Choquet integrals of weighted triangular fuzzy linguistic information and their applications to multiple attribute decision making

We investigate the multiple attribute decision making problems in which attribute values take the form of triangular fuzzy linguistic information. Firstly, the definition and some operational laws of triangular fuzzy linguistic are introduced. Then, we have developed three fuzzy linguistic Choquet integral aggregation operators: fuzzy linguistic choquet ordered averaging operator, fuzzy linguistic choquet ordered geometric operator and fuzzy linguistic choquet ordered harmonic mean operator. The prominent characteristic of the operators is that they cannot only consider the importance of the elements or their ordered positions, but also reflect the correlation among the elements or their ordered positions. We have studied some desirable properties of these operators, such as commutativity, idempotency and monotonicity, and applied these operators to multiple attribute decision making with triangular fuzzy linguistic information. Finally an illustrative example has been given to show the developed method

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This article presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area

Fuzzy set methods for object recognition in space applications

Progress on the following tasks is reported: (1) fuzzy set-based decision making methodologies; (2) feature calculation; (3) clustering for curve and surface fitting; and (4) acquisition of images. The general structure for networks based on fuzzy set connectives which are being used for information fusion and decision making in space applications is described. The structure and training techniques for such networks consisting of generalized means and gamma-operators are described. The use of other hybrid operators in multicriteria decision making is currently being examined. Numerous classical features on image regions such as gray level statistics, edge and curve primitives, texture measures from cooccurrance matrix, and size and shape parameters were implemented. Several fractal geometric features which may have a considerable impact on characterizing cluttered background, such as clouds, dense star patterns, or some planetary surfaces, were used. A new approach to a fuzzy C-shell algorithm is addressed. NASA personnel are in the process of acquiring suitable simulation data and hopefully videotaped actual shuttle imagery. Photographs have been digitized to use in the algorithms. Also, a model of the shuttle was assembled and a mechanism to orient this model in 3-D to digitize for experiments on pose estimation is being constructed

Neutrosophic Multi-Criteria Decision Making

The notion of a neutrosophic quadruple BCK/BCI-number is considered in the first article (“Neutrosophic Quadruple BCK/BCI-Algebras”, by Young Bae Jun, Seok-Zun Song, Florentin Smarandache, and Hashem Bordbar), and a neutrosophic quadruple BCK/BCI-algebra, which consists of neutrosophic quadruple BCK/BCI-numbers, is constructed. Several properties are investigated, and a (positive implicative) ideal in a neutrosophic quadruple BCK-algebra and a closed ideal in a neutrosophic quadruple BCI-algebra are studied. Given subsets A and B of a BCK/BCI-algebra, the set NQ(A,B), which consists of neutrosophic quadruple BCK/BCInumbers with a condition, is established. Conditions for the set NQ(A,B) to be a (positive implicative) ideal of a neutrosophic quadruple BCK-algebra are provided, and conditions for the set NQ(A,B) to be a (closed) ideal of a neutrosophic quadruple BCI-algebra are given. Techniques for the order of preference by similarity to ideal solution (TOPSIS) and elimination and choice translating reality (ELECTRE) are widely used methods to solve multicriteria decision-making problems. In the second research article (“Decision-Making with Bipolar Neutrosophic TOPSIS and Bipolar Neutrosophic ELECTRE-I”), Muhammad Akram, Shumaiza, and Florentin Smarandache present the bipolar neutrosophic TOPSIS method and the bipolar neutrosophic ELECTRE-I method to solve such problems. The authors use the revised closeness degree to rank the alternatives in the bipolar neutrosophic TOPSIS method. The researchers describe the bipolar neutrosophic TOPSIS method and the bipolar neutrosophic ELECTRE-I method by flow charts, also solving numerical examples by the proposed methods and providing a comparison of these methods. In the third article (“Interval Neutrosophic Sets with Applications in BCK/BCI-Algebra”, by Young Bae Jun, Seon Jeong Kim and Florentin Smarandache), the notion of (T(i,j),I(k,l),F(m,n))-interval neutrosophic subalgebra in BCK/BCI-algebra is introduced for i,j,k,l,m,n infoNumber 1,2,3,4, and properties and relations are investigated. The notion of interval neutrosophic length of an interval neutrosophic set is also introduced, and the related properties are investigated

ARTIFICIAL INTELLIGENCE-BASED APPROACH TO MODELLING OF PIPE ORGANS

The aim of the project was to develop a new Artificial Intelligence-based method to aid modeling of musical instruments and sound design. Despite significant advances in music technology, sound design and synthesis of complex musical instruments is still time consuming, error prone and requires expert understanding of the instrument attributes and significant expertise to produce high quality synthesised sounds to meet the needs of musicians and musical instrument builders. Artificial Intelligence (Al) offers an effective means of capturing this expertise and for handling the imprecision and uncertainty inherent in audio knowledge and data. This thesis presents new techniques to capture and exploit audio expertise, following extended knowledge elicitation with two renowned music technologist/audio experts, developed and embodied into an intelligent audio system. The Al combined with perceptual auditory modeling ba.sed techniques (ITU-R BS 1387) make a generic modeling framework providing a robust methodology for sound synthesis parameters optimisation with objective prediction of sound synthesis quality. The evaluation, carried out using typical pipe organ sounds, has shown that the intelligent audio system can automatically design sounds judged by the experts to be of very good quality, while significantly reducing the expert's work-load by up to a factor of three and need for extensive subjective tests. This research work, the first initiative to capture explicitly knowledge from audio experts for sound design, represents an important contribution for future design of electronic musical instruments based on perceptual sound quality will help to develop a new sound quality index for benchmarking sound synthesis techniques and serve as a research framework for modeling of a wide range of musical instruments.Musicom Lt

Hybrid-fuzzy techniques with flexibility and attitudinal parameters for supporting early product design and reliability management

The main aim of the research work presented in this thesis is to define and develop novel Hybrid Fuzzy-based techniques for supporting aspects of product development engineering, specifically product reliability at the early phase of product design under the design for reliability philosophy and concept designs assessment problems when the required information is rough and incomplete. Thus, to achieve the above-stated aim, which has been formulated in the effort to filling the identified gaps in the literature which comprise of the need for a holistic, flexible and adjustable method to facilitate and support product design concept assessment and product reliability at the early product design phase. The need for the incorporation of the attitudinal character of the DMs into the product reliability and design concept assessment and finally, the need to account for the several interrelated complex attributes in the product reliability and design concept assessment process. A combination of research methods has been employed which includes an extensive literature review, multiple case study approach, and personal interview of experts, through which data were, collected that provided information for the real-life case study. With the new Hybrid Fuzzy-based techniques (i.e. the intuitionistic fuzzy TOPSIS model which is based on an exponential-related function (IF-TOPSISEF) and the Multi-attribute group decision-making (MAGDM) method which is based on a generalized triangular intuitionistic fuzzy geometric averaging (GTIFGA) operator), a more robust method for the product reliability and design concepts assessment respectively have been achieved as displayed in the comparative analysis in the thesis. The new methods have provided a more complete and a holistic view of the assessment process, by looking at the product reliability and design concept assessment from different scenario depending on the interest of the DMs. Using the above methods, the thesis has been able to evaluated some complex mechanical systems in literature and in real-life including Crawler Crane Machine and Forklift Truck for design change with the purpose of gaining appropriate reliability knowledge and information needed at the early product design phase, and that can subsequently aid and improve the product design concepts after all such useful information have been added into the new design. With the application of the new methods, and their proven feasibility and rationality as displayed in the assessment results of the complex mechanical systems in literature and that of the real-life case studies, this thesis, therefore, can conclude that the Hybrid Fuzzy-based techniques proposed, has provided a better and a novel alternative to existing product reliability and design concepts assessment methods

Multiple-Criteria Decision Making

Decision-making on real-world problems, including individual process decisions, requires an appropriate and reliable decision support system. Fuzzy set theory, rough set theory, and neutrosophic set theory, which are MCDM techniques, are useful for modeling complex decision-making problems with imprecise, ambiguous, or vague data.This Special Issue, “Multiple Criteria Decision Making”, aims to incorporate recent developments in the area of the multi-criteria decision-making field. Topics include, but are not limited to:- MCDM optimization in engineering;- Environmental sustainability in engineering processes;- Multi-criteria production and logistics process planning;- New trends in multi-criteria evaluation of sustainable processes;- Multi-criteria decision making in strategic management based on sustainable criteria

Active Filter Modelling To Mitigate Harmonics Generated By Electric Vehicle Chargers

The Automotive industry is going through a rapid transformation to adopt electrified technology. A major share of the electrified vehicles is going to be in the Battery electric vehicles (BEVs) and plug in hybrids segments that need to connect to the grid to recharge the batteries. For customer convenience, the time required for fully charging the battery need to be brought down significantly. EV charging stations are getting installed that could bring down the charging time to less than 30 minutes. However this pose a unique issue to the power quality of the utility grid. During charging, the EV charging unit injects harmonics to the grid. When a large number of EVs are getting charge simultaneously, which is a likely scenario in the future, the degradation in the power quality of the grid would be significant. This thesis discuss the modelling of an active filter to reduce the Total harmonic distortion (THD) generated by electric vehicle (EV) chargers. The main objective of this thesis is to determine the percentage of harmonic current injected by the EV chargers to the power grid and to model an active filter to mitigate the harmonic distortion generated by these chargers. The active filter is modelled as bidirectional three-phase pulse width modulation (PWM) rectifier. The EV in this proposed model is represented as an injected current harmonic source. Positive sequence synchronous reference frame controller (SRFC) is used to generate the reference current. The hysteresis controller is used to compare the load current and injected current, and its output is used to generate the switching pulses for Metal oxide semiconductor field effect transistor (MOSFET). The DC link voltage control is achieved by using conventional Proportional and integral controller (PI) and fuzzy logic control PI. MATLAB/Simulink simulation result shows that the proposed filter can be used to mitigate the THD of EV chargers without violating the limit set by IEEE Std. 519 - 1992