Search Results (10)

In this paper, we introduce an advanced Fuzzy Record Similarity Metric (FRMS) that improves approximate record matching and models human perception of record similarity. The FRMS utilizes a newly developed similarity space with favorable properties combined with a metric space, employing a bag-of-words model with general applications in text mining and cluster analysis. To optimize the FRMS, we propose a two-stage method for approximate string matching and search that outperforms baseline methods in terms of average time complexity and F measure on various datasets. In the first stage, we construct an optimal Q-gram count filter as an optimal lower bound for fuzzy token similarities such as FRMS. The approximated Q-gram count filter achieves a high accuracy rate, filtering over 99% of dissimilar records, with a constant time complexity of $\approx \mathcal{O}\left(1\right)$. In the second stage, FRMS runs for a polynomial time of approximately $\approx \mathcal{O}\left(n^4\right)$ and models human perception of record similarity by maximum weight matching in a bipartite graph. The FRMS architecture has widespread applications in structured document storage such as databases and has already been commercialized by one of the largest IT companies. As a side result, we explain the behavior of the singularity of the Q-gram filter and the advantages of a padding extension. Overall, our method provides a more accurate and efficient approach to approximate string matching and search with real-time runtime. Full article

Since the global warming problem threatens the whole world, it is understood that countries should develop energy policies that will increase their sustainable and clean energy investments. Compared to other alternatives, the high cost of renewable energy projects is an essential obstacle in this process. Therefore, priority should be given to developing distributed energy projects to minimize this problem. The scope of the present paper is to identify the most critical items that affect the performance of distributed energy projects to have knowledge-oriented competencies. In this way, companies can focus on more critical items to provide efficiency for distributed energy projects. As a result, clean energy usage is improved, and the global warming problem is handled more successfully. A novel decision-making model is generated to examine the competencies of the knowledge economy based on collaborative filtering and bipolar q-rung orthopair fuzzy sets (q-ROFSs) with the golden ratio. The analysis concludes that learning and growth are the most critical balanced scorecard perspectives. Moreover, it was also determined that information and communication technology is the most critical competency of the knowledge economy. Therefore, it would be appropriate for investors who plan to invest in distributed energy projects to form a research and development team. Hence, new technologies will be followed instantly. In this way, companies will be able to gain a cost advantage. In this context, improving distributed energy projects is important to increase efficiency in clean energy investments. Full article

Nowadays, the integration of renewable energy sources such as solar, wind, etc. into the grid is recommended to reduce losses and meet demands. The application of power electronics devices (PED) to control non-linear, unbalanced loads leads to power quality (PQ) issues. This work presents a hybrid controller for the self-tuning filter (STF)-based Shunt active power filter (SHAPF), integrated with a wind power generation system (WPGS) and a battery storage system (BS). The SHAPF comprises a three-phase voltage source inverter, coupled via a DC-Link. The proposed neuro-fuzzy inference hybrid controller (NFIHC) utilizes both the properties of Fuzzy Logic (FL) and artificial neural network (ANN) controllers and maintains constant DC-Link voltage. The phase synchronization was generated by a self-tuning filter (STF) for the effective working of SHAPF during unbalanced and distorted supply voltages. In addition, STF also does the work of low-pass filters (LPFs) and HPFs (high-pass filters) for splitting the Fundamental component (FC) and Harmonic component (HC) of the current. The control of SHAPF works on d-q theory with the advantage of eliminating low-pass filters (LPFs) and phase-locked loop (PLL). The prime objective of the projected work is to regulate the DC-Link voltage during wind uncertainties and load variations, and minimize the total harmonic distortion (THD) in the current waveforms, thereby improving the power factor (PF).Test studies with various combinations of balanced/unbalanced loads, wind velocity variations, and supply voltage were used to evaluate the suggested method’s superior performance. In addition, the comparative analysis was carried out with those of the existing controllers such as conventional proportional-integral (PI), ANN, and FL. Full article

To express wider uncertainty, Běhounek and Daňková studied fuzzy partial logic and partial function. At the same time, Borzooei generalized t-norms and put forward the concept of partial t-norms when studying lattice valued quantum effect algebras. Based on partial t-norms, Zhang et al. studied partial residuated implications (PRIs) and proposed the concept of partial residuated lattices (PRLs). In this paper, we mainly study the related algebraic structure of fuzzy partial logic. First, we provide the definitions of regular partial t-norms and regular partial residuated implication (rPRI) through the general forms of partial t-norms and partial residuated implication. Second, we define regular partial residuated lattices (rPRLs) and study their corresponding properties. Third, we study the relations among commutative quasi-residuated lattices, commutative Q-residuated lattices, partial residuated lattices, and regular partial residuated lattices. Last, in order to obtain the filter theory of regular partial residuated lattices, we restrict certain conditions and then propose special regular partial residuated lattices (srPRLs) in order to finally construct the quotient structure of regular partial residuated lattices. Full article

Deep learning (DL) has achieved significant attention in the field of infrared (IR) and visible (VI) image fusion, and several attempts have been made to enhance the quality of the final fused image. It produces better results than conventional methods; however, the captured image cannot acquire useful information due to environments with poor lighting, fog, dense smoke, haze, and the noise generated by sensors. This paper proposes an adaptive fuzzy-based preprocessing enhancement method that automatically enhances the contrast of images with adaptive parameter calculation. The enhanced images are then decomposed into base and detail layers by anisotropic diffusion-based edge-preserving filters that remove noise while smoothing the edges. The detailed parts are fed into four convolutional layers of the VGG-19 network through transfer learning to extract features maps. These features maps are fused by multiple fusion strategies to obtain the final fused detailed layer. The base parts are fused by the PCA method to preserve the energy information. Experimental results reveal that our proposed method achieves state-of-the-art performance compared with existing fusion methods in a subjective evaluation through the visual experience of experts and statistical tests. Moreover, the objective assessment parameters are conducted by various parameters ($FMI$, $SSI{M}_a$, $API$, $\mathrm{EN}$, ${\mathrm{Q}}_{\mathrm{F}}^{\mathrm{AB}}$, and $N_F^{AB}$) which were used in the comparison method. The proposed method achieves 0.2651 to 0.3951, 0.5827 to 0.8469, 56.3710 to 71.9081, 4.0117 to 7.9907, and 0.6538 to 0.8727 gain for $FMI$, $SSI{M}_a$, $API$, $\mathrm{EN}$, and ${\mathrm{Q}}_{\mathrm{F}}^{\mathrm{AB}}$, respectively. At the same time, the proposed method has more noise reduction (0.3049 to 0.0021) that further justifies the efficacy of the proposed method than conventional methods. Full article

In this paper, we introduce the notions of $(\in ,\in )$ -fuzzy positive implicative filters and $(\in ,\in \vee q)$ -fuzzy positive implicative filters in hoops and investigate their properties. We also define some equivalent definitions of them, and then we use the congruence relation on hoop defined in blue[Aaly Kologani, M.; Mohseni Takallo, M.; Kim, H.S. Fuzzy filters of hoops based on fuzzy points. Mathematics. 2019, 7, 430; doi:10.3390/math7050430] by using an $(\in ,\in )$ -fuzzy filter in hoop. We show that the quotient structure of this relation is a Brouwerian semilattice. Full article

In this paper, we define the concepts of $(\in ,\in )$ and $(\in ,\in \vee q)$ -fuzzy filters of hoops, discuss some properties, and find some equivalent definitions of them. We define a congruence relation on hoops by an $(\in ,\in )$ -fuzzy filter and show that the quotient structure of this relation is a hoop. Full article

The concept of quantum B-algebra was introduced by Rump and Yang, that is, unified algebraic semantics for various noncommutative fuzzy logics, quantum logics, and implication logics. In this paper, a new notion of q-filter in quantum B-algebra is proposed, and quotient structures are constructed by q-filters (in contrast, although the notion of filter in quantum B-algebra has been defined before this paper, but corresponding quotient structures cannot be constructed according to the usual methods). Moreover, a new, more general, implication algebra is proposed, which is called basic implication algebra and can be regarded as a unified frame of general fuzzy logics, including nonassociative fuzzy logics (in contrast, quantum B-algebra is not applied to nonassociative fuzzy logics). The filter theory of basic implication algebras is also established. Full article

Harmonic distortion in power networks has greatly reduced power quality and this affects system stability. In order to mitigate this power quality issue, the shunt active power filter (SAPF) has been widely applied and it is proven to be the best solution to current harmonics. This paper evaluates the performance of the modified synchronous reference frame extraction (MSRF) algorithm with fuzzy logic controller (FLC) based current control pulse width modulation (PWM) inverter of three-phase three-wire SAPF to mitigate current harmonics. The proposed FLC is designed with a reduced amount of membership functions (MFs) and rules, and thus significantly reduces the computational time and memory size. Modeling and simulations of SAPF are carried out using MATLAB/Simulink R2012a with the power system toolbox under steady-state condition, and this is followed with hardware implementation using a TMS320F28335 digital signal processor (DSP), Specrum Digital Inc., Stafford, TX, USA. The results obtained demonstrate a good and satisfactory response to mitigate the harmonics in the system. The total harmonic distortion (THD) for the system has been reduced from 25.60% to 0.92% and 1.41% in the simulation study with and without FLC, respectively. Similarly for the experimental study, the SAPF can compensate for the three-phase load current by reducing THD to 5.07% and 7.4% with and without FLC, respectively. Full article

This paper analyses the complexity of multivariate electroencephalogram (EEG) signals in different frequency scales for the analysis and classification of focal and non-focal EEG signals. The proposed multivariate sub-band entropy measure has been built based on tunable-Q wavelet transform (TQWT). In the field of multivariate entropy analysis, recent studies have performed analysis of biomedical signals with a multi-level filtering approach. This approach has become a useful tool for measuring inherent complexity of the biomedical signals. However, these methods may not be well suited for quantifying the complexity of the individual multivariate sub-bands of the analysed signal. In this present study, we have tried to resolve this difficulty by employing TQWT for analysing the sub-band signals of the analysed multivariate signal. It should be noted that higher value of Q factor is suitable for analysing signals with oscillatory nature, whereas the lower value of Q factor is suitable for analysing signals with non-oscillatory transients in nature. Moreover, with an increased number of sub-bands and a higher value of Q-factor, a reasonably good resolution can be achieved simultaneously in high and low frequency regions of the considered signals. Finally, we have employed multivariate fuzzy entropy (mvFE) to the multivariate sub-band signals obtained from the analysed signal. The proposed Q-based multivariate sub-band entropy has been studied on the publicly available bivariate Bern Barcelona focal and non-focal EEG signals database to investigate the statistical significance of the proposed features in different time segmented signals. Finally, the features are fed to random forest and least squares support vector machine (LS-SVM) classifiers to select the best classifier. Our method has achieved the highest classification accuracy of 84.67% in classifying focal and non-focal EEG signals with LS-SVM classifier. The proposed multivariate sub-band fuzzy entropy can also be applied to measure complexity of other multivariate biomedical signals. Full article