Abstract:The aesthetic appearance of the microscopic instruments can reflect the advanced qualities, while improving the pleasure of the operation experience. The establishment of aesthetics indexes and evaluation method of microscopic instruments can quantify the beauty degree of instruments’ appearance, and make the objective formal beauty correspond with the subjective feeling beauty, so as to effectively guide the aesthetic design of microscopic instruments. First, based on the features of microscopic instruments, the rules of beautiful form, and the beauty calculation model of Ngo and Birkhoff, seven quantitative indicators for the beauty evaluation of microscopic instruments were constructed such as Balance, Symmetry, Proportion, Rhythm, Unity, Order and Color. Then, the correlation among 7 beauty indexes was analyzed, and the fuzzy evaluation matrix R for calculating products’ beauty was established. Finally, taking four types of laser scanning confocal biological microscopes as samples, the subjective and objective comprehensive evaluation was made by applying aesthetics indexes and the fuzzy comprehensive evaluation method. The results show that the fuzzy comprehensive evaluation vector of the sample 4 designed by Harbin Institute of Technology is B4 = [0,0. 353 4, 1. 042 5, 0. 888 3, 1. 256 2], which indicates that its beauty grade is higher than others, and it has a better performance of unity and order.

Abstract:The paper proposes a YOLOPCB network for small defects detection on printed circuit board ( PCB) using multi-channel feature fusion learning. Firstly, the last group of MPConv layer and E-ELAN layer in the YOLOv7 backbone network are removed, and the ECU module in the fusion layer and the 20 × 20 prediction head are eliminated. A cross-channel information connection module (CIC) is utilized to link the streamlined backbone and fusion networks. Secondly, a shallow feature fusion module ( SFF) and a new anchor matching strategy are designed, which add two low-level, high-resolution detection heads. Lastly, the three E-ELAN layers in the YOLOv7 backbone network are used as inputs, while the bottommost E-ELAN and two concatenation modules in the fusion layer are used as outputs, with adaptive weighted skip-connection (AWS) to increase the information within the same dimension. The average precision on the PCB Defect datasets reaches 94. 9% , with a detection speed of 45. 6 fps. Furthermore, on the Self-PCB datasets obtained from on-site enterprises, YOLOPCB achieves the highest accuracy of 76. 7% , which is a 6. 8% improvement over the detection accuracy of YOLOv7. YOLOPCB effectively enhances the detection capability of small defects on printed circuit boards.

Abstract:Visible and infrared image fusion aims to exploit the effective information between two different sensors to achieve image enhancement through complementary image features. However, current deep learning-based fusion methods tend to priorities evaluation metrics, and the models have high complexity, large weight parameters, low inference performance, poor generalization, and are not easy to deploy on the UAV edge computing platform. To address these challenges, this paper proposes a novel approach for visible and infrared image fusion, i. e. , adaptive DenseNet with knowledge distillation to learn a pre-existing fusion model, which achieves fusion effectiveness and model lightweighting through the use of hyperparameters (e. g. , width and depth). The proposed method is evaluated on a typical ground target dataset, and the experimental results show that the model parameter is only 77 KB and the inference time is 0. 95 ms, which has an ultra-light network structure, excellent image fusion effect and strong generalization ability in complex scenes.

Abstract:The technique for estimating the concentration of fine particulate matter (PM2. 5 ) based on visual cues relies on assessing its concentration by considering the overall effect of suspended fine particles on light scattering and absorption during imaging. These technologies demonstrate robust generalizability and real-time detection capabilities across large-scale areas. Existing studies depend on daytime scenes with uniform and sufficient atmospheric light, limiting their applicability to nighttime scenario with insufficient atmospheric light and uneven illumination. This paper introduces the pioneering vision-based nighttime estimation method of fine particulate matter concentration, which captures the intensity distribution of an artificial light source in various scattering directions through image processing, and utilizes the feature to correlate with fine particulate matter concentration. The proposed method innovatively leverages the artificial light source and its surrounding glowing area as the main source of nighttime haze information. Since areas dominated by natural lighting typically appear black at night, the conventional basis for daytime haze estimation (i. e. , pixel value approaching the color of “atmospheric-light / sky” as the depth of field increases), is barely useful at night. This method outperforms existing daytime haze estimation methods, achieving a mean absolute error (MAE) of 6. 187 μg / m 3 and a correlation coefficient (R 2 ) of 0. 85

Abstract:To solve the problems of small available space, complex structure, and difficult assembly of structural parts in the development of the miniature three-axis vibration sensor, this article proposes the use of three integrated module staggered combinations of the sensor design method to improve the space utilization rate and assembly accuracy. Through theoretical analysis and finite element simulation, the optimized design of the 15 mm×15 mm×15 mm miniaturized sensor is completed. The microscope imaging packaging system and the vibration testing system are established to complete the packaging and performance testing of the sensor. Experiments show that the resonant frequencies of X, Y, and Z axes of the sensor are 990, 975, and 960 Hz, respectively. The flatness is good in the frequency range of 0~ 700 Hz, and the sensitivities are 19. 10, 19. 28, and 20. 01 pm/ g, respectively, which has the advantages of lightweight and high consistency of the performance of the three-axis test. A micro-angular vibration test system is built to evaluate the performance of the sensor, which has an angular displacement measurement resolution better than 0. 21 μrad and a linearity of 0. 998.

Abstract:To improve the positioning accuracy and stability of the positioning system in GNSS-denied environments, the paper proposes a UWB quality control method considering NLOS and system errors, and achieves its integrated positioning with GNSS / INS. Firstly, considering the stability and accuracy of the positioning system, a centralized Kalman filter is constructed using loose combination of GNSS / INS and a tight combination of UWB/ INS. Baesd on this, aiming at the NLOS error existing in UWB, a two-step NLOS error identification method based on sliding window and innovation vector of the filter is designed. Finally, a method based on filter estimation is used to compensate the system error in UWB in real time. The experiment results show that the proposed UWB quality control method can effectively reduce the impact of NLOS and system errors, and the horizontal positioning error of the GNSS / UWB/ INS integrated algorithm is within 5cm. With a reasonable UWB layout, this method can achieve high positioning accuracy without relying on an excessive number of base stations.

Abstract:Aiming to simultaneously detect mechanical and insulation defects in gas insulated switchgear (GIS), this paper proposes a low-power smart wireless vibration acceleration-acoustic emission combined sensor. The design and simulation verification of the sensor fusion structure are conducted, utilizing bluetooth low energy (BLE) to establish communication between the sensor and the client. For the first time, pseudo-random M-sequences are applied to the impedance response self-calibration of a piezoelectric sensor, requiring only milliseconds and milliwatts for a single calibration. The test results show that the average vibration sensitivity of the sensor is 511 mV/ g within the frequency range of 100 Hz to 2 kHz, and the ultrasonic sensitivity is stabilized between 70 to 90 dB within the frequency range of 20 to 100 kHz, with a relative uncertainty of less than 2% . The data transmission rate exceeds 1 Mbps when the working distance is less than 8 m. A combined mechanical vibration-partial discharge test is conducted on the actual GIS equipment installed with metal particle defects. The results demonstrate that the sensor can achieve simultaneous and co-located measurement of mechanical vibration and ultrasonic signals from the equipment. This offers a novel and efficient approach for distributed detection of the operational status of power equipment.

Abstract:In order to improve the sensitivity and accuracy of the tactile perception of the fingertip of the machine, based on the analysis of the mechanism of the fingertip tactile perception machine, a feature-separated double-layer “cross”-type FBG tactile sensing unit was designed, and finite-element simulation analysis was carried out, and calibration experiments and grip sensing experiments were carried out for the sensing unit. Based on the composite perception of contact temperature and grip force, a coupling analysis was carried out, and a decoupling method based on whale optimization algorithm for optimising back propagation neural network ( WOA-BPNN) is proposed. The experimental results show that the contact temperature sensitivity of the FBG sensing unit is 11. 255 pm/ ℃ , and the grip force sensitivity is 17. 342 nm/ MPa; the average absolute error of the contact temperature of the WOA-BP decoupling model is reduced by 72. 53% , and the average absolute error of the grip force is reduced by 68. 55% .

Abstract:Magnetic fluid is one kind of intelligent material with both magnetism and fluidity, which can effectively suspend permanent magnetic mass blocks to improve the friction between the permanent magnetic mass block and the contact surface based on its unique second-order buoyancy characteristics. The function effectively promotes its wide application in electromagnetic sensing, electromagnetic energy harvesting, damping shock absorbers, etc. . This article focuses on the theoretical analysis of the second-order buoyancy of the permanent magnet in a non-submerged magnetic fluid and analyzes the magnetic field and magnetic pressure difference by using finite element simulation. Experiments are implemented to investigate quantitatively the relationship between the suspension height of the permanent magnet and the magnetic fluid injection, suspension force. Comparative tests on the application of magnetic fluid are carried out. The experiment results show that there is a good linear relationship between the magnetic fluid injection volume and the levitation height in the interval of 0. 3~ 3. 4 g. The experimental data are in good agreement with the theoretical results.

Abstract:Electromagnetic acoustic transducer (EMAT) has been widely used in the field of nondestructive testing due to its advantages of non-contact and designability. At present, most EMATs can only excite ultrasonic waves at a fixed frequency, which limits the application range of the transducer. To address this issue, this article proposes a new type of phase-controlled frequency-conversion EMAT (PC-FC-EMAT), which can achieve the purpose of variable-frequency excitation by adjusting the phase-control time delay of the transducer′s excitation signal. Firstly, the design scheme of PC-FC-EMAT is proposed, the frequency domain response model of PC-FCEMAT is formulated, and the mechanism of the phase-controlled time delay on the frequency domain response under the spatial response of the Lorentz force is revealed. Secondly, a multi-physics field coupling simulation model of PC-FC-EMAT is established to study the role of the phase-controlled delay on the PC-FC-EMAT time and frequency domain response, and simulate the modulation effect of the phase-controlled delay on the frequency response of PC-FC-EMAT. Finally, PC-FC-EMAT is developed, and experimental research is carried out. The experimental results show that the developed PC-FC-EMAT can achieve wavelength regulation in the range of 4~ 12 mm, and the frequency band covers 252~ 687 kHz, which meets the design requirements.

Abstract:Conveying solid particles through pneumatic pipes is widely used in various industrial processes. In large-scale pneumatic conveying pipelines, the particles are unevenly distributed in the pipeline and the particle flow state is complex. To accurately characterize the flow parameters of the particles in the pipeline, this article proposes a measurement method integrating the electrostatic and acoustic emission sensors. To obtain the velocity and relative volumetric distribution of particles in localized areas on the pipe crosssection, the cross-section of the square-shaped pipe with a side width of 200 mm is divided into 16 electrostatic signal sensing zones and 4 acoustic emission signal measurement areas. Experimental tests are conducted under 30 different conditions with various particle velocities and mass flow rates. Experimental results show that the particle distribution is inhomogeneous in the large-scale square pipe cross-section, and the velocity distribution is consistent with the characteristics of the asymptotic pipeline flow. Based on the analysis of mass flow rate distribution considering the local particle velocities, the relative mass flow rate distributions of the particles represented by the RMS values of electrostatic and acoustic emission signals vary in the same trend.

Abstract:Fiber optic acoustic sensors are widely used in industry, medical and other fields. In order to improve the performance of fiberoptic Fabry-Perot acoustic sensors, a L-shaped spoke structure Fabry-Perot sensing diaphragm is proposed in this work. The thickness of the diaphragm is 15 μm, the width of the beam is 0. 5 mm, and the radius of the center diaphragm is 1 mm. The diaphragm is etched on 304 stainless steel by laser processing technology. In the experiment, the sensitivity of the sensor at 1 000 Hz was investigated, and the sensor was applied to a photoacoustic spectroscopic gas detection system with a photoacoustic cell resonance frequency of 1 600 Hz, and measurement of acetylene (C2H2 ) gas concentration from 50~ 100×10 -6 was achieved. The experimental results indicate that the sound pressure sensitivity of the sensor is 25. 4 nm/ Pa at 1 000 Hz, the minimum detectable sound pressure (MDP) achievable by the sensor is 38. 2 μPa / Hz 1/ 2@ 1 kHz, and the acoustic pressure signal-to-noise ratio is 76. 8 dB. The peak of the second harmonic signal of the photoacoustic spectroscopy obtained from the experiment shows a good linear relationship with the acetylene concentration, and the response of the acetylene concentration is 1. 8 pm/ 10 -6 . The sensor has a wide range of applications in the field of single-frequency acoustic signal detection, such as photoacoustic spectroscopy.

Abstract:In order to solve the problem that the traditional Thevenin equivalent circuit models’ parameters of lithium-ion batteries cannot be identified under the constant current, constant voltage and / or constant power charge / discharge, a new equivalent timevarying internal resistance model is proposed. The analyses show that, when the open circuit voltage and internal resistance of a lithium-ion battery are characterized as an unknown time-varying voltage and internal resistance, respectively, the amp-hour integral and random walk model can be used to describe the evolutions of unknown time-varying voltage and internal resistance. Accordingly, an equivalent time-varying internal resistance model with a state space formula can be used to illustrate the characteristics of lithiumion battery. It is also shown that the transient polarization voltage produced by the battery charge and discharge can be explained by combining the product of end current at the battery and the time-varying internal resistance with the observed noise. Compared with the traditional model, the root-mean-square error of SoC estimation is reduced by 48% on average under constant current, voltage and power conditions. The correctness and effectiveness of our model and analytical results are verified by both the dataset based on the internet and experimental results.

Abstract:This study proposes a new airborne phased-array wave spectrometer (PAWS), which is a Ku-band, linear polarization twodimensional phased-array radar. It transmits a wideband linear frequency modulation signal, illuminates the sea surface at a low incidence angle, and receives echo power modulated by quasi-specular reflection from the sea surface. From the echo power, the sea surface backscattering coefficient and the one-dimensional wave spectrum can be retrieved. With conical scanning from 0° to 360°, two-dimensional wave spectrum, and main wave parameters are obtained by PAWS as well, such as significant wave height, wave direction, and wavelength. On one hand, the dynamic beam position chronogram design (DBCD) benefited from the two-dimensional phased array radar is adopted. For the extension of observation time and the suppression of speckle noise of the along-track, it makes non-uniform scanning possible instead of uniform scanning to improve the insufficient along-track signal-to-speckle noise ratio of CFOSAT SWIM. Thus, better efficiency of PAWS is achieved. On the other hand, electrical scanning of the phased-array radar without a rotating mechanism can greatly improve the compactness, adaptability, and reliability of PAWS. Therefore, it can be mounted on various aircraft platforms. The PAWS field campaign was held in the Bohai Sea near Qinhuangdao in 2023, with synchronous buoys moored there. The results show that the accuracy of PWS’s dominant wave direction is better than 15°, the accuracy of the dominant wavelength is better than 10% , and the significant wave height is better than 0. 3 m. The DBCD is proven to work for the suppression of the speckle noise along the track. The performance of the phased-array conical scanning for receiving sea surface echoes and retrieving wave spectra is also verified.

Abstract:To address the issue of low-range resolution caused by bandwidth limitation in terahertz imaging, this article proposes a multiband stitching method for the linear frequency-modulated continuous wave system. The method enhances the conventional time-domain synthetic bandwidth algorithm by incorporating a non-linear compensation algorithm based on a reference signal. This method ensures frequency and phase continuity for each frequency band while rectifying non-linear effects, thereby eliminating the need for further frequency shift and phase compensation. Experimental results using a linear FM imaging system with five frequency bands ranging from 0. 11 to 0. 75 THz show that the proposed method can significantly improve the longitudinal resolution of teraherz imaging and improve the accuracy of material dielectric constant measurement, and can be applied to the field of materials testing in the future. In addition, two surface step samples and an internal defect sample with an adhesive multilayer are designed for imaging. The imaging results reveal that the fused signals contain more detailed information compared to individual bands, enabling clearer separation of each layer within the samples. Additionally, steps with a height difference of only 0. 2 mm can be distinctly distinguished, which can meet the demands of high-precision non-destructive testing. Keywords:terahertz imaging; linear f

Abstract:This article uses eight 10 GSps ADCs to design an ultra-wideband high-speed data acquisition (DAQ) system with 80 GSps and 20 GHz bandwidth based on the bandwidth-interleaved (BI) sampling architecture and applies it to a real-time digital storage oscilloscope (DSO). Research is implemented on sub-band decomposition, acquisition synchronization between multiple sub-bands, overlapping bands, and full-band splicing. Based on the divide and conquer method, corresponding calibration and compensation methods are proposed. The comparative analysis is conducted within both the time and frequency domains, prior to and subsequent to the implementation of the compensation technique, and substantiates the efficacy of the proposed method. The findings from the experiment show that the DAQ system attains a sampling rate of 80 GSps and a bandwidth of 20 GHz. The system ENOB and SFDR could reach 6 bits and 40 dB at 20 GHz, and the changing curve of ENOB with frequency is given. The rise time of the acquisition system is 22 ps. Experimental data show that the system’s various indicators are at the leading domestic level.

Abstract:The high frequency and high voltage pulse is one of the key technologies for the insulation evaluation of inverter-fed motors. Generating, managing, and safeguarding such pulses deviates significantly from traditional high voltage DC and sinusoidal conditions, presenting critical challenges in their creation. Consequently, a high frequency and high voltage pulse generator has been devised, boasting an adjustable edge time ranging from 40 to 500 ns. This generator leverages field programmable gate array technology, achieving clock precision at the nanosecond level. Adopting a half-bridge structure initially, the design advances to a full-bridge topology capable of handling a test capacity of 1 600 pF and yielding a peak-to-peak voltage of 24 kV. Incorporating a meticulously crafted multi-level voltage drop protection algorithm, along with real-time pulse width detection, nanosecond-level pulse control, and various antiinterference measures, ensures the generator′ s reliable operation amidst high-frequency electromagnetic interference. Experimental validation confirms that the generated voltage maintains a pulse width and dead time of no less than 1 μs, with a maximum repetition frequency of 100 kHz. This innovation heralds a groundbreaking platform for comprehensively testing the insulation parameters of inverter-driven motors.

Abstract:The power supply system is an important component of the satellite, providing continuous and stable energy for satellites on orbit. Establishing a digital accompanying flight model for the satellite power supply system is of great significance for the monitoring, simulation, control, and prediction of its on-orbit status. Aiming at the problems of low fidelity and difficulty in virtual and real synchronization of existing models, a modeling method for digital accompanying flight of satellite power supply systems is proposed. Firstly, a high-fidelity mechanism model of the satellite power supply system is established through the circuit equivalent method, mathematical equivalent method, averaged model-state space and small signal method and others. Secondly, in order to obtain the dynamic input and output, the methods that telemetry data dynamic parameter identification and parameter extraction are used, thereby driving the dynamic update of the mechanism model. Finally, experiments were carried out using high-orbit remote sensing satellites to verify the digital accompanying flight of the model. As shown in the experimental results, the typical output parameters of the digital flight simulation model presented in this paper demonstrate an accuracy exceeding 95% . Moreover, the model can achieve virtual and real synchronization mapping and digital accompanying flight with on-orbit satellites every 0. 5 seconds.

Abstract:To suppress the static time error of the ultrasonic time-difference measurement system, this paper proposes a new topology based on the auxiliary impedance matching branch, and finally a prototype ultrasonic time-difference measurement system is successfully developed based on this topology. Based on the prototype and the homemade transducer, the static time errors at different driving frequencies, different matching resistances and high-temperature operating conditions (200℃ , 230℃ and 260℃ ) were tested, and the accuracy and feasibility of the whole set of system were verified by the real-flow calibration experiments. The results show that the mean value of the system static time error and its fluctuation range can be effectively suppressed by the incorporation of the auxiliary impedance matching branch, and a minimum static time error of -0. 279 ns is realized under the high-temperature environments of 200℃ , 230℃ and 260℃ . Finally, this system achieves an accuracy level of up to 0. 15% at a low flow rate of 440 m 3 / h, and an accuracy of up to 0. 05% in the range of medium and high flow rates of 1 503 ~ 4 397 m 3 / h, which verifies the feasibility of the auxiliary impedance matching branch, and provides a high-precision and low static time difference solution for ultrasonic time difference measurement.

Abstract:To solve the problem of high-precision two-dimensional attitude measurement for long-distance laser tracking cooperative targets, a small-range two-dimensional attitude measurement method based on the collimated laser is proposed in this article. Firstly, the optical characteristics of the angular prism and the principle of laser tracking based on the angular prism are described in detail. Secondly, a two-dimensional attitude measurement method using a cube-corner prism with incision and photodetector is proposed, and the mathematical relationship among laser beam vector, cube-corner prism with incision, photodetector, and the cooperative target is analyzed, the attitude solution model is established, and a cooperative target is designed. Finally, a high-precision attitude measurement system is established. Experimental data acquisition and parameter model calibration are carried out, and accuracy analysis and evaluation are implemented through verification experiments. The experimental results show that the proposed measuring device is ingenious and practical. The measuring angle error is less than 0. 011° in the working range of ±5°, which meets the demand of highprecision engineering applications.

Abstract:Accurate fault feature extraction is an important part of achieving bearing fault diagnosis. The convolutional sparse representation can characterize the shift-invariant property of features, which is very suitable for rolling bearing fault feature extraction. However, the convolutional sparse representation ignores the periodicity of fault impulse features and the difference of signal characteristics at different scales, which restricts its feature extraction ability under the interference of harmonic components and background noise. Therefore, a weighted multiscale convolutional sparse representation is proposed for separating the periodic fault impulse features in vibration signals to achieve bearing fault diagnosis. Specifically, in the constructed sparse representation model, the original signal is converted to different scales using multiscale transformation, and different weights are utilized in different scales to suppress the interferences such as harmonic components. Meanwhile, to promote fault impulse features, a regularization term that constrains the periodicity of the sparse coefficient of fault features is established to improve fault feature separation ability. In addition, the alternating direction method of multipliers and the majorization-minimization method are introduced to derive an iterative solving algorithm. Finally, by analyzing the waveform and envelope spectrum of extracted features and two quantitative evaluation indicators of fault information, the excellent capability of the proposed method in fault feature extraction and diagnosis of bearing compound faults is verified.

Abstract:The order tracking is an effective method to solve the problem of variable speed fault diagnosis. The key premise is that there is a speed signal as a reference. However, due to the influence of strong background noise and weak harmonic relations, the accuracy and adaptability of the existing speed estimation methods need to be further improved. Therefore, an improved multi-order probability approach (MOPA) based on multi-sensor signals is proposed to estimate the instantaneous speed. Firstly, according to the unity of fundamental frequency and the difference of dominant component of different sensor signals, the time-frequency diagram with a strong harmonic relationship is constructed through the normalization and fusion of instantaneous slices of the time-frequency diagram. Secondly, to eliminate the intermittent constant frequency and short-time broadband background noise in the transverse and longitudinal direction of the time-frequency diagram under time-varying conditions, a sliding noise reduction method is proposed. Finally, MOPA is implemented based on the processed time-frequency diagram to realize automatic estimation of instantaneous speed, and the fault diagnosis problem under variable speed of wind power gearbox is solved by combining the order tracking method. The measured data evaluate that the accuracy and adaptability of the instantaneous frequency estimated by the improved MOPA are better than those of the opposite methods. The mean absolute percentage error is 0. 56% , which is lower than 15. 73% , 13. 99% , and 1. 21% of the comparison methods. Combined with the order analysis, the abnormality of the wind turbine gearbox under variable speed is diagnosed.

Abstract:The decline in lithium battery capacity can compromise its safety and stability, emphasizing the importance of accurate capacity estimation for better decision-making. However, prevailing black-box data-driven models face challenges in safety-critical applications due to their lack of interpretability. Additionally, these models often rely on fixed operating conditions for feature extraction, limiting their suitability for real-world scenarios with variable conditions. To address these issues, this paper presents an enhanced adaptive neural fuzzy inference system (ANFIS) designed to accommodate random operating conditions. Firstly, the factors influencing capacity degradation are analyzed, and relevant features are extracted and refined from battery measurement data. Subsequently, an activation mechanism simplifies the system structure, while an attenuation coefficient is introduced to tailor the model to battery cell characteristics. Further refinement is achieved through continuous optimization of fuzzy clustering centers using an adaptive particle filter algorithm. Validation of the system is conducted using the NASA random walk battery dataset, resulting in a capacity estimation root mean square error (RMSE) of 3. 73% . Comparative analysis demonstrates that the proposed system offers superior accuracy and a degree of interpretability when contrasted with other methods.

Abstract:Chatter is considered as one of the important factors affecting the quality of machining processing, yet traditional chatter monitoring algorithms often lack sensitivity to chatter onset and struggle with real-time adaptability in setting monitoring thresholds. To tackle this challenge, we propose a self-adaptive online monitoring method for early chatter identification. The use of the improved wavelet packet energy entropy ( IWPEE) algorithm enhances chatter feature extraction, thereby improving recognition accuracy, robustness, and efficiency. Moreover, an improved Pauta criterion dynamically determines the chatter monitoring threshold, enabling adaptive threshold calculation under varying processing conditions. Subsequently, we develop online chatter monitoring software to meet the practical monitoring demands of machining. Validation of the proposed algorithm through simulation signals and cutting experiments demonstrates a 360% increase in sensitivity compared to traditional entropy-based methods. Additionally, the adaptive determination of the threshold by the improved Pauta criterion ensures successful monitoring of chatter onset during its growth stage. Furthermore, significant enhancements in threshold stability and adaptability relative to traditional threshold algorithms are demonstrated.

Abstract:Addressing the challenge of extracting fault characteristics from vibration signals of inter-shaft bearings in aeroengine amidst complex transmission paths and strong background noise, this paper proposes a method based on local energy density (LED) for fault feature extraction and diagnosis. Initially, singular spectrum analysis is employed for preliminary noise reduction of the fault signals and optimal reconstruction order determination using a cosine-based approach to preserve crucial fault information within the signal. Subsequently, a novel metric, LED, is introduced to quantify the energy ratio of fault characteristic frequencies and their harmonics within a local frequency range. This metric not only effectively extracts subtle fault features but also demonstrates robustness against deviations between actual and theoretical fault frequencies. Utilizing LED as the fitness function, the method enhances fault features in the denoised signal through maximum correlation kurtosis deconvolution (MCKD) optimized by the artificial hummingbird algorithm. Fault diagnosis is achieved through envelope spectrum analysis. The effectiveness of the proposed method is validated through intermediary bearing fault simulation and noise addition experiments, showing a 20. 7% to 218% increase in the fault feature coefficient (FFC) and a 22. 9% to 134% increase in LED compared to existing fault diagnosis techniques. The method accurately identifies the characteristic frequencies and harmonics of outer race faults under noise conditions of 0 dB, -4 dB, and -10 dB, indicating that the proposed SSA_MCKD can effectively reduce the influence of signal noise and extract fault features of rolling bearing.

Abstract:Time of arrival ( TOA) is a key feature utilized in localizing impacts, with the ongoing advancements in signal processing technology, a variety of time-frequency models can be found in the literature for its measurement, but the selection usually depends on human experience. In addition, considering the systematic error of the measurement model and the uncertainty caused by measurement noise, the traditional TOA-based impact localization method is insufficient. Therefore, an impact localization method for composite structures based on Bayesian estimation and data fusion is proposed in this paper. First, a variety of different time-frequency models are applied to obtain the TOA data of the impact response signal. Then, according to the uncertainty of measurement error of TOA data, the posterior probability density function of impact position is constructed by using the Bayes theorem. Afterward, the posterior distribution of the impact position parameters is estimated using the Markov chain Monte Carlo (MCMC) sampling method, and the normal distribution is used to fit the posterior distribution. Finally, the impact position probability distribution obtained from different TOA data is fused, and the final decision is made by using the fused probability distribution. The feasibility of the proposed method is verified by the drop weight impact experiment on the composite stiffened plate, the average localization error is only 0. 94 cm, which is more reliable and accurate than the traditional TOA-based impact localization method, and also has advantages in robustness and localization time.

Abstract:When the electrical contact points of the main circuit of an electric vehicle have poor contact, it is extremely easy to generate arc faults, directly threatening the life safety of the occupants. This paper proposes an arc fault detection method based on Customized differential threshold filtering, segmented maximum standardization, and statistical numerical rules. Firstly, an electric vehicle arc fault experimental platform was built around the real electric vehicle Geely Emgrand EV450, to conduct arc fault experiments under different working modes. Then, taking the power supply terminal voltage as the object, the signal is subjected to wavelet decomposition. The low-frequency coefficients obtained from wavelet decomposition were subjected to Customized differential threshold filtering and segmented maximum value standardization. Finally, the number of identical values in the normalized data was counted, and series arc faults were detected using a threshold method. The paper conducted in-depth analysis of the model′s sample length, differential threshold ratio, number of segments in maximum normalization, and preprocessing method selection, optimizing the parameters to further improve the model performance. The results show that the accuracy of the constructed model for detecting electric vehicle arc faults is 98. 35% , with good real-time performance. Through generalization analysis, algorithm time complexity analysis, and comparative analysis with other arc fault detection models, it is proven that the proposed model has good applicability for arc fault detection in electric vehicles.

Abstract:In continuous motion, based on surface electromyography ( sEMG) signals, exoskeleton robots and humans collaborate in motion control. Muscle fatigue will affect the flexibility and robustness of human-machine collaborative control. This article innovatively proposes the use of Entropy of Incremental Fuzzy Entropy and constructs a fatigue characterization model, and objectively divides the stages of muscle fatigue; Collect sEMG signals of twelve muscles in the lower limbs during repeated continuous leg lifting movements, propose a method based on the variability sensitivity coefficient SVR to determine muscle fatigue sensitivity, achieve effective muscle selection for this movement, reduce data dimensions, propose an adaptive threshold action segmentation method based on mean squared product, segment the complete signal and extract a single action signal sequence, and analyze and calculate the fatigue trend through this model. The experimental results of the subjects show that the model proposed in this paper has a more obvious gradient feature for muscle fatigue characterization compared to time-domain and frequency-domain algorithms, and has better fatigue characterization ability compared to fApEn and FFDispEn. Davies Bouldin Index for fatigue level clustering is 0. 39. This provides a reference for improving the collaborative control of exoskeletons and achieving phased compensation assistance for fatigue.

Abstract:Enhancing evacuation efficiency is of paramount importance in the field of evacuation systems research. Evacuation systems often present observability limitations, and any abnormal observation of pedestrian density at the exits can diminish the effectiveness of evacuation control. Therefore, correcting the abnormal observation information at exits becomes imperative for improving evacuation performance. To address this issue, an algorithm based on the extended Kalman filter is proposed to predict pedestrian density, and a correlation mapping between normal and abnormal pedestrian densities is established. The algorithm incorporates a neural network fitting method to identify the parameters of the state and observation functions in the extended Kalman filter algorithm, enhancing the accuracy of system modeling by approximating nonlinearity. Moreover, an iterative update mechanism utilizing the error covariance matrix allows for fast prediction and correction of pedestrian density. Additionally, the algorithm incorporates a density control algorithm to formulate a pedestrian flow evacuation control strategy for abnormal evacuation scenarios. Comparative simulations are conducted by using the evacuation model in abnormal evacuation scenarios to evaluate the effectiveness of the proposed algorithm. The results show that, compared to the evacuation control strategy without data correction, the proposed algorithm achieves efficiency improvements of up to 38. 9% and 23. 26% in abnormal evacuation simulation and human-controllable scenarios, respectively, which provides an effective solution approach for control strategies in abnormal evacuation scenarios.

Abstract:To address the problem that the positioning accuracy is reduced due to the rapid divergence of heading error in the low-cost foot-mounted pedestrian navigation system, from the perspective of reducing the attitude update error, based on the equivalent rotation vector theory and Fourier expansion, a foot-mounted pedestrian navigation algorithm based on the function fitting attitude update algorithm is proposed. Firstly, based on the equivalent rotation vector method, the sine and cosine functions are used to fit the angular velocity of the foot motion. The Taylor expansion and equation transformation are used to obtain the function fitting the attitude update method. Then, combined with the long short-term memory network (LSTM) zero velocity detection method, a foot-mounted pedestrian navigation algorithm suitable for a variety of gait is designed. Finally, WT901BC IMU is used as the hardware platform to carry out the verification experimental of multiple sets of closed-loop paths in different gaits, and the results show that, compared with the traditional foot-mounted pedestrian navigation algorithm based on the quaternion method or the two subsamples equivalent rotation vector method, the positioning error of the proposed method is reduced by 47. 66% and 42. 83% on average, and the heading error is reduced by 49. 99% and 44. 74% on average. Keywords:attitude update; equival

Abstract:Magnetic acoustic emission (MAE) is an important non-destructive testing method commonly used to evaluate the mechanical properties of ferromagnetic materials. However, there are few reports on the theory or numerical models of MAE. A theoretical MAE model considering the microstructure ( dislocation density and grain size) is proposed in this paper. The influences of magnetization parameters and microstructure parameters on the envelope of MAE signal are analyzed through numerical calculations. After that, the rationality of the MAE model is validated. Based on the MAE predicted signals with different hardness specimens, the dynamic hysteresis parameters and magnetized structural parameters in the theoretical model were inverted with the genetic algorithm. The results demonstrate that the MAE signals calculated from the theoretical model under the inversion parameters are in good agreement with the experimental signals, and the maximum error between the inversion values of the key hysteresis parameters and the theoretical calculation results is less than 15% . Therefore, the theoretical model can be used to predict the MAE signals.

Abstract:Underwater swimming manipulator ( USM), as a novel type of underwater robot, plays a significant role in the smooth completion of underwater observation and operation tasks due to its stable control. This article addresses issues such as the difficulty in accurately establishing the model, the challenge in observing external disturbances like water currents, and the difficulty in tuning the parameters of the controller for all six degrees of freedom in the stabilization control of the USM. The study proposes a control method based on fuzzy feedback linearization and an adaptive extended state observer. Firstly, the dynamic model of the USM is formulated. Secondly, considering the strong nonlinearity and strong coupling characteristics of the USM, a PD controller is designed after feedback linearization of the dynamic model, and fuzzy control logic is introduced to adjust the controller parameters. To reduce the impact of modeling errors and various uncertain disturbances present in the actual underwater environment on the control effect, an adaptive extended state observer is designed to compensate for inaccuracies of the model and external disturbances in real time. Ultimately, during lake trials, the six-dimensional root mean square error of this scheme is 0. 425 2, 0. 166 8, 0. 168 5, 0. 267 4, 0. 117 4 and 1. 003 3, which shows a significant improvement compared to traditional schemes. It shows the effectiveness of the proposed solution.

Abstract:Truck overload transportation is an enormous threat to road safety. Currently, the main identification method for truck overload has low identification efficiency and a small scope of supervision. To address these problems, this article proposes a truck overload identification method based on deep learning. Firstly, a method is designed for generating truck trajectory images specifically for the overload determination task, which can transform multidimensional spatiotemporal truck trajectory data into truck trajectory images, reducing data complexity while aggregating features. Then, we design a high-accuracy truck overload intelligent identification model TOI-Net, which consists of RepVGG modules and location attention modules. It can fully extract overload information features from truck trajectory data and efficiently complete the overload checkpoints task. Experiments are implemented on the truck overload dataset. The results show that the accuracy of the proposed method for overload identification is 96. 1% , with performance metrics higher than mainstream recognition networks, achieving precise, rapid, and comprehensive identification of overload behavior.

Abstract:Aiming at the severe waveform distortion of output vibration signal caused by nonlinear parameters of the long-stroke lowfrequency electromagnetic vibrator, the preliminary harmonic compensation control was carried out based on analysis of electromechanical coupling model and equivalent transfer function. By calculating the first and second-order approximate vibration displacement solutions of the electromechanical coupling equation that characterize the nonlinear characteristics of the vibrator, the accurate harmonic compensation control quantities that need to be superimposed on the input signal was analyzed, and then the high-precision compensation control of the output signal waveform distortion was obtained. The simulation analysis and experimental test results show that the control method based on transfer functions and approximate solutions could effectively compensate the output signal harmonics in the entire frequency range. Typically, the acceleration distortion corresponding to 0. 1 Hz was reduced from 22. 81% to 4. 5% by transfer function control, and further reduced to 4. 42% and 1. 78% by the first and second-order approximate solution control, respectively. The results show that the second-order approximate solution method exhibits higher harmonic control accuracy. Keywords:low-frequency electromagnetic vibrator; harmonic control; electromechanical couplin