-output pairs is considered and demonstrated through simulation examples.

The use of expert manpower in health and safety in France

The thesis examines the system of occupational health and safety in France. It analyses the use of expert manpower in the field with a view to establishing the possibility of a profession in health and safety. An input-output model is developed to bring together the necessary elements of prevention of accidents and occupational diseases. The role of institutions concerned with health and safety is analysed with reference to this model. The research establishes the need for a health and safety specialist role. The recognition and status of this role are found to be subject to other criteria including the acceptance by institutions of such a specialist role. The model is also used to define the role of this specialist as expected by the various institutions intervening in the field.

Application of calorimetric method for loss measurement of a SynRM drive system

Synchronous reluctance motors (SynRMs) are gaining in popularity in industrial drives due to their permanent magnet-free, competitive performance, and robust features. This paper studies the power losses in a 90-kW converter-fed SynRM drive by a calorimetric method in comparison of the traditional input-output method. After the converter and the motor were measured simultaneously in separate chambers, the converter was installed inside the large-size chamber next to the motor and the total drive system losses were obtained using one chamber. The uncertainty of both measurement methods is analyzed and discussed.

Typical performance of regular low-density parity-check codes over general symmetric channels

Typical performance of low-density parity-check (LDPC) codes over a general binary-input output-symmetric memoryless channel is investigated using methods of statistical mechanics. Relationship between the free energy in statistical-mechanics approach and the mutual information used in the information-theory literature is established within a general framework; Gallager and MacKay-Neal codes are studied as specific examples of LDPC codes. It is shown that basic properties of these codes known for particular channels, including their potential to saturate Shannon's bound, hold for general symmetric channels. The binary-input additive-white-Gaussian-noise channel and the binary-input Laplace channel are considered as specific channel models.

Multimodal 'Eyes-Free' interaction techniques for mobile devices

Mobile and wearable computers present input/output prob-lems due to limited screen space and interaction techniques. When mobile, users typically focus their visual attention on navigating their environment - making visually demanding interface designs hard to operate. This paper presents two multimodal interaction techniques designed to overcome these problems and allow truly mobile, 'eyes-free' device use. The first is a 3D audio radial pie menu that uses head gestures for selecting items. An evaluation of a range of different audio designs showed that egocentric sounds re-duced task completion time, perceived annoyance, and al-lowed users to walk closer to their preferred walking speed. The second is a sonically enhanced 2D gesture recognition system for use on a belt-mounted PDA. An evaluation of the system with and without audio feedback showed users' ges-tures were more accurate when dynamically guided by au-dio-feedback. These novel interaction techniques demon-strate effective alternatives to visual-centric interface de-signs on mobile devices.

Using the max-sum algorithm for supply chain emergence in dynamic multiunit environments

Supply chain formation (SCF) is the process of determining the set of participants and exchange relationships within a network with the goal of setting up a supply chain that meets some predefined social objective. Many proposed solutions for the SCF problem rely on centralized computation, which presents a single point of failure and can also lead to problems with scalability. Decentralized techniques that aid supply chain emergence offer a more robust and scalable approach by allowing participants to deliberate between themselves about the structure of the optimal supply chain. Current decentralized supply chain emergence mechanisms are only able to deal with simplistic scenarios in which goods are produced and traded in single units only and without taking into account production capacities or input-output ratios other than 1:1. In this paper, we demonstrate the performance of a graphical inference technique, max-sum loopy belief propagation (LBP), in a complex multiunit unit supply chain emergence scenario which models additional constraints such as production capacities and input-to-output ratios. We also provide results demonstrating the performance of LBP in dynamic environments, where the properties and composition of participants are altered as the algorithm is running. Our results suggest that max-sum LBP produces consistently strong solutions on a variety of network structures in a multiunit problem scenario, and that performance tends not to be affected by on-the-fly changes to the properties or composition of participants.

Nonlinear signalling networks and cell-to-cell variability transform external signals into broadly distributed or bimodal responses

We show theoretically and experimentally a mechanismbehind the emergence of wide or bimodal protein distributions in biochemical networks with nonlinear input-output characteristics (the dose-response curve) and variability in protein abundance. Large cell-to-cell variation in the nonlinear dose-response characteristics can be beneficial to facilitate two distinct groups of response levels as opposed to a graded response. Under the circumstances that we quantify mathematically, the two distinct responses can coexist within a cellular population, leading to the emergence of a bimodal protein distribution. Using flow cytometry, we demonstrate the appearance of wide distributions in the hypoxia-inducible factor-mediated response network in HCT116 cells. With help of our theoretical framework, we perform a novel calculation of the magnitude of cell-to-cell heterogeneity in the dose-response obtained experimentally. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

A mixture density network approach to modelling and exploiting uncertainty in nonlinear control problems

In nonlinear and stochastic control problems, learning an efficient feed-forward controller is not amenable to conventional neurocontrol methods. For these approaches, estimating and then incorporating uncertainty in the controller and feed-forward models can produce more robust control results. Here, we introduce a novel inversion-based neurocontroller for solving control problems involving uncertain nonlinear systems which could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. Based on importance sampling from these distributions a novel robust inverse control approach is obtained. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider. A nonlinear multi-variable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non-Gaussian distributions of control signal as well as processes with hysteresis. © 2004 Elsevier Ltd. All rights reserved.

Viability Theory and Economic Modeling

A brief introduction into the theory of differential inclusions, viability theory and selections of set valued mappings is presented. As an application the implicit scheme of the Leontief dynamic input-output model is considered.

The Closed Loop Model by Regional Economics or How Big is the Ants’ Footprint?

The economy is communication between Man and Nature. It is an interaction-network between our outside and inside Nature, that is, the external Nature surrounding us and the internal nature expressing our human essence. Money is an institution of the society, an infrastructure that ensures division of labour, enables the flow of information and material between the participants. The concept of regional material and financial circular flow will be more important with the oncoming peak-oil and post-carbon era. We should describe in time the outlines of closed or semi-closed loops economy. The fundamentals of Input-Output will flourish once again; it could help us formulate the link between the efficiency and resiliency of a regional complex system.

Applying consumer responsibility principle in evaluating environmental load of carbon emissions

There is a need for a proper indicator in order to assess the environmental impact of international trade, therefore using the carbon footprint as an indicator can be relevant and useful. The aim of this study is to show from a methodological perspective how the carbon footprint, combined with input- output models can be used for analysing the impacts of international trade on the sustainable use of national resources in a country. The use of the input-output approach has the essential advantage of being able to track the transformation of goods through the economy. The study examines the environmental impact of consumption related to international trade, using the consumer responsibility principle. In this study the use of the carbon footprint and input-output methodology is shown on the example of the Hungarian consumption and the impact of international trade. Moving from a production- based approach in climate policy to a consumption-perspective principle and allocation, would also help to increase the efficiency of emission reduction targets and the evaluation of the ecological impacts of international trade.

A nemzetközi kereskedelem környezeti hatásainak vizsgálata a karbon lábnyom segítségével

Napjainkban már köztudott tény az, hogy az antropogén szén-dioxid kibocsátás nagymértékben hozzájárul a klímaváltozáshoz. Ahhoz, hogy ténylegesen értékelni tudjuk a kibocsátás környezeti hatását, szükség van egy olyan indikátorra, ami figyelembe veszi a természeti erőforrások és a természet megújuló-képessége által szabott korlátokat. A karbon lábnyom, ilyen módon egy releváns indikátor erre, és segítségével kimutatható, hogy mennyire felel meg egy ország termelése, életmódja, fogyasztási szerkezete a fenntarthatóság kritériumainak. Ennek a tanulmánynak a célja, hogy megvizsgálja a magyarországi fogyasztás környezetterhelésének szerkezetét a karbon lábnyom indikátorának segítségével, középpontba helyezve a nemzetközi kereskedelem hazai hatásainak vizsgálatát. A nemzetközi kereskedelemben elfoglalt pozíció, nemcsak egy ország gazdasági szerkezetét és versenyképességét határozza meg, hanem erőteljes hatással van a fogyasztói szokásokra és a fogyasztás környezetterhelésére is. A karbon lábnyom tartalmazza annak a környezetterhelésnek az értékeit is, amely az importált termékek és szolgáltatások elfogyasztásából származik. Fontos megvizsgálni azt, hogy mely szektorokban van nagy jelentősége az importált termékek környezetterhelésének és ez hogyan járul hozzá a hazai fogyasztási mintákhoz. A tanulmány a fogyasztás környezetterhelését a fogyasztói felelősségi elvet alkalmazva vizsgálja. A karbon lábnyom fogyasztói szempontból való vizsgálata azért jelentős, mert ezáltal képes felhívni a döntéshozók figyelmét, hogy mely szektorokban és fogyasztási kategóriákban jelenik meg nem fenntartható fogyasztás. Így, felszínre kerülnek azok a területek, ahol erőteljes beavatkozásra van szükség a kibocsátások csökkentése érdekében, mindez nemcsak a termelői oldal, hanem a fogyasztói magatartás befolyásolása, jobb informálása, oktatása által. A tanulmány módszertanában kombináltan alkalmazza az ökológiai lábnyom számításból származó karbon lábnyom számítás módszertanát, kiegészítve az ágazati kapcsolatok mérlegének (input-output táblázatok) módszertanával. Ez a kombinált módszertan, a szoros ágazati összefüggéseket és kölcsönhatásokat is kezelni tudja, megjelenítve mind a direkt és indirekt környezeti hatásokat is.

Applying consumer responsibility principle in evaluating environmental load of carbon emissions

There is a need for a proper indicator in order to assess the environmental impact of international trade, therefore using the carbon footprint as an indicator can be relevant and useful. The aim of this study is to show from a methodological perspective how the carbon footprint, combined with input- output models can be used for analysing the impacts of international trade on the sustainable use of national resources in a country. The use of the input-output approach has the essential advantage of being able to track the transformation of goods through the economy. The study examines the environmental impact of consumption related to international trade, using the consumer responsibility principle. In this study the use of the carbon footprint and input-output methodology is shown on the example of the Hungarian consumption and the impact of international trade. Moving from a production- based approach in climate policy to a consumption-perspective principle and allocation, would also help to increase the efficiency of emission reduction targets and the evaluation of the ecological impacts of international trade.

Developing a high density platinum/alumina hermetic feedthrough

Typically, hermetic feedthroughs for implantable devices, such as pacemakers, use a alumina ceramic insulator brazed to a platinum wire pin. This combination of material has a long history in implantable devices and has been approved by the FDA for implantable hermetic feedthroughs. The growing demand for increased input/output (I/O) hermetic feedthroughs for implantable neural stimulator applications could be addressed by developing a new, cofired platinum/alumina multilayer ceramic technology in a configuration that supports 300 plus I/Os, which is not commercially available. Seven platinum powders with different particle sizes were used to develop different conductive cofire inks to control the densification mismatch between platinum and alumina. Firing profile (ramp rate, burn- out and holding times) and firing atmosphere and concentrations (hydrogen (wet/dry), air, neutral, vacuum) were also optimized. Platinum and alumina exhibit the alloy formation reaction in a reduced atmosphere. Formation of any compound can increase the bonding of the metal/ceramic interface, resulting in enhanced hermeticity. The feedthrough fabricated in a reduced atmosphere demonstrated significantly superior performance than that of other atmospheres. A composite structure of tungsten/platinum ratios graded thru the via structure (pure W, 50/50 W/Pt, 80/20 Pt/W and pure Pt) exhibited the best performance in comparison to the performance of other materials used for ink metallization. Studies on the high temperature reaction of platinum and alumina, previously unreported, showed that, at low temperatures in reduced atmosphere, Pt 3Al or Pt8Al21 with a tetragonal structure would be formed. Cubic Pt3Al is formed upon heating the sample to temperatures above 1350 °C. This cubic structure is the equilibrium state of Pt-Al alloy at high temperatures. The alumina dissolves into the platinum ink and is redeposited as a surface coating. This was observed on both cofired samples and pure platinum thin films coated on a 99.6 Wt% alumina and fired at 1550 °C. Different mechanisms are proposed to describe this behavior based on the size of the platinum particle