Fuzzy Logic Algorithm Research Articles

Smart Air Filter Based on Activated Charcoal and Aloe Vera Using Fuzzy Logic Algorithm

Smoking is one of the main risk factors of several chronic diseases such as lung cancer, upper respiratory tract cancer, heart disease, stroke, bronchitis, emphysema and others, even smoking can cause death. Cigarette smoke inhaled by passive smoking is the main cause of lung cancer in people who are not smokers. The risk of lung cancer increases by 20-30% in non-smokers but always surrounded by cigarette smoke, compared to non-smokers who are not exposed to smoke. This study aims to develop a smart device to filter cigarette smoke so that the air released from this device is free from the dangers of cigarette smoke. The materials used in this device as a cigarette smoke filter are activated charcoal and aloe vera, the pores of the charcoal open and can become adsorbents. This device uses fuzzy logic as a control method that can provide decisions that resemble human decisions. Arduino is used to adjust the fan rotation in absorb cigarette smoke and the LCD to display the level of cigarette smoke.

Multi-criterial analysis for modelling Matengo/Ngolo pits agro-ecological zones using fuzzy logic in Southern Tanzania

It has known that grain production is declining globally, leading to food insecurity becoming increasingly apparent in tropical countries, particularly in Sub-Saharan Africa. Countries in Sub-Saharan Africa must concentrate on indigenous agricultural methods to mitigate the impact of climate change on grain production while preserving ecological balances and achieving sustainable goals. Matengo/Ngolo pits, practised on steep slopes in the Matengo highlands, southern Tanzania, are indigenous knowledge invented by local communities over the past 300 years. Despite its effectiveness in increasing agricultural productivity, soil moisture retention, and other environmental advantages, Matengo/Ngolo agricultural technique has resulted in severe land cover changes that substantially influence other producing sectors. Understanding the agro-ecological zones is essential for enhancing policy development for the expansion and restrictive of Matengo/Ngolo pits practice that intercepting by decreasing its influence on the shrinkage of other ecological services, achieving sustainable agricultural practice in the Matengo highlands. Therefore, this study employed the multi-criteria parameters under the fuzzy logic algorithm in ArcGIS 10.8 for modelling the Matengo/Ngolo pits agro-ecological zone to realize sustainable land management in Matengo highlands.

A Review Paper on Fuzzy Logic Algorithms

Logic for gene expression analysis in a flurry. We developed a new method for analyzing gene expression data. To convert expression values into Quality descriptors, this method uses a fluid logic that can be evaluated using heuristic rules. We developed a model for identifying three different activators, repressors, and objectives in a data set for yeast gene expression in our experiments. The test predictions generated by an algorithm match the experimental data in the literature very well. Algorithms can identify a much larger number of transcription factors that could be identified at random in defining the function of unspecified proteins. Using only expression data in the form of clustering, this method allows the user to construct a linked network of genes. The interpretation of gene expression categorization models is typically difficult, however, it is an essential component of the analysis procedure. In five databases ranging in size, experimental origin, and physiological field, we investigate the effectiveness of micro rules fuzzy systems. The classifiers resulted in regulations that are simple to understand for biomedical researchers. The classifiers resulted in regulations that are simple to understand for biomedical researchers.

Model Predictive Control for Human-Centred Lower Limb Robotic Assistance

Loss of mobility and/or balance resulting from neural trauma is a critical public health issue. Robotic exoskeletons hold great potential for rehabilitation and assisted movement. However, the synergy of robot operation with human effort remains a problem. In particular, optimal assist-as-needed (AAN) control remains unresolved given pathological variance among patients. We introduce a model predictive control (MPC) architecture for lower limb exoskeletons that achieves on-the-fly transitions between modes of assistance. The architecture implements a fuzzy logic algorithm (FLA) to map key modes of assistance based on human involvement. Three modes are utilised: passive, for human relaxed and robot dominant; active-assist, for human cooperation with the task; and safety, in the case of human resistance to the robot. Electromyography (EMG) signals are further employed to predict the human torque. EMG output is used by the MPC for trajectory following and by the FLA for decision making. Experimental validation using a 1-DOF knee exoskeleton demonstrates the controller tracking a sinusoidal trajectory with relaxed, assistive, and resistive operational modes. Results demonstrate rapid and appropriate transfers among the assistance modes, and satisfactory AAN performance in each case, offering a new level of human-robot synergy for mobility assist and rehabilitation.

Early Classification of Brain Tumor Based on Image Histogram Using Fuzzy-Genetic Algorithm

A Brain Tumor Classification Framework has been outlined and created. The framework uses computer based strategies to identify tumor blocks or lesions and classify the sort of tumor utilizing matching histogram in MRI images of different patients with brain tumors. The picture processing methods such as picture segmentation, picture enhancement. Several techniques can classify the tumor such as Support vector machine (SVM), artificial neural networks (ANN), and Naive Bayes, but they did not accomplish the required accuracy. The automatic classification of tumors requires high precision since the non-accurate conclusion would cause a rise within the predominance of more serious diseases. In this paper, the proposed method using fuzzy logic and genetic algorithm based on image histogram to enhance the brain tumor classification. The experimental result showed that our technique is more effective than the previous techniques, as well as the classification accuracy efficiency is 99.9%.

Risk Assessment Algorithm for Power Transformer Fleets Based on Condition and Strategic Importance

In every electric power system, power transformers (PT) play a critical role. Under ideal circumstances, PT should receive the utmost care to maintain the highest operative condition during their lifetime. Through the years, different approaches have been developed to assess the condition and the inherent risk during the operation of PT. However, most proposed methodologies tend to analyze PT as individuals and not as a fleet. A fleet assessment helps the asset manager make sound decisions regarding the maintenance scheduling for groups of PT with similar conditions. This paper proposes a new methodology to assess the risk of PT fleets, considering the technical condition and the strategic importance of the units. First, the state of the units was evaluated using a health index (HI) with a fuzzy logic algorithm. Then, the strategic importance of each unit was assessed using a weighting technique to obtain the importance index (II). Finally, the analyzed units with similar HI and II were arranged into a set of clusters using the k-means clustering technique. A fleet of 19 PTs was used to validate the proposed method. The obtained results are also provided to demonstrate the viability and feasibility of the assessment model.

Enhanced IoT-Based Climate Control for Oyster Mushroom Cultivation Using Fuzzy Logic Approach and NodeMCU Microcontroller

Urban farming has the potential to utilise unused space in the community to alleviate food shortages and increase the community’s income through local food production. When Internet of Things (IoT) technology is integrated with urban farming, it can further improve its efficiencies and yield. The work in this paper improved our previous work of using an IoT-based climate control system to regulate the cultivation environment of oyster mushrooms automatically. Even though the climate control system could produce two batches of mushroom yields, there were several limitations, such as less efficient climate control due to threshold-based corrective action, water wastage, and system instability. This paper aims to address these stated limitations by implementing a fuzzy logic algorithm and redesigned the climate control system. Two crisp input variables from DHT22 sensors representing temperature and humidity were fed into the Node MCU microcontroller’s fuzzy logic coded in C language. The temperature and humidity conditions were divided into five fuzzy trapezoidal membership functions resulting in 25 fuzzy rules to control the duration of running the water pump and ventilation fan. An internal, lightweight web server were managed all HTTP client requests. The enhanced system also included a safety measurement to avoid overheating the microcontroller and causing water wastage. Upon analysis of the data captured in two months, the result showed a decrease of 40% in water utilisation and an increase of mushrooms yield up to 226%. The enhanced climate control system also facilitated maintaining and controlling the temperature and humidity conducive for optimal mushroom cultivation.

Research on vehicle adaptive cruise control based on BP neural network working condition recognition

To solve the problems of the vehicle following model mismatch under different driving conditions and poor vehicle following performance, this paper proposes an adaptive cruise control system based on BP neural network to identify different driving conditions. Firstly, eight typical driving cycles are selected as samples, which are redivided into low-speed mode and medium high-speed mode. According to the selected characteristic parameters and re-division of driving mode, a new BP neural network structure is designed. Secondly, the characteristic parameter values are calculated by using the characteristic parameters, and the characteristic parameters are normalized. The working condition recognition model is obtained by training the normalized data values. Finally, according to the classification of working conditions, the fuzzy control upper control algorithm and the MPC upper control algorithm for medium-high speed conditions are designed respectively. Based on the above control algorithms, the joint simulation is carried out with Car-Sim and MATLAB/Simulink. The simulation results show that the low-speed fuzzy logic algorithm and the medium high-speed MPC algorithm proposed in this paper can make the vehicle have a better following performance and comfort.

Reliable Control Applications with Wireless Communication Technologies: Application to Robotic Systems.

The nature of wireless propagation may reduce the QoS of the applications, such that some packages can be delayed or lost. For this reason, the design of wireless control applications must be faced in a holistic way to avoid degrading the performance of the control algorithms. This paper is aimed at improving the reliability of wireless control applications in the event of communication degradation or temporary loss at the wireless links. Two controller levels are used: sophisticated algorithms providing better performance are executed in a central node, whereas local independent controllers, implemented as back-up controllers, are executed next to the process in case of QoS degradation. This work presents a reliable strategy for switching between central and local controllers avoiding that plants may become uncontrolled. For validation purposes, the presented approach was used to control a planar robot. A Fuzzy Logic control algorithm was implemented as a main controller at a high performance computing platform. A back-up controller was implemented on an edge device. This approach avoids the robot becoming uncontrolled in case of communication failure. Although a planar robot was chosen in this work, the presented approach may be extended to other processes. XBee 900 MHz communication technology was selected for control tasks, leaving the 2.4 GHz band for integration with cloud services. Several experiments are presented to analyze the behavior of the control application under different circumstances. The results proved that our approach allows the use of wireless communications, even in critical control applications.

Multi-objective optimization of machining parameters in hybrid powder-mixed EDM process by response surface methodology and normalized fuzzy logic algorithm

Multi-objective optimization of machining parameters in hybrid powder-mixed EDM process by response surface methodology and normalized fuzzy logic algorithm

FUZZY LOGIC PROGRAM FOR BUS INDOOR ENVIRONMENTAL ASSESSMENT

This paper proposes a fuzzy logic algorithm that evaluates the indoor environmental conditions on an urban bus specifically in Metro Manila. This algorithm identifies the value of three indexes: IAQI, TCI, and CO2. The Indoor Air Quality Index (IAQI) quantifies the level of indoor air quality of the bus. CO2, CO, NO2, O3, TVOC, and PM10 are the input parameters for the fuzzy logic system that will determine IAQI. Thermal Comfort Index (TCI) quantifies the indoor thermal condition in four levels. The indoor temperature and humidity are the input parameters for the fuzzy logic system that will determine TCI. The fuzzy logic program in this study is designed mainly for the bus ventilation control system. The created FLS program was able to give good results. The observations from the program were the following: as the indoor air pollutants increased, the IAQI decreased; as the level of thermal parameters increased, the TCI decreased; and as the CO2 level and temperature increased, the number of passengers also increased.

Tumor Region Location and Classification Based on Fuzzy Logic and Region Merging Image Segmentation Algorithm.

Early diagnosis of tumor plays an important role in the improvement of treatment and survival rate of patients. However, breast tumors are difficult to be diagnosed by invasive examination, so medical imaging has become the most intuitive auxiliary method for breast tumor diagnosis. Although there is no universal perfect method for image segmentation so far, the consensus on the general law of image segmentation has produced considerable research results and methods. In this context, this paper focuses on the breast tumor image segmentation method based on CNN and proposes an improved DCNN method combined with CRF. This method can obtain the information of multiscale and pixels better. The experimental results show that, compared with DCNN without these methods, the segmentation accuracy is significantly improved.

A Dynamic Service Reconfiguration Method for Satellite–Terrestrial Integrated Networks

Satellite–terrestrial integrated networks (STINs) are regarded as a promising solution to meeting the demands of global high-speed seamless network access in the future. Software-defined networking and network function virtualization (SDN/NFV) are two complementary technologies that can be used to ensure that the heterogeneous resources in STINs can be easily managed and deployed. Considering the dual mobility of satellites and ubiquitous users, along with the dynamic requirements of user requests and network resource states, it is challenging to maintain service continuity and high QoE performance in STINs. Thus, we investigate the service migration and reconfiguration scheme, which are of great significance to the guarantee of continuous service provisioning. Specifically, this paper proposes a dynamic service reconfiguration method that can support flexible service configurations on integrated networks, including LEO satellites and ground nodes. We first model the migration cost as an extra delay incurred by service migration and reconfiguration and then formulate the selection processes of the location and migration paths of virtual network functions (VNFs) as an integer linear programming (ILP) optimization problem. Then, we propose a fuzzy logic and quantum genetic algorithm (FQGA) to obtain an approximate optimal solution that can accelerate the solving process efficiently with the benefits of the high-performance computing capacity of QGA. The simulation results validate the effectiveness and improved performance of the scheme proposed in this paper.

Fuzzy Logic Algorithm and GSM IoT Based Fire Fighting Robot

Artificial Intelligence (AI) covers many areas related to mechanical, electrical, psychology and philosophy. GSM IoT is the new method and idea in a simple application of fire detection. In our project, we are using IR sensor to detect a fire and it sends the information to a microcontroller, which is programed based on a fuzzy logic algorithm that helps to detect the fire and moves the nozzle head of the Fire Fighting Robot. The stepper motor is used for a 360 degree clockwise and anticlockwise rotation. The D.C motor controls the valve opens of the nozzle. The fuzzy logic algorithm is helps to detect the fire and to turn the nozzle in the direction of fire.

Improving the quality of automated VIS–grading of Scots pine seeds using fuzzy logic algorithm

The automation of Scots pine seeds grading in the visible wavelength region – VIS-grading – is promising for conducting breeding and genetic experiments. This will reduce time costs and increase the accuracy of seed color classification compared to organoleptic techniques. When controlling VIS-grading, it is necessary to accurately detect and process the optical signal reflected from a single seed. The signal is based on the wavelength and amplitude of the optical beam. Earlier, using a spectrometer for Scots pine seeds from a natural stand of the Pavlovsky district of the Voronezh region, Russia, the boundaries of three spectrometric groups were established. In the real VIS-grading process, it is necessary to take into account the probabilistic deviations of random values of wavelengths and amplitudes. Therefore, on the basis of the Mamdani fuzzy logic theory developed an analyzing algorithm for controlling the VIS-grading quality. The algorithm consists of a sequence of logical terms that clearly define the specified VIS-grading seeds spectrometric parameters by a combination of wavelength and amplitude. The efficiency of Scots pine seeds VIS-grading using the algorithm is 98.9%.

SC95 - Evaluation of multiple prebiopsy variables via artificial intelligence with Fuzzy Logic Algorithms: a step forward in prediction of targeted biopsy outcomes

SC95 - Evaluation of multiple prebiopsy variables via artificial intelligence with Fuzzy Logic Algorithms: a step forward in prediction of targeted biopsy outcomes

A Novel Experimental Method to Detect Early Rotor Faults in Induction Machines

The Induction Machines (IMs) have numerous industrial interests, as they have an uncomplicated design, strong, robust, and vulnerable to maintain. Despite that fact, a small fault in one rotor bar or a low Unbalance Rotor (UR) causes the break of this bar. Subsequently, the break of the adjacent bar because of the mechanical vibration generated by this imbalance of the rotor. This contribution proposes a new method based on the analysis of the stator line current to enhance the reliability of the monitoring system of an incipient defect in one rotor bar of the Induction Motor (IM). This new method, which combines the Electrical-Time-Synchronous-Averaging (ETSA) procedure with the Vector Park Transform (VPT) technique and Motor Current Signature Analysis (MCSA), and a Fuzzy Logic Algorithm (FLA), is performed. Indeed, an incipient rotor failure cannot be detected using the stator line current signal curve. Absolutely, in cases of the minor defect in one only rotor bar and low UR at low load. Nevertheless, the residual energy contained in each spectrum is considered as input variables for a fuzzy logic system to classify the type and the level of rotor defects. The proposed algorithm applied to the measured stator line-current has been built under the MatLab environment. The experimental obtained results confirm that the conceived algorithm can classify exactly the rotor faults.

A Fuzzy Logic-Based Algorithm to Solve the Slot Planning Problem in Container Vessels

Background: The slot planning problem is a container allocation problem within a certain location on a vessel. It is considered a sub-problem of a successful decomposition approach for the container vessel stowage planning problem. This decision has a direct effect on container handling operations and the vessel berthing time, which are key indicators for the container terminal efficiency. Methods: In this paper, an approach combining a rule-based fuzzy logic algorithm with a rule-based search algorithm is developed to solve the slot planning problem. The rules in the proposed fuzzy logic algorithm aim at improving the objective function and minimizing/eliminating constraint violation. Results: The computational results of 236 slot planning instances illustrate the efficiency and effectiveness of the proposed algorithm. Conclusions: The results show that the proposed approach is fast and can produce optimal or near-optimal solutions for a comprehensive industrial set of instances.

Real-time detection of microgrid islanding considering sources of uncertainty using type-2 fuzzy logic and PSO algorithm.

In this paper, a new innovative type-2 fuzzy-based for microgrid (MG) islanding detection is proposed in the condition of uncertainties. Load and generation uncertainties are two main sources of uncertainties in microgrids (MGs). Regardless of the uncertainties, the results cannot be confirmed. The proposed controller detects islanding in the fastest time under different conditions, and the uncertainties in the system will not considerably affect the controller’s performance. The proposed method is simulated on the sample system and examined in different scenarios. Then, a comparison is made in different conditions and scenarios between the suggested method and some common methods that have been presented so far to demonstrate the ability of the proposed method for islanding detection.

Hybrid Computational Intelligence System for Fashion Design

The domain of fashion design evolves continuously and is highly personalised, demanding intelligent and customised recommendation. The traditional artificial intelligence-based systems offer solutions based on stored knowledge; hence they can be quickly obsolete and require high effort. To meet the fashion designers’ needs and provide tailor-made recommendations effectively, a hybrid genetic-fuzzy system is proposed with interactive fitness functions. The system is based on generic hybrid architecture using fuzzy logic and genetic algorithm, which can be used to evolve various products in different domains and tested with interactive fuzzy fitness functions. The design of the generic architecture meets the research gap identified through an in-depth literature survey. To prove the utility of the architecture, an experiment is carried out showing encoding scheme, genetic operators, fuzzy membership functions, and fuzzy rules. The results are also discussed, along with the comparison, advantages, applications, and possible future enhancements.