Adaptive neuro-fuzzy systems in yarn modelling

Majumdar, Abhijit

doi:10.1533/9780857090812.2.159

Cited by 10 publications

(3 citation statements)

References 17 publications

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“…There are two different types of FIS: Mamdani and Sugeno. For the proposed study, the authors used Mamdani FIS due to its simplest structure and enhanced freedom to map antecedents to consequents with fuzzy membership [ 45 , 46 ]. In the first phase, an input assessment model is designed using fuzzy membership functions.…”

Section: Methodsmentioning

confidence: 99%

ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment

Saini

Dutta

Marques

2022

Sensors

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Air quality levels do not just affect climate change; rather, it leaves a significant impact on public health and wellbeing. Indoor air pollution is the major contributor to increased mortality and morbidity rates. This paper is focused on the assessment of indoor air quality based on several important pollutants (PM10, PM2.5, CO2, CO, tVOC, and NO2). These pollutants are responsible for potential health issues, including respiratory disease, central nervous system dysfunction, cardiovascular disease, and cancer. The pollutant concentrations were measured from a rural site in India using an Internet of Things-based sensor system. An Adaptive Dynamic Fuzzy Inference System Tree was implemented to process the field variables. The knowledge base for the proposed model was designed using a global optimization algorithm. However, the model was tuned using a local search algorithm to achieve enhanced prediction performance. The proposed model gives normalized root mean square error of 0.6679, 0.6218, 0.1077, 0.2585, 0.0667 and 0.0635 for PM10, PM2.5, CO2, CO, tVOC, and NO2, respectively. This approach was compared with the existing studies in the literature, and the approach was also validated against the online benchmark dataset.

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Section: Methodsmentioning

confidence: 99%

ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment

Saini

Dutta

Marques

2022

Sensors

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“…The flow diagram for procedure adopted to predict process factors for yarns. 60 Artificial neural network modeling for prediction of thermal transmission properties of woven fabrics and the fuzzy logic method has many advantages 60 such as translation of vague human description to fuzzy linguistic variables, translation of human expert knowledge to production rules, automatic extraction of knowledge, and efficient run-time performance. Using Image processing procedure offer an algorithm for recognizing and to send information; The glove with a visible pocket for power bank was conducted in two stages; the first one focused on the verification of the operation of tension flex sensors, and the second on the verification of indications of an orientation sensor.…”

Section: Smart E-textiles and Modern Technologiesmentioning

confidence: 99%

“…In technology side, 79 in yarn engineering using an artificial neural network, and woven fabric engineering by mathematical modeling and soft computing methods where the knowledge acquisition module facilitates the transfer and transformation of problem solving expertise from the knowledge source to the knowledge base, in addition to Garment modelling by fuzzy logic. 60 Connection between mechatronics and biodegradable polymers 93–95 Statistical modeling 96,97 could be open new field regarding to environmentally friendly applications. The design and production of apparel industry are a combination between the arts, the engineering, and the technology section or smart photochromic and thermo chromic fabrics, smart textiles perceive and adapt to changes in their surroundings.…”

Section: Market’s Needs Limitations Performance and Problemsmentioning

confidence: 99%

Smart E-textiles: A review of their aspects and applications

Younes

2023

Journal of Industrial Textiles

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The innovation in high-converging technology using electronics and textiles augments the functionality of E-textiles as Smart E-Textiles (SETs), which overcome the gap between interactivity and inter-connectivity. The current article presents a review about the recent developments in Smart E-textiles and their relationship with other modern technologies. Smart E-Textiles can be developed via smart sensors, wireless communication technologies, embedded technologies, and nanotechnology, which can monitor activity for modern applications. Investments in research and development of Smart E-Textiles will encourage the use of them as sustainable and cost-effective options for light-weight, high-performance wearable technology for monitoring a wide range of the digitization activities of Smart E-Textiles applications.

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Textronics: a review of their technological aspects and applications

Younes

2023

The Journal of The Textile Institute

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Adaptive neuro-fuzzy systems in yarn modelling

Cited by 10 publications

References 17 publications

ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment

ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment

Smart E-textiles: A review of their aspects and applications

Textronics: a review of their technological aspects and applications

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