Conversational query rewriting (CQR) addresses context dependence in conversational search by rewriting each user query into a standalone form.Recent approaches leverage reinforcement learning (RL) to directly optimize retrieval effectiveness; however, they typically rely on a single rewrite, which struggles to accommodate the divergent preferences of sparse and dense retrievers and often suffers from conflicting optimization signals.We propose DVCQR, a Dual-View CQR framework that explicitly generates two complementary rewrites for each query: a sparse-view rewrite that emphasizes distinctive lexical anchors, and a dense-view rewrite that captures complete semantic constraints.Both rewrites are produced in a single pass via a structured reasoning process.To further mitigate objective conflicts, we introduce a stage-wise RL strategy that sequentially aligns the sparse and dense views with their corresponding retrievers using rankbased feedback.Extensive experiments on four benchmarks (TopiOCQA, QReCC, CAsT-19, and CAsT-20) demonstrate that DVCQR consistently outperforms state-of-the-art methods on most metrics under both sparse and dense retrieval settings, validating the effectiveness of dual-view rewriting and stage-wise retriever alignment.

To achieve synchronization and harness the high performance of a dual-driven gantry, it is crucial to precisely compensate the desynchronization factors such as characteristic differences between parallel axes. However, these factors are usually hard to accurately identify due to the unique structure of a gantry, e.g., mechanical coupling and flexible transmissions. In this paper, a closed-loop identification method of the desynchronization factors is proposed. Firstly, based on the dynamic model of gantry with flexible transmissions, frequency analysis and reduce-order disposal of existing composite synchronous control (CSC) scheme are executed to determine key identification targets. Secondly, the coupling relationships of different desynchronization factors are revealed. To decouple and identify these factors, a modified closed-loop identification method referencing adaptive control algorithm is proposed with elaborate position motion command. Finally, the effectiveness of the proposed method in identifying and compensating the desynchronization factors is validated by simulation results.