Architecture value mapping: using fuzzy cognitive maps as a reasoning mechanism for multi-criteria conceptual design evaluation

The conceptual design phase is the most critical phase in the systems engineering life cycle. The design concept chosen during this phase determines the structure and behavior of the system, and consequently, its ability to fulfill its intended function. A good conceptual design is the first step in the development of a successful artifact. However, decision-making during conceptual design is inherently challenging and often unreliable. The conceptual design phase is marked by an ambiguous and imprecise set of requirements, and ill-defined system boundaries. A lack of usable data for design evaluation makes the problem worse. In order to assess a system accurately, it is necessary to capture the relationships between its physical attributes and the stakeholders\u27 value objectives. This research presents a novel conceptual architecture evaluation approach that utilizes attribute-value networks, designated as \u27Architecture Value Maps\u27, to replicate the decision makers\u27 cogitative processes. Ambiguity in the system\u27s overall objectives is reduced hierarchically to reveal a network of criteria that range from the abstract value measures to the design-specific performance measures. A symbolic representation scheme, the 2-Tuple Linguistic Representation is used to integrate different types of information into a common computational format, and Fuzzy Cognitive Maps are utilized as the reasoning engine to quantitatively evaluate potential design concepts. A Linguistic Ordered Weighted Average aggregation operator is used to rank the final alternatives based on the decision makers\u27 risk preferences. The proposed methodology provides systems architects with the capability to exploit the interrelationships between a system\u27s design attributes and the value that stakeholders associate with these attributes, in order to design robust, flexible, and affordable systems --Abstract, page iii

Scenarios for the development of smart grids in the UK: literature review

Smart grids are expected to play a central role in any transition to a low-carbon energy future, and much research is currently underway on practically every area of smart grids. However, it is evident that even basic aspects such as theoretical and operational definitions, are yet to be agreed upon and be clearly defined. Some aspects (efficient management of supply, including intermittent supply, two-way communication between the producer and user of electricity, use of IT technology to respond to and manage demand, and ensuring safe and secure electricity distribution) are more commonly accepted than others (such as smart meters) in defining what comprises a smart grid. It is clear that smart grid developments enjoy political and financial support both at UK and EU levels, and from the majority of related industries. The reasons for this vary and include the hope that smart grids will facilitate the achievement of carbon reduction targets, create new employment opportunities, and reduce costs relevant to energy generation (fewer power stations) and distribution (fewer losses and better stability). However, smart grid development depends on additional factors, beyond the energy industry. These relate to issues of public acceptability of relevant technologies and associated risks (e.g. data safety, privacy, cyber security), pricing, competition, and regulation; implying the involvement of a wide range of players such as the industry, regulators and consumers. The above constitute a complex set of variables and actors, and interactions between them. In order to best explore ways of possible deployment of smart grids, the use of scenarios is most adequate, as they can incorporate several parameters and variables into a coherent storyline. Scenarios have been previously used in the context of smart grids, but have traditionally focused on factors such as economic growth or policy evolution. Important additional socio-technical aspects of smart grids emerge from the literature review in this report and therefore need to be incorporated in our scenarios. These can be grouped into four (interlinked) main categories: supply side aspects, demand side aspects, policy and regulation, and technical aspects.

Prioritization of public services for digitalization using fuzzy Z-AHP and fuzzy Z-WASPAS

In this paper, public services are analyzed for implementations of Industry 4.0 tools to satisfy citizen expectations. To be able to prioritize public services for digitalization, fuzzy Z-AHP and fuzzy Z-WASPAS are used in the analysis. The decision criteria are determined as reduced cost, fast response, ease of accessibility, reduced service times, increase in the available information and increased quality. After obtaining criteria weights using fuzzy Z-AHP, health care services, waste disposal department, public transportation, information services, social care services, and citizen complaints resolution centers are compared using fuzzy Z-WASPAS that is proposed for the first time in this paper. Results show that health care services have dominant importance for the digitalization among public services.WOS:000604482500002Science Citation Index ExpandedQ2Article; Early AccessUluslararası işbirliği ile yapılmayan - HAYIROcak2021YÖK - 2020-2

Integrated Frameworks for Effective Multi-criteria Decision Making in Reliability Centred Maintenance of Industrial Machines

No abstract availabl

Security risk assessment in cloud computing domains

Cyber security is one of the primary concerns persistent across any computing platform. While addressing the apprehensions about security risks, an infinite amount of resources cannot be invested in mitigation measures since organizations operate under budgetary constraints. Therefore the task of performing security risk assessment is imperative to designing optimal mitigation measures, as it provides insight about the strengths and weaknesses of different assets affiliated to a computing platform. The objective of the research presented in this dissertation is to improve upon existing risk assessment frameworks and guidelines associated to different key assets of Cloud computing domains - infrastructure, applications, and users. The dissertation presents various informal approaches of performing security risk assessment which will help to identify the security risks confronted by the aforementioned assets, and utilize the results to carry out the required cost-benefit tradeoff analyses. This will be beneficial to organizations by aiding them in better comprehending the security risks their assets are exposed to and thereafter secure them by designing cost-optimal mitigation measures --Abstract, page iv

Multi-agent system for consumer-oriented electronic commerce

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.With the advent of the information superhighway and the exponential growth of the Internet usage, the importance of multi-agent systems is proliferating. The central theme of this thesis is to demonstrate the benefits of adopting multi-agent system (MAS) paradigm to implement consumer oriented electronic commerce system. The discipline of computational science is exploited to provide insights into the behaviour of a model of consumer behaviour that reflect the cognitive notion that the thesis has developed. For this, a multi-agent system computational environment is used to model and investigate the consumer purchase over the Internet. The MAS is developed based on a presented taxonomy, that is most relevant to the thesis application. The thesis also presents a novel approach to negotiation. Results of empirical evaluations provide a strong support that agents using the proposed approach would achieve higher payoff than human subjects. An empirical evaluation for the usability of the prototype system is also presented. Reported results are very encouraging to implement a fieldable system. To complement the perspective for a complete consumer-oriented EC system, the thesis addresses and develops approaches for searching and extracting relevant information. Example experiments are also reported to act as indicators for the effectiveness of the developed approaches

Smart Urban Water Networks

This book presents the paper form of the Special Issue (SI) on Smart Urban Water Networks. The number and topics of the papers in the SI confirm the growing interest of operators and researchers for the new paradigm of smart networks, as part of the more general smart city. The SI showed that digital information and communication technology (ICT), with the implementation of smart meters and other digital devices, can significantly improve the modelling and the management of urban water networks, contributing to a radical transformation of the traditional paradigm of water utilities. The paper collection in this SI includes different crucial topics such as the reliability, resilience, and performance of water networks, innovative demand management, and the novel challenge of real-time control and operation, along with their implications for cyber-security. The SI collected fourteen papers that provide a wide perspective of solutions, trends, and challenges in the contest of smart urban water networks. Some solutions have already been implemented in pilot sites (i.e., for water network partitioning, cyber-security, and water demand disaggregation and forecasting), while further investigations are required for other methods, e.g., the data-driven approaches for real time control. In all cases, a new deal between academia, industry, and governments must be embraced to start the new era of smart urban water systems

Consciosusness in Cognitive Architectures. A Principled Analysis of RCS, Soar and ACT-R

This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness.\ud To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST).\ud A general qualitative evaluation criteria for cognitive architectures is established based upon: a) requirements for a cognitive architecture, b) the theoretical framework based on the GST and c) core design principles for integrated cognitive conscious control systems

An overview of recent distributed algorithms for learning fuzzy models in Big Data classification

AbstractNowadays, a huge amount of data are generated, often in very short time intervals and in various formats, by a number of different heterogeneous sources such as social networks and media, mobile devices, internet transactions, networked devices and sensors. These data, identified as Big Data in the literature, are characterized by the popular Vs features, such as Value, Veracity, Variety, Velocity and Volume. In particular, Value focuses on the useful knowledge that may be mined from data. Thus, in the last years, a number of data mining and machine learning algorithms have been proposed to extract knowledge from Big Data. These algorithms have been generally implemented by using ad-hoc programming paradigms, such as MapReduce, on specific distributed computing frameworks, such as Apache Hadoop and Apache Spark. In the context of Big Data, fuzzy models are currently playing a significant role, thanks to their capability of handling vague and imprecise data and their innate characteristic to be interpretable. In this work, we give an overview of the most recent distributed learning algorithms for generating fuzzy classification models for Big Data. In particular, we first show some design and implementation details of these learning algorithms. Thereafter, we compare them in terms of accuracy and interpretability. Finally, we argue about their scalability

The Users' Perspective on the Privacy-Utility Trade-offs in Health Recommender Systems

Privacy is a major good for users of personalized services such as recommender systems. When applied to the field of health informatics, privacy concerns of users may be amplified, but the possible utility of such services is also high. Despite availability of technologies such as k-anonymity, differential privacy, privacy-aware recommendation, and personalized privacy trade-offs, little research has been conducted on the users' willingness to share health data for usage in such systems. In two conjoint-decision studies (sample size n=521), we investigate importance and utility of privacy-preserving techniques related to sharing of personal health data for k-anonymity and differential privacy. Users were asked to pick a preferred sharing scenario depending on the recipient of the data, the benefit of sharing data, the type of data, and the parameterized privacy. Users disagreed with sharing data for commercial purposes regarding mental illnesses and with high de-anonymization risks but showed little concern when data is used for scientific purposes and is related to physical illnesses. Suggestions for health recommender system development are derived from the findings.Comment: 32 pages, 12 figure