Passive acoustic monitoring(PAM)technology is increasingly becoming one of the mainstream methods for bird monitoring.However,detecting bird audio within complex natural acoustic environments using PAM devices remains...Passive acoustic monitoring(PAM)technology is increasingly becoming one of the mainstream methods for bird monitoring.However,detecting bird audio within complex natural acoustic environments using PAM devices remains a significant challenge.To enhance the accuracy(ACC)of bird audio detection(BAD)and reduce both false negatives and false positives,this study proposes a BAD method based on a Dual-Feature Enhancement Fusion Model(DFEFM).This method incorporates per-channel energy normalization(PCEN)to suppress noise in the input audio and utilizes mel-frequency cepstral coefficients(MFCC)and frequency correlation matrices(FCM)as input features.It achieves deep feature-level fusion of MFCC and FCM on the channel dimension through two independent multi-layer convolutional network branches,and further integrates Spatial and Channel Synergistic Attention(SCSA)and Multi-Head Attention(MHA)modules to enhance the fusion effect of the aforementioned two deep features.Experimental results on the DCASE2018 BAD dataset show that our proposed method achieved an ACC of 91.4%and an AUC value of 0.963,with false negative and false positive rates of 11.36%and 7.40%,respectively,surpassing existing methods.The method also demonstrated detection ACC above 92%and AUC values above 0.987 on datasets from three sites of different natural scenes in Beijing.Testing on the NVIDIA Jetson Nano indicated that the method achieved an ACC of 89.48%when processing an average of 10 s of audio,with a response time of only 0.557 s,showing excellent processing efficiency.This study provides an effective method for filtering non-bird vocalization audio in bird vocalization monitoring devices,which helps to save edge storage and information transmission costs,and has significant application value for wild bird monitoring and ecological research.展开更多
Although eye problems can occur at any age, they are often common from the age of 40. Eye diseases with a prevalence associated with age and aging will continue to increase in the coming years. Most studies conducted ...Although eye problems can occur at any age, they are often common from the age of 40. Eye diseases with a prevalence associated with age and aging will continue to increase in the coming years. Most studies conducted on problems in middle-aged people have focused on visual disorders without taking into account all the ocular morbidities that may affect this segment of the population, hence the present study, the aim of which is to determine the proportions of different eye diseases in people aged 40 and over. Materials and Methods: This was a descriptive cross-sectional study carried out in the ophthalmology department covering the period from January 1 to December 31, 2020. Results: In total, we collected 828 patients aged 40 and over out of 1811 patients who received ophthalmological consultation during the study period, representing 45.72%. The most represented age group was 40 - 50 years, with an average age of 58.84 years and a maximum of 93 years. There were slightly more women (62.3%) than men (37.7%). The main reasons for consultation were decreased visual acuity (26.4%) and pruritus (19.9%). The main eye diseases diagnosed were cataracts (23%), allergic conjunctivitis (21.1%), and bacterial conjunctivitis (14.2%). Discussions: The predominance of cataracts in the diagnosed diseases confirms the literature data, according to which the main eye morbidities in middle-aged and elderly people are cataracts, glaucoma, and age-related macular degeneration. Conclusion: It is crucial to have a mastery of these epidemiological data of eye diseases in order to adapt the technical platforms of eye care structures to the needs of different segments of the population.展开更多
We report a robust pillar-layered metal-organic framework,Zn‑tfbdc‑dabco(tfbdc:tetrafluoroterephthal-ate,dabco:1,4-diazabicyclo[2.2.2]octane),featuring the fluorinated pore environment,for the preferential binding of ...We report a robust pillar-layered metal-organic framework,Zn‑tfbdc‑dabco(tfbdc:tetrafluoroterephthal-ate,dabco:1,4-diazabicyclo[2.2.2]octane),featuring the fluorinated pore environment,for the preferential binding of propane over propylene and thus highly inverse selective separation of propane/propylene mixture.The inverse propane-selective performance of Zn‑tfbdc‑dabco for the propane/propylene separation was validated by single-component gas adsorption isotherms,isosteric enthalpy of adsorption calculations,ideal adsorbed solution theory calculations,along with the breakthrough experiment.The customized fluorinated networks served as a propane-trap to form more interactions with the exposed hydrogen atoms of propane,as unveiled by the simulation studies at the molecular level.With the advantage of inverse propane-selective adsorption behavior,high adsorption capacity,good cycling stability,and low isosteric enthalpy of adsorption,Zn‑tfbdc‑dabco can be a promising candidate adsorbent for the challenging propane/propylene separation to realize one-step purification of the target propylene substance.展开更多
In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by re...In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.展开更多
The frequency regulation reserve setting of wind-PV-storage power stations is crucial.However,the existing grid codes set up the station reserve in a static manner,where the synchronous generator characteristics and f...The frequency regulation reserve setting of wind-PV-storage power stations is crucial.However,the existing grid codes set up the station reserve in a static manner,where the synchronous generator characteristics and frequency-step disturbance scenario are considered.Thus,the advantages of flexible regulation of renewable generations are wasted,resulting in excessive curtailment of wind and solar resources.In this study,a method for optimizing the frequency regulation reserve of wind PV storage power stations was developed.Moreover,a station frequency regulation model was constructed,considering the field dynamic response and the coupling between the station and system frequency dynamics.Furthermore,a method for the online evaluation of the station frequency regulation was proposed based on the benchmark governor fitting.This method helps in overcoming the capacity-based reserve static setting.Finally,an optimization model was developed,along with the proposal of the linearized solving algorithm.The field data from the JH4#station in China’s MX power grid was considered for validation.The proposed method achieves a 24.77%increase in the station income while ensuring the system frequency stability when compared with the grid code-based method.展开更多
The discovery of advanced materials is a cornerstone of human technological development and progress.The structures of materials and their corresponding properties are essentially the result of a complex interplay of ...The discovery of advanced materials is a cornerstone of human technological development and progress.The structures of materials and their corresponding properties are essentially the result of a complex interplay of multiple degrees of freedom such as lattice,charge,spin,symmetry,and topology.This poses significant challenges for the inverse design methods of materials.Humans have long explored new materials through numerous experiments and proposed corresponding theoretical systems to predict new material properties and structures.With the improvement of computational power,researchers have gradually developed various electronic-structure calculation methods,such as the density functional theory and high-throughput computational methods.Recently,the rapid development of artificial intelligence(AI)technology in computer science has enabled the effective characterization of the implicit association between material properties and structures,thus forming an efficient paradigm for the inverse design of functional materials.Significant progress has been achieved in the inverse design of materials based on generative and discriminative models,attracting widespread interest from researchers.Considering this rapid technological progress,in this survey,we examine the latest advancements in AI-driven inverse design of materials by introducing the background,key findings,and mainstream technological development routes.In addition,we summarize the remaining challenges for future directions.This survey provides the latest overview of AI-driven inverse design of materials,which can serve as a useful resource for researchers.展开更多
As the development of new power systems progresses,the inherent inertia of power systems continues to diminish.Centralized frequency regulation,which relies on rapid communication and real-time control,can enable inve...As the development of new power systems progresses,the inherent inertia of power systems continues to diminish.Centralized frequency regulation,which relies on rapid communication and real-time control,can enable inverter-based thermostatically controlled load(ITCL)clusters to provide virtual inertia support to the power grid.However,ITCL clusters exhibit significant discrete response characteristics,which precludes the direct integration of load-side inertia support into the synchronous unit side.To address this issue,this paper elaborates on the existing technical framework and analyzes the underlying causes of the problem.It proposes a timestamp allocation mechanism for ITCL cluster control instructions,ensuring that many ITCL terminals can be triggered at staggered times,thereby allowing the load cluster power to adhere to the inertia analog control law at any moment.Building on this foundation,the paper further examines the impact of the inertia response delay of ITCL clusters,which is based on centralized frequency regulation,on the stability of the power system.A design scheme for inertia analog control parameters is proposed,taking into account dual constraints,frequency stability and load cluster regulation capacity.Finally,the feasibility and applicability of the proposed mechanism and parameter design scheme are investigated through simulations conducted via MATLAB/Simulink.展开更多
Reverse design of highly GeO2-doped silica optical fibers with broadband and flat dispersion profiles is proposed using a neural network(NN) combined with a particle swarm optimization(PSO) algorithm.Firstly,the NN mo...Reverse design of highly GeO2-doped silica optical fibers with broadband and flat dispersion profiles is proposed using a neural network(NN) combined with a particle swarm optimization(PSO) algorithm.Firstly,the NN model designed to predict optical fiber dispersion is trained with an appropriate choice of hyperparameters,achieving a root mean square error(RMSE) of 9.47×10-7on the test dataset,with a determination coefficient(R2) of 0.999.Secondly,the NN is combined with the PSO algorithm for the inverse design of dispersion-flattened optical fibers.To expand the search space and avoid particles becoming trapped in local optimal solutions,the PSO algorithm incorporates adaptive inertia weight updating and a simulated annealing algorithm.Finally,by using a suitable fitness function,the designed fibers exhibit flat group velocity dispersion(GVD) profiles at 1 400—2 400 nm,where the GVD fluctuations and minimum absolute GVD values are below 18 ps·nm-1·km-1and 7 ps·nm-1·km-1,respectively.展开更多
Terahertz(THz)metamaterials,with their exceptional ability to precisely manipulate the phase,amplitude,polarization and orbital angular momentum(OAM)of electromagnetic waves,have demonstrated significant application p...Terahertz(THz)metamaterials,with their exceptional ability to precisely manipulate the phase,amplitude,polarization and orbital angular momentum(OAM)of electromagnetic waves,have demonstrated significant application potential across a wide range of fields.However,traditional design methodologies often rely on extensive parameter sweeps,making it challenging to address the increasingly complex and diverse application requirements.Recently,the integration of artificial intelligence(AI)techniques,particularly deep learning and optimization algorithms,has introduced new approaches for the design of THz metamaterials.This paper reviews the fundamental principles of THz metamaterials and their intelligent design methodologies,with a particular focus on the advancements in AI-driven inverse design of THz metamaterials.The AI-driven inverse design process allows for the creation of THz metamaterials with desired properties by working backward from the unit structures and array configurations of THz metamaterials,thereby accelerating the design process and reducing both computational resources and time.It examines the critical role of AI in improving both the functionality and design efficiency of THz metamaterials.Finally,we outline future research directions and technological challenges,with the goal of providing valuable insights and guidance for ongoing and future investigations.展开更多
We introduce and study a new kind of generalized inverses named w-(b,c)-core inverses,which is a generalization of the(b,c)-core inverse.An example is given to show that w-(b,c)-core inverses need not be(b,c)-core inv...We introduce and study a new kind of generalized inverses named w-(b,c)-core inverses,which is a generalization of the(b,c)-core inverse.An example is given to show that w-(b,c)-core inverses need not be(b,c)-core inverses.In addition,the dual version of the w-(b,c)-core inverse is studied.Some results on(b,c)-core inverses and e-(b,c)-core inverses are unifed and generalized.展开更多
Flowfield inverse design can obtain the desired flow and contour with high design efficiency,short design cycle,and small modification need.In this study,the Euler equations are formulated in the stream-function coord...Flowfield inverse design can obtain the desired flow and contour with high design efficiency,short design cycle,and small modification need.In this study,the Euler equations are formulated in the stream-function coordinates and combined with the given boundary conditions to derive a gridless space-marching method for the inverse design of subsonic,transonic,and supersonic flowfields.Designers can prescribe the flow parameters along the reference streamline to design flowfields and aerodynamic contours.The method is validated by the theoretical transonic solution,computational fluid dynamics,and experimental data,respectively.The method supports the fabrication of a Mach 2.0 single expansion tunnel.The calibration data agree well with the prescribed pressure distribution.The method is successfully applied to inverse design of contractions,nozzles,and asymmetric channels.Compared to classical analytic contractions,the contractions designed by the space-marching method provide a more accurate transonic flow.Compared to the classical Sivells’nozzle,the nozzle designed by the space-marching method provides a smaller workload,a more flexible velocity distribution,a 20%reduction in length,and an equally uniform flow.Additionally,the space-marching method is applied to design the asymmetric channels under various Mach numbers.These asymmetric channels perfectly eliminate Mach waves,achieving the shock-free flow turning and high flow uniformity.These results validate the feasibility of the space-marching method,making it a good candidate for the inverse design of subsonic,transonic,and supersonic internal flowfields and aerodynamic contours.展开更多
The integrated waveguide polarizer is essential for photonic integrated circuits,and various designs of waveguide polarizers have been developed.As the demand for dense photonic integration increases rapidly,new strat...The integrated waveguide polarizer is essential for photonic integrated circuits,and various designs of waveguide polarizers have been developed.As the demand for dense photonic integration increases rapidly,new strategies to minimize the device size are needed.In this paper,we have inversely designed an integrated transverse electric pass(TE-pass)polarizer with a footprint of 2.88μm×2.88μm,which is the smallest footprint ever achieved.A direct binary search algorithm is used to inversely design the device for maximizing the transverse electric(TE)transmission while minimizing transverse magnetic(TM)transmission.Finally,the inverse-designed device provides an average insertion loss of 0.99 dB and an average extinction ratio of 33 dB over a wavelength range of 100 nm.展开更多
A power splitter with a wideband arbitrary splitting ratio,which provides flexibility and adaptability in forming photonic devices such as microring resonators and Mach–Zehnder interferometers,proves to be essential ...A power splitter with a wideband arbitrary splitting ratio,which provides flexibility and adaptability in forming photonic devices such as microring resonators and Mach–Zehnder interferometers,proves to be essential in photonic integrated circuits(PICs).We designed and fabricated a directional coupler-based power splitter with a wideband arbitrary splitting ratio and a microring resonator with a wideband uniform extinction ratio(ER)based on artificial gauge field(AGF)optimization.The neural network-aided inverse design method is applied to complete the target.Less than 0.9 dB power splitting variation and 1.6 dB ER variation have been achieved experimentally over a 100-nm bandwidth.Wideband performance,design efficiency,and device compactness are obtained by utilizing this optimization,which indicates great potential and universality in PIC applications.展开更多
In this paper,a linear optimization method(LOM)for the design of terahertz circuits is presented,aimed at enhancing the simulation efficacy and reducing the time of the circuit design workflow.This method enables the ...In this paper,a linear optimization method(LOM)for the design of terahertz circuits is presented,aimed at enhancing the simulation efficacy and reducing the time of the circuit design workflow.This method enables the rapid determination of optimal embedding impedance for diodes across a specific bandwidth to achieve maximum efficiency through harmonic balance simulations.By optimizing the linear matching circuit with the optimal embedding impedance,the method effectively segregates the simulation of the linear segments from the nonlinear segments in the frequency multiplier circuit,substantially improving the speed of simulations.The design of on-chip linear matching circuits adopts a modular circuit design strategy,incorporating fixed load resistors to simplify the matching challenge.Utilizing this approach,a 340 GHz frequency doubler was developed and measured.The results demonstrate that,across a bandwidth of 330 GHz to 342 GHz,the efficiency of the doubler remains above 10%,with an input power ranging from 98 mW to 141mW and an output power exceeding 13 mW.Notably,at an input power of 141 mW,a peak output power of 21.8 mW was achieved at 334 GHz,corresponding to an efficiency of 15.8%.展开更多
Chiral metamaterials are manmade structures with extraordinary mechanical properties derived from their special geometric design instead of chemical composition.To make the mechanical deformation programmable,the non-...Chiral metamaterials are manmade structures with extraordinary mechanical properties derived from their special geometric design instead of chemical composition.To make the mechanical deformation programmable,the non-uniform rational B-spline(NURBS)curves are taken to replace the traditional ligament boundaries of the chiral structure.The Neural networks are innovatively inserted into the calculation of mechanical properties of the chiral structure instead of finite element methods to improve computational efficiency.For the problem of finding structure configuration with specified mechanical properties,such as Young’s modulus,Poisson’s ratio or deformation,an inverse design method using the Neural network-based proxy model is proposed to build the relationship between mechanical properties and geometric configuration.To satisfy some more complex deformation requirements,a non-homogeneous inverse design method is proposed and verified through simulation and experiments.Numerical and test results reveal the high computational efficiency and accuracy of the proposed method in the design of chiral metamaterials.展开更多
To explore the electrostatic discharge behavior of charged powders in industrial silos,discharge experiments are conducted based on a full-size industrial silo discharge platform.Electrostatic discharge mode,frequency...To explore the electrostatic discharge behavior of charged powders in industrial silos,discharge experiments are conducted based on a full-size industrial silo discharge platform.Electrostatic discharge mode,frequency,and energy are investigated for powders of different polarities.Although the powders have low charge-to-mass ratios(+0.087μC/kg for the positively charged powders and−0.26μC/kg for the negatively charged ones),electrostatic discharges occur approximately every 10 s,with the maximum discharge energy being 800 mJ.Powder polarity considerably influences discharge energy.The positive powders exhibit higher discharge energy than the negative ones,although discharge frequency remains similar for both.Effects of powder charge,humidity,and mass flow on discharge frequency and discharge energy are quantitatively analyzed,providing important insights for the improvement of safety in industrial powder handling.展开更多
Delineation of hydrocarbon-bearing sands and the extent of accumulation using seismic data is a reoccurring challenge for many fields.This study addressed the existing challenges of delineating a known hydrocarbon reg...Delineation of hydrocarbon-bearing sands and the extent of accumulation using seismic data is a reoccurring challenge for many fields.This study addressed the existing challenges of delineating a known hydrocarbon region for a thin-pay reservoir using conventional attributes extraction methods.The efficacy of applying iso-frequency extraction and spectral frequency blending in identifying thin-pay and thick-pay reservoirs on seismic was tested by utilizing 3D seismic data and well logs data of Terra field in the Western Niger Delta Basin.Well tops of all the reservoirs in the field were picked and two reservoirs that correspond to thin-and thick-pay reservoirs,namely A and F were identified respectively.The gross pay thickness of reservoir A is 18 ft while that of reservoir F is 96 ft.Conventional attribute extraction such as RMS amplitude,minimum amplitude,and average energy can be used to identify the hydrocarbon-bearing region in reservoir F but was not applicable for identifying the thin-pay reservoir A.This prompted the interest of using iso-frequency extractions and spectral frequency blending of three iso-frequency cubes of 12 Hz,30 Hz,and 70 Hz to get a spectral frequency RGB cube.The 12 Hz isofrequency can be used to partially identify hydrocarbon-bearing region in reservoir A while the 30Hz iso-frequency can be used to partially identify hydrocarbon-bearing region in reservoir F.The results show that time slices from the spectral frequency blended cube were able to delineate both the thin-pay and thick-pay hydrocarbon-bearing regions as high amplitude.The extractions also conformed to the structure of the two reservoirs.However,there seems to be a color difference in the amplitude display for both reservoirs.The thick-pay reservoir showed a red color on the time slice while the thin-pay reservoir showed a green color.This study has shown that spectral frequency blending is a more effective tool than conventional attributes extractions in identifying hydrocarbon-bearing region using seismic data.The methodology utilized in this study can be applied to other fields with similar challenges and for identifying prospective hydrocarbon bearing areas.展开更多
Recently,many Sequential Recommendation methods adopt self-attention mechanisms to model user preferences.However,these methods tend to focus more on low-frequency information while neglecting highfrequency informatio...Recently,many Sequential Recommendation methods adopt self-attention mechanisms to model user preferences.However,these methods tend to focus more on low-frequency information while neglecting highfrequency information,which makes them ineffective in balancing users’long-and short-term preferences.At the same time,manymethods overlook the potential of frequency domainmethods,ignoring their efficiency in processing frequency information.To overcome this limitation,we shift the focus to the combination of time and frequency domains and propose a novel Hybrid Time-Frequency Dual-Branch Transformer for Sequential Recommendation,namely HyTiFRec.Specifically,we design two hybrid filter modules:the learnable hybrid filter(LHF)and the window hybrid filter(WHF).We combine these with the Efficient Attention(EA)module to form the dual-branch structure to replace the self-attention components in Transformers.The EAmodule is used to extract sequential and global information.The LHF andWHF modules balance the proportion of different frequency bands,with LHF globally modulating the spectrum in the frequency domain and WHF retaining frequency components within specific local frequency bands.Furthermore,we use a time domain residual information addition operation in the hybrid filter module,which reduces information loss and further facilitates the hybrid of time-frequency methods.Extensive experiments on five widely-used real-world datasets show that our proposed method surpasses state-of-the-art methods.展开更多
To capture the nonlinear dynamics and gain evolution in chirped pulse amplification(CPA)systems,the split-step Fourier method and the fourth-order Runge–Kutta method are integrated to iteratively address the generali...To capture the nonlinear dynamics and gain evolution in chirped pulse amplification(CPA)systems,the split-step Fourier method and the fourth-order Runge–Kutta method are integrated to iteratively address the generalized nonlinear Schrödinger equation and the rate equations.However,this approach is burdened by substantial computational demands,resulting in significant time expenditures.In the context of intelligent laser optimization and inverse design,the necessity for numerous simulations further exacerbates this issue,highlighting the need for fast and accurate simulation methodologies.Here,we introduce an end-to-end model augmented with active learning(E2E-AL)with decent generalization through different dedicated embedding methods over various parameters.On an identical computational platform,the artificial intelligence–driven model is 2000 times faster than the conventional simulation method.Benefiting from the active learning strategy,the E2E-AL model achieves decent precision with only two-thirds of the training samples compared with the case without such a strategy.Furthermore,we demonstrate a multi-objective inverse design of the CPA systems enabled by the E2E-AL model.The E2E-AL framework manifests the potential of becoming a standard approach for the rapid and accurate modeling of ultrafast lasers and is readily extended to simulate other complex systems.展开更多
Structural colors based on metasurfaces have very promising applications in areas such as optical image encryption and color printing.Herein,we propose a deep learning-enabled reverse design of polarization-selective ...Structural colors based on metasurfaces have very promising applications in areas such as optical image encryption and color printing.Herein,we propose a deep learning-enabled reverse design of polarization-selective structural color based on coding metasurface.In this study,the long short-term memory(LSTM)neural network is presented to enable the forward and inverse mapping between coding metasurface structure and corresponding color.The results show that the method can achieve 98%accuracy for the forward prediction of color and 93%accuracy for the inverse design of the structure.Moreover,a cascaded architecture is adopted to train the inverse neural network model,which can solve the nonuniqueness problem of the polarization-selective color reverse design.This study provides a new path for the application and development of structural colors.展开更多
基金supported by the Beijing Natural Science Foundation(5252014)the National Natural Science Foundation of China(62303063)。
文摘Passive acoustic monitoring(PAM)technology is increasingly becoming one of the mainstream methods for bird monitoring.However,detecting bird audio within complex natural acoustic environments using PAM devices remains a significant challenge.To enhance the accuracy(ACC)of bird audio detection(BAD)and reduce both false negatives and false positives,this study proposes a BAD method based on a Dual-Feature Enhancement Fusion Model(DFEFM).This method incorporates per-channel energy normalization(PCEN)to suppress noise in the input audio and utilizes mel-frequency cepstral coefficients(MFCC)and frequency correlation matrices(FCM)as input features.It achieves deep feature-level fusion of MFCC and FCM on the channel dimension through two independent multi-layer convolutional network branches,and further integrates Spatial and Channel Synergistic Attention(SCSA)and Multi-Head Attention(MHA)modules to enhance the fusion effect of the aforementioned two deep features.Experimental results on the DCASE2018 BAD dataset show that our proposed method achieved an ACC of 91.4%and an AUC value of 0.963,with false negative and false positive rates of 11.36%and 7.40%,respectively,surpassing existing methods.The method also demonstrated detection ACC above 92%and AUC values above 0.987 on datasets from three sites of different natural scenes in Beijing.Testing on the NVIDIA Jetson Nano indicated that the method achieved an ACC of 89.48%when processing an average of 10 s of audio,with a response time of only 0.557 s,showing excellent processing efficiency.This study provides an effective method for filtering non-bird vocalization audio in bird vocalization monitoring devices,which helps to save edge storage and information transmission costs,and has significant application value for wild bird monitoring and ecological research.
文摘Although eye problems can occur at any age, they are often common from the age of 40. Eye diseases with a prevalence associated with age and aging will continue to increase in the coming years. Most studies conducted on problems in middle-aged people have focused on visual disorders without taking into account all the ocular morbidities that may affect this segment of the population, hence the present study, the aim of which is to determine the proportions of different eye diseases in people aged 40 and over. Materials and Methods: This was a descriptive cross-sectional study carried out in the ophthalmology department covering the period from January 1 to December 31, 2020. Results: In total, we collected 828 patients aged 40 and over out of 1811 patients who received ophthalmological consultation during the study period, representing 45.72%. The most represented age group was 40 - 50 years, with an average age of 58.84 years and a maximum of 93 years. There were slightly more women (62.3%) than men (37.7%). The main reasons for consultation were decreased visual acuity (26.4%) and pruritus (19.9%). The main eye diseases diagnosed were cataracts (23%), allergic conjunctivitis (21.1%), and bacterial conjunctivitis (14.2%). Discussions: The predominance of cataracts in the diagnosed diseases confirms the literature data, according to which the main eye morbidities in middle-aged and elderly people are cataracts, glaucoma, and age-related macular degeneration. Conclusion: It is crucial to have a mastery of these epidemiological data of eye diseases in order to adapt the technical platforms of eye care structures to the needs of different segments of the population.
文摘We report a robust pillar-layered metal-organic framework,Zn‑tfbdc‑dabco(tfbdc:tetrafluoroterephthal-ate,dabco:1,4-diazabicyclo[2.2.2]octane),featuring the fluorinated pore environment,for the preferential binding of propane over propylene and thus highly inverse selective separation of propane/propylene mixture.The inverse propane-selective performance of Zn‑tfbdc‑dabco for the propane/propylene separation was validated by single-component gas adsorption isotherms,isosteric enthalpy of adsorption calculations,ideal adsorbed solution theory calculations,along with the breakthrough experiment.The customized fluorinated networks served as a propane-trap to form more interactions with the exposed hydrogen atoms of propane,as unveiled by the simulation studies at the molecular level.With the advantage of inverse propane-selective adsorption behavior,high adsorption capacity,good cycling stability,and low isosteric enthalpy of adsorption,Zn‑tfbdc‑dabco can be a promising candidate adsorbent for the challenging propane/propylene separation to realize one-step purification of the target propylene substance.
文摘In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.
基金supported by the Scientific Research Project of China Three Gorges Group Co.LTD(Contract Number:202103368).
文摘The frequency regulation reserve setting of wind-PV-storage power stations is crucial.However,the existing grid codes set up the station reserve in a static manner,where the synchronous generator characteristics and frequency-step disturbance scenario are considered.Thus,the advantages of flexible regulation of renewable generations are wasted,resulting in excessive curtailment of wind and solar resources.In this study,a method for optimizing the frequency regulation reserve of wind PV storage power stations was developed.Moreover,a station frequency regulation model was constructed,considering the field dynamic response and the coupling between the station and system frequency dynamics.Furthermore,a method for the online evaluation of the station frequency regulation was proposed based on the benchmark governor fitting.This method helps in overcoming the capacity-based reserve static setting.Finally,an optimization model was developed,along with the proposal of the linearized solving algorithm.The field data from the JH4#station in China’s MX power grid was considered for validation.The proposed method achieves a 24.77%increase in the station income while ensuring the system frequency stability when compared with the grid code-based method.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.62476278,12434009,and 12204533)supported by the National Key R&D Program of China(Grant No.2024YFA1408601)the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0302402)。
文摘The discovery of advanced materials is a cornerstone of human technological development and progress.The structures of materials and their corresponding properties are essentially the result of a complex interplay of multiple degrees of freedom such as lattice,charge,spin,symmetry,and topology.This poses significant challenges for the inverse design methods of materials.Humans have long explored new materials through numerous experiments and proposed corresponding theoretical systems to predict new material properties and structures.With the improvement of computational power,researchers have gradually developed various electronic-structure calculation methods,such as the density functional theory and high-throughput computational methods.Recently,the rapid development of artificial intelligence(AI)technology in computer science has enabled the effective characterization of the implicit association between material properties and structures,thus forming an efficient paradigm for the inverse design of functional materials.Significant progress has been achieved in the inverse design of materials based on generative and discriminative models,attracting widespread interest from researchers.Considering this rapid technological progress,in this survey,we examine the latest advancements in AI-driven inverse design of materials by introducing the background,key findings,and mainstream technological development routes.In addition,we summarize the remaining challenges for future directions.This survey provides the latest overview of AI-driven inverse design of materials,which can serve as a useful resource for researchers.
基金supported by the Key Scientific and Technological Projects(2024KJGG27)of Tianfu Yongxing Laboratorythe Experimental Platform Open Innovation Funding(209042025003)of Sichuan Energy Internet Research Institute,Tsinghua University.
文摘As the development of new power systems progresses,the inherent inertia of power systems continues to diminish.Centralized frequency regulation,which relies on rapid communication and real-time control,can enable inverter-based thermostatically controlled load(ITCL)clusters to provide virtual inertia support to the power grid.However,ITCL clusters exhibit significant discrete response characteristics,which precludes the direct integration of load-side inertia support into the synchronous unit side.To address this issue,this paper elaborates on the existing technical framework and analyzes the underlying causes of the problem.It proposes a timestamp allocation mechanism for ITCL cluster control instructions,ensuring that many ITCL terminals can be triggered at staggered times,thereby allowing the load cluster power to adhere to the inertia analog control law at any moment.Building on this foundation,the paper further examines the impact of the inertia response delay of ITCL clusters,which is based on centralized frequency regulation,on the stability of the power system.A design scheme for inertia analog control parameters is proposed,taking into account dual constraints,frequency stability and load cluster regulation capacity.Finally,the feasibility and applicability of the proposed mechanism and parameter design scheme are investigated through simulations conducted via MATLAB/Simulink.
基金supported by the Fundamental Research Funds for the Central Universities (No.2024JBZY021)the National Natural Science Foundation of China (No.61575018)。
文摘Reverse design of highly GeO2-doped silica optical fibers with broadband and flat dispersion profiles is proposed using a neural network(NN) combined with a particle swarm optimization(PSO) algorithm.Firstly,the NN model designed to predict optical fiber dispersion is trained with an appropriate choice of hyperparameters,achieving a root mean square error(RMSE) of 9.47×10-7on the test dataset,with a determination coefficient(R2) of 0.999.Secondly,the NN is combined with the PSO algorithm for the inverse design of dispersion-flattened optical fibers.To expand the search space and avoid particles becoming trapped in local optimal solutions,the PSO algorithm incorporates adaptive inertia weight updating and a simulated annealing algorithm.Finally,by using a suitable fitness function,the designed fibers exhibit flat group velocity dispersion(GVD) profiles at 1 400—2 400 nm,where the GVD fluctuations and minimum absolute GVD values are below 18 ps·nm-1·km-1and 7 ps·nm-1·km-1,respectively.
基金supported by the National Key R and D Program of China(No.2022YFF0604801)the National Natural Science Foundation of China(Nos.62271056,62171186,62201037)+3 种基金the Technology Innovation Center of Infrared Remote Sensing Metrology Technology of State Administration for Market Regulation(No.AKYKF2423)the Beijing Natural Science Foundation of China-Haidian Original Innovation Joint Fund(No.L222042)the Open Research Fund of State Key Laboratory of Millimeter Waves(No.K202326)the 111 Project of China(No.B14010).
文摘Terahertz(THz)metamaterials,with their exceptional ability to precisely manipulate the phase,amplitude,polarization and orbital angular momentum(OAM)of electromagnetic waves,have demonstrated significant application potential across a wide range of fields.However,traditional design methodologies often rely on extensive parameter sweeps,making it challenging to address the increasingly complex and diverse application requirements.Recently,the integration of artificial intelligence(AI)techniques,particularly deep learning and optimization algorithms,has introduced new approaches for the design of THz metamaterials.This paper reviews the fundamental principles of THz metamaterials and their intelligent design methodologies,with a particular focus on the advancements in AI-driven inverse design of THz metamaterials.The AI-driven inverse design process allows for the creation of THz metamaterials with desired properties by working backward from the unit structures and array configurations of THz metamaterials,thereby accelerating the design process and reducing both computational resources and time.It examines the critical role of AI in improving both the functionality and design efficiency of THz metamaterials.Finally,we outline future research directions and technological challenges,with the goal of providing valuable insights and guidance for ongoing and future investigations.
基金pported by National Natural Science Foundation of China(Grant No.12161049).
文摘We introduce and study a new kind of generalized inverses named w-(b,c)-core inverses,which is a generalization of the(b,c)-core inverse.An example is given to show that w-(b,c)-core inverses need not be(b,c)-core inverses.In addition,the dual version of the w-(b,c)-core inverse is studied.Some results on(b,c)-core inverses and e-(b,c)-core inverses are unifed and generalized.
基金supported by the National Key Research and Development Program of China(No.2019YFA0405300)the National Natural Science Foundation of China(No.12272405).
文摘Flowfield inverse design can obtain the desired flow and contour with high design efficiency,short design cycle,and small modification need.In this study,the Euler equations are formulated in the stream-function coordinates and combined with the given boundary conditions to derive a gridless space-marching method for the inverse design of subsonic,transonic,and supersonic flowfields.Designers can prescribe the flow parameters along the reference streamline to design flowfields and aerodynamic contours.The method is validated by the theoretical transonic solution,computational fluid dynamics,and experimental data,respectively.The method supports the fabrication of a Mach 2.0 single expansion tunnel.The calibration data agree well with the prescribed pressure distribution.The method is successfully applied to inverse design of contractions,nozzles,and asymmetric channels.Compared to classical analytic contractions,the contractions designed by the space-marching method provide a more accurate transonic flow.Compared to the classical Sivells’nozzle,the nozzle designed by the space-marching method provides a smaller workload,a more flexible velocity distribution,a 20%reduction in length,and an equally uniform flow.Additionally,the space-marching method is applied to design the asymmetric channels under various Mach numbers.These asymmetric channels perfectly eliminate Mach waves,achieving the shock-free flow turning and high flow uniformity.These results validate the feasibility of the space-marching method,making it a good candidate for the inverse design of subsonic,transonic,and supersonic internal flowfields and aerodynamic contours.
基金supported by the National Natural Science Foundation of China(Nos.62175076,62105028,62475085)the Natural Science Foundation of Hubei Province of China(Nos.2024AFA016,2024AFB612)the Open Project Program of Hubei Optical Fundamental Research Center.
文摘The integrated waveguide polarizer is essential for photonic integrated circuits,and various designs of waveguide polarizers have been developed.As the demand for dense photonic integration increases rapidly,new strategies to minimize the device size are needed.In this paper,we have inversely designed an integrated transverse electric pass(TE-pass)polarizer with a footprint of 2.88μm×2.88μm,which is the smallest footprint ever achieved.A direct binary search algorithm is used to inversely design the device for maximizing the transverse electric(TE)transmission while minimizing transverse magnetic(TM)transmission.Finally,the inverse-designed device provides an average insertion loss of 0.99 dB and an average extinction ratio of 33 dB over a wavelength range of 100 nm.
基金supported by the National Key R&D Program of China(Grant No.2023YFB2905502)the National Natural Science Foundation of China(Grant Nos.62122047 and 61935011).
文摘A power splitter with a wideband arbitrary splitting ratio,which provides flexibility and adaptability in forming photonic devices such as microring resonators and Mach–Zehnder interferometers,proves to be essential in photonic integrated circuits(PICs).We designed and fabricated a directional coupler-based power splitter with a wideband arbitrary splitting ratio and a microring resonator with a wideband uniform extinction ratio(ER)based on artificial gauge field(AGF)optimization.The neural network-aided inverse design method is applied to complete the target.Less than 0.9 dB power splitting variation and 1.6 dB ER variation have been achieved experimentally over a 100-nm bandwidth.Wideband performance,design efficiency,and device compactness are obtained by utilizing this optimization,which indicates great potential and universality in PIC applications.
基金Supported by the Beijing Municipal Science&Technology Commission(Z211100004421012),the Key Reaserch and Development Pro⁃gram of China(2022YFF0605902)。
文摘In this paper,a linear optimization method(LOM)for the design of terahertz circuits is presented,aimed at enhancing the simulation efficacy and reducing the time of the circuit design workflow.This method enables the rapid determination of optimal embedding impedance for diodes across a specific bandwidth to achieve maximum efficiency through harmonic balance simulations.By optimizing the linear matching circuit with the optimal embedding impedance,the method effectively segregates the simulation of the linear segments from the nonlinear segments in the frequency multiplier circuit,substantially improving the speed of simulations.The design of on-chip linear matching circuits adopts a modular circuit design strategy,incorporating fixed load resistors to simplify the matching challenge.Utilizing this approach,a 340 GHz frequency doubler was developed and measured.The results demonstrate that,across a bandwidth of 330 GHz to 342 GHz,the efficiency of the doubler remains above 10%,with an input power ranging from 98 mW to 141mW and an output power exceeding 13 mW.Notably,at an input power of 141 mW,a peak output power of 21.8 mW was achieved at 334 GHz,corresponding to an efficiency of 15.8%.
基金supported by the National Natural Science Foundation of China(grant numbers 11972287 and 12072266)the State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ23106)+1 种基金the National Key Laboratory of Aircraft Configuration Design(No.2023-JCJQ-LB-070)the Fundamental Research Funds for the Central Universities.
文摘Chiral metamaterials are manmade structures with extraordinary mechanical properties derived from their special geometric design instead of chemical composition.To make the mechanical deformation programmable,the non-uniform rational B-spline(NURBS)curves are taken to replace the traditional ligament boundaries of the chiral structure.The Neural networks are innovatively inserted into the calculation of mechanical properties of the chiral structure instead of finite element methods to improve computational efficiency.For the problem of finding structure configuration with specified mechanical properties,such as Young’s modulus,Poisson’s ratio or deformation,an inverse design method using the Neural network-based proxy model is proposed to build the relationship between mechanical properties and geometric configuration.To satisfy some more complex deformation requirements,a non-homogeneous inverse design method is proposed and verified through simulation and experiments.Numerical and test results reveal the high computational efficiency and accuracy of the proposed method in the design of chiral metamaterials.
基金The National Natural Science Foundation of China(No.51976039)。
文摘To explore the electrostatic discharge behavior of charged powders in industrial silos,discharge experiments are conducted based on a full-size industrial silo discharge platform.Electrostatic discharge mode,frequency,and energy are investigated for powders of different polarities.Although the powders have low charge-to-mass ratios(+0.087μC/kg for the positively charged powders and−0.26μC/kg for the negatively charged ones),electrostatic discharges occur approximately every 10 s,with the maximum discharge energy being 800 mJ.Powder polarity considerably influences discharge energy.The positive powders exhibit higher discharge energy than the negative ones,although discharge frequency remains similar for both.Effects of powder charge,humidity,and mass flow on discharge frequency and discharge energy are quantitatively analyzed,providing important insights for the improvement of safety in industrial powder handling.
文摘Delineation of hydrocarbon-bearing sands and the extent of accumulation using seismic data is a reoccurring challenge for many fields.This study addressed the existing challenges of delineating a known hydrocarbon region for a thin-pay reservoir using conventional attributes extraction methods.The efficacy of applying iso-frequency extraction and spectral frequency blending in identifying thin-pay and thick-pay reservoirs on seismic was tested by utilizing 3D seismic data and well logs data of Terra field in the Western Niger Delta Basin.Well tops of all the reservoirs in the field were picked and two reservoirs that correspond to thin-and thick-pay reservoirs,namely A and F were identified respectively.The gross pay thickness of reservoir A is 18 ft while that of reservoir F is 96 ft.Conventional attribute extraction such as RMS amplitude,minimum amplitude,and average energy can be used to identify the hydrocarbon-bearing region in reservoir F but was not applicable for identifying the thin-pay reservoir A.This prompted the interest of using iso-frequency extractions and spectral frequency blending of three iso-frequency cubes of 12 Hz,30 Hz,and 70 Hz to get a spectral frequency RGB cube.The 12 Hz isofrequency can be used to partially identify hydrocarbon-bearing region in reservoir A while the 30Hz iso-frequency can be used to partially identify hydrocarbon-bearing region in reservoir F.The results show that time slices from the spectral frequency blended cube were able to delineate both the thin-pay and thick-pay hydrocarbon-bearing regions as high amplitude.The extractions also conformed to the structure of the two reservoirs.However,there seems to be a color difference in the amplitude display for both reservoirs.The thick-pay reservoir showed a red color on the time slice while the thin-pay reservoir showed a green color.This study has shown that spectral frequency blending is a more effective tool than conventional attributes extractions in identifying hydrocarbon-bearing region using seismic data.The methodology utilized in this study can be applied to other fields with similar challenges and for identifying prospective hydrocarbon bearing areas.
基金supported by a grant from the Natural Science Foundation of Zhejiang Province under Grant LY21F010016.
文摘Recently,many Sequential Recommendation methods adopt self-attention mechanisms to model user preferences.However,these methods tend to focus more on low-frequency information while neglecting highfrequency information,which makes them ineffective in balancing users’long-and short-term preferences.At the same time,manymethods overlook the potential of frequency domainmethods,ignoring their efficiency in processing frequency information.To overcome this limitation,we shift the focus to the combination of time and frequency domains and propose a novel Hybrid Time-Frequency Dual-Branch Transformer for Sequential Recommendation,namely HyTiFRec.Specifically,we design two hybrid filter modules:the learnable hybrid filter(LHF)and the window hybrid filter(WHF).We combine these with the Efficient Attention(EA)module to form the dual-branch structure to replace the self-attention components in Transformers.The EAmodule is used to extract sequential and global information.The LHF andWHF modules balance the proportion of different frequency bands,with LHF globally modulating the spectrum in the frequency domain and WHF retaining frequency components within specific local frequency bands.Furthermore,we use a time domain residual information addition operation in the hybrid filter module,which reduces information loss and further facilitates the hybrid of time-frequency methods.Extensive experiments on five widely-used real-world datasets show that our proposed method surpasses state-of-the-art methods.
基金supported by the National Natural Science Foundation of China(Grant Nos.62227821,62025503,and 62205199).
文摘To capture the nonlinear dynamics and gain evolution in chirped pulse amplification(CPA)systems,the split-step Fourier method and the fourth-order Runge–Kutta method are integrated to iteratively address the generalized nonlinear Schrödinger equation and the rate equations.However,this approach is burdened by substantial computational demands,resulting in significant time expenditures.In the context of intelligent laser optimization and inverse design,the necessity for numerous simulations further exacerbates this issue,highlighting the need for fast and accurate simulation methodologies.Here,we introduce an end-to-end model augmented with active learning(E2E-AL)with decent generalization through different dedicated embedding methods over various parameters.On an identical computational platform,the artificial intelligence–driven model is 2000 times faster than the conventional simulation method.Benefiting from the active learning strategy,the E2E-AL model achieves decent precision with only two-thirds of the training samples compared with the case without such a strategy.Furthermore,we demonstrate a multi-objective inverse design of the CPA systems enabled by the E2E-AL model.The E2E-AL framework manifests the potential of becoming a standard approach for the rapid and accurate modeling of ultrafast lasers and is readily extended to simulate other complex systems.
基金supported by the National Natural Science Foundation of China(Grant Nos.62375137 and 62175114).
文摘Structural colors based on metasurfaces have very promising applications in areas such as optical image encryption and color printing.Herein,we propose a deep learning-enabled reverse design of polarization-selective structural color based on coding metasurface.In this study,the long short-term memory(LSTM)neural network is presented to enable the forward and inverse mapping between coding metasurface structure and corresponding color.The results show that the method can achieve 98%accuracy for the forward prediction of color and 93%accuracy for the inverse design of the structure.Moreover,a cascaded architecture is adopted to train the inverse neural network model,which can solve the nonuniqueness problem of the polarization-selective color reverse design.This study provides a new path for the application and development of structural colors.
