Current Issue : July - September Volume : 2020 Issue Number : 3 Articles : 5 Articles
We give a new definition of fuzzy fractional derivative called fuzzy conformable fractional derivative. Using this definition, we\nprove some results and we introduce new definition of generalized fuzzy conformable fractional derivative....
Sensor differential signals are widely used in many systems. The tracking differentiator\n(TD) is an effective method to obtain signal differentials. Differential calculation is noise-sensitive.\nThere is the characteristics of low-pass filter (LPF) in the TD to suppress the noise, but phase lag is\nintroduced. For LPF, fixed filtering parameters cannot achieve both noise suppression and phase\ncompensation lag compensation. We propose a fuzzy self-tuning tracking differentiator (FSTD)\ncapable of adaptively adjusting parameters, which uses the frequency information of the signal to\nachieve a trade-off between the phase lag and noise suppression capabilities. Based on the frequency\ninformation, the parameters of TD are self-tuning by a fuzzy method, which makes self-tuning\ndesigns more flexible. Simulations and experiments using motion measurement sensors show that\nthe proposed method has good filtering performance for low-frequency signals and improves tracking\nability for high-frequency signals compared to fixed-parameter differentiator....
This paper presents the design and development of a fuzzy logic-based multisensor fire detection and a web-based notification\nsystem with trained convolutional neural networks for both proximity and wide-area fire detection. Until recently, most\nconsumer-grade fire detection systems relied solely on smoke detectors. These offer limited protection due to the type of fire\npresent and the detection technology at use. To solve this problem, we present a multisensor data fusion with convolutional neural\nnetwork (CNN) fire detection and notification technology. Convolutional Neural Networks are mainstream methods of deep\nlearning due to their ability to perform feature extraction and classification in the same architecture. The system is designed to\nenable early detection of fire in residential, commercial, and industrial environments by using multiple fire signatures such as\nflames, smoke, and heat. The incorporation of the convolutional neural networks enables broader coverage of the area of interest,\nusing visuals from surveillance cameras. With access granted to the web-based system, the fire and rescue crew gets notified in\nreal-time with location information. The efficiency of the fire detection and notification system employed by standard fire\ndetectors and the multisensor remote-based notification approach adopted in this paper showed significant improvements with\ntimely fire detection, alerting, and response time for firefighting. The final experimental and performance evaluation results\nshowed that the accuracy rate of CNN was 94% and that of the fuzzy logic unit is 90%....
An autocatalytic set (ACS) is a graph. On the other hand, the Potential Method (PM) is an established graph based concept for\noptimization purpose. Firstly, a restricted form of ACS, namely, weak autocatalytic set (WACS), a derivation of transitive\ntournament, is introduced in this study. Then, a new mathematical concept, namely, fuzzy weak autocatalytic set (FWACS), is\ndefined and its relations to transitive PM are established. Some theorems are proven to highlight their relations. Finally, this paper\nconcludes that any preference graph is a fuzzy graph Type 5....
Settlement simulating in cohesion materials is a crucial issue due to complexity of cohesion soil texture. This research emphasis on\nthe implementation of newly developed machine learning models called hybridized Adaptive Neuro-Fuzzy Inference System\n(ANFIS)..........................
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