Kapalı Mekân Ortamında 1D-CNN Kullanarak Yapılan Doluluk Tespiti Sınıflandırması

Year 2023, Volume: 13 Issue: 1, 60 - 71, 15.03.2023

Erkan Güler Ar. Gör. Muhammet Talha Kakız Faruk Baturalp Gunay Burcu Şanal Tuğrul Çavdar

https://doi.org/10.31466/kfbd.1162332

Abstract

Derin Öğrenme modelleri kompleks deneyimlerden bilgi çıkarımına imkân sağlayan spesifik Makine Öğrenmesi yöntemleridir. Kapalı bir mekândaki bazı veri değerlerindeki değişiminin öğrenilmesi ile odada herhangi bir kişinin bulunup bulunmamasının tespit edilmesi bu deneyimlerden biridir. Bu çalışmanın amacı zaman serileri olarak oluşturulmuş ve zaman içinde ışık, sıcaklık, nem ve CO2 değerlerindeki değişimler ile kapalı bir mekânda doluluk tespiti probleminin Bir Boyutlu Evrişimli Sinir Ağı (1D-CNN) ile gerçekleştirilmesidir. Bir adet eğitim ve iki adet test veri seti kullanılarak model eğitilmiş ve daha önce tecrübe edilmeyen test veri setleri ile modelin başarısı gözlenmiştir. Keras uygulama programlama arayüzünde 1D-CNN modeli ile gerçekleştirilen testlerde doluluk tespiti sınıflandırmasının RF (Random Forest), GBM (Gradient Boosting Machines), CART (Classification and Regression Trees), LDA (Linear Discriminant Analysis) yöntemlerinden daha başarılı sonuçlar verdiği gözlenmiştir.

Keywords

Doluluk Tespiti, 1D-CNN, Zaman Serisi, Sınıflandırma, Derin Öğrenme

Occupancy Detection Classification Using 1D-CNN in Indoor Environment

Abstract

Deep Learning models are specific Machine Learning methods that allow to extract knowledge from complex experiences. Learning the change in some data values in an indoor environment and detecting whether there is any person in the room is one of these experiences. The aim of this study is to realize the problem of determining the occupancy in an indoor space with changes in light, temperature, humidity and CO2 values over time, using a One-Dimensional Convolutional Network (1D-CNN). The model has been trained using one training and two test datasets, and the success of the model has been observed with test datasets that the model has not been experienced before. In the tests performed with the 1D-CNN model in the Keras application programming interface, it has been observed that the occupancy detection classification has given more successful results than the RF (Random Forest), GBM (Gradient Boosting Machines), CART (Classification and Regression Trees), LDA (Linear Discriminant Analysis) methods.

Keywords

Occupancy Detection, 1D-CNN, Time Series, Classification, Deep Learning

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