Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors

Visual localization is an attractive problem that estimates the camera localization from database images based on the query image. It is a crucial task for various applications, such as autonomous vehicles, assistive navigation and augmented reality. The challenging issues of the task lie in various appearance variations between query and database images, including illumination variations, dynamic object variations and viewpoint variations. In order to tackle those challenges, Panoramic Annular Localizer into which panoramic annular lens and robust deep image descriptors are incorporated is proposed in this paper. The panoramic annular images captured by the single camera are processed and fed into the NetVLAD network to form the active deep descriptor, and sequential matching is utilized to generate the localization result. The experiments carried on the public datasets and in the field illustrate the validation of the proposed system.Comment: Accepted by ITSC 201

Differentiation of malignant brain tumor types using intratumoral and peritumoral radiomic features

Tumor infiltration of central nervous system (CNS) malignant tumors may extend beyond visible contrast enhancement. This study explored tumor habitat characteristics in the intratumoral and peritumoral regions to distinguish common malignant brain tumors such as glioblastoma, primary central nervous system lymphoma, and brain metastases. The preoperative MRI data of 200 patients with solitary malignant brain tumors were included from two datasets for training. Quantitative radiomic features from the intratumoral and peritumoral regions were extracted for model training. The performance of the model was evaluated using data (n = 50) from the third clinical center. When combining the intratumoral and peritumoral features, the Adaboost model achieved the best area under the curve (AUC) of 0.91 and accuracy of 76.9% in the test cohort. Based on the optimal features and classifier, the model in the binary classification diagnosis achieves AUC of 0.98 (glioblastoma and lymphoma), 0.86 (lymphoma and metastases), and 0.70 (glioblastoma and metastases) in the test cohort, respectively. In conclusion, quantitative features from non-enhanced peritumoral regions (especially features from the 10-mm margin around the tumor) can provide additional information for the characterization of regional tumoral heterogeneity, which may offer potential value for future individualized assessment of patients with CNS tumors

Gear Fault Diagnosis Based on Kurtosis Criterion VMD and SOM Neural Network

A gear fault diagnosis method based on kurtosis criterion variational mode decomposition (VMD) and self-organizing map (SOM) neural network is proposed. Firstly, the VMD algorithm is used to decompose the gear vibration signal, and the instantaneous frequency mean is calculated as the evaluation index, and the characteristic curve is drawn to screen out the most relevant intrinsic mode functions (IMFs) of the original vibration signal. Then, the number of VMD decompositions is determined, and the kurtosis value of IMFs are extracted to form the feature vectors. Then, the kurtosis value feature vectors of IMFs are normalized to form the kurtosis value normalized vectors. Finally, the normalized vectors of kurtosis value are input into SOM neural network to realize gear fault diagnosis. When the number of training times of SOM neural network is 100, the gear fault category is accurately classified by SOM neural network. The results show that when the training times of SOM neural network is 100 times, the gear fault diagnosis method, based on the kurtosis criterion VMD and SOM neural network is 100%, which indicates that the new method has a good effect on gear fault diagnosis

Mathematics Mechanization and Applications

International audienc

Computer Mathematics - Proceedings of the Fourth Asian Symposium - ASCM'2000

Ouvrage (éditeur).This volume contains selected papers presented at the Fourth Asian Symposium on Computer Mathematics. 39 peer-reviewed original contributions together with full papers and extended abstracts by the four invited speakers, G H Gonnet, D Lazard, W McCune, and W-T Wu, cover some of the most recent and significant advances in computer mathematics, including algebraic, symbolic, numeric, and geometric computation, automated mathematical reasoning, mathematical software, and computer-aided geometric design. Researchers, teachers, students, and engineers interested in doing mathematics using computers will find this volume good reading and a valuable reference

A Fault Feature Extraction Method for Rolling Bearing Based on Intrinsic Time-Scale Decomposition and AR Minimum Entropy Deconvolution

It is very difficult to extract the feature frequency of the vibration signal of the rolling bearing early weak fault and in order to extract its feature frequency quickly and accurately. A method of extracting early weak fault vibration signal feature frequency of the rolling bearing by intrinsic time-scale decomposition (ITD) and autoregression (AR) minimum entropy deconvolution (MED) is proposed in this paper. Firstly, the original early weak fault vibration signal of the rolling bearing is decomposed by the ITD algorithm to proper rotations (PRs) with fault feature frequency. Then, the sample entropy value of each PR is calculated to find the largest PRs of the sample entropy. Finally, the AR-MED filtering algorithm is utilized to filter and reduce the noise of the largest PRs of the sample entropy value, and the early weak fault vibration signal feature frequency of the rolling bearing is accurately extracted. The results show that the ITD-AR-MED method can extract the early weak fault vibration signal feature frequency of the rolling bearing accurately

Alleviation of Rheumatoid Arthritis by Inducing IDO Expression with Trichinella spiralis Recombinant Protein 43

Rheumatoid arthritis (RA) represents the autoimmune disorder that shows aggressive arthritis as the main symptom. It is difficult to treat and can lead to joint deformation and function loss. At present, Trichinella spiralis (T. spiralis) antigen has attracted much attention because it plays a role in host immune regulatory mechanisms. Therefore, we selected T. spiralis recombinant protein 43 (Tsp43) to treat the bovine collagen type II (BCII)-induced mice RA model and explored its therapeutic mechanisms. This work first verified that Tsp43 could promote the expression of indoleamine 2, 3-dioxygenase (IDO) in dendritic cells (DCs) in vitro. Then, we randomized BALB/c mice (8 weeks old) into six groups, including control, phosphate buffer saline (PBS), BCII, BCII + heat inactivated Tsp43 (HiTsp43), BCII + Tsp43, and BCII + Tsp43 + 1-methyl-troptophan (1-MT) groups. To determine the therapeutic effect of Tsp43 on the BCII-induced mice RA model, relevant cytokines in each group and pathological changes in ankle joints were detected. To explore the mechanisms of Tsp43 on the BCII-induced mice RA model, we checked the expression of IDO in each group, CD4+T cell proliferation, and apoptosis. Collectively, Tsp43 decreased tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) expression in BCII-induced mice RA model and recovered the ankle injury to a certain extent. Tsp43 promoted high expression of IDO, caused expression of related apoptotic proteins in CD4+T cells, and caused apoptosis in CD4+T cells. In addition, Tsp43 reduced the proliferation of CD4+T cells. However, these effects can be inhibited by 1-MT (IDO inhibitor). These results suggested that Tsp43 played an important role in the treatment of arthritis by inhibiting the proliferation of CD4+T cells and inducing CD4+T cells apoptosis through the high expression of IDO. The purpose of this experiment was to provide a new idea for the treatment of RA and lay a foundation for the development of parasite-derived drugs for the treatment of RA

Analysis of Thermo-Elastic Fracture Problem during Aluminium Alloy MIG Welding Using the Extended Finite Element Method

The thermo-elastic fracture problem and equations are established for aluminium alloy Metal Inert Gas (MIG) welding, which include a moving heat source and a thermoelasticity equation with the initial and boundary conditions for a plate structure with a crack. The extended finite element method (XFEM) is implemented to solve the thermo-elastic fracture problem of a plate structure with a crack under the effect of a moving heat source. The combination of the experimental measurement and simulation of the welding temperature field is done to verify the model and solution method. The numerical cases of the thermomechanical parameters and stress intensity factors (SIFs) of the plate structure in the welding heating and cooling processes are investigated. The research results provide reference data and an approach for the analysis of the thermomechanical characteristics of the welding process