1,884 research outputs found
Improving water asset management when data are sparse
Ensuring the high of assets in water utilities is critically important and requires continuous improvement. This is due to the need to minimise risk of harm to human health and the environment from contaminated drinking water. Continuous improvement and innovation in water asset management are therefore, necessary and are driven by (i) increased regulatory requirements on serviceability; (ii) high maintenance costs, (iii) higher customer expectations, and (iv) enhanced environmental and health/safety requirements.
High quality data on asset failures, maintenance, and operations are key requirements for developing reliability models. However, a literature search revealed that, in practice, there is sometimes limited data in water utilities - particularly for over-ground assets. Perhaps surprisingly, there is often a mismatch between the ambitions of sophisticated reliability tools and the availability of asset data water utilities are able to draw upon to implement them in practice.
This research provides models to support decision-making in water utility asset management when there is limited data. Three approaches for assessing asset condition, maintenance effectiveness and selecting maintenance regimes for specific asset groups were developed. Expert elicitation was used to test and apply the developed decision-support tools. A major regional water utility in England was used as a case study to investigate and test the developed approaches.
The new approach achieved improved precision in asset condition assessment (Figure 3–3a) - supporting the requirements of the UK Capital Maintenance Planning Common Framework. Critically, the thesis demonstrated that, on occasion, assets were sometimes misallocated by more than 50% between condition grades when using current approaches. Expert opinions were also sought for assessing maintenance effectiveness, and a new approach was tested with over-ground assets. The new approach’s value was demonstrated by the capability to account for finer measurements (as low as 10%) of maintenance effectiveness (Table 4-4). An asset maintenance regime selection approach was developed to support decision-making when data are sparse. The value of the approach is its versatility in selecting different regimes for different asset groups, and specifically accounting for the assets unique performance variables
Methods to Support the Project Selection Problem With Non-Linear Portfolio Objectives, Time Sensitive Objectives, Time Sensitive Resource Constraints, and Modeling Inadequacies
The United States Air Force relies upon information production activities to gain insight regarding uncertainties affecting important system configuration and in-mission task execution decisions. Constrained resources that prevent the fulfillment of every information production request, multiple information requestors holding different temporal-sensitive objectives, non-constant marginal value preferences, and information-product aging factors that affect the value-of-information complicate the management of these activities. This dissertation reviews project selection research related to these issues and presents novel methods to address these complications. Quantitative experimentation results demonstrate these methods’ significance
Gradient-based Optimization for Bayesian Preference Elicitation
Effective techniques for eliciting user preferences have taken on added
importance as recommender systems (RSs) become increasingly interactive and
conversational. A common and conceptually appealing Bayesian criterion for
selecting queries is expected value of information (EVOI). Unfortunately, it is
computationally prohibitive to construct queries with maximum EVOI in RSs with
large item spaces. We tackle this issue by introducing a continuous formulation
of EVOI as a differentiable network that can be optimized using gradient
methods available in modern machine learning (ML) computational frameworks
(e.g., TensorFlow, PyTorch). We exploit this to develop a novel, scalable Monte
Carlo method for EVOI optimization, which is more scalable for large item
spaces than methods requiring explicit enumeration of items. While we emphasize
the use of this approach for pairwise (or k-wise) comparisons of items, we also
demonstrate how our method can be adapted to queries involving subsets of item
attributes or "partial items," which are often more cognitively manageable for
users. Experiments show that our gradient-based EVOI technique achieves
state-of-the-art performance across several domains while scaling to large item
spaces.Comment: To appear in the Thirty-Fourth AAAI Conference on Artificial
Intelligence (AAAI-20
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The development of an expert systems approach to the statistical analysis of experimental data
This thesis is concerned with the application of expert systems techniques in the field of statistics. An expert statistician in industry has a twofold role; undertaking the design and analysis of data from complex experiments and providing supervision and help for research workers who analyse data from simpler designs. There is, therefore, a potential role for a statistical expert system which could be used by research workers to enable them to carry out valid analyses. The expert statistician would be freed from the more straightforward analyses and would only need to deal with referrals from the system and to initially 'tune' the system to their own application area. The design and development of such a prototype expert system, THESEUS, is the basis of this work.
The area of application chosen for the prototype system is completely randomised designs with one trial factor. It was initially important to limit the area of study so that knowledge acquisition for the system would be a manageable task. However, once the difficulties in developing an expert system have been tackled, much of the expertise used in analysing this simple type of study could be readily extended to more complex designs.
The knowledge acquisition phase, the most time consuming part of developing any expert system, concentrated on developing a rational prototype rule base by reviewing the available literature, interviewing practising statisticians and undertaking workshops where the analysis of particular data sets was discussed.
The prototype software is a production rule system and is written in Turbo Pascal on an IBM-AT. Pascal was chosen because of the need to access statistical routines during the consultation process. The prototype uses a combination of forward and backward chaining to process the rules. Information required by the system can come from the user, the data or the rules.
The overall system design also includes facilities for entering and editing data, altering and adding knowledge and a report generator. Implementation of these facilities is not incorporated as part of this thesis.
A small number of trial sites were selected for industrial trials in order to validate the system and evaluate the results of the local experts 'tuning' of the rule base to their own particular application area
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Examination of Bayesian belief network for safety assessment of nuclear computer-based systems
We report here on a continuation of work on the Bayesian Belief Network (BBN)model described in [Fenton, Littlewood et al. 1998]. As explained in the previous deliverable, our model concerns one part of the safety assessment task for computer and software based nuclear systems. We have produced a first complete, functioning version of our BBN model by eliciting a large numerical node probability table (NPT) required for our ‘Design Process Performance’ variable. The requirement for such large numerical NPTs poses some difficult questions about how, in general, large NPTs should be elicited from domain experts. We report about the methods we have devised to support the expert in building and validating a BBN. On the one hand, we have proceeded by eliciting approximate descriptions of the expert’s probabilistic beliefs, in terms of properties like stochastic orderings among distributions; on the other hand, we have explored ways of presenting to the expert visual and algebraic descriptions of relations among variables in the BBN, to assist the expert in an ongoing assessment of the validity of the BBN
A Framework to Simplify the Choice of Alternative Analysis and Selection Methods
This dissertation contributes a framework for analysts and engineering managers to investigate and choose alternative analysis and selection methods based upon their problem and its context. It began as an investigation into the alternative analysis and selection methods used in military planning. The existing military methods were inconsistent, violated the decision science body of knowledge, and provided no guidance to the practitioner on matching methods to problems. These challenges made it necessary to conduct this investigation.
This research used a three-phase mixed methods approach. The first phase applied the general inductive method to the decision making body of knowledge to elicit an evaluation theme. The second phase used content analysis to identify evaluation criteria and satisficing to choose an evaluation framework structure. The completed framework is applied to the case of U.S. Army planning in phase three as a validation case study.
This investigation\u27s results suggest that the proposed evaluation framework methodology is valid based upon the member checks and expert feedback on the case study. The research also contributes an expert-tested scalable collaborative online tool for alternative analysis and selection method research and selection. Finally, this dissertation recommends improvements for decision making in U.S. Army planning that have been validated by military planning and operations research experts
Cognitive finance: Behavioural strategies of spending, saving, and investing.
Research in economics is increasingly open to empirical results. The advances in behavioural approaches are expanded here by applying cognitive methods to financial questions. The field of "cognitive finance" is approached by the exploration of decision strategies in the financial settings of spending, saving, and investing. Individual strategies in these different domains are searched for and elaborated to derive explanations for observed irregularities in financial decision making. Strong context-dependency and adaptive learning form the basis for this cognition-based approach to finance. Experiments, ratings, and real world data analysis are carried out in specific financial settings, combining different research methods to improve the understanding of natural financial behaviour. People use various strategies in the domains of spending, saving, and investing. Specific spending profiles can be elaborated for a better understanding of individual spending differences. It was found that people differ along four dimensions of spending, which can be labelled: General Leisure, Regular Maintenance, Risk Orientation, and Future Orientation. Saving behaviour is strongly dependent on how people mentally structure their finance and on their self-control attitude towards decision space restrictions, environmental cues, and contingency structures. Investment strategies depend on how companies, in which investments are placed, are evaluated on factors such as Honesty, Prestige, Innovation, and Power. Further on, different information integration strategies can be learned in decision situations with direct feedback. The mapping of cognitive processes in financial decision making is discussed and adaptive learning mechanisms are proposed for the observed behavioural differences. The construal of a "financial personality" is proposed in accordance with other dimensions of personality measures, to better acknowledge and predict variations in financial behaviour. This perspective enriches economic theories and provides a useful ground for improving individual financial services
Design Preference Elicitation, Identification and Estimation.
Understanding user preference has long been a challenging topic in the design research community. Econometric methods have been adopted to link design and market, achieving design solutions sound from both engineering and business perspectives. This approach, however, only refines existing designs from revealed or stated preference data. What is needed for generating new designs is an environment for concept exploration and a channel to collect and analyze preferences on newly-explored concepts. This dissertation focuses on the development of querying techniques that learn and extract individual preferences efficiently. Throughout the dissertation, we work in the context of a human-computer interaction where in each iteration the subject is asked to choose preferred designs out of a set. The computer learns from the subject and creates the next query set so that the responses from the subject will yield the most information on the subject's preferences. The challenges of this research are: (1) To learn subject preferences within short interactions with enormous candidate designs; (2) To facilitate real-time interactions with efficient computation.
Three problems are discussed surrounding how information-rich queries can be made. The major effort is devoted to preference elicitation, where we discuss how to locate the most preferred design of a subject. Using efficient global optimization, we develop search algorithms that combine exploration of new concepts and exploitation of existing knowledge, achieving near-optimal solutions with a small number of queries. For design demonstration, the elicitation algorithm is incorporated with an online 3D car modeler. The effectiveness of the algorithm is confirmed by real user tests on finding car models close to the users' targets. In preference identification, we consider designs as binary labeled, and the objective is to classify preferred designs from not-preferred ones. We show that this classification problem can be formulated and solved by the same active learning technique used for preference estimation, where the objective is to estimate a preference function. Conceptually, this dissertation discusses how to extract preference information effectively by asking relevant but not redundant questions during an interaction.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91578/1/yiren_1.pd
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