Fuzzy Logic Classification System Research Articles

Design of a Fuzzy Logic Classification System for an Ocular Sun Protection Factor

There is no reliable and universal method to assess and compare protective properties of lenses, which is generally communicated to the purchasers by sunglasses categories based on lenses only, classifying the entire sun transmission spectrum (ultraviolet, infrared and visible), without accounting the geometry of the frame, nor the back reflection of the lenses, which is a serious problem. The present work develops a fuzzy classification system whose purpose is to create a reliable method to rate and compare sunglasses through their sun protective properties. This system treats the pre-acquired data relative to the lenses, i.e. ultraviolet transmittance and back reflection, in addition to the frame coverage data through the fuzzification interface, followed by the fuzzy inference process and later by the defuzzification interface. The output is an alphabetic rate of the protective eyewear to inform consumers, in a simple way to understand, regarding the sun protective properties of the sunglasses they purchase. Through the results it was possible to prove the effectiveness of choosing a fuzzy system for this classification system, as it was able to translate, in mathematical terms, the linguistic rules, classifying each of the various possible combinations of the inputs to one of the six factors stipulated for the ocular sun protection factor (OSPF) proposed in this work.

INTERVAL TYPE II FUZZY EXPERT SYSTEM FOR DIAGNOSIS VISCERAL LEISHMANIASIS

Leishmaniasis is neglected tropical a protozoan infectious disease caused by Leishmania parasites. VL (also known as Kala-Azar) is the most severe chronic form of Leishmaniasis, almost always fatal if untreated. The annual burden of VL in Ethiopia is estimated to be between 4,500 and 5,000 cases. In a medical environment, incomplete information and imprecise are driven to making an incorrect medical decision and maximize the rate of morbidity and mortality. Fuzzy logic technology enables to provide a simple way to attain to a certain conclusion from vague, imprecise and ambiguous medical data. Clinical suspicion of VL is complex because of its clinical manifestation are shared by other commonly occurring tropical disease like malaria, typhoid and tuberculosis. Several studies were conducted on VL, nevertheless, no one addresses the VL diagnosis using learning capacity. This thesis, we developed and investigated the application of an intelligent interval Type-2 Fuzzy Logic Expert System for diagnosis Visceral Leishmaniasis (VLDES) to assist health workers easy diagnosis a patient and decision-makers to prevent the expansion epidemic. The Type-2 Fuzzy Logic System would be clear to present a highly interpretable and transparent model that is very suitable for the handling uncertainties in the input factors and converting the accumulated data to linguistic formats. First we acquired well organized domain expert knowledge via interview questions and relevant document from the university of Gondar Kala-Azar Treatment and Research center. Secondly, we defined the parameters of fuzzy membership functions of Type-1 Fuzzy Logic System mentioned based on domain knowledge with intensive discussion. Finally, we employed to define uncertainty of the Footprint of Uncertainty (FOU) percentage of Interval Type-2 Fuzzy Logic System. We obtained 98.97% classification accuracy respectively from fifteen patients testing data. This show that the proposed Interval Type-2 Fuzzy Logic Classification System provides a more interpretable model that Diagnosis VL.

DESEMPENHO TÉRMICO DE PROTÓTIPOS DE AVIÁRIOS PROVIDOS DE FORRO DE PAINEL OSB DE MADEIRA BALSA RESIDUAL

This study describes the potential of using an OSB panel ceiling, made of Balsa wood wastes agglomerated with a polyurethane resin based on castor oil, in prototypes of aviaries covered with fiber cement tiles. For the study, the internal temperature, relative humidity and the thermal comfort indexes, such as the environmental thermal index of productivity for broilers (IAPfc) and enthalpy, were measured and a Fuzzy logic classification system was used. The data were collected during the spring and summer, for 15 days. The results indicate that the presence of ceiling provided a reduction in the radiation transfer to the interior of the aviary prototypes, reflecting on the reduction of the ambient temperature and the thermal comfort indexes measured during spring and summer. Based on the Fuzzy classification system and on the data obtained, it can be concluded that there is a small difference between the aviaries with and without a ceiling.

РАЗРАБОТКА БАЗЫ НЕЧЕТКИХ ПРОДУКЦИОННЫХ ПРАВИЛ НЕЧЕТКОЙ ЛОГИЧЕСКОЙ СИСТЕМЫ КЛАССИФИКАЦИИ ВОЗДУШНЫХ ОБЪЕКТОВ В ПРОЦЕССЕ КОНТРОЛЯ ВОЗДУШНОГО ПРОСТРАНСТВА И ОБОБЩЕННОГО АЛГОРИТМА ИХ РЕАЛИЗАЦИИ

Контроль воздушного пространства требует создания системы классификации воздушных объектов. Данная задача решается в нечеткой постановке. Для этого разработана база нечетких продукционных правил и обобщенный алгоритм их реализации. Утверждается, что традиционные подходы к разработке баз правил не достаточно эффективны для рассматриваемой предметной области. Цель статьи – разработка базы нечетких продукционных правил нечеткой логической системы классификации воздушных объектов. Проанализированы подходы к разработке базы нечетких продукционных правил нечетких логических систем классификации. Для классификации воздушного объекта устанавливается его принадлежность к наперед заданному классу. Наперед заданные классы формально задаются в виде множества. Элементами множества являются классы, определяемые автоматически или задаваемые автоматизировано. Определены основные признаки для классификации воздушных объектов. Значения признаков определяются по результатам обобщения трассовой информации или устанавливаются по умолчанию. Значения признаков представляют собой лингвистические переменные. В общем случае они являются нечеткими числами, описывающими термы соответствующих лингвистических переменных. Структурно нечеткие продукционные правила соответствуют правилам нечеткой логической модели Сугэно 0-го порядка. Разработан пример использования нечеткого логического вывода Сугэно 0-го порядка на базе разработанных правил. Разработанная база нечетких продукционных правил и обобщенный алгоритм их реализации являются основой метода верификации программного обеспечения нечеткой логической системы классификации воздушных объектов в процессе контроля воздушного пространства.

Multi‐layer photovoltaic fault detection algorithm

This study proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behaviour of an existing grid-connected photovoltaic (GCPV) system. For a given set of working conditions, a number of attributes such as voltage ratio (VR) and power ratio (PR) are simulated using virtual instrumentation LabVIEW software. Furthermore, a third-order polynomial function is used to generate two detection limits (high and low limits) for the VR and PR ratios. The high and low detection limits are compared with real-time long-term data measurements from a 1.1 kWp GCPV system installed at the University of Huddersfield, United Kingdom. Furthermore, samples that lie out of the detecting limits are processed by a fuzzy logic classification system which consists of two inputs (VR and PR) and one output membership function. The obtained results show that the fault detection algorithm accurately detects different faults occurring in the PV system. The maximum detection accuracy (DA) of the proposed algorithm before considering the fuzzy logic system is equal to 95.27%; however, the fault DA is increased up to a minimum value of 98.8% after considering the fuzzy logic system.

Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system

This work proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behavior of an existing PV system. For a given set of working conditions, solar irradiance and PV modules' temperature, a number of attributes such as voltage ratio (VR) and power ratio (PR) are simulated using virtual instrumentation (VI) LabVIEW software. Furthermore, a third order polynomial function is used to generate two detection limits for the VR and PR ratios obtained using VI LabVIEW simulation tool.The high and low detection limits are compared with measured data taken from 1.1 kWp PV system installed at the University of Huddersfield, United Kingdom. Samples lie out of the detection limits are processed by a fuzzy logic classification system which consists of two inputs and one output membership function.In this paper, PV faults corresponds to a short circuited PV module. The obtained results show that the fault detection algorithm can accurately detect different faults occurring in the PV system, where the maximum detection accuracy of before considering the fuzzy logic system is equal to 95.27%. However, the fault detection accuracy is increased up to a minimum value of 98.8% after considering the fuzzy system.

Diagnostic method for photovoltaic systems based on six layer detection algorithm

This work proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behaviour of an existing grid-connected photovoltaic (GCPV) plant. For a given set of working conditions, solar irradiance and PV modules’ temperature, a number of attributes such as voltage ratio (VR) and power ratio (PR) are simulated using virtual instrumentation (VI) LabVIEW software. Furthermore, a third order polynomial function is used to generate two detection limits (high and low limit) for the VR and PR ratios obtained using LabVIEW simulation tool.The high and low detection limits are compared with real-time long-term data measurements from a 1.1kWp and 0.52kWp GCPV systems installed at the University of Huddersfield, United Kingdom. Furthermore, samples that lies out of the detecting limits are processed by a fuzzy logic classification system which consists of two inputs (VR and PR) and one output membership function.The obtained results show that the fault detection algorithm can accurately detect different faults occurring in the PV system. The maximum detection accuracy of the algorithm before considering the fuzzy logic system is equal to 95.27%, however, the fault detection accuracy is increased up to a minimum value of 98.8% after considering the fuzzy logic system.

Offshore wind turbine clutter characteristics and identification in operational C‐band weather radar measurements

The aim of this work is to update ground‐clutter classification methods in weather radar rainfall measurements to more accurately identify clutter pixels from wind farms. Measurements from two dual‐polarised weather radars, based in the United Kingdom, will be used to determine the characteristics of multiple wind farms in the North Sea and the Irish Sea. Currently 21 of the top 25 largest offshore wind farms are located in these regions. The extensive area occupied by the wind farms creates problems for weather radars located in the neighbouring European countries. Datasets of wind‐farm, precipitation and ground‐clutter pixels were aggregated from Thurnham Radar measurements to form novel membership functions that can be used in a fuzzy logic classification system to identify wind‐farm clutter. When only ground‐clutter datasets were used for classification, areas of the radar scans taken up by wind‐farm clutter were misclassified as rainfall. The inclusion of wind‐farm measurements led to an increase in the ability of the algorithm to detect these pixels as clutter, as the Heidke Skill Score increased from 67.4 to 97.8%. However there was a slight increase in the number of precipitation pixels incorrectly classified as clutter, with the false alarm rate increasing from 0.05 to 1.24% when all variables are used. The algorithm performed slightly better when applied to another radar on Hameldon Hill, showing promise for application to the UK network without recalibration of membership functions.

A cloud computing based Big-Bang Big-Crunch fuzzy logic multi classifier system for Soccer video scenes classification

Soccer video summarization and classification is becoming a very important topic due to the world wide importance and popularity of soccer games which drives the need to automatically classify video scenes thus enabling better sport analysis, refereeing, training, advertisement, etc. Machine learning has been applied to the task of sports video classification. However, for some specific image and video problems (like sports video scenes classification), the learning task becomes convoluted and difficult due to the dynamic nature of the video sequence and the associated uncertainties relating to changes in light conditions, background, camera angle, occlusions and indistinguishable scene features, etc. The majority of previous techniques (such as SVM, neural network, decision tree, etc.) applied to sports video classifications did not provide a consummate solution, and such models could not be easily understood by human users; meanwhile, they increased the complexity and time of computation and the associated costs of the involved standalone machines. Hence, there is a need to develop a system which is able to address these drawbacks and handle the high levels of uncertainty in video scenes classification and undertake the heavy video processing securely and efficiently on a cloud computing based instance. Hence, in this paper we present a cloud computing based multi classifier systems which aggregates three classifiers based on neural networks and two fuzzy logic classifiers based on type-1 fuzzy logic and type-2 fuzzy logic classification systems which were optimized by a Big-Bang Big crunch optimization to maximize the system performance. We will present several real world experiments which shows the proposed classification system operating in real-time to produce high classification accuracies for soccer videos which outperforms the standalone classification systems based on neural networks, type-1 and type-2 fuzzy logic systems.