Robust and real-time lane detection filter based on adaptive neuro-fuzzy inference system

2019 ◽  
Vol 13 (7) ◽  
pp. 1181-1190 ◽  
Author(s):  
Ayhan Küçükmanisa ◽  
Orhan Akbulut ◽  
Oğuzhan Urhan
Keyword(s):  
Real Time ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Lane Detection ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Detection Filter

scholarly journals Analysis and Modeling for the Real-Time Condition Evaluating of MOSFET Power Device Using Adaptive Neuro-Fuzzy Inference System

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 6510-6518 ◽  
Author(s):  
Shengyou Xu ◽  
Xin Yang ◽  
Minyou Chen ◽  
Wei Lai ◽  
Yueyue Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Power Device ◽  
Inference System ◽  
Time Condition ◽  
The Real ◽  
Neuro Fuzzy

scholarly journals Pengendalian Lampu Lalu Lintas Cerdas di Persimpangan Empat Ruas yang Kompleks Menggunakan Algoritma Adaptive Neuro Fuzzy Inference System

2020 ◽  
Vol 6 (1) ◽  
pp. 29
Author(s):  
Budy Santoso ◽  
Azminuddin I. S. Azis ◽  
Andi Bode
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Waiting Times ◽  
Inference System ◽  
Arrival Times ◽  
Neuro Fuzzy ◽  
Transportation Type

Masalah transportasi masih sering dihadapkan pada fenomena kemacetan arus lalu lintas yang berdampak pada kecelakaan lalu lintas, polusi, dan kerugian ekonomi. Salah satu cara untuk meminimalisir fenomena tersebut melalui pengendalian sistem lampu lalu lintas yang baik terhadap arus lalu lintas jangka pendek di persimpangan jalan. Pengendalian lampu lalu lintas secara statis terbukti belum optimal dalam meminimalisir kemacetan arus lalu lintas, salah satu penyebabnya karena kondisi arus lalu lintas yang bervariasi sehingga tidak mudah diprediksi. Fuzzy Inference System (FIS) sering terbukti mampu menunjukkan hasil yang lebih baik daripada pengendalian lampu lalu lintas secara statis. Namun FIS tidak dapat diterapkan pada kondisi arus lalu lintas yang bervariasi atau di persimpangan jalan yang berbeda karena metode tersebut tidak mampu mempelajari kondisi arus lalu lintas secara real time. Agar FIS mampu melakukan pembelajaran, maka pendekatan machine learning dapat diterapkan pada FIS. Salah satu pengembangannya adalah Adaptive Neuro Fuzzy Inference System (ANFIS) yang dapat mengendalikan lampu lalu lintas cerdas secara dinamis dengan hasil yang lebih baik daripada FIS. Namun umumnya ANFIS diuji coba pada persimpangan jalan yang normal. Bagaimana jika di persimpangan yang kompleks? Persimpangan yang memiliki beberapa ruas/jalur utama yang besar (jalur poros), sementara ruas laiinya kecil, bahkan terdapat ruas yang tidak berpotongan, sehingga ada prioritas untuk setiap ruasnya. Hasilnya, penerapan ANFIS (3 GaussMf) untuk pengendalian lampu lalu lintas cerdas/dinamis di persimpangan empat ruas yang kompleks mampu mereduksi Average Waiting Times (AWT) rata-rata sebesar 3,4071E-05 detik dengan 2,7156 RMSE rata-rata, menggunakan variabel Queues Quantity dan Priority Degree. Sedangkan jika menggunakan variabel Arrival Times, Transportation Type, dan Goal Junction, ANFIS (4 TrapMf) mampu mereduksi AWT sebesar 0,0779 detik dengan 19,7646 RMSE.

scholarly journals Real Time Traffic Sign Detection and Recognition using Adaptive Neuro Fuzzy Inference System

2015 ◽  
Vol 3 (2) ◽  
pp. 1-5 ◽  
Author(s):  
Mustain Billah ◽  
Sajjad Waheed ◽  
Kawsar Ahmed ◽  
Abu Hanifa
Keyword(s):  
Real Time ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Traffic Sign ◽  
Inference System ◽  
Real Time Traffic ◽  
Neuro Fuzzy ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

scholarly journals Real-time prediction of water level change using adaptive neuro-fuzzy inference system

2017 ◽  
Vol 8 (2) ◽  
pp. 1320-1332 ◽  
Author(s):  
Mosbeh R Kaloop ◽  
Mohammed El-Diasty ◽  
Jong Wan Hu
Keyword(s):  
Water Level ◽  
Real Time ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Water Level Change ◽  
Inference System ◽  
Level Change ◽  
Time Prediction ◽  
Neuro Fuzzy

scholarly journals Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Overcrowding Level Risk Assessment in Railway Stations

2020 ◽  
Vol 10 (15) ◽  
pp. 5156
Author(s):  
Hamad Alawad ◽  
Min An ◽  
Sakdirat Kaewunruen
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Real Time ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Risk Level ◽  
Railway Stations ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Managing Risk

The railway network plays a significant role (both economically and socially) in assisting the reduction of urban traffic congestion. It also accelerates the decarbonization in cities, societies and built environments. To ensure the safe and secure operation of stations and capture the real-time risk status, it is imperative to consider a dynamic and smart method for managing risk factors in stations. In this research, a framework to develop an intelligent system for managing risk is suggested. The adaptive neuro-fuzzy inference system (ANFIS) is proposed as a powerful, intelligently selected model to improve risk management and manage uncertainties in risk variables. The objective of this study is twofold. First, we review current methods applied to predict the risk level in the flow. Second, we develop smart risk assessment and management measures (or indicators) to improve our understanding of the safety of railway stations in real-time. Two parameters are selected as input for the risk level relating to overcrowding: the transfer efficiency and retention rate of the platform. This study is the world’s first to establish the hybrid artificial intelligence (AI) model, which has the potency to manage risk uncertainties and learns through artificial neural networks (ANNs) by integrated training processes. The prediction result shows very high accuracy in predicting the risk level performance, and proves the AI model capabilities to learn, to make predictions, and to capture risk level values in real time. Such risk information is extremely critical for decision making processes in managing safety and risks, especially when uncertain disruptions incur (e.g., COVID-19, disasters, etc.). The novel insights stemmed from this study will lead to more effective and efficient risk management for single and clustered railway station facilities towards safer, smarter, and more resilient transportation systems.

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