The design and evaluation of distributed virtual environment to support learning in global operations management

The primary goal of this research is to design and develop an education technology to support learning in global operations management. The research implements a series of studies to determine the right balance among user requirements, learning methods and applied technologies, on a view of student-centred learning. This research is multidisciplinary by nature, involving topics from various disciplines such as global operations management, curriculum and contemporary learning theory, and computer aided learning. Innovative learning models that emphasise on technological implementation are employed and discussed throughout this research.

Combining spatially distributed predictions from neural networks

In this report we discuss the problem of combining spatially-distributed predictions from neural networks. An example of this problem is the prediction of a wind vector-field from remote-sensing data by combining bottom-up predictions (wind vector predictions on a pixel-by-pixel basis) with prior knowledge about wind-field configurations. This task can be achieved using the scaled-likelihood method, which has been used by Morgan and Bourlard (1995) and Smyth (1994), in the context of Hidden Markov modelling

Distributed algorithms for global optimization on sparse networks of arbitrary bandwidths

The optimization of resource allocation in sparse networks with real variables is studied using methods of statistical physics. Efficient distributed algorithms are devised on the basis of insight gained from the analysis and are examined using numerical simulations, showing excellent performance and full agreement with the theoretical results.

Analytic hierarchy process helps evaluate projects in Indian oil pipeline industry

Conventionally, oil pipeline projects are evaluated thoroughly by the owner before investment decision is made using market, technical and financial analysis sequentially. The market analysis determines pipelines throughput and supply and demand points. Subsequent, technical analysis identifies technological options and economic and financial analysis then derives the least cost option among all technically feasible options. The subsequent impact assessment tries to justify the selected option by addressing environmental and social issues. The impact assessment often suggests alternative sites, technologies, and/or implementation methodology, necessitating revision of technical and financial analysis. This study addresses these issues via an integrated project evaluation and selection model. The model uses analytic hierarchy process, a multiple-attribute decision-making technique. The effectiveness of the model has been demonstrated through a case application on cross-country petroleum pipeline project in India.

A risk-based model for inspection and maintenance of cross-country petroleum pipeline

The existing method of pipeline health monitoring, which requires an entire pipeline to be inspected periodically, is both time-wasting and expensive. A risk-based model that reduces the amount of time spent on inspection has been presented. This model not only reduces the cost of maintaining petroleum pipelines, but also suggests efficient design and operation philosophy, construction methodology and logical insurance plans. The risk-based model uses Analytic Hierarchy Process (AHP), a multiple attribute decision-making technique, to identify the factors that influence failure on specific segments and analyzes their effects by determining probability of risk factors. The severity of failure is determined through consequence analysis. From this, the effect of a failure caused by each risk factor can be established in terms of cost, and the cumulative effect of failure is determined through probability analysis. The technique does not totally eliminate subjectivity, but it is an improvement over the existing inspection method.

Multiple attribute decision making approach to petroleum pipeline route selection

Petroleum pipelines are the nervous system of the oil industry, as this transports crude oil from sources to refineries and petroleum products from refineries to demand points. Therefore, the efficient operation of these pipelines determines the effectiveness of the entire business. Pipeline route selection plays a major role when designing an effective pipeline system, as the health of the pipeline depends on its terrain. The present practice of route selection for petroleum pipelines is governed by factors such as the shortest distance, constructability, minimal effects on the environment, and approachability. Although this reduces capital expenditure, it often proves to be uneconomical when life cycle costing is considered. This study presents a route selection model with the application of an Analytic Hierarchy Process (AHP), a multiple attribute decision making technique. AHP considers all the above factors along with the operability and maintainability factors interactively. This system has been demonstrated here through a case study of pipeline route selection, from an Indian perspective. A cost-benefit comparison of the shortest route (conventionally selected) and optimal route establishes the effectiveness of the model.

Development of a co-operative distributed process mining system for quality assurance

In this paper, a co-operative distributed process mining system (CDPMS) is developed to streamline the workflow along the supply chain in order to offer shorter delivery times, more flexibility and higher customer satisfaction with learning ability. The proposed system is equipped with the ‘distributed process mining’ feature which is used to discover the hidden relationships among each working decision in distributed manner. This method incorporates the concept of data mining and knowledge refinement into decision making process for ensuring ‘doing the right things’ within the workflow. An example of implementation is given, based on the case of slider manufacturer.

Non-normally distributed data and non-parametric statistics

Different types of numerical data can be collected in a scientific investigation and the choice of statistical analysis will often depend on the distribution of the data. A basic distinction between variables is whether they are ‘parametric’ or ‘non-parametric’. When a variable is parametric, the data come from a symmetrically shaped distribution known as the ‘Gaussian’ or ‘normal distribution’ whereas non-parametric variables may have a distribution which deviates markedly in shape from normal. This article describes several aspects of the problem of non-normality including: (1) how to test for two common types of deviation from a normal distribution, viz., ‘skew’ and ‘kurtosis’, (2) how to fit the normal distribution to a sample of data, (3) the transformation of non-normally distributed data and scores, and (4) commonly used ‘non-parametric’ statistics which can be used in a variety of circumstances.

Canonical solution groups of a homomorphism : manipulator kinematics, nonparametric regression and distributed object systems

The kinematic mapping of a rigid open-link manipulator is a homomorphism between Lie groups. The homomorphisrn has solution groups that act on an inverse kinematic solution element. A canonical representation of solution group operators that act on a solution element of three and seven degree-of-freedom (do!) dextrous manipulators is determined by geometric analysis. Seven canonical solution groups are determined for the seven do! Robotics Research K-1207 and Hollerbach arms. The solution element of a dextrous manipulator is a collection of trivial fibre bundles with solution fibres homotopic to the Torus. If fibre solutions are parameterised by a scalar, a direct inverse funct.ion that maps the scalar and Cartesian base space coordinates to solution element fibre coordinates may be defined. A direct inverse pararneterisation of a solution element may be approximated by a local linear map generated by an inverse augmented Jacobian correction of a linear interpolation. The action of canonical solution group operators on a local linear approximation of the solution element of inverse kinematics of dextrous manipulators generates cyclical solutions. The solution representation is proposed as a model of inverse kinematic transformations in primate nervous systems. Simultaneous calibration of a composition of stereo-camera and manipulator kinematic models is under-determined by equi-output parameter groups in the composition of stereo-camera and Denavit Hartenberg (DH) rnodels. An error measure for simultaneous calibration of a composition of models is derived and parameter subsets with no equi-output groups are determined by numerical experiments to simultaneously calibrate the composition of homogeneous or pan-tilt stereo-camera with DH models. For acceleration of exact Newton second-order re-calibration of DH parameters after a sequential calibration of stereo-camera and DH parameters, an optimal numerical evaluation of DH matrix first order and second order error derivatives with respect to a re-calibration error function is derived, implemented and tested. A distributed object environment for point and click image-based tele-command of manipulators and stereo-cameras is specified and implemented that supports rapid prototyping of numerical experiments in distributed system control. The environment is validated by a hierarchical k-fold cross validated calibration to Cartesian space of a radial basis function regression correction of an affine stereo model. Basic design and performance requirements are defined for scalable virtual micro-kernels that broker inter-Java-virtual-machine remote method invocations between components of secure manageable fault-tolerant open distributed agile Total Quality Managed ISO 9000+ conformant Just in Time manufacturing systems.

Distributed processing, reconfigurable processes and active network

The fast spread of the Internet and the increasing demands of the service are leading to radical changes in the structure and management of underlying telecommunications systems. Active networks (ANs) offer the ability to program the network on a per-router, per-user, or even per-packet basis, thus promise greater flexibility than current networks. To make this new network paradigm of active network being widely accepted, a lot of issues need to be solved. Management of the active network is one of the challenges. This thesis investigates an adaptive management solution based on genetic algorithm (GA). The solution uses a distributed GA inspired by bacterium on the active nodes within an active network, to provide adaptive management for the network, especially the service provision problems associated with future network. The thesis also reviews the concepts, theories and technologies associated with the management solution. By exploring the implementation of these active nodes in hardware, this thesis demonstrates the possibility of implementing a GA based adaptive management in the real network that being used today. The concurrent programming language, Handel-C, is used for the description of the design system and a re-configurable computer platform based on a FPGA process element is used for the hardware implementation. The experiment results demonstrate both the availability of the hardware implementation and the efficiency of the proposed management solution.

Distributed control of computer communication systems

Flow control in Computer Communication systems is generally a multi-layered structure, consisting of several mechanisms operating independently at different levels. Evaluation of the performance of networks in which different flow control mechanisms act simultaneously is an important area of research, and is examined in depth in this thesis. This thesis presents the modelling of a finite resource computer communication network equipped with three levels of flow control, based on closed queueing network theory. The flow control mechanisms considered are: end-to-end control of virtual circuits, network access control of external messages at the entry nodes and the hop level control between nodes. The model is solved by a heuristic technique, based on an equivalent reduced network and the heuristic extensions to the mean value analysis algorithm. The method has significant computational advantages, and overcomes the limitations of the exact methods. It can be used to solve large network models with finite buffers and many virtual circuits. The model and its heuristic solution are validated by simulation. The interaction between the three levels of flow control are investigated. A queueing model is developed for the admission delay on virtual circuits with end-to-end control, in which messages arrive from independent Poisson sources. The selection of optimum window limit is considered. Several advanced network access schemes are postulated to improve the network performance as well as that of selected traffic streams, and numerical results are presented. A model for the dynamic control of input traffic is developed. Based on Markov decision theory, an optimal control policy is formulated. Numerical results are given and throughput-delay performance is shown to be better with dynamic control than with static control.

A structural method for the design of fault tolerant distributed control systems

Distributed digital control systems provide alternatives to conventional, centralised digital control systems. Typically, a modern distributed control system will comprise a multi-processor or network of processors, a communications network, an associated set of sensors and actuators, and the systems and applications software. This thesis addresses the problem of how to design robust decentralised control systems, such as those used to control event-driven, real-time processes in time-critical environments. Emphasis is placed on studying the dynamical behaviour of a system and identifying ways of partitioning the system so that it may be controlled in a distributed manner. A structural partitioning technique is adopted which makes use of natural physical sub-processes in the system, which are then mapped into the software processes to control the system. However, communications are required between the processes because of the disjoint nature of the distributed (i.e. partitioned) state of the physical system. The structural partitioning technique, and recent developments in the theory of potential controllability and observability of a system, are the basis for the design of controllers. In particular, the method is used to derive a decentralised estimate of the state vector for a continuous-time system. The work is also extended to derive a distributed estimate for a discrete-time system. Emphasis is also given to the role of communications in the distributed control of processes and to the partitioning technique necessary to design distributed and decentralised systems with resilient structures. A method is presented for the systematic identification of necessary communications for distributed control. It is also shwon that the structural partitions can be used directly in the design of software fault tolerant concurrent controllers. In particular, the structural partition can be used to identify the boundary of the conversation which can be used to protect a specific part of the system. In addition, for certain classes of system, the partitions can be used to identify processes which may be dynamically reconfigured in the event of a fault. These methods should be of use in the design of robust distributed systems.