Acoustic source localization techniques are widely applied in underwater detection,seismic monitoring,structural health monitoring,and environmental acoustic sensing.The existing acoustic source localization methods are mostly established based on the assumption of a homogeneous propagation medium.However,in practical application scenarios,the medium through which acoustic waves propagate has varying degrees of randomness,which limits the applicability of localization methods based on the homogeneity assumption.To address the issue of source localization under medium randomness,the commonly used method currently is the acoustic source localization method based on the Gaussian distribution of time difference of arrival(TDOA).Nevertheless,further verification is needed to determine whether TDOA follows a Gaussian distribution under random medium conditions and under what conditions it follows a Gaussian distribution.Therefore,random medium models with different parameter settings is constructed in this study and the acoustic travel-time field is calculated by using the fast marching method combined with Monte Carlo simulations to analyze the statistical characteristics of TDOA under random medium conditions.Experiment results indicate that TDOA does not generally follow a Gaussian distribution under random medium conditions,while it approximately conforms to a Gaussian distribution under near-field conditions with relatively small medium perturbation intensity or large correlation length.This study can provide a theoretical basis for the selection and improvement of practical acoustic source localization methods in practical applications.
Analytic hierarchy process(AHP)is widely used in the health assessment of complex equipment such as aerospace due to its structural decision-making advantages.However,the traditional AHP usually relies on a fixed scale when constructing the judgment matrix,which is difficult to reflect the dynamic evolution of index weights caused by factors such as performance degradation and operation environment changes in the whole life cycle of equipment,thus affecting the accuracy of the assessment results.Therefore,a multi-level discriminant analysis is proposed based on dynamic update of judgment matrix for health assessment of spacecraft control system.Firstly,compared with the traditional AHP,the influence of degradation rate of performance index and cumulative failure probability to construct the dynamic update model of the judgment matrix is considered in this method.Secondly,in order to enhance the accuracy and stability of the model,the time-varying adjustment parameters are introduced.The optimization problem is solved by establishing the objective function of minimizing the health degree and using the particle swarm optimization algorithm of a dynamic inertia weight strategy,and the adaptive updating of time-varying adjustment parameters is realized.Furthermore,by integrating component-level health information to the system level,the traceability of component health status and overall health status assessment are achieved.Finally,an empirical analysis is conducted using data from a spacecraft control system.The verification results show that the mean square error of the traditional AHP is 0.0438,while the mean square error of the method proposed in this paper is only 0.0081,with an accuracy rate of 95.18%.Compared to AHP,it is improved by 8.14%,effectively enhancing the accuracy of spacecraft control system health assessment and providing a theoretical basis for the health monitoring and management of spacecraft control systems.
In response to the issues of significant temperature fluctuations,short duration,and measure-ment difficulties during the explosion process of explosively formed projectile(EFP),a time division mul-tiplexing temperature measurement method based on two infrared thermal imaging cameras is proposed to test the explosion process of EFP,which solves the problem that the infrared thermal imaging camera could not capture the complete temperature information at the moment of explosion due to the limitation of frame rate.By employing a high-precision synchronous trigger for synchronous trigger and timing control of the two infrared thermal cameras,a frame rate delay of 10 μs between the two cameras is achieved,enabling high frame rate shooting and obtaining the surface temperature distribution at the moment of the EFP explosion.In the test,the maximum temperature after the EFP explosion are 2 073.72,1 779.74,1 910.87 and 2 082.56℃,with a maximum deviation of 14.1%,and the diameter of the fireball are 3.64,3.61,3.80 and 3.90 m,respectively,with a maximum deviation of 6.7%.In addition,the mor-phological changes and flight speed of the EFP are analyzed,with the flight speed of the first EFP being 2 000 m/s and the second being 2 200 m/s.
Aiming at the phased temperature measurement of the rocker test system equipment in the aerospace field and meeting the needs of various analog signal outputs,a multi-channel temperature acquisition system based on an FPGA as the core processing system is designed.The high-precision 16-bit analog-to-digital converter AD7616 and PT1000 platinum thermal resistance are used to construct a multi-channel temperature measurement network.The high-precision conditioning and temperature compensation of the sensor signal are realized by integrating a low-noise instrumentation amplifier and an adaptive filtering circuit.In addition,through the design of a data transmission protocol based on a UDP logic module and the on-demand control of Ethernet data transmission mode,the balance between the real-time transmission of multi-channel tem-perature data and the overall communication network load is achieved.To address the nonlinear characteristics of the sensor,the sliding mean filtering and Newton-Raphson method are used to optimize the temperature measurement data,which can effectively suppress the temperature drift caused by environmental noise,reduce the nonlinear error of the system,and improve the response speed and transmission efficiency of the multi-channel temperature measurement system.The experimental results show that the relative error of the temperature measured by the multi-channel temperature acquisition system is within 0.016%in the range of-40℃to+180℃.The temperature measurement system runs smoothly and stably in the overall verification test process,and has strong robustness in the industrial environment.Therefore,the temperature acquisition system can be used in multi-channel temperature detection application scenarios in aerospace and other fields with high precision and fast response.
Aiming at the problem of high-g impact protection of encapsulating materials in embedded mea-surement instruments,resin-based composites with different contents of Carboxylated-terminated liquid acrylonitrile rubber(CTBN)were prepared as encapsulating materials for measurement instruments.The effect of CTBN content on the mechanical properties of the material was studied by a uniaxial compression test.The standard Zhu-wang-tang(ZWT)nonlinear viscoelastic model was used to describe the encapsu-lating material.The penetration overload of the projectile obtained by numerical simulation is decomposed and reconstructed as the loading signal of the instrument.The influence of CTBN content,loading condi-tions and internal structure on the high-g impact of the internal components of the instrument is analyzed.The results show that the stress amplitude on the chip can be effectively reduced by installing the chip away from the center and using high-strength encapsulating materials.By changing the installation position of the chip,the energy of most detail signals can be reduced,and adjusting the content of CTBN in the encapsulating material can reduce the energy of detail signals in a specific frequency band.
With the rapid advancement of autonomous driving,security surveillance,and virtual reality technologies,the demand for wide-field-of-view imaging systems continues to grow to achieve broader visual perception ranges.Inspired by the compound eyes of insects in nature,a bionic compound eye imag-ing system is designed based on a multi-camera array.Comprising eight cameras arranged in a spherical ring to mimic the natural compound eye structure,a wide field of view of 105°×100° is achieved in this system.To address common issues in wide-field image stitching,such as poor quality,ghosting,and chromatic aberration,a coarse-to-fine image stitching method guided by agglomerative hierarchical cluster-ing,named AHC-LightGlue,is proposed.Firstly,key points and descriptors are extracted from the images using SuperPoint.Before feature matching,the topological structure of feature points is con-structed by the agglomerative hierarchical clustering algorithm.Coarse matching is performed at the clus-ter level to lock corresponding regions,followed by fine matching within these regions using LightGlue to address high mismatch rates and misalignment issues.Subsequently,the homography matrix is calculated by marginalizing sample consensus.Finally,a natural transition between the reference and target images is obtained by the proposed adaptive weighting fusion method.In this approach,superior fusion results are delivered even under significant brightness differences,large-scale color discrepancies are reduced,and image stitching quality is further enhanced.Experiments demonstrate that both image quality and stitching accuracy are improved by the proposed algorithm and color discrepancies are effectively reduced in image stitching to form smoother and more natural stitched images.Compared to traditional methods,the image stitching method based on a multi-camera array bionic compound eye has great robustness and adaptability.
To address the issue of the technical challenge encountered in the ultrasonic measurement of lubricant film thickness on thin-coated bearings,where overlapping echo signals make it difficult to sepa-rate the incident wave and prevent accurate measurement,a measurement model for a four-layer lubricant film was established under a focused acoustic field by introducing a pseudo-reflection coefficient.The film thickness was determined by calculating amplitude and phase information from the ratio of the total reflected wave to the reference wave,which circumvented the difficulties inherent in traditional methods.A finite element model was constructed to analyse the influence of different oil films and coating thick-nesses on the measurement results.Subsequently,a test bench was set up,and comparative experiments using multiple methods were conducted.Experimental results indicate that the relative error of this model is less than 5%for oil film thicknesses ranging from 5 μm to 120 μm,and less than 2%for thicknesses exceeding 60 μm.These findings demonstrate the model's excellent robustness and its ability to effec-tively enhance the accuracy and stability of thin-film bearing thickness measurements.
Vision-based measurement has been widely applied in industrial liquid level monitoring.How-ever,in weak-texture scenarios such as liquid surfaces,existing methods often rely on floaters,markers,or structured light to enhance matching features.These approaches are unsuitable in environments like pro-pellant tanks of rocket engines,where high purity and airtightness are strictly required.To improve the accuracy and robustness of stereo vision in weak-texture liquid surface scenarios,a confidence-guided fusion method of monocular and stereo depth estimation is proposed.The absolute depth map from stereo matching is used as the primary reference and the relative depth map predicted by a monocular neural net-work is also incorporated in this method.A multi-dimensional confidence map is constructed to align the monocular depth to the stereo scale,and pixel-wise weighted fusion is performed under the guidance of confidence.And the physical scale accuracy is preserved in high-confidence stereo regions,while monocu-lar structural information is used to complement details in weak-texture regions,thereby enhancing the overall accuracy and completeness of depth estimation.An experimental platform is built to measure liquid level height,and the performance of SGBM,IGEV++,and the proposed fusion algorithm is compared under different liquid levels.Experimental results show that the proposed method achieves an average error of 1.505%,which represents a reduction of approximately 90.69%compared to the SGBM method.These results demonstrate the effectiveness of the proposed approach in improving the accuracy and stability of liquid level measurement.
To address the limitation of single-domain features in comprehensively characterizing the degra-dation state for bearing remaining useful life(RUL)prediction,a method integrated multi-domain feature fusion and a parallel Transformer-bidirectional long short-term memory(BiLSTM)network are pro-posed.Firstly,time-domain,frequency-domain,time-frequency and entropy features are extracted from vibration signals.A comprehensive evaluation index based on moving average decomposition is used to select sensitive features,which are then reduced in dimensionality via principal component analysis to con-struct a composite health indicator.Subsequently,a parallel network architecture is designed.Global long-range dependencies are captured by a transformer encoder,while local bidirectional temporal patterns are learned via a BiLSTM network.An attention mechanism is introduced to adaptively fuse the outputs of the two branches.Experimental results on the PHM2012 bearing dataset show that the proposed method reduces the mean absolute error and root mean square error by 36.1%and 29.8%on average,respec-tively,compared to benchmark models(e.g.,BiLSTM,CNN),and achieves a coefficient of determina-tion(R²)of 0.95 in full-lifecycle prediction.The prediction error for the entire life cycle is below 10%.The effectiveness and stability of the method are further validated by cross-condition generalization experiments.
The crystal structure of TiO2 films is a key factor in determining their core properties,such as their photo-catalytic and optical performance.Meanwhile,sputtering power density indirectly influences the crystallization behaviour of films by regulating the deposition temperature.In this study,a quantitative model relating sputtering power density and deposition temperature was established,and a multi-physics simulation method was used to thoroughly investigate the regulation mechanism of sputtering power den-sity on the crystallization behavior of TiO2 films.Simulation results show that when the sputtering power density increases from 0.83 W/cm² to 5.00 W/cm²,the surface temperature of the film during growth rises from 197℃to 541℃,and the substrate temperature rises from 125℃to 324℃,with the surface temperature always higher than the substrate(temperature difference 72-217℃).When the surface tem-perature of the growing film is below 400℃,it is in an amorphous state;above 400℃,it transforms into the anatase phase,and the film's crystalline structure is mainly determined by the growth surface tempera-ture.This simulation work clarifies the critical role of temperature in the phase transformation of TiO2 films,provides a theoretical basis for precise control of film crystal structures,and has significant implica-tions for the industrial application of high-performance TiO2 films.
