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Accurate and reliable optical remote sensing image-based small-ship detection is crucial for maritime surveillance systems, but existing methods often struggle with balancing detection performance and computational complexity. In this paper, we propose a novel lightweight framework called HSI-ShipDetectionNet that is based on high-order spatial interactions and is suitable for deployment on resource-limited platforms, such as satellites and unmanned aerial vehicles. HSI-ShipDetectionNet includes a prediction branch specifically for tiny ships and a lightweight hybrid attention block for reduced complexity. Additionally, the use of a high-order spatial interactions module improves advanced feature understanding and modeling ability. Our model is evaluated using the public Kaggle and FAIR1M marine ship detection datasets and compared with multiple state-of-the-art models including small object detection models, lightweight detection models, and ship detection models. The results show that HSI-ShipDetectionNet outperforms the other models in terms of detection performance while being lightweight and suitable for deployment on resource-limited platforms.

Massive particles leave imprints on primordial non-Gaussianity via couplings to the inflaton, even despite their exponential dilution during inflation: practically, the Universe acts as a Cosmological Collider. We present the first dedicated search for spin-zero particles using BOSS redshift-space galaxy power spectrum and bispectrum multipoles, as well as Planck CMB non-Gaussianity data. We demonstrate that some Cosmological Collider models are well approximated by the standard equilateral and orthogonal parametrization; assuming negligible inflaton self-interactions, this facilitates us translating Planck non-Gaussianity constraints into bounds on Collider models. Many models have signatures that are not degenerate with equilateral and orthogonal non-Gaussianity and thus require dedicated searches. Here, we constrain such models using BOSS three-dimensional redshift-space galaxy clustering data, focusing on spin-zero particles in the principal series and constraining their couplings to the inflaton at varying speed and mass, marginalizing over the unknown inflaton self-interactions. This is made possible through an improvement in Cosmological Bootstrap techniques and the combination of perturbation theory and halo occupation distribution models for galaxy clustering. Our work sets the standard for inflationary spectroscopy with cosmological observations, providing the ultimate link between physics on the largest and smallest scales., Comment: 35 pages, 15 figures, 6 tables

This paper provides a comprehensive overview of how fitting of Baryon Acoustic Oscillations (BAO) is carried out within the upcoming Dark Energy Spectroscopic Instrument's (DESI) 2024 results using its DR1 dataset, and the associated systematic error budget from theory and modelling of the BAO. We derive new results showing how non-linearities in the clustering of galaxies can cause potential biases in measurements of the isotropic ($\alpha_{\mathrm{iso}}$) and anisotropic ($\alpha_{\mathrm{ap}}$) BAO distance scales, and how these can be effectively removed with an appropriate choice of reconstruction algorithm. We then demonstrate how theory leads to a clear choice for how to model the BAO and develop, implement and validate a new model for the remaining smooth-broadband (i.e., without BAO) component of the galaxy clustering. Finally, we explore the impact of all remaining modelling choices on the BAO constraints from DESI using a suite of high-precision simulations, arriving at a set of best-practices for DESI BAO fits, and an associated theory and modelling systematic error. Overall, our results demonstrate the remarkable robustness of the BAO to all our modelling choices and motivate a combined theory and modelling systematic error contribution to the post-reconstruction DESI BAO measurements of no more than $0.1\%$ ($0.2\%$) for its isotropic (anisotropic) distance measurements. We expect the theory and best-practices laid out to here to be applicable to other BAO experiments in the era of DESI and beyond., Comment: 29 pages, 18 figures, 1 table, submitted to MNRAS

The $E_G$ statistic is a discriminating probe of gravity developed to test the prediction of general relativity (GR) for the relation between gravitational potential and clustering on the largest scales in the observable universe. We present a novel high-precision estimator for the $E_G$ statistic using CMB lensing and galaxy clustering correlations that carefully matches the effective redshifts across the different measurement components to minimize corrections. A suite of detailed tests is performed to characterize the estimator's accuracy, its sensitivity to assumptions and analysis choices and the non-Gaussianity of the estimator's uncertainty is characterized. After finalization of the estimator, it is applied to $\textit{Planck}$ CMB lensing and SDSS CMASS and LOWZ galaxy data. We report the first harmonic space measurement of $E_G$ using the LOWZ sample and CMB lensing and also updated constraints using the final CMASS sample and the latest $\textit{Planck}$ CMB lensing map. We find $E_G^{Planck+CMASS} = 0.36^{+0.06}_{-0.05}$ (68.27%) and $E_G^{\rm \textit{Planck}+LOWZ} = 0.40^{+0.11}_{-0.09} $ (68.27%), with additional subdominant systematic error budget estimates of 2% and 3% respectively. Using $\Omega_{\rm m,0}$ constraints from $\textit{Planck}$ and SDSS BAO observations, $\Lambda$CDM-GR predicts $E_G^{\rm GR} (z = 0.555) = 0.401 \pm 0.005$ and $E_G^{\rm GR} (z = 0.316) = 0.452 \pm 0.005$ at the effective redshifts of the CMASS and LOWZ based measurements. We report the measurement to be in good statistical agreement with the $\Lambda$CDM-GR prediction, and report that the measurement is also consistent with the more general GR prediction of scale-independence for $E_G$. This work provides a carefully constructed and calibrated statistic with which $E_G$ measurements can be confidently and accurately obtained with upcoming survey data., Comment: 36 pages, 20 figures, Accepted for publication in PRD

Thin-walled metallic structures produced by the Directed Energy Deposition (DED) Additive Manufacturing (AM) process are prone to various fabrication defects, which hinder the wider applications of the technique in practice. In-situ inspection and monitoring methodologies are in high demand for improved quality control of printed parts. This paper presents an ultrasonic guided-wave-based method and a prototype that can potentially be used for in-situ inspection of thin-walled structures produced by DED. Lamb waves are excited by a Lead zirconate titanate (PZT) piezoelectric transducer bonded on the DED substrate remotely from the thin wall. The substrate works as a waveguide to transmit the waves which then propagate along the thin wall. A non-contact laser vibrometer is applied to measure the guide wave signals by scanning the surface of the thin wall. The mechanisms of guided wave generation and propagation along the substrate and printed part are theoretically studied. It allows for choosing proper inspection parameters to enhance the measurement sensitivity of guided waves and help interpret the signals for defect detection. Experiments were conducted with DED-produced stainless steel (316L) thin-walled structure. The new method is demonstrated in one example to detect and localize a small defect caused by inconsistent powder delivery of a fabricated thin wall sample, via analysing the B-scan ultrasonic guided wave signals. The new technique provides strong potential for in-situ online monitoring of the DED process.

Far-field wave imaging and inversion of objects with dimension and spacing much smaller than the wavelength are very difficult because of the well-known diffraction limit. The task becomes more challenging for quantitative shape reconstruction of closely spaced subwavelength impenetrable scatterers. Apart from the classic resolution limit, complex wave interaction including evanescent waves among impenetrable objects can be strong, but it is difficult to be fully utilized for correct shape inversion although it contains critical resolution information. Here we tackle this problem by proposing a level-set-based geometrical full-waveform inversion method. It makes full use of wave physics to directly reconstruct subwavelength objects of arbitrary shapes separated within half a wavelength. Shape reconstruction is controlled by the geometrical boundary gradient calculated from the finite-element formulation, and the important role of wave interaction among objects in the inversion is demonstrated. The boundaries of the objects are represented using the level-set method, allowing for reconstruction of multiple objects simultaneously. Numerical examples and experiments of ultrasonic waves in solid samples demonstrate that the method successfully reconstructs closely spaced subwavelength objects with far-field measurements, and the resolution is shown to be within a fraction of wavelength. This level of accuracy can be very useful in subsurface imaging applications such as ultrasonic nondestructive evaluation, medical diagnosis, and electromagnetic sensing.

This paper introduces a methodology to recover the morphology of a complex rough surface from ultrasonic pulse echo measurements with an array of equidistant sensors using the one dimensional convolution neural network (1DCNN). The neural network is trained by the datasets simulated from high-fidelity finite element simulations for surfaces with a range of roughness parameters and is tested on both numerical and real experimental data. To assess the performance of our proposed method, the rough surface reconstruction results from the deep learning approach are compared with those obtained from conventional ultrasonic array imaging methods. Unlike array imaging-based methods that require a large number of sensors (e.g., 128, 64 or 32), the deep learning-based method uses pulse echo signals and can achieve accurate results with much fewer sensors. The developed deep learning approach has the potential to enable low-cost, accurate, and real-time reconstruction of complex surface profiles.

Understanding the uncertainty in nondestructive evaluation measurements of rough defects requires a stochastic analysis due to the random variation between the morphology of different defects. In previous studies, large numbers of finite element models of randomly generated rough defects have been run in order to gain an insight into the statistics of the uncertain results. This approach is limited due to the time taken to run each individual model (typically of the order of minutes per model) and so the total number of models run is limited. In this paper, a surrogate model approach is proposed which produces close approximations to an original finite element model of a time-of-flight diffraction measurement, but in a small fraction of the time (<1 ms). The surrogate model is trained on pairs of input (rough defect) and output (A-scan) data from the finite element model; the surrogate then learns the relationship between inputs and outputs and is then able to generate new results. The architecture employed is a machine learning sequence-to-sequence approach similar to those used in natural language algorithms, based on the idea of ‘translating’ between defect to A-scan. In this study, a surrogate model was trained on 2160 finite element results which spanned across a range of ultrasonic incidence angles, rough defect correlation length and root-mean-square roughness. The surrogate model is shown to produce A-scans with close agreement to those produced by the original finite element model; taking approximately 0.425 ms instead of approximately 8 min. Millions of results are then possible in seconds, rather than the decades that would be required for finite element studies; more complete stochastic analysis are therefore now viable.

Endowing machines with abstract reasoning ability has been a long-term research topic in artificial intelligence. Raven's Progressive Matrix (RPM) is widely used to probe abstract visual reasoning in machine intelligence, where models will analyze the underlying rules and select one image from candidates to complete the image matrix. Participators of RPM tests can show powerful reasoning ability by inferring and combining attribute-changing rules and imagining the missing images at arbitrary positions of a matrix. However, existing solvers can hardly manifest such an ability in realistic RPM tests. In this paper, we propose a deep latent variable model for answer generation problems through Rule AbstractIon and SElection (RAISE). RAISE can encode image attributes into latent concepts and abstract atomic rules that act on the latent concepts. When generating answers, RAISE selects one atomic rule out of the global knowledge set for each latent concept to constitute the underlying rule of an RPM. In the experiments of bottom-right and arbitrary-position answer generation, RAISE outperforms the compared solvers in most configurations of realistic RPM datasets. In the odd-one-out task and two held-out configurations, RAISE can leverage acquired latent concepts and atomic rules to find the rule-breaking image in a matrix and handle problems with unseen combinations of rules and attributes.

Legged locomotion has recently achieved remarkable success with the progress of machine learning techniques, especially deep reinforcement learning (RL). Controllers employing neural networks have demonstrated empirical and qualitative robustness against real-world uncertainties, including sensor noise and external perturbations. However, formally investigating the vulnerabilities of these locomotion controllers remains a challenge. This difficulty arises from the requirement to pinpoint vulnerabilities across a long-tailed distribution within a high-dimensional, temporally sequential space. As a first step towards quantitative verification, we propose a computational method that leverages sequential adversarial attacks to identify weaknesses in learned locomotion controllers. Our research demonstrates that, even state-of-the-art robust controllers can fail significantly under well-designed, low-magnitude adversarial sequence. Through experiments in simulation and on the real robot, we validate our approach's effectiveness, and we illustrate how the results it generates can be used to robustify the original policy and offer valuable insights into the safety of these black-box policies. Project page: https://fanshi14.github.io/me/rss24.html, Comment: RSS 2024

The next generation of cosmological surveys will measure the large-scale structure (LSS) of the universe with unprecedented statistical power, covering large cosmological volumes and yielding constraints on cosmological parameters competitive with the cosmic microwave background (CMB), letting us test the standard model of cosmology at percent levels or below across cosmic history, from the recombination era to the present. Unlike CMB experiments, which probe the early universe in the linear regime, these new surveys will map the distribution of matter and galaxies at late times where the effects of nonlinearities are significant. To reliably extract fundamental physics information from this data will require theoretical models that can make accurate predictions on large, cosmological scales while being robust against the effects of nonlinear physics of small scales, due not just to gravitational collapse but also the astrophysics of galaxy formation whose precision modeling currently eludes us.In this dissertation we develop perturbation theory (PT) models for two key observables in upcoming surveys: the redshift-space clustering of galaxies in spectroscopic surveys and the cross correlation of galaxy clustering with weak gravitational lensing. Perturbation theory at the linear level has an indispensable role in CMB analyses, and the past decade has seen rapid developments in \textit{nonlinear} perturbation theories of large-scale structure, in part due to a reinterpretation of PT through the lens of effective field theories that has allowed for a robust and theoretically consistent treatment of the effects of non-perturbative small-scale physics on large-scale clustering. We review these developments in Chapter 1 before diving into the study of galaxy velocities and redshift-space distortions in Eulerian and Lagrangian perturbation theory (LPT) in Chapter 2. We use this knowledge to construct a model of the redshift-space galaxy 2-point function at 1-loop in pertu

Numerical simulations in cosmology require trade-offs between volume, resolution and run-time that limit the volume of the Universe that can be simulated, leading to sample variance in predictions of ensemble-average quantities such as the power spectrum or correlation function(s). Sample variance is particularly acute at large scales, which is also where analytic techniques can be highly reliable. This provides an opportunity to combine analytic and numerical techniques in a principled way to improve the dynamic range and reliability of predictions for clustering statistics. In this paper we extend the technique of Zel'dovich control variates, previously demonstrated for 2-point functions in real space, to reduce the sample variance in measurements of 2-point statistics of biased tracers in redshift space. We demonstrate that with this technique, we can reduce the sample variance of these statistics down to their shot-noise limit out to k ∼ 0.2 h Mpc-1. This allows a better matching with perturbative models and improved predictions for the clustering of e.g. quasars, galaxies and neutral Hydrogen measured in spectroscopic redshift surveys at very modest computational expense. We discuss the implementation of ZCV, give some examples and provide forecasts for the efficacy of the method under various conditions.

Sea state estimation (SSE) is significant to the development of autonomous ships, which can enhance the sustainable development of maritime transportation. Traditional model-based methods are limited by their drawbacks, such as high costs and inaccurate estimations. The deep learning model shows superior performance, but it requires that the sample quantity for each sea state should be almost the same. Since the occurrence probability of each state is different, the ships mainly work in low sea states, and the collected ship motion data for different sea states are highly imbalanced. This work proposes a novel class-imbalanced ship motion data-based cross-scale model for SSE. The model consists of three major components: a multi-scale feature learning module, a cross-scale feature learning module, and a prototype classifier module. The multi-scale and cross-scale feature learning modules are designed to learn abundant coarse and fine-level features from the ship motion data. The prototype classifier is utilized to overcome the limitation of the conventional softmax classifier to produce better estimates. Our research highlights our model’s remarkable scalability and versatility with 30 publicly available datasets in time series classification, demonstrating superior performance over baseline methods in 21 cases. Notably, it outperformed ShapeNet by 5.72% and EDI by 26.3%. We further validated our model’s proficiency using ship motion datasets, consistently surpassing eight state-of-the-art baselines and five class-imbalanced learning methods. Ablation and sensitivity studies, emphasize the critical role of each model component. Our findings underscore the model’s robustness and its potential to advance time series classification in diverse domains.

Qiying Zhang,1 Jing Zhang,1 Zi Liu,1,2 Juan Wang,1 Fei Wang,1 Tao Wang,1 Fan Shi,1 Jin Su,1 Yalong Zhao3 1Department of Radiation Oncology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, People’s Republic of China; 2Biobank, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, People’s Republic of China; 3Department of Medical Affairs, Guangdong Techpool Bio-Pharma Co, Ltd, Guangzhou, 510000, People’s Republic of ChinaCorrespondence: Zi Liu, Department of Radiation Oncology, the First Affiliated Hospital of Xi’an Jiaotong University, No. 277, West Yanta Road, Xi’an, Shaanxi, 710061, People’s Republic of China, Tel +86-18991232167, Email liuzmail@126.comObjective: Genomic profiles relating to H101 treatment-induced alterations are yet to be achieved. Here, we evaluated the impact of H101 via exome-sequencing approaches aiming to probe for potential biomarkers that are actionable in the treatment of persistent/recurrent/metastatic (P/R/M) cervical cancer.Methods: Whole exome sequencing (WES) was performd on paired pre- and post-H101 samples from 17 P/R/M cervical cancer patients who received serial intra-tumor injections of H101. Somatic mutations, including high-frequency mutations, microsatellite instability (MSI) status, tumor mutation burden (TMB), clonal evolution, and mutational signature were analyzed.Results: The median follow-up time after the H101 treatment was 14 months. Complete response was achieved in 9 patients, 3 patients achieved partial response, and 2 patients had stable disease, resulting in an objective response rate (ORR) of 70.6% (95% CI: 46.4%-96.7%). WES analysis showed no difference in treatment-related mutation characteristics, including non-synonymous-SNVs and TMB status. Patients with lower TMB were correlated with improved H101 response rates (P=0.044), whereas the same was not evident in high MSI (MSI-H) versus non-MSI-H patients (P=0.528). We observed a few high-frequ

Non-invasive characterization and monitoring of gas evolution during the operation of commercial Li-ion batteries (LIBs) has been a long-term challenge. This paper presents an in situ subsurface ultrasonic array imaging method to detect, locate, and characterize gases generated inside a LIB. Ultrasonic signals scattered from internal gases are collected by a full matrix capture method. A cross-section image showing gases is produced using the velocity-modified total focusing method. The locations and distribution of the gases in both the lateral and thickness directions can be clearly revealed. The finite element simulations and a controlled experiment are performed to validate the feasibility of the proposed array imaging method. Finally, a realistic long-term cycling experiment is performed on a commercial pouch cell. The appearance, evolution, and accumulation of gases are visualized and characterized by analyzing the images produced at different cycles. The experiment findings are validated against the X-ray CT results. © 2023 The Author(s)

Prediction of icing on wind turbine blades is crucial, particularly in high-latitude areas where ice accumulation is a frequent occurrence. Traditional centralized data-driven approaches for predicting blade icing have demonstrated promising performance, but they require a large amount of storage and computational resources and may also raise concerns about data privacy. Federated Learning (FL) presents a potential solution to address these issues. These challenges include redundant features in the collected data, a highly imbalanced data distribution between normal and icing samples, and slow model convergence during FL training. To tackle these challenges, we proposed a novel FL model called FedBIP. FedBIP employs a feature selection approach enhanced with human knowledge to select relevant features, a segmentation-based oversampling method to alleviate class imbalance, and a new aggregation method that takes into account data size, timestamps, and offsets of each participating client. In addition, knowledge distillation is employed in the local model training to accelerate model convergence and speed up the overall training process. The results of comprehensive experiments demonstrate that FedBIP outperforms state-of-the-art FL methods, aggregation methods, and feature extractors. Ablation and sensitivity analysis were also conducted to validate the importance of each component and key parameters in FedBIP.

We formulate the Lagrangian perturbation theory of galaxy intrinsic alignments and compute the resulting auto and cross power spectra of galaxy shapes, densities and matter to 1-loop order. Our model represents a consistent effective-theory description of galaxy shape including the resummation of long-wavelength displacements which damp baryon acoustic oscillations, and includes one linear, three quadratic and two cubic dimensionless bias coefficients at this order, along with counterterms and stochastic contributions whose structure we derive. We compare this Lagrangian model against the three-dimensional helicity spectra of halo shapes measured in N-body simulations by Akitsu et al (2023) and find excellent agreement on perturbative scales while testing a number of more restrictive bias parametrizations. The calculations presented are immediately relevant to analyses of both cosmic shear surveys and spectroscopic shape measurements, and we make a fast FFTLog-based code spinosaurus publicly available with this publication., Comment: 41 pages, 8 figures, updated to match version accepted in JCAP. Fast Python code to predict IA correlations computed in the text can be found at https://github.com/sfschen/spinosaurus

In order to address fundamental questions related to the expansion history of the Universe and its primordial nature with the next generation of galaxy experiments, we need to model reliably large-scale structure observables such as the correlation function and the power spectrum. Cosmological $N$-body simulations provide a reference through which we can test our models, but their output suffers from sample variance on large scales. Fortunately, this is the regime where accurate analytic approximations exist. To reduce the variance, which is key to making optimal use of these simulations, we can leverage the accuracy and precision of such analytic descriptions using Control Variates (CV). The power of control variates stems from utilizing inexpensive but highly correlated surrogates of the statistics one wishes to measure. The stronger the correlation between the surrogate and the statistic of interest, the larger the variance reduction delivered by the method. We apply two control variate formulations to mock catalogs generated in anticipation of upcoming data from the Dark Energy Spectroscopic Instrument (DESI) to test the robustness of its analysis pipeline. Our CV-reduced measurements offer a factor of 5-10 improvement in the measurement error compared with the raw measurements. We explore the relevant properties of the galaxy samples that dictate this reduction and comment on the improvements we find on some of the derived quantities relevant to Baryon Acoustic Oscillation (BAO) analysis. We also provide an optimized package for computing the power spectra and other two-point statistics of an arbitrary galaxy catalog as well as a pipeline for obtaining CV-reduced measurements on any of the AbacusSummit cubic box outputs. We make our scripts publicly available and report a speed improvement of $\sim$10 for a grid size of $N_{\rm mesh} = 256^3$ compared with \texttt{nbodykit}., Comment: 17 pages, 9 figures, public package (for power spectrum and control variates estimation)

In addition to the intrinsic clustering of galaxies themselves, the spatial distribution of galaxies observed in surveys is modulated by the presence of weak lensing due to matter in the foreground. This effect, known as magnification bias, is a significant contaminant to analyses of galaxy-lensing cross-correlations and must be carefully modelled. We present a method to estimate the magnification bias in spectroscopically confirmed galaxy samples based on finite differences of galaxy catalogues while marginalizing over errors due to finite step size. We use our estimator to measure the magnification biases of the CMASS and LOWZ samples in the SDSS BOSS galaxy survey, analytically taking into account the dependence on galaxy shape for fiber and PSF magnitudes, finding $\alpha_{\rm CMASS} = 2.71 \pm 0.02$ and $\alpha_{\rm LOWZ} = 2.45 \pm 0.02$ and quantify modelling uncertainties in these measurements. Finally, we quantify the redshift evolution of the magnification bias within the CMASS and LOWZ samples, finding a difference of up to a factor of three between the lower and upper redshift bounds for the former. We discuss how to account for this evolution in modelling and its interaction with commonly applied redshift-dependent weights. Our method should be readily-applicable to upcoming surveys and we make our code publicly available as part of this work., Comment: 14 pages, 9 figures, 3 tables, Accepted in MNRAS

The interest in phononic crystals and acoustic metamaterials has been an intensive subject of research in recent years. Finding a robust way to significantly expand or actively control the bandgap has received extensive attention. In this study, we propose a prestressed metamaterial beam attached with multiply local resonators connected by actively tunable piezoelectric springs. The Euler-Bernoulli beam theory and Timoshenko beam theory are applied in the theoretical analysis of the system. Further, the spectral element method is utilized to analytically compute the dispersion relation and transmission ratio and excellent agreement with reference to the benchmark is reported. The influences of an external axial force on the bandgap range and attenuation behavior are further studied. Subsequently, the effect of resonator number and mass on the local resonance bandgap structure is investigated in two parametric studies. The active control of bandgap range and frequency is then verified. By analyzing frequency response function, the tunable transmission ratio of a supercell can be observed. To conclude, this paper not only provides a guideline for designs of wave attenuation with multiple frequency regimes in a one-dimensional system, but it can also be extended to sub-wavelength wave manipulation designs.

We present the Aemulus $\nu$ simulations: a suite of 150 $(1.05 h^{-1}\rm Gpc)^3$ $N$-body simulations with a mass resolution of $3.51\times 10^{10} \frac{\Omega_{cb}}{0.3} ~ h^{-1} M_{\odot}$ in a $w\nu$CDM cosmological parameter space. The simulations have been explicitly designed to span a broad range in $\sigma_8$ to facilitate investigations of tension between large scale structure and cosmic microwave background cosmological probes. Neutrinos are treated as a second particle species to ensure accuracy to $0.5\, \rm eV$, the maximum neutrino mass that we have simulated. By employing Zel'dovich control variates, we increase the effective volume of our simulations by factors of $10-10^5$ depending on the statistic in question. As a first application of these simulations, we build new hybrid effective field theory and matter power spectrum surrogate models, demonstrating that they achieve $\le 1\%$ accuracy for $k\le 1\, h\,\rm Mpc^{-1}$ and $0\le z \le 3$, and $\le 2\%$ accuracy for $k\le 4\, h\,\rm Mpc^{-1}$ for the matter power spectrum. We publicly release the trained surrogate models, and estimates of the surrogate model errors in the hope that they will be broadly applicable to a range of cosmological analyses for many years to come., Comment: 37 pages, 15 figures, matching version accepted by JCAP

The growth of large-scale structure, as revealed in the anisotropic of clustering of galaxies in the low redshift Universe, provides a stringent test of our cosmological model. The strongest current constraints come from the BOSS and eBOSS surveys, with uncertainties on the amplitude of clustering of less than 10 per cent. A number of different approaches have been taken to fitting this signal, leading to apparently discrepant conclusions about the amplitude of fluctuations at late times. We compare in some detail two of the leading approaches, one based on a fitting a template cosmology whose amplitude and length scales are allowed to float with one based on a more traditional forward modeling approach, when fitting to the BOSS DR12 data. Holding the input data, scale cuts, window functions and modeling framework fixed we are able to isolate the cause of the differences and discuss the implications for future surveys.

This work presents the analytical acoustic model to investigate the interaction mechanism between the state of charge (SOC) of lithium-ion battery and the propagation characteristics of ultrasonic guided waves. Meanwhile, the multi-layered porous structure characteristics of lithium-ion batteries are focused on, the Biot theory and transfer matrix method are introduced to construct the analytical acoustic theory model of lithium-ion batteries. The mechanical performance (modulus and density) of the electrode is dynamically changing during cycling, which will influence the dispersion characteristics of ultrasonic guided waves in lithium-ion battery. Based on this, the intrinsic connection between the SOC and the guided wave dispersion curve of lithium-ion battery is numerically analyzed. Moreover, the frequency domain simulation model with equal structural characteristics is established to verify the validity of the above theoretical results. Theoretical results performed on the customized pouch cell show that the changes in time of flight resulting from shifts in the group velocity of guided wave A0 mode correlate strongly with the SOC. An ultrasonic guided wave detection experiment system is built to extract the variations in time of flight and amplitude of the guided wave signal during cycling, which correctly validates the range of theoretical time of flight. Furthermore, the applicability of the proposed method is verified by battery experiments with different temperature and current rate operating conditions. Furthermore, the ultrasonic guided wave detection approach has also been shown to be a sensitive means of determining the SOC of lithium-ion batteries.

This paper shows the achievement of a sensing and navigation system of aerial robot for measuring location and size of trees in a forest environment autonomously. Although forestry is an important industry in Japan, the working population of forestry is decreasing. Then, as an application of mechanization of forestry, we propose tree data collection system by aerial robots which have high mobility in three-dimensional space. First, we develop tree recognition and measurement method, along with algorithm to generate tree database. Second, we describe aerial robot navigation system based on tree recognition. Finally, we present an experimental result in which an aerial robot flies in a forest and collects tree data.

Additive manufacturing, also known as 3D printing, has become a powerful and versatile means of manufacturing for almost any applications in our daily lives. A frequently encountered problem in printing, particular in consumer or lower-end applications, is how to print a workpiece whose dimensions exceed the working space of the printer at hand. Rather than opting for the costly solution of getting a new and larger printer (and hence more expensive and also other induced hassles), a more plausible solution would be an algorithmic one, which is the objective of this paper. We present a partition-based multi-setup printing process planning algorithm that, given the CAD model of an oversized workpiece, will be able to determine a continuous printing path with multiple workpiece setups to correctly print out the workpiece on the same printer. To answer the challenge of workpiece localization during the change of workpiece setups in the middle of printing, we propose a visual-guided workpiece localization system. Both computer simulation and physical printing experiments are carried out, and the results give a positive confirmation on the feasibility and effectiveness of the proposed multi-setup printing methodology and our enabling printing process planning algorithm.

Accurate and reliable optical remote sensing image-based small-ship detection is crucial for maritime surveillance systems, but existing methods often struggle with balancing detection performance and computational complexity. In this paper, we propose a novel lightweight framework called \textit{HSI-ShipDetectionNet} that is based on high-order spatial interactions and is suitable for deployment on resource-limited platforms, such as satellites and unmanned aerial vehicles. HSI-ShipDetectionNet includes a prediction branch specifically for tiny ships and a lightweight hybrid attention block for reduced complexity. Additionally, the use of a high-order spatial interactions module improves advanced feature understanding and modeling ability. Our model is evaluated using the public Kaggle marine ship detection dataset and compared with multiple state-of-the-art models including small object detection models, lightweight detection models, and ship detection models. The results show that HSI-ShipDetectionNet outperforms the other models in terms of recall, and mean average precision (mAP) while being lightweight and suitable for deployment on resource-limited platforms.

This comprehensive paper delves into the forefront of personalized voice synthesis within artificial intelligence (AI), spotlighting the Dynamic Individual Voice Synthesis Engine (DIVSE). DIVSE represents a groundbreaking leap in text-to-voice (TTS) technology, uniquely focusing on adapting and personalizing voice outputs to match individual vocal characteristics. The research underlines the gap in current AI-generated voices, which, while technically advanced, fall short in replicating the unique individuality and expressiveness intrinsic to human speech. It outlines the challenges and advancements in personalized voice synthesis, emphasizing the importance of emotional expressiveness, accent and dialect variability, and capturing individual voice traits. The architecture of DIVSE is meticulously detailed, showcasing its three core components: Voice Characteristic Learning Module (VCLM), Emotional Tone and Accent Adaptation Module (ETAAM), and Dynamic Speech Synthesis Engine (DSSE). The innovative approach of DIVSE lies in its adaptive learning capability, which evolves over time to tailor voice outputs to specific user traits. The paper presents a rigorous experimental setup, utilizing accepted datasets and personalization metrics like Mean Opinion Score (MOS) and Emotional Alignment Score, to validate DIVSE's superiority over mainstream models. The results depict a clear advancement in achieving higher personalization and emotional resonance in AI-generated voices., Comment: 8 pages

In this research, we introduce RefineNet, a novel architecture designed to address resolution limitations in text-to-image conversion systems. We explore the challenges of generating high-resolution images from textual descriptions, focusing on the trade-offs between detail accuracy and computational efficiency. RefineNet leverages a hierarchical Transformer combined with progressive and conditional refinement techniques, outperforming existing models in producing detailed and high-quality images. Through extensive experiments on diverse datasets, we demonstrate RefineNet's superiority in clarity and resolution, particularly in complex image categories like animals, plants, and human faces. Our work not only advances the field of image-to-text conversion but also opens new avenues for high-fidelity image generation in various applications., Comment: 8 pages

Transferring human motion skills to humanoid robots remains a significant challenge. In this study, we introduce a Wasserstein adversarial imitation learning system, allowing humanoid robots to replicate natural whole-body locomotion patterns and execute seamless transitions by mimicking human motions. First, we present a unified primitive-skeleton motion retargeting to mitigate morphological differences between arbitrary human demonstrators and humanoid robots. An adversarial critic component is integrated with Reinforcement Learning (RL) to guide the control policy to produce behaviors aligned with the data distribution of mixed reference motions. Additionally, we employ a specific Integral Probabilistic Metric (IPM), namely the Wasserstein-1 distance with a novel soft boundary constraint to stabilize the training process and prevent mode collapse. Our system is evaluated on a full-sized humanoid JAXON in the simulator. The resulting control policy demonstrates a wide range of locomotion patterns, including standing, push-recovery, squat walking, human-like straight-leg walking, and dynamic running. Notably, even in the absence of transition motions in the demonstration dataset, robots showcase an emerging ability to transit naturally between distinct locomotion patterns as desired speed changes.

The abstract visual reasoning ability in human intelligence benefits discovering underlying rules in the novel environment. Raven's Progressive Matrix (RPM) is a classic test to realize such ability in machine intelligence by selecting from candidates. Recent studies suggest that solving RPM in an answer-generation way boosts a more in-depth understanding of rules. However, existing generative solvers cannot discover the global concept-changing rules without auxiliary supervision (e.g., rule annotations and distractors in candidate sets). To this end, we propose a deep latent variable model for Concept-changing Rule ABstraction (CRAB) by learning interpretable concepts and parsing concept-changing rules in the latent space. With the iterative learning process, CRAB can automatically abstract global rules shared on the dataset on each concept and form the learnable prior knowledge of global rules. CRAB outperforms the baselines trained without auxiliary supervision in the arbitrary-position answer generation task and achieves comparable and even higher accuracy than the compared models trained with auxiliary supervision. Finally, we conduct experiments to illustrate the interpretability of CRAB in concept learning, answer selection, and global rule abstraction.

The audible fire alarm system of the building makes a sharp sound to alert all occupants when fires occur. According to the fire code, the fire alarm should be loud enough to be heard in any corner of the building. Thus, this work explores a smart technology of using alarm attenuation to reveal the fire scene information. Different alarms with frequencies from 500 to 2000 Hz are tested. The propanol pool fires of different sizes and shapes are selected as the detecting targets. Results show that the sound pressure attenuation by the fire plume is positively correlated with the fire scene heat release rate. The sound-pressure attenuation is also greater if the flame thickness is larger along the sound path. Hence, a sound-field fire monitoring model is proposed and verified by experiments using larger wood-crib fire and liquid-pool fire. This work provides a practical sound-based fire monitoring model and helps establish a scientific framework for the smart technology of using the existing audible alarm system to continuously monitor the building fire development. © 2022 Elsevier Ltd

Topological phononic crystals (PnCs) have attracted tremendous research attention in recent years. A significant hallmark of these structures is that these crystals can support interface modes that are robust to structural disturbance and protected by topology. In this study, we propose a new type of active sandwiched PnC beam for inducing topological geometric phase transition and topologically protected interface modes (TPIMs) for one-dimensional (1D) systems. The layered system comprises two commonly used active materials, i.e., barium titanate (BaTiO3) and cobalt ferric oxide (CoFe2O4). Analytical modeling for this layered system is derived in the framework of linearly constitutive relations of a magneto-electro-elastic (MEE) material with temperature effects. Two analytical approaches, i.e., the spectral element method (SEM) and the plane wave expansion (PWE) method, are applied to derive the theoretical band structure of the system and excellent agreement is reported. A numerical analysis based on the finite element method (FEM) is adopted for further validation. The influence of outer fields in the bandgap frequency is examined and the size-dependent properties are also analyzed. Moreover, the transmission response is determined via analytical modeling and numerical analysis. It is found that the robust TPIMs are immune to defects and disorders. Conclusively, this study puts forward a new type of beam system for inducing topological phase transition. It can be readily extended to more complex systems and higher-order models.

The audible fire alarm system of the building makes a sharp sound to alert all occupants when fires occur. According to the fire code, the fire alarm should be loud enough to be heard in any corner of the building. Thus, this work explores a smart technology of using alarm attenuation to reveal the fire scene information. Different alarms with frequencies from 500 to 2000 Hz are tested. The propanol pool fires of different sizes and shapes are selected as the detecting targets. Results show that the sound pressure attenuation by the fire plume is positively correlated with the fire scene heat release rate. The sound-pressure attenuation is also greater if the flame thickness is larger along the sound path. Hence, a sound-field fire monitoring model is proposed and verified by experiments using larger wood-crib fire and liquid-pool fire. This work provides a practical sound-based fire monitoring model and helps establish a scientific framework for the smart technology of using the existing audible alarm system to continuously monitor the building fire development. © 2022 Elsevier Ltd

We investigate the stochastic properties of typical red galaxy samples in a controlled numerical environment. We use halo occupation distribution (HOD) modelling to create mock realizations of three separate bright red galaxy samples consistent with data sets used for clustering and lensing analyses in modern galaxy surveys. Second-order Hybrid Effective Field Theory (HEFT) is used as a field-level forward model to describe the full statistical distribution of these tracer samples, and their stochastic power spectra are directly measured and compared to the Poisson shot-noise prediction. While all of the galaxy samples we consider are hosted within haloes with sub-Poisson stochasticity, we observe that the galaxy samples themselves possess stochasticities that range from sub-Poisson to super-Poisson, in agreement with predictions from the halo model. As an application of our methodology, we place priors on the expected degree of non-Poisson stochasticity in cosmological analyses using such samples. We expect these priors will be useful in reducing the complexity of the full parameter space for future analyses using second-order Lagrangian bias models. More generally, the techniques outlined here present the first application of HEFT methods to characterize models of the galaxy–halo connection at the field level, revealing new connections between once-disparate modelling frameworks.

The optical depth $\tau$ is the least well determined parameter in the standard model of cosmology, and one whose precise value is important for both understanding reionization and for inferring fundamental physics from cosmological measurements. We forecast how well future epoch of reionization experiments could constraint $\tau$ using a symmetries-based bias expansion that highlights the special role played by anisotropies in the power spectrum on large scales. Given a parametric model for the ionization evolution inspired by the physical behavior of more detailed reionization simulations, we find that future 21cm experiments could place tight constraints on the timing and duration of reionization and hence constraints on $\tau$ that are competitive with proposed, space-based CMB missions provided they can measure $k\approx 0.1\,h\,\text{Mpc}^{-1}$ with a clean foreground wedge across redshifts spanning the most active periods of reionization, corresponding to ionization fractions $0.2 \lesssim x \lesssim 0.8$. Significantly improving upon existing CMB-based measurements with next-generation 21cm surveys would require substantially longer observations ($\sim5$ years) than standard $\mathcal{O}(1000 \,\,\text{hour})$ integration times. Precise measurements of smaller scales will not improve constraints on $\tau$ until a better understanding of the astrophysics of reionization is achieved. In the presence of noise and foregrounds even future 21cm experiments will struggle to constrain $\tau$ if the ionization evolution deviates significantly from simple parametric forms.

Simulations have become an indispensable tool for accurate modelling of observables measured in galaxy surveys, but can be expensive if very large dynamic range in scale is required. We describe how to combine Lagrangian perturbation theory models with N-body simulations to reduce the effects of finite computational volume in the prediction of ensemble average properties in the simulations within the context of control variates. In particular we use the fact that Zel'dovich displacements, computed during initial condition generation for any simulation, correlate strongly with the final density field. Since all the correlators of biased tracers can be computed with arbitrary precision for these displacements, pairing the Zel'dovich `simulation' with the N-body realization allows hundredfold reductions in sample variance for power spectrum or correlation function estimation. Zel'dovich control variates can accurately extend matter or tracer field emulators to larger scales than previously possible, as well as improving measurements of statistics in simulations which are inherently limited to small volumes, such as hydrodynamical simulations of galaxy formation and reionization.

Human cognition has compositionality. We understand a scene by decomposing the scene into different concepts (e.g., shape and position of an object) and learning the respective laws of these concepts, which may be either natural (e.g., laws of motion) or man-made (e.g., laws of a game). The automatic parsing of these laws indicates the model's ability to understand the scene, which makes law parsing play a central role in many visual tasks. This paper proposes a deep latent variable model for Compositional LAw Parsing (CLAP), which achieves the human-like compositionality ability through an encoding-decoding architecture to represent concepts in the scene as latent variables. CLAP employs concept-specific latent random functions instantiated with Neural Processes to capture the law of concepts. Our experimental results demonstrate that CLAP outperforms the baseline methods in multiple visual tasks such as intuitive physics, abstract visual reasoning, and scene representation. The law manipulation experiments illustrate CLAP's interpretability by modifying specific latent random functions on samples. For example, CLAP learns the laws of position-changing and appearance constancy from the moving balls in a scene, making it possible to exchange laws between samples or compose existing laws into novel laws.

The paper presents a stochastic approach to generate randomly rough surfaces with a prescribed mean angular intensity distribution for elastic wave scattering. The analytical and numerical technique is implemented for a linearly elastic medium-vacuum interface, to generate band-limited uniform diffusers which may have applications for wave manipulation and ultrasonic non-destructive evaluation. Longitudinal incidence is assumed, and the theory allows for bulk wave mode conversions, with the surfaces designed to produce prescribed intensity distributions for both reflected compression wave modes, as well as the mode-converted vertically polarized shear wave modes. The angular distribution of these two cases is shown to be significantly different; additional, more exotic intensity distributions are also demonstrated to highlight the versatility of the model.

Wind farms are usually located at high latitudes, leading to an increased risk of blade icing. Data-driven approaches offer promising solutions for blade icing detection but rely on a considerable amount of data. Data exchange between several wind farms would improve the performance of detection models, due to spatio-temporal dependencies capable of reflecting different weather conditions. However, due to commercial competition, data owners may be reluctant to share their data. To address the privacy issue, this research proposes a heterogeneous federated learning (FL) model. Unlike traditional FL, the structures of the server and client models are different and a privacy-preserving approach is incorporated to solve the class-imbalance problem in the sensor data. Using real data from 20 turbines at two wind farms, this model was evaluated and compared to two state-of-the-art FL models and five well-known class-imbalance methods. The experiment results verify the effectiveness and superiority of the proposed model.

Wind energy is of great importance for future energy development. In order to fully exploit wind energy, wind farms are often located at high latitudes, a practice that is accompanied by a high risk of icing. Traditional blade icing detection methods are usually based on manual inspection or external sensors/tools, but these techniques are limited by human expertise and additional costs. Model-based methods are highly dependent on prior domain knowledge and prone to misinterpretation. Data-driven approaches can offer promising solutions but require a massive amount of labeled training data, which are not generally available. In addition, the data collected for icing detection tend to be imbalanced because, most of the time, wind turbines operate under normal conditions. To address these challenges, this article presents a novel deep class-imbalanced semisupervised (DCISS) model for estimating blade icing conditions. DCISS integrates class-imbalanced and semisupervised learning (SSL) using a prototypical network that can rebalance features and measure the similarities between labeled and unlabeled samples. In addition, a channel calibration attention module is proposed to improve the ability to extract features from raw data. The proposed model has been evaluated using the blade icing datasets of three wind turbines. Compared to the classical anomaly detection and state-of-the-art SSL algorithms, DCISS shows significant advantages in terms of accuracy. Compared to five different class-imbalanced loss functions, the proposed DCISS is competitive. The generalization and practicability of the proposed model are further verified in the use case of online estimation.

Wind farms are often located at high latitudes, which entails a high risk of icing for wind turbine blades. Traditional anti-icing methods rely primarily on manual observation, the use of special materials, or external sensors/tools, but these methods are limited by human experience, additional costs, and understanding of the mechanical mechanism. Model-based approaches rely heavily on prior knowledge and are subject to misinterpretation. Data-driven approaches can deliver promising solutions but require large datasets for training, which might face significant challenges with respect to data management, e.g., privacy protection and ownership. To address these issues, this paper proposes a federated learning (FL) based model for blade icing detection. The proposed approach first creates a prototype-based model for each client and then aggregates all client models into a globally weighted model. The clients use a prototype-based modeling method to address the data imbalance problem, while using the FL-based learning method to ensure data security and safety. The proposed model is comprehensively evaluated using data from two wind farms, with 70 wind turbines. The results validate the effectiveness of the proposed prototype-based client model for feature extraction, and the superiority over the five baselines in terms of icing detection accuracy. In addition, the experiment demonstrates the promising result of online blade icing detection, with almost 100% accuracy.

Numerical simulations in cosmology require trade-offs between volume, resolution and run-time that limit the volume of the Universe that can be simulated, leading to sample variance in predictions of ensemble-average quantities such as the power spectrum or correlation function(s). Sample variance is particularly acute at large scales, which is also where analytic techniques can be highly reliable. This provides an opportunity to combine analytic and numerical techniques in a principled way to improve the dynamic range and reliability of predictions for clustering statistics. In this paper we extend the technique of Zel'dovich control variates, previously demonstrated for 2-point functions in real space, to reduce the sample variance in measurements of 2-point statistics of biased tracers in redshift space. We demonstrate that with this technique, we can reduce the sample variance of these statistics down to their shot-noise limit out to $k \sim 0.2\, h\rm Mpc^{-1}$. This allows a better matching with perturbative models and improved predictions for the clustering of e.g.~quasars, galaxies and neutral Hydrogen measured in spectroscopic redshift surveys at very modest computational expense. We discuss the implementation of ZCV, give some examples and provide forecasts for the efficacy of the method under various conditions., Comment: 17 pages main text, 5 pages of appendices, 9 figures

Simulations have become an indispensable tool for accurate modelling of observables measured in galaxy surveys, but can be expensive if very large dynamic range in scale is required. We describe how to combine Lagrangian perturbation theory models with N-body simulations to reduce the effects of finite computational volume in the prediction of ensemble average properties in the simulations within the context of control variates. In particular we use the fact that Zel'dovich displacements, computed during initial condition generation for any simulation, correlate strongly with the final density field. Since all the correlators of biased tracers can be computed with arbitrary precision for these displacements, pairing the Zel'dovich `simulation' with the N-body realization allows hundredfold reductions in sample variance for power spectrum or correlation function estimation. Zel'dovich control variates can accurately extend matter or tracer field emulators to larger scales than previously possible, as well as improving measurements of statistics in simulations which are inherently limited to small volumes, such as hydrodynamical simulations of galaxy formation and reionization., Comment: Updated to reflect published version

The optical depth $\tau$ is the least well determined parameter in the standard model of cosmology, and one whose precise value is important for both understanding reionization and for inferring fundamental physics from cosmological measurements. We forecast how well future epoch of reionization experiments could constraint $\tau$ using a symmetries-based bias expansion that highlights the special role played by anisotropies in the power spectrum on large scales. Given a parametric model for the ionization evolution inspired by the physical behavior of more detailed reionization simulations, we find that future 21cm experiments could place tight constraints on the timing and duration of reionization and hence constraints on $\tau$ that are competitive with proposed, space-based CMB missions provided they can measure $k\approx 0.1\,h\,\text{Mpc}^{-1}$ with a clean foreground wedge across redshifts spanning the most active periods of reionization, corresponding to ionization fractions $0.2 \lesssim x \lesssim 0.8$. Significantly improving upon existing CMB-based measurements with next-generation 21cm surveys would require substantially longer observations ($\sim5$ years) than standard $\mathcal{O}(1000 \,\,\text{hour})$ integration times. Precise measurements of smaller scales will not improve constraints on $\tau$ until a better understanding of the astrophysics of reionization is achieved. In the presence of noise and foregrounds even future 21cm experiments will struggle to constrain $\tau$ if the ionization evolution deviates significantly from simple parametric forms., Comment: published in JCAP

We present a formalism for jointly fitting pre- and post-reconstruction redshift-space clustering (RSD) and baryon acoustic oscillations (BAO) plus gravitational lensing (of the CMB) that works directly with the observed 2-point statistics. The formalism is based upon (effective) Lagrangian perturbation theory and a Lagrangian bias expansion, which models RSD, BAO and galaxy-lensing cross correlations within a consistent dynamical framework. As an example we present an analysis of clustering measured by the Baryon Oscillation Spectroscopic Survey in combination with CMB lensing measured by Planck. The post-reconstruction BAO strongly constrains the distance-redshift relation, the full-shape redshift-space clustering constrains the matter density and growth rate, and CMB lensing constrains the clustering amplitude. Using only the redshift space data we obtain $\Omega_\mathrm{m} = 0.303\pm 0.008$, $H_0 = 69.21\pm 0.78$ and $\sigma_8 = 0.743\pm 0.043$. The addition of lensing information, even when restricted to the Northern Galactic Cap, improves constraints to $\Omega_m = 0.300 \pm 0.008$, $H_0 = 69.21 \pm 0.77$ and $\sigma_8 = 0.707 \pm 0.035$, in tension with CMB and cosmic shear constraints. The combination of $\Omega_m$ and $H_0$ are consistent with Planck, though their constraints derive mostly from redshift-space clustering. The low $\sigma_8$ value are driven by cross correlations with CMB lensing in the low redshift bin ($z\simeq 0.38$) and at large angular scales, which show a $20\%$ deficit compared to expectations from galaxy clustering alone. We conduct several systematics tests on the data and find none that could fully explain these tensions., Comment: 46 pages, 15 figures, updated to match version accepted by JCAP

Lamb waves have infinite number dispersion modes; however, no every mode is excitable and detectable. Traditional matrix methods can calculate the dispersion curve of each mode over the full range of possible frequencies. However, the resulting numerically calculated multimodal dispersion curves do not fully represent the dispersion curves measured in real experiments, which are most often dominated by energy from specific modes. An improved dynamic stiffness matrix method is proposed herein to overcome such challenges of the traditional matrix methods. The first step of the improved method is to calculate the displacement response of a plate-like structure under a vertical dynamic loading using the global stiffness matrix of the structure, then the dominant dispersion trend is extracted from the displacement using the phase-velocity scanning scheme. The improved method is verified with three case studies representing typical plate-like structures in structural engineering. The results demonstrate that dispersion trends calculated with the improved method have good agreement with those obtained from experimental measurements. © 2022 John Wiley & Sons Ltd.

The audible fire alarm system of the building makes a sharp sound to alert all occupants when fires occur. According to the fire code, the fire alarm should be loud enough to be heard in any corner of the building. Thus, this work explores a smart technology of using alarm attenuation to reveal the fire scene information. Different alarms with frequencies from 500 to 2000 Hz are tested. The propanol pool fires of different sizes and shapes are selected as the detecting targets. Results show that the sound pressure attenuation by the fire plume is positively correlated with the fire scene heat release rate. The sound-pressure attenuation is also greater if the flame thickness is larger along the sound path. Hence, a sound-field fire monitoring model is proposed and verified by experiments using larger wood-crib fire and liquid-pool fire. This work provides a practical sound-based fire monitoring model and helps establish a scientific framework for the smart technology of using the existing audible alarm system to continuously monitor the building fire development. © 2022 Elsevier Ltd

We investigate robust large-area elastic transverse wave propagation in an actively tunable membrane-type acoustic metamaterial. The waveguide with multiple degrees of freedom to control the width of the interface mode is realized by designing a heterostructure including three domains. One central domain is constructed by periodic unit cells in an ordinary state, where a Dirac cone can be observed in the band structure. The other two domains consist of periodic unit cells possessing opposite valley Chern numbers, respectively. By employing a finite element model, the topologically protected interface states with tunable degrees of freedom are exhibited. The energy of interface states distributes equally in the large-central region. Although a larger degree of freedom leads to lower amplitudes of interface states, larger total energy is demonstrated by defining a quality factor. Moreover, we design several waveguides with straight lines and sharp corners with different angles and denote three different notations to show clearly that the large-area transverse wave can propagate robustly through sharp corners. Finally, it is found that the large-area transverse wave transport shows immunity to disorders and defects in the propagation path.

Topological phononic crystals (PnCs) have attracted tremendous research attention in recent years. A significant hallmark of these structures is that these crystals can support interface modes that are robust to structural disturbance and protected by topology. In this study, we propose a new type of active sandwiched PnC beam for inducing topological geometric phase transition and topologically protected interface modes (TPIMs) for one-dimensional (1D) systems. The layered system comprises two commonly used active materials, i.e., barium titanate (BaTiO3) and cobalt ferric oxide (CoFe2O4). Analytical modeling for this layered system is derived in the framework of linearly constitutive relations of a magneto-electro-elastic (MEE) material with temperature effects. Two analytical approaches, i.e., the spectral element method (SEM) and the plane wave expansion (PWE) method, are applied to derive the theoretical band structure of the system and excellent agreement is reported. A numerical analysis based on the finite element method (FEM) is adopted for further validation. The influence of outer fields in the bandgap frequency is examined and the size-dependent properties are also analyzed. Moreover, the transmission response is determined via analytical modeling and numerical analysis. It is found that the robust TPIMs are immune to defects and disorders. Conclusively, this study puts forward a new type of beam system for inducing topological phase transition. It can be readily extended to more complex systems and higher-order models.

A hybrid geometrical full waveform inversion (GFWI) method is presented in this article for accurate ultrasonic defect characterisation using a phased array. The proposed method combines two steps: (1) A newly developed multi-mode topological energy method (TEM) is used to provide a high-resolution image for defect classification and a good initial defect model for the second step. (2) A GFWI procedure is executed to reconstruct the defect shape through iteratively reducing the mismatch in waveforms between the measurements and synthetic data from the defect model. The GFWI procedure enables direct inversion of the defect shape by deforming the elements attached at its boundaries from finite element formulation, controlled by the preconditioned boundary gradient. To suppress the cross-talk among different geometrical parameters with distinct units, the BFGS and the subspace methods are implemented together with GFWI, which enables reconstructing defects of various shapes by taking all the wave physics into the inversion automatically. Numerical examples are shown with a volumetric defect, a straight crack, a curved crack and a surface-breaking notch, giving accuracy of defect sizing within fractions of a wavelength. Two experiments using an ultrasonic phased array system further demonstrate the feasibility of this method. © 2022 Elsevier Ltd