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EN
The article presents the application of fuzzy logic to risk assessment in assembly and forming production processes. The fuzzy FMEA method was used, enabling the assessment of risk parameters based on expert opinions. This resulted in the development of a system that allows for greater flexibility and increased resistance to errors associated with human factors, enabling risk assessment through the use of linguistic variables. This allows organisations to analyse and manage risk, improving the efficiency and safety of their operations. This article presents an analysis of the benefits of using fuzzy logic in risk assessment in production in conjunction with the FMEA method, which is one of the most widely used risk assessment methods in industry. It discusses how fuzzy logic can help capture uncertainties in production processes and provide a more flexible framework for their evaluation. A case study is also presented, in which fuzzy logic was applied to risk assessment, highlighting the benefits it brings to production efficiency and safety.
EN
Infectious diseases significantly impact global mortality rates, with their complex symptoms complicating the assessment and determination of infection severity. Various countries grapple with different forms of these diseases. This research utilizes three AI-based decision-making techniques to refine diagnostic processes through the analysis of medical imagery. The goal is achieved by developing a mathematical model that identifies potential infectious diseases from medical images, adopting a multi-criteria decision-making approach. The avant-garde, AI-centric methodologies are introduced, harnessing an innovative amalgamation of hypersoft sets in a fuzzy context. Decision-making might include recommendations for isolation, quarantine in domestic or specialized environments, or hospital admission for treatment. Visual representations are used to enhance comprehension and underscore the importance and efficacy of the proposed method. The foundational theory and outcomes associated with this innovative approach indicate its potential for broad application in areas like machine learning, deep learning, and pattern recognition.
EN
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.
PL
W artykule przedstawiono technologię informacyjną do oceny stopnia wszczepienia implantów stomatologicznych u pacjentów z przewlekłą chorobą wątroby za pomocą zbiorów rozmytych, co pozwala na zastosowanie tej metody do medycznych zadań diagnostycznych. Główne wyniki naukowe: opracowano algorytmy i modele matematyczne formalizujące proces wspomagania podejmowania decyzji diagnostycznych w oparciu o logikę rozmytą; opracowano matematyczne modele funkcji przynależności formalizujące reprezentację ilościowych i jakościowych cech informacyjnych opartych na regułach logiki rozmytej, które mogą być wykorzystane w informatycznych systemach ekspertowych do oceny stopnia wszczepienia implantów stomatologicznych u pacjentów z przewlekłą chorobą wątroby.
4
Content available Fuzzy logic-based prediction data for the CNC lathe
EN
Purpose: The research aims to predict the parameters between the cutting speed range correlated to the depth of cut for the CNC lathe. Design/methodology/approach: The model predicts the cutting speed parameters carried out based on the data range between the depth of the cut and the cutting speed. That information has been derived from the machine tool handbook and expert engineer recommendations. The fuzzy logic-based methods were used to predict cutting speed parameters for three different materials: aluminium, machine steel, and stainless steel. The data range in each material was used to condition the membership function. Findings: The result shows that the prediction cutting speed parameters are related to the range of the depth of the cut between 0.15 and 0.4 mm. It is observed that if the depth of the cut is very high, the cutting speed is lower. The information obtained is slightly different from the machine tool handbook. It can be used with the feed rate parameters to perform the machining process of the CNC lathes in the smart factory. Research limitations/implications: Further research should focus on predicting surface roughness and tool wear in the turning. Practical implications: The cutting speed selection has a significant impact on manufacturing. It affects production time, tool wear, cost, etc. Generally, the parameter has been derived from machining handbooks or machine tools textbooks, and some data is vague because it has only maximum and minimum. The data between ranges is unclear for operation. Executing production planning for new engineers was hard, which can affect manufacturing systems. Therefore, proper and precise cutting parameters are required. Originality/value: General machine tool manuals often provide vague information on recommended parameters and only show the maximum and minimum values. In past research, it has only a determined parameters range for the experiment. The data between ranges is unclear for operation. In this research, the parameter prediction was performed between the cutting speed range related to the cutting depth, which is for use in the CNC lathe process.
EN
Purpose: The study aims to investigate and assess the application of Fuzzy Logic to construct a predictive maintenance model for rotating machinery. Design/methodology/approach: The research uses a mixed approach, with both quantitative and qualitative approaches, and are four main steps: 1) surveying and analysing existing predictive maintenance techniques; 2) determining appropriate expert assessment criteria for predictive maintenance techniques; 3) vibration analysis by the experts; 4) evaluate the performance of rotating machinery with fuzzy logic. Findings: The result of the study will be used to build a rotating machinery predictive maintenance model, which is very similar to the traditional method. The obtained data showed that the efficiency of the rotating machinery and the vibration level were compliant with the standard ISO 10816-3. Thus, such data can be planned for maintenance to maximize benefit. Research limitations/implications: Future research should optimise the model and add additional modules for automatic data collection. The production monitoring system should help collect data by considering downtime, predicting the functional service life of rotating machinery, etc. Practical implications: The proposed model can be used in small water pumps in order to perform predictive maintenance. The conceptual framework was tested, particularly with rotating machinery. Originality/value: The fuzzy logic model is described as the fuzzy of a process with linguistics for greater clarity.
EN
The widespread desire to automate the CNC machine control process and optimize it is leading to the development of new algorithms. The article presents both a novel approach to this task based on a fuzzy decision-making system as well as an evaluation of the proposed solution on a large database containing data from multiple machining processes and a comparison with the Reference Points Realization Optimization (RPRO) algorithm used in industry. In addition to achieving the intended accuracy of the machining process, the presented system is also easily interpretable for the expert operating the machine. It is also possible to manipulate the presented system easily and shape it according to specific needs.
7
Content available remote Management model for the construction’s waste use in walls manufacturing
EN
The mathematical model, which could be represented as a fuzzy inference tree for project man-agement aimed at waste usage in wall manufacturing, is proposed in the article. The factors classification that affect production's environmental and economic efficiency is presented. The proposed ecological and economic efficiency criterion and influence factors are linguistic variables consisting of fuzzy terms on the corresponding universal sets. The proposed hierarchical system of mathematical models allows the intelligent choice of proper building material, depending on the influence of environmental parameters, socio-economic parameters, and engineering and technological parameters of a building object, based on fuzzy logical expressions “IF-THEN”. The proposed model could be used as a support system model in the decision-making process at the early feasibility stage. Estimating pros and cons based on the results of a virtual experiment in terms of proposed criterion value for specific construction waste allows proper planning of construction waste usage in the construction sector. The proposed model can be used as a design and engineering tool in the decision-making process for forecasting the ecological and economic efficiency of the use of waste in the manufacture of walls.
EN
The purpose of this article is to propose a fuzzy logic system as a tool for automated risk identification of potential technical challenges and social barriers during the implementation of artificial intelligence-based co-bots on workstations in manufacturing enterprises. On the basis of an extensive literature review, as well as industry reports and expert consultations, the basic challenges and enterprise barriers occurring during the implementation of changes in enterprises, especially during the implementation of the latest technologies, were selected. A fuzzy logic model was then developed that, based on the values of the input factors, generates an answer as to whether there is a risk of technical or social challenges in an enterprise when implementing the latest technologies. The results generated by the developed model, when confronted with expert knowledge, experience and subjective assessments, showed that the model works as expected. The results of the study suggest that the use of fuzzy logic can effectively support companies in detecting challenges and obstacles, thereby facilitating decision-making in reducing the risk of their occurrence. Adaptation to the conditions currently prevailing in the company allows for dynamic adjustment of co-bot deployment strategies, which in turn can lead to more effective management of technological changes and minimization of potential operational disruptions.
EN
Nano-additives are generally blended with the base lubricant oil, to enhance the lubricant characteristics such as wear, coefficient of friction (CoF), thermal conductivity, density, and flash and fire points of the lubricant. In this research, nano-additives of SiO2, Al2O3 and TiO2 are blended with the base SN500 oil with different proportions of mixture. When these three nanoparticles are used together in base oil, they enhance most of the desirable properties of a lubricant; 27 samples with three different levels of a mixture of nano-additives are identified using factorial design of experiments. The experimental outcomes for the selected three characteristics of interest of density, flash point and fire point are determined. Conducting experiments for ‘n’ number of samples with different proportions of mixture of nano-additives is a cumbersome, expensive and time-consuming process, in order to determine the optimum mix of nano-additives for the desirable level of characteristics of interest. In this research, attempt has been made to apply fuzzy logic to simulate a greater number of samples with different proportions of a mixture of three nano-additives with the respective outcomes of characteristics of three thermophysical properties. Out of the numerous samples simulated using fuzzy logic, the sample with the optimum mix of three nano-additives of SiO2, Al2O3 and TiO2 blended with the base oil is identified for the desirable level of characteristics of interest of density, flash point and fire point. The values of the identified sample are found to be at the desirable level of 0.9008 gm/ml, 231°C and 252°C, respectively.
EN
The article presents an approach to formulating a ship control process model in order to solve the problem of determining a safe ship trajectory in collision situations. Fuzzy process properties are included in the model to bring it closer to reality, as in many situations the navigator makes a subjective decision. A special neural network was used to solve the presented problem. This artificial neural network is characterized by minimum and maximum operations when set. In order to confirm the correctness of the operation of the proposed algorithm, the results of the simulations obtained were presented and an discussion was conducted.
EN
Maintaining the high level of operational readiness of the army requires ensuring constant high technical readiness of its technical equipment. The reliability of a technical device results from its design and operational features, and in the operation phase it depends solely on the efficiency of the logistics supply system, service, and diagnosis. The principles of implementing these processes and the functioning of the systems are determined by the adopted equipment exploitation strategy. The study presents the results of the assessment of the technical equipment maintenance system in the Polish Army. Methods, procedures and tools for obtaining data to achieve the set goal, methods of data processing, and analysis of the obtained information were characterized. Information and data characterizing the functioning of the maintenance system were obtained as a result of the use of interview techniques with experts with theoretical and practical knowledge about the functioning of the system in the armed forces, using observation techniques, analysis of operational documentation in paper form as well as collected in IT systems. The method of surveying of two groups of experts, participants of the logistics course involved in the implementation and managing the operation of the equipment was of fundamental importance in the research process. IT tools such as MS Excel, MatLab and Statistica were used to develop the obtained numerical and linguistic results. The study determined the parameters of descriptive statistics, histograms and probability distributions and used fuzzy logic. As a result of the study, the reasons for the currently not fully satisfactory level of operation of the equipment maintenance system were revealed and possible solutions were identified to increase the efficiency and effectiveness of the system, and thus the technical readiness of the equipment.
PL
Utrzymanie wysokiego poziomu gotowości operacyjnej wojska wymaga zapewnienia stałej wysokiej gotowości technicznej sprzętu stanowiącego jego wyposażenie. Niezawodność urządzenia technicznego wynika z jego cech konstrukcyjnych i eksploatacyjnych, a w fazie eksploatacji jest zależna wyłącznie od sprawności działania systemu zaopatrzenia logistycznego, obsługiwania, w tym diagnozowania. Zasady realizacji tych procesów i funkcjonowania systemów determinuje przyjęta strategia eksploatacji sprzętu. W opracowaniu przedstawiono wyniki oceny funkcjonowania systemu obsługiwania sprzętu technicznego w wojsku polskim. Scharakteryzowano metody, procedury i narzędzia pozyskiwania danych dla realizacji postawionego celu, metody przetwarzania i opracowania danych oraz analizy uzyskanej informacji. Informacje i dane charakteryzujące funkcjonowanie systemu obsługiwania uzyskano w wyniku zastosowania techniki wywiadu z ekspertami dysponującymi wiedzą teoretyczną jak i praktyczną o funkcjonowaniu systemu w siłach zbrojnych, za pomocą techniki obserwacji, analizy dokumentacji eksploatacyjnej w formie papierowej, jak również zgromadzonej w systemach informatycznych. Podstawowe znaczenie w procesie badawczym miała metoda ankietowania dwóch grup ekspertów, uczestników kursu logistycznego związanych z realizacją i zarządzających eksploatacją sprzętu. Do opracowania uzyskanych wyników liczbowych i lingwistycznych wykorzystano narzędzia informatyczne w postaci MS Excel, MatLab i Statistica. W opracowaniu wyznaczono parametry statystyki opisowej, histogramy i rozkłady prawdopodobieństwa oraz wykorzystano logikę rozmytą. W wyniku przeprowadzonego badania ujawniono przyczyny aktualnie nie w pełni zadowalającego poziomu funkcjonowania systemu utrzymania stanu technicznego sprzętu oraz wskazano możliwe do zastosowania rozwiązania pozwalające na podniesienie sprawności i efektywności systemu, a tym samym gotowości technicznej sprzętu.
12
Content available remote Rozmyty regulator stanu układu dwumasowego
PL
Sterowanie napędem elektrycznym oparte o wektor zmiennych stanu umożliwia precyzyjne odtwarzanie sygnału zadanego. Jednak zakłócenia parametryczne lub pomiarowe zdecydowanie utrudniają zachowanie poprawności działania struktury sterowania. W związku z tym, w niniejszej publikacji, zaproponowano rozszerzenie regulatora o część bazującą na logice rozmytej. Zastosowany element wpływa, w zależności od uchybu prędkości, na wartości wzmocnień w sprzężeniach zwrotnych, w efekcie odpowiednio kształtowane są zmienne stanu oraz dynamika napędu. W procesie projektowania zastosowany został optymalizacyjny algorytm metaheurystyczny - Symbiotic Organisms Search (SOS). Obiektem sterowanym jest układ napędowy z elastycznym sprzęgłem, które zostało wprowadzone do części mechanicznej. Wyniki badań symulacyjnych potwierdzają poprawność analizowanej koncepcji. Przeprowadzone zostały również testy eksperymentalne (algorytm został zaimplementowany w karcie dSPACE1103, napęd zawierał dwa silniki prądu stałego o mocy 0,5kW).
EN
Application of control methods based on state vector leads to precise tracking of reference signal. However, the parametric or measurement disturbance definitely make it difficult to maintain the correct operation of the system. Therefore, in this paper, implementation of an additional fuzzy element is proposed. It can recalculate, using information about the speed error, the coefficients in the feedback paths. Then, shape of the state variables and the dynamics of the system are forced properly. In the design process, the metaheuristic algorithm - Symbiotic Organisms Search (SOS) - was used. The electrical drive contains elastic shaft. The results of the simulations confirm the correctness of the analyzed method. Moreover, the experimental tests were performed (with the dSPACE 1103 card, the system contains two DC motor with 0.5kW).
EN
Torque by means the space vector modulation (DTC-SVM) technique using a fuzzy logic controller and applied to the induction drive powered by a two-level inverter. Direct torque control (DTC) is used to achieve torque decoupling fuzzy regulation and space vector modulation in order to keep torque ripple and flux to a minimum. This paper uses a DTC-SVM method based on fuzzy logic controllers to overcome the limitation of DTC-SVM based PI controllers, such as sensitivity to machine parameter variation and external disturbance. In the end, the feasibility and effectiveness of the suggested methods are experimentally confirmed Validation with a real-time Matlab / Simulink program DSpace-based interface1104.
PL
Moment obrotowy za pomocą techniki modulacji wektora przestrzennego (DTC-SVM) z wykorzystaniem sterownika logiki rozmytej i zastosowany do napędu indukcyjnego zasilanego przez dwupoziomowy falownik. Bezpośrednie sterowanie momentem obrotowym (DTC) służy do uzyskania rozmytej regulacji z odsprzęganiem momentu obrotowego i modulacji wektora przestrzennego w celu ograniczenia tętnienia i strumienia momentu obrotowego do minimum. W artykule wykorzystano metodę DTC-SVM opartą na sterownikach z logiką rozmytą w celu przezwyciężenia ograniczeń sterowników PI opartych na DTC-SVM, takich jak wrażliwość na zmiany parametrów maszyny i zakłócenia zewnętrzne. Ostatecznie wykonalność i skuteczność proponowanych metod jest potwierdzona eksperymentalnie Walidacja z programem czasu rzeczywistego Matlab / Simulink opartym na interfejsie DSpace1104.
EN
In order to obtain high performance speed response in pulse-width-modulation direct torque controlled drive of permanent magnet synchronous motor, fuzzy logic is utilized to update parameters of proportional-integral speed controller. However, overshoot and undershoot of speed response are still high, especially at times of load torque change. For reduction of the overshoot and undershoot, boundaries of integral time constant in fuzzy logic are tuned according to speed error during overshoot and undershoot. Theoretical assumptions are validated via simulations.
PL
Aby uzyskać wysoką wydajność odpowiedzi prędkości w sterowanym bezpośrednio momentem napędowym silnika synchronicznego z magnesami trwałymi z modulacją szerokości impulsu, do aktualizacji parametrów proporcjonalno-całkującego regulatora prędkości wykorzystywana jest logika rozmyta. Jednak przeregulowanie i niedoregulowanie odpowiedzi prędkości są nadal wysokie, zwłaszcza w okresach zmiany momentu obciążenia. W celu zmniejszenia przeregulowania i niedoregulowania, granice stałej czasowej całkowania w logice rozmytej są dostrajane zgodnie z błędem prędkości podczas przeregulowania i niedoregulowania. Założenia teoretyczne są weryfikowane za pomocą symulacji.
EN
Unbalanced network voltage damages utility and end-user equipment. Electrified trains, single-phase distributed generators, and line-toline connected industrial loads can increase voltage imbalances. Using Dynamic Voltage Restorer (DVR) at appropriate places is one way to reduce imbalance in practical networks. This research proposes a new technique for managing DVR to improve voltage profile. The simulation results imply that real-time implementation of the suggested controller is practical and resilient.
PL
Niezrównoważone napięcie sieciowe uszkadza sprzęt komunalny i użytkownika końcowego. Zelektryfikowane pociągi, jednofazowe rozproszone generatory i obciążenia przemysłowe połączone między liniami mogą zwiększać nierównowagę napięcia. Używanie dynamicznego przywracania napięcia (DVR) w odpowiednich miejscach jest jednym ze sposobów zmniejszenia asymetrii w praktycznych sieciach. Badanie to proponuje nową technikę zarządzania DVR w celu poprawy profilu napięcia. Wyniki symulacji sugerują, że implementacja sugerowanego kontrolera w czasie rzeczywistym jest praktyczna i odporna.
16
Content available remote ANFIS based inverse controller design for liquid level control of a spherical tank
EN
In this study, an adaptive neuro fuzzy inference system (ANFIS) based inverse controller design is presented for liquid level control application of a spherical tank. First, an excitation signal is applied to the system and the corresponding output signal is obtained. ANFIS-based fuzzy model of the nonlinear spherical tank system is constructed by using this input-output data set. While constructing the fuzzy model, a fuzzy model structure with two inputs and one output is preferred considering design simplicity. The input-output data used for constructing the fuzzy model of the system are exchanged, and by using this new data set, an ANFIS based inverse controller is designed. To improve the control performance against disturbances and model mismatches, the inverse controller is used in an internal model control structure. The performance of the proposed controller is compared to that of classical PI and fuzzy PI controllers under set point variation and disturbance conditions. The results of comparisons reveal that the proposed inverse controller outperforms both the classical and fuzzy PI controllers.
PL
W niniejszym opracowaniu przedstawiono projekt regulatora odwrotnego opartego na adaptacyjnym neurorozmytym systemie wnioskowania (ANFIS) do zastosowania w kontroli poziomu cieczy w zbiorniku kulistym. Najpierw do systemu doprowadzany jest sygnał wzbudzenia i uzyskiwany jest odpowiedni sygnał wyjściowy. Oparty na ANFIS model rozmyty nieliniowego systemu zbiorników sferycznych jest tworzony przy użyciu tego zestawu danych wejściowych i wyjściowych. Podczas konstruowania modelu rozmytego preferowana jest struktura modelu rozmytego z dwoma danymi wejściowymi i jednym wynikiem, biorąc pod uwagę prostotę projektowania. Dane wejściowe-wyjściowe wykorzystywane do budowy modelu rozmytego systemu są wymieniane, a przy użyciu tego nowego zestawu danych projektowany jest sterownik odwrotny oparty na ANFIS. W celu poprawy wydajności sterowania w przypadku zakłóceń i niezgodności modelu, w wewnętrznej strukturze sterowania modelu zastosowano regulator odwrotny. Wydajność proponowanego regulatora jest porównywana z klasycznymi regulatorami PI i rozmytymi regulatorami PI w warunkach zmienności wartości zadanej i zakłóceń. Wyniki porównań pokazują, że proponowany regulator odwrotny przewyższa zarówno klasyczne, jak i rozmyte regulatory PI.
EN
Particle Filters (PF) accomplish nonlinear system estimation and have received high interest from numerous engineering domains over the past decade. The main problem of PF is to degenerate over time due to the loss of particle diversity. One of the essential causes of losing particle diversity is sample impoverishment (most of particle’s weights are insignificant) which affects the result from the particle depletion in the resampling stage and unsuitable prior information of process and measurement noise. To address this problem, a new Adaptive Fuzzy Particle Filter (AFPF) is used to improve the precision and efficiency of the state estimation results. The error in AFPF state is avoided from diverging by using Fuzzy logic. This method is called tuning weighting factor (α) as output membership function of fuzzy logic and input memberships function is the mean and the covariance of residual error. When the motion model is noisier than measurement, the performance of the proposed method (AFPF) is compared with the standard method (PF) at various particles number. The performance of the proposed method can be compared by keeping the noise level acceptable and convergence of the particle will be measured by the standard deviation. The simulation experiment findings are discussed and evaluated.
PL
Adaptacyjny filtr cząstek rozmytych (AFPF) służy do poprawy precyzji i wydajności wyników szacowania stanu. Metoda ta nazywana jest dostrajaniem współczynnika ważenia (α), ponieważ wyjściowa funkcja przynależności logiki rozmytej, a wejściowa funkcja przynależności jest średnią i kowariancją błędu resztowego. Wydajność proponowanej metody jest porównywana przez utrzymanie dopuszczalnego poziomu hałasu, a zbieżność cząstki będzie mierzona przez odchylenie standardowe. Wyniki eksperymentu symulacyjnego są omawiane i oceniane.
EN
In hydrology and water resources engineering, predicting the flow coefficient is a crucial task that helps estimate the precipitation resulting in a surface flow. Accurate flow coefficient prediction is essential for efficient water management, flood control strategy development, and water resource planning. This investigation calculated the flow coefficient using models based on Simple Membership functions and fuzzy Rules Generation Technique (SMRGT) and an Adaptive Neuro-Fuzzy Inference System (ANFIS) model. The fuzzy logic methods are used to model the intricate connections between the inputs and the output. Statistical parameters such as the coefficient of determination (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) were used to evaluate the performance of models. The statistical tests outcome for the SMRGT model was (RMSE:0.056, MAE:1.92, MAPE:6.88, R2:0.996), and for the ANFIS was (RMSE:0.96, MAE:2.703, MAPE:19.97, R2:0.8038). According to the findings, the SMRGT, a physics-based model, exhibited superior accuracy and reliability in predicting the flow coefficient compared to ANFIS. This is attributed to the SMRGT’s ability to integrate expert knowledge and domain-specific information, rendering it a viable solution for diverse issues.
EN
Nowadays, applied computer-oriented and information digitalization technologies are developing very dynamically and are widely used in various industries. One of the highest priority sectors of the economy of Ukraine and other countries around the world, the needs of which require intensive implementation of high-performance information technologies, is agriculture. The purpose of the article is to synthesise scientific and practical provisions to improve the information technology of the comprehensive monitoring and control of microclimate in industrial greenhouses. The object of research is nonstationary processes of aggregation and transformation of measurement data on soil and climatic conditions of the greenhouse microclimate. The subject of research is methods and models of computer-oriented analysis of measurement data on the soil and climatic state of the greenhouse microclimate. The main scientific and practical effect of the article is the development of the theory of intelligent information technologies for monitoring and control of greenhouse microclimate through the development of methods and models of distributed aggregation and intellectualised transformation of measurement data based on fuzzy logic.
EN
Wireless Sensor Networks (WSN) acquired a lot of attention due to their widespread use in monitoring hostile environments, critical surveillance and security applications. In these applications, usage of wireless terminals also has grown significantly. Grouping of Sensor Nodes (SN) is called clustering and these sensor nodes are burdened by the exchange of messages caused due to successive and recurring re-clustering, which results in power loss. Since most of the SNs are fitted with nonrechargeable batteries, currently researchers have been concentrating their efforts on enhancing the longevity of these nodes. For battery constrained WSN concerns, the clustering mechanism has emerged as a desirable subject since it is predominantly good at conserving the resources especially energy for network activities. This proposed work addresses the problem of load balancing and Cluster Head (CH) selection in cluster with minimum energy expenditure. So here, we propose hybrid method in which cluster formation is done using unsupervised machine learning based kmeans algorithm and Fuzzy-logic approach for CH selection.
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