Fuzzy Logic Algorithm Research Articles

Risk Assessment of Water Pollution Engineering Emergencies Based on Fuzzy Logic Algorithm

Risk Assessment of Water Pollution Engineering Emergencies Based on Fuzzy Logic Algorithm

Improving semi-active vibration control of an 11-story structure with non-linear behavior and floating fuzzy logic algorithm

In recent years, vibration control of structures subjected to the wind, earthquakes and traffic loads has been broadly studied. Based on this, different types of dampers and control systems have been used and evaluated in the form of active, semi-active, passive, and hybrid control. In this study, to control the vibrations of an 11-story structure under the effect of seismic load, the semi-active control approach of structural vibrations is used, with the help of Magneto-Rheological, MR, damper and using fuzzy logic algorithm decision system. In addition to using fuzzy inference system to determine the appropriate control voltage of the MR damper, the floating fuzzy inference system is also used to improve the performance of control system. In other words, to improve the performance of the fuzzy system with fixed membership functions, floating fuzzy system has been used with variable range for membership functions. The proposed fuzzy system is implemented in two different decision states; first, making decision based on velocity and second, making decision based on the velocity and displacement. Implementation of fuzzy inference system is conducted in MATLAB, and linear and nonlinear structural model in OpenSEES, where TCP/IP connection between them is used. The results of incremental dynamic analysis with maximum acceleration of 0.1 g to 1.0 g with incremental step of 0.1 g indicate that using floating fuzzy control system for decision based on velocity reduces the average displacement of the roof about 17.6% in linear structure and 22% in nonlinear structure, while in decision making based on velocity and displacement, these values are about 1.3% and 1.34% in linear and nonlinear structures, respectively.

Design and Build Smart Agriculture Using Cognitive Internet of Things (C IoT)

In Indonesia, agriculture is required to be able to develop growth in the region and be able to produce agricultural products that have high competitiveness while also empowering the community. IoT, or commonly known as the Internet of Things (Internet of Things), allows us to connect to various things. For example, we can use a smart farm. This research leads to the implementation of a plant sprayer that uses the Cognitive internet of things that can be integrated into farmers and factories whose raw material uses corn. This irrigation system uses a NodeMCU microcontroller, automatic discharge using a DC water pump, a soil moisture sensor using a capacitive soil moisture v.2 sensor. The system used uses fuzzy logic algorithms as data processing. The sensor data shows the 70% soil moisture value displayed on the smartphone so that the system will water or not automatically until the soil moisture value is as needed. Soil moisture in this system is in 3 criteria, namely dry, normal, and wet. Watering produced by this system with an average time duration of 8 seconds. This farming system is designed to increase productivity and predict problems in agriculture. This analysis illustrates that the Cognitive internet of things has great potential in agricultural technology because it can increase and facilitate corn production for farmers.

Design and Build Smart Agriculture Using Cognitive Internet of Things (C IoT)

In Indonesia, agriculture is required to be able to develop growth in the region and be able to produce agricultural products that have high competitiveness while also empowering the community. IoT, or commonly known as the Internet of Things (Internet of Things), allows us to connect to various things. For example, we can use a smart farm. This research leads to the implementation of a plant sprayer that uses the Cognitive internet of things that can be integrated into farmers and factories whose raw material uses corn. This irrigation system uses a NodeMCU microcontroller, automatic discharge using a DC water pump, a soil moisture sensor using a capacitive soil moisture v.2 sensor. The system used uses fuzzy logic algorithms as data processing. The sensor data shows the 70% soil moisture value displayed on the smartphone so that the system will water or not automatically until the soil moisture value is as needed. Soil moisture in this system is in 3 criteria, namely dry, normal, and wet. Watering produced by this system with an average time duration of 8 seconds. This farming system is designed to increase productivity and predict problems in agriculture. This analysis illustrates that the Cognitive internet of things has great potential in agricultural technology because it can increase and facilitate corn production for farmers.

Power quality improvement in distribution system based on dynamic voltage restorer using PI tuned fuzzy logic controller

Purpose. This article proposes a new control strategy for Dynamic Voltage Restorer (DVR) in utility grid for distribution system. The proposed DVR using PI tuned fuzzy logic scheme is based on replacement of conventional DVR and voltage sag compensation in distribution system network. The novelty of the proposed work consists in presenting an enhanced PI tuned fuzzy logic algorithm to control efficiently the dynamic voltage restorer when voltage sag is suddenly occurred. Methods. The proposed algorithm which provides sophisticated and cost-effective solution for power quality problems. Our strategy is based on tuned fuzzy control of reactive powers and also closed loop for harmonic reduction in distribution system. The proposed control technique strategy is validated using MATLAB / Simulink software to analysis the working performances. Results. The results obtained clearly show that DVR using PI tuned fuzzy logic have good performances (sag compensation, harmonic reduction) compared to conventional DVR. Originality. Compensation of voltage sag/ swell in distribution for reactive power and current harmonic reduction by using DVR based PI tuned fuzzy logic controller. Practical value. The work concerns the comparative study and the application of DVR based on PI tuned fuzzy techniques to achieve a good performance control system of the distribution system. This article presents a comparative study between the conventional DVR control and PI tuned fuzzy DVR control. The strategy based on the use of a PI tuned fuzzy controller algorithm for the control of the continuous voltage sag and harmonic for the distribution network-linear as well as non-linear loads in efficient manner. The study is validated by the simulation results based on MATLAB / Simulink software.

Study of a Semi-Active Control System to Reduce Lateral Displacement in Framed Structures under Seismic Load

This study presents the numerical implementation of a semi-active control system used to reduce lateral displacements in framed structures under seismic loads. To manage forces in the structures, two controllable fluid devices called magnetorheological (MR) dampers were used, and a fuzzy logic (FL) algorithm was employed to determine the optimal control forces. The FL controller has been programmed based on a set of 49 inference rules using two input parameters: displacement and velocity of the first story of the uncontrolled structure. The voltage to be applied to the MR dampers was the output parameter of the control algorithm, thus altering the damping forces in the system. To evaluate the performance of the proposed controller, three plane frame structures with different geometric configurations were modeled and subjected to four different real ground accelerations records. The results obtained in this study showed considerable reductions in displacement, acceleration, and interstory drift for all the structures studied, demonstrating the effectiveness and efficiency of the controller to improve the damping characteristics in structures.

Application Of Sugeno Fuzzy Logic In The Determination Of Employee Incentives

Suzuya superstore is one of the marketers of human needs products that have various types of products. Suzuya already has several branches in Indonesia which are located in North Sumatra, Aceh, West Sumatra, and Riau. Suzuya was founded in 1983 and currently suzuya already has more than 22 outlets, one of which is in Pematangsiantar. Suzuya superstore Pematangsiantar has employees who are divided into several teams, one of which is the cashier team. The cashier team is the spearhead of the sale of focus items, where the focus item is the flagship product of suzuya superstore Pematangsiantar. The cashier team is also responsible for the sales transaction service process at suzuya superstore Pematangsiantar. The existence of incentives can motivate the work spirit of the cashier team in increasing sales, work speed, and good transaction processing service attitude at suzuya superstore Pematangsiantar. In determining the feasibility of the cashier team receiving incentives at suzuya superstore Pematangsiantar, three variables were used, namely sales of focus items, assessment of work attitudes, and assessment of work speed. Based on the results of processing the cashier team's data using the fuzzy logic algorithm, the Sugeno method, manually and using Matlab software, there was no significant difference. So that Fuzzy Logic with the Sugeno method can be used to determine employee incentives at suzuya superstore Pematangsiantar

Optimization of Heterogeneous Clustering Routing Protocol for Internet of Things in Wireless Sensor Networks

Wireless sensor network technology is widely used in various modern scenarios, and various industries have higher and higher requirements for the performance indicators of wireless sensor networks. A reasonable and effective layout of wireless sensor networks is conducive to the monitoring of environmental quality, various transactions, and status and transmits a large number of sensing data to the data aggregation center for processing and analysis. However, the operation and development of traditional wireless sensor networks are extremely dependent on the energy supply of the network. When the corresponding supply energy is limited, the operation life of the corresponding wireless sensor network will be greatly reduced. Based on the above situation, this paper proposes a nonuniform clustering routing protocol optimization algorithm from the energy loss of cluster head and clustering form algorithm in wireless sensor networks. At the level of cluster head calculation in wireless sensor networks, firstly, based on the adaptive estimation clustering algorithm, the core density is used as the estimation element to calculate the cluster head radius of wireless sensor networks. At the same time, this paper creatively proposes a fuzzy logic algorithm to further solve the uncertainty of cluster head selection, integrate the residual energy of cluster head nodes, and finally complete the reasonable distribution of cluster heads and realize the balance of node energy consumption. In order to further reduce the algorithm overhead of transmission between cluster heads and realize energy optimization, an intercluster routing optimization algorithm based on the ant colony algorithm is proposed. The pheromone is updated and disturbed by introducing chaotic mapping to ensure the optimal solution of the algorithm, and the optimal path is selected from the perspective of energy dispersion coefficient and distance coefficient, so as to optimize the energy consumption between cluster heads. The experimental results show that compared with the traditional algorithm, the proposed nonuniform clustering routing protocol optimization algorithm prolongs the corresponding life cycle by 75% and reduces the total network energy consumption by about 20%. Therefore, the algorithm achieves the purpose of optimizing network energy consumption and prolonging network life to a certain extent and has certain practical value.

EmcFIS: Evolutionary multi-criteria Fuzzy Inference System for virtual network function placement and routing

With the increasing demands for low-delay network services, mobile edge computing (MEC) has emerged as an appealing solution to provide computing resources in close to the end users. Network function virtualization (NFV) is a new network architecture which replaces dedicated hardware middleboxes with software instances to run network functions via software virtualization on general-purpose servers deployed at edge clouds. Because of the resource limitation at network edges, efficient placement and routing for online virtual network function requests (VNF-PRO) is a challenging task. The VNF-PRO has proven to be NP-hard, and thus, metaheuristic algorithms are the best choice in term of the solution quality. However, metaheuristics suffer from high computational complexity, and cannot be performed for online requests in the VNF-PRO. In this paper, a combined model based on fuzzy logic and genetic algorithm is proposed to achieve proper solution quality-speed trade-off in the VNF-PRO. In this method, a multi-criteria fuzzy inference system (named mcFIS) is used for the online VNF placement and routing. To achieve the best performance, a multi-objective evolutionary algorithm based on genetic algorithm (GA) is utilized in an offline procedure for automatic rule tuning of the mcFIS, once before applying it for online applications. Simulation results on two NFV benchmark instances demonstrate the efficiency of the proposed model against the existing techniques.

Strict-Feedback Backstepping Digital Twin and Machine Learning Solution in AE Signals for Bearing Crack Identification

Bearings are nonlinear systems that can be used in several industrial applications. In this study, the combination of a strict-feedback backstepping digital twin and machine learning algorithm was developed for bearing crack type/size diagnosis. Acoustic emission sensors were used to collect normal and abnormal data for various crack sizes and motor speeds. The proposed method has three main steps. In the first step, the strict-feedback backstepping digital twin is designed for acoustic emission signal modeling and estimation. After that, the acoustic emission residual signal is generated. Finally, a support vector machine is recommended for crack type/size classification. The proposed digital twin is presented in two steps, (a) AE signal modeling and (b) AE signal estimation. The AE signal in normal conditions is modeled using an autoregressive technique, the Laguerre algorithm, a support vector regression technique and a Gaussian process regression procedure. To design the proposed digital twin, a strict-feedback backstepping observer, an integral term, a support vector regression and a fuzzy logic algorithm are suggested for AE signal estimation. The Ulsan Industrial Artificial Intelligence (UIAI) Lab’s bearing dataset was used to test the efficiency of the combined strict-feedback backstepping digital twin and machine learning technique for bearing crack type/size diagnosis. The average accuracies of the crack type diagnosis and crack size diagnosis of acoustic emission signals for the bearings used in the proposed algorithm were 97.13% and 96.9%, respectively.

Mikro-Irigasi Cerdas dengan Sprinkler Menggunakan Fuzzy Logic Pada Lahan Terbatas Untuk Pertanian 4.0

<span lang="EN-US">Most irrigation systems in Indonesia use surface irrigation systems or conventional irrigation, which are still heavily influenced by Earth's gravity, making it very difficult to manage and monitor. while current technological developments are almost evenly distributed throughout Indonesia with an already very good internet network, so that it can be used to support agricultural systems 4.0. In this study, researchers used intelligent computing technology on micro irrigation with Fuzzy Logic algorithm and Sugeno inference to decide when irrigation water is distributed to sprinkler irrigation systems based on a predetermined range value, then the results are evaluated to see the accuracy of the model that has been made before testing in the actual environment. The purpose of this research is to produce smart micro irrigation technology that can be used on limited land and lack of water so that it can help facilitate the work of farmers.</span>

Mate Analysis of Hepatocellular Carcinoma Immune Subtypes and Their Functional Effects Based on Fuzzy Logic and Evolutionary Algorithms.

Functional analysis of immune subtypes in hepatocellular carcinoma has attracted much attention due to its advantages in solving some optimization problems. At present, the research on the immune subtype of hepatocellular carcinoma is still in its infancy, and the high stability of its system still has problems. Based on fuzzy logic and evolutionary algorithms, this paper constructs a Mate analysis of the optimization problem of immune subtypes and dynamic optimization problems of hepatocellular carcinoma. The model conducts in-depth analysis and research on the biological immune subtype system, solving the problems of reliable information processing and body defense. Tested with existing test functions, very competitive results were achieved. The simulation results show that the improved algorithm based on data statistics has global search ability, the solution accuracy reaches 0.931, and the stability reaches 88.1%.

Comparative analysis of thrust force, roughness and roundness error in drilling of aluminium composites using RSM, ANN and fuzzy logic

Making holes without any defect in a solid product made of hybrid Aluminium composite material, is a difficult job in assembly industries. Hybrid composite of Aluminium 7075 reinforced with ceramic materials like silicon carbide, boron carbide, graphite and mica are used in automobile and structural industries due to their excellent mechanical properties. Thrust force developed during drilling, Error in the circularity and poor surface finish of the drilled holes are some of the common problems faced in the drilling process. Hence, to optimise the quality of drilling, analysing these responses under various conditions of drilling by varying the drilling factor become essential. This study discusses the development of various models to predict the thrust force developed, roughness and circularity error in the holes drilled on a hybrid metal matrix composite. Testing of drilling is conducted in CNC vertical machining centre using Titanium aluminium nitride (TiAlN) coated carbide drill tool of 5 mm diameter. Various drilling factors considered in our study are point angle of the drill tool, drilling speed and feed rate. Multiple regression equations using RSM, Artificial neural network (ANN) and Fuzzy Logic algorithms are used to develop the prediction models. The predicted values of thrust force, surface roughness and circularity error from these models are found to be matching with the observed experimental values.

Online DE Optimization for Fuzzy-PID Controller of Semi-Active Suspension System Featuring MR Damper

In pursue of a comfortable vehicle driving in different road profiles, intelligent methods used to improve the suspension system of the vehicle. Semi-active suspension system outperformed other suspension systems because it contains an intelligent actuator that can give the appropriate force to dissipate unwanted vibration using intelligent and real-time controllers. Here in this paper MR fluid damper with Fuzzy-PID controller examined to optimized using modified DE algorithm. However, in the Fuzzy-PID controller, the fuzzy logic algorithm is used to autotune the PID controller, but it cannot be considered as a fully real-time controller since the fuzzy algorithm uses a previous knowledge base built offline. The offline design of Fuzzy algorithm cannot cope with real-time unexpected vibrations occurred while driving car in different road profiles. In this paper, a Fuzzy-DE-PID controller proposed based on modified DE algorithm to enhance the Fuzzy logic gains in order to increase vehicle semi-active suspension system performance. To prove the effectiveness of the proposed controllers, a simulation and experimental tests were conducted using the proposed controller with different disturbances. The results of the simulation and the experimental tests for the proposed controller showed improvement of the vehicle’s ride comfort over the Fuzzy-PID and the passive system. We believe that using this proposed controller in any other real-time application will improve the performance to the highest levels without the need for a previous knowledge base for designing a real-time Fuzzy-PID controller.

DESIGN OF UAV CONTROL SYSTEM WITH FUZZY LOGIC CONTROLLER AND ITS IMPORTANCE IN THE ECONOMY

This article presents a model of the control system of a multi-rotor Unmanned aerial vehicle (uav) using Fuzzy logic controller. During the execution of the work, the Simulink environment of the MATLAB software system was used as a tool. As it is known, multi-rotor drones have a clear place in the modern world and are used in almost all areas of the economy, that is why a designed control system is a necessity. During the work, membership functions were calculated and defined, the adjustment of which was made by means of an automatic adjustment tool created in the form of a graphical interface, which selects independent values in the defined range, giving the Fuzzy inference system as an input. Mamdani fuzzy method (if and then) was selected as Fuzzy logic algorithm. At the end of the article, the results obtained from the designed graphic interface and control system are presented.

Reduced False Alarm for Forest Fires Detection and Monitoring using Fuzzy Logic Algorithm

The purpose of this research was to detect forest fires in efforts to minimize the incidence of false alarms. The research method used is divided into several stages, namely planning, analysis, design, implementation, testing, and maintenance. Arduino acts as a data collector on the field, which will later be used to detect forest fires and false alarms. Fuzzy logic is used as the essence of the algorithm and will provide a higher level of accuracy for forest fire detection and false alarms. In testing the fuzzy program, the fuzzy output between Arduino and the fuzzy on the monitoring dashboard has a small difference of 0.99%. It can be concluded; the application can minimize the occurrence of false alarms to reduce the user's workload.

Fuzzy Support Vector Machine based Fall Detection Method for Traumatic Brain Injuries

Falling is a major health issue that can lead to both physical and mental injuries. Detecting falls accurately can reduce the severe effects and improve the quality of life for disabled people. Therefore, it is critical to develop a smart fall detection system. Many approaches have been proposed in wearable-based systems. In these approaches, machine learning techniques have been conducted to provide automatic classification and to improve accuracy. One of the most commonly used algorithms is Support Vector Machine (SVM). However, classical SVM can neither use prior knowledge to process accurate classifications nor solve problems characterized by ambiguity. More specifically, some values of falls are inaccurate and similar to the features of normal activities, which can also greatly impact the performance of the learning ability of SVMs. Hence, it became necessary to look for an effective fall detection method based on a combination of Fuzzy Logic (FL) and SVM algorithms so as to reduce false positive alarms and improve accuracy. In this paper, various training data are assigned to the corresponding membership degrees. Some data points with a high chance of falling are assigned a high degree of membership, yielding a high contribution for SVM decision-making. This does not only achieve accurate fall detection, but also reduces the hesitation in labeling the outcomes and improves the heuristic transparency of the SVM. The experimental results achieved 100% specificity and precision, with an overall accuracy of 99.96%. Consequently, the experiment proved to be effective and yielded better results than the conventional approaches.

Coyote Optimization Using Fuzzy System for Energy Efficiency in WSN

In recent days, internet of things is widely implemented in Wireless Sensor Network (WSN). It comprises of sensor hubs associated together through the WSNs. The WSN is generally affected by the power in battery due to the linked sensor nodes. In order to extend the lifespan of WSN, clustering techniques are used for the improvement of energy consumption. Clustering methods divide the nodes in WSN and form a cluster. Moreover, it consists of unique Cluster Head (CH) in each cluster. In the existing system, Soft-K means clustering techniques are used in energy consumption in WSN. The soft-k means algorithm does not work with the large –scale wireless sensor networks, therefore it causes reliability and energy consumption problems. To overcome this, the proposed Load-Balanced Clustering conjunction with Coyote Optimization with Fuzzy Logic (LBC-COFL) algorithm is used. The main objective is to perform the lifespan by balancing the gateways with the load of less energy. The proposed algorithm is evaluated using the metrics such as energy consumption, throughput, central tendency, network lifespan, and total energy utilization.

Cuckoo search assisted fuzzy logic algorithm for smart WSN routing system

Cuckoo search assisted fuzzy logic algorithm for smart WSN routing system

Development of the fuzzy logic algorithm of data survey and analysis for disaster relief systems

The article is devoted to the development of an algorithm for collecting and analyzing data with fuzzy logic about the need for a resource in logistics zones under emergency circumstances to create a prototype management decision support system.