Scopus
🔓 Açık Erişim YÖKSİS DOI Eşleşti
SJR Q1
The Innovative Approach to Real-Time Detection of Fuel Types Based on Ultrasonic Sensor and Machine Learning
Arabian Journal for Science and Engineering · Aralık 2024
YÖKSİS Kayıtları
The Innovative Approach to Real-Time Detection of Fuel Types Based on Ultrasonic Sensor and Machine Learning
Arabian Journal for Science and Engineering · 2024 SCI-Expanded
Dr. Öğr. Üyesi UĞUR TAŞKIRAN →
The Innovative Approach to Real-Time Detection of Fuel Types Based on Ultrasonic Sensor and Machine Learning
Arabian Journal for Science and Engineering · 2024 SCI-Expanded
Prof. Dr. MEHMET ÇUNKAŞ →
YÖKSİS Kayıtları — ISSN Eşleşmesi
New WOA Variants for Superior Meta-heuristic Optimization with Multiple Hunter Whale Leading
2025 ISSN: 2193-567X SCI-Expanded Q2
Dr. Öğr. Üyesi SEMA SERVİ →
A New Lifetime Model: Properties, Estimation, and Applications in Quality Control
2025 ISSN: 2193-567X SCI-Expanded Q2
Arş. Gör. ERDEM CANKUT →
Brain Tumor Detection with Transfer Learning Models Based on Attention Modules
2026 ISSN: 2193-567X SCI-Expanded Q2
Dr. Öğr. Üyesi ZÜLEYHA YILMAZ ACAR →
Effect of Silica/Graphene Nanohybrid Particles on the Mechanical Properties of Epoxy Coatings
2019 ISSN: 2193-567X SCI-Expanded Q3
Doç. Dr. ŞAKİR YAZMAN →
Heuristic Optimization Based on Penalty Approach for Surface Permanent Magnet Synchronous Machines
2020 ISSN: 2193-567X SCI-Expanded Q1
Doç. Dr. MEHMET AKİF ŞAHMAN →
A Method of Classification Performance Improvement Via a Strategy of Clustering-Based Data Elimination Integrated with k-Fold Cross-Validation
2021 ISSN: 2193-567X SCI-Expanded Q2
Dr. Öğr. Üyesi ONUR İNAN →
Training Feed-Forward Multi-Layer Perceptron Artificial Neural Networks with a Tree-Seed Algorithm
2020 ISSN: 2193-567X SCI-Expanded Q3
Doç. Dr. AHMET CEVAHİR ÇINAR →
Solving Multi‑Objective Resource Allocation Problem Using Multi‑Objective Binary Artificial Bee Colony Algorithm
2021 ISSN: 2193-567X SCI-Expanded Q3
Prof. Dr. FATİH BAŞÇİFTÇİ →
Theoretical and Experimental Investigation of the Performance of an Atkinson Cycle Engine
2021 ISSN: 2193-567X SCI-Expanded Q3
Dr. Öğr. Üyesi HALİL ERDİ GÜLCAN →
Optimization of Electricity Generation Parameters with Microbial Fuel Cell Using the Response Surface Method
2022 ISSN: 2193-567X SCI-Expanded Q2
Prof. Dr. ŞAKİR TAŞDEMİR →
The Innovative Approach to Real-Time Detection of Fuel Types Based on Ultrasonic Sensor and Machine Learning
2024 ISSN: 2193-567X SCI-Expanded Q2
Prof. Dr. MEHMET ÇUNKAŞ →
Enhanced Gain Dual-Port Compact Printed Meandered Log-Periodic Monopole Array Antenna Design with Octagonal-Ring Shaped FSS for Broadband 28 GHz Applications
2024 ISSN: 2193-567X SCI-Expanded Q2
Dr. Öğr. Üyesi MEHMET YERLİKAYA →
The Innovative Approach to Real-Time Detection of Fuel Types Based on Ultrasonic Sensor and Machine Learning
2024 ISSN: 2193-567X SCI-Expanded Q2
Dr. Öğr. Üyesi UĞUR TAŞKIRAN →
Determination of Temperature Effects on Cortical Bone Milling Using Taguchi Method
2025 ISSN: 2193-567X SCI-Expanded Q2
Prof. Dr. SÜLEYMAN NEŞELİ →
Characterizing Machining Indicators with Machine Learning Models Under Cellulose Nanocrystal and Graphene-Based Nanofluid Conditions
2025 ISSN: 2193-567X SCI-Expanded Q2
Doç. Dr. MUSTAFA KUNTOĞLU →
Makale Bilgileri
ISSN2193567X
Yayın TarihiAralık 2024
Cilt / Sayfa49 · 16571-16591
Scopus ID2-s2.0-85192871222
Erişim🔓 Açık Erişim
Özet
This paper presents an approach to prevent the incorrect transfer of fuel to the wrong tank during refueling. An experimental setup was developed to perform ultrasonic and dielectric measurements on diesel, gasoline, ethanol, and water. The fuel types were determined using an ultrasonic sensor and time-of-flight values were measured at various temperatures. Additionally, the dielectric coefficients of these liquids were measured to determine the liquid types using a dielectric sensor. The results obtained from both the ultrasonic and dielectric methods are systematically compared, and the advantages and disadvantages of each approach are thoroughly discussed. It was observed that dielectric method does not always yield accurate results. The proposed system effectively prevents erroneous transfer of fuel to the tank during refueling. The developed system may be used in practice to distinguish fuel types. In addition, a new approach using machine learning techniques to determine fuel type is presented. Fuel types were classified using 33 machine learning algorithms such as support vector machines, artificial neural networks and K-Nearest neighbors. It seems that artificial neural network with first layer size 25 and quadratic discriminant classifiers have achieved remarkable results with a success rate of 94% in classification. The results highlight the important and effective role of ultrasonic sensors in accurately identifying fuel types, leading to more efficient and safer storage and transportation of fuel. It is also concluded that machine learning techniques can be used effectively in identifying and classifying fuel types. The approach involving ultrasonic and artificial intelligence techniques was particularly innovative in distinguishing fuel types.
Scimago Dergi (ISSN Eşleşmesi)
Arabian Journal for Science and Engineering
Q1
SJR Skoru0,545
H-Index89
ÜlkeGermany
Multidisciplinary (Q1)
Metrikler
2
Atıf