Transmission characteristics of planetary gear wear in multistage gear transmission system

Gear tooth surface wear is a common failure mode. It takes a long time and can cause other major faults. The wear fault signal is weak and hard to identify. This paper reveals the transmission characteristics of the planetary gear wear and the relationship between multistage gear meshing through the transmission characteristic analysis. Taking the multistage gear transmission system fault simulation test rig as the research object, a three-dimensional solid rigid-flexible coupling model of the system was established. The contact force signals from each pair of gears in the state of planetary gear wear were obtained by dynamic simulation. The test signals of the fixed-axis gearbox and planetary gearbox were obtained under normal and planetary gear wear states. Comparing the transmission characteristics of the system under normal and planetary gear wear states, the vibration mechanism of the wear fault and the correlative characteristic between gearboxes were revealed

Frame-wise streaming end-to-end speaker diarization with non-autoregressive self-attention-based attractors

This work proposes a frame-wise online/streaming end-to-end neural diarization (FS-EEND) method in a frame-in-frame-out fashion. To frame-wisely detect a flexible number of speakers and extract/update their corresponding attractors, we propose to leverage a causal speaker embedding encoder and an online non-autoregressive self-attention-based attractor decoder. A look-ahead mechanism is adopted to allow leveraging some future frames for effectively detecting new speakers in real time and adaptively updating speaker attractors. The proposed method processes the audio stream frame by frame, and has a low inference latency caused by the look-ahead frames. Experiments show that, compared with the recently proposed block-wise online methods, our method FS-EEND achieves state-of-the-art diarization results, with a low inference latency and computational cost

A unified fused Lasso approach for sparse and blocky feature selection in regression and classification

In many applications, sparse and blocky coefficients often occur in regression and classification problems. The fused Lasso was designed to recover these sparse structured features especially when the design matrix encounters the situation of ultrahigh dimension. Quantile loss is well known as a robust loss function in regression and classification. In this paper, we combine quantile loss and fused Lasso penalty together to produce quantile fused Lasso which can achieve sparse and blocky feature selection in both regression and classification. Interestingly, our proposed model has the unified optimization formula for regression and classification. For ultrahigh dimensional collected data, we derive multi-block linearized alternating direction method of multipliers (LADMM) to deal with it. Moreover, we prove convergence and derive convergence rates of the proposed LADMM algorithm through an elegant method. Note that the algorithm can be easily extended to solve many existing fused Lasso models. Finally, we present some numerical results for several synthetic and real world examples, which illustrate the robustness, scalability, and accuracy of the proposed method

Motion-aware Memory Network for Fast Video Salient Object Detection

Previous methods based on 3DCNN, convLSTM, or optical flow have achieved great success in video salient object detection (VSOD). However, they still suffer from high computational costs or poor quality of the generated saliency maps. To solve these problems, we design a space-time memory (STM)-based network, which extracts useful temporal information of the current frame from adjacent frames as the temporal branch of VSOD. Furthermore, previous methods only considered single-frame prediction without temporal association. As a result, the model may not focus on the temporal information sufficiently. Thus, we initially introduce object motion prediction between inter-frame into VSOD. Our model follows standard encoder--decoder architecture. In the encoding stage, we generate high-level temporal features by using high-level features from the current and its adjacent frames. This approach is more efficient than the optical flow-based methods. In the decoding stage, we propose an effective fusion strategy for spatial and temporal branches. The semantic information of the high-level features is used to fuse the object details in the low-level features, and then the spatiotemporal features are obtained step by step to reconstruct the saliency maps. Moreover, inspired by the boundary supervision commonly used in image salient object detection (ISOD), we design a motion-aware loss for predicting object boundary motion and simultaneously perform multitask learning for VSOD and object motion prediction, which can further facilitate the model to extract spatiotemporal features accurately and maintain the object integrity. Extensive experiments on several datasets demonstrated the effectiveness of our method and can achieve state-of-the-art metrics on some datasets. The proposed model does not require optical flow or other preprocessing, and can reach a speed of nearly 100 FPS during inference.Comment: 12 pages, 10 figure

General Rotation Invariance Learning for Point Clouds via Weight-Feature Alignment

Compared to 2D images, 3D point clouds are much more sensitive to rotations. We expect the point features describing certain patterns to keep invariant to the rotation transformation. There are many recent SOTA works dedicated to rotation-invariant learning for 3D point clouds. However, current rotation-invariant methods lack generalizability on the point clouds in the open scenes due to the reliance on the global distribution, \ie the global scene and backgrounds. Considering that the output activation is a function of the pattern and its orientation, we need to eliminate the effect of the orientation.In this paper, inspired by the idea that the network weights can be considered a set of points distributed in the same 3D space as the input points, we propose Weight-Feature Alignment (WFA) to construct a local Invariant Reference Frame (IRF) via aligning the features with the principal axes of the network weights. Our WFA algorithm provides a general solution for the point clouds of all scenes. WFA ensures the model achieves the target that the response activity is a necessary and sufficient condition of the pattern matching degree. Practically, we perform experiments on the point clouds of both single objects and open large-range scenes. The results suggest that our method almost bridges the gap between rotation invariance learning and normal methods.Comment: 4 figure

EgoVM: Achieving Precise Ego-Localization using Lightweight Vectorized Maps

Accurate and reliable ego-localization is critical for autonomous driving. In this paper, we present EgoVM, an end-to-end localization network that achieves comparable localization accuracy to prior state-of-the-art methods, but uses lightweight vectorized maps instead of heavy point-based maps. To begin with, we extract BEV features from online multi-view images and LiDAR point cloud. Then, we employ a set of learnable semantic embeddings to encode the semantic types of map elements and supervise them with semantic segmentation, to make their feature representation consistent with BEV features. After that, we feed map queries, composed of learnable semantic embeddings and coordinates of map elements, into a transformer decoder to perform cross-modality matching with BEV features. Finally, we adopt a robust histogram-based pose solver to estimate the optimal pose by searching exhaustively over candidate poses. We comprehensively validate the effectiveness of our method using both the nuScenes dataset and a newly collected dataset. The experimental results show that our method achieves centimeter-level localization accuracy, and outperforms existing methods using vectorized maps by a large margin. Furthermore, our model has been extensively tested in a large fleet of autonomous vehicles under various challenging urban scenes.Comment: 8 page

Integrating machine learning and single-cell trajectories to analyze T-cell exhaustion to predict prognosis and immunotherapy in colon cancer patients

IntroductionThe incidence of colon adenocarcinoma (COAD) has recently increased, and patients with advanced COAD have a poor prognosis due to treatment resistance. Combining conventional treatment with targeted therapy and immunotherapy has shown unexpectedly positive results in improving the prognosis of patients with COAD. More study is needed to determine the prognosis for patients with COAD and establish the appropriate course of treatment.MethodsThis study aimed to explore the trajectory of T-cell exhaustion in COAD to predict the overall survival and treatment outcome of COAD patients. Clinical data were derived from the TCGA-COAD cohort through "UCSC", as well as the whole genome data. Prognostic genes driving T-cell trajectory differentiation were identified on the basis of single-cell trajectories and univariate Cox regression. Subsequently, T-cell exhaustion score (TES) was created by iterative LASSO regression. The potential biological logic associated with TES was explored through functional analysis, immune microenvironment assessment, immunotherapy response prediction, and in vitro experiments.ResultsData showed that patients with significant TES had fewer favorable outcomes. Expression, proliferation, and invasion of COAD cells treated with TXK siRNA were also examined by cellular experiments. Both univariate and multivariate Cox regression indicated that TES was an independent prognostic factor in patients with COAD; in addition, subgroup analysis supported this finding. Functional assay revealed that immune response and cytotoxicity pathways are associated with TES, as the subgroup with low TES has an active immune microenvironment. Furthermore, patients with low TES responded better to chemotherapy and immunotherapy.ConclusionIn this study, we systematically explored the T-cell exhaustion trajectory in COAD and developed a TES model to assess prognosis and provide guidelines for the treatment decision. This discovery gave rise to a fresh concept for novel therapeutic procedures for the clinical treatment of COAD