A real-time roundabout detection and navigation system for smart vehicles and cities using laser simulator-fuzzy logic algorithms and sensor fusion in a road environment is presented in this paper. A wheeled mobile robot (WMR) is supposed to navigate autonomously on the road in real-time and reach a predefined goal while discovering and detecting the road roundabout. A complete modeling and path planning of the road's roundabout intersection was derived to enable the WMR to navigate autonomously in indoor and outdoor terrains. A new algorithm, called Laser Simulator, has been introduced to detect various entities in a road roundabout setting, which is later integrated with fuzzy logic algorithm for making the right decision about the existence of the roundabout. The sensor fusion process involving the use of a Wi-Fi camera, laser range finder, and odometry was implemented to generate the robot's path planning and localization within the road environment. The local maps were built using the extracted data from the camera and laser range finder to estimate the road parameters such as road width, side curbs, and roundabout center, all in two-dimensional space. The path generation algorithm was fully derived within the local maps and tested with a WMR platform in real-time.
Motion control involving DC motors requires a closed-loop system with a suitable compensator if tracking performance with high precision is desired. In the case where structural model errors of the motors are more dominating than the effects from noise disturbances, accurate system modelling will be a considerable aid in synthesizing the compensator. The focus of this paper is on enhancing the tracking performance of a wheeled mobile robot (WMR), which is driven by two DC motors that are subject to model parametric uncertainties and uncertain deadzones. For the system at hand, the uncertain nonlinear perturbations are greatly induced by the time-varying power supply, followed by behaviour of motion and speed. In this work, the system is firstly modelled, where correlations between the model parameters and different input datasets as well as voltage supply are obtained via polynomial regressions. A robust H ∞ -fuzzy logic approach is then proposed to treat the issues due to the aforementioned perturbations. Via the proposed strategy, the H ∞ controller and the fuzzy logic (FL) compensator work in tandem to ensure the control law is robust against the model uncertainties. The proposed technique was validated via several real-time experiments, which showed that the speed and path tracking performance can be considerably enhanced when compared with the results via the H ∞ controller alone, and the H ∞ with the FL compensator, but without the presence of the robust control law.
Due to the rise in awareness of environmental issues and the depletion of virgin resources, many firms have attempted to increase the sustainability of their activities. One efficient way to elevate sustainability is the consideration of corporate social responsibility (CSR) by designing a closed loop supply chain (CLSC). This paper has developed a mathematical model to increase corporate social responsibility in terms of job creation. Moreover the model, in addition to increasing total CLSC profit, provides a range of strategic decision solutions for decision makers to select a best action plan for a CLSC. A proposed multi-objective mixed-integer linear programming (MILP) model was solved with non-dominated sorting genetic algorithm II (NSGA-II). Fuzzy set theory was employed to select the best compromise solution from the Pareto-optimal solutions. A numerical example was used to validate the potential application of the proposed model. The results highlight the effect of CSR in the design of CLSC.
One of the most common reasons why researchers seek help from statistician is sample size calculation. However despite the common believe that it only involves formula and calculation, researchers often ignore other aspects of research design that leads to proper sample size calculation. In this article, the author outlines basic steps toward sample size calculation. The author also introduces the logic behind sample size calculation for single mean and single proportion in simplified and less intimidating forms to those not statistically inclined.
Fuzzy Logic is a popular method to tune a PID controller. By using Fuzzy Logic, the PID is tuned automatically based on information of output error, which is better than other tuning rule methods. Fuzzy Logic Control will tune gains of PID controller by using a set of fuzzy rules designed specifically for that. However, specific transient requirements of the process output cannot be assigned to the controller. This research proposes a new method to overcome this problem by using a reference model. Step input from the reference model that contains the desired response information will be compared against the actual output. The reference model can be pre-selected by the user as desired. This study was simulated on a steam temperature process model while few sets of first-order model were used as reference. The results showed that the proposed Fuzzy PID controller with reference model provides better performance with perfect tracking during transient and steady-state.
Shunt active power filter (SAPF) is the most effective solution for current harmonics. In its controller, DC-link capacitor voltage regulation algorithm with either proportional-integral (PI) or fuzzy logic control (FLC) technique has played a significant role in maintaining a constant DC voltage across all the DC-link capacitors. However, PI technique performs poorly with high overshoot and significant time delay under dynamic state conditions, as its parameters are difficult to be tuned without requiring complete knowledge of the designated system. Although FLC technique has been developed to overcome limitations of PI technique, it is mostly developed with high complexity thereby increases computational burden of the designed controller. This paper presents a fuzzy-based DC-link capacitor voltage regulation algorithm with reduced computational efforts to enhance performance of three-phase three-level neutralpoint diode clamped (NPC) inverter-based SAPF in overall DC-link voltage regulation. The proposed method is called effort-reduction FLC technique. The proposed algorithm is developed and evaluated in MATLAB-Simulink. Moreover, conventional algorithm with PI technique is tested for comparison purposes. Simulation results have confirmed improvement achieved by the proposed algorithm in comparison to the conventional algorithm.
Eddy current testing (ECT) is an accurate, widely used and well-understood inspection technique, particularly in the aircraft and nuclear industries. The coating thickness or lift-off will influence the measurement of defect depth on pipes or plates. It will be an uncertain decision condition whether the defects on a workpiece are cracks or scratches. This problem can lead to the occurrence of pipe leakages, besides causing the degradation of a company’s productivity and most importantly risking the safety of workers. In this paper, a novel eddy current testing error compensation technique based on Mamdani-type fuzzy coupled differential and absolute probes was proposed. The general descriptions of the proposed ECT technique include details of the system design, intelligent fuzzy logic design and Simulink block development design. The detailed description of the proposed probe selection, design and instrumentation of the error compensation of eddy current testing (ECECT) along with the absolute probe and differential probe relevant to the present research work are presented. The ECECT simulation and hardware design are proposed, using the fuzzy logic technique for the development of the new methodology. The depths of the defect coefficients of the probe’s lift-off caused by the coating thickness were measured by using a designed setup. In this result, the ECECT gives an optimum correction for the lift-off, in which the reduction of error is only within 0.1% of its all-out value. Finally, the ECECT is used to measure lift-off in a range of approximately 1 mm to 5 mm, and the performance of the proposed method in non-linear cracks is assessed.
To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent) to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS) and particle swarm optimization (PSO). Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE) and ant colony optimization (ANFIS-ACO).
There are a lot of factors and conditions to be considered by tour and travel companies when designing quality product due to the fact that the product being sold is intangible and their ultimate goal is to sustain customers' loyalty. Fuzzy Logic Controller (FLC) has been observed to be compatible to this 'intangible' factor thus giving better result when compared to other methods. By using FLC, all communications are clear and have precise meaning.
Maximum k-Satisfiability (MAX-kSAT) consists of the most consistent interpretation that generate the maximum number
of satisfied clauses. MAX-kSAT is an important logic representation in logic programming since not all combinatorial
problem is satisfiable in nature. This paper presents Hopfield Neural Network based on MAX-kSAT logical rule. Learning
of Hopfield Neural Network will be integrated with Wan Abdullah method and Sathasivam relaxation method to obtain
the correct final state of the neurons. The computer simulation shows that MAX-kSAT can be embedded optimally in
Hopfield Neural Network.
Obesity and its complications is one of the main issues in today's world and is increasing rapidly. A wide range of non-contagious diseases, for instance, diabetes type 2, cardiovascular, high blood pressure and stroke, numerous types of cancer, and mental health issues are formed following obesity. According to the WHO, Malaysia is the sixth Asian country with an adult population suffering from obesity. Therefore, identifying risk factors associated with obesity among Malaysian adults is necessary. For this purpose, this study strives to investigate and assess the risk factors related to obesity and overweight in this country. A quantitative approach was employed by surveying 26 healthcare professionals by questionnaire. Collected data were analyzed with the DEMATEL and Fuzzy Rule-Based methods. We found that lack of physical activity, insufficient sleep, unhealthy diet, genetics, and perceived stress were the most significant risk factors for obesity.
The accuracy level for reservoir evaporation prediction is an important issue for decision making in the water resources field. The traditional methods for evaporation prediction could encounter numerous obstacles owing to the effect of several parameters on the shape of the evaporation pattern. The current research presented modern model called the Coactive Neuro-Fuzzy Inference System (CANFIS). Modification for such model has been achieved for enhancing the evaporation prediction accuracy. Genetic algorithm was utilized to select the effective input combination. The efficiency of the proposed model has been compared with popular artificial intelligence models according to several statistical indicators. Two different case studies Aswan High Dam (AHD) and Timah Tasoh Dam (TTD) have been considered to explore the performance of the proposed models. It is concluded that the modified GA-CANFIS model is better than GA-ANFIS, GA-SVR, and GA-RBFNN for evaporation prediction for both case studies. GA-CANFIS attained minimum RMSE (15.22 mm month-1 for AHD, 8.78 mm month-1 for TTD), minimum MAE (12.48 mm month-1 for AHD, 5.11 mm month-1 for TTD), and maximum determination coefficient (0.98 for AHD, 0.95 for TTD).
In this study, we are reporting a novel prediction model for forecasting the carbon dioxide (CO2) fixation of microalgae which is based on the hybrid approach of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA). The CO2 fixation rate of various algal strains was collected and the cultivation conditions of the microalgae such as temperature, pH, CO2 %, and amount of nitrogen and phosphorous (mg/L) were taken as the input variables, while the CO2 fixation rate was taken as the output variable. The optimization of ANFIS parameters and the formation of the optimized fuzzy model structure were performed by genetic algorithm (GA) using MATLAB in order to achieve optimum prediction capability and industrial applicability. The best-fitting model was figured out using statistical analysis parameters such as root mean square error (RMSE), coefficient of regression (R2), and average absolute relative deviation (AARD). According to the analysis, GA-ANFIS model depicted a greater prediction capability over ANFIS model. The RMSE, R2, and AARD for GA-ANFIS were observed to be 0.000431, 0.97865, and 0.044354 in the training phase and 0.00056, 0.98457, and 0.032156 in the testing phase, respectively, for the GA-ANFIS Model. As a result, it can be concluded that the proposed GA-ANFIS model is an efficient technique having a very high potential to accurately predict the CO2 fixation rate.
The probabilistic hesitant elements (PHFEs) are a beneficial augmentation to the hesitant fuzzy element (HFE), which is intended to give decision-makers more flexibility in expressing their biases while using hesitant fuzzy information. To extrapolate a more accurate interpretation of the decision documentation, it is sufficient to standardize the organization of the elements in PHFEs without introducing fictional elements. Several processes for unifying and arranging components in PHFEs have been proposed so far, but most of them result in various disadvantages that are critically explored in this paper. The primary objective of this research is to recommend a PHFE unification procedure that avoids the deficiencies of operational practices while maintaining the inherent properties of PHFE probabilities. The prevailing study advances the hypothesis of permutation on PHFEs by suggesting a new sort of PHFS division and subtraction compared with the existing unification procedure. Eventually, the proposed PHFE-unification process will be used in this study, an innovative PHFEs based on the Weighted Aggregated Sum Product Assessment Method-Analytic Hierarchy Process (WASPAS-AHP) perspective for selecting flexible packaging bags after the prohibition on single-use plastics. As a result, we have included the PHFEs-WASPAS in our selection of the most effective fuzzy environment for bio-plastic bags. The ranking results for the suggested PHFEs-MCDM techniques surpassed the existing AHP methods in the research study by providing the best solution. Our solutions offer the best bio-plastic bag alternative strategy for mitigating environmental impacts.
Global seaport network efficiency can be measured using the Liner Shipping Connectivity Index (LSCI) with Gross Domestic Product. This paper utilizes k-means and hierarchical strategies by leveraging the results obtained from Data Envelopment Analysis (DEA) and Fuzzy Data Envelopment Analysis (FDEA) to cluster 133 countries based on their seaport network efficiency scores. Previous studies have explored hkmeans clustering for traffic, maritime transportation management, swarm optimization, vessel trajectory prediction, vessels behaviours, vehicular ad hoc network etc. However, there remains a notable absence of clustering research specifically addressing the efficiency of global seaport networks. This research proposed hkmeans as the best strategy for the seaport network efficiency clustering where our four newly founded clusters; low connectivity (LC), medium connectivity (MC), high connectivity (HC) and very high connectivity (VHC) are new applications in the field. Using the hkmeans algorithm, 24 countries have been clustered under LC, 47 countries under MC, 40 countries under HC and 22 countries under VHC. With and without a fuzzy dataset distribution, this demonstrates that the hkmeans clustering is consistent and practical to form grouping of general data types. The findings of this research can be useful for researchers, authorities, practitioners and investors in guiding their future analysis, decision and policy makings involving data grouping and prediction especially in the maritime economy and transportation industry.
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.
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.
The specific texture on B-scan images is believed to be related to both ultrasound machine characteristics and tissue properties, i.e., the pathological states of the soft tissue. Therefore, for classification, features can be extracted with the use of image texture analysis techniques. In this paper a novel fuzzy approach for texture characterization is used for classification of normal liver and diffused liver diseases, here fatty liver, liver cirrhosis, and hepatitis are emphasized. The texture analysis techniques are diversified by the existence of several approaches. We propose fuzzy features for the analysis of the texture image. For this, a membership function is constructed to represent the effect of the neighboring pixels on the current pixel in a window. Using these membership function values, we find a feature by weighted average method for the current pixel. This is repeated for all pixels in the window treating each time one pixel as the current pixel. Using these fuzzy based features, we derive three descriptors: maximum, entropy, and energy as used in co-occurrence method, for each window.
Accurate medical disease diagnosis is considered to be an important classification problem. The main goal of the classification process is to determine the class to which a certain pattern belongs. In this article, a new classification technique based on a combination of The Teaching Learning-Based Optimization (TLBO) algorithm and Fuzzy Wavelet Neural Network (FWNN) with Functional Link Neural Network (FLNN) is proposed. In addition, the TLBO algorithm is utilized for training the new hybrid Functional Fuzzy Wavelet Neural Network (FFWNN) and optimizing the learning parameters, which are weights, dilation and translation. To evaluate the performance of the proposed method, five standard medical datasets were used: Breast Cancer, Heart Disease, Hepatitis, Pima-Indian diabetes and Appendicitis. The efficiency of the proposed method is evaluated using 5-fold cross-validation and 10-fold cross-validation in terms of mean square error (MSE), classification accuracy, running time, sensitivity, specificity and kappa. The experimental results show that the efficiency of the proposed method for the medical classification problems is 98.309%, 91.1%, 91.39%, 88.67% and 93.51% for the Breast Cancer, Heart Disease, Hepatitis, Pima-Indian diabetes and Appendicitis datasets, respectively, in terms of accuracy after 30 runs for each dataset with low computational complexity. In addition, it has been observed that the proposed method has efficient performance compared with the performance of other methods found in the related previous studies.
Sand management is essential for enhancing the production in oil and gas reservoirs. The critical total drawdown (CTD) is used as a reliable indicator of the onset of sand production; hence, its accurate prediction is very important. There are many published CTD prediction correlations in literature. However, the accuracy of most of these models is questionable. Therefore, further improvement in CTD prediction is needed for more effective and successful sand control. This article presents a robust and accurate fuzzy logic (FL) model for predicting the CTD. Literature on 23 wells of the North Adriatic Sea was used to develop the model. The used data were split into 70% training sets and 30% testing sets. Trend analysis was conducted to verify that the developed model follows the correct physical behavior trends of the input parameters. Some statistical analyses were performed to check the model's reliability and accuracy as compared to the published correlations. The results demonstrated that the proposed FL model substantially outperforms the current published correlations and shows higher prediction accuracy. These results were verified using the highest correlation coefficient, the lowest average absolute percent relative error (AAPRE), the lowest maximum error (max. AAPRE), the lowest standard deviation (SD), and the lowest root mean square error (RMSE). Results showed that the lowest AAPRE is 8.6%, whereas the highest correlation coefficient is 0.9947. These values of AAPRE (<10%) indicate that the FL model could predicts the CTD more accurately than other published models (>20% AAPRE). Moreover, further analysis indicated the robustness of the FL model, because it follows the trends of all physical parameters affecting the CTD.