arXiv subjects

Songqi Li

Publications and source records attributed to Songqi Li.

5 recordsLinked to original sources

arXiv2026

Canonical Procedural Actions: An Auditable Annotation Protocol for Tool-Use Agent Traces

Tool-use agent traces identify messages and API calls, but procedural analyses also need explicit units of action and inspectable links to their evidence. We present Canonical Procedural Actions (CPAs), an annotation protocol that records a procedural function, its first agent-event anchor, the agent events that realize it, and separate contextual evidence. Multiple actions may share a message anchor without an inferred within-message order. A retail case study produces a versioned 24-entry codebook through open induction, recorded consolidation, and successive application audits. Two isolated LLM contexts annotate 32 trajectories disjoint from development at the trajectory level, producing 499 and 491 occurrences with anchor-label overlap A=0.982. Requiring identical context-event references reduces overlap to 0.798. These are structural repeatability measures, not semantic accuracy: 16 of 26 task IDs also occur in development, and historical tool payloads were truncated to 110 characters. Retrospective controls show that collapsing all labels raises overlap to 0.986, while simple endpoint rules reproduce the tool-anchored portion with 0.997 overlap. Assistant-message actions have 0.971 overlap, with a per-label minimum of 0.816. Applying the frozen codebook to 244 further trajectories yields 4,058 records, including eight diagnostic outcomes. The contribution is an explicit, auditable annotation instrument and a case study of its construction and measurement limits; human-reference validity and downstream utility remain to be established.

cs.CL↗

arXiv2023

Smart Skin separation control using distributed-input distributed-output, multi-modal actuators, and machine learning

Efficient flow separation control represents significant economic benefit. This study applies a machine learning algorithm to minimize flow separation in Smart Skin, a flow control device that features distributed-input and distributed-output (DIDO). Smart Skin comprises 30 hybrid actuator units, each integrating a height-adjustable vortex generator and a mini-jet actuator. These units are deployed on a backward-facing ramp to reduce flow separation in a distributed manner. To monitor the flow state, distributed pressure taps are deployed around the multi-modal actuators. Parametric studies indicate that the mapping between control parameters and separation control performance is complex. To optimize separation control, a cutting-edge variant of the particle swarm optimization (PSO-TPME) is used for the control parameters in the Smart Skin. This algorithm is capable of achieving fast optimization in high-dimensional parameter spaces. The results demonstrate the efficiency of PSO-TPME, and the optimized solution significantly outperforms the best result from the parametric study. These findings represent a promising future of machine learning-based flow control using distributed actuators and sensors.

eess.SY↗

arXiv2023

Aerodynamic Characterization of a Fan Array Wind Generator

Experimental assessment of safe and precise flight control algorithms for unmanned aerial vehicles (UAVs) under gusty wind conditions requires the capability to generate a large range of velocity profiles. In this study, we employ a small fan array wind generator which can generate flows with large spatial and temporal variability. We perform a thorough aerodynamic characterization operating the fans uniformly from a low to the maximum level. PIV and hot-wire measurements indicate a jet-like flow with nearly uniform core which monotonously contracts in streamwise direction and surrounding growing unsteady shear-layers. These complex dynamics results in a limited region with desired flow profile and turbulence level. The experimental results shed light on the flow generated by a full-scale fan array wind generator, and indicate the need for further improvements via properly designed add-ons and dedicated control algorithms.

physics.flu-dyn↗

arXiv2022

Machine-learned control-oriented flow estimation for multiactuator multi-sensor systems exemplified for the fluidic pinball

We propose the first machine-learned control-oriented flow estimation for multiple-input multiple-output plants. Starting point is constant actuation with open-loop actuation commands leading to a database with simultaneously recorded actuation commands, sensor signals and flow fields. A key enabler is an estimator input vector comprising sensor signals and actuation commands. The mapping from the sensor signals and actuation commands to the flow fields is realized in an analytically simple, data-centric and general nonlinear approach. The analytically simple estimator generalizes Linear Stochastic Estimation (LSE) for actuation commands. The data-centric approach yields flow fields from estimator inputs by interpolating from the database -- similar to Loiseau et al. (2018) for unforced flow. The interpolation is performed with k Nearest Neighbors (kNN). The general global nonlinear mapping from inputs to flow fields is obtained from a Deep Neural Network (DNN) via an iterative training approach. The estimator comparison is performed for the fluidic pinball plant, which is a multiple-input, multiple-output wake control benchmark (Deng et al. 2020) featuring rich dynamics under steady controls. We conclude that the machine learning methods clearly outperform the linear model. The performance of kNN and DNN estimators are comparable for periodic dynamics. Yet, DNN performs consistently better when the flow is chaotic. Moreover, a thorough comparison regarding to the complexity, computational cost, and prediction accuracy is presented to demonstrate the relative merits of each estimator. The proposed method can be generalized for closed-loop flow control plants.

physics.flu-dyn↗

arXiv2021

Pressure-Informed Velocity Estimation in a Subsonic Jet

This work aims to estimate time-resolved velocity field that is directly associated with pressure fluctuations in a subsonic round jet. To achieve this goal, synchronous measurements of the velocity field and in-flow pressure fluctuations were performed at Mach number 0.3. Two different experiment campaigns were conducted, the first experimental campaign aims to explore the time-resolved dynamics of the axisymmetric velocity components, and second experiment focuses on the time-resolved, 2D velocity estimates on a streamwise plane. Two different methods were utilized to estimate the input-output relation between velocity and in-flow pressure measurements. A hybrid approach based on the spectral linear stochastic estimation and the proper orthogonal decomposition was applied to setup the model in a linear manner, and a wavelet-based filter was implemented to attenuate the noise level in the cross-correlation functions. In addition, the pressure-velocity relationship was also described by neural network architectures based on the multi-layer perceptron (MLP) and bidirectional long-short-term-memory (LSTM). In both experimental sets, pressure fluctuations inside the flow are found to be connected to the streamwise convection of large-scale coherent structures in the flow. A unique advantage of the bidirectional LSTM method was found among all estimation schemes is also reported in this work. The estimation result represents the space-time dynamics of the acoustic sources in the jet flow field, and it is of great importance to understand the noise generation mechanism.

physics.flu-dyn↗