Special issue on fuzzy theoretical model analysis for signal processing

Balas, Valentina E.; Hong, Jer Lang; Lin, Tsung-Chih

doi:10.3233/jifs-179272

Cited by 5 publications

(2 citation statements)

References 48 publications

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“…FML acts as a fundamental tool for the human readable and hardwareindependent modeling of fuzzy logic controllers [8]. It also allows FLS designers to define their structures irrespective of their remnant interpretations and offers a set of facilities that accelerate the entire procedure of designing a fuzzy structure [9,10]. It included complete data elements for executing Mamdani, TSK, Tsukamoto, and AnYa systems, as in its XML Schema Definition (XSD).…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Stand-alone and Cloud-Based Fuzzy Logic Systems for Human Fall Detection

Pandya

Pourabdollah

Lotfi

2022

Int. J. Fuzzy Syst.

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Traditionally, fuzzy logic systems are linked to specific hardware or software systems. Observations reveal that dispersed and distributed designs of intelligent systems are gaining attraction. Due to the possible complexities of fuzzy logic computations, distributed architectures have the potential to add value to the development of fuzzy systems. However, the absence of best practices and standard methodologies may prevent widespread adoption. By broadening the IEEE-1855 (2016) standard in terms of system definition and data exchange, this research offers a standard solution for building a Service-Oriented Architecture (SOA) as a novel method of implementing fuzzy logic systems by means of a cloud-based collecting, processing, and examining data over the web. A comparison between the performances of a stand-alone hardware-dependent solution and a cloud-based solution (known as fuzzy-as-a-service) is performed. The analysis is also carried out on two different cloud service providers and software libraries (Amazon Web Services using JFML as a java-based library and Azure Web Services using Simpful as a python-based library). The analysis and evaluation are performed on a human fall detection scenario involving wearable sensors. The proposed algorithm can identify between fall and non-fall events. However, the results show that the processing time taken per 10,000 samples using smartwatch and mobile was 2220 s and 101 s for a cloud-based non-fuzzy machine learning system, 1111 s and 45 s for a cloud-based fuzzy system with AWS and JFML, and 1250 s and 97 s for a cloud-based fuzzy system with Microsoft Azure and Simpful libraries. It has been observed that a smartwatch with a fuzzy stand-alone crashed after processing 5000 samples and a mobile phone requires 179.42 s to process 10,000 samples.

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Section: Introductionmentioning

confidence: 99%

A Comparative Study of Stand-alone and Cloud-Based Fuzzy Logic Systems for Human Fall Detection

Pandya

Pourabdollah

Lotfi

2022

Int. J. Fuzzy Syst.

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“…The fuzzy markup language is a web communication language that is evolving as a basic method for the humanunderstandable and hardware-independent modelling of fuzzy logic controllers [4]. Fuzzy markup language enables fuzzy logic control designers to describe their structures independently of their legacy representations by means of these characteristics and provide them with with a set of facilities that speed up the entire process of creating a fuzzy structure [5], [6]. Nevertheless, the XML nature of the fuzzy markup language, in addition to the hardware independence function, makes it possible to incorporate fuzzy logic in computational scenarios marked by high degrees of omnipresence.…”

Section: Introductionmentioning

confidence: 99%

Developing a cloud-based service-oriented architecture for fuzzy logic systems

Pandya

Pourabdollah

Lotfi

2021

2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Fuzzy logic systems are customarily related to specific hardware or software systems. Nevertheless, it has been observed that distributed and cloud-based architectures of various intelligent systems are pouring intensifying attention. While the distributed architectures can potentially add values in developing fuzzy systems, a lack of standard methods and practices may limit their public use. This study aims to provide a standard solution for developing cloud-based service-oriented architectures for fuzzy logic systems, based on extending IEEE-1855 (2016) in the defining system and exchanging data. Experiments were performed employing simulation concerning collection, processing and monitoring of data in a distributed manner over the web. A real-time human activity recognition simulated scenario is also demonstrated through a cloud-based fuzzy system.

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