Search Results (2,892)

Poor classroom acoustic conditions can impair speech intelligibility, increase cognitive load, and reduce the quality of learning environments. Simulation-guided optimization provides a promising approach for improving building acoustic performance and indoor environmental quality while reducing trial-and-error material selection in renovation practice. The field-validated optimized configuration combined slotted wood sound-absorbing panels and mineral wool panels, with a total material cost of 1512 RMB. Field measurements showed that this configuration reduced RT from 2.42–1.76 s to 0.78–0.43 s. In addition, the simulation-based STI evaluation increased from 0.19 to 0.75, indicating a potential improvement in speech intelligibility. Since STI was not directly measured in the field, this result is interpreted as a model-based prediction supported by the calibrated RT validation. The average relative error between simulated and measured RT values was 6.83%, demonstrating the predictive reliability of the calibrated model for RT prediction. The proposed framework provides a practical decision-support method for cost-controlled acoustic renovation of educational buildings and was validated using an existing classroom case. The framework was demonstrated using one existing classroom and provides a methodological basis for adaptation to other educational spaces by updating room-specific inputs. Its external transferability requires validation in additional classrooms. Full article

Interoperability remains a central issue in multi-performance building simulation, where heterogeneous domain-specific tools must be combined despite differences in modeling formalisms, numerical solvers, and execution schemes. Existing approaches, including data exchange standards and component-based frameworks such as the Functional Mock-up Interface (FMI), address specific levels of interoperability but often require model-level access, component wrapping, Functional Mock-up Unit (FMU) packaging, or framework-specific integration. This paper examines service-level interoperability, where domain-specific simulation tools are exposed as autonomous web services coordinated through an external orchestration mechanism. A structured, JSON-based Pivot DataSet (PDS) organizes data exchange between services, while coupling strategies are implemented at the orchestration level to manage interactions without accessing internal model structures. The approach is evaluated using a classroom case study from the Agence Nationale de la Recherche (ANR) COSIMPHI research project, focusing on communication overhead, synchronization constraints, and coupling behavior in distributed co-simulation. Under the investigated weak-coupling conditions, the waveform relaxation method (WRM) reduces synchronization iterations by 144× over one day and by approximately 3319× over one month compared with minute-by-minute sequential chaining. These results, obtained under weak thermal–acoustic coupling conditions, highlight the relevance of service-level interoperability and orchestration-level coupling for distributed building-performance simulation workflows involving independently developed domain tools. Their generalization to stronger coupling regimes, however, remains a direction for future work. Full article

In outdoor road environments, vehicle acoustic source direction-of-arrival (DOA) estimation is challenged by a low signal-to-noise ratio (SNR), dynamic-noise interference, and stringent real-time requirements. Under such conditions, conventional methods often struggle to achieve an effective balance among estimation accuracy, computational efficiency, and robustness against noise. To address this issue, this paper proposes a DOA estimation method that integrates a dynamic-pruning strategy with an adaptive subspace tracking mechanism. The proposed approach reduces computational complexity while enhancing algorithmic stability in complex and time-varying noise environments. Extensive experiments conducted on simulated data, the LOCATA dataset, and real-world outdoor road measurements demonstrate that the proposed method achieves comparable or superior DOA accuracy relative to conventional approaches, while significantly reducing computational cost. Furthermore, it exhibits stronger stability and robustness in real-world static and dynamic vehicle localization scenarios. Our method achieves a more favorable trade-off among multiple performance metrics. The results show that this method has good engineering application potential in complex outdoor environments, and can provide a practical solution for real-world vehicle monitoring. Full article

Background: Head-related transfer functions (HRTFs) contain time and level cues and may be utilized in automatic algorithms to identify locations of sound, a desirable feature for next-generation hearing devices. Due to substantial variability in individual head sizes and ear acoustics, individualized HRTFs are expected to provide the best localization results. However, acquiring individualized HRTFs for each user is time-consuming. Methods: This study constructed three binaural and/or monaural algorithms suitable for hearing devices. A linear classifier was trained on HRTF databases from a subset of subjects and used to predict sound locations for other individuals to evaluate cross-subject variability. Results: Using the CIPIC Database, a “two-step” method achieved a horizontal localization error of 1.0° and a vertical error of 30.4° sequentially. With the 3D3A Database, the horizontal and vertical errors were 5.6° and 36.5°, respectively. Both datasets yielded improved accuracy when frontal and rear hemifields were simulated separately, with trends remaining consistent across databases. When subjects were grouped by gender, classifiers trained on women’s HRTFs performed well in predicting men’s localization, whereas classifiers trained on men’s HRTFs resulted in significantly larger errors. Conclusions: These findings offer insights into the localization cues embedded in HRTFs and demonstrate the influences of inter-subject variability for spatial hearing devices. Full article

To address localization error accumulation in autonomous underwater vehicle (AUV) swarms due to underwater acoustic communication interruptions, this paper proposes a cooperative localization method that integrates Long Short-Term Memory (LSTM) prediction and factor graph optimization. During the real-time stage, each AUV uses a trained LSTM to predict observations, ensuring the Unscented Kalman filter (UKF) maintains continuous state estimation during interruptions and mitigates error accumulation. During the post-processing stage, a factor graph comprising motion model factors, cooperative observation factors, and LSTM prediction factors is constructed on the AUV swarm master node. By adaptively switching factor types based on communication status, global nonlinear optimization is performed on the AUV states. Simulation results show that compared with UKF + LSTM, the proposed method reduces the Average Localization Error (ALE) by 55% and the Root Mean Square Error (RMSE) by 60%; compared with the Rauch–Tung–Striebel (RTS) smoothing algorithm, it reduces the ALE by 36% and the RMSE by 44%. This fully verifies that the strategy combining real-time state maintenance and post-processing global optimization can more effectively correct AUV localization errors in communication-interrupted regions. Experiments under different communication interruption durations further confirm the robustness of the proposed algorithm, with the maximum error-to-range ratio remaining below 0.2% of the range. Full article

Long-delay multipath pushes underwater acoustic mixing beyond the instantaneous model assumed by many classical algorithms; spectral overlap among mechanically and biologically generated sources compounds the difficulty, and low signal-to-noise ratios erode the higher-order statistical cues used by methods such as FastICA and JADE. This work adapts frequency-domain second-order decorrelation (FSD) to convolutive underwater mixtures by using multi-block joint diagonalization of cross-power spectral density matrices in the short-time Fourier transform domain together with compact time-domain regularization of the demixing filters. To provide a controlled and traceable evaluation, we introduce ShipsEarBSS, a simulated benchmark that combines single-source ShipsEar recordings with deep-water BELLHOP arrival responses to form virtual multichannel mixtures with known reference sources. Under a five-trial, eight-SNR protocol spanning $-5$ to 30 dB, an optimized compact FSD configuration is evaluated against the frozen reference FSD, PCA-SVD, and AuxIVA, and its main design choices are further examined through filter-length, multi-block CPSD, and output-ordering ablations. The results support a cautious conclusion: under the tested ShipsEarBSS protocol, compact time-domain regularization improves the FSD operating point, while the choices of filter support, CPSD block count, and output ordering remain empirical configuration decisions rather than universal optima. Full article

The paper presents the concept of a turbine-based energy harvester designed to recover kinetic energy from airflow generated by a moving vehicle and integrated with a roadside acoustic barrier. The proposed solutions employ a vertical-axis aerodynamic turbine positioned within a cavity in the barrier and various airflow guiding structures intended to enhance the efficiency of energy transfer from turbulent airflow to the turbine rotor. To evaluate the effectiveness of the proposed concepts, two-dimensional CFD simulations were conducted in the ANSYS Fluent environment using the k–ε turbulence model. Three airflow deflector geometries and one reference configuration without a deflector were analyzed. The performance of each configuration was assessed based on the maximum instantaneous power and the average power generated by the turbine during a single vehicle pass-by event. The results demonstrated a significant influence of the airflow guide geometry on system performance. The most effective configuration achieved an average power output of approximately 7 W during a single vehicle pass-by event, whereas the configuration without an airflow guide exhibited significantly lower energy recovery efficiency. The obtained findings confirm the potential of the analyzed technology as a power source for autonomous low-power roadside infrastructure systems. Full article

Hypersonic air-breathing propulsion via the Oblique Detonation Wave Engine (ODWE) offers superior thermodynamic efficiency compared to conventional scramjets by utilizing a stationary oblique detonation wave (ODW). While fundamental research has predominantly focused on two-dimensional planar wedges, realistic applications feature axisymmetric conical configurations. Over a cone, radial Taylor–Maccoll (TM) compression decelerates the flow and, in the mixed flow regime, establishes a localized subsonic pocket near the cone surface. However, the unsteady structures, triple-point kinetics, and cellular evolution under the competing influences of stabilizing TM compression and destabilizing Prandtl–Meyer (PM) expansions induced by a finite-length cone remain poorly understood. To address this gap, high-resolution numerical simulations of axisymmetric conical ODWs on a finite cone (semi-cone angle θ = 49°) were conducted at an inflow Mach number of Ma₀ = 7.5 using OpenFOAM. The methodology solves the reactive Euler equations coupled with a single-step Arrhenius model and three levels of adaptive mesh refinement to resolve fine-scale wave structures. Numerical results reveal that the localized subsonic pocket completely obliterates the smooth ZND-like initiation zone typical of purely supersonic configurations. Within this subsonic channel, acoustic disturbances propagate upstream against the bulk flow at a relative velocity of c − u, bypassing the supersonic wave-blocking effect to continuously impinge upon the detonation front. This acoustic feedback loop disrupts shock–reaction coupling, accelerating wave front bifurcation into single triple-point, dual triple-point, and PM-affected segments. Shock polar analysis validates that upstream-facing triple points exhibit greater shock strength, driving slow upstream migration and causing adjacent triple points to collide and reform into distinct, chaotic cell morphologies. Trajectory tracking confirms that the mixed flow cells are substantially larger and more chaotic than supersonic cases, directly reflecting amplified perturbations from the subsonic pockets. These insights provide crucial design criteria for optimizing cone angles to suppress irregular modes and stabilize conical ODWs. Full article

Gas pipeline leakages in urban underground utility tunnels pose a severe threat to public safety. Leakages of varying aperture sizes trigger differentiated risks of diffusion and explosion; thus, achieving precise identification of leakage hole size has become a critical issue in safety management. To address the difficulty of traditional methods in effectively separating the acoustic features of different leakage levels within complex utility tunnel environments, this paper proposes a gas pipeline leakage risk level identification method based on a lightweight Spatial–Temporal Graph Neural Network (ST-GNN). First, relying on a real utility tunnel simulation platform, acoustic signals under different pressures and leakage hole size are collected, and time-frequency magnitude features are constructed through Short-Time Fourier Transform (STFT). Furthermore, each acoustic sample is independently converted into a graph with STFT time frames as nodes, where temporal neighborhood edges and K-nearest neighbor edges jointly encode local dynamics and non-local spectral similarities. This transforms unstructured acoustic signals into graph-structured data that embodies spatial–temporal coupling relationships. Building upon this, a lightweight Chebyshev graph convolutional network is designed to progressively extract discriminative features strongly correlated with leakage levels using multi-layer convolution. Experimental results on the actual utility tunnel simulation platform dataset demonstrate that the proposed method achieves excellent performance in a three-level leakage classification task. The t-SNE visualization reveals the effective separation of features, progressing from complete mixing in the input layer to distinct separation in the output layer. Through multiple training statistics and ablation experiments, the impact of dataset size and the number of network layers on the identification performance is analyzed, validating the robustness of the proposed model under limited samples and the effectiveness of its lightweight structure. This provides a feasible solution for the automated and refined identification of gas pipeline leakage levels in underground utility tunnels. Full article

The continuous, narrowband time-frequency structure of dolphin whistles is an important information carrier for target detection, behavioral analysis, and ecological monitoring in passive acoustic monitoring. However, ocean noise can easily obscure whistle time-frequency contours, blur their boundaries, and cause local discontinuities, thereby reducing the reliability of subsequent acoustic analysis. Existing denoising methods based on transform-domain thresholding and spectral-domain statistical modeling can attenuate background interference to some extent. However, without explicit structural constraints, these methods still have difficulty achieving a satisfactory balance between noise suppression and preservation of the whistle time–frequency structure. To address this problem, this study proposes a Structural Prior-Guided Adaptive Wavelet Denoising (SPG-AWD) method for single-channel unsupervised scenarios. The proposed method introduces structural priors at two levels: adaptive subband selection and terminal node denoising. At the first level, subband nodes are adaptively split, retained, or suppressed based on stationary wavelet packet recursive decomposition and the distribution of candidate structures. At the second level, a structural mask satisfying local grouped-energy and two-dimensional time–frequency connectivity constraints is extracted, and a continuous whistle-presence probability is obtained through a signed distance transform. This probability is then used to jointly guide local noise power spectral density estimation and protective Wiener gain fusion. Simulation results show that, under real recorded background noise and ship noise conditions, SPG-AWD achieves favorable overall denoising performance when the input SNR is higher than −16 dB, while maintaining a more stable balance between target region energy preservation and non-target region noise suppression. Experiments on real recordings further demonstrate that the proposed method can effectively suppress in-band noise components within the whistle-bearing frequency range, better preserve continuous main frequency contours, and improve the overall perceptibility of whistle contours, confirming its applicability to single-channel dolphin whistle denoising in complex underwater noise environments. Full article

In Underwater Acoustic Target Recognition (UATR), accurately extracting spectral lines from time–frequency spectra in complex ocean environments faces three critical challenges: low-frequency spectral confusion, line spectrum and noise mixture, and a computational efficiency vs. performance trade-off. To address these, we propose a deep learning-integrated framework based on application-oriented integration and adaptation of established techniques tailored to the underwater acoustic domain. The framework consists of the following: (1) the Line Spectrum Separation Network (LSS-Net), which integrates a Time–Frequency Joint LSTM and a Temporal Gated Cross-Attention (TGCA) module within an encoder–decoder architecture adapted for high-resolution underwater acoustic time–frequency spectra; (2) a physics-informed signal simulation approach that realistically models Doppler frequency drift and intensity fluctuations; and (3) a Peak-Tracking Line Extractor (PTLE) algorithm that leverages underwater acoustic-specific temporal constraints. The proposed framework achieves an MOTA of 0.89 on simulated data and 0.52 on real sea trial data, outperforming existing methods by 0.06-2.14 in MOTA and significantly suppressing high-resolution background noise. Full article

Underwater acoustic communication (UAC) is of great strategic importance for marine resource exploration and security collaboration. However, its open physical nature exposes communication links to severe eavesdropping and localization threats, while limited bandwidth and severe attenuation further exacerbate the difficulty of secure transmission. To address this, this study introduces the underwater acoustic reconfigurable intelligent surface (UARIS) to reconfigure acoustic propagation paths, leveraging its programmable reflection capability to enhance link quality and provide additional spatial degrees of freedom for location privacy protection. Accounting for the partial observability caused by the coarse observations of a mobile eavesdropping user (EU), noisy channel state information (CSI), and the practical constraint of UARIS discrete phase quantization, a utility maximization problem is formulated to jointly optimize the secrecy rate and location privacy. To tackle the strong non-convexity and coupled constraints in dynamic environments, a Gated Recurrent and Conformal-calibrated Soft Actor–Critic (GC-SAC) algorithm is proposed. Specifically, GC-SAC employs a gated recurrent unit (GRU) to capture the temporal statistical features of channel evolution. By integrating a risk prediction network with a conformal calibration mechanism, conservative estimation and robust regulation of multidimensional constraint risks are enhanced. Simulation results demonstrate that the GC-SAC algorithm achieves faster convergence and superior stability in dynamic underwater environments. Compared with representative baselines, the proposed algorithm exhibits significant advantages in secrecy rate and location privacy protection, validating its effectiveness for UARIS-assisted secure resource optimization in underwater scenarios. Full article

In therapeutic applications, ultrasound is widely used in physiotherapy, tissue repair, and cancer treatment. Regarding cancer treatment, as an emerging field for technology, significant research efforts have been devoted to the area of ultrasound therapy. The derived energy from beams can be deposited in tissues not only through heating but also through non-thermal mechanisms, whereby cancer cells are subject to cell death. Ultrasound-induced heating can generate localized temperature elevations within biological tissues, making it a subject of interest for thermal therapeutic applications. Nevertheless, excessive temperature elevations outside the primary exposure region may result in undesirable thermal effects within the surrounding tissue. In this study, we used continuous 3 MHz ultrasound waves at the powers of 0.4 to 1.4 W on ex vivo chicken breast tissue in a water bath to prevent fluctuations in temperature. The process was also numerically modeled with a maximum error of 0.4% from the measured data. Temperature measurements revealed a significant difference between the region of maximum acoustic pressure along the beam axis and deeper tissue locations (in some cases, above 3.5 °C). These findings indicate that temperature gradients can develop within homogeneous tissue during ultrasound exposure, emphasizing the importance of controlling acoustic power and exposure conditions. Moreover, increasing the temperature was significant during the first moments of treatment, which highlights the importance of precise controls for rate and precision in therapy. The numerical simulations also showed that increasing acoustic power elevates tissue temperature while simultaneously producing a less uniform temperature distribution. These observations may be useful for the optimization of future ultrasound-based thermal treatment strategies; however, direct clinical extrapolation requires further investigation using physiologically representative tissue models. Full article

Acoustic propagation in stratified shallow seas is governed by finite-depth waveguiding, impedance contrasts at the seawater–seabed interface, and coupled space–time wave dynamics. Conventional numerical solvers are accurate but often require detailed environmental priors, mesh generation, and explicit time marching, increasing the cost of simulations involving complex boundaries or repeated evaluations. This study proposes a physics-informed residual network (ResNet-PINN) for continuous simulation of two-dimensional acoustic fields in shallow-sea stratified media. The framework embeds a variable-density, variable-sound-speed acoustic pressure wave equation, initial and boundary constraints, and interface-focused collocation into network training. A Gaussian initial wave packet and temporal gating are incorporated through the output transformation to improve early-time physical consistency. The model is validated against SPECFEM2D simulations and a stratified semi-analytical modal benchmark. The results show that it captures source-region spreading, main wavefront evolution, and transmission–reflection structures near the seawater–seabed interface at an equivalent frequency of approximately 477 Hz. Supplementary tests with sloping and arched interfaces and modified boundary conditions indicate adaptability to smooth interface variations. Overall, the framework provides a physically consistent neural network strategy for continuous shallow-sea acoustic field simulation and a complementary basis for future extensions to higher-frequency propagation, more complex environments, and dynamically varying ocean conditions. Full article

Residual stress (RS) is a key integrity parameter after welding and additive manufacturing, motivating portable sensing methods for in-situ assessment. Phased Array Ultrasonics for Residual Stress (PAURS) treats a phased-array probe as a time-of-flight (ToF) sensor and infers RS from ToF changes of the longitudinal critically refracted (LCR) wave propagating near the surface. In practical deployments, however, the ToF sensing chain can be susceptible to systematic bias from sensor–specimen interface variability (couplant layer thickness) which can dominate the inferred stress uncertainty if not quantified and corrected. This study combines numerical modelling with experimental validation to (i) characterise couplant-induced sensitivity in LCR ToF sensing, (ii) propagate this effect into RS error/uncertainty, and (iii) demonstrate a model-informed compensation strategy suitable for practical calibration workflows. Simulations show that couplant thickness variations can introduce RS errors of ~36 MPa (~13% of yield strength). The proposed compensation reduces ToF bias to 0 ns under idealised simulated conditions and to ~0.3 ns in experiments, corresponding to ~1.1 MPa RS error (~0.4% of yield strength). These results provide configuration-specific guidance for sensor calibration and uncertainty reporting in phased-array ultrasonic RS sensing, and establish a foundation for future in-process sensing of residual stress and microstructure evolution. Full article