An Algorithm Based on Neuro-Fuzzy Controller Implemented in A Smart Clothing System For Obstacle Avoidance

Bahadır, Senem Kurşun; Thomassey, Sébastien; Končar, Vladan; Kalaoğlu, Fatma

doi:10.1080/18756891.2013.781336

Cited by 3 publications

References 27 publications

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RETRACTED ARTICLE: A fuzzy control model based on BP neural network arithmetic for optimal control of smart city facilities

Xia

2019

Pers Ubiquit Comput

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In order to analyze and study the fuzzy control model of smart city optimization control, this paper designs a fully networked fuzzy controller based on BP neural network arithmetic, so that the realization process of fuzzy reasoning is networked and clear. The evaluation mode of wisdom town is constructed, and the index system is simplified according to the evaluation index system of the develop possible of wisdom town. The analog outcome shows that the above arithmetic can availably optimize the parameters and structure of the neuro vagueness control, and the designed neuro vagueness control has good performance. The build of wisdom cities is advantageous to further promoting the deep integration of industrialization, informationization, urbanization, and agricultural modernization, which is of great significance for solving urban development problems and promoting continuable town develop. To improve the development capacity of intelligent city, we should highlight the characteristics of smart cities; advocate the concept of sustainable development and low-carbon development, market-oriented, overall planning, top-level design, and micro-regulation; and take the road of development with local characteristics.

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RETRACTED ARTICLE: A fuzzy control model based on BP neural network arithmetic for optimal control of smart city facilities

Xia

2019

Pers Ubiquit Comput

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Smart shirt for obstacle avoidance for visually impaired persons

Bahadır

Končar

Kalaoğlu

2016

Smart Textiles and Their Applications

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Human Action Recognition Using Deep Learning Methods on Limited Sensory Data

et al. 2020

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In recent years, due to the widespread usage of various sensors action recognition is becoming more popular in many fields such as person surveillance, human-robot interaction etc. In this study, we aimed to develop an action recognition system by using only limited accelerometer and gyroscope data. Several deep learning methods like Convolutional Neural Network(CNN), Long-Short Term Memory (LSTM) with classical machine learning algorithms and their combinations were implemented and a performance analysis was carried out. Data balancing and data augmentation methods were applied and accuracy rates were increased noticeably. We achieved new stateof-the-art result on the UCI HAR dataset by 97.4% accuracy rate with using 3 layer LSTM model. Also, we implemented same model on collected dataset (ETEXWELD) and 99.0% accuracy rate was obtained which means a solid contribution. Moreover, the performance analysis is not only based on accuracy results, but also includes precision, recall and f1-score metrics. Additionally, a real-time application was developed by using 3 layer LSTM network for evaluating how the best model classifies activities robustly.

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An Algorithm Based on Neuro-Fuzzy Controller Implemented in A Smart Clothing System For Obstacle Avoidance

Cited by 3 publications

References 27 publications

RETRACTED ARTICLE: A fuzzy control model based on BP neural network arithmetic for optimal control of smart city facilities

RETRACTED ARTICLE: A fuzzy control model based on BP neural network arithmetic for optimal control of smart city facilities

Smart shirt for obstacle avoidance for visually impaired persons

Human Action Recognition Using Deep Learning Methods on Limited Sensory Data

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