Anti-rollover control of a heavy-duty vehicle based on lateral load transfer rate

With the rapid development of the highway network construction and heavy-duty vehicle market, the rollover accidents of heavy-duty vehicle continue to increase. In order to improve rollover stability of vehicle, a four degree of freedom (DOF) heavy-duty vehicle model is established. An anti-rollover control strategy is designed by using differential braking system to control the lateral load transfer rate (LTR). The dynamic simulation of vehicle with and without control is fulfilled in Matlab/Simulink. Then, the vehicle responses under typical angle step input are compared and analyzed with different road surface adhesion coefficient, vehicle speed, steering wheel angle and vehicle load. The results show that the proposed control strategy is able to improve vehicle rollover stability greatly and is also beneficial to vehicle yaw stability. The increase of road surface adhesion coefficient, vehicle speed, steering wheel angle or vehicle load has positive correlation with the rollover control effect

Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning

The abundant spatial and contextual information provided by the advanced remote sensing technology has facilitated subsequent automatic interpretation of the optical remote sensing images (RSIs). In this paper, a novel and effective geospatial object detection framework is proposed by combining the weakly supervised learning (WSL) and high-level feature learning. First, deep Boltzmann machine is adopted to infer the spatial and structural information encoded in the low-level and middle-level features to effectively describe objects in optical RSIs. Then, a novel WSL approach is presented to object detection where the training sets require only binary labels indicating whether an image contains the target object or not. Based on the learnt high-level features, it jointly integrates saliency, intraclass compactness, and interclass separability in a Bayesian framework to initialize a set of training examples from weakly labeled images and start iterative learning of the object detector. A novel evaluation criterion is also developed to detect model drift and cease the iterative learning. Comprehensive experiments on three optical RSI data sets have demonstrated the efficacy of the proposed approach in benchmarking with several state-of-the-art supervised-learning-based object detection approaches

Effect of hyperbaric oxygen therapy on cognitive impairment after aneurysm subarachnoid hemorrhage

Purpose: To evaluate the effect of hyperbaric oxygen therapy (HBOT) on cognitive impairment after aneurysm subarachnoid hemorrhage (aSAH). Methods: The current study was carried out in a regional neurosurgical center in Taiyuan, Shanxi Province of China from January 2019 to September 2020. A total of 150 patients with persistent cognitive dysfunction at 3 months after aSAH onset were enrolled, which were randomly classified into group A (HBOT) and group B (control) via the random number table method. The outcome was evaluated by Montreal cognitive assessment (MoCA). Results: There were no significant differences between group A and group B with regard to MoCA score and proportions of normal MoCA patients at 3 months after HBOT (p > 0.05). Both groups showed no significant differences in proportions of normal MoCA patients at 6 months after HBOT (p > 0.05). However, there were significant differences between group A and group B with MoCA score of patients at 6 months after HBOT (p < 0.05). There were also significant differences in MoCA score and proportions of normal MoCA patients at 9 months after HBOT. Conclusion: HBOT alleviates cognitive impairment after aSAH, and thus may be used to manage cognitive impairment in patients after aSAH. However, further clinical trials are required prior to application in clinical practice

Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images

Land-use classification using remote sensing images covers a wide range of applications. With more detailed spatial and textural information provided in very high resolution (VHR) remote sensing images, a greater range of objects and spatial patterns can be observed than ever before. This offers us a new opportunity for advancing the performance of land-use classification. In this paper, we first introduce an effective midlevel visual elements-oriented land-use classification method based on “partlets,” which are a library of pretrained part detectors used for midlevel visual elements discovery. Taking advantage of midlevel visual elements rather than low-level image features, a partlets-based method represents images by computing their responses to a large number of part detectors. As the number of part detectors grows, a main obstacle to the broader application of this method is its computational cost. To address this problem, we next propose a novel framework to train coarse-to-fine shared intermediate representations, which are termed “sparselets,” from a large number of pretrained part detectors. This is achieved by building a single-hidden-layer autoencoder and a single-hidden-layer neural network with an L0-norm sparsity constraint, respectively. Comprehensive evaluations on a publicly available 21-class VHR land-use data set and comparisons with state-of-the-art approaches demonstrate the effectiveness and superiority of this paper

Background prior-based salient object detection via deep reconstruction residual

Detection of salient objects from images is gaining increasing research interest in recent years as it can substantially facilitate a wide range of content-based multimedia applications. Based on the assumption that foreground salient regions are distinctive within a certain context, most conventional approaches rely on a number of hand designed features and their distinctiveness measured using local or global contrast. Although these approaches have shown effective in dealing with simple images, their limited capability may cause difficulties when dealing with more complicated images. This paper proposes a novel framework for saliency detection by first modeling the background and then separating salient objects from the background. We develop stacked denoising autoencoders with deep learning architectures to model the background where latent patterns are explored and more powerful representations of data are learnt in an unsupervised and bottom up manner. Afterwards, we formulate the separation of salient objects from the background as a problem of measuring reconstruction residuals of deep autoencoders. Comprehensive evaluations on three benchmark datasets and comparisons with 9 state-of-the-art algorithms demonstrate the superiority of the proposed work

SAFDet: a semi-anchor-free detector for effective detection of oriented objects in aerial images.

An oriented bounding box (OBB) is preferable over a horizontal bounding box (HBB) in accurate object detection. Most of existing works utilize a two-stage detector for locating the HBB and OBB, respectively, which have suffered from the misaligned horizontal proposals and the interference from complex backgrounds. To tackle these issues, region of interest transformer and attention models were proposed, yet they are extremely computationally intensive. To this end, we propose a semi-anchor-free detector (SAFDet) for object detection in aerial images, where a rotation-anchor-free-branch (RAFB) is used to enhance the foreground features via precisely regressing the OBB. Meanwhile, a center-prediction-module (CPM) is introduced for enhancing object localization and suppressing the background noise. Both RAFB and CPM are deployed during training, avoiding increased computational cost of inference. By evaluating on DOTA and HRSC2016 datasets, the efficacy of our approach has been fully validated for a good balance between the accuracy and computational cost