Search Results (16)

Utilizing underwater vehicles for hydropower infrastructure inspection is increasingly vital. However, these GNSS-denied and confined environments pose significant navigation challenges: Inertial Navigation Systems (INSs) suffer cumulative drift, Doppler Velocity Logs (DVLs) face acoustic blind zones near walls, and visual navigation frequently fails in highly turbid waters. To address these issues, this paper proposes a tightly coupled multi-source (INS/acoustic/optical/vision) navigation algorithm leveraging prior wall geometry constraints. Developed within an Error-State Kalman Filter (ESKF) framework, the model seamlessly accommodates sensor spatiotemporal heterogeneity. To overcome optical failures, a structural surface constraint model is innovatively constructed using single-beam sonar ranging. The core contribution involves transforming sonar ranging data into 6-DOF spatial pose constraints based on the dam’s planar characteristics, effectively bounding the localization drift perpendicular to the surface. Field experiments at the hydropower station dam demonstrate that under extreme conditions with total visual failure, the proposed algorithm effectively constrains critical motion degrees of freedom. By maintaining the wall-tracking error within 0.08 m (Root Mean Square Error, RMSE)—which effectively represents the relative localization error given the known absolute position of the structural wall—this method significantly enhances the operational robustness and precision of close-wall inspections in extreme underwater environments. Full article

Distributed seismic sensor networks integrated into the Internet of Things (IoT) infrastructure enable continuous condition monitoring of large-scale engineering structures. During long-term operation, however, measurement channels are subject to sensitivity drift, increased noise, and pulse artifacts that statistically mimic real vibration events. Related deep-learning techniques for noisy and ill-posed inverse problems have demonstrated the value of combining principled physical priors with deep models. Although the application domain differs, the underlying methodological insight—that constrained, physics-aware feature mappings can stabilize learning under noisy and partially observed conditions—directly motivates the use of a parameterized quantum circuit as a nonlinear feature transformer in the present work, where Hilbert space mapping serves as an analogous structural prior for the latent representation. Three principal fault modes are considered in this work, corresponding to the dominant degradation mechanisms observed in long-term seismic instrumentation: sensor drift, increased noise, and sensor failure. Each fault mode produces a distinct signature in the windowed feature space; the proposed model is trained to discriminate between them based on the latent CNN-LSTM-VQC representation. We propose a hybrid quantum-inspired deep-learning model (QC-DL) for the detection and diagnosis of channel-degradation anomalies. The architecture combines a 1D-CNN+LSTM feature extractor with a parameterized variational quantum circuit (VQC) used as a nonlinear feature transformer. All quantum experiments were performed on the QPanda3 CPUQVM simulator. The data were split chronologically prior to windowing to avoid information leakage. On real-world labeled accelerometric data with four operating modes (normal/drift/high-noise/failure), the QC-DL model achieved a macro-averaged F1 score of approximately 0.69 and per-class AUC values in the range 0.88–0.99. The mean early-detection latency was 1.6 s versus 2.1 s for the CNN-LSTM baseline (~24% reduction). An ablation study against a parameter-matched classical MLP showed that the gain is modest and not solely attributable to additional nonlinearity. The reported p-values (p = 0.70, p = 0.29) do not establish statistical significance. The results support the feasibility of hybrid quantum-inspired deep learning for sensor-channel verification, while highlighting the need for evaluation on real NISQ hardware. This paper proposes a hybrid quantum-inspired approach for detecting and diagnosing such anomalies in the time series of distributed seismic networks. The architecture combines a classical temporal feature extraction module based on one-dimensional convolutional layers and a recurrent long short-term memory (LSTM) network, which generates a latent window representation of the signal, with a parameterized variational quantum circuit used as a nonlinear feature processor in a hybrid computational circuit. Experimental validation was performed on real-world labeled data with multiple sensor degradation modes. The evaluation was organized in a scoring framework aligned with autonomous operation through window ranking and threshold alarm generation. In the experiments, the proposed model provided a macro-averaged F1 score of approximately 0.69 and area under the receiver operating characteristic (AUC) curve values in the range of 0.88–0.99 across classes, outperforming baseline deep models. The average early detection latency was 1.6 s versus 2.1 s for the baseline recurrent model (a 24% reduction). An ablative comparison with a control model based on a classical multilayer perceptron of comparable dimension confirmed that the improvement is not limited to the addition of additional nonlinearity. The obtained results indicate the potential of quantum-supported deep learning for improving the reliability of long-term vibration monitoring and verifying the correctness of sensor channels in distributed seismic networks. Full article

Objective: The aim of this study was to characterize the epidemiological features and whole-genome characteristics of H5 subtype avian influenza viruses circulating in Jining City during 2024–2025, and to provide scientific evidence for avian influenza prevention and control. Methods: A total of 748 poultry-related environmental samples were collected in March, June, September, and December of 2024–2025. Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) was used to detect influenza A virus and H5 subtype viral RNA. H5-positive samples were subjected to whole-genome sequencing and analyzed using bioinformatics tools. Results: Among the 748 samples, the positivity rate of influenza A virus was 16.04% (120/748), and that of the H5 subtype was 8.16% (61/748). The H5 positivity rate in 2025 (11.88%) was significantly higher than that in 2024 (5.37%). Higher positivity rates were observed in March and December compared to June and September. Twelve complete H5 genomes were obtained, including nine H5N1 and three H5N6 strains. All HA genes belonged to clade 2.3.4.4b. Key mutations related to antigenic drift, replication and adaptation were detected in multiple viral proteins. Conclusions: The positivity rate of H5 subtype avian influenza viruses in Jining City showed an increasing trend during 2024–2025, with higher prevalence in winter and spring. The circulating strains predominantly belonged to clade 2.3.4.4b. Antigenic drift-associated mutations in the HA protein were identified in some strains, which may affect vaccine matching. Enhanced surveillance of H5 viruses and regular evaluation of antigenic compatibility between vaccine and circulating strains are recommended to mitigate potential risks posed by viral genetic variation. Full article

Near-fault ground motions exhibit significant characteristics such as velocity pulses, rupture directivity, and strong vertical components, which pose serious threats to structural safety. However, near-fault ground motion records remain scarce and have not been adequately accounted for in current seismic design codes. This paper proposes a data-driven simulation method for non-stationary near-fault ground motions based on Stationary Wavelet Transform (SWT) combined with Hilbert’s instantaneous frequency estimation. First, to address the baseline drift issue commonly observed in measured seismic motions, a baseline correction technique combining the least squares method and the Iwan method is proposed to enhance the reliability of seismic time histories. Subsequently, statistical distributions of velocity pulses and vertical-to-horizontal (V/H) acceleration ratios, along with their relationships with fault distance and magnitude, are analyzed based on more than 900 ground motion records. The results show that these near-fault motions generally contain pronounced long-period components, which will have significant implications for the seismic response of long-period structures. Additionally, unidirectional pulses dominate in near-fault records. Among the 107 selected long-period pulse records, unidirectional pulses account for 69.2%. Based on this, seismic motions are decomposed using SWT, and stochastic reconstruction is performed, combined with multivariate response spectrum matching to optimize the generation of near-fault time histories consistent with the target spectrum. Compared with the results obtained without optimization, the proposed method reduces the mean square error by about 40% or more, demonstrating a clear improvement in accuracy and reliability. This method provides reliable seismic input support for seismic analysis and performance-based design of bridges in near-fault regions. Full article

Under extreme snowfall conditions, wind-induced snow drifting can lead to the redistribution of snow accumulation on roofs, resulting in localized overloads that pose a serious threat to building structural safety. Notably, morphological differences in snow particles significantly alter their aerodynamic characteristics, causing variations in their motion trajectories and increasing the uncertainty in determining roof snow loads. Therefore, this study develops a numerical simulation method that accounts for snow morphologies based on the drag coefficients of typical snow crystals, and further investigates the accumulation characteristics of differently shaped snow particles on typical roofs. Analysis results demonstrate that the observed variations in snow particle motion characteristics primarily originate from differences in their respective drag coefficients. The drag coefficient exerts a direct influence on particle settling velocity, which subsequently governs spatial distribution patterns of snow concentration and final accumulation patterns. Under identical inflow snow concentration conditions, particles with higher drag coefficients exhibit reduced depositional accumulation on roof surfaces. Notably, this shape-dependent effect diminishes with increasing roof span and slope. Full article

Lightweight steel-framing (LSF) systems have become increasingly prominent in modern construction due to their structural efficiency, design flexibility, and sustainability. However, traditional facade materials such as stone are often cost-prohibitive, and brick veneers—despite their popularity—pose seismic performance concerns. This study introduces an innovative porcelain sheathing system for cold-formed steel (CFS) shear walls. Porcelain has no veins thus it offers integrated and reliable strength unlike granite. Four full-scale CFS shear walls incorporating screwed porcelain sheathing (SPS) were tested under combined cyclic lateral and constant gravity loading. The experimental program investigated key seismic characteristics, including lateral stiffness and strength, deformation capacity, failure modes, and energy dissipation, to calculate the system response modification factor (R). The test results showed that configurations with horizontal sheathing, double mid-studs, and three blocking rows improved performance, achieving up to 21.1 kN lateral resistance and 2.5% drift capacity. The average R-factor was 4.2, which exceeds the current design code values (AISI S213: R = 3; AS/NZS 4600: R = 2), suggesting the enhanced seismic resilience of the SPS-CFS system. This study also proposes design improvements to reduce the risk of brittle failure and enhance inelastic behavior. In addition, the results inform discussions on permissible building heights and contribute to the advancement of CFS design codes for seismic regions. Full article

The Northeastern China Gravity Monitoring Network (NCGMN; 40–50°N), a pioneering time-variable gravity monitoring system in high-latitude cold-temperate environments, serves as a critical infrastructure for geodynamic investigations of the Songliao Basin, Changbai Mountain volcanic zone, and northern Tan-Lu Fault Zone. To address the data reliability challenges posed by nonlinear dynamic drifts in spring-type relative gravimeters during mobile surveys, this study quantifies—for the first time—the non-smooth normal distribution characteristics of such drifts using the inaugural 2015 dataset from two CG-5 instruments. Results demonstrate a 7–15% reduction in mean dynamic drift rates compared to static conditions, with spatiotemporal variability governed by multi-physics field coupling (terrain undulation, thermal fluctuation, and barometric perturbation). A comprehensive correction framework—integrating a gravimetric line drift rate computation, multi-model validation, and absolute datum cross-validation—reveals gravity value discrepancies up to ±10 μGal across models. The innovative hybrid scheme combines local drift preprocessing (initial-point modeling, line fitting, variance-sum optimization) with global adjustment optimization, achieving the significant suppression of nonlinear drift errors. The variance-sum optimal and Bayesian adjustment hybrid synergizes local variance minimization and global temporal correlation priors, delivering the following: (1) 34% and 29% reductions in segment self-difference standard deviations versus classical and Bayesian adjustments; (2) 24% and 14% decreases in segment residual standard deviations; (3) 12% and 6% improvements in absolute datum cross-validation precision. This study establishes a foundation for the reliable extraction of μGal-level gravity signals, advancing high-precision gravity monitoring of seismicity, volcanic unrest, and fault zone deformation in complex terrains. By harmonizing local-scale accuracy with network-wide consistency, the framework sets a new benchmark for time-variable gravity studies in challenging environments. Full article

The present paper describes the measurement of the drift of unidirectional pose accuracy, repeatability, and static compliance of a collaborative robot employing a measurement methodology that relies on the description of a virtual ISO cube placed in the robot’s workspace. The measurements aimed to investigate and assess the suitability of the UR5 six-axis collaborative robot for its application in robotic 3D printing. An experimental laboratory measurement workstation was constructed to perform the measurements, and control measurements were performed. The measurements involved describing the TCP point of the robot tool at five measurement points located in a virtual ISO cube during a minimum of 30 repeated measurement cycles. A camera and six linear incremental sensors with assessment units were used for the measurements. The measurements were performed in compliance with the regulations of STN ISO 9283 standard for this type of measurement. As a result of the measurements, the technical specifications of the drift and static compliance of the controlled robotic arm were verified, and the results were compared with the values specified by the manufacturer. Following the measurements and assessment of the results, it was possible to assess the suitability of the used UR5 robotic arm for its application in robotic 3D printing and to propose possible recommendations for the calibration of the robot and the process settings of the printing system for the production of objects using FDM technology. Full article

This paper proposes a robotic state estimation and map construction method. The traditional lidar SLAM methods are affected by sensor measurement noise, which causes the estimated trajectory to drift, especially along the altitude direction caused by lidar noise. In this paper, ground parameters in the environment are extracted to construct the ground factors to compress the trajectory estimation drifting along the altitude direction using the characteristics of constant robot pose relative to the ground. Our method uses tightly coupled lidar and inertial to obtain low-drift lidar odometry factors by factor graph optimization. The optimized lidar odometry factors are then added to a global factor graph, together with ground, loop closure, and GPS factors to obtain accurate robot state estimation and mapping after factor graph optimization. The experimental results show that our method has comparable results with advanced lidar SLAM methods, and even performs better in some complex and large-scale environments. Full article

Point cloud registration is the basis of real-time environment perception for robots using 3D LiDAR and is also the key to robust simultaneous localization and mapping (SLAM) for robots. Because LiDAR point clouds are characterized by local sparseness and motion distortion, the point cloud features of coal mine roadway environments show a weak texture and degradation. Therefore, for these environments, the traditional point cloud registration method to register directly will lead to problems, such as a decline in registration accuracy, z-axis drift, and map ghosting. To solve the above problems, we propose a point cloud registration method based on IMU preintegration with the sensor characteristics of LiDAR and IMU. The system framework of this method mainly consists of four modules: IMU preintegration, point cloud preprocessing, point cloud frame matching and point cloud registration. First, IMU sensor data are introduced, and IMU linear interpolation is used to correct the motion distortion in LiDAR scanning, and the IMU preintegration error function is constructed. Second, the point cloud segmentation is performed using the ground segmentation method of RANSAC to provide additional ground constraints for the z-axis displacement and to remove the unstable flawed points from the point cloud. On this basis, the LiDAR point cloud registration error function is constructed by extracting the feature corner points and feature plane points. Finally, the Gaussian Newton solution is used to optimize the constraint relationship between the LiDAR odometry frames to minimize the error function, complete the LiDAR point cloud registration and better estimate the position and pose of the mobile robot. The experimental results show that compared with the traditional point cloud registration method, the proposed method has a higher point cloud registration accuracy, success rate and computational efficiency. The LiDAR odometry constructed using this method can better reflect the authenticity of the robot trajectory and has higher trajectory accuracy and smaller absolute position and pose error. Full article

Inertial localisation is an important technique as it enables ego-motion estimation in conditions where external observers are unavailable. However, low-cost inertial sensors are inherently corrupted by bias and noise, which lead to unbound errors, making straight integration for position intractable. Traditional mathematical approaches are reliant on prior system knowledge, geometric theories and are constrained by predefined dynamics. Recent advances in deep learning, which benefit from ever-increasing volumes of data and computational power, allow for data-driven solutions that offer more comprehensive understanding. Existing deep inertial odometry solutions rely on estimating the latent states, such as velocity, or are dependent on fixed-sensor positions and periodic motion patterns. In this work, we propose taking the traditional state estimation recursive methodology and applying it in the deep learning domain. Our approach, which incorporates the true position priors in the training process, is trained on inertial measurements and ground truth displacement data, allowing recursion and learning both motion characteristics and systemic error bias and drift. We present two end-to-end frameworks for pose invariant deep inertial odometry that utilises self-attention to capture both spatial features and long-range dependencies in inertial data. We evaluate our approaches against a custom 2-layer Gated Recurrent Unit, trained in the same manner on the same data, and tested each approach on a number of different users, devices and activities. Each network had a sequence length weighted relative trajectory error mean $\le 0.4594$ m, highlighting the effectiveness of our learning process used in the development of the models. Full article

In the era of big data, industrial process data are often generated rapidly in the form of streams. Thus, how to process such sequential and high-speed stream data in real time and provide critical quality variable predictions has become a critical issue for facilitating efficient process control and monitoring in the process industry. Traditionally, soft sensor models are usually built through offline batch learning, which remain unchanged during the online implementation phase. Once the process state changes, soft sensors built from historical data cannot provide accurate predictions. In practice, industrial process data streams often exhibit characteristics such as nonlinearity, time-varying behavior, and label scarcity, which pose great challenges for building high-performance soft sensor models. To address this issue, an online-dynamic-clustering-based soft sensor (ODCSS) is proposed for industrial semi-supervised data streams. The method achieves automatic generation and update of clusters and samples deletion through online dynamic clustering, thus enabling online dynamic identification of process states. Meanwhile, selective ensemble learning and just-in-time learning (JITL) are employed through an adaptive switching prediction strategy, which enables dealing with gradual and abrupt changes in process characteristics and thus alleviates model performance degradation caused by concept drift. In addition, semi-supervised learning is introduced to exploit the information of unlabeled samples and obtain high-confidence pseudo-labeled samples to expand the labeled training set. The proposed method can effectively deal with nonlinearity, time-variability, and label scarcity issues in the process data stream environment and thus enable reliable target variable predictions. The application results from two case studies show that the proposed ODCSS soft sensor approach is superior to conventional soft sensors in a semi-supervised data stream environment. Full article

Traditionally, inertial measurement unit (IMU)-based human joint angle estimation techniques are evaluated for general human motion where human joints explore all of their degrees of freedom. Pure human walking, in contrast, limits the motion of human joints and may lead to unobservability conditions that confound magnetometer-free IMU-based methods. This work explores the unobservability conditions emergent during human walking and expands upon a previous IMU-based method for the human knee to also estimate human hip angles relative to an assumed vertical datum. The proposed method is evaluated ($N=12$) in a human subject study and compared against an optical motion capture system. Accuracy of human knee flexion/extension angle (7.87${}^{\circ }$ absolute root mean square error (RMSE)), hip flexion/extension angle (3.70${}^{\circ }$ relative RMSE), and hip abduction/adduction angle (4.56${}^{\circ }$ relative RMSE) during walking are similar to current state-of-the-art self-calibrating IMU methods that use magnetometers. Larger errors of hip internal/external rotation angle (6.27${}^{\circ }$ relative RMSE) are driven by IMU heading drift characteristic of magnetometer-free approaches and non-hinge kinematics of the hip during gait, amongst other error sources. One of these sources of error, soft tissue perturbations during gait, is explored further in the context of knee angle estimation and it was observed that the IMU method may overestimate the angle during stance and underestimate the angle during swing. The presented method and results provide a novel combination of observability considerations, heuristic correction methods, and validation techniques to magnetic-blind, kinematic-only IMU-based skeletal pose estimation during human tasks with degenerate kinematics (e.g., straight line walking). Full article

In this paper, we investigate the design of distributed damping systems (DDSs) for the overall seismic protection of multiple adjacent buildings. The considered DDSs contain interstory dampers implemented inside the buildings and also interbuilding damping links. The design objectives include mitigating the buildings seismic response by reducing the interstory-drift and story-acceleration peak-values and producing small interbuilding approachings to decrease the risk of interbuilding collisions. Designing high-performance DDS configurations requires determining convenient damper positions and computing proper values for the damper parameters. That allocation-tuning optimization problem can pose serious computational difficulties for large-scale multibuilding systems. The design methodology proposed in this work—(i) is based on an effective matrix formulation of the damped multibuilding system; (ii) follows an $H_{\infty }$ approach to define an objective function with fast-evaluation characteristics; (iii) exploits the computational advantages of the current state-of-the-art genetic algorithm solvers, including the usage of hybrid discrete-continuous optimization and parallel computing; and (iv) allows setting actuation schemes of particular interest such as full-linked configurations or nonactuated buildings. To illustrate the main features of the presented methodology, we consider a system of five adjacent multistory buildings and design three full-linked DDS configurations with a different number of actuated buildings. The obtained results confirm the flexibility and effectiveness of the proposed design approach and demonstrate the high-performance characteristics of the devised DDS configurations. Full article

In this paper, a method for induction machine (IM) torque/speed tracking control derived from the 3-D non-holonomic integrator including drift terms is proposed. The proposition builds on a previous result derived in the form of a single loop non-linear state controller providing implicit rotor flux linkage vector tracking. This concept was appropriate only for piecewise constant references and assured minimal norm of the stator current vector during steady-states. The extended proposition introduces a second control loop for the rotor flux linkage vector magnitude that can be either constant, programmed, or optimized to achieve either maximum torque per amp ratio or high dynamic response. It should be emphasized that the same structure of the controller can be used either for torque control or for speed control. Additionally, it turns out that the proposed controller can be easily adapted to meet different objectives posed on the drive system. The introduced control concept assures stability of the closed loop system and significantly improves tracking performance for bounded but arbitrary torque/speed references. Moreover, the singularity problem near zero rotor flux linkage vector length is easily avoided. The presented analyses include nonlinear effects due to magnetic saturation. The overall IM control scheme includes cascaded high-gain current controllers based on measured electrical and mechanical quantities together with a rotor flux linkage vector estimator. Simulation and experimental results illustrate the main characteristics of the proposed control. Full article