Vehicle Influence on the Earth’s Magnetic Field Changes

Markevičius, Vytautas; Navikas, Dangirutis; Daubaras, A.; Cepenas, Mindaugas; Zilys, Mindaugas; Andriukaitis, Darius

doi:10.5755/j01.eee.20.4.4552

Cited by 11 publications

(5 citation statements)

References 4 publications

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“…Manyetik sensörlerin kullanılmasının esas temeli manyetik kuvveti veya alan verisini hesaplamaya dayalı değildir [21]- [23]. Araç tespiti yapmak, aracın hızlanmasını [24]- [26] saptamak veya manyetik arkın var olduğunu tespit etmek gibi çokça sebebi de vardır.…”

Section: Kablosuz Manyetik Sensörler Kullanılarak Araç Sınıflandırma Sisteminin Tasarımıunclassified

Implementation of the vehicle classification based-on decision tree algorithm using wireless magnetic sensors

Vançin¹,

Erdem²

2018

Pamukkale J Eng Sci

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Kablosuz sensör ağları kullanarak akıllı ulaşım sistemleri (Intelligent Transportation Systems, ITS) tasarlamak, hem maliyet hem de enerji verimliliği açısından avantajlı olup herhangi bir yolun trafiğini gözlemlemek, o yol hakkında trafik bilgisi edinmek veya sadece araçları tespit edip tipleri ve hızlarını saptamak son zamanlarda araştırmacıların ilgi odağı haline gelmiştir. Bu çalışmada sensör düğümü, manyetometre, güç kartı ve pilden oluşan ve diğer çalışmalarda kullanılan düğümlerden daha doğru ve anlaşılır veriler sunabilen bir sensör devresi kullanılmıştır. Bu sensör devreleri ile aracın tipini belirlemek için iki farklı yöntem sunulmuştur. İlk yöntemde, yoldan geçen araçlar, önerilen algoritma ve İ (Manyetik İmza Uzunluğu) paremetresine göre otomobil, minibus, otobüs ve kamyon olarak sınıflandırılmıştır. Bu yöntemle elde edilen doğruluk payı %89 olmuştur. Diğer yöntemde ise araç sınıflandırması, makine öğrenmesi algoritması olan J48 kullanılarak yapılmış ve önerilen yöntem esas alınarak elde edilen sonuçların eniyilemesi yapılmıştır. Bir makine öğrenmesi yazılım paketi olan Weka'da uygulanan J48 sınıflandırma algoritmasını kullanır. Karar ağacı modeli, 3 eksenli HMC5983L manyetik sensöründen geçen araçlardan çıkarılan manyetik ham veri, ölçüm süresi gibi bir dizi özellikten oluşturulmuştur. Özellikler, çapraz geçerlilik temelinde değişen sınıflandırma oranları derecelerine sahip bir karar ağacı modeli üretmek için J48 eğitim algoritmasına doğru sınıflandırmalarla sağlanan niteliklerdir. Makine öğrenmesi algoritması olan J48 kullanımı araç sınıflandırmasında daha verimli ve doğru sonuçlar verdiği görülmüştür. İlk yöntemle elde edilen İ değerleri hesaplama aşamasında zorluklar doğurmuştur. Ancak J48 algoritması kullanılarak daha belirgin ve hassas sınır ve eşik değerleri elde edilmiştir. Çalışmanın sonucu, araç sınıflandırma sisteminde önerilen algoritmanın eniyilemesiyle yaklaşık % 100 doğruluk payı ile etkili ve verimli olduğunu göstermektedir. The design of Intelligent Transportation Systems (ITS) using wireless sensor networks to observe any road traffic, get road information, or just identify road vehicles has recently become an interesting and popular research topic because of its advantages in cost and energy efficiency. To perform this study, sensor circuit consisting of sensor node, magnetometer, power board and battery, is used. This sensor structure presents more accurate and intelligible results than sensor nodes used in other studies. Two different methods have been proposed to determine the type of vehicle with these sensor circuits. In the first method, vehicles passing by the road are classified as cars, minibuses, buses and trucks according to the proposed algorithm and (Magnetic Signature Length) parameters. The accuracy achieved with this method was 89%. In the other method, vehicle classification was performed using machine learning algorithm J48 which is a machine learning decision tree extension and the obtained results were optimized based on the proposed method. It uses the J48 class...

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Section: Kablosuz Manyetik Sensörler Kullanılarak Araç Sınıflandırma Sisteminin Tasarımıunclassified

Implementation of the vehicle classification based-on decision tree algorithm using wireless magnetic sensors

Vançin¹,

Erdem²

2018

Pamukkale J Eng Sci

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“…Assume sensor observability is between x � −30 m and 30 m. A small car such as the Toyota Vitz moves along the road (x-axis). e magnetic susceptibility of a ferromagnetic parts content of a car is quite high, ≈ 1000 [22]. Assume the wireless sensor node and road are aligned to the Earth magnetic North direction (φ � 0 0 ).…”

Section: Anomalous Signal Graphsmentioning

confidence: 99%

Performance Evaluation of Magnetic Wireless Sensor Networks Algorithm for Traffic Flow Monitoring in Chaotic Cities

Fimbombaya

Mvungi

Ndyetabura

et al. 2018

Modelling and Simulation in Engineering

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Traffic flow monitoring involves the capturing and dissemination of real-time traffic flow information for a road network. When a vehicle, a ferromagnetic object, travels along a road, it disturbs the ambient Earth’s magnetic field, causing its distortion. The resulting distortion carries vehicle signature containing traffic flow related information such as speed, count, direction, and classification. To extract such information in chaotic cities, a novel algorithm based on the resulting magnetic field distortion was developed using nonintrusive sensor localization. The algorithm extracts traffic flow information from resulting magnetic field distortions sensed by magnetic wireless sensor nodes located on the sides of the road. The model magnetic wireless sensor networks algorithm for local Earth’s magnetic field performance was evaluated through simulation using Dar es Salaam City traffic flow conditions. Simulation results for vehicular detection and count showed 93% and 87% success rates during normal and congested traffic states, respectively. Travel Time Index (TTI) was used as a congestion indicator, where different levels of congestion were evaluated depending on the traffic state with a performance of 87% and 88% success rates during normal and congested traffic flow, respectively.

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“…For example, heat transfer or temperature measurements can be used to work against forest fires, whereas measurements with moisture, temperature or pressure sensors can be used in weather forecasting and so forth. To measure the magnetic field of the Earth, to identify a certain material or to determine the metallic properties of objects, anisotropic magnetoresistive sensors (AMRs) are used [21][22][23]. Because metals such as iron, nickel or cobalt affect the background magnetic field, many objects containing metals can be detected by these sensors.…”

Section: Wireless Sensor Networkmentioning

confidence: 99%

Implementation of the vehicle recognition systems using wireless magnetic sensors

Vançin

Erdem

2017

Sādhanā

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Wireless network sensors and their use in traffic monitoring, traffic density determination or vehicle speed detection and classification have recently been the focus of interest for researchers. This article describes how a new sensor circuit was designed to deliver instantaneous, real-time and novel solutions as a vehicle detection system, which is more powerful than the nodes used in other studies, and gives results with smaller error margins due to its serial communication qualification. With the proposed logic algorithm, it was possible to categorise the instantaneous traffic status of a road in four levels: no traffic, mild traffic, heavy traffic and very heavy traffic. Additionally, with the nodes placed at the beginning and the end of the road, the number of vehicles per hour for a day was determined and traffic was analysed. Then, vehicles passing by were classified with a proposed classification algorithm and magnetic signature length (MSL) parameter as cars, minibuses, buses and trucks, and an accuracy rate of 95% was obtained. As the last application, the direction of motion of the vehicle on the x-axis as well as left-to-right or right-to-left directions was determined, and the result was 94% accurate. The simplicity of the proposed algorithms, the absence of any complex mathematical calculations, the low cost of the sensor node and circuit and the low power consumption of the communication system demonstrate the superiority of this system in comparison with other studies.

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Vehicle Influence on the Earth’s Magnetic Field Changes

Cited by 11 publications

References 4 publications

Implementation of the vehicle classification based-on decision tree algorithm using wireless magnetic sensors

Implementation of the vehicle classification based-on decision tree algorithm using wireless magnetic sensors

Performance Evaluation of Magnetic Wireless Sensor Networks Algorithm for Traffic Flow Monitoring in Chaotic Cities

Implementation of the vehicle recognition systems using wireless magnetic sensors

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