Search Results (401)

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

A photoelectric tracking system is a typical bearing-only target tracking system that faces significant challenges arising from measurement origin uncertainty due to clutter and the discrepancy between continuous-time target dynamics and discrete-time optical sampling, as well as the inherent nonlinearity of bearing-only tracking. This paper addresses these issues by proposing a novel distributed probabilistic data association feedback particle filter (DPDA-FPF) framework. To resolve the tracking ambiguity at the local level, we extend the feedback particle filter to a continuous-discrete setting integrated with probabilistic data association. Subsequently, the local state estimates and covariances from spatially separated tracking systems are transmitted to a fusion center and integrated using an optimal linear covariance-weighted fusion rule to improve global observability and mitigate biases of individual systems. Numerical simulations in a 3D scenario with moderate clutter density demonstrate that while individual sensor tracks suffer from fluctuations, the proposed fused estimate achieves substantially lower root mean square errors in both position and velocity. The results validate the efficiency of the proposed architecture as a robust solution for photoelectric tracking applications. Full article

Accurate estimation of the State of Charge (SOC) for lithium-ion batteries is a core function of the Battery Management System (BMS). However, LiFePO₄ batteries present specific challenges for SOC estimation due to the characteristic plateau in their open-circuit voltage (OCV) versus SOC relationship. Moreover, data-driven estimation approaches often face significant difficulties stemming from measurement noise and interference, the highly nonlinear internal dynamics of the battery, and the time-varying nature of key battery parameters. To address these issues, this paper proposes a Long Short-Term Memory (LSTM) model integrated with feature engineering, physical constraints, and the Extended Kalman Filter (EKF). First, the model’s temporal perception of the historical charge–discharge states of the battery is enhanced through the fusion of temporal voltage information. Second, a post-processing strategy based on physical laws is designed, utilizing the Particle Swarm Optimization (PSO) algorithm to search for optimal correction factors. Finally, the SOC obtained from the previous steps serves as the observation input to EKF filtering, enabling a probabilistically weighted fusion of the data-driven model output and the EKF to improve the model’s dynamic tracking performance. When applied to SOC estimation of LiFePO₄ batteries under various operating conditions and temperatures ranging from 0 °C to 50 °C, the proposed model achieves average Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) as low as 0.46% and 0.56%, respectively. These results demonstrate the model’s excellent robustness, adaptability, and dynamic tracking capability. Additionally, the proposed approach only requires derived features from existing input data without the need for additional sensors, and the model exhibits low memory usage, showing considerable potential for practical BMS implementation. Furthermore, this study offers an effective technical pathway for state estimation under a “physical information–data-driven–filter fusion” framework, enabling accurate SOC estimation of lithium-ion batteries across multiple operating scenarios. Full article

This study presents a passive mechanical filter designed to enhance sub-Hertz Venusquake detection by shaping the seismic transfer path. The mechanism uses a tunable, high-Q pendulum mounted inside a cylindrical enclosure on a three-ring gimbal to ensure self-leveling and alignment in gravity on uneven terrain. Unlike approaches that rely on broadband digitization and require active control and a stable power supply, this housing–gimbal mechanism performs mechanical filtering for sub-Hz signal amplification and higher frequency attenuation without power. Response spectrum analysis shows that the transmissibility can be tuned to achieve peak sensitivities in the 0.5–0.8 Hz range. When tuned to 50–55 mm pendulum length and under assumed undamping, the pendulum-mounted mechanism improves detectability at best by 10–100× relative to a bare sensor for moderate magnitude ($M_s$ = 3–6) in a 12 h observation window, with signal-to-noise (SNR) ratio of 3, and amplitude spectrum density (ASD) of 10⁻⁸ m/s²/√Hz. Furthermore, we extrapolate that the predicted minimum detectable event rates follow $N_m^{min}\propto {\left(\mathrm{S}\mathrm{N}\mathrm{R}\right)}^{1.2}{\left(\mathrm{A}\mathrm{S}\mathrm{D}\right)}^{1.2}{f}_s^{0.6}$, where $f_s$ is the quake wave frequency. The damping ratio, considering both structural damping and viscous drag, is estimated to be in the order of 10⁻³ to 10⁻². A probabilistic sensitivity analysis is performed to account for the inherent uncertainty in the spectral mismatch between the narrowband sub-Hz resonance of the designed mechanical filter and the peak frequencies of seismic events; the derived probability model suggests strategies for improving the detection probability in the 0.01–1 Hz range. Full article

Wireless sensor networks comprise many resource-constrained nodes that must protect both local readings and routing metadata. The sensors collect data from the environment or from the individual to whom they are attached and transmit it to the nearest gateway node via a wireless network for further delivery to external users. Due to wireless communication, the transmitted messages may be intercepted, rerouted, or even modified by an attacker. Consequently, security and privacy issues are of utmost importance, and the nodes must be protected against unauthorized access during transmission over a public wireless channel. To address these issues, we propose the Probabilistic Bit-Similarity-Based Key Agreement Protocol (PBS-KAP). This novel method enables two nodes to iteratively converge on a shared secret key without transmitting it or relying on pre-installed keys. PBS-KAP enables two nodes to agree on a symmetric session key using probabilistic similarity alignment with explicit key confirmation (MAC). Optimized Garbled Circuits facilitate secure computation with minimal computational and communication overhead, while Secure Sketches combined with Fuzzy Extractors correct residual errors and amplify entropy, producing reliable and uniformly random session keys. The resulting protocol provides a balance between security, privacy, and usability, standing as a practical solution for real-world WSN and IoT applications without imposing excessive computational or communication burdens. Security relies on standard computational assumptions via a one-time elliptic–curve–based base Oblivious Transfer, followed by an IKNP Oblivious Transfer extension and a small garbled threshold circuit. No pre-deployed long-term keys are required. After the bootstrap, only symmetric operations are used. We analyze confidentiality in the semi-honest model. However, entity authentication, though feasible, requires an additional Authenticated Key Exchange step or malicious-secure OT/GC. Under the semi-honest OT/GC assumption, we prove session-key secrecy/indistinguishability; full entity authentication requires an additional AKE binding step or malicious-secure OT/GC. Full article

Autonomous navigation in cluttered and dynamic industrial environments remains a major challenge for mobile robots. Traditional occupancy-grid and geometric planning approaches often struggle in such unstructured settings due to partial observability, sensor noise, and the frequent presence of moving agents (machinery, vehicles, humans). These limitations seriously undermine long-term reliability and safety compliance—both essential for Industry 4.0 applications. This paper introduces S3PM, a lightweight entropy-regularized framework for simultaneous mapping and path planning that operates directly on dense 3D point clouds. Its key innovation is a dynamics-aware entropy field that fuses per-voxel occupancy probabilities with motion cues derived from residual optical flow. Each voxel is assigned a risk-weighted entropy score that accounts for both geometric uncertainty and predicted object dynamics. This representation enables (i) robust differentiation between reliable free space and ambiguous/hazardous regions, (ii) proactive collision avoidance, and (iii) real-time trajectory replanning. The resulting multi-objective cost function effectively balances path length, smoothness, safety margins, and expected information gain, while maintaining high computational efficiency through voxel hashing and incremental distance transforms. Extensive experiments in both real-world and simulated settings, conducted on a Raspberry Pi 5 (with and without the Hailo-8 NPU), show that S3PM achieves 18–27% higher IoU in static/dynamic segmentation, 0.94–0.97 AUC in motion detection, and 30–45% fewer collisions compared to OctoMap + RRT* and standard probabilistic baselines. The full pipeline runs at 12–15 Hz on the bare Pi 5 and 25–30 Hz with NPU acceleration, making S3PM highly suitable for deployment on resource-constrained embedded platforms. Full article

Accurate cropland classification within smallholder farming systems is essential for effective land management, efficient resource allocation, and informed agricultural decision-making. This study evaluates cropland classification performance using Red, Green, Blue (RGB) and multi-spectral (blue, green, red, red-edge, near-infrared) unmanned aerial vehicle (UAV) imagery. Both datasets were derived from imagery acquired using a MicaSense Altum sensor mounted on a DJI Matrice 300 UAV. Cropland classification was performed using machine learning algorithms implemented within the Google Earth Engine (GEE) platform, applying both a non-binary classification of five land cover classes and a binary classification within a probabilistic framework to distinguishing cropland from non-cropland areas. The results indicate that multi-spectral imagery achieved higher classification accuracy than RGB imagery for non-binary classification, with overall accuracies of 75% and 68%, respectively. For binary cropland classification, RGB imagery achieved an area under the receiver operating characteristic curve (AUC–ROC) of 0.75, compared to 0.77 for multi-spectral imagery. These findings suggest that, while multi-spectral data provides improved classification performance, RGB imagery can achieve comparable accuracy for fundamental cropland delineation. This study contributes baseline evidence on the relative performance of RGB and multi-spectral UAV imagery for cropland mapping in heterogeneous smallholder farming landscapes and supports further investigation of RGB-based approaches in resource-constrained agricultural contexts. Full article

Uncertainty-aware decision-making increasingly relies on multimodal sensing pipelines that must fuse correlated measurements, propagate uncertainty, and trigger reliable control actions. This study develops a unified mathematical framework for multimodal data fusion and Bayesian decision-making under uncertainty. The approach integrates adaptive Covariance Intersection (aCI) for correlation-robust sensor fusion, a Gaussian state–space backbone with Kalman filtering, heteroskedastic Bayesian regression with full posterior sampling via an affine-invariant MCMC sampler, and a Bayesian likelihood-ratio test (LRT) coupled to a risk-sensitive proportional–derivative (PD) control law. Theoretical guarantees are provided by bounding the state covariance under stability conditions, establishing convexity of the aCI weight optimization on the simplex, and deriving a Bayes-risk-optimal decision threshold for the LRT under symmetric Gaussian likelihoods. A proof-of-concept agro-environmental decision-support application is considered, where heterogeneous data streams (IoT soil sensors, meteorological stations, and drone-derived vegetation indices) are fused to generate early-warning alarms for crop stress and to adapt irrigation and fertilization inputs. The proposed pipeline reduces predictive variance and sharpens posterior credible intervals (up to 34% narrower 95% intervals and 44% lower NLL/Brier score under heteroskedastic modeling), while a Bayesian uncertainty-aware controller achieves 14.2% lower water usage and 35.5% fewer false stress alarms compared to a rule-based strategy. The framework is mathematically grounded yet domain-independent, providing a probabilistic pipeline that propagates uncertainty from raw multimodal data to operational control actions, and can be transferred beyond agriculture to robotics, signal processing, and environmental monitoring applications. Full article

The prevalence of massive, multi-scale, high-dimensional, and dynamic data sets resulting from advances in information and network communication technologies is frequently hampered by data incompleteness, a consequence of complex network structures and constrained sensor capabilities. The necessity of complete data for effective data analysis and mining mandates robust preprocessing techniques. This comprehensive survey systematically reviews missing value interpolation methodologies specifically tailored for time series flow network data, organizing them into four principal categories: classical statistical algorithms, matrix/tensor-based interpolation methods, nearest-neighbor-weighted methods, and deep learning generative models. We detail the evolution and technical underpinnings of diverse approaches, including mean imputation, the ARMA family, matrix factorization, KNN variants, and the latest deep generative paradigms such as GANs, VAEs, normalizing flows, autoregressive models, diffusion probabilistic models, causal generative models, and reinforcement learning generative models. By delineating the strengths and weaknesses across these categories, this survey establishes a structured foundation and offers a forward-looking perspective on state-of-the-art techniques for missing data generation and imputation in complex networks. Full article

Three-dimensional multi-object tracking (3D MOT) plays a vital role in enabling reliable perception for LiDAR-based autonomous systems. However, LiDAR measurements often exhibit sparsity, occlusion, and sensor noise that lead to uncertainty and instability in downstream tracking. To address these challenges, we propose FGO-PMB, a unified probabilistic framework that integrates the Poisson Multi-Bernoulli (PMB) filter from Random Finite Set (RFS) theory with Factor Graph Optimization (FGO) for robust LiDAR-based object tracking. In the proposed framework, object states, existence probabilities, and association weights are jointly formulated as optimizable variables within a factor graph. Four factors, including state transition, observation, existence, and association consistency, are formulated to uniformly encode the spatio-temporal constraints among these variables. By unifying the uncertainty modeling capability of RFS with the global optimization strength of FGO, the proposed framework achieves temporally consistent and uncertainty-aware estimation across continuous LiDAR scans. Experiments on KITTI and nuScenes indicate that the proposed method achieves competitive 3D MOT accuracy while maintaining real-time performance. Full article

This study develops a statistical and technological framework to analyze extreme rainfall in Colima, Mexico, by integrating historical precipitation records, probabilistic modeling, and spatial visualization. Using data from CONAGUA meteorological stations, we identify high-intensity rainfall events and model their recurrence using the Generalized Extreme Value (GEV) distribution to estimate key return periods. The results support flood-risk assessment and territorial planning in Colima. Spatial interpolation was performed in Python (version 3.13), and QGIS (version 3.38) produces exceedance maps that illustrate geographic variations in rainfall intensity across the state. These exceedance maps reveal a consistent spatial pattern, with the northern and western areas of Colima experiencing the highest frequencies of extreme events. Based on these results, the integration of real-time sensor technologies and satellite observations may improve flood monitoring and risk management frameworks. Full article

The ultrasonic guided wave method is successfully used for structural health monitoring (SHM) of aircraft structures utilizing PZT (Pb-Zr-Ti based piezoceramic material) sensors for guided wave generation and detection. To increase the mechanical durability of the sensors in operational conditions, this paper demonstrates the feasibility of the integration of PZTs into the Glass fiber/Polymethyl methacrylate (G/PMMA) composite plate and evaluates the possibility of impact damage detection using generated guided waves. Two types of PZT sensors were embedded into different layers during the manufacturing process. Generally, radial mode disc sensors are used for Lamb wave generation, and thickness-shear square-shaped sensors are used for both Lamb and shear wave generation. First, the wave propagation was analyzed considering the sensor type and sensor placement within the layup. The main objective was to propose the optimal sensor network with embedded sensors for successful impact damage detection. Lamb wave frequency tuning of disk sensors and unique vibrational characteristics of integrated shear sensors were exploited to selectively actuate only one guided wave mode. Finally, the Reconstruction Algorithm for the Probabilistic Inspection of Damage (RAPID) was utilized for damage localization and visualization. Full article

Remaining Useful Life (RUL) prediction has become a crucial component in predictive maintenance and condition-based operation with the rapid advancement of industrial automation and the increasing complexity of mechanical systems. Although existing deep learning models, such as Long Short-Term Memory (LSTM) networks and conventional Convolutional Neural Networks (CNNs), have demonstrated effectiveness in modeling equipment degradation from multivariate sensor data, they still face several limitations. Recurrent architectures often suffer from vanishing gradients and struggle to capture long-term dependencies, while CNN-based methods typically rely on small convolutional kernels and deterministic feature extractors, limiting their ability to model long-range dependencies and stochastic degradation transitions. To address these challenges, this study proposes a novel hybrid deep learning framework that integrates large-kernel convolutional feature extraction with Markov transition modeling for RUL prediction. Specifically, the large-kernel CNN captures both local and global degradation patterns, while the Markov feature module encodes probabilistic state transitions to characterize the stochastic evolution of equipment health. Furthermore, a lightweight channel attention mechanism is incorporated to adaptively emphasize degradation-sensitive sensor information, thereby enhancing feature discriminability. Extensive experiments conducted on the NASA C-MAPSS turbofan engine dataset demonstrate that the proposed model consistently outperforms conventional CNN, LSTM, and hybrid baselines in terms of Root Mean Square Error (RMSE) and the NASA scoring metric. The results verify that combining deep convolutional representations with probabilistic transition information significantly enhances prediction accuracy and robustness in industrial RUL estimation tasks. Full article

This study introduces the Hybrid Integrated Prediction-Error Reconstruction-based Anomaly Detection (HIPER-CHAD) model, which addresses the challenge of reliably detecting subtle anomalies in noisy multivariate indoor environmental time-series data. The main objective is to separate temporal modeling of normal behavior from probabilistic modeling of prediction uncertainty, ensuring that the anomaly score becomes robust to stochastic fluctuations while remaining sensitive to truly abnormal events. The HIPER-CHAD architecture first employs a Long Short-Term Memory (LSTM) network to forecast the next time step’s sensor readings, subsequently forming a residual error vector that captures deviations from the expected temporal pattern. A Variational Autoencoder (VAE) is then trained on these residual vectors rather than on the raw sensor data to learn the distribution of normal prediction errors and quantify their probabilistic unicity. The final anomaly score integrates the VAE’s reconstruction error with its Kullback–Leibler (KL) divergence, yielding a statistically grounded measure that jointly reflects the magnitude and distributional abnormality of the residual. The proposed model is evaluated on a real-world multivariate indoor environmental dataset and compared against eight traditional machine learning and deep learning baselines using a synthetic ground truth generated by a 99th percentile-based criterion. HIPER-CHAD achieves an F1-score of 0.8571, outperforming the next best model, the LSTM Autoencoder (F1 = 0.8095), while maintaining perfect recall. Furthermore, a time-step sensitivity analysis demonstrates that a 20-step window yields an optimal F1-score of 0.884, indicating that the proposed residual-based hybrid design provides a reliable and accurate framework for anomaly detection in complex multivariate time-series data. Full article

Accurate, real-time monitoring of meat freshness is essential for reducing food waste and safeguarding consumer health, yet conventional methods rely on costly, laboratory-grade spectroscopy or destructive analyses. This work presents a low-cost electronic-nose platform that integrates a compact array of metal-oxide gas sensors (Figaro TGS2602, TGS2603, and Sensirion SGP30) with a Gaussian Process Regression (GPR) model to estimate a continuous freshness index under refrigerated storage. The pipeline includes headspace sensing, baseline normalization and smoothing, history-window feature construction, and probabilistic prediction with uncertainty. Using factorial analysis and response-surface optimization, we identify history length and sampling interval as key design variables; longer temporal windows and faster sampling consistently improve accuracy and stability. The optimized configuration (≈143-min history, ≈3-min sampling) reduces mean absolute error from ~0.51 to ~0.05 on the normalized freshness scale and shifts the error distribution within specification limits, with marked gains in process capability and yield. Although it does not match the analytical precision or long-term robustness of spectrometric approaches, the proposed system offers an interpretable and energy-efficient option for short-term, laboratory-scale monitoring under controlled refrigeration conditions. By enabling probabilistic freshness estimation from low-cost sensors, this GPR-driven e-nose demonstrates a proof-of-concept pathway that could, after further validation under realistic cyclic loads and operational disturbances, support more sustainable meat management in future smart refrigeration and cold-chain applications. This study should be regarded as a methodological, laboratory-scale proof-of-concept that does not demonstrate real-world performance or operational deployment. The technical implications described herein are hypothetical and require extensive validation under realistic refrigeration conditions. Full article