Sabreen J. Siwan; Waleed Fawwaz Shareef; Ahmed Nasser
Abstract
Petroleum is the economic infrastructure in Iraq because it generates a significant portion of the country's revenue and can be considered as the primary source of financial costs each year. As a consequence, it is critical to protect the sector and continue to develop it. Therefore, it is important ...
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Petroleum is the economic infrastructure in Iraq because it generates a significant portion of the country's revenue and can be considered as the primary source of financial costs each year. As a consequence, it is critical to protect the sector and continue to develop it. Therefore, it is important to track and maintain pipelines regularly to detect defects on time. In pipeline monitor and control, the advent of the Internet of Things (IoT) technology and the deployment of embedded sensing systems enable successful pipeline maintenance with the simple requirement for real-time precise measurements. In this paper, a wireless network based on an IoT system and integrated with cloud service is proposed for structure monitoring of oil pipelines, to detect the risks on the structure such as tampering and/or wear and tear effects. The method is based on collecting data from a sensor node equipped with an RF module attached to the pipeline structure. These nodes collectively form a network of IoT devices connected to the cloud server. The raw data is collected, stored, and statistically analyzed to be accessible by the user anytime and anywhere through the Internet. The performance of the system is evaluated in different cases, including the distance about the node to detect events on the pipe and to discriminate the distance of event to determine the location the event it was tested by using four different states of the transmitted data.
Mahmoud M. Mahmoud; Ahmed R. Nasser
Volume 21, Issue 2 , June 2021, , Page 36-43
Abstract
Object detection of autonomous vehicles presents a big challenge forresearchers due to the requirements of accuracy and precision in real-time.This work presents a deep learning approach based on a dual architecturedesign of the network. A highly accurate multi-class network of convolutionalneural networks ...
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Object detection of autonomous vehicles presents a big challenge forresearchers due to the requirements of accuracy and precision in real-time.This work presents a deep learning approach based on a dual architecturedesign of the network. A highly accurate multi-class network of convolutionalneural networks (CNN) is presented for input data classification. A Region-Based Convolutional Neural Networks (Faster R-CNN) network with a modifiedFeature Pyramid Networks (FPN) is used for better detection of tiny objects andYou Only Look Once (YOLOv3) network is used for general detection. Eachnetwork independently detects the existence of an object. The decision maps arethen fused and compared to decide whether an object is present or not. FasterR-CNN with FPN model reported a higher intersection over Union (IoU) andmean average precision (mAP) than the YOLOv3. This approach is reliabledemonstrating an upgrade on the existing state-of-the-art methods of fullyconnected networks.
