Data-driven fuzzy analysis in quantitative mineral resource assessment

The integration of geo-information from multiple sources and of diverse nature in developing mineral favourability indexes (MFIs) is a well-known problem in mineral exploration and mineral resource assessment. Fuzzy set theory provides a convenient framework to combine and analyse qualitative and quantitative data independently of their source or characteristics. A novel, data-driven formulation for calculating MFIs based on fuzzy analysis is developed in this paper. Different geo-variables are considered fuzzy sets and their appropriate membership functions are defined and modelled. A new weighted average-type aggregation operator is then introduced to generate a new fuzzy set representing mineral favourability. The membership grades of the new fuzzy set are considered as the MFI. The weights for the aggregation operation combine the individual membership functions of the geo-variables, and are derived using information from training areas and L, regression. The technique is demonstrated in a case study of skarn tin deposits and is used to integrate geological, geochemical and magnetic data. The study area covers a total of 22.5 km(2) and is divided into 349 cells, which include nine control cells. Nine geo-variables are considered in this study. Depending on the nature of the various geo-variables, four different types of membership functions are used to model the fuzzy membership of the geo-variables involved. (C) 2002 Elsevier Science Ltd. All rights reserved.

Achieving completeness: from constructive set theory to large cardinals

This thesis is an exploration of several completeness phenomena, both in the constructive and the classical settings. After some introductory chapters in the first part of the thesis where we outline the background used later on, the constructive part contains a categorical formulation of several constructive completeness theorems available in the literature, but presented here in an unified framework. We develop them within a constructive reverse mathematical viewpoint, highlighting the metatheory used in each case and the strength of the corresponding completeness theorems. The classical part of the thesis focuses on infinitary intuitionistic propositional and predicate logic. We consider a propositional axiomatic system with a special distributivity rule that is enough to prove a completeness theorem, and we introduce weakly compact cardinals as the adequate metatheoretical assumption for this development. Finally, we return to the categorical formulation focusing this time on infinitary first-order intuitionistic logic. We propose a first-order system with a special rule, transfinite transitivity, that embodies both distributivity as well as a form of dependent choice, and study the extent to which completeness theorems can be established. We prove completeness using a weakly compact cardinal, and, like in the constructive part, we study disjunction-free fragments as well. The assumption of weak compactness is shown to be essential for the completeness theorems to hold.

Strategic logistics outsourcing:an integrated QFD and fuzzy AHP approach

This paper develops an integratedapproach, combining quality function deployment (QFD), fuzzy set theory, and analytic hierarchy process (AHP) approach, to evaluate and select the optimal third-party logistics service providers (3PLs). In the approach, multiple evaluating criteria are derived from the requirements of company stakeholders using a series of house of quality (HOQ). The importance of evaluating criteria is prioritized with respect to the degree of achieving the stakeholder requirements using fuzzyAHP. Based on the ranked criteria, alternative 3PLs are evaluated and compared with each other using fuzzyAHP again to make an optimal selection. The effectiveness of proposed approach is demonstrated by applying it to a Hong Kong based enterprise that supplies hard disk components. The proposed integratedapproach outperforms the existing approaches because the outsourcing strategy and 3PLs selection are derived from the corporate/business strategy.

A fuzzy levelised energy cost method for renewable energy technology assessment

Renewable energy project development is highly complex and success is by no means guaranteed. Decisions are often made with approximate or uncertain information yet the current methods employed by decision-makers do not necessarily accommodate this. Levelised energy costs (LEC) are one such commonly applied measure utilised within the energy industry to assess the viability of potential projects and inform policy. The research proposes a method for achieving this by enhancing the traditional discounting LEC measure with fuzzy set theory. Furthermore, the research develops the fuzzy LEC (F-LEC) methodology to incorporate the cost of financing a project from debt and equity sources. Applied to an example bioenergy project, the research demonstrates the benefit of incorporating fuzziness for project viability, optimal capital structure and key variable sensitivity analysis decision-making. The proposed method contributes by incorporating uncertain and approximate information to the widely utilised LEC measure and by being applicable to a wide range of energy project viability decisions. © 2013 Elsevier Ltd. All rights reserved.

The Development of the Generalization Algorithm Based on the Rough Set Theory

This paper considers the problem of concept generalization in decision-making systems where such features of real-world databases as large size, incompleteness and inconsistence of the stored information are taken into account. The methods of the rough set theory (like lower and upper approximations, positive regions and reducts) are used for the solving of this problem. The new discretization algorithm of the continuous attributes is proposed. It essentially increases an overall performance of generalization algorithms and can be applied to processing of real value attributes in large data tables. Also the search algorithm of the significant attributes combined with a stage of discretization is developed. It allows avoiding splitting of continuous domains of insignificant attributes into intervals.

Identifying the configurational paths to innovation in SMEs:a fuzzy-set qualitative comparative analysis

Using fuzzy-set qualitative comparative analysis (fsQCA), this study investigates the conditions leading to a higher level of innovation. More specifically, the study explores the impact of inter-organisational knowledge transfer networks and organisations' internal capabilities on different types of innovation in Small to Medium size Enterprises (SMEs) in the high-tech sector. A survey instrument was used to collect data from a sample of UK SMEs. The findings show that although individual factors are important, there is no need for a company to perform well in all the areas. The fsQCA, which enables the examination of the impacts of different combinations of factors, reveals that there are a number of paths to achieve better incremental and radical innovation performance. Companies need to choose the one that is closest to their abilities and fits best with their resources.

Modulating membership grades to gain consensus for fuzzy set uncertainty values in a clinical decision support system

This paper deals with a very important issue in any knowledge engineering discipline: the accurate representation and modelling of real life data and its processing by human experts. The work is applied to the GRiST Mental Health Risk Screening Tool for assessing risks associated with mental-health problems. The complexity of risk data and the wide variations in clinicians' expert opinions make it difficult to elicit representations of uncertainty that are an accurate and meaningful consensus. It requires integrating each expert's estimation of a continuous distribution of uncertainty across a range of values. This paper describes an algorithm that generates a consensual distribution at the same time as measuring the consistency of inputs. Hence it provides a measure of the confidence in the particular data item's risk contribution at the input stage and can help give an indication of the quality of subsequent risk predictions. © 2010 IEEE.

Partner selection for reverse logistics centres in green supply chains: a fuzzy artificial immune optimization approach

The design of reverse logistics networks has now emerged as a major issue for manufacturers, not only in developed countries where legislation and societal pressures are strong, but also in developing countries where the adoption of reverse logistics practices may offer a competitive advantage. This paper presents a new model for partner selection for reverse logistic centres in green supply chains. The model offers three advantages. Firstly, it enables economic, environment, and social factors to be considered simultaneously. Secondly, by integrating fuzzy set theory and artificial immune optimization technology, it enables both quantitative and qualitative criteria to be considered simultaneously throughout the whole decision-making process. Thirdly, it extends the flat criteria structure for partner selection evaluation for reverse logistics centres to the more suitable hierarchy structure. The applicability of the model is demonstrated by means of an empirical application based on data from a Chinese electronic equipment and instruments manufacturing company.

Entrepreneurship rates the fuzzy-set approach

A fuzzy-set qualitative comparative analysis is applied to determine the necessary and sufficient conditions for higher entrepreneur rates. Based on Global Entrepreneurship Monitor data, it is shown that the most relevant conditions are Media Attention to Entrepreneurship, as well as Perceived Capabilities and Perceived Opportunities. The non-existence of Fear of Failure is also an important factor in determining higher entrepreneurship rates. When the sample is split, this condition is more important for most developed countries. This can be viewed as relevant information for policymakers to better define their policies to promote entrepreneurship, which is a key to more sustainable growth in countries.

What are the conditions for good innovation results? A fuzzy-set approach for European Union

Innovation is one of the main concerns of European Union countries since the beginning of the century. Despite failing to reach their targets, innovation remains a priority because innovation enables countries to achieve better economic performance. This study analyzes the relation between the level of innovation and the economic effects and applies a fuzzy-set qualitative comparative analysis to study the relation between six conditions and two different outcomes. The data comes from the Union Innovation Scoreboard. The study finds that research systems, linkages and entrepreneurship, and intellectual assets are necessary conditions for the outcomes of a high level of innovation and positive economic effects. The main sufficient condition for both outcomes is a good research system.

An assessment system for evaluation of driving competencies

Automobiles have deeply impacted the way in which we travel but they have also contributed to many deaths and injury due to crashes. A number of reasons for these crashes have been pointed out by researchers. Inexperience has been identified as a contributing factor to road crashes. Driver’s driving abilities also play a vital role in judging the road environment and reacting in-time to avoid any possible collision. Therefore driver’s perceptual and motor skills remain the key factors impacting on road safety. Our failure to understand what is really important for learners, in terms of competent driving, is one of the many challenges for building better training programs. Driver training is one of the interventions aimed at decreasing the number of crashes that involve young drivers. Currently, there is a need to develop comprehensive driver evaluation system that benefits from the advances in Driver Assistance Systems. A multidisciplinary approach is necessary to explain how driving abilities evolves with on-road driving experience. To our knowledge, driver assistance systems have never been comprehensively used in a driver training context to assess the safety aspect of driving. The aim and novelty of this thesis is to develop and evaluate an Intelligent Driver Training System (IDTS) as an automated assessment tool that will help drivers and their trainers to comprehensively view complex driving manoeuvres and potentially provide effective feedback by post processing the data recorded during driving. This system is designed to help driver trainers to accurately evaluate driver performance and has the potential to provide valuable feedback to the drivers. Since driving is dependent on fuzzy inputs from the driver (i.e. approximate distance calculation from the other vehicles, approximate assumption of the other vehicle speed), it is necessary that the evaluation system is based on criteria and rules that handles uncertain and fuzzy characteristics of the driving tasks. Therefore, the proposed IDTS utilizes fuzzy set theory for the assessment of driver performance. The proposed research program focuses on integrating the multi-sensory information acquired from the vehicle, driver and environment to assess driving competencies. After information acquisition, the current research focuses on automated segmentation of the selected manoeuvres from the driving scenario. This leads to the creation of a model that determines a “competency” criterion through the driving performance protocol used by driver trainers (i.e. expert knowledge) to assess drivers. This is achieved by comprehensively evaluating and assessing the data stream acquired from multiple in-vehicle sensors using fuzzy rules and classifying the driving manoeuvres (i.e. overtake, lane change, T-crossing and turn) between low and high competency. The fuzzy rules use parameters such as following distance, gaze depth and scan area, distance with respect to lanes and excessive acceleration or braking during the manoeuvres to assess competency. These rules that identify driving competency were initially designed with the help of expert’s knowledge (i.e. driver trainers). In-order to fine tune these rules and the parameters that define these rules, a driving experiment was conducted to identify the empirical differences between novice and experienced drivers. The results from the driving experiment indicated that significant differences existed between novice and experienced driver, in terms of their gaze pattern and duration, speed, stop time at the T-crossing, lane keeping and the time spent in lanes while performing the selected manoeuvres. These differences were used to refine the fuzzy membership functions and rules that govern the assessments of the driving tasks. Next, this research focused on providing an integrated visual assessment interface to both driver trainers and their trainees. By providing a rich set of interactive graphical interfaces, displaying information about the driving tasks, Intelligent Driver Training System (IDTS) visualisation module has the potential to give empirical feedback to its users. Lastly, the validation of the IDTS system’s assessment was conducted by comparing IDTS objective assessments, for the driving experiment, with the subjective assessments of the driver trainers for particular manoeuvres. Results show that not only IDTS was able to match the subjective assessments made by driver trainers during the driving experiment but also identified some additional driving manoeuvres performed in low competency that were not identified by the driver trainers due to increased mental workload of trainers when assessing multiple variables that constitute driving. The validation of IDTS emphasized the need for an automated assessment tool that can segment the manoeuvres from the driving scenario, further investigate the variables within that manoeuvre to determine the manoeuvre’s competency and provide integrated visualisation regarding the manoeuvre to its users (i.e. trainers and trainees). Through analysis and validation it was shown that IDTS is a useful assistance tool for driver trainers to empirically assess and potentially provide feedback regarding the manoeuvres undertaken by the drivers.

Exploring the challenges to industrialized residential building in China

Industrialised building (IB) is considered by many to have an important role to play in China's residential construction industry due to its potential for improved quality, productivity, efficiency, safety and sustainability. It is surprising, therefore, that although a large number of construction programmes have been completed in the country in recent years, very few have been built in this manner. Quite why this situation exists is unknown. The well-known problems with IB, such as the constraints placed on designer freedom, may be the cause. It is equally possible that, as is typical with developing countries such as China, cost or government issues dominate. On the other hand, in comparison with other countries, the construction industry in China has been widely criticised for its lack of modernity. Either way, there is an urgent need to assess and understand the hindrances to the adoption of IB in residential construction in order to identify what corrective measures, if any, need to be taken. Towards this end, we first identify a set of critical factors (CFs) for assessing the hindrances to IB adoption in China. This involves the analysis of research data collected by a questionnaire survey of experienced housing developers and professionals working in China's construction industry sector. Fuzzy set theory is used in the selection of the CFs. These CFs comprise, in rank order: higher initial cost; lack of skilled labour in IB; manufacturing capability and involvement issues and product quality problems; lack of supply chain; lack of codes and standards; and lack of government incentives, directives and promotion. The establishment of the CFs provides a basis for local construction sectors to better equip themselves for future implementation of IB. The findings also indicate a current need for formulating improved policies and strategies to encourage the further development of IB in China at present.

Measuring corruption in public construction projects in China

Corruption has been identified as the greatest obstacle to economic and social development. Public construction projects, in particular, face high corruption risk as public construction sector has been consecutively deemed as the most corrupt one. Despite considerable efforts have been undertaken to measure corruption at a nation level, few focus on the measurement of corruption in construction projects. This paper develops a fuzzy measurement model for the potential corruption in public construction projects in China. Through semi-structured interviews with 14 experts, followed by a questionnaire survey with 188 respondents, 24 measurement items of corruption were identified and further categorized into five constructs. The fuzzy set theory was then adopted to quantify each measurement item, construct and the overall corruption level. This model can facilitate in evaluating, revealing and monitoring corruption in public construction projects. Although this study focuses on measuring corruption in public construction projects in China, similar research methods can be applied in other countries around the world and thus contribute to the global body of knowledge of corruption.

Improved Kalman filter initialisation using neurofuzzy estimation

It is traditional to initialise Kalman filters and extended Kalman filters with estimates of the states calculated directly from the observed (raw) noisy inputs, but unfortunately their performance is extremely sensitive to state initialisation accuracy: good initial state estimates ensure fast convergence whereas poor estimates may give rise to slow convergence or even filter divergence. Divergence is generally due to excessive observation noise and leads to error magnitudes that quickly become unbounded (R.J. Fitzgerald, 1971). When a filter diverges, it must be re initialised but because the observations are extremely poor, re initialised states will have poor estimates. The paper proposes that if neurofuzzy estimators produce more accurate state estimates than those calculated from the observed noisy inputs (using the known state model), then neurofuzzy estimates can be used to initialise the states of Kalman and extended Kalman filters. Filters whose states have been initialised with neurofuzzy estimates should give improved performance by way of faster convergence when the filter is initialised, and when a filter is re started after divergence

Devolution of forest-related rights: Comparative analyses of six developing countries

The study focused on the different ways that forest-related rights can be devolved to the local level according to the current legal frameworks in Laos, Nepal, Vietnam, Kenya, Mozambique and Tanzania. The eleven case studies represented the main ways in which forest-related rights can be devolved to communities or households in these countries. The objectives of this study were to 1) analyse the contents and extent of forest-related rights that can be devolved to the local level, 2) develop an empirical typology that represents the main types of devolution, and 3) compare the cases against a theoretical ideal type to assess in what way and to what extent the cases are similar to or differ from the theoretical construct. Fuzzy set theory, Qualitative Comparative Analysis and ideal type analysis were used in analysing the case studies and in developing an empirical typology. The theoretical framework, which guided data collection and analyses, was based on institutional economics and theories on property rights, common pool resources and collective action. On the basis of the theoretical and empirical knowledge, the most important attributes of rights were defined as use rights, management rights, exclusion rights, transfer rights and the duration and security of the rights. The ideal type was defined as one where local actors have been devolved comprehensive use rights, extensive management rights, rights to exclude others from the resource and rights to transfer these rights. In addition, the rights are to be secure and held perpetually. The ideal type was used to structure the analysis and as a tool against which the cases were analysed. The contents, extent and duration of the devolved rights varied greatly. In general, the results show that devolution has mainly meant the transfer of use rights to the local level, and has not really changed the overall state control over forest resources. In most cases the right holders participate, or have a limited role in the decision making regarding the harvesting and management of the resource. There was a clear tendency to devolve the rights to enforce rules and to monitor resource use and condition more extensively than the powers to decide on the management and development of the resource. The empirical typology of the cases differentiated between five different types of devolution. The types can be characterised by the devolution of 1) restricted use and control rights, 2) extensive use rights but restricted control rights, 3) extensive rights, 4) insecure, short term use and restricted control rights, and 5) insecure extensive rights. Overall, the case studies conformity to the ideal type was very low: only two cases were similar to the ideal type, all other cases differed considerably from the ideal type. The restricted management rights were the most common reason for the low conformity to the ideal type (eight cases). In three cases, the short term of the rights, restricted transfer rights, restricted use rights or restricted exclusion rights were the reason or one of the reasons for the low conformity to the ideal type. In two cases the rights were not secure.