Automation of the inspection process using novel computer vision and machine learning algorithms can be a more efficient and safe solution to prevent further deterioration of the masonry structures.
Experimental results indicate that the proposed MAV-based inspection approach can effectively collect images from multiple viewing angles, and accurately assess critical RC column damages.
In this paper, the robotic crane with advanced AI algorithms is proposed to provide resources for infrastructure reconstruction after an earthquake.
This work addresses the ecological-adaptive cruise control problem for connected electric vehicles by a computationally efficient robust control strategy.
However, such SPC-based representation i) optimizes under the volatile observation space which leads to the pose-misalignment between training and inference stages, and ii) lacks the global relationships among human parts that is critical for handling the incomplete painted SMPL.
Masked Language Models (MLMs) have proven to be effective for second-pass rescoring in Automatic Speech Recognition (ASR) systems.
The control objective of the RMPC is to optimise the electric energy efficiency of the ego vehicle with consideration of a bounded model mismatch disturbance subject to satisfaction of physical and safety constraints.
The development of electric and connected vehicles as well as automated driving technologies are key towards the smart city, with convenient urban mobility and high energy economy performance.
To address this issue, we propose the Ranking-based Backward Compatible Learning (RBCL), which directly optimizes the ranking metric between new features and old features.
Cooperative vehicle management emerges as a promising solution to improve road traffic safety and efficiency.
The paper first derives two important analytic results: a) analytic EM optimal solutions of fundamental and commonly used series HEV frameworks, and b) proof of optimality of charge sustaining operation in series HEVs.
By contrast, pixel-level optimization is more explicit, however, it is sensitive to the visual quality of training data and is not robust to object deformation.
In this study, artificial neural networks are developed with adaptive training algorithms, which enables automatic nodes generation and layers addition.
Then, the target-free object tracking algorithm based on optical flow is implemented, to continuously monitor and quantify the rotation of structural bolts.
The proposed YOLO-v2 is used in combination with the classification neural network, which improves the identification accuracy for critical damage state of reinforced concrete structures by 7. 5%.
Existing multilingual machine translation approaches mainly focus on English-centric directions, while the non-English directions still lag behind.
We investigate the following question for machine translation (MT): can we develop a single universal MT model to serve as the common seed and obtain derivative and improved models on arbitrary language pairs?
Ranked #3 on Machine Translation on WMT2014 English-French (using extra training data)
Abstract—Superpixel generation, which is an essential step in many image processing applications, has attracted increasing attention from researchers.