Da Ruan

Dealing with heterogeneous information in engineering evaluation processes

Before selecting a design for a large engineering system several design proposals are evaluated studying different key aspects. In such a design assessment process, different criteria need to be evaluated, which can be of both of a quantitative and qualitative nature, and the knowledge provided by experts may be vague and/or incomplete. Consequently, the assessment problems may include different types of information (numerical, linguistic, interval-valued). Experts are usually forced to provide knowledge in the same domain and scale, resulting in higher levels of uncertainty. In this paper, we propose a flexible framework that can be used to model the assessment problems in different domains and scales. A fuzzy evaluation process in the proposed framework is investigated to deal with uncertainty and manage heterogeneous information in engineering evaluation processes.

Self-tuning of fuzzy belief rule bases for engineering system safety analysis

A framework for modelling the safety of an engineering system using a fuzzy rule-based evidential reasoning (FURBER) approach has been recently proposed, where a fuzzy rule-base designed on the basis of a belief structure (called a belief rule base) forms a basis in the inference mechanism of FURBER. However, it is difficult to accurately determine the parameters of a fuzzy belief rule base (FBRB) entirely subjectively, in particular for complex systems. As such, there is a need to develop a supporting mechanism that can be used to train in a locally optimal way a FBRB initially built using expert knowledge. In this paper, the methods for self-tuning a FBRB for engineering system safety analysis are investigated on the basis of a previous study. The method consists of a number of single and multiple objective nonlinear optimization models. The above framework is applied to model the system safety of a marine engineering system and the case study is used to demonstrate how the methods can be implemented.

A Cumulative Belief Degree-Based Approach for Missing Values in Nuclear Safeguards Evaluation

Nuclear safeguards are a set of activities to verify that a State is living up to its international undertakings not to use nuclear programs for nuclear weapons purposes. Nuclear safeguards experts of International Atomic Energy Agency (IAEA) evaluate indicators by benefitting from several sources such as State declarations, on-site inspections, the IAEA databases, and other open sources. The IAEA expert evaluations are aggregated to make a final decision, which usually incomplete because of over 900 indicators, lack of expertise, and unavailability of information sources. In this study, a cumulative belief degree approach is introduced based on the belief structure. It is used to aggregate the incomplete expert evaluations that are represented with fuzzy linguistic terms. Moreover, a reliability index is employed to find the trustworthiness of the final result depending on the available evaluations. A numerical example illustrates the applicability of the proposed methodology.

A comparison study of fuzzy MADM methods in nuclear safeguards evaluation

Nuclear safeguards evaluation (NSE) is to verify that a State is living up to its international undertakings not to use nuclear programs for nuclear weapons purposes. The main issue in NSE is on the aggregation of expert evaluations for numerous indicators to make a final decision about the State’s nuclear activity. Fuzzy multiple attribute decision making (FMADM) methods are capable of dealing with such an issue. In this study, we propose a new FMADM methodology to solve the NSE problem. To this end, we investigate the applicability of four basic FMADM methods, namely a simple additive weighting method, a TOPSIS method, a linguistic method, and a non-compensatory method, to the NSE issue. As a result of the assessment of the basic methods, we propose a new FMADM methodology based on a new aggregation operator in which a cumulative belief structure is used to represent the expert evaluations. The basic methods and the proposed method as well are applied to an example from the literature for illustration purposes.

An ordered weighted averaging operator-based cumulative belief degree approach for energy policy evaluation

In the context of a long-term for sustainable energy policy development, a set of interlinked decisions produces a process as a strategy. An ordered weighted averaging operator-based cumulative belief degree approach is proposed for a case-study of energy policy evaluation. The approach is realised in a developed software tool called advanced uncertain information processing tool (AdUnIT) for the case study. The tool can be flexibly used to solve other related policy evaluation problems.

Optimization algorithm for learning consistent belief rule-base from examples

A belief rule-based inference approach and its corresponding optimization algorithm deal with a rule-base with a belief structure called a belief rule base (BRB) that forms a basis in the inference mechanism. In this paper, a new learning method is proposed based on the given sample data for optimally generating a consistent BRB. The focus is given on the consistency of BRB knowing that the consistency conditions are often violated if the system is generated from real world data. The measurement of BRB inconsistency is incorporated in the objective function of the optimization algorithm. This process is formulated as a non-linear constraint optimization problem and solved using the optimization tool provided in MATLAB. A numerical example is demonstrated the effectiveness of the proposed algorithm.

A cumulative belief-degree approach for nuclear safeguards evaluation

Nuclear safeguards are a set of activities to verify that a State is living up to its international undertakings not to use nuclear programs for nuclear weapons purposes. International Atomic Energy Agency (IAEA) uses a hierarchical assessment system that is composed of critical activities in the nuclear fuel cycle, processes required for the activities, and the indicators to evaluate the processes. IAEA experts benefit from several sources to evaluate the indicators such as State declarations, on-site inspections, IAEA databases, and open sources. One of the most important problems in nuclear safeguards evaluation (NSE) is observed in the aggregation of the multiple expert evaluations. In this study, a methodology is proposed to solve this problem where fuzzy linguistic terms are proposed to represent the expert evaluations and cumulative belief degrees (CBDs) are applied to aggregate the evaluations. CBD is introduced based on belief structure, which is developed on the basis of decision theory and Dempster-Shafer theory of evidence. The study also presents a numerical example to show the applicability of the proposed methodology.

Supporting nuclear safety culture assessment using Intelligent Decision System software

This paper illustrates how efficiency, effectiveness, consistency and reliability of nuclear safety culture assessment can be improved by the Intelligent Decision System (IDS) tool. It describes how IDS can be used as both a knowledge management and assessment supporting tool for nuclear safety culture assessment using the IAEA Safety Culture Assessment Review Team (SCART) guidelines. Once the assessment knowledge is built into IDS, the tool provides support at the data gathering stage by retrieving the right guidance at when and where it is needed so that the assessment can be made more efficiently, consistently and objectively. At the Discussion and closing stage of the assessment, the tool generates aggregated assessment results for both a team of and individual reviewers in text and graphical formats. The text report highlights the strengths and the areas for improvement, including advice on how to improve and examples of best practices in the areas. The various graphical interfaces and charts provide side by side comparisons, panoramic view of the strengths and weaknesses, trends and profiles of assessment outcomes made by different reviewers, for different organisations or for the same organisation at different time. Those outcomes give a speedy and accurate account of the review so that discussions and feedback can be made more productively and constructively.

Analyzing environmental samples in nuclear safeguards evaluation by a cumulative belief degrees approach

In this study we apply a cumulative belief degrees approach to an environmental sampling example in nuclear safeguards evaluation. Transformation formulas for each indicator are defined for distinguishing anthropogenic natural uranium from mineral uranium. An order weighted averaging operator-based aggregation method is proposed to make a final decision. The methodology is illustrated by a numerical example.

Dealing with missing values in nuclear safeguards evaluation

Nuclear Safeguards are a set of activities to verify that a State is living up to its international undertakings not to use nuclear programs for nuclear weapons purposes. In the nuclear safeguards evaluation, the experts from International Atomic Energy Agency evaluate and aggregate different indicators to make the final decision. However, some of the expert evaluation values are usually missing. In this study a cumulative belief-degrees based approach is proposed to aggregate die expert evaluations and deal with the missing values. An index is employed to find the reliability of the final result. A numerical example is provided to illustrate the applicability of the methodology.