Search results for: fuzzy logic based analysis

Authors: Vinay A. Sharma, Shiva Prasad H. C.

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The initial stages of any project are often observed to be in a mixed set of conditions. Setting up the project is a tough task, but taking the initial decisions is rather not complex, as some of the critical factors are yet to be introduced into the scenario. These simple initial decisions potentially shape the timeline and subsequent events that might later be plotted on it. Proceeding towards the solution for a problem is the primary objective in the initial stages. The optimization in the solutions can come later, and hence, the resources deployed towards attaining the solution are higher than what they would have been in the optimized versions. A ‘logic’ that counters the problem is essentially the core of the desired solution. Thus, if the problem is solved, the deployment of resources has led to the required logic being attained. As the project proceeds along, the individuals working on the project face fresh challenges as a team and are better accustomed to their surroundings. The developed, optimized solutions are then considered for implementation, as the individuals are now experienced, and know better of the consequences and causes of possible failure, and thus integrate the adequate tolerances wherever required. Furthermore, as the team graduates in terms of strength, acquires prodigious knowledge, and begins its efficient transfer, the individuals in charge of the project along with the managers focus more on the optimized solutions rather than the traditional ones to minimize the required resources. Hence, as time progresses, the authorities prioritize attainment of the required logic, at a lower amount of dedicated resources. For empirical analysis of the stated theory, leaders and key figures in organizations are surveyed for their ideas on appropriate logic required for tackling a problem. Key-pointers spotted in successfully implemented solutions are noted from the analysis of the responses and a metric for measuring logic is developed. A graph is plotted with the quantifiable logic on the Y-axis, and the dedicated resources for the solutions to various problems on the X-axis. The dedicated resources are plotted over time, and hence the X-axis is also a measure of time. In the initial stages of the project, the graph is rather linear, as the required logic will be attained, but the consumed resources are also high. With time, the authorities begin focusing on optimized solutions, since the logic attained through them is higher, but the resources deployed are comparatively lower. Hence, the difference between consecutive plotted ‘resources’ reduces and as a result, the slope of the graph gradually increases. On an overview, the graph takes a parabolic shape (beginning on the origin), as with each resource investment, ideally, the difference keeps on decreasing, and the logic attained through the solution keeps increasing. Even if the resource investment is higher, the managers and authorities, ideally make sure that the investment is being made on a proportionally high logic for a larger problem, that is, ideally the slope of the graph increases with the plotting of each point.

Keywords: decision-making, leadership, logic, strategic management

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Authors: Aman Berhe, Camille Guinaudeau, Claude Barras

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In order to facilitate information seeking in large collections of multimedia documents with long and progressive content (such as broadcast news or TV series), one can extract the semantic links that exist between semantically coherent parts of documents, i.e., scenes. The links can then create a coherent collection of scenes from which it is easier to perform content analysis, topic extraction, or information retrieval. In this paper, we focus on TV series structuring and propose two approaches for scene linking at different levels of granularity (episode and season): a fuzzy online clustering technique and a graph-based community detection algorithm. When evaluated on the two first seasons of the TV series Game of Thrones, we found that the fuzzy online clustering approach performed better compared to graph-based community detection at the episode level, while graph-based approaches show better performance at the season level.

Keywords: multimedia collection structuring, progressive content, scene linking, fuzzy clustering, community detection

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Authors: Reginaldo Cardoso, Magno Enrique Mendoza Meza

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This work focuses on simulation and comparison of two control skyhook techniques applied to a quarter-car of the active suspension. The objective is to provide comfort to the driver. The main idea of skyhook control is to imagine a damper connected to an imaginary sky; thus, the feedback is performed with the resultant force between the imaginary and the suspension damper. The first control technique is the Mandani fuzzy skyhook and the second control technique is a Takagi-Sugeno fuzzy skyhook controller, in the both controllers the inputs are the relative velocity between the two masses and the vehicle body velocity, the output of the Mandani fuzzy skyhook is the coefficient of imaginary damper viscous-friction and the Takagi-Sugeno fuzzy skyhook is the force. Finally, we compared the techniques. The Mandani fuzzy skyhook showed a more comfortable response to the driver, followed closely by the Takagi- Sugeno fuzzy skyhook.

Keywords: active suspention, Mandani, quarter-car, skyhook, Sugeno

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Authors: Masoumeh Mofarrah, Amir NahanMoghadam

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Higher oil prices and increasing oil demand are main reasons for great attention to Enhanced Oil Recovery (EOR). Comprehensive researches have been accomplished to develop, appraise and improve EOR methods and their application. Recently Artificial Intelligence (AI) gained popularity in petroleum industry that can help petroleum engineers to solve some fundamental petroleum engineering problems such as reservoir simulation, EOR project risk analysis, well log interpretation and well test model selection. This study presents a historical overview of most popular AI tools including neural networks, genetic algorithms, fuzzy logic and expert systems in petroleum industry and discusses two case studies to represent the application of two mentioned AI methods for selecting an appropriate EOR method based on reservoir characterization in feasible and effective way.

Keywords: artificial intelligence, EOR, neural networks, expert systems

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Authors: Imed Feki, Faouzi Msahli

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Selection of relevant parameters from a high dimensional process operation setting space is a problem frequently encountered in industrial process modelling. This paper presents a method for selecting the most relevant fabric physical parameters for each sensory quality feature. The proposed relevancy criterion has been developed using two approaches. The first utilizes a fuzzy sensitivity criterion by exploiting from experimental data the relationship between physical parameters and all the sensory quality features for each evaluator. Next an OWA aggregation procedure is applied to aggregate the ranking lists provided by different evaluators. In the second approach, another panel of experts provides their ranking lists of physical features according to their professional knowledge. Also by applying OWA and a fuzzy aggregation model, the data sensitivity-based ranking list and the knowledge-based ranking list are combined using our proposed percolation technique, to determine the final ranking list. The key issue of the proposed percolation technique is to filter automatically and objectively the relevant features by creating a gap between scores of relevant and irrelevant parameters. It permits to automatically generate threshold that can effectively reduce human subjectivity and arbitrariness when manually choosing thresholds. For a specific sensory descriptor, the threshold is defined systematically by iteratively aggregating (n times) the ranking lists generated by OWA and fuzzy models, according to a specific algorithm. Having applied the percolation technique on a real example, of a well known finished textile product especially the stonewashed denims, usually considered as the most important quality criteria in jeans’ evaluation, we separate the relevant physical features from irrelevant ones for each sensory descriptor. The originality and performance of the proposed relevant feature selection method can be shown by the variability in the number of physical features in the set of selected relevant parameters. Instead of selecting identical numbers of features with a predefined threshold, the proposed method can be adapted to the specific natures of the complex relations between sensory descriptors and physical features, in order to propose lists of relevant features of different sizes for different descriptors. In order to obtain more reliable results for selection of relevant physical features, the percolation technique has been applied for combining the fuzzy global relevancy and OWA global relevancy criteria in order to clearly distinguish scores of the relevant physical features from those of irrelevant ones.

Keywords: data sensitivity, feature selection, fuzzy logic, OWA operators, percolation technique

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Authors: Javeria Arain, Saad Malik

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The management of solid waste is a crucial and essential aspect of urban environmental management especially in a city with an ever increasing population such as Karachi. The total amount of municipal solid waste generated from Gulshan e Iqbal town on average is 444.48 tons per day and landfill sites are a widely accepted solution for final disposal of this waste. However, an improperly selected site can have immense environmental, economical and ecological impacts. To select an appropriate landfill site a number of factors should be kept into consideration to minimize the potential hazards of solid waste. The purpose of this research is to analyse the study area for the construction of an appropriate landfill site for disposal of municipal solid waste generated from Gulshan e-Iqbal Town by using geospatial techniques considering hydrological, geological, social and geomorphological factors. This was achieved using analytical hierarchy process and fuzzy analysis as a decision support tool with integration of geographic information sciences techniques. Eight most critical parameters, relevant to the study area, were selected. After generation of thematic layers for each parameter, overlay analysis was performed in ArcGIS 10.0 software. The results produced by both methods were then compared with each other and the final suitability map using AHP shows that 19% of the total area is Least Suitable, 6% is Suitable but avoided, 46% is Moderately Suitable, 26% is Suitable, 2% is Most Suitable and 1% is Restricted. In comparison the output map of fuzzy set theory is not in crisp logic rather it provides an output map with a range of 0-1, where 0 indicates least suitable and 1 indicates most suitable site. Considering the results it is deduced that the northern part of the city is appropriate for constructing the landfill site though a final decision for an optimal site could be made after field survey and considering economical and political factors.

Keywords: Analytical Hierarchy Process (AHP), fuzzy set theory, Geographic Information Sciences (GIS), Multi-Criteria Decision Analysis (MCDA)

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Authors: Albert Alexander Stonier

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Due to the fossil fuel exhaustion and environmental pollution, renewable energy sources especially solar photovoltaic system plays a predominant role in providing energy to the consumers. It has been estimated that by 2050 the renewable energy sources will satisfy 50% of the total energy requirement of the world. In this context, the faults in the conversion process require a special attention which is considered as a major problem. A fault which remains even for a few seconds will cause undesirable effects to the system. The presentation comprises of the analysis, causes, effects and mitigation methods of various faults occurring in the entire solar photovoltaic energy conversion process. In order to overcome the faults in the system, an intelligent based artificial neural networks and fuzzy logic are proposed which can significantly mitigate the faults. Hence the presentation intends to find the problem in renewable energy and provides the possible solution to overcome it with simulation and experimental results. The work performed in a 3kWp solar photovoltaic plant whose results cites the improvement in reliability, availability, power quality and fault tolerant ability.

Keywords: solar photovoltaic, power electronics, power quality, PWM

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Authors: C. B. Le, V. N. Pham

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In modern data analysis, multi-source data appears more and more in real applications. Multi-source data clustering has emerged as a important issue in the data mining and machine learning community. Different data sources provide information about different data. Therefore, multi-source data linking is essential to improve clustering performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a versatile machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. This paper proposes a new clustering ensemble method for multi-source data analysis. The fuzzy optimized multi-objective clustering ensemble method is called FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. The experiments were performed on the standard sample data set. The experimental results demonstrate the superior performance of the FOMOCE method compared to the existing clustering ensemble methods and multi-source clustering methods.

Keywords: clustering ensemble, multi-source, multi-objective, fuzzy clustering

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Authors: R. Mellah, R. Toumi

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This paper presents a new adaptive neuro-fuzzy controller equipped with compensatory fuzzy control (CNFI) in order to not only adjusts membership functions but also to optimize the adaptive reasoning by using a compensatory learning algorithm. The proposed control structure includes both CNFI controllers for which one is used to control in force the master robot and the second one for controlling in position the slave robot. The experimental results obtained, show a fairly high accuracy in terms of position and force tracking under free space motion and hard contact motion, what highlights the effectiveness of the proposed controllers.

Keywords: compensatory fuzzy, neuro-fuzzy, control adaptive, teleoperation

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Authors: Elena M. Capitanul, Carlos A. Nunes Cosenza, Walid El Moudani, Felix Mora Camino

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The construction of a new airport or the extension of an existing one requires massive investments and many times public private partnerships were considered in order to make feasible such projects. One characteristic of these projects is uncertainty with respect to financial and environmental impacts on the medium to long term. Another one is the multistage nature of these types of projects. While many airport development projects have been a success, some others have turned into a nightmare for their promoters. This communication puts forward a new approach for airport investment risk assessment. The approach takes explicitly into account the degree of uncertainty in activity levels prediction and proposes milestones for the different stages of the project for minimizing risk. Uncertainty is represented through fuzzy dual theory and risk management is performed using dynamic programming. An illustration of the proposed approach is provided.

Keywords: airports, fuzzy logic, risk, uncertainty

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Authors: Georgios Charizanos, Haydar Demirhan, Duygu Icen

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Two of the most impactful issues in classical logistic regression are class imbalance and complete separation. These can result in model predictions heavily leaning towards the imbalanced class on the binary response variable or over-fitting issues. Fuzzy methodology offers key solutions for handling these problems. However, most studies propose the transformation of the binary responses into a continuous format limited within [0,1]. This is called the possibilistic approach within fuzzy logistic regression. Following this approach is more aligned with straightforward regression since a logit-link function is not utilized, and fuzzy probabilities are not generated. In contrast, we propose a method of fuzzifying binary response variables that allows for the use of the logit-link function; hence, a probabilistic fuzzy logistic regression model with the Monte Carlo method. The fuzzy probabilities are then classified by selecting a fuzzy threshold. Different combinations of fuzzy and crisp input, output, and coefficients are explored, aiming to understand which of these perform better under different conditions of imbalance and separation. We conduct numerical experiments using both synthetic and real datasets to demonstrate the performance of the fuzzy logistic regression framework against seven crisp machine learning methods. The proposed framework shows better performance irrespective of the degree of imbalance and presence of separation in the data, while the considered machine learning methods are significantly impacted.

Keywords: fuzzy logistic regression, fuzzy, logistic, machine learning

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Authors: Abdelkhalek Bakkari

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Image segmentation is a critical step in image processing and pattern recognition. In this paper, we proposed a new robust automatic image classification based on a dynamic fuzzy c-means algorithm and mathematical morphology. The proposed segmentation algorithm (DFCM_MM) has been applied to MR perfusion images. The obtained results show the validity and robustness of the proposed approach.

Keywords: segmentation, classification, dynamic, fuzzy c-means, MR image

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Authors: Hashem Shariatmadar, Mozhgansadat Momtazdargahi

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The purpose of control structure against earthquake is to dissipate earthquake input energy to the structure and reduce the plastic deformation of structural members. There are different methods for control structure against earthquake to reduce the structure response that they are active, semi-active, inactive and hybrid. In this paper two different combined control systems are used first system comprises base isolator and multi tuned mass dampers (BI & MTMD) and another combination is hybrid base isolator and multi tuned mass dampers (HBI & MTMD) for controlling an eight story isolated benchmark steel structure. Active control force of hybrid isolator is estimated by fuzzy logic algorithms. The influences of the combined systems on the responses of the benchmark structure under the two near-field earthquake (Newhall & Elcentro) are evaluated by nonlinear dynamic time history analysis. Applications of combined control systems consisting of passive or active systems installed in parallel to base-isolation bearings have the capability of reducing response quantities of base-isolated (relative and absolute displacement) structures significantly. Therefore in design and control of irregular isolated structures using the proposed control systems, structural demands (relative and absolute displacement and etc.) in each direction must be considered separately.

Keywords: base-isolated benchmark structure, multi-tuned mass dampers, hybrid isolators, near-field earthquake, fuzzy algorithm

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Authors: Najla Althuniyan

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Online discussions take place across different platforms. These discussions have the potential to extract crowd wisdom and capture the collective intelligence from a different perspective. However, certain phenomena, such as controversy, often appear in online argumentation that makes the discussion between participants heated. Heated discussions can be used to extract new knowledge. Therefore, detecting the presence of controversy is an essential task to determine if collective intelligence can be extracted from online discussions. This paper uses existing measures for estimating controversy quantitatively in cyber-argumentation. First, it defines controversy in different fields, and then it identifies the attributes of controversy in online discussions. The distributions of user opinions and the distance between opinions are used to calculate the controversial degree of a discussion. Finally, the results from each controversy measure are discussed and analyzed using an empirical study generated by a cyber-argumentation tool. This is an improvement over the existing measurements because it does not require ground-truth data or specific settings and can be adapted to distribution-based or distance-based opinions.

Keywords: online argumentation, controversy, collective intelligence, agreement analysis, collaborative decision-making, fuzzy logic

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Authors: Mohammad Sarchami, Mohsen Zeinalkhani

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Always, financial reporting system faces some problems to win public ear. The increase in the number of fraud and representation, often combined with the bankruptcy of large companies, has raised concerns about the quality of financial statements. So, investors, legislators, managers, and auditors have focused on significant fraud detection or prevention in financial statements. This article aims to investigate the Sugeno fuzzy classification to consider fraud detection in financial reporting of accepted firms by Tehran stock exchange. The hypothesis is: Sugeno fuzzy classification may detect fraud in financial reporting by financial ratio. Hypothesis was tested using Matlab software. Accuracy average was 81/80 in Sugeno fuzzy classification; so the hypothesis was confirmed.

Keywords: fraud, financial reporting, Sugeno fuzzy classification, firm

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Authors: Aquil Ahmed, Srikant Gupta, Irfan Ali

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In this paper, we study a multi-objective capacitated transportation problem (MOCTP) with mixed constraints. This paper is comprised of the modelling and optimisation of an MOCTP in a fuzzy environment in which some goals are fractional and some are linear. In real life application of the fuzzy goal programming (FGP) problem with multiple objectives, it is difficult for the decision maker(s) to determine the goal value of each objective precisely as the goal values are imprecise or uncertain. Also, we developed the concept of linearization of fractional goal for solving the MOCTP. In this paper, imprecision of the parameter is handled by the concept of fuzzy set theory by considering these parameters as a trapezoidal fuzzy number. α-cut approach is used to get the crisp value of the parameters. Numerical examples are used to illustrate the method for solving MOCTP.

Keywords: capacitated transportation problem, multi objective linear programming, multi-objective fractional programming, fuzzy goal programming, fuzzy sets, trapezoidal fuzzy number

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Authors: Zahra Abdolkarimi, Naser Zouri

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The actual purpose of RoboCup is creating independent team of robots in 2050 based of FiFa roles to bring the victory in compare of world star team. There is unbelievable growing of Robots created a collection of complex and motivate subject in robotic and intellectual ornate, also it made a mechatronics style base of theoretical and technical way in Robocop. Decision making of robots depends to environment reaction, self-player and rival player with using inductive Fuzzy system valuation subsidiary to solve issue of robots in land game. The measure of selection in compare with other methods depends to amount of victories percentage in the same team that plays accidentally.

Keywords: particle swarm optimization, chaos theory, inference fuzzy system, simulation environment rational fuzzy system, mamdani and assilian, deffuzify

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Authors: Arash Sadeghi, Taimaz Larimian

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This study explores the intricate interplay between community-based social capital (CBSC) and neighbourhood dynamics in enhancing resilience of Iranian SMEs, particularly under the strain of international sanctions. Utilising fuzzy-set Qualitative Comparative Analysis (fsQCA), we examine how different dimensions of CBSC—structural, relational, and cognitive—interact with neighbourhood socio-economic and built-environment characteristics to influence SME resilience. Findings reveal four configurations that contribute to the presence of resistance and five configurations associated with the adaptation outcome. Each configuration demonstrates a distinct combination of social capital elements, which vary according to the specific socio-economic and built-environmental characteristics of the neighbourhoods. The first configuration highlights the importance of structural social capital in deprived areas for building resistance, while the second emphasises the role of relational social capital in low-density, minimally deprived areas. Overall, cognitive social capital seems to be less effective in driving economic resilience compared to structural and relational types. This research contributes to the literature by providing a nuanced understanding of the synergistic effects of CBSC dimensions and neighbourhood characteristics on SME resilience. By adopting a configurational approach, we move beyond traditional methodologies, offering a comprehensive view of the complex dynamics of CBSC and neighbourhood characteristics and their impact on SME resilience in varying neighbourhoods.

Keywords: community-based social capital, fuzzy-set qualitative comparative analysis (fsQCA), place-based resilience, resistance

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Authors: Karunambigai Mevani Govindasamy, Sathishkumar Ayyappan

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In this paper, we formulate the algorithm to find out the dominating function parameters of Intuitionistic Fuzzy Graphs(IFG). The methodology we adopted here is converting any physical problem into an IFG, and that has been transformed into Intuitionistic Fuzzy Matrix. Using Linear Program Solver software (LiPS), we found the defined parameters for the given IFG. We obtained these parameters for a path and cycle IFG. This study can be extended to other varieties of IFG. In particular, we obtain the definition of edge dominating function, minimal edge dominating function, fractional edge domination number (γ_if^') and upper fractional edge domination number (Γ_if^') of an intuitionistic fuzzy graph. Also, we formulated an algorithm which is appropriate to work on LiPS to find fractional edge domination number and upper fractional edge domination number of an IFG.

Keywords: fractional edge domination number, intuitionistic fuzzy cycle, intuitionistic fuzzy graph, intuitionistic fuzzy path

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Authors: Merve Uzuner Sahin, Kumru D. Atalay, Berna Dengiz

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In today’s competitive market, there are many alternative products that can be used in similar manner and purpose. Therefore, the utility of the product is an important issue for the preferability of the brand. This utility could be measured in terms of its functionality, durability, reliability. These all are affected by the system capabilities. Reliability is an important system design criteria for the manufacturers to be able to have high availability. Availability is the probability that a system (or a component) is operating properly to its function at a specific point in time or a specific period of times. System availability provides valuable input to estimate the production rate for the company to realize the production plan. When considering only the corrective maintenance downtime of the system, mean time between failure (MTBF) and mean time to repair (MTTR) are used to obtain system availability. Also, the MTBF and MTTR values are important measures to improve system performance by adopting suitable maintenance strategies for reliability engineers and practitioners working in a system. Failure and repair time probability distributions of each component in the system should be known for the conventional availability analysis. However, generally, companies do not have statistics or quality control departments to store such a large amount of data. Real events or situations are defined deterministically instead of using stochastic data for the complete description of real systems. A fuzzy set is an alternative theory which is used to analyze the uncertainty and vagueness in real systems. The aim of this study is to present a novel approach to compute system availability using representation of MTBF and MTTR in fuzzy numbers. Based on the experience in the system, it is decided to choose 3 different spread of MTBF and MTTR such as 15%, 20% and 25% to obtain lower and upper limits of the fuzzy numbers. To the best of our knowledge, the proposed method is the first application that is used fuzzy MTBF and fuzzy MTTR for fuzzy system availability estimation. This method is easy to apply in any repairable production system by practitioners working in industry. It is provided that the reliability engineers/managers/practitioners could analyze the system performance in a more consistent and logical manner based on fuzzy availability. This paper presents a real case study of a repairable multi-stage production line in lead-acid battery production factory in Turkey. The following is focusing on the considered wet-charging battery process which has a higher production level than the other types of battery. In this system, system components could exist only in two states, working or failed, and it is assumed that when a component in the system fails, it becomes as good as new after repair. Instead of classical methods, using fuzzy set theory and obtaining intervals for these measures would be very useful for system managers, practitioners to analyze system qualifications to find better results for their working conditions. Thus, much more detailed information about system characteristics is obtained.

Keywords: availability analysis, battery production system, fuzzy sets, triangular fuzzy numbers (TFNs)

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Authors: Kemal Polat

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In this paper, an attribute weighting method called fuzzy C-means clustering based attribute weighting (FCMAW) for classification of Diabetes disease dataset has been used. The aims of this study are to reduce the variance within attributes of diabetes dataset and to improve the classification accuracy of classifier algorithm transforming from non-linear separable datasets to linearly separable datasets. Pima Indians Diabetes dataset has two classes including normal subjects (500 instances) and diabetes subjects (268 instances). Fuzzy C-means clustering is an improved version of K-means clustering method and is one of most used clustering methods in data mining and machine learning applications. In this study, as the first stage, fuzzy C-means clustering process has been used for finding the centers of attributes in Pima Indians diabetes dataset and then weighted the dataset according to the ratios of the means of attributes to centers of theirs. Secondly, after weighting process, the classifier algorithms including support vector machine (SVM) and k-NN (k- nearest neighbor) classifiers have been used for classifying weighted Pima Indians diabetes dataset. Experimental results show that the proposed attribute weighting method (FCMAW) has obtained very promising results in the classification of Pima Indians diabetes dataset.

Keywords: fuzzy C-means clustering, fuzzy C-means clustering based attribute weighting, Pima Indians diabetes, SVM

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Authors: M. S. Annie Christi

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Transportation Problem (TP) is based on supply and demand of commodities transported from one source to the different destinations. Usual methods for finding solution of TPs are North-West Corner Rule, Least Cost Method Vogel’s Approximation Method etc. The transportation costs tend to vary at each time. We can use fuzzy numbers which would give solution according to this situation. In this study the Best Candidate Method (BCM) is applied. For ranking Centroid Ranking Technique (CRT) and Robust Ranking Technique have been adopted to transform the fuzzy TP and the above methods are applied to EDWARDS Vacuum Company, Crawley, in West Sussex in the United Kingdom. A Comparative study is also given. We see that the transportation cost can be minimized by the application of CRT under BCM.

Keywords: best candidate method, centroid ranking technique, fuzzy transportation problem, robust ranking technique, transportation problem

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Authors: Sukhveer Singh, Sandeep Singh

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A bi-objective fuzzy transportation problem with the objectives to minimize the total fuzzy cost and fuzzy time of transportation without according priorities to them is considered. To the best of our knowledge, there is no method in the literature to find efficient solutions of the bi-objective transportation problem under uncertainty. In this paper, a bi-objective transportation problem in an uncertain environment has been formulated. An algorithm has been proposed to find efficient solutions of the bi-objective transportation problem under uncertainty. The proposed algorithm avoids the degeneracy and gives the optimal solution faster than other existing algorithms for the given uncertain transportation problem.

Keywords: uncertain transportation problem, efficient solution, ranking function, fuzzy transportation problem

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Authors: Mehrdad Mahmudizad, Roya Ahmadi Ahangar

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This paper presents the method of designing the type 2 fuzzy PID controllers in order to solve the problem of Load Frequency Control (LFC). The Harmony Search (HS) algorithm is used to regulate the measurement factors and the effect of uncertainty of membership functions of Interval Type 2 Fuzzy Proportional Integral Differential (IT2FPID) controllers in order to reduce the frequency deviation resulted from the load oscillations. The simulation results implicitly show that the performance of the proposed IT2FPID LFC in terms of error, settling time and resistance against different load oscillations is more appropriate and preferred than PID and Type 1 Fuzzy Proportional Integral Differential (T1FPID) controllers.

Keywords: load frequency control, fuzzy-pid controller, type 2 fuzzy system, harmony search algorithm

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Authors: Nana Kwasi Amoateng

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The paper uses secondary information to investigate some longstanding limitations in International Relations (IR) theory. Specifically, the analysis highlights IR theory through the lens of J. C. Sharman’s brilliant concept of outside-in state-building logic in which some states, particularly those in Africa, have relied mainly on foreign resources to address local threats. The key findings are that IR theory has been largely understood from the perspective of an inside-out state-building logic, whereby Western and other advanced states have heavily relied on local resources to address foreign threats. In this vein, IR theorists, including Critical Theorists, have not been able to fully grasp African states and states elsewhere that have generally relied on an outside-in logic of state-building. The paper helps to fill a major gap in IR theory, which has mainly addressed criticisms of being Euro- or Western-centric or failing to include the unique experiences of states and other actors in the Global South by developing critical theories such as post-colonial theory and neo-colonialism. Although this has helped to understand some experiences of actors in the Global South, the fundamental difference between state-building in the West and the Global South, particularly Africa, has not been adequately explored to fully comprehend why, despite the works of Critical Theorists, IR theory still fails to capture many political and socioeconomic realities in Africa and elsewhere.

Keywords: international relations theory, outside-in state-building logic, inside-out state-building logic, Africa

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Authors: O. P. Rahi, Manoj Kumar

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Power transformers are essential and expensive equipments in electrical power system. Dissolved gas analysis (DGA) is one of the most useful techniques to detect incipient faults in power transformers. However, the identification of the faulted location by conventional method is not always an easy task due to variability of gas data and operational variables. In this paper, an extension theory based power transformer fault diagnosis method is presented. Extension theory tries to solve contradictions and incompatibility problems. This paper first briefly introduces the basic concept of matter element theory, establishes the matter element models for three-ratio method, and then briefly discusses extension set theory. Detailed analysis is carried out on the extended relation function (ERF) adopted in this paper for transformer fault diagnosis. The detailed diagnosing steps are offered. Simulation proves that the proposed method can overcome the drawbacks of the conventional three-ratio method, such as no matching and failure to diagnose multi-fault. It enhances diagnosing accuracy.

Keywords: DGA, extension theory, ERF, fault diagnosis power transformers, fault diagnosis, fuzzy logic

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Authors: Abderrahmen Bouguerra, Kamel Kara, Djamel Saigaa, Samir Zeghlache, Keltoum Loukal

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In this paper, a Fault-Tolerant control of 2 DOF Helicopter (TRMS System) Based on Fuzzy Gain-Adaptive PID is presented. In particular, the introduction part of the paper presents a Fault-Tolerant Control (FTC), the first part of this paper presents a description of the mathematical model of TRMS, an adaptive PID controller is proposed for fault-tolerant control of a TRMS helicopter system in the presence of actuator faults, A fuzzy inference scheme is used to tune in real-time the controller gains, The proposed adaptive PID controller is compared with the conventional PID. The obtained results show the effectiveness of the proposed method.

Keywords: fuzzy control, gain-adaptive PID, helicopter model, PID control, TRMS system

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Authors: Jorge Anibal Restrepo Morales, Sonia Martín Gómez

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A model based on a logical framework was developed to quantify SMEs' internationalization capacity. To do so, linguistic variables, such as human talent, infrastructure, innovation strategies, FTAs, marketing strategies, finance, etc. were integrated. It is argued that a company’s management of international markets depends on internal factors, especially capabilities and resources available. This study considers internal factors as the biggest business challenge because they force companies to develop an adequate set of capabilities. At this stage, importance and strategic relevance have to be defined in order to build competitive advantages. A fuzzy inference system is proposed to model the resources, skills, and capabilities that determine the success of internationalization. Data: 157 linguistic variables were used. These variables were defined by international trade entrepreneurs, experts, consultants, and researchers. Using expert judgment, the variables were condensed into18 factors that explain SMEs’ export capacity. The proposed model is applied by means of a case study of the textile and clothing cluster in Medellin, Colombia. In the model implementation, a general index of 28.2 was obtained for internationalization capabilities. The result confirms that the sector’s current capabilities and resources are not sufficient for a successful integration into the international market. The model specifies the factors and variables, which need to be worked on in order to improve export capability. In the case of textile companies, the lack of a continuous recording of information stands out. Likewise, there are very few studies directed towards developing long-term plans, and., there is little consistency in exports criteria. This method emerges as an innovative management tool linked to internal organizational spheres and their different abilities.

Keywords: business strategy, exports, internationalization, fuzzy set methods

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Authors: Nemanja Kajtez, Yue Zhan, Basel Halak

Abstract:

The significant rise in the cost of manufacturing of nanoscale integrated circuits (IC) has led the majority of IC design companies to outsource the fabrication of their products to other companies, often located in different countries. This multinational nature of the hardware supply chain has led to a host of security threats, including IP piracy, IC overproduction, and Trojan insertion. To combat that, researchers have proposed logic locking techniques to protect the intellectual properties of the design and increase the difficulty of malicious modification of its functionality. However, the adoption of logic locking approaches is rather slow due to the lack of the integration with IC production process and the lack of efficacy of existing algorithms. This work automates the logic locking process by developing software using Python that performs the locking on a gate-level netlist and can be integrated with the existing digital synthesis tools. Analysis of the latest logic locking algorithms has demonstrated that the SFLL-HD algorithm is one of the most secure and versatile in trading-off levels of protection against different types of attacks and was thus selected for implementation. The presented tool can also be expanded to incorporate the latest locking mechanisms to keep up with the fast-paced development in this field. The paper also presents a case study to demonstrate the functionality of the tool and how it could be used to explore the design space and compare different locking solutions. The source code of this tool is available freely from (https://www.researchgate.net/publication/353195333_Source_Code_for_The_Lockit_Tool).

Keywords: design automation, hardware security, IP piracy, logic locking

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Authors: F. Berna Benli, Özgür Keskin

Abstract:

Lately, many mathematicians have been studied the statistical convergence of a sequence of fuzzy numbers. We know that Lambda-statistically convergence is a kind of convergence between ordinary convergence and statistical convergence. In this paper, we will introduce the new kind of convergence such as λ-levelwise statistical convergence. Then, we will define the concept of the λ-levelwise statistical cluster and limit points of a sequence of fuzzy numbers. Also, we will discuss the relations between the sets of λ-levelwise statistical cluster points and λ-levelwise statistical limit points of sequences of fuzzy numbers. This work has been extended in this paper, where some relations have been considered such that when lambda-statistical limit inferior and lambda-statistical limit superior for lambda-statistically convergent sequences of fuzzy numbers are equal. Furthermore, lambda-statistical boundedness condition for different sequences of fuzzy numbers has been studied.

Keywords: fuzzy number, λ-levelwise statistical cluster points, λ-levelwise statistical convergence, λ-levelwise statistical limit points, λ-statistical cluster points, λ-statistical convergence, λ-statistical limit points

Procedia PDF Downloads 444