This work presents an offshore pipeline scour monitoring sensor network system based on active thermometry. The system consists of thermal cables, data acquisition unit, and data processing unit. As the thermal cables emit heats, the distributed DS18B20 digital temperature sensors record temperature information over time. The scour-induced exposure and free spanning can be identified by analyzing the temperature curves. Pipeline exposure and free-spanning experiments were carried out in laboratory, whose results show that the system is able to give overall information about the development of pipeline scour. Difference values analysis reveals the changing patterns of heat transfer behavior for line heat source in sediment and water scenarios. Two features, magnitude and temporal instability, are extracted from temperature curves to better differentiate sediment and water scenarios. Based on these two features, K-means clustering algorithm is adopted for pattern classification of the system, which was implemented in MATLAB and facilitated the automatic detection of the scour monitoring sensor network system. The proposed sensor network has the advantages of low cost, high precision and construction flexiblility, providing a promising approach for offshore pipeline scour monitoring, especially suitable for nearshore environment.
Bridge management and maintenance work is an important part for the assessment the health state of bridge. The conventional management and maintenance work mainly relied on experienced engineering staffs by visual inspection and filling in survey forms. However, the human-based visual inspection is a difficult and time-consuming task and its detection results significantly rely on subjective judgement of human inspectors. To address the drawbacks of human-based visual inspection method, this paper proposes an image-based comprehensive maintenance and inspection method for bridges using deep learning. To classify the types of bridges, a convolutional neural network (CNN) classifier established by fine-turning the AlexNet is trained, validated and tested using 3832 images with three types of bridges (arch, suspension and cable-stayed bridge). For the recognition of bridge components (tower and deck of bridges), a Faster Region-based Convolutional Neural Network (Faster R-CNN) based on modified ZF-net is trained, validated and tested by utilizing 600 bridge images. To implement the strategy of a sliding window technique for the crack detection, another CNN from fine-turning the GoogLeNet is trained, validated and tested by employing a databank with cropping 1455 raw concrete images into 60000 intact and cracked images. The performance of the trained CNNs and Faster R-CNN is tested on some new images which are not used for training and validation processes. The test results substantiate the proposed method can indeed recognize the types and components and detect cracks for a bridges.
Three dimensional Network-on-chip (3D NoC) is proposed as an effective architecture to optimize system performance. However, thermal issues bring significant challenges on 3D NoC due to high power density. In this paper, we propose a 3D matrix synthesis problem (MSP) based thermalaware mapping approach under performance constraints for 3D NoC architecture to realize temperature equilibrium and achieve better performance. Genetic algorithm is taken in the approach to obtain the optimal placements. Experimental results show that the proposed approach can achieve a temperature deviation of 45.3% on average compared with the state of art thermal optimization approaches. Moreover, our approach achieves 9.43% power saving and 14.88% delay reduction. Keywords: 3D Network-on-Chip, MSP, thermal, mapping Classification: Electron devices, circuits, and systems IEICE Electronics Express, Vol.13, No.7,[1][2][3][4][5][6][7][8][9] Hardware/Software Codesign (1988).
References
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.