Qiang Lu

EAAI Journal 2023 Journal Article

Unsupervised image-to-image translation in multi-parametric MRI of bladder cancer

• Zhiying Chen
• Lingkai Cai
• Chunxiao Chen
• Xue Fu
• Xiao Yang
• Baorui Yuan
• Qiang Lu
• Huiyu Zhou

AAAI Conference 2021 Short Paper

Improving the Morphology and Control Policy of Self-reconfiguring Modular Robots in Dynamic Environment (Student Abstract)

• Dong Xue
• Qiang Lu
• Jian Li

Ideally, self-reconfiguration modular robots (SMR) can change their morphology and perform actions related to a specific task in any scene. However, most SMRs only adapt to several specific scenes because their morphology and control policies are designed or trained based on these scenes. Once SMRs meet an unknown scene, especially multiply unknown scenes (called dynamic environment), these policies will be useless. Although some of these policies import evolutionary algorithms to enhance the ability of SMR to explore unknown scenes, they are very time-consuming. The reason is that individual fitness depends on the interaction between SMR and these scenes. We propose a two-stage reconfiguration algorithm (TSRA) without any prior knowledge to address the time-consuming problem. In the two stages, the reconfiguration methods use the evolutionary algorithm (GA) to simultaneously generate scene-fitted morphology and actions. The first stage method uses the estimation neural network to evaluate the individual fitness to run faster and can recommend better policies to the second stage. The second stage method obtains this fitness from scenes and updates the neural network to approximate these scenes. Through experiments, TSRA can find better morphology and control policies than the other two canonical algorithms — GA and GEM-RL.

TIST Journal 2019 Journal Article

A Visual Analysis Approach for Understanding Durability Test Data of Automotive Products

• Ying Zhao
• Lei Wang
• Shijie Li
• Fangfang Zhou
• Xiaoru Lin
• Qiang Lu
• Lei Ren

People face data-rich manufacturing environments in Industry 4.0. As an important technology for explaining and understanding complex data, visual analytics has been increasingly introduced into industrial data analysis scenarios. With the durability test of automotive starters as background, this study proposes a visual analysis approach for understanding large-scale and long-term durability test data. Guided by detailed scenario and requirement analyses, we first propose a migration-adapted clustering algorithm that utilizes a segmentation strategy and a group of matching-updating operations to achieve an efficient and accurate clustering analysis of the data for starting mode identification and abnormal test detection. We then design and implement a visual analysis system that provides a set of user-friendly visual designs and lightweight interactions to help people gain data insights into the test process overview, test data patterns, and durability performance dynamics. Finally, we conduct a quantitative algorithm evaluation, case study, and user interview by using real-world starter durability test datasets. The results demonstrate the effectiveness of the approach and its possible inspiration for the durability test data analysis of other similar industrial products.

EAAI Journal 2014 Journal Article

Situation-aware composition and execution in dynamic environments by automated planning

• Qiang Lu
• Justin Wilson
• Yixin Chen
• Christopher Gill
• Louis Thomas
• Gruia-Catalin Roman
• Guoliang Chen

TIST Journal 2013 Journal Article

A SAT-based approach to cost-sensitive temporally expressive planning

• Qiang Lu
• Ruoyun Huang
• Yixin Chen
• You Xu
• Weixiong Zhang
• Guoliang Chen

Complex features, such as temporal dependencies and numerical cost constraints, are hallmarks of real-world planning problems. In this article, we consider the challenging problem of cost-sensitive temporally expressive (CSTE) planning, which requires concurrency of durative actions and optimization of action costs. We first propose a scheme to translate a CSTE planning problem to a minimum cost (MinCost) satisfiability (SAT) problem and to integrate with a relaxed parallel planning semantics for handling true temporal expressiveness. Our scheme finds solution plans that optimize temporal makespan, and also minimize total action costs at the optimal makespan. We propose two approaches for solving MinCost SAT. The first is based on a transformation of a MinCost SAT problem to a weighted partial Max-SAT (WPMax-SAT), and the second, called BB-CDCL, is an integration of the branch-and-bound technique and the conflict driven clause learning (CDCL) method. We also develop a CSTE customized variable branching scheme for BB-CDCL which can significantly improve the search efficiency. Our experiments on the existing CSTE benchmark domains show that our planner compares favorably to the state-of-the-art temporally expressive planners in both efficiency and quality.

AAAI Conference 2013 Conference Paper

Goal-Oriented Euclidean Heuristics with Manifold Learning

• Wenlin Chen
• Yixin Chen
• Kilian Weinberger
• Qiang Lu
• Xiaoping Chen

Recently, a Euclidean heuristic (EH) has been proposed for A* search. EH exploits manifold learning methods to construct an embedding of the state space graph, and derives an admissible heuristic distance between two states from the Euclidean distance between their respective embedded points. EH has shown good performance and memory efficiency in comparison to other existing heuristics such as differential heuristics. However, its potential has not been fully explored. In this paper, we propose a number of techniques that can significantly improve the quality of EH. We propose a goal-oriented manifold learning scheme that optimizes the Euclidean distance to goals in the embedding while maintaining admissibility and consistency. We also propose a state heuristic enhancement technique to reduce the gap between heuristic and true distances. The enhanced heuristic is admissible but no longer consistent. We then employ a modified search algorithm, known as B0 algorithm, that achieves optimality with inconsistent heuristics using consistency check and propagation. We demonstrate the effectiveness of the above techniques and report un-matched reduction in search costs across several non-trivial benchmark search problems.