Fuzzy Logic Functions Research Articles

A comparative assessment of fuzzy logic and evidential belief function models for mapping artesian zone boundary in an arid region, Iraq

Abstract This paper discusses and compares the potential application of the evidential belief function model and fuzzy logic inference system technique for spatial delineation of a groundwater artesian zone boundary in an arid region of central Iraq. First, a flowing well inventory of a total of 93 perennial flowing wells was constructed and randomly partitioned into two data sets: 70% (65 wells) for training and 30% (28 wells) for validation. Twelve groundwater conditioning factors were considered in the geospatial analysis depending on data availability and literature review. The random forest (RF) algorithm was first applied to investigate the most important conditioning factors in groundwater potential analysis. The most important factors with training flowing wells were used to develop predictive models. The prediction accuracy of the developed models was checked using the area under the relative operating characteristic curve. Results showed that the best model with a higher prediction accuracy of 86% was a fuzzy AND model followed by the evidential model with 84%. The main conclusion of this study is that the integrated use of the adapted models with RF offer a rapid assessment tool in groundwater exploration and can be helpful in groundwater management.

A GIS-based Fuzzy-AHP method for the evaluation of solar farms locations: Case study in Khuzestan province, Iran

Abstract This study was conducted to prioritize the land of Khuzestan province in Iran to install solar photovoltaic farms, based on techno-economic and environmental aspects. Fuzzy logic and fuzzy membership functions were used to create criteria layers in the environment of GIS to draw the map of the suitability of lands. Also the Analytic Hierarchy Process (AHP) technique was used to weigh the techno-economic and environmental criteria and to draw the final map of the suitability of lands as solar farms. Results showed that Khuzestan province has a great potential to generate solar electricity via photovoltaic arrays. Based on the results, the potential of solar electricity generation in Khuzestan through the worst scenario is approximately 1.75 times more than the gross electricity produced in Iran in 2013. On the other hand, installing solar farms on the lands located at the south and the southwest of the province which are in danger of turning to desert, is a good opportunity to combat with the spread of desertification. By using solar power facilities for multipurpose application (generating solar power and combating with desertification), the costs of solar power generation will be amortized and the overall cost of solar power will be reduced. Based on the map of the suitability of the lands as solar farm, the desert and semi-desert areas of Khuzestan province have the excellent potential of electricity generation.

A manufacturing system energy-efficient optimisation model for maintenance-production workforce size determination using integrated fuzzy logic and quality function deployment approach

In maintenance systems, the current approach to workforce analysis entails the utilisation of metrics that focus exclusively on workforce cost and productivity. This method omits the “green” concept, which principally hinges on energy-efficient manufacturing and also ignores the production-maintenance integration. The approach is not accurate and could not be heavily relied upon for sound maintenance decisions. Consequently, comprehensive, scientifically-motivated, cost–effective and environmentally-conscious approaches are needed. With this in view, a deviation from the traditional approach through employing a combined fuzzy, quality function deployment interacting with three meta-heuristics (colliding bodies’ optimisation, big-bang big-crunch and particle swarm optimisation) for optimisation is made in the current study. The workforce size parameters are determined by maximising workforce size’s earned-valued as well as electric power efficiency maximisation subject to various real-life constraints. The efficacy and robustness of the model is tested with data from an aluminium products manufacturing system operating in a developing country. The results obtained indicate that the proposed colliding bodies’ optimisation framework is effective in comparison with other techniques. This implies that the proposed methodology potentially displays tremendous benefit of conserving energy, thus aiding environmental preservation and cost of energy savings. The principal novelty of the paper is the uniquely new method of quantifying the energy savings contributions of the maintenance workforce.

Sleptsov Nets Run Fast

We show that Sleptsov place-transition nets (that allow transition firing in multiple instances at a step) run fast by implementing multiplication and division operations in polynomial time. In comparison, Petri nets (PNs) implement the mentioned operations in exponential time. Moreover, PNs are obtained as a special case of Sleptsov nets (SNs) using loops with places having unit marking attached to each transition. In addition, we develop basics of an SN programming technology including basic operations and program composition rules. We provide examples of programs written in SN language for encryption/decryption with the RSA algorithm, calculation of fuzzy logic functions, and parallel calculation of the solutions to Laplace equations. SN computers promise hyper-performance because of a concurrent programming style consisting of a concise graphical language and small granulation of parallel processes on the level of separate events.

Polypyridyl-imidazole based smart Ru(II) complex mimicking advanced Boolean and Fuzzy logic functions

A terpyridyl-imidazole based heteroditopic Ru(II) complex of composition [(phen)2Ru(tpy-Hbzim-dipy)](ClO4)2] (1), where tpy-Hbzim-dipy=4′-[4-(4,5-Di-pyridin-2-yl-1H-imidazol-2-yl)-phenyl]-[2,2′;6′,2″] terpyridine and phen=1,10-phenanthroline is designed and structurally characterized in this work for the construction of multifunctional Boolean and Fuzzy logic devices. The designed complex acts as a red-color emitter and has a free terpyridine moiety which can be coordinated to a cationic guest and an imidazole motif capable of interacting with selective anionic guests through hydrogen bonding. Consequently, modulation of physicochemical properties of the complex by the anionic and cationic guests was examined through different optical channels. The metalloreceptor acts as a chromogenic sensor for F−, AcO−, CN−, and H2PO4− among the anions, while for Fe2+ among bivalent 3d cations in acetonitrile. Based on various output signals with a particular set of anionic and cationic inputs, the complex mimics the functions of four-inputs NOR logic gate and complicated function of memory device. More importantly, we also report the implementation of Fuzzy logic approach to develop an infinite-valued logic system based on either the absorbance or luminescence dependence of the complex upon concentration of different ionic inputs. In conjunction with the experimental investigation, density functional theory (DFT) and time-dependent density functional theory (TD-DFT) studies were also carried out to investigate the structural and electronic properties of the receptor.

Real-Time Onboard Global Nonlinear Aerodynamic Modeling from Flight Data

Flight test and modeling techniques were developed to accurately identify global nonlinear aerodynamic models onboard an aircraft. The techniques were developed and demonstrated during piloted flight testing of an Aermacchi MB-326M Impala jet aircraft. Advanced piloting techniques and nonlinear modeling techniques based on fuzzy logic and multivariate orthogonal function methods were implemented with efficient onboard calculations and flight operations to achieve real-time maneuver monitoring, near-real-time global nonlinear aerodynamic modeling, and prediction validation testing in flight. Results demonstrated that global nonlinear aerodynamic models for a large portion of the flight envelope were identified rapidly and accurately using piloted flight test maneuvers during a single flight, with the final identified and validated models available before the aircraft landed.

Fuzzy neural network approach to optimizing process performance by using multiple responses

This research proposes a method for optimizing process performance; the method involves the use of multiple quality characteristics, fuzzy logic, and radial basis function neural networks (RBFNNs). In the method, each quality characteristic is transformed into a signal-to-noise ratio, and all the ratios are then provided as inputs to a fuzzy model to obtain a single comprehensive output measure (COM). The RBFNNs are used to generate a full factorial design. Finally, the average COM values are calculated for different factor levels, where for each factor, the level that maximizes the COM value is identified as the optimal level. Three case studies are presented for illustrating the method, and for all of them, the proposed method affords the largest total anticipated improvements in multiple quality responses compared with previously used methods, including the fuzzy, grey-Taguchi, Taguchi, and principal component analysis methods. The main advantages of the proposed method are its effectiveness in obtaining global optimal factor levels, its applicability and the requirement of less computational effort, and its efficiency in improving performance. In conclusion, the proposed method may enable practitioners optimize process performance by using multiple quality characteristics.

Liver and tumour segmentation from abdominal CT images using adaptive threshold method

Liver tumour segmentation from abdominal CT image is a challenging task in biomedical image processing. Complex segmentation methods may provide good segmentation results but are difficult for implementing in clinical environment. Hence, in this paper, we propose a simple thresholding technique based on fuzzy logic and Shannon's entropy function to segment the tumour from liver CT images. Tumour dimensions are then measured subsequently. The proposed method provides better results than the classical segmentation methods.

Wavelet-Based Power Quality Disturbances Detection and Classification Using RBFNN and Fuzzy Logic

This paper presents a new approach to detect and classify power quality disturbances in the power system using fuzzy logic (FL) and radial basis function neural networks (RBFNN). Feature extracted through the wavelet is used for training; after training, the obtained weight is used to classify the power quality problems in RBFNN, but it suffers from extensive computation and low convergence speed. Then to detect and classify the events, FL is proposed, the extracted characters are used to find out membership functions and fuzzy rules being determined from the power quality inherence. For the classification, five types of disturbance are taken into account. The classification performance of FL is compared with RBFNN. The classification accuracy of FL is improved with the help of cognitive as well as the social behavior of particles along with fitness value using particle swarm optimization, just by determining the ranges of the feature of the membership function for each rules to identify each disturbance specifically. The simulation result using FL possesses significant improvements and gives classification results in less than a cycle when compared over other considered approach.

Hybrid Fuzzy Centroid with MDV-Hop BAT Localization Algorithms in Wireless Sensor Networks

Many applications employing wireless sensor networks have been available in real-world scenarios. Their popularity is due to distinctive characteristics, for example, small scale, multisensing capability, and cost-effective deployment. However, there are constraints including routing, reliability, and especially localization, in particular without the aid of global positioning services, the lack of satellite coverage. In addition, if embedded, the overhead will be increased with hardware costs and shortened battery life. Thus, a range-free-based localization scheme is promising and is being pursued as a cost-effective approach. Centroid is one of the pioneer low complexity range-free estimation algorithms, and DV-Hop is another algorithm that has no requirements for distance information. However, their main drawbacks are location estimation precision. Recently, a soft-computing-based approach used to address uncertainty and approximation has been proposed as a low cost solution to gain precision, and, therefore, this research investigates its integration and then proposes a novel hybrid localization algorithm utilizing key characteristics of Centroid and DV-Hop. This hybrid scheme employs an extra weight with signal normalization derived from a fuzzy logic function in Centroid. The research also integrates a BAT algorithm of the modified DV-Hop. These combinations demonstrate the effectiveness in the simulation and location error reduction with time complexity trade-off.

Formalization and Use of Fuzzy Predicates for Hybrid Processes Implementation

The technique of formalization of fuzzy predicates together with crisp logical variables for the specification of fuzzy logic-dynamic situations and crisp logical commands is considered. The technique is based on the submission of crisp and fuzzy logic variables by means of membership functions and on the use of fuzzy inference rules. Here we only used the forms of presentation of fuzzy logic functions which are also suitable for presentation of crisp logic functions. By the examples the possibility of using the considered technique for the computer implementation of hybrid processes is shown.

Networks of elementary fuzzy solvers for synthesizing and analyzing fuzzy data processing and control systems

An approach for describing fuzzy data processing systems based on networks of elementary solvers of fuzzy logic functions that makes it possible to improve the quality and reduce the computational complexity of the analysis and synthesis of embedded control systems with fuzzy decision-making subsystems is proposed. Simulation of fuzzy network structures is considered and examples of their practical application are discusses.

AutoRELAX: automatically RELAXing a goal model to address uncertainty

Dynamically adaptive systems (DAS) must cope with system and environmental conditions that may not have been fully understood or anticipated during development. RELAX is a fuzzy logic-based specification language for identifying and assessing sources of environmental uncertainty, thereby making DAS requirements more tolerant of unanticipated conditions. This paper presents AutoRELAX, an approach that automatically generates RELAXed goal models to address environmental uncertainty. Specifically, AutoRELAX identifies goals to RELAX, which RELAX operators to apply, and the shape of the fuzzy logic function that establishes the goal satisfaction criteria. AutoRELAX generates different solutions by making tradeoffs between minimizing the number of RELAXed goals and maximizing delivered functionality by reducing the number of adaptations triggered by minor and adverse environmental conditions. In a recent extension, AutoRELAX uses a stepwise adaptation of weights to balance these two competing concerns and thereby further improve the utility of AutoRELAX. We apply it to two industry-based applications involving network management and a robotic controller, respectively.

An Autonomic Approach to Risk-Aware Data Center Overbooking

Elasticity is a key characteristic of cloud computing that increases the flexibility for cloud consumers, allowing them to adapt the amount of physical resources associated to their services over time in an on-demand basis. However, elasticity creates problems for cloud providers as it may lead to poor resource utilization, specially in combination with other factors, such as user overestimations and pre-defined VM sizes. Admission control mechanisms are thus needed to increase the number of services accepted, raising the utilization without affecting services performance. This work focuses on implementing an autonomic risk-aware overbooking architecture capable of increasing the resource utilization of cloud data centers by accepting more virtual machines than physical available resources. Fuzzy logic functions are used to estimate the associated risk to each overbooking decision. By using a distributed PID controller approach, the system is capable of self-adapting over time—changing the acceptable level of risk—depending on the current status of the cloud data center. The suggested approach is extensively evaluated using a combination of simulations and experiments executing real cloud applications with real-life available workloads. Our results show a 50 percent increment at both resource utilization and capacity allocated with acceptable performance degradation and more stable resource utilization over time.

Development of a Remote Sensing-Based Method to Map Likelihood of Common Ragweed (Ambrosia artemisiifolia) Presence in Urban Areas

Common Ragweed (Ambrosia artemisiifolia) is a plant that constitutes an important and growing public health concern worldwide as it is probably expanding with climate change, which brings forward the need for improved mapping tools. Our final purpose is to operationalize the use of optical remote sensing for the automated mapping and surveillance of Ambrosia artemisiifolia. Analyses considering the probable spectral instability originating from the variability of the urban landscape and from that of sensors characteristics were developed. Worldview 2, Rapid Eye and SPOT 4 HRVIR sensors were used together with geolocalized surveys of Common Ragweed in Montreal and Valleyfield (Quebec, Canada). Images were standardized and various derivatives variables such as multiple vegetation indexes were created. Spectral confusion, statistical analyses, object-oriented technology and Fuzzy-logic functions were used to develop predictive risks maps of Common Ragweed potential presence. The results showed that the green bands (510-590 nm) of higher spatial resolutions sensors had a higher potential to cope with spectral confusions and changing landscape characteristics and to predict the likelihood of Ambrosia artemisiifolia presence with a recurrent stability. The good agreement between observed and predicted ragweed revealed an important potential for the operationalization of this method.

Assessing Coastal Squeeze of Tidal Wetlands

Torio, D.D. and Chmura, G.L., 2013. Assessing coastal squeeze of tidal wetlands.As sea level rise accelerates and land development intensifies along coastlines, tidal wetlands will become increasingly threatened by coastal squeeze. Barriers that protect inland areas from rising sea level prevent or reduce tidal flows, and impermeable surfaces prevent wetland migration to the adjacent uplands. As vegetation succumbs to submergence by rising sea levels on the seaward edge of a wetland, those wetlands prevented from inland migration will decrease in area, if not disappear completely. Tools to identify locations where coastal squeeze is likely to occur are needed for coastal management. We have developed a “Coastal Squeeze Index” that can be used to assess the potential of coastal squeeze along the borders of a single wetland and to rank the threats faced by multiple wetlands. The index is based on surrounding topography and impervious surfaces derived from light detection and ranging and advanced spaceborne thermal emission and reflection radiometry imagery, respectively, and uses a fuzzy logic approach. We assume that coastal squeeze varies continuously over the coastal landscape and tested several fuzzy logic functions before assigning a continuous weight, from 0 to 1, corresponding to the influence of slope and impervious surfaces on coastal squeeze. We then combined the ranked variables to produce a map of coastal squeeze as a continuous index. Using this index, we compare the present and future threat of coastal squeeze to marshes in Wells and Portland, Maine, in the United States and Kouchibouguac National Park in New Brunswick, Canada.

Integration of Rolling Wheel Deflectometer Deflection Measurements into Pavement Management Systems

The Louisiana Department of Transportation and Development (DOTD) conducted a research project with the rolling wheel deflectometer (RWD) on approximately 1,400 mi of asphalt concrete roadways. The major outcomes of the study were evaluation of the effectiveness of the RWD as a structural assessment tool on Louisiana highways and the introduction of the RWD index (RI). The purpose of this study was to define RI ranges for treatment selections or pavement structural conditions linked to current methods of the Louisiana DOTD's pavement management system; multivariate (factor analysis) and descriptive [analysis of variance (ANOVA)] statistical methods and fuzzy logic functions were used. The factor analysis revealed that the RI could be used as a single index for assessing pavement structural conditions. ANOVA revealed that the RI could not be used to establish the four treatment methods typically used by the Louisiana DOTD but that it could be used to infer whether a pavement was structurally sound or deficient. If the pavement were determined to be structurally deficient, additional structural evaluation, such as with a falling weight deflectometer, would be required. Fuzzy logic and set theory were used to establish fuzzy functions for structurally sound and structurally deficient pavement conditions along with RI threshold ranges per thickness group. Equations were derived to produce RI threshold values for pavement thicknesses less than 12 in. The equations may be used for pavements thicker than 12 in., but with caution. For large segments of roadways, algorithms were used to assess whether pavements were structurally sound or structurally deficient on the basis of RI.

From binary logic functions to fuzzy logic functions

Copyright c 2013 Omar Salazar, Jairo Soriano, and Humberto Serrano. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract It is known that any binary logic function (in binary logic) can be rep- resented by at least one logic formula, however this is not always true for a ternary logic function (in ternary logic). A ternary logic function can be represented by at least one logic formula if and only if it satises a mathematical condition which is called regularity. Also, it is known that a fuzzy logic formula (in standard fuzzy logic) can be obtained by means of truth tables in ternary logic. This paper presents a method destined for extending a binary logic function into one (or more) ter- nary logic function(s) representable by at least one logic formula, and therefore, to obtain fuzzy logic formulas from a binary logic function. Mathematics Subject Classication: 03B52, 06D72, 08A72, 94D05

Modeling and multiple performance optimization of ultrasonic micro-hole machining of PCD using fuzzy logic and taguchi quality loss function

Polycrystalline diamond is an ideal material for parts with micro-holes and has been widely used as dies and cutting tools in automotive, aerospace and woodworking industries due to its superior wear and corrosion resistance. In this research paper, the modeling and simultaneous optimization of multiple performance characteristics such as material removal rate and surface roughness of polycrystalline diamond (PCD) with ultrasonic machining process has been presented. The fuzzy logic and taguchi's quality loss function has been used. In recent years, fuzzy logic has been used in manufacturing engineering for modeling and monitoring. Also the effect of controllable machining parameters like type of abrasive slurry, their size and concentration, nature of tool material and the power rating of the machine has been determined by applying the single objective and multi-objective optimization techniques. The analysis of results has been done using the MATLAB 7.5 software and results obtained are validated by conducting the confirmation experiments. The results show the considerable improvement in S/N ratio as compared to initial cutting conditions. The surface roughness of machined surface has been measured by using the Perthometer (M4Pi, Mahr Germany).

Death of Paradox: The Killer Logic Beneath the Standards of Proof

The prevailing but contested view of proof standards is that factfinders should determine facts by probabilistic reasoning. Given imperfect evidence, they first should ask themselves what they think the chances are that the burdened party would be right were the truth to become known, and they then should compare those chances to the applicable standard of proof. I contend that for understanding the standards of proof, the modern versions of logic — in particular, fuzzy logic and belief functions — work better than classical probability. This modern logic suggests that factfinders first assess evidence of an imprecisely perceived and described reality to form a fuzzy degree of belief in a fact’s existence, and they then apply the standard of proof by comparing their belief in a fact’s existence to their belief in its negation. This understanding nicely explains how the standard of proof actually works in the law world. While conforming more closely to what we know of people’s cognition, the new understanding captures better how the law formulates and manipulates the standards and it also gives a superior mental image of the factfinders’ task. One virtue of this conceptualization is that it is not a radical reconception. Another virtue is that it nevertheless manages to resolve some stubborn problems of proof, including the infamous conjunction paradox.