ANALYSIS OF LATERAL DISPLACEMENT AND EVALUATION OF TREATMENT MEASURES OF CURVED BEAM: A CASE STUDY

The curved beam bridge exhibits lateral displacement during construction and operation. Taking a curved beam bridge as an example, the status of lateral displacement of the bridge is investigated in detail in this paper. To understand the mechanism of the curved beam lateral displacement, further to determine the curved beam lateral displacement under temperature effect, using ANSYS software to establish solid element model of the curved beam, steady state thermal analysis method is applied to analyze temperature field. Based on the analysis, the lateral displacement under temperature effect is analyzed. Then in order to further explain the lateral displacement mechanism, to discuss the frictional force causing the residual deformation of the rubber bearing to make the lateral displacement of the curved beam, the mechanical mechanism of curved beam under temperature effect is approximately analyzed. On the basis of clarifying the mechanism of lateral displacement, the paper puts forward the reinforcement measures for the curved beam bridge. In order to verify the treatment effect, long-term displacement monitoring is performed on the bridge. Numerical studies and monitoring data show that temperature is the main factor that causes the lateral displacement. Monitoring data over the past year shows that the displacement of the bearing is less than the value of allowable displacement after the reinforcement measures are adopted, and the bridge is in a safe state

Efficient Deep Reinforcement Learning via Adaptive Policy Transfer

Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks. Existing transfer approaches either explicitly computes the similarity between tasks or select appropriate source policies to provide guided explorations for the target task. However, how to directly optimize the target policy by alternatively utilizing knowledge from appropriate source policies without explicitly measuring the similarity is currently missing. In this paper, we propose a novel Policy Transfer Framework (PTF) to accelerate RL by taking advantage of this idea. Our framework learns when and which source policy is the best to reuse for the target policy and when to terminate it by modeling multi-policy transfer as the option learning problem. PTF can be easily combined with existing deep RL approaches. Experimental results show it significantly accelerates the learning process and surpasses state-of-the-art policy transfer methods in terms of learning efficiency and final performance in both discrete and continuous action spaces.Comment: Accepted by IJCAI'202

Spatial Path Following for AUVs Using Adaptive Neural Network Controllers

The spatial path following control problem of autonomous underwater vehicles (AUVs) is addressed in this paper. In order to realize AUVs’ spatial path following control under systemic variations and ocean current, three adaptive neural network controllers which are based on the Lyapunov stability theorem are introduced to estimate uncertain parameters of the vehicle’s model and unknown current disturbances. These controllers are designed to guarantee that all the error states in the path following system are asymptotically stable. Simulation results demonstrated that the proposed controller was effective in reducing the path following error and was robust against the disturbances caused by vehicle's uncertainty and ocean currents

Energy-resolved Photoconductivity Mapping in a Monolayer-bilayer WSe2 Lateral Heterostructure

Vertical and lateral heterostructures of van der Waals materials provide tremendous flexibility for band structure engineering. Since electronic bands are sensitively affected by defects, strain, and interlayer coupling, the edge and heterojunction of these two-dimensional (2D) systems may exhibit novel physical properties, which can be fully revealed only by spatially resolved probes. Here, we report the spatial mapping of photoconductivity in a monolayer-bilayer WSe2 lateral heterostructure under multiple excitation lasers. As the photon energy increases, the light-induced conductivity detected by microwave impedance microscopy first appears along the hetero-interface and bilayer edge, then along the monolayer edge, inside the bilayer area, and finally in the interior of the monolayer region. The sequential emergence of mobile carriers in different sections of the sample is consistent with the theoretical calculation of local energy gaps. Quantitative analysis of the microscopy and transport data also reveals the linear dependence of photoconductivity on the laser intensity and the influence of interlayer coupling on carrier recombination. Combining theoretical modeling, atomic scale imaging, mesoscale impedance microscopy, and device-level characterization, our work suggests an exciting perspective to control the intrinsic band-gap variation in 2D heterostructures down to the few-nanometer regime.Comment: 18 pages, 5 figures; Nano Lett., Just Accepted Manuscrip

Study on Application of T-S Fuzzy Observer in Speed Switching Control of AUVs Driven by States

Considering the inherent strongly nonlinear and coupling performance of autonomous underwater vehicles (AUVs), the speed switching control method for AUV driven by states is presented. By using T-S fuzzy observer to estimate the states of AUV, the speed control strategies in lever plane, vertical plane, and speed kept are established, respectively. Then the adaptive switching law is introduced to switch the speed control strategies designed in real time. In the simulation, acoustic Doppler current profile/side scan sonar (ADCP/SSS) observation case is employed to demonstrate the effectiveness of the proposed method. The results show that the efficiency of AUV was improved, the trajectory tracking error was reduced, and the steady-state ability was enhanced

WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmark for Autonomous Driving on Water Surfaces

Autonomous driving on water surfaces plays an essential role in executing hazardous and time-consuming missions, such as maritime surveillance, survivors rescue, environmental monitoring, hydrography mapping and waste cleaning. This work presents WaterScenes, the first multi-task 4D radar-camera fusion dataset for autonomous driving on water surfaces. Equipped with a 4D radar and a monocular camera, our Unmanned Surface Vehicle (USV) proffers all-weather solutions for discerning object-related information, including color, shape, texture, range, velocity, azimuth, and elevation. Focusing on typical static and dynamic objects on water surfaces, we label the camera images and radar point clouds at pixel-level and point-level, respectively. In addition to basic perception tasks, such as object detection, instance segmentation and semantic segmentation, we also provide annotations for free-space segmentation and waterline segmentation. Leveraging the multi-task and multi-modal data, we conduct numerous experiments on the single modality of radar and camera, as well as the fused modalities. Results demonstrate that 4D radar-camera fusion can considerably enhance the robustness of perception on water surfaces, especially in adverse lighting and weather conditions. WaterScenes dataset is public on https://waterscenes.github.io

Boosting visible-light-driven photocatalytic performance of waxberry-like CeO<inf>2</inf> by samarium doping and silver QDs anchoring

In this work, waxberry-like CeO2 photocatalyst (denoted ASC) with prominent visible-light-driven photocatalytic performances for multi-model reactions was achieved by Sm doping and Ag quantum dots (QDs) anchoring. For instance, the as-fabricated ASC acquired 7.08-times and 6.83-times higher activities for CH3CHO removal and H2 production than those of pure CeO2 counterpart, respectively. The concentration of oxygen vacancies (Ov) in CeO2 is distinctly increased by Sm doping, resulting in a narrower bandgap of the Sm-doped CeO2 (SC). Under visible light irradiation, the Ov caused by doping can capture the photo-excited electrons and construct a doping-related transition state between the conduction band (CB) and the valence band (VB), which can effectively limit the recombination of photo-excited electrons and holes. These captured electrons further fleetly transfer to the co-catalytic sites of anchored Ag QDs, strengthening the absorption utilization for visible-light synchronously. The migration of charge carriers and proposed mechanisms were well elaborated by transient photovoltage (TPV), surface photovoltage (SPV) and density functional theory (DFT) calculation. It is hoped our work in this paper could provide potential and meaningful strategies for the design of noble metal quantum dots modified metal oxide semiconductors and facilitate their applications in other photocatalytic fields effectively