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Fulltext An adaptive handover prediction scheme for seamless mobility based wireless networks

Sadiq AS, Fisal NB, Ghafoor KZ, Lloret J

ScientificWorldJournal, 2014;2014:610652.
PMID: 25574490 DOI: 10.1155/2014/610652

We propose an adaptive handover prediction (AHP) scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches.

Matched MeSH terms: Fuzzy Logic
Fulltext Fuzzy mixed assembly line sequencing and scheduling optimization model using multiobjective dynamic fuzzy GA

Tahriri F, Dawal SZ, Taha Z

ScientificWorldJournal, 2014;2014:505207.
PMID: 24982962 DOI: 10.1155/2014/505207

A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model.

Matched MeSH terms: Fuzzy Logic*
Fulltext Robust fuzzy logic stabilization with disturbance elimination

Danapalasingam KA

ScientificWorldJournal, 2014;2014:171597.
PMID: 25177713 DOI: 10.1155/2014/171597

A robust fuzzy logic controller is proposed for stabilization and disturbance rejection in nonlinear control systems of a particular type. The dynamic feedback controller is designed as a combination of a control law that compensates for nonlinear terms in a control system and a dynamic fuzzy logic controller that addresses unknown model uncertainties and an unmeasured disturbance. Since it is challenging to derive a highly accurate mathematical model, the proposed controller requires only nominal functions of a control system. In this paper, a mathematical derivation is carried out to prove that the controller is able to achieve asymptotic stability by processing state measurements. Robustness here refers to the ability of the controller to asymptotically steer the state vector towards the origin in the presence of model uncertainties and a disturbance input. Simulation results of the robust fuzzy logic controller application in a magnetic levitation system demonstrate the feasibility of the control design.

Matched MeSH terms: Fuzzy Logic*
Fulltext Comparative study on interaction of form and motion processing streams by applying two different classifiers in mechanism for recognition of biological movement

Yousefi B, Loo CK

ScientificWorldJournal, 2014;2014:723213.
PMID: 25276860 DOI: 10.1155/2014/723213

Research on psychophysics, neurophysiology, and functional imaging shows particular representation of biological movements which contains two pathways. The visual perception of biological movements formed through the visual system called dorsal and ventral processing streams. Ventral processing stream is associated with the form information extraction; on the other hand, dorsal processing stream provides motion information. Active basic model (ABM) as hierarchical representation of the human object had revealed novelty in form pathway due to applying Gabor based supervised object recognition method. It creates more biological plausibility along with similarity with original model. Fuzzy inference system is used for motion pattern information in motion pathway creating more robustness in recognition process. Besides, interaction of these paths is intriguing and many studies in various fields considered it. Here, the interaction of the pathways to get more appropriated results has been investigated. Extreme learning machine (ELM) has been implied for classification unit of this model, due to having the main properties of artificial neural networks, but crosses from the difficulty of training time substantially diminished in it. Here, there will be a comparison between two different configurations, interactions using synergetic neural network and ELM, in terms of accuracy and compatibility.

Matched MeSH terms: Fuzzy Logic
Fulltext Age estimation based on children's voice: a fuzzy-based decision fusion strategy

Mirhassani SM, Zourmand A, Ting HN

ScientificWorldJournal, 2014;2014:534064.
PMID: 25006595 DOI: 10.1155/2014/534064

Automatic estimation of a speaker's age is a challenging research topic in the area of speech analysis. In this paper, a novel approach to estimate a speaker's age is presented. The method features a "divide and conquer" strategy wherein the speech data are divided into six groups based on the vowel classes. There are two reasons behind this strategy. First, reduction in the complicated distribution of the processing data improves the classifier's learning performance. Second, different vowel classes contain complementary information for age estimation. Mel-frequency cepstral coefficients are computed for each group and single layer feed-forward neural networks based on self-adaptive extreme learning machine are applied to the features to make a primary decision. Subsequently, fuzzy data fusion is employed to provide an overall decision by aggregating the classifier's outputs. The results are then compared with a number of state-of-the-art age estimation methods. Experiments conducted based on six age groups including children aged between 7 and 12 years revealed that fuzzy fusion of the classifier's outputs resulted in considerable improvement of up to 53.33% in age estimation accuracy. Moreover, the fuzzy fusion of decisions aggregated the complementary information of a speaker's age from various speech sources.

Matched MeSH terms: Fuzzy Logic*
Fulltext An application of a hybrid MCDM method for the evaluation of entrepreneurial intensity among the SMEs: a case study

Rostamzadeh R, Ismail K, Bodaghi Khajeh Noubar H

ScientificWorldJournal, 2014;2014:703650.
PMID: 25197707 DOI: 10.1155/2014/703650

This study presents one of the first attempts to focus on critical success factors influencing the entrepreneurial intensity of Malaysian small and medium sized enterprises (SMEs) as they attempt to expand internationally. The aim of this paper is to evaluate and prioritize the entrepreneurial intensity among the SMEs using multicriteria decision (MCDM) techniques. In this research FAHP is used for finding the weights of criteria and subcriteria. Then for the final ranking of the companies, VIKOR (in Serbian: VlseKriterijumska Optimizacija I Kompromisno Resenje) method was used. Also, as an additional tool, TOPSIS technique, is used to see the differences of two methods applied over the same data. 5 main criteria and 14 subcriteria were developed and implemented in the real-world cases. As the results showed, two ranking methods provided different ranking. Furthermore, the final findings of the research based on VIKOR and TOPSIS indicated that the firms A3 and A4 received the first rank, respectively. In addition, the firm A4 was known as the most entrepreneurial company. This research has been done in the manufacturing sector, but it could be also extended to the service sector for measurement.

Matched MeSH terms: Fuzzy Logic
Fulltext Hamming distance method with subjective and objective weights for personnel selection

Saad R, Ahmad MZ, Abu MS, Jusoh MS

ScientificWorldJournal, 2014;2014:865495.
PMID: 24782670 DOI: 10.1155/2014/865495

Multicriteria decision making (MCDM) is one of the methods that popularly has been used in solving personnel selection problem. Alternatives, criteria, and weights are some of the fundamental aspects in MCDM that need to be defined clearly in order to achieve a good result. Apart from these aspects, fuzzy data has to take into consideration that it may arise from unobtainable and incomplete information. In this paper, we propose a new approach for personnel selection problem. The proposed approach is based on Hamming distance method with subjective and objective weights (HDMSOW's). In case of vagueness situation, fuzzy set theory is then incorporated onto the HDMSOW's. To determine the objective weight for each attribute, the fuzzy Shannon's entropy is considered. While for the subjective weight, it is aggregated into a comparable scale. A numerical example is presented to illustrate the HDMSOW's.

Matched MeSH terms: Fuzzy Logic*
Fulltext Minimal solution of singular LR fuzzy linear systems

Nikuie M, Ahmad MZ

ScientificWorldJournal, 2014;2014:517218.
PMID: 24737977 DOI: 10.1155/2014/517218

In this paper, the singular LR fuzzy linear system is introduced. Such systems are divided into two parts: singular consistent LR fuzzy linear systems and singular inconsistent LR fuzzy linear systems. The capability of the generalized inverses such as Drazin inverse, pseudoinverse, and {1}-inverse in finding minimal solution of singular consistent LR fuzzy linear systems is investigated.

Matched MeSH terms: Fuzzy Logic*
Fulltext Development of biological movement recognition by interaction between active basis model and fuzzy optical flow division

Yousefi B, Loo CK

ScientificWorldJournal, 2014;2014:238234.
PMID: 24883361 DOI: 10.1155/2014/238234

Following the study on computational neuroscience through functional magnetic resonance imaging claimed that human action recognition in the brain of mammalian pursues two separated streams, that is, dorsal and ventral streams. It follows up by two pathways in the bioinspired model, which are specialized for motion and form information analysis (Giese and Poggio 2003). Active basis model is used to form information which is different from orientations and scales of Gabor wavelets to form a dictionary regarding object recognition (human). Also biologically movement optic-flow patterns utilized. As motion information guides share sketch algorithm in form pathway for adjustment plus it helps to prevent wrong recognition. A synergetic neural network is utilized to generate prototype templates, representing general characteristic form of every class. Having predefined templates, classifying performs based on multitemplate matching. As every human action has one action prototype, there are some overlapping and consistency among these templates. Using fuzzy optical flow division scoring can prevent motivation for misrecognition. We successfully apply proposed model on the human action video obtained from KTH human action database. Proposed approach follows the interaction between dorsal and ventral processing streams in the original model of the biological movement recognition. The attained results indicate promising outcome and improvement in robustness using proposed approach.

Matched MeSH terms: Fuzzy Logic*
Fulltext A novel approach toward fuzzy generalized bi-ideals in ordered semigroups

Khan FM, Sarmin NH, Khan HU

ScientificWorldJournal, 2014;2014:275947.
PMID: 24883375 DOI: 10.1155/2014/275947

In several advanced fields like control engineering, computer science, fuzzy automata, finite state machine, and error correcting codes, the use of fuzzified algebraic structures especially ordered semigroups plays a central role. In this paper, we introduced a new and advanced generalization of fuzzy generalized bi-ideals of ordered semigroups. These new concepts are supported by suitable examples. These new notions are the generalizations of ordinary fuzzy generalized bi-ideals of ordered semigroups. Several fundamental theorems of ordered semigroups are investigated by the properties of these newly defined fuzzy generalized bi-ideals. Further, using level sets, ordinary fuzzy generalized bi-ideals are linked with these newly defined ideals which is the most significant part of this paper.

Matched MeSH terms: Fuzzy Logic*
Fulltext Expanded DEMATEL for determining cause and effect group in bidirectional relations

Falatoonitoosi E, Ahmed S, Sorooshian S

ScientificWorldJournal, 2014;2014:103846.
PMID: 24693224 DOI: 10.1155/2014/103846

Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology has been proposed to solve complex and intertwined problem groups in many situations such as developing the capabilities, complex group decision making, security problems, marketing approaches, global managers, and control systems. DEMATEL is able to realize casual relationships by dividing important issues into cause and effect group as well as making it possible to visualize the casual relationships of subcriteria and systems in the course of casual diagram that it may demonstrate communication network or a little control relationships between individuals. Despite of its ability to visualize cause and effect inside a network, the original DEMATEL has not been able to find the cause and effect group between different networks. Therefore, the aim of this study is proposing the expanded DEMATEL to cover this deficiency by new formulations to determine cause and effect factors between separate networks that have bidirectional direct impact on each other. At the end, the feasibility of new extra formulations is validated by case study in three numerical examples of green supply chain networks for an automotive company.

Matched MeSH terms: Fuzzy Logic*
Fulltext Solving fuzzy fractional differential equations using Zadeh's extension principle

Ahmad MZ, Hasan MK, Abbasbandy S

ScientificWorldJournal, 2013;2013:454969.
PMID: 24082853 DOI: 10.1155/2013/454969

We study a fuzzy fractional differential equation (FFDE) and present its solution using Zadeh's extension principle. The proposed study extends the case of fuzzy differential equations of integer order. We also propose a numerical method to approximate the solution of FFDEs. To solve nonlinear problems, the proposed numerical method is then incorporated into an unconstrained optimisation technique. Several numerical examples are provided.

Matched MeSH terms: Fuzzy Logic
A novel approach to detect respiratory phases from pulmonary acoustic signals using normalised power spectral density and fuzzy inference system

Palaniappan R, Sundaraj K, Sundaraj S, Huliraj N, Revadi SS

Clin Respir J, 2016 Jul;10(4):486-94.
PMID: 25515741 DOI: 10.1111/crj.12250

BACKGROUND: Monitoring respiration is important in several medical applications. One such application is respiratory rate monitoring in patients with sleep apnoea. The respiratory rate in patients with sleep apnoea disorder is irregular compared with the controls. Respiratory phase detection is required for a proper monitoring of respiration in patients with sleep apnoea.
AIMS: To develop a model to detect the respiratory phases present in the pulmonary acoustic signals and to evaluate the performance of the model in detecting the respiratory phases.
METHODS: Normalised averaged power spectral density for each frame and change in normalised averaged power spectral density between the adjacent frames were fuzzified and fuzzy rules were formulated. The fuzzy inference system (FIS) was developed with both Mamdani and Sugeno methods. To evaluate the performance of both Mamdani and Sugeno methods, correlation coefficient and root mean square error (RMSE) were calculated.
RESULTS: In the correlation coefficient analysis in evaluating the fuzzy model using Mamdani and Sugeno method, the strength of the correlation was found to be r = 0.9892 and r = 0.9964, respectively. The RMSE for Mamdani and Sugeno methods are RMSE = 0.0853 and RMSE = 0.0817, respectively.
CONCLUSION: The correlation coefficient and the RMSE of the proposed fuzzy models in detecting the respiratory phases reveals that Sugeno method performs better compared with the Mamdani method.

Matched MeSH terms: Fuzzy Logic
Evaluation of traditional and consolidated rice farms in Guilan Province, Iran, using life cycle assessment and fuzzy modeling

Khoshnevisan B, Rajaeifar MA, Clark S, Shamahirband S, Anuar NB, Mohd Shuib NL, et al.

Sci Total Environ, 2014 May 15;481:242-51.
PMID: 24602908 DOI: 10.1016/j.scitotenv.2014.02.052

In this study the environmental impact of consolidated rice farms (CF) - farms which have been integrated to increase the mechanization index - and traditional farms (TF) - small farms with lower mechanization index - in Guilan Province, Iran, were evaluated and compared using Life cycle assessment (LCA) methodology and adaptive neuro-fuzzy inference system (ANFIS). Foreground data were collected from farmers using face-to-face questionnaires and background information about production process and inventory data was taken from the EcoInvent®2.0 database. The system boundary was confined to within the farm gate (cradle to farm gate) and two functional units (land and mass based) were chosen. The study also included a comparison of the input-output energy flows of the farms. The results revealed that the average amount of energy consumed by the CFs was 57 GJ compared to 74.2 GJ for the TFs. The energy ratios for CFs and TFs were 1.6 and 0.9, respectively. The LCA results indicated that CFs produced fewer environmental burdens per ton of produced rice. When compared according to the land-based FU the same results were obtained. This indicates that the differences between the two types of farms were not caused by a difference in their production level, but rather by improved management on the CFs. The analysis also showed that electricity accounted for the greatest share of the impact for both types of farms, followed by P-based and N-based chemical fertilizers. These findings suggest that the CFs had superior overall environmental performance compared to the TFs in the study area. The performance metrics of the model based on ANFIS show that it can be used to predict the environmental burdens of rice production with high accuracy and minimal error.

Matched MeSH terms: Fuzzy Logic
Agricultural drought risk assessment of Northern New South Wales, Australia using geospatial techniques

Hoque MA, Pradhan B, Ahmed N, Sohel MSI

Sci Total Environ, 2020 Nov 17.
PMID: 33248778 DOI: 10.1016/j.scitotenv.2020.143600

Droughts are recurring events in Australia and cause a severe effect on agricultural and water resources. However, the studies about agricultural drought risk mapping are very limited in Australia. Therefore, a comprehensive agricultural drought risk assessment approach that incorporates all the risk components with their influencing criteria is essential to generate detailed drought risk information for operational drought management. A comprehensive agricultural drought risk assessment approach was prepared in this work incorporating all components of risk (hazard, vulnerability, exposure, and mitigation capacity) with their relevant criteria using geospatial techniques. The prepared approach is then applied to identify the spatial pattern of agricultural drought risk for Northern New South Wales region of Australia. A total of 16 relevant criteria under each risk component were considered, and fuzzy logic aided geospatial techniques were used to prepare vulnerability, exposure, hazard, and mitigation capacity indices. These indices were then incorporated to quantify agricultural drought risk comprehensively in the study area. The outputs depicted that about 19.2% and 41.7% areas are under very-high and moderate to high risk to agricultural droughts, respectively. The efficiency of the results is successfully evaluated using a drought inventory map. The generated spatial drought risk information produced by this study can assist relevant authorities in formulating proactive agricultural drought mitigation strategies.

Matched MeSH terms: Fuzzy Logic
Improving the environmental impact of palm kernel shell through maximizing its production of hydrogen and syngas using advanced artificial intelligence

Rezk H, Nassef AM, Inayat A, Sayed ET, Shahbaz M, Olabi AG

Sci Total Environ, 2019 Mar 25;658:1150-1160.
PMID: 30677979 DOI: 10.1016/j.scitotenv.2018.12.284

Fossil fuel depletion and the environmental concerns have been under discussion for energy production for many years and finding new and renewable energy sources became a must. Biomass is considered as a net zero CO2 energy source. Gasification of biomass for H2 and syngas production is an attractive process. The main target of this research is to improve the production of hydrogen and syngas from palm kernel shell (PKS) steam gasification through defining the optimal operating parameters' using a modern optimization algorithm. To predict the gaseous outputs, two PKS models were built using fuzzy logic based on the experimental data sets. A radial movement optimizer (RMO) was applied to determine the system's optimal operating parameters. During the optimization process, the decision variables were represented by four different operating parameters. These parameters include; temperature, particle size, CaO/biomass ratio and coal bottom ash (CBA) with their operating ranges of (650-750 °C), (0.5-1 mm), (0.5-2) and wt% (0.02-0.10), respectively. The individual and interactive effects of different combinations were investigated on the production of H2 and syngas yield. The optimized results were compared with experimental data and results obtained from Response Surface Methodology (RSM) reported in literature. The obtained optimal values of the operating parameters through RMO were found 722 °C, 0.92 mm, 1.72 and 0.06 wt% for the temperature, particle size, CaO/biomass ratio and coal bottom ash, respectively. The results showed that syngas production was significantly improved as it reached 65.44 vol% which was better than that obtained in earlier studies.

Matched MeSH terms: Fuzzy Logic*
Optimized fuzzy inference system to enhance prediction accuracy for influent characteristics of a sewage treatment plant

Ansari M, Othman F, El-Shafie A

Sci Total Environ, 2020 Jun 20;722:137878.
PMID: 32199382 DOI: 10.1016/j.scitotenv.2020.137878

Sewage treatment plants (STPs) keep sewage contamination within safe levels and minimize the risk of environmental disasters. To achieve optimum operation of an STP, it is necessary for influent parameters to be measured or estimated precisely. In this research, six well-known influent chemical and biological characteristics, i.e., biochemical oxygen demand (BOD), chemical oxygen demand (COD), Ammoniacal Nitrogen (NH3-N), pH, oil and grease (OG) and suspended solids (SS), were modeled and predicted using the Sugeno fuzzy logic model. The membership function range of the fuzzy model was optimized by ANFIS, the integrated Genetic algorithms (GA), and the integrated particle swarm optimization (PSO) algorithms. The results were evaluated by different indices to find the accuracy of each algorithm. To ensure prediction accuracy, outliers in the predicted data were found and replaced with reasonable values. The results showed that both integrated GA-FIS and PSO-FIS algorithms performed at almost the same level and both had fewer errors than ANFIS. As the GA-FIS algorithm predicts BOD with fewer errors than PSO-FIS and the aim of this study is to provide an accurate prediction of missing data, GA-FIS was only used to predict the BOD parameter; the other parameters were predicted by PSO-FIS algorithm. As a result, the model successfully could provide outstanding performance for predicting the BOD, COD, NH3-N, OG, pH and SS with MAE equal to 3.79, 5.14, 0.4, 0.27, 0.02, and 3.16, respectively.

Matched MeSH terms: Fuzzy Logic
Pesticide applicators questionnaire content validation: A fuzzy delphi method

Manakandan SK, Rosnah I, Mohd Ridhuan J, Priya R

Med J Malaysia, 2017 08;72(4):228-235.
PMID: 28889134 MyJurnal

BACKGROUND: The most crucial step in forming a set of survey questionnaire is deciding the appropriate items in a construct. Retaining irrelevant items and removing important items will certainly mislead the direction of a particular study. This article demonstrates Fuzzy Delphi method as one of the scientific analysis technique to consolidate consensus agreement within a panel of experts pertaining to each item's appropriateness. This method reduces the ambiguity, diversity, and discrepancy of the opinions among the experts hence enhances the quality of the selected items. The main purpose of this study was to obtain experts' consensus on the suitability of the preselected items on the questionnaire.
METHODS: The panel consists of sixteen experts from the Occupational and Environmental Health Unit of Ministry of Health, Vector-borne Disease Control Unit of Ministry of Health and Occupational and Safety Health Unit of both public and private universities. A set of questionnaires related to noise and chemical exposure were compiled based on the literature search. There was a total of six constructs with 60 items in which three constructs for knowledge, attitude, and practice of noise exposure and three constructs for knowledge, attitude, and practice of chemical exposure. The validation process replicated recent Fuzzy Delphi method that using a concept of Triangular Fuzzy Numbers and Defuzzification process.
RESULTS: A 100% response rate was obtained from all the sixteen experts with an average Likert scoring of four to five. Post FDM analysis, the first prerequisite was fulfilled with a threshold value (d) ≤ 0.2, hence all the six constructs were accepted. For the second prerequisite, three items (21%) from noise-attitude construct and four items (40%) from chemical-practice construct had expert consensus lesser than 75%, which giving rise to about 12% from the total items in the questionnaire. The third prerequisite was used to rank the items within the constructs by calculating the average fuzzy numbers. The seven items which did not fulfill the second prerequisite similarly had lower ranks during the analysis, therefore those items were discarded from the final draft.
CONCLUSION: Post FDM analysis, the experts' consensus on the suitability of the pre-selected items on the questionnaire set were obtained, hence it is now ready for further construct validation process.

Matched MeSH terms: Fuzzy Logic
Fulltext FuGeF: A Resource Bound Secure Forwarding Protocol for Wireless Sensor Networks

Umar IA, Mohd Hanapi Z, Sali A, Zulkarnain ZA

Sensors (Basel), 2016 Jun 22;16(6).
PMID: 27338411 DOI: 10.3390/s16060943

Resource bound security solutions have facilitated the mitigation of spatio-temporal attacks by altering protocol semantics to provide minimal security while maintaining an acceptable level of performance. The Dynamic Window Secured Implicit Geographic Forwarding (DWSIGF) routing protocol for Wireless Sensor Network (WSN) has been proposed to achieve a minimal selection of malicious nodes by introducing a dynamic collection window period to the protocol's semantics. However, its selection scheme suffers substantial packet losses due to the utilization of a single distance based parameter for node selection. In this paper, we propose a Fuzzy-based Geographic Forwarding protocol (FuGeF) to minimize packet loss, while maintaining performance. The FuGeF utilizes a new form of dynamism and introduces three selection parameters: remaining energy, connectivity cost, and progressive distance, as well as a Fuzzy Logic System (FLS) for node selection. These introduced mechanisms ensure the appropriate selection of a non-malicious node. Extensive simulation experiments have been conducted to evaluate the performance of the proposed FuGeF protocol as compared to DWSIGF variants. The simulation results show that the proposed FuGeF outperforms the two DWSIGF variants (DWSIGF-P and DWSIGF-R) in terms of packet delivery.

Matched MeSH terms: Fuzzy Logic
Fulltext Design and Implementation of Fuzzy Compensation Scheme for Temperature and Solar Irradiance Wireless Sensor Network (WSN) on Solar Photovoltaic (PV) System

Pazikadin AR, Rifai D, Ali K, Mamat NH, Khamsah N

Sensors (Basel), 2020 Nov 25;20(23).
PMID: 33255797 DOI: 10.3390/s20236744

Photovoltaic (PV) systems need measurements of incident solar irradiance and PV surface temperature for performance analysis and monitoring purposes. Ground-based network sensor measurement is preferred in many near real-time operations such as forecasting and photovoltaic (PV) performance evaluation on the ground. Hence, this study proposed a Fuzzy compensation scheme for temperature and solar irradiance wireless sensor network (WSN) measurement on stand-alone solar photovoltaic (PV) system to improve the sensor measurement. The WSN installation through an Internet of Things (IoT) platform for solar irradiance and PV surface temperature measurement was fabricated. The simulation for the solar irradiance Fuzzy Logic compensation (SIFLC) scheme and Temperature Fuzzy Logic compensation (TFLC) scheme was conducted using Matlab/Simulink. The simulation result identified that the scheme was used to compensate for the error temperature and solar irradiance sensor measurements over a variation temperature and solar irradiance range from 20 to 60 °C and from zero up to 2000 W/m2. The experimental results show that the Fuzzy Logic compensation scheme can reduce the sensor measurement error up to 17% and 20% for solar irradiance and PV temperature measurement.

Matched MeSH terms: Fuzzy Logic

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