Current Issue : October - December Volume : 2018 Issue Number : 4 Articles : 5 Articles
This paper represents the clinical decision support system for video head impulse test (vHIT) based on fuzzy inference system. It\nexamines the eye and head movement recorded by the eye movement tracking device, calculates the vestibulo-ocular reflex (VOR)\ngain, and applies fuzzy inference system to output the normality and artifact index of the test result. The position VOR gain and the\nproportion of covert and overt catch-up saccades (CUS) within the dataset are used as the input of the inference system. In addition,\nthis system yields one more factor, the artifact index, which represents the current interference in the dataset. Data of fifteen\nvestibular neuritis patients and two of normal subjects were evaluated. The artifact index appears to be very high in the lesion\nside of vestibular neuritis (VN) patients, indicating highly theoretical contradictions, which are low gain but without CUS, or\nnormal gain with the appearance of CUS. Both intact side and normal subject show high normality and low artifact index, even\nthough the intact side has slightly lower normality and higher artifact index. In conclusion, this is a robust system, which is the\nfirst one that takes gain and CUS into account, to output not only the normality of the vHIT dataset, but also the artifacts....
We propose a new method for ordering bipolar fuzzy numbers. In this method, for comparison of bipolar LR fuzzy numbers, we\nuse an extension of Kerre�s method being used in ordering of unipolar fuzzy numbers. We give a direct formula to compare two\nbipolar triangular fuzzy numbers in ...
A marine energy system, which is fundamentally not paired with electric grids, should\nwork for an extended period with high reliability. To put it in another way, by employing electrical\nutilities on a ship, the electrical power demand has been increasing in recent years. Besides, fuel cells\nin marine power generation may reduce the loss of energy and weight in long cables and provide a\nplatform such that each piece of marine equipment is supplied with its own isolated wire connection.\nHence, fuel cells can be promising power generation equipment in the marine industry. Besides,\nfailure modes and effects analysis (FMEA) is widely accepted throughout the industry as a valuable\ntool for identifying, ranking, and mitigating risks. The FMEA process can help to design safe\nhydrogen fueling stations. In this paper, a robust FMEA has been developed to identify the potentially\nhazardous conditions of the marine propulsion system by considering a general type-2 fuzzy logic\nset. The general type-2 fuzzy system is decomposed of several interval type-2 fuzzy logic systems\nto reduce the inherent highly computational burden of the general type-2 fuzzy systems. Linguistic\nrules are directly incorporated into the fuzzy system. Finally, the results demonstrate the success\nand effectiveness of the proposed approach in computing the risk priority number as compared to\nstate-of-the-art methods....
We introduce an approach to predict deterioration of reaction state for people having neurological movement disorders such as\nhand tremors and nonvoluntary movements. These involuntary motor features are closely related to the symptoms occurring in\npatients suffering from Huntington�s disease (HD). We propose a hybrid (neurofuzzy) model that combines an artificial neural\nnetwork (ANN) to predict the functional capacity level (FCL) of a person and a fuzzy logic system (FLS) to determine a stage of\nreaction. We analyzed our own dataset of 3032 records collected from 20 test subjects (both healthy and HD patients) using\nsmart phones or tablets by asking a patient to locate circular objects on the device�s screen. We describe the preparation and\nlabelling of data for the neural network, selection of training algorithms, modelling of the fuzzy logic controller, and\nconstruction and implementation of the hybrid model. The feed-forward backpropagation (FFBP) neural network achieved the\nregression R value of 0.98 and mean squared error (MSE) values of 0.08, while the FLS provides a final evaluation of subject�s\nreaction condition in terms of FCL....
The present paper deals with the concept of generalized fuzzy invex monotonocities and generalized weakly fuzzy invex functions\nare introduced. Some necessary conditions for weakly fuzzy invex monotonocities are presented. Moreover, the concept of fuzzy\nstrong invex monotonocities and fuzzy strong invex functions are also discussed. To strengthen our definitions, we provide\nnontrivial examples of fuzzy invex monotonocities and weakly fuzzy invex functions....
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