Su boru hatlarında sızıntı konumlarını ses ve ımu sensör verileri kullanarak tespit eden bir robot tasarlamak Genişletilmiş kalman filtresi kullanarak sensör füzyonu yapılması
Sarcastic text is a type of text that contains a kind of irony, in which negative expressions are expressed as positive by attributing meanings to words that contradict their real meanings during communication. During face-to-face communication, changes in tone of voice, body language, eye contact or word stress make it easier for the other person to detect the sarcastic expression. However, it is challenging to detect sarcastic expressions only through text in machine learning-based systems since human-centred qualities cannot be transferred. Newspapers often use sarcastic expressions in their headlines to attract people's attention. However, many people cannot fully understand whether these expressions are sarcastic or not without reading the content of the text. As a result, they can transmit false information to the people around them through direct communication or social media. In this study, to prevent such misinformation, newspaper headlines with and without sarcasm are tried to be classified on three different GPUs with deep learning methods. As a result, the developed model can successfully detect news headlines containing sarcasm.
Water is vital for all living beings, especially for a human. Automatic position detection of water leakage in water pipelines is very important to minimize the loss of labour, time, money spent on exploration and excavation in pipe inspection procedures. The main goal of detection is to prevent water loss. In this study, sensitive position detection, crack frequency band detection and external sphere studies of an in-pipe robot prototype have performed. During the precise position estimation, classical EKF, stationary region detection and location estimation using EHDE are performed with two different ANNs. In this way, online precise position estimation can be done on hardware that has not sufficient computational power for indoor robotic studies. In addition, the sound characteristics resulting from the crack at different hole size and water pressure intensity levels have investigated. Finally, a new sealing sphere design has devised and three different hydrophone sensor data have recorded on the SD card simultaneously. It has been found that the proposed ANN method has the performance to work online and can make a similar position estimation with the classical IMU position estimation method by 99%.
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