Fuzzy Logic Rules Research Articles

Rancang Bangun Kontrol Pemadam Kebakaran Menggunakan Sensor MQ-2 dan Flame Sensor 5 Channel Berbasis Fuzzy Logic

Fire is an event that often occurs due to human negligence. Losses from fire include damage to goods, business delays and can even cause loss of life. For early handling of fires, a "Fire Fighting Control Design Using MQ-2 Sensor and Fuzzy Logic Based 5 Channel Flame Sensor" has been created. This tool uses the MQ-2 sensor to detect gases related to fires, such as hydrocarbon gas. A 5 channel flame sensor is used to detect flames. The predetermined fuzzy logic rules will be used to control the fire extinguisher and implemented using the Arduino Atmega 2560 microcontroller as the brain of the system. Next, the microcontroller will send a control signal to the 12V DC pump output as a water spray system to deal with fires. The test results of this system show that the tool can detect early fire hotspots and respond well to fire extinguishing according to the fuzzy rule decisions carried out by testing the tool with 30 fire spots at different distances.

Type-3 fuzzy neural networks for dynamic system control

This paper presents a Type-3 Fuzzy Neural Network (T3FNN) for dynamic system control. The structure of the T3FNN controller, which is based on the compositional rule of inference of interval type-3 fuzzy systems, is proposed. TSK-type fuzzy rules, employing three-dimensional membership functions (MFs) in the antecedent part and linear functions with coefficients represented as interval sets in the consequent part, are utilized to design T3FNN. Gaussian functions with uncertain centers are employed to describe the type-3 membership functions. By employing three-dimensional MFs, type-3 fuzzy logic rules can accurately model a high degree of uncertainty and effectively handle uncertain information. The design of the T3FNN was accomplished by integrating the type-3 fuzzy logic system (T3FLS) with neural networks. The learning of the T3FNN was conducted using the genetic algorithms and gradient descent algorithm. As a result, the type-3 rule base of the system was effectively developed. The learning algorithms are presented and the proposed T3FNN structure is employed for the development of dynamic system control. The simulations of the T3FNN system for the control of the nonlinear and time-varying dynamic systems were carried out. The results obtained from the simulations indicate the suitability of using the T3FNN in controlling dynamic systems.

Optimization and characterization of boric acid powder mixed electrical discharge machining of Ti-6Al-4V ELI

The powder mixed electrical discharge machining (PMEDM) process is used for enhancing machining efficiency and improving the surface characteristics of the workpiece compared to normal EDM. The present study evaluates the effect of boric acid powder mixed in deionized water in PMEDM of Ti-6Al-4V ELI (extra low interstitial). The PMEDM process is assessed by evaluating material removal rate (MRR) and surface roughness (SR) at varying powder concentration (PC), current and pulse duration ( Ton). Boric acid PMEDM exhibits advantageous for both MRR and SR, where MRR increases and SR decreases with an increase in PC. To simultaneously achieve higher MRR and lower SR, the grey-fuzzy logic method is employed. Initially, the raw input data is normalized into grey numbers and a single index is obtained through grey relational analysis. Subsequently, triangular membership functions are assigned to the inputs and output. Fuzzy logic rules are then formulated, and grey fuzzy relational grade (GFRG) is calculated for each condition. The optimum condition (GFRG = 0.787) is attained at 15 g/l PC, 9 A current and 106 µs Ton, resulting in a 63% improvement compared to the initial condition. At the optimum condition, MRR increased by 145% and SR decreased by 29% compared to the initial condition. The effect of boric acid in PMEDM can be visualized through the formation of smooth surfaces, exhibiting reduced SR, smaller and shallower craters, and fewer debris and globules. Additionally, the boric acid PMEDM experiments resulted in the formation of a hard TiB phase on Ti-6Al-4V, thus improving its wear resistance.

Evaluation of Earthquake Performance of Reinforced Concrete Buildings with Fuzzy Logic Method

Reinforced concrete building is a type of building whose structural system consists of reinforced concrete columns, beams, wall wall, slabs and foundations. Due to the strong earthquakes in our country, it has been observed that reinforced concrete buildings have been severely damaged or collapsed from past to present. The evaluation of buildings after earthquakes and their performances are quite important for the safety of life and property. In the literature, different methods have been developed for post-earthquake evaluation of buildings, either low-cost and fast or slow and high-cost and tool-demanding. In this study, in order to overcome the gap in the literature, the evaluation of earthquake performance of buildings with fuzzy logic method is discussed. In this context, the performance of the building was evaluated by taking into account the concrete compressive strength, number of floors, ground floor area, area of column and shear walls, ground floor area and architectural parameters. The data of 18, 28 and 146 buildings affected by the earthquakes in Afyon, Bingöl and Van provinces in 2002, 2003 and 2011, respectively, were used in the study. Out of the total 192 building data, 94 buildings were processed as data and fuzzy logic rules were applied. The remaining 98 buildings were tested with this method. The buildings considered are light, moderate and severely damaged or collapsed. Matlab program was used in the study. The method and result used in the study are in the class of fast and second stage methods in the literature, which are reliable but do not reflect the final result. With the method revealed in the study, the damage status of reinforced concrete buildings can be determined quickly and reliably. In this context, the buildings were evaluated as no damage/slight, moderate and severely damaged or collapsed. According to the results, the correct estimation rate of the damage status of 98 buildings was found to be 88%. It is seen that the fuzzy logic method can predict building performances at a quite high rate. Compared to the existing second stage assessment methods in the literature, this result is more conservative.

Active contour-based tooth segmentation in radiographs using fuzzy logic and CNN

Radiographic imaging is a crucial tool frequently employed by dentists for initial diagnosis and treatment planning. However, these images often suffer from distortion or inaccuracies due to incorrect exposure settings, making it challenging to identify critical regions such as tooth roots and margins. This study addresses these issues by presenting two innovative methods for tooth segmentation from radiographs, aimed at isolating the tooth regions for better analysis. The first method utilizes fuzzy logic rules to detect edges within the radiographic images. These detected edges are then used as a mask for the Active Contour Method (ACM) to segment the teeth accurately. The second method involves the creation of a Convolutional Neural Network (CNN) for tooth segmentation. The segmentation performance of the CNN is further refined using the ACM, leveraging the initial segmentation as a mask. Both methods demonstrated notable results with varying performance metrics. Specifically, the Fuzzy-Based Active Contour Method achieved precision, recall, and F1 score values of 0.6246, 0.4169, and 0.50, respectively. In contrast, the CNN-Based Active Contour Method calculated accuracy and specificity values of 0.9706 and 0.9872, respectively. These findings indicate that both approaches have distinct strengths in different performance aspects. Our study suggests that these advanced segmentation techniques can significantly enhance the diagnostic capabilities of dental professionals by providing clearer images of tooth structures, aiding in the detection of issues such as root problems, fractures, and wear patterns. Implementing these methods either independently or in combination could lead to more accurate diagnoses and better patient outcomes. Future work could explore the integration of these techniques to leverage their complementary strengths, potentially leading to even greater segmentation accuracy and reliability.

Simulation and analysis of the electro-hydraulic coordinated system for the disc brake system in mining belt conveyor

The present study aims to investigate the dynamic performance of the disk hydraulic brake system in a mining belt conveyor. To achieve this, a simulation model of the hydraulic system for the belt conveyor is designed and constructed using AMESim. It is recognized that brake disk deformation can lead to brake shock, thus an ADAMS rigid-flexible coupling model for hydraulic disk brakes is established. Furthermore, the concept of rigid-flexible coupling mechanical systems is introduced into the hydraulic system to enable interaction between physical quantities. Use fuzzy logic rules to adjust parameters in Simulink, and in response to the suboptimal control of braking and acceleration, a compact non-model-based adaptive algorithm is incorporated into the conventional PID control algorithm. Employing mechanical modeling and multidisciplinary dynamic collaborative simulation technology, a simulation system integrating electromechanical and hydraulic aspects is developed for the disk hydraulic brake of belt conveyors. This system is employed to conduct simulation studies on the closed-loop control of braking disk speed and deceleration/acceleration for belt conveyors. The study findings reveal that the non-model-based adaptive PID control algorithm, in comparison to fuzzy PID, not only demonstrates quicker and more stable responses across various dynamic performance curves but also markedly reduces the axial oscillation amplitude of the braking disk under braking conditions.

A Deep Learning-Based Reliable Link Prediction Model for Achieving Traffic-Aware Routing in Mobile Ad-hoc Networks

One of the most promising wireless network architectures is the mobile ad-hoc network (MANET). Researchers have introduced enormous protocols for efficient routing, but it does not provide a reliable communication link for data transmission. Therefore, this research proposes a reliable link prediction-based traffic-aware deep learning routing protocol in MANET to maintain path stability and reliability to construct efficient routing. The reliable traffic-aware link prediction model used in this research is Fuzzy-based Deep Extreme [Formula: see text]-Learning (FDEQL) model. The fuzzy logic rule is used to compute the status of a wireless link to build stable and faster paths toward destinations. Traffic patterns can affect the efficiency of a node. So, to cope with the traffic pattern in MANET, the point-to-point (P2P) and end-to-end (E2E) traffic matrices are initially constructed. To evaluate whether the wireless link is reliable or not, the proposed approach utilizes fuzzy rules by considering essential parameters such as neighborhood overlap (NOVER), bipartivity index (BI), node mobility (NM), data rate (DR), received signal strength indicator (RSSI) and buffer occupancy (BO). The output is the [Formula: see text]-value for reliable link prediction. The performance of a proposed model is validated with other baseline methods based on various measures such as energy consumption, route failure, throughput, delay, packet delivery ratio (PDR), normalized routing load (NRL) and buffer occupancy by varying the mobility speed from 5 to 30[Formula: see text]m/sec, number of nodes and simulation time, respectively. At the mobility speed of 10[Formula: see text]m/sec, the proposed model has a delay of 0.08 sec, PDR of 99% and throughput of 1903.4[Formula: see text]kbps. The proposed model achieves a delay of 19.37[Formula: see text]msec, PDR of 96.22%, and throughput of 132.95[Formula: see text]kbps, respectively, for 30 nodes. If the simulation runs for 300 sec, the suggested model achieves a delay of 2.98 sec and a PDR of 0.946, respectively.

Sliding mode control for nonlinear semi-Markov jump systems with cyber attacks via fuzzy logic

This paper studies the security control problem of semi-Markov jump systems. First, the parameter uncertainty, the time-varying delay, the nonlinear function and the cyber attack are considered in the system. Second, the nonlinear function is linearized by the fuzzy logic rule. A sliding mode surface is designed to obtain an equivalent controller and get a sliding mode dynamic system. By constructing Lyapunov functions of the mode dependence, a sufficient condition for H∞ asymptotic stability of the system is obtained. Then, an adaptive sliding mode controller is established, and the original system reaches the sliding mode surface in a finite time. Finally, two examples verify the correctness and practicality of the proposed theory.

MEDICAL FUZZY-EXPERT SYSTEM FOR PREDICTION OF ENGRAFTMENT DEGREE OF DENTAL IMPLANTS IN PATIENTS WITH CHRONIC LIVER DISEASE

The paper presents an information technology for assessing the degree of engraftment of dental implants in the event of a pathology violation through the use of fuzzy sets, which allows using this method for medical diagnostic tasks. Main scientific results: developed algorithms and mathematical models that formalize the process supporting diagnostic decisions based on fuzzy logic; developed mathematical models of membership functions that formalize the presentation of qualitative and qualitative informational features based on the rules of fuzzy logic, which can be used in information expert systems when assessing the degree of engraftment of dental implants in case of disease with pathological diseases.

РАЗРАБОТКА МОДЕЛИ BP-СХЕМЫ НА ОСНОВЕ ТЕОРИИ АВТОМАТОВ И С ПРИМЕНЕНИЕМ МЕТОДОВ КВАНТОВОГО ПРОГРАММИРОВАНИЯ

The paper describes the concept of a new type of abstract automata “BP-scheme” (Business Process) that effectively distributes management commands between entities with their permanent growth in the paradigm of complexly modelled systems. The paper investigates the proposed model of the BP-scheme implementation, which is an artificial neural network, functioning based on fuzzy logic rules using the Takagi-Sugeno algorithm, for deriving rules in the form of functional dependencies. The model uses QPU (Quantum Processing Unit) in computing system behaviour, which implies a quick response to changes in the system entities. QPU used taking into account the developed rules of fuzzy neural network allows improving scenarios and predictive models of a complex system behaviour.

A Fuzzy Based Intelligent Traffic Light Control (ITLC) Method: An Implementation in Ankara City

The escalating global population and increased vehicle usage have worsened traffic congestion in metropolitan areas, a significant urban challenge. Addressing this, adaptive traffic light control methods, especially at intersections, are being developed to improve traffic flow and reduce waiting times. This study significantly contributes to this field by implementing Fuzzy Logic in intelligent traffic light systems, focusing on Ankara's Polatlı Refik Cesur intersection. Using the SUMO simulation platform and Python programming, it analyzed waiting times and queue lengths. The initial phase used queue length for each intersection arm as an input. Fuzzy logic rules then determined the output, prioritizing street or phase order for optimal flow. The study further proposed an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based control plan. ANFIS merges neural network capabilities with fuzzy logic, using waiting time and queue length as inputs to regulate the green light duration. Compared to existing traffic systems, this model showed a substantial improvement. It achieved a 36.5% reduction in waiting times, underlining the efficiency of the Fuzzy Logic-based method. This approach not only enhances traffic management but also contributes significantly to the literature on intelligent traffic light control systems. By addressing key urban traffic issues, the study paves the way for future advancements in traffic management technologies. The findings highlight the potential of combining advanced computational methods, like ANFIS, with traditional traffic control techniques to optimize urban traffic flow, offering a blueprint for similar challenges in other metropolitan areas.

Multisensor Fuzzy Logic Approach for Enhanced Fire Detection in Smart Cities

As smart cities expand rapidly, the demand for strong fire protection has become even more essential. A significant challenge lies in ensuring that fire detection systems integrate seamlessly into our modern infrastructures. Leveraging multisensor systems can yield reliable data on potential fires. Particularly in smart buildings, the effectiveness of multisensor fire detection algorithms becomes paramount. This research introduces a fuzzy logic-driven method that harnesses the power of smoke, flame, and temperature sensors. While smoke, flames, and elevated temperatures are primary fire indicators, they can manifest concurrently or sequentially. We have amalgamated various fuzzy logic rules (IF-THEN structures) to gauge the intensity of fires. When a fire ignites, the sensors spring into action, identifying its source and promptly notifying users via the Internet and GSM modems. Moreover, they relay the fire’s precise geographic coordinates to fire departments. The fire’s status will be consistently updated on a dedicated online portal. It was observed from our results that the system proficiently sends fire alerts to residents, and the fire status undergoes regular updates at 45-second intervals. This refreshing is initiated by the identification of a designated percentage of smoke and flame. These outcomes validate the system’s effectiveness in enhancing the precision and responsiveness of fire detection capabilities.

Fuzzy adaptive event-triggered synchronization control mechanism for T–S fuzzy RDNNs under deception attacks

In this paper, a fuzzy-dependent adaptive event-triggered mechanism (FAETM) for synchronizing Takagi–Sugeno (T–S) fuzzy reaction–diffusion neural networks (RDNNs) is developed while considering deception attacks. Firstly, a general neural network model considering both fuzzy logic rules and reaction–diffusion terms is established. Secondly, a FAETM based on an aperiodic sampling period is presented under deception attacks to alleviate the communication burden, wherein the adaptive threshold function is dependent on membership functions. Moreover, a membership-function-dependent asymmetric Lyapunov functional (LF) is constructed, and some positive-definite and symmetric constraints of matrices are removed as a result. Based on the LF method and integral inequality techniques, the H∞ synchronization criteria for the T–S fuzzy RDNNs are presented by linear matrix inequalities (LMIs). Finally, one simulation example is exploited to demonstrate the feasibility and validity of the established method, and the outcome is applied to image secure communication.

CTJIF-ICN: A Coadjuvant Trust Joint Interest Forwarding Mechanism in Information Centric Networks

The Information centric networks (ICN) transforms the focal point of current Internet paradigm to data centric approach from host centric approach by allowing content driven forwarding and in-network caching mechanisms. Though NDN (Named data networking) paradigm of ICN assures a secure content communication, it is vulnerable to different attacks by the malicious nodes. To minimize the hazards from compromised nodes and to improve the network security, the remaining nodes should transparently receive information about such nodes. This will restrict the forwarding strategy to exploit these malicious nodes for forwarding interest and content as well. Our protocol introduces a dynamic model for prediction of trust in order to evaluate the node trust. Proposed approach observes the historical behaviors of node and uses extended fuzzy logic rules for the prediction of future behaviors to evaluate the node’s trust value. This prediction model is incorporated within the trust based forwarding mechanism that aims to forward interest through secure and shortest path. The extensive simulation study has been carried out to analyze the protocol performance in ns-3 driven ndnSIM-2.0 simulator for performance metrics such as data discovery latency, packet delivery ratio, network overhead, detection ratio and cache hit ratio. When we integrate our trust joint forwarding strategy to state-of-the-art protocols, their performance is significantly improved up to approximately 10-35% against stated performance measures for realistic network topology.

СИСТЕМА УПРАВЛЕНИЯ НАКОПИТЕЛЯМИ ЭЛЕКТРИЧЕСКОЙ ЭНЕРГИИ НА ПРИНЦИПАХ НЕЧЕТКОЙ ЛОГИКИ

In rapidly developing world, the complexity of electric energy storage management systems and a significant data flow affect the development of technologies and contribute to the transition from classical to more efficient control methods based on fuzzy logic, which are able to function with complex structures and incomplete information. (Research Purpose) The research purpose is developing the optimal structure of the control system according to the criterion of minimizing the number of rules of fuzzy logic and increasing the energy efficiency of the electric power storage and justifying further research and implementation of this system in the software package. (Materials and methods) Used the methods of analysis, synthesis and modeling used to create the structure of the control system being developed based on the fuzzy logic method. (Results and discussion) The final structure of the power storage management system was determined on the basis of the fuzzy logic method, which is presented as a list of rules. The main variables on which the work of the control system being developed is based have been formulated. (Conclusions) The results obtained are the basis for further research and development of a control system based on a software package capable of influencing the controls of an electric power storage device.

APPLICATION OF NATURAL LANGUAGE PROCESSING AND FUZZY LOGIC TO DISINFORMATION DETECTION

Natural language processing (NLP) is a field of computer science that is concerned with processing, collection and analysis of data encoded in natural language, such as speech, written text, online posts, etc. This paper explores the integration of Natural Language Processing (NLP) methods, specifically TF-IDF and n-gram analysis, with fuzzy logic rules employing Gaussian membership functions to detect disinformation in text data. The approach emphasizes reducing false positives by assessing the probability of disinformation rather than binary decisions, enhancing the accuracy and reliability of text analysis under informational uncertainty.

Architecture of intelligent SIEM for detecting cyber incidents in databases of military information and communication systems.

The article looks at the current supply of cyber defense to the databases of information and communication systems. The effectiveness of continuous visits to databases is analyzed. The analysis indicates that existing systems are not immune to security, which is a critical aspect in the field of cybersecurity. A new approach to the architecture of the SIEM system is being introduced, which is aimed at different parts of the protection circuit of the information and communication system. The fragmented architecture makes it possible to effectively detect and respond to cyber attacks at all levels of protection, from the operating system to databases. A new approach is being introduced to ensure cyber security of databases with the benefit of the intelligent capabilities of the SIEM system. The main aspect of this architecture is rich database protection, which allows you to effectively detect and respond to cyber attacks. The registration approach includes the addition of data from different levels to the security circuit of the information and communication system, the module for analyzing data about categories in the database, which operates on the basis of The use of fuzzy multiplicity theory methods, fuzzy logic inference and rule correlation module to improve the detection of cyber incidents. And also the integration of OLAP technologies to provide a deep and analytical view of the database security system. The architecture for identifying cyber incidents has been designed to improve the efficiency of identifying cyber incidents related to the functioning of the database of information and communication systems and. The result of the investigation is a reduction in the capabilities of the SIEM system in identifying and responding to cyber identities in the database of the military information system. Further, we directly monitor the operational model of the cybersecurity system of the information and communication system database.

DFPC: Dynamic Fuzzy-based Primary User Aware clustering for Cognitive Radio Wireless Sensor Networks

Clustering-based routing solutions have proven to be efficient for wireless networks such as Wireless Sensor Networks (WSNs), Vehicular Ad Hoc Networks (VANETs), etc. Cognitive Radio WSN (CR-WSN) is a class of WSNs that consists of several resource-constrained Secondary Users (SUs), sink, and Primary Users (PUs). Compared to WSNs, there are several challenges in designing the clustering technique for CR-WSNs. As a result, one cannot directly apply the WSN clustering protocols to CR-WSNs. Developing a clustering protocol for CR-WSNs must address challenges such as ensuring PU protection, and SU connectivity, selecting the optimal Cluster Head (CH), and discovering the optimal cluster size. Present CR-WSN clustering solutions failed to resolve the trade-off among all essential clustering objectives. To address these challenges, this study presents a novel approach called Dynamic Fuzzy-based PU aware Clustering (DFPC) for CR-WSNs. DFPC uses a dynamic approach to discover the number of clusters, a fuzzy-based algorithm for optimal CH selection, and reliable multi-hop data transmission to ensure PU protection. To enhance the performance of CR-WSNs, an effective strategy was designed to define the optimal number of clusters using the network radius and live node. Fuzzy logic rules were formulated to assess the four CR-specific parameters for optimal CH selection. Finally, reliable intra- and intercluster data transmission routes are discovered to protect the PUs from malicious activities. The simulation results showed that the DFPC protocol achieved an improved average throughput of 48.04 and 46.49, a PDR of 93.36 and 84.37, and a reduced delay of 0.0271 and 0.0276 in static and dynamic topologies, respectively, which were better than those of ABCC, ATEEN, and LEACH protocols.

Combining MUSHRA Test and Fuzzy Logic in the Evaluation of Benefits of Using Hearing Prostheses

Assessing the effectiveness of hearing aid fittings based on the benefits they provide is crucial but intricate. While objective metrics of hearing aids like gain, frequency response, and distortion are measurable, they do not directly indicate user benefits. Hearing aid performance assessment encompasses various aspects, such as compensating for hearing loss and user satisfaction. The authors suggest enhancing the widely used APHAB (Abbreviated Profile of Hearing Aid Benefit) questionnaire by integrating it with the MUSHRA test. APHAB, a self-completed questionnaire for users, evaluates specific sound scenarios on a seven-point scale, with each point described by a letter, percentage, and description. Given the complexities, especially for older users, we propose converting the seven-point APHAB scale to a clearer 100-point MUSHRA scale using fuzzy logic rules. The paper starts with presenting the goals of the study, focused on the assessment of the benefits of hearing aid use, especially in the case of the elderly population. The introductory part includes an overview of methods for evaluating the effectiveness of hearing aid use. Then, the methodology for the data collection is presented. This is followed by a method modification that combines the MUSHRA (MUltiple Stimuli with Hidden Reference and Anchor) test and fuzzy logic processing and the commonly used hearing aid benefit assessment questionnaire, APHAB (Abbreviated Profile of Hearing Aid Benefit). The results of such a process are examined. A summary of the findings is given in the form of fuzzy logic-based rules, followed by a short discussion. Finally, the overall conclusion and possible future directions for the method development are presented.

Longitudinal and Lateral Stability Control Strategies for ACC Systems of Differential Steering Electric Vehicles

To ensure lateral stability during the cruising of a differential steering vehicle (DSV), this paper presents a curving adaptive cruise control (ACC) system coordinated with a differential steering control (DSC) system, which considers both longitudinal cruising capability and lateral stability on curved roads. Firstly, a DSV dynamics model is developed and a control strategy architecture for a curving ACC system is designed. Then, the car-following control strategy for the curving ACC system is designed based on the fuzzy model predictive control (FMPC) algorithm. The strategy aims to improve the economy and balances car following, safety, comfort and economy. Moreover, fuzzy logic rules are designed to update the weight coefficients of the performance indicators in real time. Finally, the lateral stability controller is designed based on the preview algorithm and the sliding mode control (SMC) algorithm. The simulation results show that the lateral stability of the DSV during the curving cruise is realized via the control of the differential drive torque of the two front wheels. The proposed FMPC controller and SMC controller based on the preview control algorithm satisfy the performance in terms of vehicle following and lateral stable driving in the process of cruising.