Identification of Contact Stiffness between Brake Disc and Brake Pads Using Modal Frequency Analysis

The contact stiffness between brake disc and brake pads is a vital parameter that affects brake NVH performance through increasing the system stiffness and modal frequencies. In order to establish accurate contact behavior between brake parts for further research on precise modeling of disc brakes, a method of identifying the normal contact stiffness of a floating caliper disc brake was developed in this study based on modal frequency testing and finite element analysis. The results showed that contact stiffness increases with brake pressure due to compression of the friction material and increases with the disc mode order at lower-order modes but almost stays invariant at higher-order ones due to contact area variation

RAIN: RegulArization on Input and Network for Black-Box Domain Adaptation

Source-Free domain adaptation transits the source-trained model towards target domain without exposing the source data, trying to dispel these concerns about data privacy and security. However, this paradigm is still at risk of data leakage due to adversarial attacks on the source model. Hence, the Black-Box setting only allows to use the outputs of source model, but still suffers from overfitting on the source domain more severely due to source model's unseen weights. In this paper, we propose a novel approach named RAIN (RegulArization on Input and Network) for Black-Box domain adaptation from both input-level and network-level regularization. For the input-level, we design a new data augmentation technique as Phase MixUp, which highlights task-relevant objects in the interpolations, thus enhancing input-level regularization and class consistency for target models. For network-level, we develop a Subnetwork Distillation mechanism to transfer knowledge from the target subnetwork to the full target network via knowledge distillation, which thus alleviates overfitting on the source domain by learning diverse target representations. Extensive experiments show that our method achieves state-of-the-art performance on several cross-domain benchmarks under both single- and multi-source black-box domain adaptation.Comment: Accepted by IJCAI 202

Single-port laparoscopic sacrospinous ligament suspension via the natural vaginal cavity (SvNOTES) for pelvic prolapse: The first feasibility study

ObjectiveThis study aims to investigate the feasibility and short-term efficacy of single-port laparoscopic-assisted transvaginal natural cavity endoscopic sacrospinous ligament suspensions (SvNOTES).MethodsA total of 30 patients diagnosed with anterior or/and middle pelvic organ prolapse Stages III and IV underwent natural vaginal cavity (SvNOTES), and 30 patients who underwent conventional sacrospinous ligament (SSLF) were used as a control group. The operation time, blood loss, postoperative POP-Q score, length of hospital stay, and complications were compared between the two groups.ResultsThe operation time for SvNOTE was (60 ± 13) min, which was longer than (30 ± 15) min for SSLF (P = 0.04). However, the bleeding amount in SvNOTE was 29.44 ± 2.56, significantly lower than that in the SSLF group (80 ± 10; P = 0.02), and the postoperative hospital stay in the SvNOTE group was (4 ± 2) days, longer than (3 ± 1) days in SSLF (P = 0.02). However, there were no intraoperative complications in the SvNOTE group, whereas one ureteral injury occurred in the SSLF group; in addition, the postoperative POP-Q score was significantly better in the SvNOTE group than that in the SSLF group with increasing time (P < 0.001).ConclusionCompared with SSLF, single-port laparoscopic sacrospinous ligament suspension via the natural vaginal cavity is visualized, greatly improving the success rate of sacrospinous ligament fixation, with less blood loss and fewer complications, arguably a safer and minimally invasive surgical approach

Distributed Fixed-Time Control for Leader-Steered Rigid Shape Formation with Prescribed Performance

Resorting to the principle of rigid body kinematics, a novel framework for a multi-robot network is proposed to form and maintain an invariant rigid geometric shape. Unlike consensus-based formation, this approach can perform both translational and rotational movements of the formation geometry, ensuring that the entire formation motion remains consistent with the leader. To achieve the target formation shape and motion, a distributed control protocol for multiple Euler-Lagrange robotic vehicles subject to nonholonomic constraints is developed. The proposed protocol includes a novel prescribed performance control (PPC) algorithm that addresses the second-order dynamics of the robotic vehicles by employing a combination of nonsingular sliding manifold and adaptive law. Finally, the effectiveness of the proposed formation framework and control protocol is demonstrated through the numerical simulations and practical experiments with a team of four robotic vehicles

Bundle folding type III Bricard linkages

The paper presents a set of one-degree-of-freedom overconstrained linkages, which can be folded into a bundle and deployed into a polygon on a plane. The proposed mechanisms are movable Bricard octahedra of type III, characterized by the existence of two configurations where all joints are coplanar. The possible geometries of doubly-collapsible Bricard linkages are parameterized and their kinematics is analyzed. A line-intersection method is proposed to construct a bundle-folding mechanism of this type. Necessary and sufficient conditions are derived for the deployed-configuration polygon to be a square. Simulation and prototype experiment results validate the analysis and design.This research has been supported by the National Natural Science Foundation of China under Grant 51605011, 51635002(Key Program), the Fundamental Research Funds for the Central Universities (YWF-19-BJ-J-336), the State Key Laboratory of Robotics and System (HIT), and the AUTORECON project funded under the Seventh Framework Program of the European Commission (Collaborative Project NMP-FOF-2011-285189). The authors gratefully acknowledge the supporting agencies

On-Device Model Fine-Tuning with Label Correction in Recommender Systems

To meet the practical requirements of low latency, low cost, and good privacy in online intelligent services, more and more deep learning models are offloaded from the cloud to mobile devices. To further deal with cross-device data heterogeneity, the offloaded models normally need to be fine-tuned with each individual user's local samples before being put into real-time inference. In this work, we focus on the fundamental click-through rate (CTR) prediction task in recommender systems and study how to effectively and efficiently perform on-device fine-tuning. We first identify the bottleneck issue that each individual user's local CTR (i.e., the ratio of positive samples in the local dataset for fine-tuning) tends to deviate from the global CTR (i.e., the ratio of positive samples in all the users' mixed datasets on the cloud for training out the initial model). We further demonstrate that such a CTR drift problem makes on-device fine-tuning even harmful to item ranking. We thus propose a novel label correction method, which requires each user only to change the labels of the local samples ahead of on-device fine-tuning and can well align the locally prior CTR with the global CTR. The offline evaluation results over three datasets and five CTR prediction models as well as the online A/B testing results in Mobile Taobao demonstrate the necessity of label correction in on-device fine-tuning and also reveal the improvement over cloud-based learning without fine-tuning

SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles

As shown by recent studies, machine intelligence-enabled systems are vulnerable to test cases resulting from either adversarial manipulation or natural distribution shifts. This has raised great concerns about deploying machine learning algorithms for real-world applications, especially in the safety-critical domains such as autonomous driving (AD). On the other hand, traditional AD testing on naturalistic scenarios requires hundreds of millions of driving miles due to the high dimensionality and rareness of the safety-critical scenarios in the real world. As a result, several approaches for autonomous driving evaluation have been explored, which are usually, however, based on different simulation platforms, types of safety-critical scenarios, scenario generation algorithms, and driving route variations. Thus, despite a large amount of effort in autonomous driving testing, it is still challenging to compare and understand the effectiveness and efficiency of different testing scenario generation algorithms and testing mechanisms under similar conditions. In this paper, we aim to provide the first unified platform SafeBench to integrate different types of safety-critical testing scenarios, scenario generation algorithms, and other variations such as driving routes and environments. Meanwhile, we implement 4 deep reinforcement learning-based AD algorithms with 4 types of input (e.g., bird's-eye view, camera) to perform fair comparisons on SafeBench. We find our generated testing scenarios are indeed more challenging and observe the trade-off between the performance of AD agents under benign and safety-critical testing scenarios. We believe our unified platform SafeBench for large-scale and effective autonomous driving testing will motivate the development of new testing scenario generation and safe AD algorithms. SafeBench is available at https://safebench.github.io