FLC based on static var compensator for power system transient stability enhancement

Transient Stability is the capability of a system to be able to return to its normal state after experiencing large disturbances. The static var compensator (SVC) is a shunt device of the flexible AC transmission systems (FACTS) family using power electronics to improve transient stability in power system. For the SVC control, it is usually used a PI controller, although PI controller is simpler and cheaper but not suitable when power system is subjected to transient stability since power system become non-linear system. In order to overcome this problem, the PI controller combined with Fuzzy controller is designed. Two types of faults were considered for this study to examine the effect of the fuzzy-SVC controller on system transient stability, the proposed fault types are single line to ground fault and three lines to ground fault. The performance and behavior of the designed fuzzy controller compared with that of the conventional PI controller in term of terminal voltage, rotor angle, and transmission line active power

Fuzzy Set Ranking Methods and Multiple Expert Decision Making

The present report further investigates the multi-criteria decision making tool named Fuzzy Compromise Programming. Comparison of different fuzzy set ranking methods (required for processing fuzzy information) is performed. A complete sensitivity analysis concerning decision maker’s risk preferences was carried out for three water resources systems, and compromise solutions identified. Then, a weights sensitivity analysis was performed on one of the three systems to see whether the rankings would change in response to changing weights. It was found that this particular system was robust to the changes in weights. An inquiry was made into the possibility of modifying Fuzzy Compromise Programming to include participation of multiple decision makers or experts. This was accomplished by merging a technique known as Group Decision Making Under Fuzziness, with Fuzzy Compromise Programming. Modified technique provides support for the group decision making under multiple criteria in a fuzzy environment.https://ir.lib.uwo.ca/wrrr/1001/thumbnail.jp

On nearness measures in fuzzy relational data models

AbstractIt has been widely recognized that the imprecision and incompleteness inherent in real-world data suggest a fuzzy extension for information management systems. Various attempts to enhance these systems by fuzzy extensions can be found in the literature. Varying approaches concerning the fuzzification of the concept of a relation are possible, two of which are referred to in this article as the generalized fuzzy approach and the fuzzy-set relation approach. In these enhanced models, items can no longer be retrieved by merely using equality-check operations between constants; instead, operations based on some kind of nearness measures have to be developed. In fact, these models require such a nearness measure to be established for each domain for the evaluation of queries made upon them. An investigation of proposed nearness measures, often fuzzy equivalences, is conducted. The unnaturalness and impracticality of these measures leads to the development of a new measure: the resemblance relation, which is defined to be a fuzzified version of a tolerance relation. Various aspects of this relation are analyzed and discussed. It is also shown how the resemblance relation can be used to reduce redundancy in fuzzy relational database systems

A fuzzy knowledge based system for clinical diagnosis of tropical fever

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Sıtma ve tifo Sahra-altı Afrika'nın en büyük tropikal ateş enfeksiyonlarıdır. Her ikisi de bölgenin hastalık, ölüm ve ekonomik kayıplarının sebebidir. Tifo ateşi sebebiyle, her 100.000 kişiden 725 tifo vakasına yakalanmakta ve bu hastalardan da 7 adedi ölümle sonuçlandığı tahmin edilmektedir ve Dünya'nın sıtma ölümlerinin %90'ı Sahra-altı Afrika'da meydana gelmektedir. Bu iki hastalığın teşhisinde önemli olan çok sayıda belirti bulunması ve birçoğunun da ortak olması dolayısıyla teşhis zorlaşmaktadır. Bulanık küme teorisine ve insan gibi sonuçlandırma üzerine dayanan bulunak mantık, insani bilimlerde yaygın olarak kullanılmakta ve birçok problemi başarılı bir şekilde çözmektedir. Sınıflandırma ve karar verme görevlerine ihtiyaç duyulan tıbbi teşhis bu cazip uygulamalardan biridir. Belirsizliklerin olduğu teşhis özelliklerindeki karmaşıklıklar bilgisayar sistemlerinde kullanılan doğal dil ile üstesinden gelinmiştir. Bu çalışmada, Sahra-altı Afrika'da sıtma ve tifo ateşinin klinik teşhisi için bilgi tabanlı teşhis sisteminin (TROPFEV) tasarımında bulanık mantık kullanımı anlatılmaktadır. Bilgiler, tıp uzmanları danışmanlığında Uganda Sağlıklı Bakanlığı tarafından hazırlanan UCG-2012'den (Uganda Klinik Klavuzu 2012) çıkarım yapılmıştır. Bu kaynaklardan edinilmiş bilgiler modellenip, bulanık kural tabanlı mantık kullanılarak tanımlanmış ve Matlab 2012a gerçeklenmiştir. Toplanan bilgilere göre, 21 adet teşhis özellikleri, ateş hastalığının durumuna ya da şiddetine göre sistemi oluşturmak için düzenlenmiştir. Kullanıcı, karmaşık-sıtma, karmaşık olmayan-sıtma, karmaşık-tifo, karmaşık olmayan-tifo veya bilinmeyen ateş cevabını sistemden beklemektedir. Test ve performansını değerlendirmek için, TROPFEV sistemin sonuçları ile doktor tarafından yapılan teşhis sonuçlarıyla karşılaştırılmıştır. Uzman teşhisleri ve sistem teşhisleri arasındaki % 86 oranında doğruluk olduğunu görülmüştür. Sonuç olarak, tıbbi teşhis için tecrübesiz hekimlerin teşhislerine daha hızlı ve verimli bir şekilde teşhis koyabilmek için yardımcı olması amacıyla bulanık mantık kullanımına ağırlık verilebilir.. Çünkü bulanık mantık belirtilerdeki kesin olmama sıkıntılarının üstesinden gelebilmek için bulanıklık kümelerini kullanır ve bir sınıflandırmaya ilişkilendirir.Malaria and typhoid fever are major tropical fever infections. Both are responsible for significant morbidity, mortality and economic loss in the region. Typhoid fever is estimated to cause 725 incident cases and 7 deaths per 100,000 people in the year and on the other side 90% of the total world malaria deaths occur in the Sub-Saharan Africa. The two diseases malaria and typhoid fever have several diagnosis features with overlapping signs and symptoms which are a task in medical diagnosis. Fuzzy logic that lies on the fuzzy set theory and similar to human reasoning is widely used for human-related sciences, and successfully solves many problems. Medical diagnosis is one of these attractive applications, which requires classification and decision making tasks. It uses natural language to represent data into computer systems where complications in diagnosis features such as vagueness are perfectly handled. This thesis describes the use of fuzzy logic to design a knowledge based system for clinical diagnosis of malaria and typhoid fever (TROPFEV) in Sub-Saharan Africa. Knowledge was extracted from the documentary of UCG-2012 (Uganda Clinical Guidelines 2012) prepared by the ministry of healthy in Uganda as well as consulting medical experts. The knowledge acquired from these resources is modelled, represented using fuzzy rule based reasoning and implemented in Matlab 2012 a. According to the collected knowledge, 21 diagnosis features have been organised with their situations or severity during fever infections to build the system. The user is expected to get the answer of complicated malaria, uncomplicated malaria, complicated typhoid, uncomplicated typhoid or unknown fever. For testing and evaluating its performance, the results of the TROPFEV system were compared with the results of diagnosis made by a real doctor The difference in results between expert diagnosis and system diagnosis showed that the expert system have similarity with the real experts with 86% accuracy. In conclusion, the use of fuzzy logic in medical diagnosis can be emphasized because it provides an efficient way to assist inexperienced physicians to arrive at the final diagnosis of fever more quickly and efficiently. This is because fuzzy logic applies fuzzy sets to handle vagueness existing in symptoms

A stochastic multi-criteria assessment of security of transportation assets

Transportation project evaluation and prioritization use traditional performance measures including travel time, safety, user costs, economic efficiency, and environmental quality. The project impacts in terms of enhancing the infrastructure resilience or mitigating the consequences of infrastructure damage in the event of disaster occurrence are rarely considered in project evaluation. This dissertation presents a methodology to address this issue so that in evaluating and prioritizing investments, infrastructure with low security can receive the attention they deserve. Secondly, the methodology can be used for evaluating and prioritizing candidate investments dedicated specifically to security enhancement. In defining security as a function of threat likelihood, asset resilience and damage consequences, this dissertation uses security-related considerations in investment prioritization thus adding further robustness in traditional evaluations. As this leads to an increase in the number of performance criteria in the evaluation, the dissertation adopts a multiple-criteria analysis approach. The methodology quantifies the overall security level for an infrastructure in terms of the threats it faces, its resilience to damage, and the consequences in the event of the infrastructure damage. The dissertation demonstrates that it is feasible to develop a security-related measure that can be used as a performance criterion in the evaluation of general transportation projects or projects dedicated specifically towards security improvement. Through a case study, the dissertation applies the methodology by measuring the risk (and hence, security) of each for bridge infrastructure in Indiana. The method was also fuzzified and a Monte Carlo simulation was run to account for unknown data and uncertainty. On the basis of the multiple types of impacts including risk impacts such as the increase in security due to each candidate investment, this dissertation shows how to prioritize security investments across the multiple infrastructure assets using multiple-criteria analysis

Water Resources Decision Making Under Uncertainty

Uncertainty is in part about variability in relation to the physical characteristics of water resources systems. But uncertainty is also about ambiguity (Simonovic, 2009). Both variability and ambiguity are associated with a lack of clarity because of the behaviour of all system components, a lack of data, a lack of detail, a lack of structure to consider water resources management problems, working and framing assumptions being used to consider the problems, known and unknown sources of bias, and ignorance about how much effort it is worth expending to clarify the management situation. Climate change, addressed in this research project (CFCAS, 2008), is another important source of uncertainty that contributes to the variability in the input variables for water resources management. This report presents a set of examples that illustrate (a) probabilistic and (b) fuzzy set approaches for solving various water resources management problems. The main goal of this report is to demonstrate how information provided to water resources decision makers can be improved by using the tools that incorporate risk and uncertainty. The uncertainty associated with water resources decision making problems is quantified using probabilistic and fuzzy set approaches. A set of selected examples are presented to illustrate the application of probabilistic and fuzzy simulation, optimization, and multi-objective analysis to water resources design, planning and operations. Selected examples include dike design, sewer pipe design, optimal operations of a single purpose reservoir, and planning of a multi-purpose reservoir system. Demonstrated probabilistic and fuzzy tools can be easily adapted to many other water resources decision making problems.https://ir.lib.uwo.ca/wrrr/1035/thumbnail.jp

Development of Conceptual Constructs for Organisational BIM Adoption and their Systematic Application within the UK Architecture Sector

Building Information Modelling (BIM) is an innovation that is transforming practices within the Architectural, Engineering, Construction and Operation (AECO) sectors. The investigation of the process of BIM adoption and diffusion has attracted significant interest from industry and academia. Drivers and factors influencing BIM adoption were examined at different levels, ranging from individual and group through organisations and supply chains to whole market level. However, there is still a dearth of studies that extensively integrate drivers and factors affecting the decision to adopt BIM by organisations. Existing studies often seek to develop approaches for forecasting BIM diffusion, and are generally focused on the diffusion phase, after BIM has been adopted. Therefore, this study aims to improve the understanding of the BIM adoption process within organisations and across markets by developing the necessary conceptual constructs (e.g., BIM adoption taxonomy, adoption process model, adoption two-dimensional characterisation model, and systems thinking models) and providing the supporting empirical evidence. This study provided an in-depth analysis of the BIM adoption process within organisations. It developed a unified BIM adoption taxonomy that contains an extensive array of adoption factors. Following the validation of the taxonomy, its factors were used within a proposed conceptual model, which combined the Innovation Diffusion Theory with the Institutional Theory, to perform a multifaceted analysis of the BIM adoption process. A set of 11 most influencing factors on BIM adoption process was identified and included: Willingness to adopt BIM, Communication behaviour of an organisation, Observability of BIM benefits, Compatibility of BIM, Social motivations among organisation's members, Relative advantage of BIM, Organisational culture, Top management support, Organisational readiness, Coercive pressures (Governmental mandate, informal mandate), and Organisation size. Focussing on these 11 most influencing factors, several analyses were performed to understand the interplays between these factors - while considering specific instances of certain factors (i.e. organisation size, and external isomorphic pressure) over time (i.e., Pre-2011, 2011-2016, and Post-2016 exemplifying three key time periods in the UK national BIM strategy). The results showed that the Relative advantage of BIM is the most important and influencing factor across all the three stages of the adoption process (i.e., Awareness stage, Intention stage, and Decision stage) of the BIM adoption process. Coercive pressures (e.g. Governmental mandate, informal mandate) had a direct influence on both formulating the intention and the decision to adopt BIM across the three-time horizons (i.e., Pre-2011, 2011-2016, and Post-2016). For the Pre-2011 period, the coercive pressures were mostly informal mandate/pressures by the parent companies and partners, while during 2011-2016 and Post-2016 periods, it is predominantly the UK Government mandate which was announced in 2011 and entered into effect in 2016. Several Systems Thinking models were developed to show the interdependencies among the factors that affect the BIM adoption process at different time periods and stages of the BIM adoption process. Such models infer patterns of behaviour of BIM adoption as complex systems and can be used to guide the development and implementation of BIM strategies. For example, by relating each factor within the system thinking model to the player group(s) who can exert influence upon it, the complementary role of the player groups can be planned to facilitate the BIM adoption process according to the patterns identified in the corresponding systems thinking model. The different patterns developed through the specialised systems thinking models can be used to develop tailored BIM adoption strategies for the different scenarios involved. At a global level (overall aim), this study provided an understanding of how intra-organisational BIM adoption and inter-organisational BIM diffusion occurs. At a local level (individual objectives), the key knowledge deliverables in this study (i.e., the taxonomy, conceptual model for BIM adoption process, two-dimensional characterisation model of BIM adoption, and systems thinking models) and the empirical investigation represent a new contribution to knowledge with each contributing from a specific standpoint. The Unified BIM Adoption Taxonomy is the first – if not the sole – statistically validated BIM adoption taxonomy that includes an extensive array of adoption drivers and factors and combines constructs from both the Institutional and the Innovation Diffusion theories. The conceptual model for analysing BIM adoption and its use for the empirical investigation of BIM adoption within the UK Architecture sector explored and identified relationships that were not known before (i.e., triggering the BIM Awareness and formulating an Intention about BIM adoption is not limited to Internal Environment Characteristics and the Innovation Characteristics respectively - as suggested by Rogers’ theory, but occurs by a combination of both characteristics). The two-dimensional characterisation model of BIM adoption clarified new interplays between adoption factors, the organisation size, and time (i.e., pairs of positively and negatively correlated factors vary based on time horizon). The classification of factors into cause and effect groups using the F-DEMATEL provided a new understanding of the independencies between factors which can be used to tailor and prioritise implementation actions and investments. The developed Systems Thinking Models enabled an attentive analysis of mutual interactions between adoption factors as part of a causal relationship networks. The developed instances of such models for different temporal scenarios and stages of the BIM adoption stage can be exploited by the industry player groups (i.e., Policy-makers, decision-makers, change agents, etc.) to promote BIM adoption process within the organisations and BIM diffusion across a market. The key knowledgeable deliverables can be used to perform various analyses of the BIM adoption process, providing evidence and insights for decision-makers within organisations and across a whole market when formulating BIM adoption and diffusion strategies. In particular, they can assist researchers, decision-makers, and policy-makers with a better understanding of the BIM adoption process and can guide the development of BIM strategies and plan for BIM adoption and diffusion. Ultimately, they contribute to promote BIM adoption within the architectural sector through the suggested adoption patterns

Mathematical and fuzzy modelling of high-speed interconnections in integrated circuits.

Microstrip line are the most popular interconnection type mainly due to its planar geometry. The mode of propagation is almost a transverse electromagnetic mode of wave propagation (TEM) and can be described by the Telegrapher's equations. These facts make mathematical and fuzzy modelling of microstrip lines possible.Two types of nonuniformly coupled microstrip lines, namely, nonuniformly spaced and strictly nonuniform, are presented in this study. A new model of capacitance matrix was developed for nonuniformly spaced coupled microstrip lines. The model obtained was then translated into a Mathematica program in order to be utilised in real systems. Furthermore, a new matrix; mutual capacitance ratio matrix, was deduced from the previous model. A few valuable properties were then established from this matrix. Novel concepts were introduced to approximate capacitance of strictly nonuniform coupled microstrip lines and Mathematica programs were coded to implement these methods. The study then continued with the development of new algorithms to calculate the time delay and characteristic impedance using capacitance matrices of both types of nonuniform lines. These algorithms finally became a generalised algorithm which could be used in any type of coupled microstrip lines, uniform and nonuniform. The time delay and characteristic impedance were later used as parameters to simulate crosstalk using SPICE. Analysis of geometrical and electrical parameters of microstrip lines was performed mathematically and simulations modelled using the Mathematica package. Experimental work was also carried out to investigate the characteristic of crosstalk. All information obtained from these analyses were then fed into the developed novel fuzzy model. The model was designed to minimise crosstalk and to optimise the geometrical and electrical parameters of coupled microstrip lines simultaneously. These models have the potential to become 'multi purpose on board designing tools' for a designer before the system is finally fabricated

Application of decision trees and multivariate regression trees in design and optimization

Induction of decision trees and regression trees is a powerful technique not only for performing ordinary classification and regression analysis but also for discovering the often complex knowledge which describes the input-output behavior of a learning system in qualitative forms;In the area of classification (discrimination analysis), a new technique called IDea is presented for performing incremental learning with decision trees. It is demonstrated that IDea\u27s incremental learning can greatly reduce the spatial complexity of a given set of training examples. Furthermore, it is shown that this reduction in complexity can also be used as an effective tool for improving the learning efficiency of other types of inductive learners such as standard backpropagation neural networks;In the area of regression analysis, a new methodology for performing multiobjective optimization has been developed. Specifically, we demonstrate that muitiple-objective optimization through induction of multivariate regression trees is a powerful alternative to the conventional vector optimization techniques. Furthermore, in an attempt to investigate the effect of various types of splitting rules on the overall performance of the optimizing system, we present a tree partitioning algorithm which utilizes a number of techniques derived from diverse fields of statistics and fuzzy logic. These include: two multivariate statistical approaches based on dispersion matrices, an information-theoretic measure of covariance complexity which is typically used for obtaining multivariate linear models, two newly-formulated fuzzy splitting rules based on Pearson\u27s parametric and Kendall\u27s nonparametric measures of association, Bellman and Zadeh\u27s fuzzy decision-maximizing approach within an inductive framework, and finally, the multidimensional extension of a widely-used fuzzy entropy measure. The advantages of this new approach to optimization are highlighted by presenting three examples which respectively deal with design of a three-bar truss, a beam, and an electric discharge machining (EDM) process