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The categorization and retrieval of brain tumors using Magnetic Resonance Imaging (MRI) is a difficult but necessary process for brain tumor diagnosis. In this study, a reinforcement learning agent is proposed that can interact with an environment that includes brain tumor images and retrieve and categorize the most comparable images to an unknown query image. This article proposes a unique fuzzy and Deep Learning (DL)-based Reinforcement Learning (RL) strategy for categorizing three types of brain tumors as well as no tumors. Deep Brain Incep Res Architecture 2.0 based Reinforcement Learning Network (DBIRA2.0-RLN), the proposed Convolutional Neural Network (CNN)-based technique, benefits from a novel architecture in which brain tumor descriptors are established using the inception block and effective skip-connection mapping arrangement. To improve the efficiency of DBIRA2.0-RLN, improved samples are created by training and testing the system with a fuzzy logic-based technique. To lower the dimension of the descriptor vector for improved image categorization and retrieval, the descriptor vector obtained from DBIRA2.0 is binary coded using Multilinear Principal Component Analysis. DBIRA2.0 produces and preserves brain tumors and no tumor descriptors in several layers, which are then used sequentially in numerous units to construct the final brain tumor categorization and retrieval. The proposed method’s output is tested using a dataset, and the accuracy rates obtained for meningioma tumor, glioma tumor, pituitary tumor, and no tumor are 97.1%, 98.7%, 94.3%, and 100% respectively, indicating that the proposed approach outperforms the other brain tumor categorization and retrieval approaches used in the literature.

Tidying up objects poses a complex challenge for service robots, particularly when it involves scheduling operations that require real-time action for achieving optimal results. This paper presents a decision system aimed at selecting priority locations to address the problem of robots searching for miscellaneous items in indoor environments. Initially, three datasets with varying complexities are generated using real environmental data, and the environmental information is integrated into feature matrices. Subsequently, the rule base of the fuzzy inference system is trained and optimized using an equilibrium optimizer, and its performance is compared with other commonly used algorithms. The feature matrices and the optimized rule base are then incorporated into the fuzzy inference system, leveraging the traveling salesman problem to determine the optimal sequence for visiting locations. The accuracy and efficiency of the proposed method are validated through physical implementation tests using analog data. Finally, the system is applied and evaluated in real-world scenarios to validate its effectiveness. The video of experiment is available at https://youtu.be/rjMDgopM-9M.

Emperor Penguin Optimizer (EPO) is a recently developed population-based meta-heuristic algorithm that simulates the huddling behavior of emperor penguins. Mixed results have been observed on the performance of EPO in solving general optimization problems. Within the EPO, two parameters need to be tuned (namely ${f}$ and ${l}$ ) to ensure a good balance between exploration (i.e., roaming unknown locations) and exploitation (i.e., manipulating the current known best). Since the search contour varies depending on the optimization problem, the tuning of $f$ and $l$ is problem-dependent, and there is no one-size-fits-all approach. To alleviate these problems, an adaptive mechanism can be introduced in EPO. This paper proposes a fuzzy adaptive variant of EPO, namely Fuzzy Adaptive Emperor Penguin Optimizer (FAEPO), to solve this problem. As the name suggests, FAEPO can adaptively tune the parameters $f$ and $l$ throughout the search based on three measures (i.e., quality, success rate, and diversity of the current search) via fuzzy decisions. A test suite of twelve optimization benchmark test functions and three global optimization problems (Team Formation Optimization - TFO, Low Autocorrelation Binary Sequence - LABS, and Modified Condition/Decision Coverage - MC/DC test case generation) were solved using the proposed algorithm. The respective solution results of the benchmark meta-heuristic algorithms were compared. The experimental results demonstrate that FAEPO significantly improved the performance of its predecessor (EPO) and gives superior performance against the competing meta-heuristic algorithms, including an improved variant of EPO (IEPO).

Snake-like modular robots (MRs) are highly flexible, but, to traverse a challenging terrain or explore a region of interest, MR needs to attain efficient locomotion depending on a tradeoff between objectives like forward velocity and power consumption of the robot. The objectives can vary with different weights depending upon the situation, reflecting relative objective importance. This study developed a multiobjective reinforcement learning algorithm based on a fuzzy inference system (FI-MORL) to select the most appropriate gait parameters of snake-like MRs according to the objective weights. The developed algorithm employs a fuzzy inference system to reduce the number of states in an environment, which results in faster learning. The proposed approach uses the previously learned experience to rapidly achieve the best objective values in response to a change in weights. While setting equal importance to the objectives, FI-MORL delivers superior performance than single-objective reinforcement learning algorithms by consuming 2% less power and gaining 2.5% higher velocity since it mitigates the effect of weight change, similar performance found comparing an actor-critic algorithm. Likewise, the proposed method outperforms by consuming 14% less power and achieving 11% higher velocity than traditional methods like proximal policy optimization, deep Q-network, and vanilla policy gradient. Even after weight change, FI-MORL achieved a 14% higher reward than the above methods. The proposed FI-MORL framework can effectively converge quicker and efficiently handle the changes in objective weights.

This study provides a complete Selective Compliance Assembly Robot Arm (SCARA) wafer inspection system. This system mainly includes two phases. In the first phase, a SCARA manipulator acts as an inspection application platform, and a depth camera is used to position the position vector of an inspection point and a cassette together with a single-axis visual frame so that SCARA can automatically perform calibration in the working area. In the second phase, the wafer ready for inspection is optimized for procedure sorting to perform all detection items in the shortest time when SCARA is in the process of inspection. This study has solved the problem of calibrating positions of a manipulator and a working area with consumption of manpower in a traditional wafer inspection field in such a way that the manipulator can complete a predetermined task with the economic benefits of the inspection center without colliding with the working area. In addition, optimized scheduling reduces the time for the manipulator to complete all inspection tasks to allow more wafers to be inspected per unit time. Finally, the relative positions of all inspection points are automatically calculated by the manipulator by placing SCARA in a field not calibrated by the inspection points. The predefined wafer inspection scheduling is sent to the system for optimization calculation, and the experimental results are presented by SCARA.

Electric vehicles (EVs) need to be recharged at intermediate locations, such as shopping malls, restaurants, and parking lots, to meet the daily commute requirements of their users. Currently, public electric vehicle supply equipment (EVSE) serve EVs by conventional methods, which can result in long waiting time for users. This issue reduces the travel efficiency of EVs and thus affects user comfort. Most previous research has studied energy consumption and charging cost optimization; however, comparatively less work has focused on waiting time optimization despite its great importance from the EV user’s perspective. In this paper, we formulate the waiting time optimization as a fuzzy integer linear programming problem and propose a novel heuristic fuzzy inference system-based algorithm (FISA) that resolves the objective function and minimizes the waiting time of EVs at public EVSE installations. We developed the underlying fuzzy inference system by defining the membership functions, expert rules, and formulation for obtaining the optimal solution. The novel FISA automates the correlations of the uncertain and independent input parameters into weighted control variables and resolves the objective function in each sampling period to optimize the waiting time for EVs with the most urgent service requirements. A java language-based simulator is developed for a parking lot to evaluate the effectiveness of the proposed FISA. The simulation results indicate higher efficiency of the proposed FISA compared with state-of-art scheduling algorithms. [ABSTRACT FROM AUTHOR]

Advancement of technology has made available data of different modalities that can be integrated effectively through multiple view learning for modeling real-world problems. Although multiple view learning has achieved great success in many applications, it still faces several challenges. One of them is how to reduce the negative impact of the missing views in incomplete multiple view datasets by fully exploiting the information available. Another challenge is how to enhance the interpretability of the multiple view model for scenarios with high transparency requirement. To address these challenges, this article proposes a novel modeling method for incomplete multiple view fuzzy system. Based on fuzzy interpretable rules, the method integrates missing view imputation and hidden view learning as one single process to yield a model of high interpretability, where cooperative learning is used to mine the complementary information between the visible views and the hidden view. The proposed method has four advantages when compared with existing approaches: 1) the method is more interpretable, attributed to the fuzzy interpretable rules that it is based on, 2) missing view imputation is integrated into the modeling to make it more efficient than the existing two-step strategy, 3) the method not only imputes missing views, but also mines the hidden view shared by the multiple visible views, and 4) cooperative learning is used to mine the complementary information, which significantly reduces the negative impact of missing views. Experiments on real datasets demonstrate the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

The densified deployment of heterogeneous networks coexisting with a variety of overlapping cells has emerged as a viable solution for next generation wireless networks. Despite numerous advantages, the heterogeneity and denseness also raise complicated handover management issue. Nonetheless, most existing handover methods generally depend on one or more objective attributes, and rarely consider the subjective demands of personalized users and specific applications that demand differentiated services. Through decomposing the control plane and data plane, software defined network(SDN) offers a flexible architectural paradigm to overcome these challenges. In this article, we first develop an SDN-driven handover architecture that is capable of perceiving global network status and requirements from various perspectives, including the physical layer, users, and applications. Then, a context-aware multi-criteria handover mechanism is developed in the SDN edge to provide differentiated services. Considering the numerous complicated factors, the handover decision is made based on a hierarchical fuzzy inference system to process diverse attributes and vague requirements described in natural language. Finally, we evaluate the performance of our proposed scheme through a combination of extensive simulations and real-world experiments. The results demonstrate that our solution outperforms the baseline handover schemes, more efficiently providing differentiated services with respect to throughput, bandwidth cost, and application satisfaction, and is efficient and feasible in practice. [ABSTRACT FROM AUTHOR]

This paper proposes a new hybrid algorithm for secure communication applications. The proposed algorithm includes a fuzzy brain emotional learning controller (FBELC), a recurrent cerebellar model articulation controller (RCMAC), and a robust compensator (RC). The main brain-imitated neural network controller is a combination of the RCMAC and the FBELC, which is a mathematical model that approximates the decision and emotional activity of a human brain. A fuzzy inference system is also merged into the FBELC to produce an efficient hybrid structure, then it is used for secure communication applications. The 3-dimensional (3D) Genesio chaotic system is used for audio and image secure communication systems to show the potency and performance of the proposed algorithm. In the first application, a new image encryption algorithm is proposed to enhance security for information transmission, then several standard images are applied for the chaotic synchronization of image secure communication. In the second application, the audio signal is embedded in a 3D chaotic trajectory, which is used as an encryption carrier signal, after using the proposed method for the decryption, the source signal can be retrieved. The comparisons of simulation results using security analyses and root mean square error for recent algorithms are performed to validate the performance and efficiency of the proposed hybrid algorithm. The simulation results point out that our algorithm can attain better synchronization performance, and achieve more efficient audio and image secure communications.

Conventionally, risk assessment methods suffer from ignoring the dynamic nature of risks. Therefore, this study demonstrates an interactive approach to risks and prioritizes risks based on their overall cyclic events acting on the attainment of the goals of a project during its life cycle. Hence, the risks of the project of gas condensate storage tank construction (G.C.S.T.C) are first identified and evaluated by using a fuzzy inference system (F.I.S); then, the created risk network is simulated over the project life cycle by using the results of the F.I.S; finally, the impacts of the risks are assessed by applying purposeful indicators. Based on the results, the risk of the increase in the economic sanctions most significantly impacts on achieving the project goals, and lack of the governmental support, the variation of the fiscal governmental policy, high inflation rates, and sharp changes in the exchange rate are considered as the important risks. The results also demonstrated that these five risks improved the attainment of the project goals by more than 55%, while the total improvement on the 21 other risks enhanced the achievement of the project goals by less than 45%.

This paper investigates the opinion dynamics on the adaptive networks with the community structure. A coevolution model of opinions and networks is proposed, in which the influence weights among agents can not be precisely obtained, and there exist the confidence heterogeneities and the frequency difference of exchanging opinion between the inner-community and the inter-community. A two-dimensional fuzzy inference system is designed to obtain the influence weights among agents’ opinions, which deals with the difficulties that the influence weights can not be precisely obtained. The simulation results show that the imbalance levels between the inner-community confidence and the inter-community confidence not only affect strongly the average relative frequency of survival opinions, but also dominate the distribution of survival opinions. The polarization state of opinions easily appears in the population with the higher inner-community confidence, whereas when the inter-community confidence is higher, the system tends to exhibit a large number of small-size clusters and a plural state is achieved. Moreover, the simulation results also show that the convergence time to the stable state varies non-monotonically with the increase of the inner-community confidence or the inter-community confidence.

Nowadays, under the pressure of numerous research publications, researchers increasingly pay attention to writing survey papers to track and understand one research topic they are interested in, and then begin to conduct more in-depth research. Until this moment, there are two types of survey papers: traditional review analysis and bibliometric statistical analysis. Compared with traditional review analysis, due to the analysis of various bibliometric information that can be quickly summarized to assess and predict one research field’s development, the bibliometric statistical analysis is progressively proposed. However, no research relied on the bibliometric approach to explore fuzzy inference system (FIS) -based classifiers. More importantly, since the current open-ended bibliometric analysis approaches have different assessment focuses, choosing a suitable approach is problematic. Therefore, based on the extraction, integration, and expansion of previous bibliometric analysis theories, this research proposes a new systematic and time-saving bibliometric statistical analysis approach. It is worth noting that the proposed approach eliminates the need to read the internal content of all publications. Two core parts (Publication Information and TOP 20 SETs) are generated by the proposed analysis approach. Among them, analyzing Author Keywords and TOP 20 SETs are unprecedented guiding features to assist researchers in exploring research topic. Significantly, this research relies on the proposed approach to explore FIS-based classifiers. Various assessments cover the bibliometric information of the entire related publications. In addition, these results may need to be considered to increase the citation rate of future publications.

One of the most important researches in wireless sensor networks is energy saving. The clustering algorithm can efficiently save energy. However, most of the existing clustering algorithms use a fixed cluster head election algorithm and perform each round of clustering, so that unsuitable nodes are continuously elected as cluster heads, leading to rapid exhaustion of node energy and short network life. In order to solve these problems, this paper proposes an Energy-saving Fuzzy Clustering Routing algorithm (EFCR) for wireless sensor networks,which designs two types of clustering, Clustering Type1 and Clustering Type2, and alternately executes them in different rounds according to the threshold. Clustering Type1 considers the parameters that affect the energy consumption of the node: the distance from the node to the base station, the number of neighbor nodes of the node, and the remaining energy of the node as fuzzy inputs, and calculates the fitness of the node as a cluster head through fuzzy inference to avoid the occurrence of unsuitable nodes being successively elected as cluster heads. Clustering Type2 performs data transmission by trusting the last round of cluster heads, which reduces the number of control data packets in each round of clustering and reduces the overall energy consumption of the network. This paper conducted simulations in four different scenarios, compared other clustering algorithms of the same type, and analyzed the remaining energy of the network, the number of surviving nodes, and the life of the network. The results show that EFCR algorithm is better than others. [ABSTRACT FROM AUTHOR]

In an islanded microgrid with multiple distributed generations (DGs), the difference in line impedance may cause local voltage deviation, which leads to a series of problems such as lower power allocation accuracy and bus voltage drop under traditional droop control. In this respect, a method for optimizing the droop control using an improved particle swarm optimization (IPSO) is proposed. Firstly, the microgrid structure and influence of line parameters on traditional droop control strategy is analyzed. Then, an improved particle swarm optimization is proposed. Based on the basic particle swarm optimization (PSO) algorithm, a fuzzy inference system (FIS) is introduced to dynamically adjust the particle swarm optimization, which can effectively improve the global search ability and local search ability of the algorithm. After that, the improved algorithm is applied to the droop controller, simultaneously, the range of stable operation of the system is determined by small signal analysis. Finally, the simulation and experiment results show that the proposed improved droop control strategy can achieve accurate allocation of active and reactive power effectively while maintaining bus voltage and system frequency stability, enhance the dynamic performance and transient stability of the microgrid system.

To make intelligent vehicles obtain human drivers' steering characteristic, a new single point preview-based human-like driver model is proposed, which contains a preview decision module and a steering wheel angle calculation module. Enlightened by the visual gaze mechanism of human drivers when passing a curved road, the preview decision module is established using Takagi-Sugeno fuzzy inference system (T-S FIS) to adaptively adjust the preview point position in both longitudinal and lateral direction, and the steering angle calculation module would use the adjusted preview point to generate steering command based on pure pursuit algorithm. Ant colony optimization (ACO) method is used to optimize the fuzzy rules in the preview decision module according to the similarity of trajectories between the proposed driver model and human drivers. The proposed human-like driver model is verified on a two-lane urban curved road. Five experienced human drivers' driving trajectories under different speeds are collected for the verification. After the preview decision module optimization done in the PreScan/Simulink simulation platform, the proposed human-like driver model shows higher similarity with experienced human drivers than the driver model with fixed preview distance or the driver model only with changeable longitudinal preview distance.

This letter presents a multi-objective optimization strategy for switched reluctance generator based on fuzzy inference non-dominated sorting genetic algorithm III, which is able to search and rank solutions in Pareto front with the preference of designer to overcome the secondary choice difficulty. Mamdani fuzzy inference system is established in line with the decision maker's way of thinking, and is introduced into non-dominated sorting genetic algorithm III, the competitive optimal solutions with relative intensity value are achieved with consideration of the designer's preferences so that the practical decision-making efficiency is improved. The effectiveness of the proposed multi-objective optimization strategy for switched reluctance generator is validated by the simulation verification. [ABSTRACT FROM AUTHOR]

This article presents an adaptive fusion method for a multisensor odometry using a fuzzy inference system and applies it to a visual-inertial measurement unit (IMU)-wheel odometry. This article first presents the mechanism of the multisensor odometry with an error state Kalman filter and points out that the assumption of the constant covariance in the prediction and measurement models are invalid due to dynamical interior and exterior abnormities. The previous work of fuzzy logic Kalman filter can only deal with uncertain uncertainty in the measurement model and thus not suitable for this double stage variant uncertainty problem. This article provides a solution using adaptive network fuzzy inference systems. The abnormal working conditions of the two models can be detected online separately, and the fusion gains are tuned accordingly. The training method of the networks is also provided to further improve the fuzzy inference system when the ground truth of the trajectories is available. Experimental results show that the proposed adaptive fusion method can significantly improve the robustness and accuracy of the fused trajectory estimation, even under severe working conditions. [ABSTRACT FROM AUTHOR]

The size of the remote sensing (RS) data is increasing exponentially with modern technological evolution. Efficient mapping with these data using a supervised model demands ample labeled samples, and obtaining them is expensive and time-consuming. For that reason, domain adaptation (DA) methodologies that work with limited available knowledge in terms of labeled samples or trained models are highly successful. DA uses the model trained for a domain called “source” and aims to classify the data of a related domain called “target.” However, an insufficient amount of labeled samples in the target domain data set is still a bottleneck for DA models used for RS images. To address this, we have proposed an efficient DA classification model using an interpretable rule-based fuzzy extreme learning machine (IRF-ELM). These rules are derived using the maximum fuzzy membership value of features characterized by class-belonging fuzzification and two rule extraction matrices. A partially connected ELM architecture is then designed with these rules. The fuzzification process typically aligns the target domain’s feature space to the source domain in the best possible ways to transfer knowledge between them. The proposed model’s superiority to similar other methods is verified using different RS data sets in terms of various performance measurement indexes. [ABSTRACT FROM AUTHOR]

This paper deals with the analysis of sleep quality, which involves a non-invasive sleep stage detection method with home deployability. Some physiological signals, such as heart rate, heart rate variation, and the number of times the subject rolled over, are collected to determine the sleep stage. A fuzzy inference system is adopted to evaluate the division of sleep stage. Then, a preliminary sleep depth is calculated. Furthermore, a finite-state machine is developed to detect the sleep stage changes. The difference between our research and other existing studies is that, first, both the pressure sensors and the heart rate device are employed; then, the fuzzy inference and a finite-state machine are introduced, which give us a higher precision than the traditional methods to evaluate the sleep stage. The experimental results show that the proposed method can well evaluate the sleep quality that is almost consistent with a polysomnography test. The latter is currently recognized as the best way to measure sleep quality, which, however, requires a variety of monitoring sensors and has to be performed by the nursing staff in a professional setting. The reported approach can be used for monitoring sleep quality or sleep disorder screening at home.

Tasks related to traffic and safety operations under low visibility conditions remain a challenge for transportation agencies nationwide. Transportation managers are required to model weather and road variables to make operational decisions, while simultaneously prioritizing the deployment of available resources. This research presents a fuzzy inference system (FIS) framework to assist transportation managers with prioritizing the deployment of resources to areas of low visibility originated by fog. The framework is composed of three fuzzy systems that characterize fog occurrence, road risk conditions, and deployment of resources. Fuzzy logic is considered a decision-making technique that could be effectively applied when dealing with ambiguous, imprecise and complex variables like fog in multiple domains, including but not limited to weather, transportation, and resource allocation. The main benefit of this technique is the ability to generate solutions to a problem where a rigorous analytical solution do not exist, e.g., visibility forecasting. The framework was developed using GUIDE, a user interface design environment, and the Fuzzy Logic Toolbox of MATLAB.

This paper studies a split-complex-valued neuro-fuzzy algorithm for fuzzy inference system, which realizes a frequently used zero-order Takagi-Sugeno-Kang system. Here, adaptive momentum is utilized to speed up the learning convergence. Some strong convergence results are demonstrated based on the weak convergence results, which expresses that the weight sequence of fuzzy parameters converges to a fixed point. Simulation results support the theoretical findings.

Diabetes is a serious chronic disease. The importance of clinical decision support systems (CDSSs) to diagnose diabetes has led to extensive research efforts to improve the accuracy, applicability, interpretability, and interoperability of these systems. However, this problem continues to require optimization. Fuzzy rule-based systems are suitable for the medical domain, where interpretability is a main concern. The medical domain is data-intensive, and using electronic health record data to build the FRBS knowledge base and fuzzy sets is critical. Multiple variables are frequently required to determine a correct and personalized diagnosis, which usually makes it difficult to arrive at accurate and timely decisions. In this paper, we propose and implement a new semantically interpretable FRBS framework for diabetes diagnosis. The framework uses multiple aspects of knowledge-fuzzy inference, ontology reasoning, and a fuzzy analytical hierarchy process (FAHP) to provide a more intuitive and accurate design. First, we build a two-layered hierarchical and interpretable FRBS; then, we improve this by integrating an ontology reasoning process based on SNOMED CT standard ontology. We incorporate FAHP to determine the relative medical importance of each sub-FRBS. The proposed system offers numerous unique and critical improvements regarding the implementation of an accurate, dynamic, semantically intelligent, and interpretable CDSS. The designed system considers the ontology semantic similarity of diabetes complications and symptoms concepts in the fuzzy rules' evaluation process. The framework was tested using a real data set, and the results indicate how the proposed system helps physicians and patients to accurately diagnose diabetes mellitus.

This article proposes an efficient intelligent control structure for uncertain nonlinear systems. This controller is a new self-organizing fuzzy cerebellar model articulation controller (CMAC), which has a framework that includes a CMAC and which uses sliding mode control. A new mixed Gaussian membership function (GMF) is created using a prior GMF and a present GMF for each layer of the CMAC, which reuses relevant data in the prior GMF to more accurately detect tracking errors. This is more general than the local-feedback of a recurrent unit because inputs can simultaneously stir the present state and the prior state to regulate suitable errors. Using a self-organizing algorithm allows increasing or decreasing the layers so that the structure of the new self-organizing fuzzy CMAC (NSOFC) is constructed automatically. The proposed control system consists of a NSOFC and a compensation controller. The NSOFC is the main tracking controller, and imitates an ideal controller; and the compensator expels the approximation error. A Lyapunov stability function is used to make the system stable, and an adaptive proportional integral method allows online updating of the parameters for efficient control. An inverted double pendulum system and a magnetic levitation system are used to demonstrate that the proposed method gives good tracking performance. [ABSTRACT FROM AUTHOR]

This article presents aerodynamic modeling of the fixed-wing aircraft using the Mamdani fuzzy inference network (MFIN). A Mamdani fuzzy inference system with a Gaussian membership function has been used as a nonlinear regression functional node to create a multilayer network, called MFIN. The multilayered MFIN incorporates the nonlinear function approximation capability of the multilayered neural network in addition to robustness against uncertainties and measurement noises. The limited-memory Broyden–Fletcher–Goldfarb–Shanno optimization technique has been used to optimize network parameters to learn the nonlinear yawing moment dynamics of the Advanced Technology Testing Aircraft System (ATTAS) aircraft. Since every node in the network learns the nonlinearity of the dynamics, the proposed MFIN becomes capable of learning highly nonlinear dynamics. The adequacy of the proposed network is validated using the recorded flight data from the ATTAS aircraft of the DLR German Aerospace Centre in two cases: 1) trimmed low-angle-of-attack flight condition and 2) quasi-steady stall high-angle-of-attack (highly nonlinear complex) flight condition. The simulated time history tracking performance, mean square error, $R^2$ score, and explained variance score of the proposed network are compared with state-of-the-art methods. Also, the robustness of the proposed approach is demonstrated by evaluating its performance against test data corrupted with additive white Gaussian noise. [ABSTRACT FROM AUTHOR]

Granger causality (GC) is one of the most popular measures to investigate causality influence among brain regions and has been achieved significant results for exploring brain networks based on functional magnetic resonance imaging (fMRI). However, the predictors and order selection of conventional GC are based on linear models which result in such restrictions as poorly detection of nonlinearity and so on, in the application. This paper proposes a novel GC model called back propagation (BP) based kernel function Granger causality (BP_KFGC), in which symplectic geometry is used for embedding dimension and fuzzy inference system for predicting time series. The proposed method doesn’t depend on the prediction of the vector auto-regression model, so that time series don’t need to be wide-sense stationary as linear GC and kernel GC. In addition, it is a multivariate approach which is applicable to both linear and nonlinear systems and eliminates the effects of latent variables. The performance of the new method is evaluated and compared with linear GC, partial GC, neural network GC and kernel GC by simulated data with multiple adjustments to the nonlinearity. The results show that BP_KFGC outperforms the other four methods in detecting both linear and nonlinear causalities. Furthermore, we applied BP_KFGC to construct directed weight network (DWN) of Alzheimer’s disease (AD) patients and health controls (HCs), and then nine graph-based features of DWN were used for classification by the classifier of support vector machine with radial basis kernel function. The accuracy of 95.89%, sensitivity of 93.31%, and specificity of 94.97% were achieved which may provide an auxiliary mean for the clinical diagnosis of AD. [ABSTRACT FROM AUTHOR]

Historical financial data are frequently used in technical analysis to identify patterns that can be exploited to achieve trading profits. Although technical analysis using a variety of technical indicators has proven to be useful for the prediction of price trends, it is difficult to use them to formulate trading rules that could be used in an automatic trading system due to the vague nature of the rules. Moreover, it is challenging to determine a specified combination of technical indicators that can be used to detect good trading points and trading rules since different stock may be affected by different set of factors. In this paper, we propose a novel trading point forecasting framework that incorporates a bicluster mining technique to discover significant trading patterns, a method to establish the fuzzy rule base, and a fuzzy inference system optimized for trading point prediction. The proposed method (called BM-FM) was tested on several historical stock datasets and the average performance was compared with the conventional buy-and-hold strategy and five previously reported intelligent trading systems. Experimental results demonstrated the superior performance of the proposed trading system. [ABSTRACT FROM AUTHOR]

In this paper, we elaborate the design, implementation, and testing of a system—FallSense, which is able to detect accidental falls of human beings in a precise way. The distinguishing feature of FallSense is its endeavors beyond the scope of accelerometer, which is a component of traditional body sensor network. Along with this acceleration measuring unit, FallSense uses the benefits of an Internet-of-Things-enabled environment, which consists of a number of infrared transmitter–receiver pairs and ultrasonic sensors. Employing a fuzzy inference system, FallSense fuses the data from multiple sensors and becomes over sure before inferring that a fall has occurred. Depending on the inputs from multiple sensors, FallSense generates a value between 0 and 1, which signifies the chance of fall. Results show that multi-sensor-based FallSense achieves overall 16% improvement in comparison with the existing approaches, on an average. Beyond the theoretical modeling, this paper also practically implemented the same with the help of the real-sensing units. [ABSTRACT FROM AUTHOR]

In this work the design, implementation and real-time tests of a navigation system for the autonomous surface vehicle MACÁBOT is presented. This vehicle represents a versatile platform to perform several tasks in the marine environment, such as; ports maintenance, marine productive ecosystems studies and bathymetries. The navigation system is responsible for accurately determining the position, velocity and attitude of the vehicle. It represents a fundamental component to autonomously carry out any of the aforementioned tasks. In this work, the navigation system is developed based on a GPS aided strap-down inertial navigation system using an extended Kalman filter sensor fusion algorithm. In order to provide an adaptive approach to the sensor fusion algorithm tuning a fuzzy inference system is used. The navigation system was implemented as a package for the Robot Operating System, benefiting from the advantages of heterogeneity, integration and hardware abstraction. Real time tests of the MACÁBOT on a local creek were carried out, showing satisfactory performance of the navigation system in both position and velocity estimates. In addition to these tests, simulations of GPS outages were carried out with the registered data to evaluate the performance of the navigation system in such cases [ABSTRACT FROM AUTHOR]

The asymmetric-paths winding is a novel ac winding with a fractional ratio of the number of poles to the number of paths, which is beneficial for the optimized design of machines. Resulting from the numerous combinations of slot-vectors for path windings, the optimized arrangement with a low asymmetric degree can hardly be achieved by existing methods. Focusing on the optimization for arrangements of this winding, the mathematical optimization model considering two objectives for the percentage of the electromotive force error and the total harmonic distortion is built. In addition, an improved discrete particle swarm optimization approach is proposed and includes a setting method for the initial positions, a discrete operator with chaotic mutation for the velocity update, and an aggregate strategy combined with fuzzy inference. To validate the proposed approach, a winding design of a hydrogenerator is presented combined with the finite element analysis. The results show that the optimized arrangement presents a low asymmetric degree and harmonic. This approach provides a technical support for the arrangement optimization of an asymmetric-paths winding. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to make a nanofluid/ultrasonic atomization minimum quality lubrication (MQL) system, and to explore the effectiveness of the system used in the grinding process of SKD11 mold steel with heat treatment. Besides, a set of optimized multiple quality characteristics of grinding parameter combinations is obtained by using the Taguchi method and the fuzzy inference system. The experimental parameter combinations design is performed by using $L_{18}(2^{1}\times 3^{7})$ orthogonal table of the Taguchi method, where the experimental parameters are the type of nanoparticles, nanofluid concentration, tangential velocity, table rate, nozzle angle, nozzle distance, air pressure, and spray volume. The single-quality characteristics such as grinding force ratio, grinding temperature, and surface roughness are considered in this paper. The gray relation analysis and the fuzzy inference system are adopted to obtain the multiple performance characteristic index (MPCI), then the combination of maximum MPCI is the optimized multiple quality characteristics of grinding parameter combinations. This paper also compares the difference of nanofluid/ultrasonic atomization MQL and nanofluid/air MQL through the optimized multiple quality characteristics of grinding parameter combinations. Experimental results indicate that the nanofluid parameters containing the type of nanoparticles, nanofluid concentration, and spray volume are the most influential control factors. Comparing nanofluid/ultrasonic atomization MQL with nanofluid/air MQL can also get the best of grinding force ratio, grinding temperature, surface roughness, and surface morphology. Note to Practitioners—The study finds the optimal process parameters for the grinding of hardened mold steel in a nanofluid/ultrasonic atomization minimum quantity lubrication system. At present, grinding hardened mold steel is a costly and time-consuming undertaking, because it lacks an efficient lubricating method. The study considered grinding force ratio, grinding temperature, and surface roughness as single-quality characteristics for gray relational analysis and fuzzy inference to calculate multiple performance characteristic indexes. These indexes identified the optimal parameter combination for grinding hardened mold steel. The results suggested that the proposed lubrication system is highly effective in this regard. [ABSTRACT FROM AUTHOR]

To protect vulnerable road users (VRU) like children and elderly, this paper presents a new warning system on smartphones. This system has three phases. First, we propose a new geometric approach to activate the system for necessary risky situations. Second, we extract some important features for VRUs and drivers based on their smartphones sensors to estimate the collision risk by using a fuzzy inference engine. Finally, we divide the warning alarms into low risk, medium risk, and high risk. To improve system accuracy, we consider the effects of vehicle acceleration, weather condition, time of day, pedestrian age, and driver age in our system and use 4G wireless communications between VRUs and drivers. Experimental results on 608 samples from six important types of accident situations show that our system for in danger VRUs, has 96% accuracy, 63% recall, and 90% precision. The improvements in accuracy, precision, and F-measure are 5%, 70%, and 42% compared with the previous works. Moreover, the activation phase of our system led to 400-ms reduction in run time while the accuracy improves 22%. Besides, on the random samples extracted from the accident simulator software, the accuracy, recall, and precision of the proposed system improve 98%, 75%, and 60%, which are better than the previous similar systems. [ABSTRACT FROM AUTHOR]

Clustering is one of the effective approaches for prolonging the lifetime of a wireless sensor network and increasing its scalability. In current clustering protocols, load balancing is achieved by rotating the costly role of the cluster head among the sensors. To achieve this, the network operation is divided into fixed time durations called rounds. Network nodes are clustered for one round and are reclustered for the next round, i.e., round-based policy. Using this policy, loads of nodes are somewhat balanced. However, the imposed overhead from consecutive reclusterings wastes the energy resource of network nodes. Although many attempts have been made to introduce energy-efficient clustering protocols, the reclustering overhead still remains a serious drawback of these protocols. To mitigate this problem, this paper proposes a fuzzy-based hyper round policy (FHRP) to efficiently and flexibly schedule the clustering-task. In FHRP, instead of every round, clustering is performed at the beginning of every Hyper Round (HR), which is composed of many rounds. During the network lifetime, the length of an HR is not fixed and is computed using a fuzzy inference system. The node’s residual energy and its distance from the sink are used as the inputs of this fuzzy system and the HR length is its output. Thus, the nodes’ situation is taken into account for determining the reclustering time. Simulation results reveal the effectiveness of FHRP in reducing the clustering energy overhead, lengthening the network lifetime, and conserving the network nodes’ energy. [ABSTRACT FROM PUBLISHER]

This paper presents a new Fuzzy Inference System (FIS)-based Risk Priority Number (RPN) model for the prioritization of failures in Failure Mode and Effect Analysis (FMEA). In FMEA, the monotonicity property of the RPN scores is important. To maintain the monotonicity property of an FIS-based RPN model, a complete and monotonically-ordered fuzzy rule base is necessary. However, it is impractical to gather all (potentially a large number of) fuzzy rules from FMEA users. In this paper, we introduce a new two-stage approach to reduce the number of fuzzy rules that needs to be gathered, and to satisfy the monotonicity property. In stage-1, a Genetic Algorithm (GA) is used to search for a small set of fuzzy rules to be gathered from FMEA users. In stage-2, the remaining fuzzy rules are deduced approximately by a monotonicity-preserving similarity reasoning scheme. The monotonicity property is exploited as additional qualitative information for constructing the FIS-based RPN model. To assess the effectiveness of the proposed approach, a real case study with information collected from a semiconductor manufacturing plant is conducted. The outcomes indicate that the proposed approach is effective in developing an FIS-based RPN model with only a small set of fuzzy rules, which is able to satisfy the monotonicity property for prioritization of failures in FMEA. [ABSTRACT FROM AUTHOR]