Shengbo Eben Li
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View article: Research on disassembly technology of aero-engine rotor interference fit based on induction heating
Research on disassembly technology of aero-engine rotor interference fit based on induction heating Open
To address the issue of damage occurring during the disassembly of interference fit structures in aero engines, a disassembly method based on induction heating is proposed. This paper establishes a transient heat conduction theory for the …
View article: Scalable Synthesis of Formally Verified Neural Value Function for Hamilton-Jacobi Reachability Analysis
Scalable Synthesis of Formally Verified Neural Value Function for Hamilton-Jacobi Reachability Analysis Open
Hamilton-Jacobi (HJ) reachability analysis provides a formal method for guaranteeing safety in constrained control problems. It synthesizes a value function to represent a long-term safe set called feasible region. Early synthesis methods …
View article: Algorithm Design and Comparative Test of Natural Gradient Gaussian Approximation Filter
Algorithm Design and Comparative Test of Natural Gradient Gaussian Approximation Filter Open
Popular Bayes filters typically rely on linearization techniques such as Taylor series expansion and stochastic linear regression to use the structure of standard Kalman filter. These techniques may introduce large estimation errors in non…
View article: Air-ground collaborative multi-source orbital integrated detection system: Combining 3D imaging and intrusion recognition
Air-ground collaborative multi-source orbital integrated detection system: Combining 3D imaging and intrusion recognition Open
With the rapid expansion of railway networks globally, ensuring rail infrastructure safety through efficient detection methods has become critical. Traditional inspection systems face limitations in flexibility, adaptability to adverse wea…
View article: Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion
Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion Open
Reinforcement learning (RL) has shown great potential in enabling quadruped robots to perform agile locomotion. However, directly training policies to simultaneously handle dual extreme challenges, i.e., extreme underactuation and extreme …
View article: Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks
Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks Open
End-to-end autonomous driving has emerged as a dominant paradigm, yet its highly entangled black-box models pose significant challenges in terms of interpretability and safety assurance. To improve model transparency and training flexibili…
View article: An intelligent carbon emission measurement model based on high-frequency power data
An intelligent carbon emission measurement model based on high-frequency power data Open
This paper introduces genetic learning strategy into the traditional artificial bee colony algorithm, proposes an evolutionary artificial bee colony algorithm, and gives full play to its global search ability to automatically search for th…
View article: Enhanced DACER Algorithm with High Diffusion Efficiency
Enhanced DACER Algorithm with High Diffusion Efficiency Open
Due to their expressive capacity, diffusion models have shown great promise in offline RL and imitation learning. Diffusion Actor-Critic with Entropy Regulator (DACER) extended this capability to online RL by using the reverse diffusion pr…
View article: Distributional Soft Actor-Critic with Harmonic Gradient for Safe and Efficient Autonomous Driving in Multi-lane Scenarios
Distributional Soft Actor-Critic with Harmonic Gradient for Safe and Efficient Autonomous Driving in Multi-lane Scenarios Open
Reinforcement learning (RL), known for its self-evolution capability, offers a promising approach to training high-level autonomous driving systems. However, handling constraints remains a significant challenge for existing RL algorithms, …
View article: Design and Experimental Test of Datatic Approximate Optimal Filter in Nonlinear Dynamic Systems
Design and Experimental Test of Datatic Approximate Optimal Filter in Nonlinear Dynamic Systems Open
Filtering is crucial in engineering fields, providing vital state estimation for control systems. However, the nonlinear nature of complex systems and the presence of non-Gaussian noises pose significant challenges to the performance of co…
View article: Study on Mach-Zehnder optical isolator based on magneto-optic photonic crystal fiber
Study on Mach-Zehnder optical isolator based on magneto-optic photonic crystal fiber Open
Optical isolators are important components in fiber optic communication and sensing systems. Conventional optical isolators, including bulk and in-line fiber types, mainly rely on the Faraday rotation effect and require external magnetic f…
View article: NANO-SLAM : Natural Gradient Gaussian Approximation for Vehicle SLAM
NANO-SLAM : Natural Gradient Gaussian Approximation for Vehicle SLAM Open
Accurate localization is a challenging task for autonomous vehicles, particularly in GPS-denied environments such as urban canyons and tunnels. In these scenarios, simultaneous localization and mapping (SLAM) offers a more robust alternati…
View article: Minds on the Move: Decoding Trajectory Prediction in Autonomous Driving with Cognitive Insights
Minds on the Move: Decoding Trajectory Prediction in Autonomous Driving with Cognitive Insights Open
In mixed autonomous driving environments, accurately predicting the future trajectories of surrounding vehicles is crucial for the safe operation of autonomous vehicles (AVs). In driving scenarios, a vehicle's trajectory is determined by t…
View article: Predictive Lagrangian Optimization for Constrained Reinforcement Learning
Predictive Lagrangian Optimization for Constrained Reinforcement Learning Open
Constrained optimization is popularly seen in reinforcement learning for addressing complex control tasks. From the perspective of dynamic system, iteratively solving a constrained optimization problem can be framed as the temporal evoluti…
View article: Quantitative Representation of Autonomous Driving Scenario Difficulty Based on Adversarial Policy Search
Quantitative Representation of Autonomous Driving Scenario Difficulty Based on Adversarial Policy Search Open
Autonomous vehicles with self-evolution capabilities are expected to improve their performance through learning algorithms, to automatically adapt to the external environment. However, due to the infinity, complexity, and variability of th…
View article: A Review of the Current Status and Prospects of Improving Indoor Environment for Lightweight Buildings in High-Altitude Cold Regions
A Review of the Current Status and Prospects of Improving Indoor Environment for Lightweight Buildings in High-Altitude Cold Regions Open
Lightweight structures, characterized by rapid assembly, are vital for creating habitats in outdoor environments, but their implementation in high-plateau cold regions encounters significant challenges in heating and ventilation. This pape…
View article: Conformal Symplectic Optimization for Stable Reinforcement Learning
Conformal Symplectic Optimization for Stable Reinforcement Learning Open
Training deep reinforcement learning (RL) agents necessitates overcoming the highly unstable nonconvex stochastic optimization inherent in the trial-and-error mechanism. To tackle this challenge, we propose a physics-inspired optimization …
View article: An Explicit Discrete-Time Dynamic Vehicle Model with Assured Numerical Stability
An Explicit Discrete-Time Dynamic Vehicle Model with Assured Numerical Stability Open
Numerical stability is of great significance for discrete-time dynamic vehicle model. Among the unstable factors, low-speed singularity stands out as one of the most challenging issues, which arises from that the denominator of tire side a…
View article: Robust State Estimation for Legged Robots with Dual Beta Kalman Filter
Robust State Estimation for Legged Robots with Dual Beta Kalman Filter Open
Existing state estimation algorithms for legged robots that rely on proprioceptive sensors often overlook foot slippage and leg deformation in the physical world, leading to large estimation errors. To address this limitation, we propose a…
View article: Controllability test for nonlinear datatic systems
Controllability test for nonlinear datatic systems Open
Controllability is a fundamental property of control systems, serving as the prerequisite for controller design. While controllability test is well established in modelic (i.e., model-driven) control systems, extending it to datatic (i.e.,…
View article: Nonlinear Bayesian Filtering with Natural Gradient Gaussian Approximation
Nonlinear Bayesian Filtering with Natural Gradient Gaussian Approximation Open
Practical Bayes filters often assume the state distribution of each time step to be Gaussian for computational tractability, resulting in the so-called Gaussian filters. When facing nonlinear systems, Gaussian filters such as extended Kalm…
View article: Quantitative Representation of Scenario Difficulty for Autonomous Driving Based on Adversarial Policy Search
Quantitative Representation of Scenario Difficulty for Autonomous Driving Based on Adversarial Policy Search Open
Adversarial scenario generation is crucial for autonomous driving testing because it can efficiently simulate various challenge and complex traffic conditions. However, it is difficult to control current existing methods to generate desire…
View article: Scalable Synthesis of Formally Verified Neural Value Function for Hamilton-Jacobi Reachability Analysis
Scalable Synthesis of Formally Verified Neural Value Function for Hamilton-Jacobi Reachability Analysis Open
Hamilton-Jacobi (HJ) reachability analysis provides a formal method for guaranteeing safety in constrained control problems. It synthesizes a value function to represent a long-term safe set called feasible region. Early synthesis methods …
View article: Rocket Landing Control with Random Annealing Jump Start Reinforcement Learning
Rocket Landing Control with Random Annealing Jump Start Reinforcement Learning Open
Rocket recycling is a crucial pursuit in aerospace technology, aimed at reducing costs and environmental impact in space exploration. The primary focus centers on rocket landing control, involving the guidance of a nonlinear underactuated …
View article: Risk-Aware Vehicle Trajectory Prediction Under Safety-Critical Scenarios
Risk-Aware Vehicle Trajectory Prediction Under Safety-Critical Scenarios Open
Trajectory prediction is significant for intelligent vehicles to achieve high-level autonomous driving, and a lot of relevant research achievements have been made recently. Despite the rapid development, most existing studies solely focuse…
View article: Hierarchical and Decoupled BEV Perception Learning Framework for Autonomous Driving
Hierarchical and Decoupled BEV Perception Learning Framework for Autonomous Driving Open
Perception is essential for autonomous driving system. Recent approaches based on Bird's-eye-view (BEV) and deep learning have made significant progress. However, there exists challenging issues including lengthy development cycles, poor r…