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When performing data classification tasks, it often occurs to them the curse of dimensionality problem. To address the issue, a manifold learning method termed locally conjugated orthogonal subspace (LCOS) is put forward for dimensionality reduction or feature extraction in this paper. Note that point to feature space (P2S) distance contributes to mining local geometry information, both a local margin characterizing data apartness and a locally conjugated orthogonal constraint beneficial to removing data redundancy are well studied from the P2S distance metric. They are all exploited to model the proposed LCOS. Then, a low dimensional subspace can be explored by maximizing the P2S distance induced local margin under the constraint. Compared with some other related dimensionality reduction methods, experimental results on benchmark face and object data sets validate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Real-time shield tail clearance measurement and monitoring is a key task during shield tunneling construction. The shield tail clearance measurement and monitoring technology development is still in its infancy, the current methods are mainly designed manually based on intuition. In order to fill the gap between the requirement of shield tail clearance measurement and monitoring and the limitations of the current methods, this paper systematically studies the existing mechanisms related to shield tail clearance measurement and monitoring, and develops a high-precision intelligent monitoring system for shield tail clearance. The proposed monitoring system includes four components: 1) two types of shield tail clearance calculation models, 2) the integrated hardware of the monitoring system which is composed of a data acquisition unit, a signal transmission unit and a control unit, 3) the region of interest (ROI) extraction method based on deep neural network, and the image processing algorithms for image enhancement and feature extraction, 4) the custom-developed software built on mature integrated development environment (IDE). After the calculation model of shield tail clearance is established, the system uses monitoring devices equipped with industrial cameras to obtain the on-site image, and then applies image processing technologies along with deep learning approach to extract the key features, which are brought into the model to calculate the values of shield tail clearance, finally displays these values and simulates the current tunneling attitude of the shield machine in real time. The experimental results show that the system proposed in this paper achieves the goal of high precision measuring and real-time monitoring of the shield tail clearance. • A high-precision intelligent monitoring system for shield tail clearance is proposed and invented. • Two types of shield tail clearance calculation models are presented. • The ROI extraction is taken as an object detection problem and solved by an end-to-end solution. • Comprehensive algorithms of image processing are used for image enhancement and feature extraction. • The high accuracy, stability and real-time performance of the system have been confirmed through experimental testing. [ABSTRACT FROM AUTHOR]