measurement system design

This paper addresses the reliability of calibration evaluation for machine learning models, identifying systematic biases in the widely used Expected Calibration Error (ECE) under distributional shift and varying binning strategies. Methodologically, it clarifies the logical hierarchy among multi-level calibration definitions, and systematically exposes ECE’s limitations through visualization, binning-based statistical analysis, and theoretical derivation—demonstrating its failure to satisfy key requirements of robustness and consistency in calibration assessment. Building on this critique, the paper introduces and explicates emerging calibration paradigms—including distribution-level and instance-level calibration—alongside their corresponding evaluation methodologies, thereby constructing a rigorous, interpretable, and practice-oriented calibration knowledge framework. The results equip researchers with principled guidance for selecting appropriate evaluation metrics and advance calibration assessment from ad hoc, heuristic practices toward standardization and formalization.

This work addresses the limitations of traditional Gaussian assumptions in accurately representing complex uncertainties, which often lead to information loss and reduced accuracy in multi-stage measurement and control processes. To overcome these challenges, the paper proposes a scalable precision framework based on Gaussian Mixture Models (GMMs), leveraging GMMs as universal approximators of probability density functions. The approach integrates closed-form uncertainty propagation algorithms with memory-efficient computational strategies, thereby transcending the representational constraints of Gaussian methods while maintaining computational tractability. Experimental evaluations in manufacturing and metrology scenarios—such as circular factories—demonstrate that the proposed method significantly enhances the fidelity of uncertainty characterization and propagation, outperforming conventional Gaussian-based techniques.

Deming regression suffers from bias under heteroscedasticity—non-constant error variances—common in wide-range measurement data. To address this, we propose a weighted errors-in-variables (EIV) regression method. Our key contribution is the first systematic integration of a generalized precision profile model into the Deming regression framework, enabling concentration-dependent adaptive weighting that accurately captures heteroscedastic error structure. The method unifies EIV modeling, precision profile estimation, and weighted least squares, implemented via a standardized R package. Empirical evaluation demonstrates substantial improvements in accuracy and robustness for method comparison, particularly in high-stakes clinical assay validation where analytical consistency is critical. By explicitly modeling heteroscedasticity through measurement-concentration–dependent weights, our approach provides a statistically principled, broadly applicable solution for measurement verification under non-constant error variance.

When machine learning models are employed as measurement instruments, it remains unclear whether their outputs genuinely reflect stable and consistent latent constructs beyond merely achieving predictive performance. This work formally introduces the concept of “learned measurement functions” and proposes “measurement stability” as a distinct evaluation criterion. Through theoretical analysis and empirical case studies, we demonstrate that conventional metrics—such as generalization error, calibration, and robustness—do not guarantee measurement consistency. Our findings reveal that models with comparable predictive accuracy can implement systematically inequivalent measurement functions, and that these discrepancies become pronounced under distributional shifts, thereby exposing critical limitations in current evaluation frameworks.

In novel sensor development, conventional characterization and parameter optimization heavily rely on expert knowledge and are time-consuming, forming a critical bottleneck. This paper introduces the first closed-loop Bayesian optimization framework specifically designed for sensor characteristic characterization. By integrating real-time measurement feedback with a Gaussian process surrogate model, the method enables fully automated, human-in-the-loop-free exploration of high-dimensional parameter spaces and identification of optimal operating points. It eliminates manual trial-and-error, significantly improving optimization efficiency and reproducibility. Validated on a low-noise CCD sensor, the approach completes full-parameter-space characterization and optimization within two days—accelerating the process by over an order of magnitude compared to conventional methods—while maintaining comparable accuracy. This work establishes a generalizable, automation-first paradigm for intelligent instrument development.

Although large language models (LLMs) can achieve agreement with human annotators in text coding, their judgments may rely on superficial features unrelated to the underlying theoretical construct, thereby lacking construct validity. To address this issue, this work proposes a “fine-grained calibration” approach that decomposes theoretical constructs into clause-level components, validates each component against extractive evidence, and aggregates results according to explicit theoretical rules to assess whether LLMs genuinely measure the target construct. This method shifts the validation of construct validity from output consistency to process interpretability, enabling identification of errors stemming either from missing components or confusion with neighboring constructs. It establishes a transparent and interpretable paradigm for trustworthy measurement using LLMs in the social sciences.

This study addresses the quality control challenges in additive manufacturing arising from its layer-by-layer fabrication process and high degree of customization. It presents the first systematic adaptation of the Six Sigma DMAIC methodology tailored to this context. By integrating multi-source sensing and measurement data across the entire manufacturing chain—including materials, design, process parameters, and post-processing—the work employs advanced techniques such as deep learning, machine learning, design of experiments, simulation, ontological analysis, and network science to model the complex relationships among design inputs, process variations, and final part quality. The proposed optimization framework enables real-time anomaly detection and simultaneously optimizes lead time and energy consumption, thereby significantly enhancing the quality stability and process controllability of additive manufacturing systems.

This study addresses the limitations of existing SysML verification approaches, which are often tool-dependent and restricted to performance properties, lacking support for automated validation of behavioral and interface requirements. To overcome these shortcomings, this work proposes a tool-agnostic, automated verification workflow driven by SysML test cases, integrating UML Testing Profile and behavioral diagram constructs to enable unified validation of multidimensional attributes—including behavior, timing, and state responses. The methodology was developed through a mixed-methods research strategy combining literature review and stakeholder interviews, and its efficacy was empirically validated across two independent SysML toolchains. The approach not only transcends the constraints of conventional parametric methods but also enables automatic traceability of verification results back to the original model elements.

Industrial prediction and soft sensing often fail due to field data suffering from bias, latency, or seemingly plausible yet unreliable measurements. This work proposes a large language model (LLM)-guided Measurement Credibility Correction (MCC) method that, for the first time, leverages semantic information from process documentation to construct an external reference—requiring neither numerical correlations, fault labels, nor explicit process equations—for lightweight pre-inference correction. MCC translates document semantics into reference signals compatible with numerical models and integrates them at the front end of the prediction pipeline. Evaluated on multiple real-world industrial tasks, MCC reduces average relative MAE by 30.7% on authentic test data and by 80.3% under controlled contamination, while adding only 0.5–2.0k online parameters and incurring a maximum inference latency of 0.089 ms per step.

This study addresses the lack of optimal experimental design guidance for the standard addition method under non-decreasing measurement error structures. Building on c-optimality theory and integrating linear response modeling with analysis of variance, the authors systematically derive an optimal two-concentration-point design that minimizes estimation variance under constant, linear, or quadratic error growth. This work represents the first application of optimal experimental design theory to the standard addition method and demonstrates that the proposed two-point design achieves universal optimality across all considered non-decreasing error scenarios. Notably, the optimal allocation of replicate measurements deviates from the conventional 50:50 ratio and yields minimum-variance unbiased estimates without requiring weighted regression.