Search Results (378)

Passive distributed acoustic sensing (DAS) is an attractive tool for monitoring geological CO₂ storage, but its dense, continuous recordings create a data-volume problem: a multi-month, multi-well archive yields enormous numbers of detector triggers, of which confirmed seismic events form a vanishingly small fraction, and conventional supervised classification is ill-posed when labels remain scarce and the negative class undefined because the non-event population is open-ended and spans noise families that vary over time and between wells. We present Argus, a sparse-label machine-learning workflow that converts continuous DAS recordings into a reproducible, auditable catalogue of event candidates. A deterministic front end reduces the archive to comparable trigger objects, each described by a 67-feature interpretable representation of its two-dimensional time–channel character (e.g., duration and channel span, detector-mask morphology, apparent moveout, inter-channel waveform coherence, and spectral shape); a retrieval-first machine-learning layer then ranks these triggers by their similarity, in this interpretable feature space, to a small seed catalogue of independently confirmed events, within an iterative human-in-the-loop process that introduces local supervised noise-rejection gates only for recurrent artefact families once they have been labelled. Applied to the CO2CRC Otway Stage 4 dataset—120 days of recordings on two wells, comprising roughly 23 TB and 14.14 million raw triggers—the workflow expanded a 39-event seed catalogue into 631 analyst-reviewed events, demonstrating complementarity with an independent template-matching analysis: two additional induced-event candidates were recovered, one within the CRC4 template-matching coverage and one on CRC7 during a CRC4 data gap. The induced-event class itself grew only from four to six candidates, and its counts are reported as a reviewed lower bound rather than a complete census. The result is a provenance-preserving, conservatively interpreted event inventory rather than an opaque classifier output, an outcome aligned with the reproducibility and audit requirements of CO₂ storage assurance. Full article

Existing EMTR methods typically adopt passive location schemes that rely on transient signals generated by faults. When faults occur at a low inception angle, the resulting traveling-wave signals are typically weak and suffer from poor detectability. Active pulse injection provides controllable excitation and improves signal identification. The transfer function similarity method achieves high accuracy; its practical application is constrained by the requirement for transient voltage at the fault point. To address these limitations, this paper proposes a fault location method based on active pulse injection and systematically investigates the characteristics of fault voltage in both frequency and time domains. First, the frequency-domain formulation of fault voltage in the reversed-time process is derived, and the applicable scope of the energy metric is evaluated. The waveform features of the reversed-time fault voltage are then analyzed to assess the similarity between the fault voltage and the injected pulse voltage, as well as the applicability of the MCCC metric. An improved IMCCC criterion is developed to quantify the similarity between the fault voltage and the forward-time voltage. Furthermore, a symmetry similarity coefficient (SSC) is defined by leveraging the inherent symmetry property of fault voltage waveforms. The four metrics were validated using reduced-scale experiments and simulation studies. All metrics achieved accurate fault location in simple lines. In complex networks, the energy metric showed significant deviation from the real fault location. The IMCCC and SSC metrics provided higher accuracy than the MCCC metric and maintained reliable fault location for grounding faults up to 300 Ω. Full article

Surface electromyography (sEMG), increasingly delivered through wireless wearable systems, is a key non-invasive tool for the objective monitoring of muscle activation during repetitive motor tasks. The clinical and longitudinal usefulness of wearable sEMG during rowing depends on the test–retest reliability of the parameters extracted from the signal during high-intensity, multi-muscle cyclic locomotor tasks. This study aimed to use advanced sEMG processing to quantify the between-session reliability of EMG-derived parameters (onset, offset, active duration, and peak position) across seven major muscles, to characterize the between-session similarity of ensemble-averaged activation waveforms, and to describe within-trial activation dynamics. Fifteen competitive rowers (10 males, five females; aged 14–22 years) performed two identical 2000 m all-out trials seven days apart, with sEMG recorded by a wireless wearable system. Reliability was assessed by ICC(A,1) with 95% CIs, SEM, MDC₉₅, CV%, and Bland–Altman analysis. The waveform similarity of the session ensemble cycles was quantified by Pearson correlation, cosine similarity, normalized cross-correlation maximum, and normalized dynamic time warping (DTW). Within-trial dynamics were assessed across ten consecutive stroke-count windows. Onset showed excellent reliability across all seven muscles (ICC = 0.943–0.995); offset, moderate-to-excellent (0.524–0.907); peak position, poor-to-excellent (0.114–0.948); active duration, poor-to-good (0.077–0.814). Ensemble-waveform similarity between sessions was high for each athlete across all muscles (Pearson r = 0.832–0.972; cosine similarity = 0.924–0.983), confirming that the individual activation fingerprint of the mean stroke cycle is stable over a 7-day interval. Both amplitude (FMPR) and active duration revealed a reproducible U-shaped within-trial pattern. These findings highlight the potential of wearable sEMG to provide reliable, personalized insights into rowing-specific muscle activation patterns, supporting more individualized monitoring and training optimization in rowers. Full article

High-voltage transmission lines are critical carriers of electric power, and rapid and accurate fault location is essential for secure and stable power-system operation. This paper proposes a fault-location method that integrates distributed-parameter physics-based features with a convolutional block attention module-based convolutional neural network (CBAM-CNN). Voltages and currents measured at both line terminals are transformed into modal quantities, after which a distributed-parameter line model is used to derive compensated-voltage waveforms along the line and construct a physically meaningful feature matrix. The CBAM-CNN is trained offline using multiple waveform samples, and the fault location is determined from the minimum similarity value among the observation points. Under identical test settings, the proposed model achieves an overall mean absolute fault-location error of 0.0746 km across four simulated test locations, representing reductions of 79.23%, 85.15%, and 68.44% relative to CNN, SE-CNN, and ECA-CNN, respectively. For a field record whose operation and maintenance record places the fault 51.0 km from the M terminal, the proposed model estimates 51.2089 km, corresponding to an absolute error of 0.2089 km. These results demonstrate high fault-location accuracy within the scope tested and provide preliminary evidence of applicability to field recordings. Full article

Background: Robot-assisted gait training is increasingly used in pediatric neurorehabilitation, but its effects on the full time-course of gait kinematics remain insufficiently understood. This study aimed to determine whether intensive gait training with the automated PRODROBOT device could improve frontal- and transverse-plane kinematic profiles in children with neurological disorders and shift them toward the gait pattern of healthy peers. Methods: A prospective single-arm pre–post study was conducted in 10 children with cerebral palsy aged 8–13 years who underwent 20 PRODROBOT training sessions over 4 weeks. A control group comprised 18 healthy age-matched children. Three-dimensional gait analysis was performed before intervention (KF1) and after intervention (KF2). The primary outcome was the similarity of full frontal- and transverse-plane kinematic waveforms of the hip, knee, and ankle/foot relative to pre–post changes and to the healthy reference profiles, assessed using the difference factor (f1) and similarity factor (f2). Results: Only two within-group pre–post comparisons met both similarity criteria: hip rotation KF2 versus KF1 (f1 = 13.9%, f2 = 83.7) and ankle rotation KF2 versus KF1 (f1 = 4.1%, f2 = 67.4). These findings indicate similarity between the pre- and post-intervention profiles within the intervention group, rather than normalization relative to the control group. In comparisons with healthy controls, most variables retained elevated f1 values, indicating persistent quantitative deviation from the reference gait pattern. Conclusions: PRODROBOT-assisted gait training was associated with partial reorganization of selected kinematic waveforms rather than full normalization of gait. The intervention appeared more effective in improving waveform consistency and selected transverse-plane features than in correcting persistent distal and rotational deviations linked to more complex structural or neuromuscular factors. Full article

This paper is a first step toward providing answers to the question of whether fractal pattern formation gives rise to power-law (fractional) kinetics and how such kinetics relate to geometric properties such as fractal dimension. The study focuses on Lichtenberg figures produced by high-voltage discharges on wood, a heterogeneous dielectric medium with anisotropic conductivity and variable moisture content. During breakdown, the discharge propagates through branching streamers and carbonization fronts, exhibiting scale-free growth, long-tailed waiting times, and memory effects. The associated current signals are analyzed using the theory of fractal elements developed by Nigmatullin and Chen. This framework allows complex self-similar waveforms to be decomposed into elementary fractal modes characterized by power-law exponents and amplitudes. The results show that the electrical response is governed by fractional dynamics encoded in these modes. However, no direct one-to-one relationship is found between the fractal dimension of the discharge patterns and the kinetic power-law exponents. This decoupling is attributed to the influence of the heterogeneous medium and the percolation pathways through which the discharge propagates. Full article

Smartphones incorporate acoustic components, including a speaker and multiple microphones, which can be used as a low-cost, contactless platform for vital signs monitoring. However, extracting breathing rate (BR) and heart rate (HR) from smartphone acoustic reflections remains challenging in indoor environments because thoracic reflections are weak and are often mixed with static clutter, hand motion, environmental multipath, and other dynamic sources. In this work, we present a smartphone-based frequency-modulated continuous wave (FMCW) acoustic sensing system that enables simultaneous BR and HR estimation using the integrated speaker and two physical microphones. Instead of processing the received signal as a single, mixed signal, the proposed method leverages distance information from the FMCW beat frequency and an angular phase index (AoA information), derived from dual-microphone and virtual aperture processing, to organize moving reflectors into a joint distance–angle–time representation. A 3D-DBSCAN clustering module is then applied to this representation to separate candidate dynamic sources from static and multipath components, without presupposing the number of sources. To further handle ambiguous cases where multiple candidate dynamic sources are detected, a Siamese similarity network is introduced as a conditional second-stage source-association module. The Siamese model compares candidate thoracic waveforms and estimates whether multiple detected components are likely to originate from the same physical source or different sources, thus improving source selection without resorting to classical blind source separation. The system was evaluated on 20 participants in two indoor environments, a laboratory and a bedroom, using three consumer smartphones and an electrocardiogram (ECG) reference device. In the smartphone-only blind configuration, the proposed pipeline achieved MAEs of 2.312 bpm for HR and 1.394 bpm for BR. In the ECG-assisted calibrated configuration, which is used to evaluate physiological coherence rather than deployable smartphone-only performance, the errors decreased to 0.462 bpm for HR and 0.091 bpm for BR. These results demonstrate that spatial clustering and conditional Siamese source pairing improve the robustness of acoustic vital sign detection using smartphones in indoor environments. Full article

Accurate evaluation of the shank-to-vertical angle (SVA) is important for optimizing lower-limb alignment during gait, particularly during ankle–foot orthosis (AFO) tuning. This study investigated the validity of a wearable posture sensor for continuous SVA measurement during walking using an optical motion capture system as the reference standard. Nine healthy adults participated, and 88 gait cycles were analyzed. SVA was measured using a shank-mounted wearable sensor and a three-dimensional motion capture system. Gait events for the wearable sensor were identified independently using synchronized tablet-based video recordings rather than inertial signals. Agreement was evaluated at the gait-cycle and participant levels using waveform correlation, mean bias, root mean square error (RMSE), and Bland–Altman analysis. Across the full gait cycle, the trial-level waveform correlation was 0.935 ± 0.064 and the RMSE was 9.74 ± 3.71°. During the individually identified stance phase (mean toe-off, 63.67 ± 1.78% of the gait cycle), waveform correspondence increased to 0.990 ± 0.010 and the RMSE decreased to 6.78 ± 3.51°. Participant-level estimates were similar. For SVA range, participant-level Bland–Altman analysis yielded a bias of −5.58° with 95% limits of agreement from −14.01° to 2.86°, and repeated-measures analysis produced similar estimates. Temporal error analysis showed smaller and more stable deviations during the majority of the stance phase than during swing, with larger deviations in late swing. These findings support the potential use of the wearable posture sensor as a targeted tool for stance-phase SVA assessment, although further validation of pathological gait and actual orthotic tuning is required. Full article

To validate the validity of a driving simulation platform, this study constructs a dual dataset using field data and driving simulation experiments on an urban arterial road. Comprehensive validation is conducted from both subjective perception and objective quantification perspectives. Subjectively, questionnaires assessed simulation fidelity, driving workload, and physiological symptoms. Objectively, five core car-following indicators (speed, acceleration, relative speed, headway, and time headway) were analyzed using dynamic time warping and statistical methods. The results demonstrate that the simulated scenarios exhibit high subjective fidelity, reasonable task loads, and controllable motion sickness risks. Objectively, dynamic time warping confirms strong temporal pattern similarity, with waveform consistency proportions across core indicators ranging from 91.67% to 100.0%. Macroscopically, satisfactory relative aggregate similarity is demonstrated, with relative difference between means consistently constrained within a 20% threshold. However, strict absolute behavioral validity is unsupported due to significant statistical differences in sequence means, establishing clear boundary constraints for trend replication. It is speculated that systematic biases are primarily related to differences in risk perception within virtual environments. Full article

The incipient cable fault in active low-resistance-grounded distribution networks is a typical asymmetrical fault and is difficult to locate because the fault current is weak, short-lasting, and easily affected by distributed generation (DG). To address this typical asymmetrical problem, this paper proposes a fault section location method based on multidimensional information correlation analysis. First, an equivalent incipient fault model is established by combining the Kizilcay arc model with an insulation-defect resistance, so that the intermittent arc behavior and the conductive path of degraded insulation can be represented simultaneously. Then, the generalized S-transform is used to extract three features from the transient zero-sequence current, namely the transient current energy index, group phase-angle polarity, and waveform similarity. On this basis, a multidimensional feature vector and a comprehensive similarity coefficient are constructed to identify the fault section, and an auxiliary downstream energy comparison rule is introduced to distinguish the actual fault section from DG-connected pseudo-fault sections. The method is verified in MATLAB/Simulink R2025a under different fault locations, DG access conditions, penetration levels, noise levels, and key parameter variations. The simulation results under the tested conditions indicate that the proposed method can effectively identify asymmetrical incipient cable fault sections in active low-resistance-grounded distribution networks. Full article

Rotating machinery is a key component in modern industry, and its operating condition directly affects equipment safety and production reliability. However, discrepancies among different machines cause source–target distribution shifts, while fault annotation for target machines is costly, limiting the performance of deep learning-based diagnosis under cross-machine scenarios with limited labels. To address these issues, this paper proposes a multi-scale time–frequency semantic consistency model based on self-supervised transferable representation learning, termed MTFSC. First, augmented waveform views and multi-scale frequency-domain views are constructed from unlabeled source-domain vibration signals for self-supervised pre-training without source labels. Then, a time-domain impulse-aware feature extractor and a time–frequency decoupled spectral feature extractor are designed to enhance local impulsive responses and emphasize fault-sensitive time–frequency patterns. Furthermore, a semantic-aware soft contrastive loss is developed to mine potential semantic neighbors from multi-scale frequency-domain structural similarity, reducing false-negative effects in conventional hard-label contrastive learning. Finally, the pre-trained time-domain extractor is transferred to the target machine and fine-tuned with limited labeled samples. Experimental results show that MTFSC outperforms comparison methods under different labeled sample ratios and achieves an average accuracy of 97.5% across four cross-machine diagnostic tasks. Full article

Partial discharge (PD) source-type classification is essential for condition-based maintenance of high-voltage apparatus. Existing approaches based on grid discretizations of phase-resolved partial discharge (PRPD) patterns suffer from performance degradation under stochastic interference and multi-source conditions. This paper proposes a graph-based framework that integrates the morphological characterization of raw high-frequency PD waveforms with the phase-amplitude position of individual discharge events to enable multi-label classification, identifying multiple PD sources coexisting within a single test. The framework operates through three stages: a multi-task neural network extracts per-pulse embeddings and confidence scores; a construction procedure establishes selective graph connectivity based on spatial proximity and morphological similarity; and an edge-conditioned graph neural network performs classification via message passing weighted by multimodal edge attributes. Experimental evaluation on PD measurements acquired in accordance with IEC 60270 shows that the proposed framework achieves a Matthews correlation coefficient (MCC) of 0.98 and an exact match ratio of 0.97 across single-source, noisy, and multi-source conditions, substantially outperforming histogram- and set-based baselines. The framework maintains an MCC of 0.97 in multi-source scenarios, where its advantage over existing methods is most pronounced. Cross-domain evaluation on an independent dataset acquired with different laboratory equipment confirms the approach’s robustness, achieving an MCC of 0.93 without retraining. Finally, an ablation study demonstrates that the joint removal of morphological similarity filtering and confidence-based node filtering and edge gating reduces the MCC by 0.25, confirming the critical role of the waveform-informed relational structure. Full article

Magnetic anomaly detection is vulnerable to environmental noise and insufficient prior target information, making non-periodic anomaly signals difficult to detect at low-signal-to-noise-ratio (SNR) conditions. This paper proposes a detection method based on a multi-parameter-constrained mirror dual-branch biased monostable stochastic resonance (SR) system. Nonlinear odd-order bias terms are introduced into the conventional biased monostable potential function to build a multi-parameter-controllable SR model. This improves regulation of potential-well width, depth, and wall morphology, enhancing noise-energy utilization and responses to non-periodic features. Considering peak-type, valley-type, and bipolar anomaly morphologies, a mirror dual-branch SR structure is developed to cooperatively detect features with different polarities. To preserve temporal waveforms and time–frequency structures during parameter optimization, a composite metric combining the correlation coefficient and wavelet-domain image structural similarity index is constructed. Multi-fidelity robust Bayesian optimization is used to obtain a unified robust parameter set for the magnetic anomaly signal family. Experiments with simulated colored noise and measured geomagnetic noise show that the proposed method effectively recovers magnetic anomaly features under strong noise. At −19 dB SNR, its detection probability remains above 80%. Compared with orthogonal basis function decomposition, empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise, the method achieves better noise suppression, feature preservation, and detection performance under low-SNR conditions. Full article

Similar to a web search engine, we have developed a computer-based acoustic search engine tailored for the critical scenario of high-speed aircraft ground explosion monitoring, addressing the long-standing challenge of real-time localization for such high-impact events. Unlike conventional acoustic source localization techniques, our method uniquely resolves the separation and localization of multiple aliasing events, which are prevalent in high-speed aircraft explosion scenarios due to complex shock wave propagation and overlapping signatures. We first calculate the waveforms of all possible acoustic sources over 2D grids. Then, a dimensionality reduction method and fast search technology are applied to the database. Once a high-speed aircraft ground explosion occurs, the real-time system returns detection feedback by matching real-time data with the pre-established search database. Different from other artificial intelligence (AI)-based approaches, the acoustic search engine can handle multiple aliased acoustic events in real time and does not require any prior information or input parameters—a key advantage for emergency response to high-speed aircraft explosions where predefined parameters are often unavailable. Both synthetic tests and field data applications (using actual acoustic records from high-speed aircraft ground explosion experiments) demonstrate the method’s credibility in detecting and localizing multiple acoustic sources. Full article

This paper aims to explore non-traveling fractal solutions to an extended Hirota–Satsuma–Ito equation (gHSI) that contains several well-known equations arising in fluid dynamics. Our approach is based on the application of a new variable-separation technique that transfers the governing equation into several solvable forms. Some of these equations can also be solved with standard analytical methods. We employ the modified generalized exponential rational function method (mGERFM), resulting in a varied set of exact analytical solutions. These solutions exhibit a wide range of structural types, such as periodic, rational, hyperbolic, and hybrid configurations. A notable feature of our solutions is that the obtained solutions include several free functions, which provide a systematic way to modify the structure of the waveforms in the solutions. By appropriately selecting these free functions, several categories of dromion-type solutions are introduced. These non-traveling fractal solutions appear to be the first of their kind derived for this equation. The analytical findings are supported by illustrations that demonstrate the complex temporal and spatial dynamics that are characteristic of these solutions. The proposed approach opens a systematic path to non-traveling waves in higher-dimensional systems, where functional flexibility gives rise to self-similar fractal structures, and could be adapted to other equations in physics and engineering. Full article