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.

Typical performance of 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. The binary-input additive-white-Gaussian-noise channel and the binary-input Laplace channel are considered as specific channel noise models.

The impact of procurement-driven technological change on U.S. manufacturing productivity growth

As we enter the 21st Century, technologies originally developed for defense purposes such as computers and satellite communications appear to have become a driving force behind economic growth in the United States. Paradoxically, almost all previous econometric models suggest that the largely defense-oriented federal industrial R&D funding that helped create these technologies had no discernible effect on U.S. industrial productivity growth. This paper addresses this paradox by stressing that defense procurement as well as federal R&D expenditures were targeted to a few narrowly defined manufacturing sub-sectors that produced high tech weaponry. Analysis employing data from the NBER Manufacturing Productivity Database and the BEA' s Input Output tables then demonstrates that defense procurement policies did have significant effects on the productivity performance of disaggregated manufacturing industries because of a process of procurement-driven technological change.

Foreign direct investment, transactions linkages, and the performance of the domestic sector

Using data from the UK Census of Production, including foreign ownership data, and information from UK industry input-output tables, this paper examines whether the intensity of transactions linkages between foreign and domestic firms affects productivity growth in domestic manufacturing industries. The implications of the findings for policies promoting linkages between multinational and domestic firms in the UK economy are outlined.

Feasibility studies of an on-line identification technique

This thesis is concerned with the study of a non-sequential identification technique, so that it may be applied to the identification of process plant mathematical models from process measurements with the greatest degree of accuracy and reliability. In order to study the accuracy of the technique under differing conditions, simple mathematical models were set up on a parallel hybrid. computer and these models identified from input/output measurements by a small on-line digital computer. Initially, the simulated models were identified on-line. However, this method of operation was found not suitable for a thorough study of the technique due to equipment limitations. Further analysis was carried out in a large off-line computer using data generated by the small on-line computer. Hence identification was not strictly on-line. Results of the work have shovm that the identification technique may be successfully applied in practice. An optimum sampling period is suggested, together with noise level limitations for maximum accuracy. A description of a double-effect evaporator is included in this thesis. It is proposed that the next stage in the work will be the identification of a mathematical model of this evaporator using the teclmique described.

Developing a decomposable measure of profit efficiency using DEA

In for-profit organizations efficiency measurement with reference to the potential for profit augmentation is particularly important as is its decomposition into technical, and allocative components. Different profit efficiency approaches can be found in the literature to measure and decompose overall profit efficiency. In this paper, we highlight some problems within existing approaches and propose a new measure of profit efficiency based on a geometric mean of input/output adjustments needed for maximizing profits. Overall profit efficiency is calculated through this efficiency measure and is decomposed into its technical and allocative components. Technical efficiency is calculated based on a non-oriented geometric distance function (GDF) that is able to incorporate all the sources of inefficiency, while allocative efficiency is retrieved residually. We also define a measure of profitability efficiency which complements profit efficiency in that it makes it possible to retrieve the scale efficiency of a unit as a component of its profitability efficiency. In addition, the measure of profitability efficiency allows for a dual profitability interpretation of the GDF measure of technical efficiency. The concepts introduced in the paper are illustrated using a numerical example.

Zero weights and non-zero slacks:different solution to the same problem

This paper re-assesses three independently developed approaches that are aimed at solving the problem of zero-weights or non-zero slacks in Data Envelopment Analysis (DEA). The methods are weights restricted, non-radial and extended facet DEA models. Weights restricted DEA models are dual to envelopment DEA models with restrictions on the dual variables (DEA weights) aimed at avoiding zero values for those weights; non-radial DEA models are envelopment models which avoid non-zero slacks in the input-output constraints. Finally, extended facet DEA models recognize that only projections on facets of full dimension correspond to well defined rates of substitution/transformation between all inputs/outputs which in turn correspond to non-zero weights in the multiplier version of the DEA model. We demonstrate how these methods are equivalent, not only in their aim but also in the solutions they yield. In addition, we show that the aforementioned methods modify the production frontier by extending existing facets or creating unobserved facets. Further we propose a new approach that uses weight restrictions to extend existing facets. This approach has some advantages in computational terms, because extended facet models normally make use of mixed integer programming models, which are computationally demanding.

A circular Malmquist-type index for measuring productivity

This paper develops two new indices for measuring productivity in multi-input multi-output situations. One index enables the measure of productivity change of a unit over time while the second index makes it possible to compare two units on productivity at the same or different points in time. Productivity in a single input single output context is defined as the ratio of output to input. In multi-input multi-output contexts this ratio is not defined. Instead, one of the methods traditionally used is the Malmquist Index of productivity change over time. This is computed by reference to the distances of the input-output bundles of a production unit at two different points in time from the efficient boundaries corresponding to those two points in time. The indices developed in this paper depart form the use of two different reference boundaries and instead they use a single reference boundary which in a sense is the most efficient boundary observed over two or more successive time periods. We discuss the assumptions which make possible the definition of such a single reference boundary and proceed to develop the two Malmquist-type indices for measuring productivity. One key advantage of using a single reference boundary is that the resulting index values are circular. That is it is possible to use the index values of successive time periods to derive an index value of productivity change over a time period of any length covered by successive index values or vice versa. Further, the use of a single reference boundary makes it possible to construct an index for comparing the productivities of two units either at the same or at two different points in time. This was not possible with the traditional Malmquist Index. We decompose both new indices into components which isolate production unit from industry or comparator unit effects. The components themselves like the indices developed are also circular. The components of the indices drill down to reveal more clearly the performance of each unit over time relative either to itself or to other units. The indices developed and their components are aimed at managers of production units to enable them to diagnose the performance of their units with a view to guiding them to improved performance.

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.

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.

Fuzzy data envelopment analysis:a discrete approach

Data envelopment analysis (DEA) as introduced by Charnes, Cooper, and Rhodes (1978) is a linear programming technique that has widely been used to evaluate the relative efficiency of a set of homogenous decision making units (DMUs). In many real applications, the input-output variables cannot be precisely measured. This is particularly important in assessing efficiency of DMUs using DEA, since the efficiency score of inefficient DMUs are very sensitive to possible data errors. Hence, several approaches have been proposed to deal with imprecise data. Perhaps the most popular fuzzy DEA model is based on a-cut. One drawback of the a-cut approach is that it cannot include all information about uncertainty. This paper aims to introduce an alternative linear programming model that can include some uncertainty information from the intervals within the a-cut approach. We introduce the concept of "local a-level" to develop a multi-objective linear programming to measure the efficiency of DMUs under uncertainty. An example is given to illustrate the use of this method.

Data envelopment analysis model for the appraisal and relative performance evaluation of nurses at an intensive care unit

The appraisal and relative performance evaluation of nurses are very important and beneficial for both nurses and employers in an era of clinical governance, increased accountability and high standards of health care services. They enhance and consolidate the knowledge and practical skills of nurses by identification of training and career development plans as well as improvement in health care quality services, increase in job satisfaction and use of cost-effective resources. In this paper, a data envelopment analysis (DEA) model is proposed for the appraisal and relative performance evaluation of nurses. The model is validated on thirty-two nurses working at an Intensive Care Unit (ICU) at one of the most recognized hospitals in Lebanon. The DEA was able to classify nurses into efficient and inefficient ones. The set of efficient nurses was used to establish an internal best practice benchmark to project career development plans for improving the performance of other inefficient nurses. The DEA result confirmed the ranking of some nurses and highlighted injustice in other cases that were produced by the currently practiced appraisal system. Further, the DEA model is shown to be an effective talent management and motivational tool as it can provide clear managerial plans related to promoting, training and development activities from the perspective of nurses, hence increasing their satisfaction, motivation and acceptance of appraisal results. Due to such features, the model is currently being considered for implementation at ICU. Finally, the ratio of the number DEA units to the number of input/output measures is revisited with new suggested values on its upper and lower limits depending on the type of DEA models and the desired number of efficient units from a managerial perspective.

Managing performance of organisational units with PIMsoft:a new DEA software

DEA literature continues apace but software has lagged behind. This session uses suitably selected data to present newly developed software which includes many of the most recent DEA models. The software enables the user to address a variety of issues not frequently found in existing DEA software such as: -Assessments under a variety of possible assumptions of returns to scale including NIRS and NDRS; -Scale elasticity computations; -Numerous Input/Output variables and truly unlimited number of assessment units (DMUs) -Panel data analysis -Analysis of categorical data (multiple categories) -Malmquist Index and its decompositions -Computations of Supper efficiency -Automated removal of super-efficient outliers under user-specified criteria; -Graphical presentation of results -Integrated statistical tests

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.

Classifying flexible measures in data envelopment analysis:a slack-based measure

Data envelopment analysis (DEA) has gained a wide range of applications in measuring comparative efficiency of decision making units (DMUs) with multiple incommensurate inputs and outputs. The standard DEA method requires that the status of all input and output variables be known exactly. However, in many real applications, the status of some measures is not clearly known as inputs or outputs. These measures are referred to as flexible measures. This paper proposes a flexible slacks-based measure (FSBM) of efficiency in which each flexible measure can play input role for some DMUs and output role for others to maximize the relative efficiency of the DMU under evaluation. Further, we will show that when an operational unit is efficient in a specific flexible measure, this measure can play both input and output roles for this unit. In this case, the optimal input/output designation for flexible measure is one that optimizes the efficiency of the artificial average unit. An application in assessing UK higher education institutions used to show the applicability of the proposed approach. © 2013 Elsevier Ltd. All rights reserved.