Search Results (240)

Background and Objectives: Decision-making capacity is essential for informed consent, yet its assessment in emergency departments (EDs) is often subjective and inconsistently documented. Older adults are particularly vulnerable to impaired capacity during acute illness. We aimed to develop a scalable, electronic health record (EHR)-based approach to support early identification of older ED patients at risk of decisional vulnerability using the Medical Information Mart for Intensive Care (MIMIC)-IV database. Methods: We conducted a retrospective cohort study of 51,195 ED patients aged ≥65 years. Clinicians manually reviewed 2000 patients using a conservative consensus protocol to establish a consensus-derived proxy for decisional vulnerability. Such an approach yielded a definitive reference subset (capacity vs. no capacity) and an “uncertain” category when consensus was not achieved. We developed a weak-supervision expectation–maximization (EM) label model that combined multiple noisy labeling functions derived from triage vital signs, acuity measures, arrival mode, and large language model (LLM)-classified chief complaints to estimate the probabilistic risk of impaired capacity. Model discrimination and calibration were assessed on an independent holdout subset of definitive reference labels using receiver operating characteristic area under the curve (ROC-AUC), precision–recall area under the curve (PR-AUC), calibration plots, and Brier score. To support clinically conservative use, operating thresholds were a priori constrained to limit automated flagging to ≤15% of patients, with the remaining ones deferred for clinician review. Results: On the definitive holdout set, the weak-supervision model achieved an ROC-AUC of approximately 0.855 and a PR-AUC of approximately 0.837. Calibration assessment demonstrated residual miscalibration in the generative posterior, which improved after a lightweight discriminative refinement step (logistic regression trained on EM-derived probabilistic labels), reducing the Brier score to approximately 0.224 on holdout evaluation. Under the prespecified operational constraint (≤15% auto-flagged), the model functioned as a conservative, selective alerting strategy, achieving high specificity and positive predictive value while identifying only a minority of patients with decisional vulnerability. Conclusions: This study demonstrates a methodological proof of concept for using weak supervision to model a retrospectively defined proxy for decisional vulnerability from routinely collected ED EHR data. The framework is intended to support conservative, triage-oriented prioritization. Further prospective validation, external testing, and workflow governance are needed before clinical implementation. Full article

The advancing perception capabilities of an individual unmanned underwater vehicle (UUV) pose new challenges for multi-target perceptual consistency in underwater bearing-only tracking (BOT) systems. Accurate target state estimation necessitates two key prerequisites: sensor bias compensation and precise data association. The biases encompass both sensor spatial bias and signal propagation delay between the target and sensor. This paper introduces a measurement model that explicitly accounts for these factors. To address the lack of prior target information, an initial target state estimation algorithm is developed based on maximum likelihood estimation (MLE), with a refined bio-inspired variant incorporating particle swarm optimization (PSO). To this end, a cost function is formulated to transform the BOT data association problem into an assignment problem. Thereafter, an iterative multi-target data association (MDA) algorithm, integrated with the expectation-maximization (EM) method, is designed to jointly mitigate the effects of signal delay and spatial bias. Monte Carlo simulation scenarios validate the overall effectiveness of the proposed MDA framework. Specifically, the EM-based spatial bias estimation method demonstrates accurate bias estimation capability. Full article

Receiver mobility complicates channel state information (CSI) acquisition in fluid antenna system (FAS)-assisted grant-free random access (GFRA), because user activity and multi-port channels evolve across pilot frames. Existing FAS acquisition methods are mainly frame-wise, while temporal recovery schemes do not directly combine receiver geometry with Doppler information. This article proposes geo-temporal expectation-maximization approximate message passing (GT-EM-AMP), which transfers posterior information between frames and refines the channel prior using receiver trajectory, effective Doppler, and coarse geometry. The proposed recursion preserves the low-complexity structure of EM-AMP while introducing only limited additional state updates. Simulations over an SNR range from $-14$ to 8 dB show that GT-EM-AMP achieves lower channel-estimation error and a favorable activity-detection tradeoff relative to static, temporal-only, geometry-only, and greedy baselines. Ablation, robustness, mobility, scalability, and statistical evaluations characterize the operating range of GT-EM-AMP and show that its activity-detection advantage depends on the SNR regime. GT-EM-AMP introduces modest runtime and memory overhead relative to static EM-AMP. The evaluation focuses on short acquisition windows with coarse geometry information under a Jakes-type temporal model. Full article

This study proposes a novel measurement system repeatability and reproducibility (R&R) framework for zero-inflated correlated defect count data in semiconductor wafer automated optical inspection (AOI). In advanced semiconductor manufacturing environments, AOI systems are extensively used to detect wafer defects such as particles, scratches, and structural abnormalities. However, conventional Gauge R&R methods are primarily developed for continuous Gaussian-type measurements and are therefore not fully appropriate for high-yield semiconductor inspection data characterized by discrete defect counts, excessive zero observations, and correlated defect categories. To address these limitations, this study develops a zero-inflated bivariate Poisson (ZIBP) measurement system model capable of simultaneously capturing correlated defect generation mechanisms and structural zero-defect states. A latent variable representation is introduced to model shared and category-specific defect sources, while a zero inflation mechanism accounts for defect-free wafer observations commonly encountered in precision manufacturing. An expectation–maximization (EM) algorithm is further developed for parameter estimation, including latent common defect counts and structural zero probabilities. Based on the fitted model, the repeatability variance, reproducibility variance, total measurement variation, and Percent R&R are estimated under the proposed probabilistic framework. In addition, bootstrap resampling is employed to construct confidence intervals for the proposed R&R measures. Theoretical properties of the proposed framework, including covariance structure, identifiability, EM monotonicity, estimator consistency, and asymptotic behavior of the Percent R&R estimator, are analytically established. The proposed framework extends traditional Gauge R&R analysis from continuous Gaussian measurements to zero-inflated correlated count-type defect inspection data and provides a statistically rigorous methodology for evaluating AOI measurement system reliability in semiconductor wafer manufacturing environments. Full article

Accurately predicting the remaining useful life (RUL) of UAV bearings is challenging due to maneuver-shock-induced stochastic jumps during their two-phase degradation, while existing numerical methods are computationally too costly for UAV onboard computing. To address this, an analytical RUL prediction method considering stochastic jumps is proposed. A two-phase Wiener process incorporating stochastic jumps is constructed to model degradation processes involving shocks. Subsequently, a combined Kalman Filter–Rauch–Tung–Striebel Smoothing–Expectation Maximization (KF-EM-RTS) framework is developed for simultaneous online updating of drift and diffusion coefficients. Furthermore, utilizing Stein’s Lemma, an analytical expression under a fixed-change-point assumption for the RUL probability density function (PDF) of the proposed model is derived, thereby reducing the reliance on repeated numerical integration. Under the experimental settings used in this study, the analytical implementation reduces the single-point PDF calculation time by approximately 90% compared with the corresponding numerical integration implementation, which is important for compute-limited UAV platforms. Moreover, RMSE is decreased by 48% and 76% versus models ignoring jumps. This approach offers a lightweight solution for real-time predictive maintenance of UAVs. Full article

Accurate identification of ship maneuvering dynamics is a fundamental prerequisite for realizing autonomous navigation in intelligent ship systems. Existing nonparametric identification methods face critical limitations under realistic data-stream conditions: batch-mode algorithms cannot process streaming sensor data in real time, while parametric approaches impose rigid assumptions on the underlying system structure. This paper proposes an online nonparametric identification framework for intelligent ship maneuvering dynamics based on the Expectation-Maximization (EM) algorithm, specifically, an Online EM (OEM) variant adapted for sequential data streams. The proposed method treats ship maneuvering forces and moments with a probabilistic Gaussian mixture framework and iteratively refines both model parameters and latent structure using incoming sensor observations, without requiring a pre-specified model order. The method is designed to handle the nonlinearity and non-Gaussianity of ship motion under environmental disturbances, including wind and current. Systematic experiments are conducted on the SR108 container ship dataset, encompassing turning tests and zigzag tests. Comparative evaluations against the incremental Gaussian mixture model (IGMM) demonstrate that the proposed OEM-based method achieves superior prediction accuracy and real-time adaptability. The proposed framework provides a computationally efficient and practically deployable solution for online, structure-free modeling of intelligent ship maneuvering systems. Full article

All-in-one weather-degraded image restoration aims to restore clean images from diverse weather-degraded observations (such as rain, haze, and snow) using a unified model. However, this topic remains challenging due to its ill-posed nature and the scarcity of large-scale paired training data. This article develops a novel semi-supervised learning framework, termed Semi-Supervised Degradation-Aware Learning (S²DAL), to adjust the feature space to align with the unified parameter space for all-in-one adverse weather removal. Specifically, the proposed S²DAL consists of two backbone networks: a Degradation-guided Histogram Transformer (DHformer) for weather-degraded image restoration and a Degradation-guided Convolutional Neural Network (DCNN) for degradation generation. A key component, the Degradation-guided Histogram Transformer (DHT) block, is designed to effectively capture intrinsic image features while suppressing diverse degradation interference through channel shuffling modulation, dynamic-range histogram self-attention, and dual-scale gated feed forward. Furthermore, a Monte Carlo-based Expectation-Maximization (EM) algorithm is introduced to jointly optimize latent variables and network parameters under both labeled and unlabeled data. Extensive quantitative and qualitative results on synthetic and real-world datasets consistently demonstrate that the proposed S²DAL achieves superior restoration performance compared to multiple state-of-the-art fully supervised and semi-supervised approaches. Full article

The likelihood equations arising from a progressively Type II censored sample drawn from a Type I Generalized Logistic Distribution do not yield closed-form solutions for the scale and shape parameters. To address this, we derive maximum likelihood estimators of the unknown parameters by means of the Expectation–Maximization (EM) algorithm. The expected Fisher information matrix is obtained using the missing information principle, allowing for the computation of asymptotic standard errors. A simulation study is presented to illustrate the performance and practical implementation of the proposed inferential procedures. Full article

This paper presents a computationally efficient Multivariate Mixture Model Thresholding (MMMT) technique for sparse signal denoising and recovery, with the goal of improving data quality in modern sensing and biomedical systems. The proposed method extends classical thresholding approaches by modeling nonzero signal coefficients using a multivariate Gaussian mixture prior, thereby capturing cross-channel and intercomponent dependencies commonly observed in multi-sensor and physiological signals. The thresholding rule is analytically derived through maximum a posteriori (MAP) estimation within a majorization–minimization (MM) optimization framework, while the associated model parameters are adaptively estimated using an expectation–maximization (EM) algorithm. Experimental results on noisy sinusoidal signals and synthetic ECG data demonstrate that MMMT consistently achieves higher correlation with ground-truth signals and improved preservation of pulse amplitude and morphological characteristics compared with benchmark methods, including the ${\mathcal{l}}_1$-fused lasso and convex–non-convex (CNC) fused lasso. Quantitative evaluations based on correlation metrics, signal-to-noise ratio (SNR), and peak signal-to-noise ratio (PSNR) further confirm the effectiveness of the proposed approach. Owing to its scalability, robustness, and strong statistical interpretability, MMMT provides a promising framework for real-time ECG signal enhancement. Although the proposed framework is general and can be adapted to other biomedical modalities such as EEG, CT, and MRI, experimental validation in this study is limited to ECG signals. Full article

Expectation Maximization (EM) iterations for Gaussian mixture models (GMMs) are highly sensitive to initial parameters, which calls for developing robust initialization methods. For multidimensional GMMs, the problem is even more severe than for univariate GMMs because of the larger number of parameters. For univariate or low-dimensional GMMs, several studies on their initialization have appeared in the literature, whereas research on initializing multivariate, high-dimensional GMMs remains limited. In this study, we compare several initializations for Multivariate Diagonal Gaussian Mixture Models (MDGMMs). In our study, we have included methods already used in the literature for initializing MDGMMs: Hierarchical Clustering, K-means, and random initialization. We have also used a new method, namely, Ensemble Clustering. A review of the existing literature suggests that Ensemble Clustering has not been used previously as an initialization strategy for MDGMM. Several metrics were used to evaluate the clustering quality. Our study demonstrates that Ensemble Clustering, while computationally intensive, is competitive with other methods for initializing MDGMMs. Full article

The Poisson exponentially weighted moving average (PEWMA) control chart rests upon the equidispersion assumption of the pure Poisson distribution, a structural symmetry condition stipulating that the process mean and variance are equal. In manufacturing environments characterized by latent process heterogeneity, this assumption is systematically violated: the resulting distributions are inherently asymmetric, heavily right-skewed, and overdispersed. This structural asymmetry renders standard PEWMA control limits artificially narrow, inducing a substantial inflation of false alarm rates. This paper introduces the Poisson mixture EWMA (PM-EWMA) control chart, which models the latent heterogeneous structure of count data as a finite Poisson mixture distribution, with parameters estimated via the Expectation–Maximization (EM) algorithm without requiring prior labeling of process states. The optimal number of components is determined via the Bayesian Information Criterion (BIC) as the primary criterion, supplemented by the Akaike Information Criterion (AIC), its bias-corrected variant (AICc), and the log-likelihood ratio diagnostic. The PM-EWMA chart incorporates the exact mixture variance, accounting for both within-component and between-component variability, into the EWMA control limit structure, thereby providing a theoretically justified correction under the fitted Poisson mixture assumption. A Monte Carlo simulation study comprising 495 factorial configurations benchmarks the PM-EWMA chart against both the standard PEWMA chart and the negative binomial EWMA (NB-EWMA) chart with oracle dispersion calibration, confirming stable in-control ARL performance and demonstrating improved discrimination relative to the misspecified PEWMA baseline. Empirical validation using fabric defect count data from two textile manufacturers in Türkiye, with Overdispersion Indices of 6.01 and 2.74, respectively, demonstrates false alarm reductions ranging from 40.9% to 89.2% relative to the standard PEWMA chart, depending on the smoothing parameter and degree of overdispersion. Full article

This paper proposes a modeling framework for nonlinear degradation under constant-stress accelerated degradation testing (CSADT) to predict failure lifetime. The proposed employs a generalized Wiener process to characterize degradation, wherein the drift coefficient is stress-dependent and the heterogeneity in the diffusion coefficient is explicitly modeled. Random effects are introduced to capture volatility variability across degradation trajectories, and model parameters are estimated via the expectation–maximization (EM) algorithm. Using the law of total probability, the probability density function (PDF) and reliability function of failure lifetime under normal operating conditions are derived. The proposed model is validated using crack propagation simulation data and experimental wear scar width data from an alloy product. The results demonstrate that the proposed model improves prediction accuracy for failure lifetime and reliability, highlighting its potential utility in engineering applications. Full article

Aiming to improve radar multi-target tracking (MTT) accuracy and association performance in complex scenarios involving dense clutter, missed detections, and maneuvering targets, an improved tracklet generation approach based on the expectation–maximization (EM) framework is proposed in which data association variables and motion model variables are jointly modeled as latent variables. These variables are estimated through iterative updates based on the loopy belief propagation (LBP) algorithm and the interacting multiple model (IMM) filtering and smoothing algorithms to generate high-confidence tracklets. Then, a delayed decision-making strategy based on the multi-hypothesis approach is employed to associate these tracklets into complete target trajectories. The resulting algorithm is named IMM-TrackletMHT. The performance of the IMM-TrackletMHT algorithm is evaluated and compared with several baseline algorithms in simulated scenarios under different clutter rates and detection probabilities. The simulation results demonstrate that the proposed algorithm consistently outperforms the baseline methods in terms of tracking accuracy, exhibits strong robustness to variations in the operating environment, and achieves higher computational efficiency in multi-scan measurement processing, thereby demonstrating the effectiveness and superiority of the proposed tracklet generation approach for maneuvering MTT. Full article

In recent years, the growing availability of large-scale data across a wide range of disciplines has created new opportunities for developing models that improve the predictive accuracy of statistical models. Although techniques such as regularization and Bayesian hierarchical methods are commonly used for building predictive models, substantial challenges remain, particularly when dealing with high-dimensional datasets that contain considerable noise. In this study, we propose a Bayesian hierarchical model that employs a spike-and-slab hierarchical elastic net prior that regularizes the Cox Proportional Hazards (Cox-PH) model. The method combines Bayesian modeling with the regularized partial log-likelihood of the Cox-PH framework, incorporating an Elastic Net penalty to estimate the joint posterior distribution under a hierarchical elastic net prior. We compute this posterior using an Expectation–Maximization Cyclic Coordinate Descent Algorithm (EM-CCDA), which streamlines feature selection and enhances overall predictive performance. We evaluate the algorithm’s performance through Monte Carlo simulations and apply it to three real-world datasets, comparing the results with those from established classical and Bayesian survival analysis approaches. The findings demonstrate notable gains in both feature selection and predictive accuracy, highlighting the model’s strong ability to predict patient survival and identify relevant genes in real biological datasets. Full article

Estimating the remaining useful life (RUL) of engineering systems is crucial for maintenance planning and the reliability of complex mechanical units. Accurate RUL predictions support timely interventions and help to prevent unexpected failures. This study proposes a statistically robust framework that models degradation signals up to the end of life using a hidden Markov model (HMM) with a simple-failure structure and an absorbing terminal state. The proposed method estimates state-dependent linear emission parameters and transition probabilities using a ridge-regularized expectation–maximization (EM) algorithm. The ridge penalty stabilizes slope estimates under limited data, while a robust Huber-based scale estimator reduces sensitivity to outliers in the sensor-derived health indicator. RUL is computed as a weighted expected time to absorption, combining transient-state survival characteristics with smoothed posterior-state probabilities obtained via the forward–backward algorithm. This yields a low-variance state-aware estimator that preserves the probabilistic structure of the HMM. Simulation studies show that the proposed ridge-regularized EM significantly reduces parameter variance and improves predictive accuracy compared with the baseline weighted least squares EM (WLS-EM). A real-data case analysis demonstrates further improvements in RUL estimation accuracy and smoother, more reliable prediction trajectories. Overall, the framework provides a robust and interpretable approach for practical prognostics applications. Full article