Data Fusion Using Fuzzy Logic Techniques Supported by Modified Weighting Factors (FLMW)

Abstract

This article describes a data fusion algorithm that uses fuzzy logic techniques. The algorithm involves weighting factors, the values of which change depending on whether the conditions dictated by processes of fuzzy logic have been met. By modifying the values of weighting factors, one can achieve a measurement signal with expected properties. The article includes a general description of the algorithm and an example of its application. The algorithm was developed for the purposes of data fusion for contactless measurement of the linear and angular position of an automatic laparoscopic device or a laparoscopic camera (hereinafter Laparoscope sleeve or LS).These measurements are used in the article to close the feedback loop on the position within a servomechanism with two degrees of freedom.

The Measurement of Displacement with the Use of MEMS Sensors: Accelerometer, Gyroscope and Magnetometer

“…However it should be remembered that each sensor has different accuracy described as the noise of measurement covariance matrix and his uniqueness should be included while gauging the real location of arm. The references to Kalman filtration can be seen in bibliography [5][6][7][8]. It includes all of foregoing factors and sets the estimate of the state vector having the smallest covariance error.…”

Subscribing to fuzzy temporal aggregation of heterogeneous sensor streams in real‐time distributed environments

“…This innovative approach provides an intuitive linguistic representation of mobile and ambient sensors as well as implies a drastic reduction of the communication burden. The proposed data modeling is based on the fuzzy linguistic approach and fuzzy logic [28] because it has provided successful results in developing intelligent systems using the data provided by the sensors [11,[29][30][31][32][33] highlighting the development of renewable energy and energy-saving systems [34][35][36][37].The managing of uncertainty and vagueness is key in intelligent systems, such as activity recognition [38], to obtain high performance and results [39]. Finally, in order to illustrate the usefulness and effectiveness of our proposal, we present the results of the fuzzy temporal aggregation of sensor streams with alpha-cut subscriptions in a case study where an inhabitants performs an daily activities in an intelligent environment.…”

“…The proposed data modeling is based on the fuzzy linguistic approach and fuzzy logic [28] because it has provided successful results in developing intelligent systems using the data provided by the sensors [11,[29][30][31][32][33] highlighting the development of renewable energy and energy-saving systems [34][35][36][37]. The proposed data modeling is based on the fuzzy linguistic approach and fuzzy logic [28] because it has provided successful results in developing intelligent systems using the data provided by the sensors [11,[29][30][31][32][33] highlighting the development of renewable energy and energy-saving systems [34][35][36][37].…”

“…Among them, it is noteworthy ZeroC Ice [24], an object-oriented distributed computing tool with support for several languages and platforms, Global Sensor Network (GSN) that applies sliding window management [25] in changing data stream [26] and, finally, OpenIot, which increases the characteristics of GSN adding top-k subscriptions over sliding windows (top-k/w subscriptions) [27] and semantic open-linked data techniques.In the context of this state of the art, the aim of this paper is to provide a straightforward approach that processes heterogeneous sensor streams using linguistic terms in real-time intelligent environments. The proposed data modeling is based on the fuzzy linguistic approach and fuzzy logic [28] because it has provided successful results in developing intelligent systems using the data provided by the sensors [11,[29][30][31][32][33] highlighting the development of renewable energy and energy-saving systems [34][35][36][37].The managing of uncertainty and vagueness is key in intelligent systems, such as activity recognition [38], to obtain high performance and results [39]. In this way, sensor-based activity recognition is a relevant research topic that integrates pervasive and mobile computing [40,41], context-aware computing [42][43][44], and ambient assisted living [45,46].In order to illustrate the usefulness and effectiveness of our proposal, we present the results in terms of reduction of the communication burden of the fuzzy temporal aggregation of sensor streams with alpha-cut subscriptions in a case study where an inhabitants performs an daily activities in an intelligent environment.…”

“…In the context of this state of the art, the aim of this paper is to provide a straightforward approach that processes heterogeneous sensor streams using linguistic terms in real-time intelligent environments. The proposed data modeling is based on the fuzzy linguistic approach and fuzzy logic [28] because it has provided successful results in developing intelligent systems using the data provided by the sensors [11,[29][30][31][32][33] highlighting the development of renewable energy and energy-saving systems [34][35][36][37].…”