Fuzzy Logic Algorithm Research Articles

Data-driven approaches for predicting wax deposition

Deposition of Wax is a challenging issue in the upstream oil and gas production process. Thus, precise estimating of this deposition/precipitation issue can increase the performance of petroleum products. To solve this issue, enormous research has been done previously, but unfortunately, the outcomes of the addressed studies are not a proper solution for the mentioned problem. In this research, a method of coupling the fuzzy logic and genetic algorithm (GA) has been developed to propose a robust and efficient method for predicting the amount of deposition of Wax. The input variables in the developed model are oil composition, temperature, pressure, and oil-specific gravity, which evolved in conventional approaches to estimate wax deposition amount. Different statistical indexes like mean square error and relative deviation were implemented to determine the results' accuracy and integrity. Based on the addressed statistical criteria, the gained results of the evolved model have high performance and integrity in contrast with other methods or correlations in the determination of the wax deposition values. It is worth noting that the suggested method's advantages are wide ranges of applicability and simple utilization. Outcomes gained from this communication confirmed the applicability of fuzzy logic in wax deposition measurement, which can better understand wax deposition in pipelines, wellbores, and reservoirs.

Underwater Image Classification Algorithm Based on Convolutional Neural Network and Optimized Extreme Learning Machine

In order to deal with the target recognition in the complex underwater environment, we carried out experimental research. This includes filtering noise in the feature extraction stage of underwater images rich in noise, or with complex backgrounds, and improving the accuracy of target classification in the recognition process. This paper discusses our contribution to improving the accuracy of underwater target classification. This paper proposes an underwater target classification algorithm based on the improved flow direction algorithm (FDA) and search agent strategy, which can simultaneously optimize the weight parameters, bias parameters, and super parameters of the extreme learning machine (ELM). As a new underwater target classifier, it replaces the full connection layer in the traditional classification network to build a classification network. In the first stage of the network, the DenseNet201 network pre-trained by ImageNet is used to extract features and reduce dimensions of underwater images. In the second stage, the optimized ELM classifier is trained and predicted. In order to weaken the uncertainty caused by the random input weight and offset of the introduced ELM, the fuzzy logic, chaos initialization, and multi population strategy-based flow direction algorithm (FCMFDA) is used to adjust the input weight and offset of the ELM and optimize the super parameters with the search agent strategy at the same time. We tested and verified the FCMFDA-ELM classifier on Fish4Knowledge and underwater robot professional competition 2018 (URPC 2018) datasets, and achieved 99.4% and 97.5% accuracy, respectively. The experimental analysis shows that the FCMFDA-ELM underwater image classifier proposed in this paper has a greater improvement in classification accuracy, stronger stability, and faster convergence. Finally, it can be embedded in the recognition process of underwater targets to improve the recognition performance and efficiency.

Review of Intelligent Control Systems with Robotics

Interactive between human and robot assumes a significant job in improving the productivity of the instrument in mechanical technology. Numerous intricate undertakings are cultivated continuously via self-sufficient versatile robots. Current automated control frameworks have upset the creation business, making them very adaptable and simple to utilize. This paper examines current and up and coming sorts of control frameworks and their execution in mechanical technology, and the job of AI in apply autonomy. It additionally expects to reveal insight into the different issues around the control frameworks and the various approaches to fix them. It additionally proposes the basics of apply autonomy control frameworks and various kinds of mechanical technology control frameworks. Each kind of control framework has its upsides and downsides which are talked about in this paper. Another kind of robot control framework that upgrades and difficulties the pursuit stage is man-made brainpower. A portion of the speculations utilized in man-made reasoning, for example, Artificial Intelligence (AI) such as fuzzy logic, neural network and genetic algorithm, are itemized in this paper. At long last, a portion of the joint efforts between mechanical autonomy, people, and innovation were referenced. Human coordinated effort, for example, Kinect signal acknowledgment utilized in games and versatile upper-arm-based robots utilized in the clinical field for individuals with inabilities. Later on, it is normal that the significance of different sensors will build, accordingly expanding the knowledge and activity of the robot in a modern domain

Dimensional synthesis of four-bar mechanisms using Rao algorithms and their variants

This paper presents the dimensional synthesis of four-bar mechanisms for path generation. The synthesis of the four-bar mechanisms is proposed using nine advanced optimization algorithms, namely the Rao-1 algorithm, Rao-2 algorithm, Rao-3 algorithm, self-adaptive multi-population (SAMP) Rao-1 algorithm, SAMP Rao-2 algorithm, SAMP Rao-3 algorithm, quasi-oppositional-based (QO) Rao-1 algorithm, QO Rao-2 algorithm, and QO Rao-3 algorithm. Six different cases of synthesis of a four-bar mechanism are considered. The brief literature review based on the synthesis of mechanisms is also presented. The designs of mechanisms obtained using Rao algorithms and their variants are compared with the designs acquired using other advanced optimization algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO) algorithm, differential evolution (DE), imperialist competitive algorithm (ICA), hybrid GA-DE algorithm, simulated annealing (SA), Malaga university mechanism synthesis algorithm (MUMSA), IMMa optimization algorithm (IOA), hybrid Taguchi random coordinate search algorithm (HTRCA), hybrid GA and fuzzy logic (GA–FL) algorithm, ant-gradient method (AGM), teaching–learning-based optimization algorithm (TLBO), and hybrid Lagrange interpolation differential evolution algorithm (HLIDE). The comparison of results reveals the efficacy of Rao algorithms and their variants for synthesizing the four-bar mechanism.

Modern Farming Biofloc ponds for tilapia aquaculture based on the internet of things use a fuzzy logic algorithm

This study aims to create a smart system in controlling tilapia biofloc ponds. The type of research is laboratory experiments and the category of system testing, namely Functionality testing, is used to measure the performance of the system hardware. Testing the functionality of the prototype is tested in 2 stages, the first test begins with knowing the accuracy of sensor data and testing manually. The second test is to test the prototype as a whole, namely by giving treatment so that the performance of the prototype can be known as desired. The results obtained when the temperature is cold, the water heater heater will turn on and treatment to the pH sensor is obtained, namely an acidic pH condition, then the water pump will turn on and drain the water until the ultrasonic sensor detects the maximum water level that can be drained. Testing on feeding is by monitoring the servo successfully rotating, opening and closing the feed storage container based on the time that has been set. The results of monitoring feeding in the morning, afternoon and evening are successful and can send notifications. Based on the test results, the resulting " Modern Farming Biofloc ponds for tilapia aquaculture based on the internet of things use a fuzzy logic algorithm" can run as expected.

The Decision Tree Aided Neuro-Fuzzy Inference Characterization of the Stochastic Hydrology of the Tana Alluvial Aquifer

The Tana Alluvial Aquifer is the name given to the little-understood aquifer which is active in the areas bordering the River Tana Flow course as the river weaves its way through the sedimentary plains of Balambala, Garissa, Fafi and Ijara and, finally, into the Tana Delta areas, with the common denominator being the proximity to the Lower Tana catchment, especially the riparian corridor of the River itself, and beyond. The aquifer may extend to between five to fifteen kilometers away from the river channels course way, and at times, it may be felt even 20 kilometers away. The geology of the locality is heterogeneous and comprise sediments whose soil mechanics may not be easily deciphered, since some areas close to the river have very fresh water while others are saline (Bura East in Fafi Sub County easily comes to mind here). There are areas far from the river but bearing fresh water (Mulanjo comes to mind). In some areas, sites close to the river discharge low yield figures, whereas those located farther afield discharge favorably. The water quality and discharge are therefore stochastic variables, subject to chance occurrence. In view of this inconsistency, and on the account of data scarcity, the neuro-fuzzy inference algorithm was developed to map the Universe of Discourse of the Tana Alluvial Aquifer, aka the T.A.A., as it relates to the longitudes, latitudes, depths, and discharges of the aquifers in the study area. The mapping was with respect to aquifer discharge, the variable used to characterize an aquifer, in terms of Transmissivity and Hydraulic Conductivity, thereby defining aquifer recharge propensity. Membership functions were developed using the trapezoidal membership family, and fuzzy rules were appropriately evolved from the fuzzified aquifer data, before finally employing the Sugeno inference engines (in Python) to make predictions of discharge, at each of the T.A.A. aquifer subsets mapped for fresh, saline, hard and brackish water species. The accuracy in the outputs achieved in the areas mapped vindicated the power of the neuro-fuzzy inference systems, as the accuracy oscillated between 92 and 99 percent, when the discharge values predicted were compared with the actual known discharge values of the wells mapped. The water quality class characterization was then undertaken using the decision tree (DT) algorithm in python which gave rise to a 100 percent prediction accuracy. The same DT algorithm could not successfully predict the discrete values of aquifer discharge or EC values, with as much accuracy (but performed excellently with salinity class data), and that was why fuzzy logic was employed. The study vindicated the use of the DT and Fuzzy Logic Algorithms as simple, yet powerful analytical tools, in characterizing the Stochastic Hydrology of the Tana Alluvial Aquifer.

State of Health Estimation and Remaining Useful Life Prediction Using Hybrid Kmeans CNN-Lstm Network

Lithium-ion batteries have become among the most used storage systems in different fields such as electric vehicles, lighting, robotics, etc. These storage systems are known for their fast charging and high energy density (they can store 3 or 4 times more energy per unit mass than other battery technologies). Lithium-ion batteries are being used in more and more areas; however, the major drawback of this system is its high price, and the degradation of its performance makes good maintenance necessary to optimize the battery operation. This good maintenance consists on the real-time follow-up of its state of health (SOH) and the prediction of its remaining useful life (RUL). For this purpose, there are two main categories of methods: model-based methods (such as the sliding mode, and the Kalman filter, etc.) and data-based methods (such as fuzzy logic, genetic programming and artificial intelligence algorithms, etc.). Many authors have based their studies on artificial intelligence models and more specifically artificial neural networks which are known for their high accuracy and their ability to solve complex problems that model based methods find difficulties to deal with. Among the most used neural networks, we find recurrent neural networks (RNN) and more specifically Long Short Term Memory (LSTM). LSTM networks have an internal memory allowing them to process time series flexibly and with high accuracy. These networks have shown good performance in SOH estimation and RUL prediction in several studies. Convolutional neural networks are networks originally dedicated to image processing, but recently several studies have proposed to use these networks for processing time series and have demonstrated good performance in this field. Some studies propose to combine different models for the estimation of the SOH and the prediction of the RUL, among these models, we find the CNN LSTM combination which improves the accuracy of the model and decreases its calculation time. This is the case for our study, where the CNN network was used for data filtering and the LSTM network for processing the filtered data. In addition to that, we added a K-means clustering network which is used to classify the data and makes their processing by the CNN LSTM hybrid model, easier and faster. The major drawback of neural network models is that they need a lot of data for their training. In our study, we used the NASA open source dataset which was extracted from an experiment consisting on charging and discharging LCO 18650 lithium ion batteries with randomly chosen currents between -4.5A and 4.5A to simulate the operation of a lithium battery in electric vehicle driving conditions. The dataset contains the data of 4 batteries, we used the data of 3 of them for training the model and we validated our model on the data of the 4th one.The following figure shows the estimation results of the state of health of the 4th battery obtained by our model.To evaluate the performance of our model, we used three metrics: Root Mean Square Error (RMSE), Mean Square Error (MSE) and Mean Absolute Error (MAE). The performance evaluation demonstrated the improvement in the accuracy of the model and the computation time compared to each of the networks used separately. The following table summarizes the obtained results: Metric/ Model MSE RMSE MAE Hybrid model 0.0002 0.01 0.008 For the prediction of the RUL, we developed a model that predicts the evolution of the capacitance value. For its training, we used the same dataset. The model trains on a percentage of 90% of the data from each battery and its role is to predict the remaining capacitance values until the end of life of the battery is reached. The following figure shows the model results for the prediction of the 4th battery data Figure 1

Development of Web-Based Flood Detection System and Flood Control Using Nodemcu

Internet of Things is a concept and method for remote control, monitoring, data transmission, and various other tasks. For some areas in Indonesia, flooding may be unavoidable, but this can be done to minimize the number of disaster losses by providing early warnings if there are indications that a flood will occur. This study will create an internet of things system to monitor the river’s water level and the sluice gate control system. The manufacture of this system will be made in the form of a prototype, to detect the water level will use ultrasonic sensors, and to drive the floodgates in this system will use a stepper motor. In this system, to determine the speed of movement of the openings of the sluice gate is determined based on the water level obtained as input data and calculated using fuzzy logic algorithms. The fuzzy logic algorithm here is one of the methods used in the field of control system studies. Testing on the fuzzy algorithm the author uses the fuzzy logic toolbox in the MATLAB application as a comparison to the fuzzy algorithm made on the prototype, from the results of testing the flood monitoring system runs perfectly by providing the appropriate output results and the average error of controlling the floodgates is 0.58%.

Design and Simulation of Grid-Connected Photovoltaic System’s Performance Analysis with Optimal Control of Maximum Power Point Tracking Based on Artificial Intelligence

The research presented in this paper is part of a more significant effort to improve the dynamic and static performance of power generation systems using solar panels under specific climatic conditions. The solar panel can produce the greatest power only at the specified voltage and electric current levels. Environmental variables, such as random atmospheric oscillations, have a significant effect on the performance of a solar system connected to the network. Irradiation and ambient temperature are the two inputs to a PV (photovoltaic) system. Because solar radiation varies in nature, the PV system efficiency is low. To improve the efficiency of a solar PV system, several maximum power point tracking (MPPT) approaches are used. The purpose of this paper is to improve the performance of DC/DC chopper controllers and PV inverters in the face of abrupt climate change. To that end, the primary goal of this paper is to compare the following maximum power point search (MPPT) algorithms: the incremental conductance (IC) algorithm, the fuzzy logic (FL) algorithm, VSI controller, and the particle swarm optimization (PSO) strategy. These algorithms were evaluated in terms of efficiency, stability, and speed. A 100 kW PV system is design using MATLAB software 2021a version.

NanoDoc: Designing an Adaptive Serious Game for Programming With Working Examples Support

There is an increased interest in serious games about programming, particularly for younger ages. The ability to evoke motivation and retain engagement leads to better learning efficiency and a positive educational impact. Serious games usually include support systems to assist novice users. In recent years there have been attempts to enhance support with intelligent tutoring systems. These systems adapt and provide personalized learning by analyzing user behaviour. Although this is a positive evolution, support still remains in the form of hints, images and videos. The paper describes an alternative adaptive method for support with working examples. An expert solution to a similar problem is displayed to the students allowing them to address the problem and start the solving process by analogy. Our previous research showed that working examples can reduce cognitive load during problem-solving and increase the student's educational results in comparison to traditional techniques. This paper presents the design of NanoDoc, a serious game for teaching the programming concepts of sequence and iteration to elementary school students. It features adaptive support with editable and executable working examples. Each example can be examined, edited and executed freely by the user until the required knowledge level is sufficient to solve the similar problem at hand. The adaptation method is based on a Fuzzy Logic algorithm and the dynamic modelling of user knowledge level. The paper also provides some preliminary results on student usage in the classroom and a questionnaire about the game experience. Plans for future research include investigating if adaptive support with working examples can reduce cognitive load even further and how learning is affected.

A Fuzzy Logic Path Planning Algorithm Based on Geometric Landmarks and Kinetic Constraints

This paper mainly focuses on the path planning of mobile robots in complex two-dimensional terrain. It proposes a fuzzy rule-based path planning algorithm for multiple guide points by changing the spatial point-taking method and combining Dijkstra's algorithm and fuzzy logic algorithm. The planning process of this algorithm divide into three stages. The first stage identifies the edge points of the forbidden area by designing the search space, marks the feasible area widths of the edge points in X and Y directions, and marks their midpoints. The second stage uses Dijkstra's algorithm that does the road map sorting on these marked points and the starting and ending points and takes the lowest cost sequence as the search road map. In the third stage, using a fuzzy logic system to search these road signs one by one until the endpoint area is searched. The simulation results show that this algorithm can solve the complex environment that traditional fuzzy inference algorithms cannot plan. Compared with the graph search algorithm, this algorithm dramatically reduces the planning time and provides more flexible turning angles. This algorithm can better consider the robot's size and the relationship between speed and turning angles while estimating the motion state at each step compared with the sampling algorithm. This algorithm will extend to group path planning and dynamic environment planning in subsequent studies.

Caracterización del acicalamiento de Leistes bellicosus machos en el aeropuerto de Jaén, Perú

Preening in birds is a biologic aspect very important, its allow to ensure their welfare through the maintenance from external parts of theirs body. In relation to that was numerically characterized the preening of male Leistes bellicosus (specie most hit by airplanes Jaen aerodrome), between February and July 2022 at Jaén airport. A continuous recording for 2 hours and focal behavior for 5 minutes was performed, also using the fuzzy logic algorithm and exploratory analysis to measure this behavior. The preening is characterized by having a frequency between 1-4times/5minutes, a duration concentrated mainly between 1-50 seconds and an intensity of 4.7 points (Membership function=0.8). These variables have no correlation with temperature, humidity, wind speed and cloudiness. The wings and the back are the structures that are most cleaned (29 and 21 times/80 observations, respectively).

Implementation of Mean of Maximum on Cigarette Smoke Control in a Room

Home automation is an IoT technology that has the ability to communicate between devices. This technology requires a method for making the decision process. Fuzzy logic method is a solution to this problem. Fuzzy Logic is a method that functions to replicate and run human knowledge to control a system. In fuzzy logic algorithm, there are three main processes, which are fuzzification, inference, and defuzzification. Defuzzification process requires a method to obtain the value of crisp solutions. One of the many methods is Mean of Maximum (MoM). MoM is an algorithm that calculates the average of fuzzy conclusions or outputs that have the highest degree. In this research, we apply the MoM method in the defuzzification process to build a smoke emission control system in a room using an exhaust fan. Wemos D1 mini is used as a controller and processor in the system. And using telegram chatbot for communication media between users and the system. There are 27 rules in this system with 100% functionality. This research topic is very useful to keep the air clean and fresh in the room and avoid the dangers of cigarette smoke that can damage health.

Novel Approach to Monitoring and Mitigating Power Quality Disturbances in Hybrid Renewable Energy System

Renewable energy sources are continually increasing their share in the energy system. The introduction of Hybrid Renewable Energy Sources (H.R.E.S.) in the electrical power system has gained momentum. The increased share of Renewable sources has resulted in an increase in Power Quality (P.Q.) disturbances at the user and consumer levels. This research will engineers dedicate the P.Q. disturbances produced in the electrical system due to Hybrid Renewable energy sources, especially solar and wind. The research will predict what type of P.Q. disturbances are introduced during power production. The study will focus on a stand-alone Hybrid Renewable Energy System (H.R.E.S.). The energy system data will be collected and used to produce a Fuzzy Logic (F.L.) algorithm to improve the mitigating techniques to reduce P.Q. disturbances. The suggested algorithm analyzes the stored and continuous data from different sources. The data will be collected and used to drive the hardware to take appropriate actions at the mitigation level. The hardware used in the system consists of a Multiplexer (MUX) and different types of filters. The output of the multiplexer chooses the filters. The algorithm will improve the system's efficiency and help the designers improve the system's design capabilities. The monitoring system will help predict what type of P.Q. disturbances are produced when different energy sources are used to produce power. The proposed system monitors these P.Q. disturbances and classifies them according to their severity.

Adaptive and intelligent control of permanent magnet synchronous motor (PMSM) using a combination of fuzzy logic and gray wolf algorithm under fault condition

Adaptive and intelligent control of permanent magnet synchronous motor (PMSM) using a combination of fuzzy logic and gray wolf algorithm under fault condition

Soft Computing-Based Schemes for Handover Management in Future Networks

The progression to next generation networks is replete with abundant co-existing technologies. To comply with the always best connected paradigm, several vertical handover decision approaches have been proposed in literature, using advanced techniques and tools. This paper discusses the application of soft computing techniques in the vertical handover decision-making process with emphasis on the state-of-the-art techniques. For a comprehensive evaluation, the algorithms are classified into three sets based on the soft computing technique used, namely fuzzy logic, machine learning, and evolutionary algorithms, and representative handover algorithms in each group are discussed. These papers are categorized in a well-defined structure to bring out their contribution, to underline the pretermitted notions, and to bring forth the emerging issues for future research. This paper summarizes the soft computing concepts and reviews its applications in candidate network selection, QoE enhancement, and reducing the unnecessary handovers.

Hierarchical speed planning and energy management for autonomous plug-in hybrid electric vehicle in vehicle-following environment

In this paper, a hierarchical energy management control strategy is investigated for autonomous plug-in hybrid electric vehicle in vehicle-following environment. With the target of safety and comfort, the designed algorithm is divided into two layers. The grey neural network is leveraged in the upper layer controller to predict the future speed trend of preceding vehicle, and the target speed of ego vehicle is planned by fuzzy adaptive control algorithm. By combining with the planned state of charge reference trajectory, genetic algorithm is exploited in the adaptive equivalent consumption minimization strategy-based lower layer controller to determine the initial equivalent factor map by offline iterative calculation, and the fuzzy logic algorithm is employed to update the equivalent factor in real time according to the state of charge difference. Finally, the simulation and hardware-in-the-loop experiment are conducted to validate the performance of the proposed strategy. The simulation results highlight the capability of the proposed strategy in solving multi-objective optimization for autonomous plug-in hybrid electric vehicle in vehicle-following environment, and the experiment results validate that the energy consumption economy of the proposed strategy reaches 95.43% optimality of the results derived by dynamic programming while ensuring the satisfied driving comfort and safety.

Study of Integration of Neural Networks with Other Existing or Developing Technologies: A Review

The popularity of Artificial Neural Networks (ANN) as significant intelligence methods has been growing. Their applications in numerous fields have been greatly influenced by their capacity to emulate the cognitive processes and decision-making of the human brain. The fundamentals of an ANN's nature and operation are described in this study. This paper's main goal is to provide a quick overview of how neural networks might be combined with other forms of artificial intelligence, such as fuzzy logic, expert systems, and genetic algorithms. It describes the working models of these hybrid systems as well as the areas in which they can be used. Finally, it discusses current developments in the study of neural networks.

Optimal coalition formation and maximum profit allocation for distributed energy resources in smart grids based on cooperative game theory

Over the past decades, significant revolutions have occurred on electricity market to reduce the electricity cost and increase profits. In particular, the novel structures facilitate the electricity manufacturers to participate in the market and earn more profit by cooperate with other producers. This paper presents a three-level gameplay-based intelligent structure to evaluate individual and collaborative strategies of electricity manufacturers, considering network and physical constraints. At the Level I, the particle swarm optimization (PSO) algorithm is implemented to determine the optimum power of distributed energy resources (DERs) in the power grid, to maximize the profits. Further, the fuzzy logic algorithm is applied to model the intermittent nature of the renewable sources and implement load demand in the power grid. At the Level II, DERs are classified into two different fuzzy logic groups to secure the fairness between every participant. Finally, at the Level III, the DERs in each group are combined each other by cooperative game theory-based algorithms to increase the coalition profits. Thereafter, Shapley, Nucleolus, and merge/split methods are applied to allocate a fair profit allocation by coalition formation. Ultimately, the results verify the proposed model influence electric players to find effective collaborative strategies under different conditions and environments.

An energy‐efficient auto clustering framework for enlarging quality of service in Internet of Things‐enabled wireless sensor networks using fuzzy logic system

SummaryThe advancement of the Internet of Things (IoT) technologies will play a vital role in the evolution of the smart city, smart healthcare, and smart grid applications. Wireless sensor network (WSN) is one of the futuristic technologies utilized for sensing and data exchange processes in IoT‐enabled applications. However, the hotspot problem, control packet overhead, and inefficient clustering are the most significant challenges to attaining an energy‐efficient IoT‐enabled WSN (IWSN) model. Most of the existing clustering schemes lagged to mitigate these aforesaid constraints since it consumes extra energy for data computation and forwarding tasks under any environmental conditions. In this article, a novel energy‐efficient auto clustering (EEAC) framework has been proposed to develop an effective IWSN model with enhanced quality of service. The EEAC framework comprises three phases such as zone formation, node classification, and auto clustering phases. The objective of the first phase is to significantly form the different zones by alleviating the hotspot problem. Subsequently, the fuzzy logic algorithm is employed in the second phase to classify the nodes as master, sub‐master, and member nodes. Finally, the third phase of the proposed framework will accomplish the auto clustering mechanism based on hop information received from the reported packet. The performance results evident that the proposed EEAC framework obtains a lesser energy consumption of 0.01 J during a dense network and the network lifetime is prolonged up to 52% when compared with existing state‐of‐the‐art clustering models.