arXiv subjects

Qi Hao

Publications and source records attributed to Qi Hao.

At least 55 records · Page 3Linked to original sources

arXiv2022

Adaptive Environment Modeling Based Reinforcement Learning for Collision Avoidance in Complex Scenes

The major challenges of collision avoidance for robot navigation in crowded scenes lie in accurate environment modeling, fast perceptions, and trustworthy motion planning policies. This paper presents a novel adaptive environment model based collision avoidance reinforcement learning (i.e., AEMCARL) framework for an unmanned robot to achieve collision-free motions in challenging navigation scenarios. The novelty of this work is threefold: (1) developing a hierarchical network of gated-recurrent-unit (GRU) for environment modeling; (2) developing an adaptive perception mechanism with an attention module; (3) developing an adaptive reward function for the reinforcement learning (RL) framework to jointly train the environment model, perception function and motion planning policy. The proposed method is tested with the Gym-Gazebo simulator and a group of robots (Husky and Turtlebot) under various crowded scenes. Both simulation and experimental results have demonstrated the superior performance of the proposed method over baseline methods.

cs.RO↗

arXiv2022

Statistical analysis of the Si I 6560.58 Å line observed by CHASE

The Si I 6560.58 Å line in the H$α$ blue wing is blended with a telluric absorption line from water vapor in ground-based observations. Recent observations with the space-based telescope CHASE provide a new window to study this line. We aim to study the Si I line statistically and to explore possible diagnostics. We select three scannings in the CHASE observations, and measure the equivalent width (EW) and the full width at half maximum (FWHM) for each pixel on the solar disk. We then calculate the theoretical EW and FWHM from the VALC model. An active region is also studied in particular for difference in the quiet Sun and the sunspots. The Si I line is formed at the bottom of the photosphere. The EW of this line increases from the disk center to $μ$ = 0.2, and then decreases toward the solar limb, while the FWHM shows a monotonically increasing trend. Theoretically predicted EW agrees well with observations, while the predicted FWHM is far smaller due to the absence of unresolved turbulence in models. The macroturbulent velocity is estimated to be 2.80 km s$^{-1}$ at the disk center, and increases to 3.52 km s$^{-1}$ at $μ$ = 0.2. We do not find any response to flare heating in current observations. Doppler shifts and line widths of the Si I 6560.58 Å and Fe I 6569.21 Å lines can be used to study the mass flows and turbulence of the different photospheric layers. The Si I line has good potentials to diagnose the dynamics and energy transport in the photosphere.

astro-ph.SR↗

arXiv2022

Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles

The technology of dynamic map fusion among networked vehicles has been developed to enlarge sensing ranges and improve sensing accuracies for individual vehicles. This paper proposes a federated learning (FL) based dynamic map fusion framework to achieve high map quality despite unknown numbers of objects in fields of view (FoVs), various sensing and model uncertainties, and missing data labels for online learning. The novelty of this work is threefold: (1) developing a three-stage fusion scheme to predict the number of objects effectively and to fuse multiple local maps with fidelity scores; (2) developing an FL algorithm which fine-tunes feature models (i.e., representation learning networks for feature extraction) distributively by aggregating model parameters; (3) developing a knowledge distillation method to generate FL training labels when data labels are unavailable. The proposed framework is implemented in the Car Learning to Act (CARLA) simulation platform. Extensive experimental results are provided to verify the superior performance and robustness of the developed map fusion and FL schemes.

cs.LG↗

arXiv2022

SLAM-TKA: Real-time Intra-operative Measurement of Tibial Resection Plane in Conventional Total Knee Arthroplasty

Total knee arthroplasty (TKA) is a common orthopaedic surgery to replace a damaged knee joint with artificial implants. The inaccuracy of achieving the planned implant position can result in the risk of implant component aseptic loosening, wear out, and even a joint revision, and those failures most of the time occur on the tibial side in the conventional jig-based TKA (CON-TKA). This study aims to precisely evaluate the accuracy of the proximal tibial resection plane intra-operatively in real-time such that the evaluation processing changes very little on the CON-TKA operative procedure. Two X-ray radiographs captured during the proximal tibial resection phase together with a pre-operative patient-specific tibia 3D mesh model segmented from computed tomography (CT) scans and a trocar pin 3D mesh model are used in the proposed simultaneous localisation and mapping (SLAM) system to estimate the proximal tibial resection plane. Validations using both simulation and in-vivo datasets are performed to demonstrate the robustness and the potential clinical value of the proposed algorithm.

cs.RO↗

arXiv2022

Calibration procedures for the CHASE/HIS science data

The Hα line is an important optical line in solar observations containing the information from the photosphere to the chromosphere. To study the mechanisms of solar eruptions and the plasma dynamics in the lower atmosphere, the Chinese Hα Solar Explorer (CHASE) was launched into a Sun-synchronous orbit on October 14, 2021. The scientific payload of the CHASE satellite is the Hα Imaging Spectrograph (HIS). The CHASE/HIS acquires, for the first time, seeing-free Hα spectroscopic observations with high spectral and temporal resolutions. It consists of two observational modes. The raster scanning mode provides full-Sun or region-of-interest spectra at Hα (6559.7-6565.9 Å) and Fe I (6567.8-6570.6 Å) wavebands. The continuum imaging mode obtains full-Sun photospheric images at around 6689 Å. In this paper, we present detailed calibration procedures for the CHASE/HIS science data, including the dark-field and flat-field correction, slit image curvature correction, wavelength and intensity calibration, and coordinate transformation. The higher-level data products can be directly used for scientific research.

astro-ph.SR↗

arXiv2022

Edge Federated Learning Via Unit-Modulus Over-The-Air Computation

Edge federated learning (FL) is an emerging paradigm that trains a global parametric model from distributed datasets based on wireless communications. This paper proposes a unit-modulus over-the-air computation (UMAirComp) framework to facilitate efficient edge federated learning, which simultaneously uploads local model parameters and updates global model parameters via analog beamforming. The proposed framework avoids sophisticated baseband signal processing, leading to low communication delays and implementation costs. Training loss bounds of UMAirComp FL systems are derived and two low-complexity large-scale optimization algorithms, termed penalty alternating minimization (PAM) and accelerated gradient projection (AGP), are proposed to minimize the nonconvex nonsmooth loss bound. Simulation results show that the proposed UMAirComp framework with PAM algorithm achieves a smaller mean square error of model parameters' estimation, training loss, and test error compared with other benchmark schemes. Moreover, the proposed UMAirComp framework with AGP algorithm achieves satisfactory performance while reduces the computational complexity by orders of magnitude compared with existing optimization algorithms. Finally, we demonstrate the implementation of UMAirComp in a vehicle-to-everything autonomous driving simulation platform. It is found that autonomous driving tasks are more sensitive to model parameter errors than other tasks since the neural networks for autonomous driving contain sparser model parameters.

cs.IT↗

arXiv2022

Reinforcement Learned Distributed Multi-Robot Navigation with Reciprocal Velocity Obstacle Shaped Rewards

The challenges to solving the collision avoidance problem lie in adaptively choosing optimal robot velocities in complex scenarios full of interactive obstacles. In this paper, we propose a distributed approach for multi-robot navigation which combines the concept of reciprocal velocity obstacle (RVO) and the scheme of deep reinforcement learning (DRL) to solve the reciprocal collision avoidance problem under limited information. The novelty of this work is threefold: (1) using a set of sequential VO and RVO vectors to represent the interactive environmental states of static and dynamic obstacles, respectively; (2) developing a bidirectional recurrent module based neural network, which maps the states of a varying number of surrounding obstacles to the actions directly; (3) developing a RVO area and expected collision time based reward function to encourage reciprocal collision avoidance behaviors and trade off between collision risk and travel time. The proposed policy is trained through simulated scenarios and updated by the actor-critic based DRL algorithm. We validate the policy in complex environments with various numbers of differential drive robots and obstacles. The experiment results demonstrate that our approach outperforms the state-of-art methods and other learning based approaches in terms of the success rate, travel time, and average speed. Source code of this approach is available at https://github.com/hanruihua/rl_rvo_nav.

cs.RO↗

arXiv2022

Robotic Wireless Energy Transfer in Dynamic Environments: System Design and Experimental Validation

Wireless energy transfer (WET) is a ground-breaking technology for cutting the last wire between mobile sensors and power grids in smart cities. Yet, WET only offers effective transmission of energy over a short distance. Robotic WET is an emerging paradigm that mounts the energy transmitter on a mobile robot and navigates the robot through different regions in a large area to charge remote energy harvesters. However, it is challenging to determine the robotic charging strategy in an unknown and dynamic environment due to the uncertainty of obstacles. This paper proposes a hardware-in-the-loop joint optimization framework that offers three distinctive features: 1) efficient model updates and re-optimization based on the last-round experimental data; 2) iterative refinement of the anchor list for adaptation to different environments; 3) verification of algorithms in a high-fidelity Gazebo simulator and a multi-robot testbed. Experimental results show that the proposed framework significantly saves the WET mission completion time while satisfying collision avoidance and energy harvesting constraints.

cs.RO↗

arXiv2022

Phase-SLAM: Phase Based Simultaneous Localization and Mapping for Mobile Structured Light Illumination Systems

Structured Light Illumination (SLI) systems have been used for reliable indoor dense 3D scanning via phase triangulation. However, mobile SLI systems for 360 degree 3D reconstruction demand 3D point cloud registration, involving high computational complexity. In this paper, we propose a phase based Simultaneous Localization and Mapping (Phase-SLAM) framework for fast and accurate SLI sensor pose estimation and 3D object reconstruction. The novelty of this work is threefold: (1) developing a reprojection model from 3D points to 2D phase data towards phase registration with low computational complexity; (2) developing a local optimizer to achieve SLI sensor pose estimation (odometry) using the derived Jacobian matrix for the 6 DoF variables; (3) developing a compressive phase comparison method to achieve high-efficiency loop closure detection. The whole Phase-SLAM pipeline is then exploited using existing global pose graph optimization techniques. We build datasets from both the unreal simulation platform and a robotic arm based SLI system in real-world to verify the proposed approach. The experiment results demonstrate that the proposed Phase-SLAM outperforms other state-of-the-art methods in terms of the efficiency and accuracy of pose estimation and 3D reconstruction. The open-source code is available at https://github.com/ZHENGXi-git/Phase-SLAM.

cs.CV↗

arXiv2021

Re-ranking With Constraints on Diversified Exposures for Homepage Recommender System

The homepage recommendation on most E-commerce applications places items in a hierarchical manner, where different channels display items in different styles. Existing algorithms usually optimize the performance of a single channel. So designing the model to achieve the optimal recommendation list which maximize the Click-Through Rate (CTR) of whole homepage is a challenge problem. Other than the accuracy objective, display diversity on the homepage is also important since homogeneous display usually hurts user experience. In this paper, we propose a two-stage architecture of the homepage recommendation system. In the first stage, we develop efficient algorithms for recommending items to proper channels while maintaining diversity. The two methods can be combined: user-channel-item predictive model with diversity constraint. In the second stage, we provide an ordered list of items in each channel. Existing re-ranking models are hard to describe the mutual influence between items in both intra-channel and inter-channel. Therefore, we propose a Deep \& Hierarchical Attention Network Re-ranking (DHANR) model for homepage recommender systems. The Hierarchical Attention Network consists of an item encoder, an item-level attention layer, a channel encoder and a channel-level attention layer. Our method achieves a significant improvement in terms of precision, intra-list average distance(ILAD) and channel-wise Precision@k in offline experiments and in terms of CTR and ILAD in our online systems.

cs.IR↗

arXiv2021

Runtime Safety Assurance for Learning-enabled Control of Autonomous Driving Vehicles

Providing safety guarantees for Autonomous Vehicle (AV) systems with machine-learning-based controllers remains a challenging issue. In this work, we propose Simplex-Drive, a framework that can achieve runtime safety assurance for machine-learning enabled controllers of AVs. The proposed Simplex-Drive consists of an unverified Deep Reinforcement Learning (DRL)-based advanced controller (AC) that achieves desirable performance in complex scenarios, a Velocity-Obstacle (VO) based baseline safe controller (BC) with provably safety guarantees, and a verified mode management unit that monitors the operation status and switches the control authority between AC and BC based on safety-related conditions. We provide a formal correctness proof of Simplex-Drive and conduct a lane-changing case study in dense traffic scenarios. The simulation experiment results demonstrate that Simplex-Drive can always ensure operation safety without sacrificing control performance, even if the DRL policy may lead to deviations from the safe status.

cs.RO↗

arXiv2021

Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimization

Wireless federated learning (FL) is an emerging machine learning paradigm that trains a global parametric model from distributed datasets via wireless communications. This paper proposes a unit-modulus wireless FL (UMWFL) framework, which simultaneously uploads local model parameters and computes global model parameters via optimized phase shifting. The proposed framework avoids sophisticated baseband signal processing, leading to both low communication delays and implementation costs. A training loss bound is derived and a penalty alternating minimization (PAM) algorithm is proposed to minimize the nonconvex nonsmooth loss bound. Experimental results in the Car Learning to Act (CARLA) platform show that the proposed UMWFL framework with PAM algorithm achieves smaller training losses and testing errors than those of the benchmark scheme.

eess.SP↗

arXiv2021

Capture Uncertainties in Deep Neural Networks for Safe Operation of Autonomous Driving Vehicles

Uncertainties in Deep Neural Network (DNN)-based perception and vehicle's motion pose challenges to the development of safe autonomous driving vehicles. In this paper, we propose a safe motion planning framework featuring the quantification and propagation of DNN-based perception uncertainties and motion uncertainties. Contributions of this work are twofold: (1) A Bayesian Deep Neural network model which detects 3D objects and quantitatively captures the associated aleatoric and epistemic uncertainties of DNNs; (2) An uncertainty-aware motion planning algorithm (PU-RRT) that accounts for uncertainties in object detection and ego-vehicle's motion. The proposed approaches are validated via simulated complex scenarios built in CARLA. Experimental results show that the proposed motion planning scheme can cope with uncertainties of DNN-based perception and vehicle motion, and improve the operational safety of autonomous vehicles while still achieving desirable efficiency.

cs.RO↗

arXiv2021

Joint Multi-Object Detection and Tracking with Camera-LiDAR Fusion for Autonomous Driving

Multi-object tracking (MOT) with camera-LiDAR fusion demands accurate results of object detection, affinity computation and data association in real time. This paper presents an efficient multi-modal MOT framework with online joint detection and tracking schemes and robust data association for autonomous driving applications. The novelty of this work includes: (1) development of an end-to-end deep neural network for joint object detection and correlation using 2D and 3D measurements; (2) development of a robust affinity computation module to compute occlusion-aware appearance and motion affinities in 3D space; (3) development of a comprehensive data association module for joint optimization among detection confidences, affinities and start-end probabilities. The experiment results on the KITTI tracking benchmark demonstrate the superior performance of the proposed method in terms of both tracking accuracy and processing speed.

cs.CV↗

arXiv2021

Revisiting the Spectral Features of Ellerman Bombs and UV Bursts. I. Radiative Hydrodynamic Simulations

Ellerman bombs (EBs) and UV bursts are both small-scale solar activities that occur in active regions. They are now believed to form at different heights in the lower atmosphere. In this paper, we use one-dimensional radiative hydrodynamic simulations to calculate various line profiles in response to heating in different atmospheric layers. We confirm that heating in the upper photosphere to the lower chromosphere can generate spectral features of typical EBs, while heating in the mid to upper chromosphere can generate spectral features of typical UV bursts. The intensity evolution of the H$α$ line wing in EBs shows a rise--plateau pattern, while that of the Si IV 1403 Å line center in UV bursts shows a rise--fall pattern. However, the predicted enhancement of FUV continuum near 1400 Å for EBs is rarely reported and requires further observations to check it. With two heating sources or an extended heating source in the atmosphere, both EB and UV burst features could be reproduced simultaneously.

astro-ph.SR↗

arXiv2021

Nearly constant Q dissipative models and wave equations for general viscoelastic anisotropy

The quality factor (Q) links seismic wave energy dissipation to physical properties of the Earth's interior, such as temperature, stress and composition. Frequency independence of Q, also called constant Q for brevity, is a common assumption in practice for seismic Q inversions. Although exactly and nearly constant Q dissipative models are proposed in the literature, it is inconvenient to obtain constant Q wave equations in differential form, which explicitly involve a specified Q parameter. In our recent research paper, we proposed a novel weighting function method to build the first- and second-order nearly constant Q dissipative models. Of importance is the fact that the wave equations in differential form for these two models explicitly involve a specified Q parameter. This behavior is beneficial for time-domain seismic waveform inversion for Q, which requires the first derivative of wavefields with respect to Q parameters. In this paper, we extend the first- and second-order nearly constant Q models to the general viscoelastic anisotropic case. We also present a few formulation of the nearly constant Q viscoelastic anisotropic wave equations in differential form.

physics.geo-ph↗

arXiv2021

Nearly constant Q models of the generalized standard linear solid type and the corresponding wave equations

Time-domain seismic forward and inverse modeling for a dissipative medium is a vital research topic to investigate the attenuation structure of the Earth. Constant Q, also called frequency independence of the quality factor, is a common assumption for seismic Q inversion. We propose the first- and second-order nearly constant Q dissipative models of the generalized standard linear solid type, using a novel Q-independent weighting function approach. The two new models, which originate from the Kolsky model (a nearly constant Q model) and the Kjartansson model (an exactly constant Q model), result in the corresponding wave equations in differential form. Even for extremely strong attenuation (e.g., Q = 5), the quality factor and phase velocity for the two new models are close to those for the Kolsky and Kjartansson models, in a frequency range of interest. The wave equations for the two new models involve explicitly a specified Q parameter and have compact and simple forms. We provide a novel perspective on how to build a nearly constant Q dissipative model which is beneficial for time-domain large scale wavefield forward and inverse modeling. This perspective could also help obtain other dissipative models with similar advantages. We also discuss the extension beyond viscoacousticity and other related issues, for example, extending the two new models to viscoelastic anisotropy.

physics.geo-ph↗

arXiv2021

The origin of ultrasensitive SERS sensing beyond plasmonics

Plasmon-free surface-enhanced Raman scattering (SERS) substrates have attracted tremendous attention for their abundant sources, excellent chemical stability, superior biocompatibility, good signal uniformity, and unique selectivity to target molecules. Recently, researchers have made great progress in fabricating novel plasmon-free SERS substrates and exploring new enhancement strategies to improve their sensitivity. This review summarizes the recent developments of plasmon-free SERS substrates and specially focuses on the enhancement mechanisms and strategies. Furthermore, the promising applications of plasmon-free SERS substrates in biomedical diagnosis, metal ions and organic pollutants sensing, chemical and biochemical reactions monitoring, and photoelectric characterization are introduced. Finally, current challenges and future research opportunities in plasmon-free SERS substrates are briefly discussed.

physics.optics↗