arXiv subjects

Xiangyu Xie

Publications and source records attributed to Xiangyu Xie.

11 recordsLinked to original sources

arXiv2026

Design and Control of a Cable-Driven Switchable Actuator with Torque/Tension Dual Modes for Exoskeletons

Existing wearable exoskeleton architectures are typically constrained by a single mechanical output modality, providing either joint torque around an anatomical joint or linear traction along a limb-training-oriented direction, which limits adaptability to diverse training scenarios. This letter presents a cable-driven switchable actuator (CDSA) that can rapidly switch between torque and tension modes while centralizing all sensing and actuation components at the proximal drive unit. A Coupled Movable Pulley Mechanism (CMPM) provides tension amplification at the distal end-effector, while a bidirectional Cable-Driven Ratchet Mechanism (CDRM) enables mode switching and preload regulation. To eliminate the need for distal instrumentation, multi-source proximal sensors are integrated with a data-driven fusion model to estimate distal output forces. An adaptive dual-mode force control strategy based on iterative learning control (ILC) is further developed. Platform experiments demonstrate transmission efficiencies of $(92.4 \pm 2.0)\%$ and $(96.5 \pm 3.3)\%$ in the torque and tension modes, respectively, along with a tension amplification ratio of $2.77 \pm 0.10$ under tension mode. Tracking tests on simulated knee-joint gait trajectories and short-stroke tension profiles yield stable control, with RMSEs of $(4.52 \pm 0.51)\%$ and $(3.15 \pm 0.19)\%$ of the uncontrolled peak value, respectively. Finally, seated human-coupled experiments validate the system's controllable force generation in both joint-torque and linear-traction application modes.

cs.RO↗

arXiv2026

Optimization Design and Simulation Validation of a Variable Stiffness Actuator Based on a Crossed Four-Bar Mechanism

This paper presents a bio-inspired antagonistic variable stiffness actuator (VSA) based on two crossed four-bar compliant transmission elastic units (CFB-CTEs). The design addresses the difficulty of combining nonlinear elastic shaping with low structural inertia in antagonistic VSA mechanisms. Inspired by the crossed constraint behavior of the anterior and posterior cruciate ligaments during knee flexion, the proposed actuator uses geometric transmission, elastic energy storage, and bilateral antagonistic arrangement to shape the output torque and equivalent stiffness. A multi-objective optimization model is established to balance torque tracking accuracy, equivalent inertia, and mass. The selected compromise design achieved a torque root-mean-square error (RMSE) of $0.883~\mathrm{N\,mm}$ and a total mass of $50.2~\mathrm{g}$. Its average equivalent inertia was reduced by about 78% compared with an independent torque-only optimized design. An ADAMS multibody model was further built to verify the structural response under single-input, opposite-input, and same-input conditions. The results support the feasibility of the proposed crossed four-bar elastic unit as a lightweight nonlinear elastic branch for antagonistic VSAs.

eess.SY↗

arXiv2026

Non-destructive 3D doping imaging of silicon sensors

Silicon sensors are the foundational detection medium for X-rays and charged particles. While their bulk dopant distribution determines device performance, it is conventionally assumed homogeneous because traditional profiling is destructive, spatially restricted, and insensitive at the relevant concentrations. Here we introduce a non-destructive 3D doping imaging technique that turns the readout electronics of a charge-integrating hybrid pixel detector into a massively parallelized capacitance-voltage profiler. With a few tens of micrometres of 3D resolution over wafer-scale areas at concentrations on the order of $10^{11}$ cm$^{-3}$, we image the bulk doping concentration of operational sensors. Macroscopically, we resolve depth-evolving concentric doping rings; microscopically, we uncover scattered doping anomalies that distort local electric fields. The rings modulate the depletion voltage, while the anomalies disrupt local charge collection, a previously overlooked cause of pixel yield and performance degradation. By bridging manufacturing signatures with microscopic defects, this approach provides a non-destructive framework for sensor characterization and yield optimization.

physics.ins-det↗

arXiv2026

Clair Obscur: an Illumination-Aware Method for Real-World Image Vectorization

Image vectorization aims to convert raster images into editable, scalable vector representations while preserving visual fidelity. Existing vectorization methods struggle to represent complex real-world images, often producing fragmented shapes at the cost of semantic conciseness. In this paper, we propose COVec, an illumination-aware vectorization framework inspired by the Clair-Obscur principle of light-shade contrast. COVec is the first to introduce intrinsic image decomposition in the vector domain, separating an image into albedo, shade, and light layers in a unified vector representation. A semantic-guided initialization and two-stage optimization refine these layers with differentiable rendering. Experiments on various datasets demonstrate that COVec achieves higher visual fidelity and significantly improved editability compared to existing methods. The code will be released at https://github.com/decade-de/COVec.

cs.CV↗

arXiv2026

Long-Term Probabilistic Forecast of Vegetation Conditions Using Climate Attributes in the Four Corners Region

Weather conditions can drastically alter the state of crops and rangelands, and in turn, impact the incomes and food security of individuals worldwide. Satellite-based remote sensing offers an effective way to monitor vegetation and climate variables on regional and global scales. The annual peak Normalized Difference Vegetation Index (NDVI), derived from satellite observations, is closely associated with crop development, rangeland biomass, and vegetation growth. Although various machine learning methods have been developed to forecast NDVI over short time ranges, such as one-month-ahead predictions, long-term forecasting approaches, such as one-year-ahead predictions of vegetation conditions, are not yet available. To fill this gap, we develop a two-phase machine learning model to forecast the one-year-ahead peak NDVI over high-resolution grids, using the Four Corners region of the Southwestern United States as a testbed. In phase one, we identify informative climate attributes, including precipitation and maximum vapor pressure deficit, and develop the generalized parallel Gaussian process that captures the relationship between climate attributes and NDVI. In phase two, we forecast these climate attributes using historical data at least one year before the NDVI prediction month, which then serve as inputs to forecast the peak NDVI at each spatial grid. We developed open-source tools that outperform alternative methods for both gross NDVI and grid-based NDVI one-year forecasts, providing information that can help farmers and ranchers make actionable plans a year in advance.

stat.AP↗

arXiv2026

Sub-Pixel Electron Beam Alignment for Machine Learning Characterization of Hybrid Pixel Detectors

Due to their radiation hardness, kilohertz frame rates, and high dynamic range, hybrid pixel detectors have recently expanded their application range to electron diffraction and recently also electron imaging. However, these detectors typically have pixel sizes about ten times larger than those of direct electron detectors commonly used for imaging and more prominent electron multiple scattering effects. To overcome these limitations, machine learning approaches can be utilized to reconstruct the electron entrance point and achieve super-resolution. As this process is inherently stochastic, and machine learning relies on suitable training data, high-quality, representative training data are essential for developing models that achieve the best possible resolution. In this work, we present two novel experimental methods for generating such training data. The first method employs precise microscope alignment to scan the detector plane using a finely focused electron beam of 2 μm diameter, enabling controlled sub-pixel mapping. The second method utilizes specially designed aperture masks with sub-pixel-sized holes to accurately localize electron entry points. We developed and validated two experimental strategies for collecting training data at acceleration voltages of 60, 80, 120, and 200 keV, which enable sub-pixel labeling for hybrid pixel detectors. Notably, our methodology is broadly applicable to a wide range of hybrid pixel detectors.

physics.ins-det↗

arXiv2024

OnlySportsLM: Optimizing Sports-Domain Language Models with SOTA Performance under Billion Parameters

This paper explores the potential of a small, domain-specific language model trained exclusively on sports-related data. We investigate whether extensive training data with specially designed small model structures can overcome model size constraints. The study introduces the OnlySports collection, comprising OnlySportsLM, OnlySports Dataset, and OnlySports Benchmark. Our approach involves: 1) creating a massive 600 billion tokens OnlySports Dataset from FineWeb, 2) optimizing the RWKV architecture for sports-related tasks, resulting in a 196M parameters model with 20-layer, 640-dimension structure, 3) training the OnlySportsLM on part of OnlySports Dataset, and 4) testing the resultant model on OnlySports Benchmark. OnlySportsLM achieves a 37.62%/34.08% accuracy improvement over previous 135M/360M state-of-the-art models and matches the performance of larger models such as SomlLM 1.7B and Qwen 1.5B in the sports domain. Additionally, the OnlySports collection presents a comprehensive workflow for building high-quality, domain-specific language models, providing a replicable blueprint for efficient AI development across various specialized fields.

cs.CL↗

arXiv2023

Characterization of iLGADs using soft X-rays

Experiments at synchrotron radiation sources and X-ray Free-Electron Lasers in the soft X-ray energy range ($250$eV--$2$keV) stand to benefit from the adaptation of the hybrid silicon detector technology for low energy photons. Inverse Low Gain Avalanche Diode (iLGAD) sensors provide an internal gain, enhancing the signal-to-noise ratio and allowing single photon detection below $1$keV using hybrid detectors. In addition, an optimization of the entrance window of these sensors enhances their quantum efficiency (QE). In this work, the QE and the gain of a batch of different iLGAD diodes with optimized entrance windows were characterized using soft X-rays at the Surface/Interface:Microscopy beamline of the Swiss Light Source synchrotron. Above $250$eV, the QE is larger than $55\%$ for all sensor variations, while the charge collection efficiency is close to $100\%$. The average gain depends on the gain layer design of the iLGADs and increases with photon energy. A fitting procedure is introduced to extract the multiplication factor as a function of the absorption depth of X-ray photons inside the sensors. In particular, the multiplication factors for electron- and hole-triggered avalanches are estimated, corresponding to photon absorption beyond or before the gain layer, respectively.

physics.ins-det↗

arXiv2022

Simulation of the Signal Propagation for Thin-gap RPC in the ATLAS Phase-II Upgrade

Thin-gap Resistive Plate Chambers (RPCs) with a 1 mm gap size are introduced in the Phase-II ATLAS upgrade. Smaller avalanche charge due to the reduced gap size raises concerns for signal integrity. This work focuses on the RPC signal propagation process in lossless conditions, and an analytical study is implemented for the ATLAS RPC. Detector modeling is presented, and the simulation of the RPC signal is discussed in detail. Simulated characteristic impedance and crosstalk have been compared with the measured value to validate this model. This method is applied to different RPC design geometries, including the newly proposed $η-η$ readout scheme.

physics.ins-det↗

arXiv2021

A new layout about the graphite layers in RPC

The Resistive Plate Chamber (RPC) is widely used in experiments of high energy physics as trigger detector as its good time resolution and high efficiency. In the traditional layout of RPC, the graphite layers are indispensable parts. The working voltage is applied on these layers and the charge of avalanche dissipates through them. In this paper, a new design which removes the graphate layers is proposed to improve the structure of this detector. With this new design, the negative effect from the ununiformity of graphite is eliminated and the structure of detector is simplified.

physics.ins-det↗

arXiv2020

Room-temperature quasi-continuous-wave pentacene maser pumped by an invasive Ce:YAG luminescent concentrator

We present in this work a quasi-continuous-wave (CW) pentacene maser operating at 1.45 GHz in the Earth's magnetic field at room temperature with a duration of $\sim$4 ms and an output power of up to -25 dBm. The maser is optically pumped by a cerium-doped YAG (Ce:YAG) luminescent concentrator (LC) whose wedge-shaped output is embedded inside a 0.1% pentacene-doped para-terphenyl (Pc:Ptp) crystal. The pumped crystal is located inside a ring of strontium titanate (STO) that supports a TE$_{01δ}$ mode of high magnetic Purcell factor. Combined with simulations, our results indicate that CW operation of pentacene masers at room-temperature is perfectly feasible so long as excessive heating of the crystal is avoided.

physics.app-ph↗