Research on Axial Mechanical Properties of the Grouted Connection Section Considering Installation Errors

[Introduction] With the development of offshore wind turbine works to deep sea areas, the challenging construction environment tends to result in errors in the installation of the grouted connection for the jacket foundation. These errors can subsequently affect the axial mechanical properties of the grouted connection. Therefore, it is necessary to study the impact laws of installation errors on the axial mechanical properties of the grouted connection section. [Method] The study was commenced by conducting axial static loading tests on reduced-scale test piece of the grouted connection section, which was followed by simulating the axial loading process of the corresponding test piece using the finite element analysis method. The simulation results were found to align well with the experimental data, indicating a successful outcome. [Result] According to the research findings, the increasing in longitudinal and transverse installation errors can lead to an increase in the axial stiffness of the grouted connection section. This, in turn, further alters the longitudinal strain distribution of the casing and pile pipe. Additionally, the increase in installation errors can lead to an increase in the maximum value of the third principal stress in the grouting materials during the axial loading process, as well as changes in its distribution location. [Conclusion] In conclusion, the influence of installation errors on the axial mechanical properties of the grouted connection section for the jacket foundation can cause alterations in failure modes of the grouted connection section. Therefore, it is needed to consider and evaluate the harm caused by the impact laws of installation errors based on their influence rules

Photoactive platinum(IV) complex conjugated to a cancer-cell-targeting cyclic peptide

A conjugate of cancer-cell targeting cyclic disulphide non a-peptide c(CRWYDENAC) consisting of nine L-amino acids with the photoactive succinate platinum(IV)complex trans, trans-[Pt(N3)2(py)2(OH)(succinate)] (Pt-cP) has been synthesised and characterised. The conjugate was stable in dark, but released succinate–peptide and Pt(II) species upon irradiation with visible light, and formed photoproducts with guanine. Conjugate Pt-cP exhibited higher photocytotoxicity than parent complextrans, trans, trans-[Pt(N3)2(OH)2(py)2] (FM-190) towards cancer cells, including ovarian A2780, lung A549 and prostate PC3 human cancer cells upon irradiation with blue light (465 nm, 17.28 J cm−2) with IC50values of 2.8–22.4μM and the highest potency for A549 cells. Even though the dark cellular accumulation of Pt-cP in A2780 cells was lower than that of parent FM-190, Pt from Pt-cP accumulated in cancer cells upon irradiation to a level >3× higher than that fromFM-190. In addition, the cellular accumulation of Pt from Pt-cPwas enhanced ca. 47× after irradiation

Alleviate Similar Object in Visual Tracking via Online Learning Interference-Target Spatial Structure

Correlation Filter (CF) based trackers have demonstrated superior performance to many complex scenes in smart and autonomous systems, but similar object interference is still a challenge. When the target is occluded by a similar object, they not only have similar appearance feature but also are in same surrounding context. Existing CF tracking models only consider the target’s appearance information and its surrounding context, and have insufficient discrimination to address the problem. We propose an approach that integrates interference-target spatial structure (ITSS) constraints into existing CF model to alleviate similar object interference. Our approach manages a dynamic graph of ITSS online, and jointly learns the target appearance model, similar object appearance model and the spatial structure between them to improve the discrimination between the target and a similar object. Experimental results on large benchmark datasets OTB-2013 and OTB-2015 show that the proposed approach achieves state-of-the-art performance

Adaptive Correlation Model for Visual Tracking Using Keypoints Matching and Deep Convolutional Feature

Although correlation filter (CF)-based visual tracking algorithms have achieved appealing results, there are still some problems to be solved. When the target object goes through long-term occlusions or scale variation, the correlation model used in existing CF-based algorithms will inevitably learn some non-target information or partial-target information. In order to avoid model contamination and enhance the adaptability of model updating, we introduce the keypoints matching strategy and adjust the model learning rate dynamically according to the matching score. Moreover, the proposed approach extracts convolutional features from a deep convolutional neural network (DCNN) to accurately estimate the position and scale of the target. Experimental results demonstrate that the proposed tracker has achieved satisfactory performance in a wide range of challenging tracking scenarios

Consistently Sampled Correlation Filters with Space Anisotropic Regularization for Visual Tracking

Most existing correlation filter-based tracking algorithms, which use fixed patches and cyclic shifts as training and detection measures, assume that the training samples are reliable and ignore the inconsistencies between training samples and detection samples. We propose to construct and study a consistently sampled correlation filter with space anisotropic regularization (CSSAR) to solve these two problems simultaneously. Our approach constructs a spatiotemporally consistent sample strategy to alleviate the redundancies in training samples caused by the cyclical shifts, eliminate the inconsistencies between training samples and detection samples, and introduce space anisotropic regularization to constrain the correlation filter for alleviating drift caused by occlusion. Moreover, an optimization strategy based on the Gauss-Seidel method was developed for obtaining robust and efficient online learning. Both qualitative and quantitative evaluations demonstrate that our tracker outperforms state-of-the-art trackers in object tracking benchmarks (OTBs)

Multi-View Structural Local Subspace Tracking

In this paper, we propose a multi-view structural local subspace tracking algorithm based on sparse representation. We approximate the optimal state from three views: (1) the template view; (2) the PCA (principal component analysis) basis view; and (3) the target candidate view. Then we propose a unified objective function to integrate these three view problems together. The proposed model not only exploits the intrinsic relationship among target candidates and their local patches, but also takes advantages of both sparse representation and incremental subspace learning. The optimization problem can be well solved by the customized APG (accelerated proximal gradient) methods together with an iteration manner. Then, we propose an alignment-weighting average method to obtain the optimal state of the target. Furthermore, an occlusion detection strategy is proposed to accurately update the model. Both qualitative and quantitative evaluations demonstrate that our tracker outperforms the state-of-the-art trackers in a wide range of tracking scenarios

Commutation Torque Ripple Suppression Strategy of Brushless DC Motor Considering Back Electromotive Force Variation

This paper presents a commutation torque ripple suppression strategy for brushless DC motor (BLDCM) in the high-speed region, which considers the back electromotive force (back-EMF) variation during the commutation process. In the paper, the influence of actual back-EMF variation on the torque and outgoing phase current during the commutation process is analyzed. A modified smooth torque mechanism is then reconstructed considering the back-EMF variation, based on which a novel torque ripple suppression strategy is further designed. Compared with the traditional strategy which controls the chopping duty cycle relatively smoothly in the commutation process, the proposed strategy dynamically regulates the chopping duty cycle, which makes it show a gradual decrease. This strategy can suppress the commutation torque ripple even in a long commutation process, and broaden the speed range of the commutation torque ripple reduction. Under the experimental conditions of this paper, the proposed strategy can effectively reduce the commutation torque ripple in the high-speed region, and avoid the outgoing phase current cannot be reduced to zero. The experimental results verify the correctness of the theoretical analysis and the feasibility of the proposed strategy

Alleviate Similar Object in Visual Tracking via Online Learning Interference-Target Spatial Structure

Correlation Filter (CF) based trackers have demonstrated superior performance to many complex scenes in smart and autonomous systems, but similar object interference is still a challenge. When the target is occluded by a similar object, they not only have similar appearance feature but also are in same surrounding context. Existing CF tracking models only consider the target’s appearance information and its surrounding context, and have insufficient discrimination to address the problem. We propose an approach that integrates interference-target spatial structure (ITSS) constraints into existing CF model to alleviate similar object interference. Our approach manages a dynamic graph of ITSS online, and jointly learns the target appearance model, similar object appearance model and the spatial structure between them to improve the discrimination between the target and a similar object. Experimental results on large benchmark datasets OTB-2013 and OTB-2015 show that the proposed approach achieves state-of-the-art performance