Search Results (1,244)

Magnetic field ripple in the audible-frequency range can induce magnetostriction in ferromagnetic components, causing vibration and noise even in non-rotating systems such as electric vehicle battery systems. Magnetic shielding can reduce such excitation; however, shielding materials used in this range are generally ferromagnetic and may themselves undergo magnetostriction, potentially degrading NVH performance. Therefore, magnetic shielding materials should provide effective field attenuation while suppressing their own vibration to improve system-level NVH performance. In this study, a magnetic shielding test setup and an electromagnetic finite-element model were developed. The electromagnetic model was compared with experimental plate-shield measurements and subsequently used for comparative investigations of the effects of shielding configuration, open-area ratio, and thickness, while the associated NVH performance was evaluated experimentally. A mesh-type structure with a continuous magnetic flux path achieved shielding performance comparable to that of a plate-type structure. Experiments using a 0.635 mm steel-wire mesh provided a magnetic shielding effectiveness of approximately 3.73 dB at 3 kHz and reduced the target vibration component at 6 kHz by 55.8% and system noise by 5 dB. These results demonstrate that mesh-type magnetic shielding can attenuate audible-frequency magnetic field ripple while improving system-level NVH performance, indicating potential application to electric vehicle battery systems and other electromagnetic systems. Full article

Surface roughness (Ra) of drilled hole walls is difficult to measure non-destructively because the recessed surface is inaccessible to a stylus unless the workpiece is sectioned. This study presents an imaging rig and a stylus ground-truth procedure for 150 holes (4–9 mm) drilled in Al-7075, and evaluates whether gray-level co-occurrence matrix (GLCM), first-order and wavelet features from grayscale hole-wall images, together with spindle speed and feed rate, can estimate Ra using support vector regression (SVR) with and without mutual information feature selection. Two evaluation schemes were compared: an image-level split of 900 augmented images, and hole-level cross-validation in which all images of a hole are kept together and tests use original images of unseen holes. The image-level split gave R² = 0.80, whereas hole-level cross-validation gave R² between −0.04 and 0.08 for all feature sets, showing that image-level splitting overstates performance for this kind of data. Flips left the features unchanged, whereas noise injection and 90° rotation altered them and their correlation with Ra. The work provides a reproducible acquisition and ground-truth procedure, a 150-hole dataset and an evaluation protocol for image-based roughness estimation of drilled holes, and identifies the data and imaging conditions needed for estimation on unseen holes. Full article

As the core rotating components of the air supply system, the compressor and its blades are prone to crack initiation under long-term cyclic loading and high-temperature erosion. Failure to detect cracks in a timely manner may lead to blade fracture, subsequently causing catastrophic unit failure. Affected by environmental noise and the weak vibration signatures of blade cracks, the existing methods for detecting blade cracks lack sufficient accuracy and reliability. To address this problem, a cracked blade detection method is proposed based on a vibration deep fusion framework for compressors. Specifically, the proposed vibration deep fusion framework includes two parts, namely data-level and decision-level fusion. The proposed data-level fusion method divides the original vibration signals into low-frequency and high-frequency bands based on the sampling frequencies of the two vibration sensors and fuses these two frequency bands to suppress noise and enhance defect features. Moreover, the proposed decision-level fusion method can further fuse the preliminary results obtained from the one-dimensional convolutional neural network with two raw vibration signals and the data-level fusion signal, thereby obtaining the final blade crack detection result. To verify the proposed method, a compressor blade crack detection experimental platform is built to simulate the crack detection effect of blades under different working conditions, which covers blades with artificial cracks of different lengths in noisy and noiseless environments. The results show that the proposed method has a detection accuracy of over 96% with 10-fold cross-validation, and the ablation experiment verifies that the proposed method is superior to traditional single signal or single-stage fusion methods, which demonstrates its effectiveness for compressor blade crack detection. Full article

Aircraft wake vortices pose a hazard to following aircraft, yet their forward-looking radar detection in rain is complicated by propagation through intervening precipitation. In a near-axial geometry, the weak line-of-sight projection of wake rotational velocity motivates a complementary polarimetric observable. This study numerically investigates how wake-induced raindrop redistribution and reorientation form a two-core linear depolarization ratio (LDR) signature, how intervening rain distorts this signature, and whether full-matrix propagation retrieval and compensation can recover it. A physics-guided convolutional neural network (CNN) combined with bidirectional long short-term memory (BiLSTM) retrieves range-resolved forward-scattering matrices from polarimetric echo profiles and reconstructs the cumulative propagation matrix for two-sided echo compensation. Under the modeled Boeing 747-class wake, a rain rate of 10 mm ${\mathrm{h}}^{-1}$, and a 20 m × 20 m transverse averaging footprint, the intrinsic wake-to-background LDR contrast is 9.92 dB. For the prescribed sustained path-orientation-bias scenario, full-matrix propagation reduces the contrast to 2.87 dB, while network-based compensation restores it to 8.89 dB. A separate numerical evaluation using disjoint calibration and test physical-background fields further shows improved two-core detection under the tested strong-path conditions, with the benefit governed jointly by propagation coupling and received path and target signal-to-noise ratios (SNRs). These results provide numerical evidence for full-matrix propagation compensation as a means of recovering the cross-polarized wake signature under the modeled conditions. Full article

Ball screw feed systems are critical components of CNC machine tools, and their health condition directly affects positioning accuracy and operational reliability. However, variations in rotational speed under changing operating conditions can cause substantial shifts in fault characteristics, limiting the generalization capability of conventional fault identification methods developed under a single operating condition. To address this issue, a feature-enhanced multi-kernel domain adaptation framework is proposed for cross-domain fault identification of ball screw feed systems. First, an attention-based feature enhancement module is developed to adaptively reweight vibration features, thereby emphasizing informative feature components and suppressing redundant and noise-related information. Subsequently, a residual feature extraction and fusion module is employed to learn hierarchical fault representations and improve feature discriminability. Finally, a multi-kernel maximum mean discrepancy (MK-MMD) strategy is integrated with adversarial domain adaptation to reduce feature distribution discrepancies between source and target domains under different rotational speeds and ball screw specimens. Local spalling fault experiments were conducted on the developed test bench, where vibration signals were collected using an accelerometer under various operating conditions and subsequently used to validate the proposed framework. Experimental results demonstrate that the proposed method achieves an average identification accuracy of 92.9% across the 12 investigated cross-domain tasks, compared with 69.7% for the optimized source-only CNN, with the performance gap becoming particularly pronounced under cross-specimen transfer conditions. The results indicate that the proposed framework improves the learning of fault-discriminative and domain-invariant representations, thereby improving the robustness of ball screw fault identification under variable operating conditions. Full article

Bearings are critical components of modern large-scale rotating machinery, such as wind turbines, and their operating condition directly affects system safety, reliability, and operation and maintenance costs. Vibration and current signals reflect the mechanical responses and electromechanically coupled responses induced by bearing faults, respectively, and the two modalities differ in their characteristic representations of fault information. When vibration and current features are fused only once at the end of a network, early complementary information cannot be incorporated into subsequent feature learning; moreover, a single-level weighting scheme cannot capture variations in modality contributions across different samples and feature depths. To address these limitations, a Multi-Scale Feature Enhancement and Adaptive Fusion Network (MSFEAF-Net) is developed. The network employs a dual-branch multi-scale residual architecture to extract vibration and current features, enables early cross-modal interaction through bidirectional cross-attention after the first residual stage, and enhances the vibration and current modalities using one-dimensional spatial attention and channel attention, respectively, according to their distinct feature representations. Subsequently, sample-dependent modality weights are generated at three feature levels, and a feature pyramid is used to aggregate feature information across different levels. Finally, parallel multi-scale convolutions and channel–spatial attention are employed to further refine the features for bearing condition identification. Experimental results show that MSFEAF-Net achieves a test accuracy of 99.88% on the PU dataset under conventional operating conditions. Under strong noise at −8 dB, it maintains accuracies of 91.78% and 91.32% on the PU and KAIST datasets, respectively. In the cross-condition task involving simultaneous variations in rotational speed, torque, and radial load, the model achieves an accuracy of 92.45%. These results validate the effectiveness of the proposed method for fault diagnosis under strong noise and complex operating conditions. Full article

Systematic measurements on a subsonic axial-flow fan rig investigated how major noise sources scale with inflow distortion parameters. Common in compact nacelles for next-generation UHBR engines, these distortions significantly amplify fan noise and present an aeroacoustic design challenge. The Co/Counter Rotating Acoustic Fan Test Rig (CRAFT) was developed by the German Aerospace Center (DLR) in Berlin specifically for the investigation of sound sources in ducted, subsonic fan stages. A diverse set of distortion conditions was created, varying distortion type and intensity through scaled and modified distortion screens, resulting in a rich dataset of simultaneous acoustic and aerodynamic measurements. Advanced signal-processing techniques are employed to isolate the distortion-induced noise contributions and to quantify their dependence on key flow features. The resulting data are used to calibrate and validate data-driven models that predict noise generation in the presence of distorted inflows to illustrate the potential of the dataset. This work combines advanced aeroacoustic signal processing with interpretable machine learning analysis. The generated dataset allows a better understanding of the impact of inflow distortions on the sound sources at the rotor and stator. Shapley value analysis shows that low-order circumferential inflow harmonics, rig power setting, and rotor–stator distance significantly affect most outputs. In this way, prediction methods can be optimized in the long term. This dataset enables data-driven modelling of aeroacoustic dependencies. Due to the nonlinear relationship between aerodynamics, operating conditions, and radiated noise, it can be used to evaluate machine learning approaches. Full article

Background/Objectives: Computer-assisted posturography is utilized to quantify human postural control, yet existing dedicated systems frequently present physical constraints, such as limited sensing surfaces and rigid hardware barriers. In this study, a methodological framework is presented and a structural arrangement (OpenBalance) is designed to implement balance assessment accommodating a wider anthropometric range through zone-based foot placement using a decentralized load cell array, with preliminary implementation details provided in a repository. Methods: The configuration comprises an array of 16 discrete uniaxial vertical-force load cells embedded within four mechanically decoupled sub-platforms to minimize mechanical cross-talk. Biomechanical moment equations were implemented in a custom Python pipeline to compute a localized Center of Pressure (COP) for each sub-platform independently. A vector-based data fusion algorithm maps these local coordinates into a unified global coordinate system. The system evaluation incorporated baseline signal-to-noise ratio (SNR) analysis, mechanical crosstalk testing, and digital low-pass filtering. Results: A proof-of-concept evaluation using empirical data confirmed that the cascading coordinate model produces a continuous global COP trajectory and quadrant-specific load distributions. The platform dimensions (355 × 460 mm) and sensor topography geometrically accommodate natural external foot rotation (incorporating a 10° toe-out angle projection) and foot lengths corresponding to EU shoe sizes up to 55. Mechanical crosstalk between adjacent sub-platforms remained minimal (<1% of applied load). Conclusions: The OpenBalance framework confirms the technical feasibility of deriving a continuous global COP from a decentralized array of distributed load cells. While baseline component specifications and static verifications are established, comprehensive dynamic cross-validation against reference standards remains a necessary next step for the future development of open hardware that can be produced using 3D printing. Full article

In previous works, it has been highlighted how the confinement of electromagnetic (EM) coherent modes—arising from their coupling with the water connectome of living matter—acts as a self-emergent ‘mold’ that spatially organizes molecules, membranes, vesicles and larger cellular structures through ponderomotive and dielectrophoretic forces: the standing waves of the self-trapped fields pattern matter into nodes and antinodes—as in cymatics—driving the template-free self-assembly of compartments from the nano- to the macroscale. In this perspective report, we extend that picture and propose specific physical mechanisms by which the same confined EM modes may act as the agent of one of the deepest levels of morphogenesis: the handedness/chirality of biomolecules. We first recall that the chiral content of light is not captured by polarization (spin angular momentum, SAM) alone: the true measure of the chirality of light is the helicity (ℋ), whereas the orbital angular momentum (OAM), carried by helical-wavefront (Laguerre–Gaussian-like) modes, governs global rotational transformations. We then show that (i) the geometric (Berry) phase implied by the chiral light–matter coupling is the quantum manifestation of optical rotation, both being governed by the same molecular pseudoscalar—the Rosenfeld rotational strength—that builds the mixed electric–magnetic polarizability responsible for chiral optical forces; (ii) the decohered one-dimensional (1D) lanes generated by the Anderson–Higgs–Kibble mechanism for the self-focusing of the fields traveling through the coherent aqueous medium are themselves optical vortices carrying a quantized flux, and, therefore, natural cavities for OAM-carrying modes whose topological charge selects, through an optical OAM torque, the handedness of supramolecular helices (like α-helices, as well as more complex geometries such as several collagen types); (iii) the helicity gradient developed at the boundaries of coherence domains acts as a ‘chiral mold’ that breaks the energy degeneracy of the ‘enantiomer + field mode’ pair and exerts an enantioselective SAM torque on point-like species (amino acids and sugars), lowering the activation barrier of the preferred enantiomer. Although small, this chiral bias operates within the coherent, low-entropy medium of biological water, being thus fueled and protected against thermal noise. We conclude that the homochirality of life is a direct and necessary consequence of the QED coherence of the aqueous matrix. Full article

Seafarers inhabit acoustically hostile environments continuously across deployment rotations of four to six months, yet how shipboard noise relates to operational-safety risk is poorly characterised. We surveyed 242 maritime cadets with sea-service experience at Korea Maritime and Ocean University and applied factor analysis, structural regression and mediation modelling to the resulting cross-sectional, self-reported data. A two-factor model—Noise-Induced Sleep Disturbance and Fatigue (F1; α = 0.929) and Noise-Related Communication and Safety Impairment (F2; α = 0.871)—was retained over competing specifications on parsimony and interpretability grounds, with high reliability and supported discriminant validity (φ = 0.667), although absolute fit was modest (CFI = 0.865, RMSEA = 0.106) and the model awaits independent replication. F1 was strongly associated with F2 (β = 0.592, p < 0.001, R² = 0.349), independent of academic department. An exploratory mediation analysis indicated that sleep disturbance and fatigue statistically accounted for 58% of the association between perceived noise burden and perceived safety impairment (indirect = 0.321, 95% CI [0.16, 0.49]), and 34% of cadets self-reported a noise-related near-miss. Across all shipboard spaces, cadets favouring stricter, WHO-aligned noise limits outnumbered those satisfied with current IMO ceilings by about 2.5 to 1. These cross-sectional findings are consistent with—but cannot causally demonstrate—a sleep–fatigue pathway linking shipboard noise to operational safety, and they offer stakeholder-derived input for future discussions of IMO Resolution MSC.337(91). Full article

Restricting the $\left[[3L^3,2L^3+2,3]\right]$ FCC lattice code to a single triad sheet gives the $\left[[L^3,2L,L]\right]$ sheet code, with uniform weight-4 stabilizers and $K=4$ active connectivity. This code is isomorphic to L disjoint rotated 2D toric codes, so its memory is the toric code’s, and no density advantage is claimed. The contribution is a logical interconnect: native fault-tolerant joint Pauli measurement between selected logical qubits in independently encoded, co-located code blocks, with no dedicated routing region. Its natural use is entanglement generation and parity measurement between such blocks, since the $ZZ$ and $XX$ merges characterized here are exactly the two measurements a logical Bell measurement requires. Every FCC triangle has one edge in each triad sheet, so products of triangle measurements implement joint Pauli measurements across sheets while the merged code retains $d=L$. Finite-size crossing estimates under circuit-level depolarizing noise at $L\in \{4,6,8\}$ are $1.07\pm 0.05\%$ for the $ZZ$-merge, $0.76\pm 0.05\%$ with planar boundaries, and $0.91\pm 0.05\%$ for the $XX$-merge. The value of the primitive is that, for the pairs it reaches, it needs no routing region. Surface-code surgery merges only adjacent patches, so joining arbitrary pairs costs a reservation of roughly $1.5$ tiles per logical qubit; within the directly reachable set, the sheet architecture avoids that reservation, using $L^2$ physical qubits per logical, against $1.5L^2$ for a bussed toric array. That set is structured and limited: at the verified sizes of $L\in \{4,6,8\}$, each basis logical reaches L partners in one other sheet, giving $3L^2$ directly measurable pairs, corresponding to about $17\%$ of all logical pairs, and the same pattern is conjectured for general even L values. Three limits are stated rather than deferred: only the joint measurements are fault-tolerant primitives, the composed three-sheet CNOT being verified but not yet fault-tolerantly characterized; all thresholds are finite-size crossings at three lattice sizes, not asymptotic values; and pairs outside the reachable set would need routing as in any other architecture. Full article

The three triad sheets of the face-centered cubic (FCC) lattice, at even lattice size L, are edge-disjoint but share their vertex and octahedral-void ancilla positions. Running the three sheet codes in a three-round time-multiplexed cycle on one chip needs $4L^3$ physical qubits against $6L^3$ for three separate blocks, a saving of exactly one third at every L, for instance, 864 qubits instead of 1296 at $L=6$. The schedule is implemented explicitly, with all three sheets on one shared ancilla set; comparing it against the effective single-sheet model used for the parameter sweeps shows agreement to within $5\%$ at $L=4$ and $2.2\%$ at $L=6$. The cost of the saving is that each sheet idles for two of every three sub-rounds, and the finite-size crossing estimate of the circuit-level threshold falls from $1.13\pm 0.08\%$ to $0.86\pm 0.06\%$: a factor of $1.3$, that is a $24\%$ relative reduction, or $0.27$ percentage points. Sweeping the idle-to-gate noise ratio r over $0,1,2,4$ gives estimates decreasing monotonically from $1.13\%$ to $0.71\%$. The dual X-basis memory gives $0.78\pm 0.05\%$, overlapping the Z value. All values are finite-size crossings at $L=4,6,8$ from the full FCC circuit, not asymptotic thresholds, and the quoted spreads are dominated by finite-size drift rather than statistical error. Each sheet decomposes exactly into L independent rotated 2D toric codes (Proposition 1), so the memory is the toric code’s and no encoding-rate advantage is claimed; at $d=4$, the rate equals the rotated toric code’s. In the simulated noise model, where no channel couples two sheets, the sheets fail independently. Sub-threshold suppression survives the sharing at $\mathsf{\Lambda }(4\to 6)=12.3\pm 1.1$ and $\mathsf{\Lambda }(6\to 8)=5.6\pm 1.2$ at $p={10}^{-3}$, on a memory carrying 24 to 48 logical qubits. Block-level failure probabilities are reported alongside, since they grow with the logical count: at $L=6$ and $p={10}^{-3}$, the 36-logical chip loses at least one logical qubit in about one experiment in sixty. The $L=6$ instance, $\left[\right[648,36,6\left]\right]$, needs 864 physical qubits, within the nominal atom count of current neutral-atom processors; no implementability claim is made beyond that count. Full article

Rotating machinery sits at the heart of petrochemical, power generation, and other heavy industries. Its reliability directly shapes production output and cost. Infrared thermography enables non-contact, real-time monitoring of equipment surface temperatures. This makes it a useful tool for early fault detection. Yet images captured in factories typically carry mixed noise from sensors, transmission, and the environment. Such noise often obscures the thermal signatures that indicate faults. This paper puts forward a complete edge-deployable pipeline that denoises infrared images and then diagnoses faults. The first stage is a denoising method built on the stationary wavelet transform (SWT) and a parallel dual-branch U-Net. Under mild, moderate, and severe mixed noise, it reaches 40.99, 35.08, and 31.76 dB PSNR, respectively. The second stage is a lightweight CNN-Transformer-CBAM classifier. It uses depthwise separable convolution and a CBAM attention module. On an 11-class induction motor dataset, it scores 98.67% accuracy (five-run average). The model also achieves 98.75% on a 9-class transformer dataset and 99.50% on a 5-class pump dataset, demonstrating cross-device generalization. The model occupies only 2.758 MB. After INT8 quantization, it runs on the RK3588S edge board. Average CPU inference takes 103.026 ms. The 99th-percentile latency stays under 124.778 ms. These numbers support the feasibility of factory-floor monitoring. Full article

Reliable local three-dimensional (3D) wind estimates are important for fixed-wing UAV flight under wind disturbances, but low-cost platforms lack direct 3D flow sensing. We propose PIRNN-AKF, which combines a physics-informed gated recurrent unit (PI-GRU) with a measurement-noise-adaptive Kalman smoother. PI-GRU embeds the wind triangle and attitude rotations in an airspeed-closure loss and predicts covariance scales for adaptive fusion. In PX4/JSBSim simulations with Dryden turbulence, five-seed PI-GRU attains a 3D RMSE of $0.208\pm 0.003$ m/s and a direction MAE of 3.22° on Test-ID. PIRNN-AKF attains $0.544\pm 0.007$ m/s and 1.97° on Test-OOD, reducing RMSE by 37.4% relative to Vanilla GRU; KalmanNet yields lower OOD RMSE but weaker ID accuracy and direction estimation. Session-aware evaluation shows that the AKF reduces OOD jitter by 24.8% while preserving RMSE. In ten frozen HITL sessions of the final 41-input, 100-step model, ID/OOD RMSEs are $0.569\pm 0.054$/$0.832\pm 0.318$ m/s, the companion-side processing $p_{95}$ is $20.4\pm 0.5$ ms, and the achieved loop rate is $83.2\pm 1.6$ Hz with $5.6\pm 0.7\%$ deadline misses. These results support 50 Hz feasibility under simulation truth; real-flight accuracy and closed-loop benefits remain untested. Full article

Background/Objectives: Intensive care units (ICUs) are acoustically demanding environments in which nurses are continuously exposed to alarms, medical equipment, and staff activity. This study assessed ICU nurses’ perceived internal and external noise levels and their perceived noise-related adverse effects, and examined whether these perceptions differed according to demographic and work-related characteristics. Methods: A cross-sectional design was used with 244 ICU nurses in Saudi Arabia. Data were collected using a structured questionnaire adapted to assess perceived internal and external ICU noise and four effect domains (subjective, emotional, physiological, and work performance). Results: Perceived ICU noise was moderate (internal 64.68; external 64.51). Effect-domain means were 50.95 (subjective), 54.67 (emotional), 54.74 (physiological), and 51.28 (work performance). Female nurses scored significantly higher than male nurses for internal and external noise and for subjective, emotional, and physiological effects (all p < 0.05). Staff nurses, rotating-shift nurses, and nurses with <5 years of ICU experience reported higher effects in several domains; significant differences were also found by age group, marital status, and work-shift pattern. In multivariable models adjusting for all characteristics simultaneously, staff-nurse role, female gender and rotating shift remained independently associated with higher scores, whereas age group and years of ICU experience did not. Conclusions: ICU noise was perceived as a significant occupational stressor, with certain nurse subgroups reporting greater adverse effects. Healthcare organizations should prioritize comprehensive noise-reduction policies, effective alarm management, and targeted support strategies to mitigate noise-related impacts on nurses and improve the ICU work environment. Full article