TY  - JOUR
T1  - Kablosuz manyetik sensörler kullanarak karar ağacı algoritma tabanlı araç sınıflandırmasının gerçekleştirilmesi
TT  - Implementation of the vehicle classification based-on decision tree algorithm using wireless magnetic sensors
AU  - Vançin, Sercan
AU  - Erdem, Ebubekir
PY  - 2018
DA  - April
JF  - Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi
PB  - Pamukkale Üniversitesi
WT  - DergiPark
SN  - 2147-5881
SP  - 302
EP  - 310
VL  - 24
IS  - 2
LA  - tr
AB  - Kablosuzsensör ağları kullanarak akıllı ulaşım sistemleri (Intelligent TransportationSystems, ITS) tasarlamak, hem maliyet hem de enerji verimliliği açısındanavantajlı olup herhangi bir yolun trafiğini gözlemlemek, o yol hakkında trafikbilgisi edinmek veya sadece araçları tespit edip tipleri ve hızlarını saptamakson zamanlarda araştırmacıların ilgi odağı haline gelmiştir. Bu çalışmadasensör düğümü, manyetometre, güç kartı ve pilden oluşan ve diğer çalışmalardakullanılan düğümlerden daha doğru ve anlaşılır veriler sunabilen bir sensördevresi kullanılmıştır. Bu sensör devreleri ile aracın tipini belirlemek içiniki farklı yöntem sunulmuştur. İlk yöntemde, yoldan geçen araçlar, önerilenalgoritma ve                                (Manyetik İmza Uzunluğu) paremetresine göreotomobil, minibus, otobüs ve kamyon olarak sınıflandırılmıştır. Bu yöntemleelde edilen doğruluk payı %89 olmuştur. Diğer yöntemde ise araçsınıflandırması, makine öğrenmesi algoritması olan J48 kullanılarak yapılmış veönerilen yöntem esas alınarak elde edilen sonuçların eniyilemesi yapılmıştır.Bir makine öğrenmesi yazılım paketi olan Weka'da uygulanan J48 sınıflandırmaalgoritmasını kullanır. Karar ağacı modeli, 3 eksenli HMC5983L manyetiksensöründen geçen araçlardan çıkarılan manyetik ham veri, ölçüm süresi gibi birdizi özellikten oluşturulmuştur. Özellikler, çapraz geçerlilik temelindedeğişen sınıflandırma oranları derecelerine sahip bir karar ağacı modeliüretmek için J48 eğitim algoritmasına doğru sınıflandırmalarla sağlananniteliklerdir. Makine öğrenmesi algoritması olan J48 kullanımı araçsınıflandırmasında daha verimli ve doğru sonuçlar verdiği görülmüştür. İlk yöntemle elde edilen   değerleri hesaplama aşamasında zorluklardoğurmuştur. Ancak J48 algoritması kullanılarak daha belirgin ve hassas sınırve eşik değerleri elde edilmiştir. Çalışmanın sonucu, araç sınıflandırmasisteminde önerilen algoritmanın eniyilemesiyle yaklaşık % 100 doğruluk payıile etkili ve verimli olduğunu göstermektedir.
KW  - Kablosuz sensör ağları
KW  - Manyetik sensör
KW  - Araç sınıflandırması
KW  - Makine öğrenmesi algoritması
KW  - Manyetik imza uzunluğu
N2  - Thedesign of Intelligent Transportation Systems (ITS) using wireless sensornetworks to observe any road traffic, get road information, or just identifyroad vehicles has recently become an interesting and popular research topicbecause of its advantages in cost and energy efficiency. To perform this study,sensor circuit consisting of sensor node, magnetometer, power board andbattery, is used. This sensor structure presents more accurate and intelligibleresults than sensor nodes used in other studies. Two different methods havebeen proposed to determine the type of vehicle with these sensor circuits. Inthe 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. Theaccuracy achieved with this method was 89%. In the other method, vehicleclassification was performed using machine learning algorithm J48 which is amachine learning decision tree extension and the obtained results wereoptimized based on the proposed method. It uses the J48 classificationalgorithm implemented in Weka, a machine learning software package. TheDecision Tree model was built from a series of features like magnetic raw data,measurement time derived from vehicles passing through the 3-axis HMC5983Lmagnetic sensor. The properties are those provided by the correctclassification into the J48 training algorithm to produce a decision tree modelwith grading ratios that vary on the basis of cross validity. The use of J48, amachine learning algorithm, has been shown to yield more efficient and accurateresults in vehicle classification. The MSL values obtained by the first methodhave caused difficulties in the calculation process. However, by using the J48algorithm, more specific and sensitive boundary and threshold values wereobtained. The result of the study illustrates that the vehicle classificationsystem is so effective and efficient with an accuracy rate of about 100% withoptimization of the proposed system.
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UR  - https://dergipark.org.tr/tr/pub/pajes/issue//419727
L1  - https://dergipark.org.tr/tr/download/article-file/465788
ER  -