arXiv subjects

Zhiling Chen

Publications and source records attributed to Zhiling Chen.

9 recordsLinked to original sources

arXiv2026

How Should a Robot Configure Its Laser Scanner for Inspection?

Robotic inspection relies on accurate sensing to acquire high-fidelity geometric measurements for defect detection and metrology. While prior work has focused on robot motion and viewpoint planning, how to configure sensing parameters remains largely underexplored, despite their decisive impact on measurement quality. We propose SenseHD, a robotic sensing system that formulates scanner configuration as an instruction-conditioned sensing decision. Instead of predicting precise parameter values, SenseHD treats sensing parameters as discrete sensing actions and selects stable sensing regimes through hyperdimensional associative memory. Experiments on a real robotic inspection platform demonstrate that SenseHD robustly selects appropriate sensing configurations and significantly improves inspection reliability, while remaining lightweight and efficient compared to baseline methods.

cs.RO↗

arXiv2026

Task-Aware Scanning Parameter Configuration for Robotic Inspection Using Vision Language Embeddings and Hyperdimensional Computing

Robotic laser profiling is widely used for dimensional verification and surface inspection, yet measurement fidelity is often dominated by sensor configuration rather than robot motion. Industrial profilers expose multiple coupled parameters, including sampling frequency, measurement range, exposure time, receiver dynamic range, and illumination, that are still tuned by trial-and-error; mismatches can cause saturation, clipping, or missing returns that cannot be recovered downstream. We formulate instruction-conditioned sensing parameter recommendation; given a pre-scan RGB observation and a natural-language inspection instruction, infer a discrete configuration over key parameters of a robot-mounted profiler. To benchmark this problem, we develop Instruct-Obs2Param, a real-world multimodal dataset linking inspection intents and multi-view pose and illumination variation across 16 objects to canonical parameter regimes. We then propose ScanHD, a hyperdimensional computing framework that binds instruction and observation into a task-aware code and performs parameter-wise associative reasoning with compact memories, matching discrete scanner regimes while yielding stable, interpretable, low-latency decisions. On Instruct-Obs2Param, ScanHD achieves 92.7% average exact accuracy and 98.1% average Win@1 accuracy across the five parameters, with strong cross-split generalization and low-latency inference suitable for deployment, outperforming rule-based heuristics, conventional multimodal models, and multimodal large language models. This work enables autonomous, instruction-conditioned sensing configuration from task intent and scene context, eliminating manual tuning and elevating sensor configuration from a static setting to an adaptive decision variable.

cs.RO↗

arXiv2025

Contour-informed inter-patient deformable registration of Head-and-Neck patients

Background and Purpose: Voxel-based analysis (VBA) helps to identify dose-sensitive regions by aligning individual dose distributions within a common coordinate system (CCS). Accurate deformable image registration (DIR) is essential for addressing anatomical variability across patients. To improve both global and region-specific alignment, we enhanced our in-house DIR algorithm (CPT-DIR) with contour-informed regularisations. We tested its performance for head-and-neck (HN) CT images. Materials and Methods: We developed and evaluated contour-informed CPT-DIR on 37 HN CTs, including 7 with ground-truth dose for warped dose validation. Bone contours were generated using TotalSegmentator, while other organs at risk (OARs) were manually delineated. Contour-based constraints, such as Dice Similarity, were integrated to enhance registration outcome. The global registration results were evaluated using MAE, SSIM and PSNR. Geometric accuracy and warped dose accuracy were assessed using Dice Similarity Coefficient (DSC) and Dose-Organ Overlap (DOO). Constrained and unconstrained CPT-DIR were compared to B-spline. Results: CPT-DIR achieved superior accuracy with a MAE of 98.9\pm6.3 HU, lower than 179.1\pm17.8 HU for B-spline. Incorporating brainstem contours as regularisation improved the DSC from 0.604\pm0.116 to 0.878\pm0.017 and DOO from 0.430\pm0.117 to 0.753\pm0.043 for brainstem. Across all metrics, the enhanced CPT-DIR outperformed the B-spline, confirming its advantages in geometric accuracy. Conclusions: The integration of contour-informed regularisation in CPT-DIR improved DIR accuracy, particularly in dosimetrically relevant regions. This enhanced spatial alignment enabled more precise dose mapping for VBA and demonstrated strong potential for advancing reliable inter-patient dosimetric studies in HN radiotherapy.

physics.med-ph↗

arXiv2025

Privacy-Preserving Decentralized Federated Learning via Explainable Adaptive Differential Privacy

Decentralized federated learning enables collaborative model training without a central server, but shared model updates can still leak sensitive information through inversion, reconstruction, and membership inference attacks. Differential privacy offers formal protection, yet existing decentralized methods operate without visibility into the noise already injected by previous participants. Each client therefore adds a full, worst-case perturbation at every step, and the accumulated noise degrades accuracy well below what the privacy requirement actually demands. We present PrivateDFL, a decentralized and privacy-preserving framework that pairs hyperdimensional computing with a transparent noise accountant. The accountant tracks the cumulative perturbation present in the shared model and lets each client add only the minimal incremental noise needed to satisfy its privacy budget. We prove that every transmitted model satisfies the target privacy guarantee, and that under this accounting the cumulative noise grows only logarithmically in the number of clients and rounds, rather than the far faster super-linear growth incurred without accounting. This yields a substantially tighter balance between privacy and accuracy than prior approaches. Across image, speech, and wearable-sensor benchmarks, and under both identically and non-identically distributed data, PrivateDFL surpasses centrally trained Transformer-based and deep neural network baselines, improving accuracy by 16 percent on images, 62 percent on speech, and 14 percent on wearable sensing over the strongest baseline in each case, while reducing inference latency by up to 119 times and energy consumption by up to 143 times. These properties make PrivateDFL a practical solution for privacy-preserving collaborative learning in settings where sensitive data cannot be centralized, such as healthcare and human-activity monitoring.

cs.CR↗

arXiv2025

ScanBot: A Benchmark for Precision Robotic Surface Scanning with Industrial Laser Profilers

We introduce ScanBot, a benchmark for instruction-conditioned, high-precision surface scanning with robot-mounted industrial laser profilers. Unlike existing robot learning datasets that emphasize coarse behaviors such as grasping, navigation, or dialogue, ScanBot targets sensing-centric tasks where sub-millimeter motion continuity, strict stand-off control, and stable scanner settings are essential for acquiring usable geometry. The dataset contains scanning trajectories over twenty objects, including electronic components and structured 3D-printed parts, and spans six task types that range from broad inspection to fine-grained detail scanning and geometry-critical operations, including metrology and registration. Each episode is specified by natural language instructions and paired with synchronized first-person RGB-D, third-person video, laser height profiles, robot joint and pose traces, and scanner-parameter logs. These requirements expose a gap: despite recent progress, learning-based models often fail to produce stable and feasible scan motions under fine-grained instructions and real laser-profiling constraints. To reflect how industrial scanning is actually done, we evaluate methods through a two-stage pipeline. Stage I asks the model to "set up the sensor" by recommending scanner parameters, while Stage II asks it to "move like a scanner" by producing smooth, feasible trajectories that maintain stand-off and cover the intended region under precision demands.

cs.RO↗

arXiv2025

Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Models

Additive manufacturing enables the fabrication of complex designs while minimizing waste, but faces challenges related to defects and process anomalies. This study presents a novel multimodal Retrieval-Augmented Generation-based framework that automates anomaly detection across various Additive Manufacturing processes leveraging retrieved information from literature, including images and descriptive text, rather than training datasets. This framework integrates text and image retrieval from scientific literature and multimodal generation models to perform zero-shot anomaly identification, classification, and explanation generation in a Laser Powder Bed Fusion setting. The proposed framework is evaluated on four L-PBF manufacturing datasets from Oak Ridge National Laboratory, featuring various printer makes, models, and materials. This evaluation demonstrates the framework's adaptability and generalizability across diverse images without requiring additional training. Comparative analysis using Qwen2-VL-2B and GPT-4o-mini as MLLM within the proposed framework highlights that GPT-4o-mini outperforms Qwen2-VL-2B and proportional random baseline in manufacturing anomalies classification. Additionally, the evaluation of the RAG system confirms that incorporating retrieval mechanisms improves average accuracy by 12% by reducing the risk of hallucination and providing additional information. The proposed framework can be continuously updated by integrating emerging research, allowing seamless adaptation to the evolving landscape of AM technologies. This scalable, automated, and zero-shot-capable framework streamlines AM anomaly analysis, enhancing efficiency and accuracy.

cs.AI↗

arXiv2025

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?

In industrial settings, the accurate detection of anomalies is essential for maintaining product quality and ensuring operational safety. Traditional industrial anomaly detection (IAD) models often struggle with flexibility and adaptability, especially in dynamic production environments where new defect types and operational changes frequently arise. Recent advancements in Multimodal Large Language Models (MLLMs) hold promise for overcoming these limitations by combining visual and textual information processing capabilities. MLLMs excel in general visual understanding due to their training on large, diverse datasets, but they lack domain-specific knowledge, such as industry-specific defect tolerance levels, which limits their effectiveness in IAD tasks. To address these challenges, we propose Echo, a novel multi-expert framework designed to enhance MLLM performance for IAD. Echo integrates four expert modules: Reference Extractor which provides a contextual baseline by retrieving similar normal images, Knowledge Guide which supplies domain-specific insights, Reasoning Expert which enables structured, stepwise reasoning for complex queries, and Decision Maker which synthesizes information from all modules to deliver precise, context-aware responses. Evaluated on the MMAD benchmark, Echo demonstrates significant improvements in adaptability, precision, and robustness, moving closer to meeting the demands of real-world industrial anomaly detection.

cs.CV↗

arXiv2024

Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing

Federated Learning (FL) has become a key method for preserving data privacy in Internet of Things (IoT) environments, as it trains Machine Learning (ML) models locally while transmitting only model updates. Despite this design, FL remains susceptible to threats such as model inversion and membership inference attacks, which can reveal private training data. Differential Privacy (DP) techniques are often introduced to mitigate these risks, but simply injecting DP noise into black-box ML models can compromise accuracy, particularly in dynamic IoT contexts, where continuous, lifelong learning leads to excessive noise accumulation. To address this challenge, we propose Federated HyperDimensional computing with Privacy-preserving (FedHDPrivacy), an eXplainable Artificial Intelligence (XAI) framework that integrates neuro-symbolic computing and DP. Unlike conventional approaches, FedHDPrivacy actively monitors the cumulative noise across learning rounds and adds only the additional noise required to satisfy privacy constraints. In a real-world application for monitoring manufacturing machining processes, FedHDPrivacy maintains high performance while surpassing standard FL frameworks - Federated Averaging (FedAvg), Federated Proximal (FedProx), Federated Normalized Averaging (FedNova), and Federated Optimization (FedOpt) - by up to 37%. Looking ahead, FedHDPrivacy offers a promising avenue for further enhancements, such as incorporating multimodal data fusion.

cs.LG↗

arXiv2024

Vision Language Model for Interpretable and Fine-grained Detection of Safety Compliance in Diverse Workplaces

Workplace accidents due to personal protective equipment (PPE) non-compliance raise serious safety concerns and lead to legal liabilities, financial penalties, and reputational damage. While object detection models have shown the capability to address this issue by identifying safety items, most existing models, such as YOLO, Faster R-CNN, and SSD, are limited in verifying the fine-grained attributes of PPE across diverse workplace scenarios. Vision language models (VLMs) are gaining traction for detection tasks by leveraging the synergy between visual and textual information, offering a promising solution to traditional object detection limitations in PPE recognition. Nonetheless, VLMs face challenges in consistently verifying PPE attributes due to the complexity and variability of workplace environments, requiring them to interpret context-specific language and visual cues simultaneously. We introduce Clip2Safety, an interpretable detection framework for diverse workplace safety compliance, which comprises four main modules: scene recognition, the visual prompt, safety items detection, and fine-grained verification. The scene recognition identifies the current scenario to determine the necessary safety gear. The visual prompt formulates the specific visual prompts needed for the detection process. The safety items detection identifies whether the required safety gear is being worn according to the specified scenario. Lastly, the fine-grained verification assesses whether the worn safety equipment meets the fine-grained attribute requirements. We conduct real-world case studies across six different scenarios. The results show that Clip2Safety not only demonstrates an accuracy improvement over state-of-the-art question-answering based VLMs but also achieves inference times two hundred times faster.

cs.CV↗