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Progress and prospect of memristive sensor
Hu Wei, Wang Youhong, Kang Kaijin, Dong Caili, He Yong
Abstract:
As the fourth type of passive electronic device, memristor is considered to be a strong competitor of the next generation of non-volatile memory and artificial synaptic device due to its simple structure, fast storage speed and high integration density. Currently, resistive, capacitive, and inductive sensors have clear principles, and are extensively investigated and widely applied, which are constructed based on the three types of passive electronic devices—resistor, capacitor, and inductor. However, the research of memristor based memristive sensor is still in the preliminary stage. In this paper, the properties, materials, mechanism and applications of memristor are introduced briefly. Based on the research of memristor in sensing field, the concept and definition of the memristive sensor are proposed. Then, the research progress on the sensing characteristics of physical quantity, chemical quantity and biological quantity by memristor is reviewed. Finally, the challenges and future prospects of memristive sensors are discussed, providing feasible directions for the future research of memristive sensor.
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A joint optimization super resolution imaging method of optical neural network based on a telescopic system
Sun Youhong, Zhang Tao, Liu Jianan, Liu Jianhua, Wang Chao
Abstract:
Optical telescope is an important tool to obtain optical information of distant objects, and has wide application in astronomical observation, remote sensing, and optical surveillance. Resolution is an important indicator of a telescope's ability to observe objects in detail, and the traditional way to improve the resolution of a telescope is to build a larger aperture telescope, which leads to a significant increase in construction and maintenance costs. In this paper, an optic-neural network joint optimization method is proposed. The point diffusion function of the telescopic system is equivalent to a single-core convolution layer, which is integrated into the front end of the image super-resolution reconstruction network for joint training, and the point diffusion function obtained by phase mask reconstruction training is introduced into the optical path, so as to achieve collaborative optimization of the two, and effectively improve the resolution of the observed image. In this paper, a high-performance generative adversarial network is constructed, whose training parameters are smaller than the existing unsupervised networks, and the reconstruction speed is much faster than the existing unsupervised networks. This network adopts double discriminator architecture to improve the ability to extract detailed features. The designed cascade residuals make full use of the extracted feature information at all levels, expand the information propagation path, and improve the reconstruction efficiency. The simulation results show that compared with the simple deep learning method, the PSNR and SSIM of the super-resolution image reconstructed by the joint optimization method in this paper are increased by 3.98 and 0.06 respectively in the simulation data set, and the image details are rich and easy to distinguish. Verification experiments show that the fringe image reconstructed by the joint optimization method in this paper has the highest contrast and is easier to distinguish.
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Research on ice bonding state sensing method for energy-efficient electro-thermal de-icing
Zhou Yi, Guo Yudong, Gui Kang, Ge Junfeng, Ye Lin
Abstract:
To address the difficulty of sensing the ice bonding state during aircraft electrothermal deicing, an in-situ in-body detection method based on the relaxation polarization effect is proposed. First, the mechanism of the influence of the phase state and molecular thermal motion on the ice adhesion effect is revealed, and the principle of detecting the four ice bonding states based on the complex impedance measurement is elucidated. Then, an integrated functional film for detection and de-icing is designed, and the dynamic change process of the interfacial phase state is characterized in ice melting experiments, which identifies the key features of the ice bonding state sensing, and then establishes the recognition method for the weakening of the ice bonding state. In the pre-stressed de-icing experiment, the method can effectively determine the time range of the ice shedding, which is expected to significantly improve the accuracy of the ice bonding state sensing in the electro-thermal de-icing process and provide key technical support for the energy-efficient electro-thermal de-icing of aircrafts.
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Method for suppressing near-field interference in thin-film nanocomposite piezoresistive ultrasonic sensor
Pan Xingchen, Yuan Keyi, Dong Rui, Wang Kai, Rao Jing
Abstract:
Compared to conventional ultrasonic sensors, the flexible piezoresistive film ultrasonic sensor based on nanographene/polyvinylpyrrolidone (GNP/PVP) offers advantages such as light weight, flexibility, wide bandwidth, easy integration, and the ability to conform to curved surfaces, showing great potential in non-destructive testing and structural health monitoring. However, when the piezoresistive film ultrasonic sensor is close to the ultrasonic emission device, instability in the connection between the sensor and the conversion circuit, or inadequate electromagnetic shielding in the testing system, can introduce significant electromagnetic interference. This interference results in strong signal crosstalk, affecting the acquisition of acoustic field information and the quality of ultrasonic signals within the near-field range. To effectively suppress crosstalk signals, this paper employs phase shift and differential amplification compensation methods, as well as an iterative approximation method based on cross-correlation to reconstruct the ultrasonic signals. Experimental results show that signals processed with phase shift and differential amplification compensation methods can effectively remove the crosstalk received by the GNP/PVP piezoresistive film ultrasonic sensor. Additionally, the iterative approximation method based on cross--correlation reduces the variance of the crosstalk signal interval by 99.18%, thereby more effectively suppressing the impact of crosstalk on the ultrasonic signal.
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Design of bandwidth extension controller for MEMS ring gyroscope
Cui Rang, Wei Wenqiang, Cai Qi, Liu Yupeng, Cao Huiliang
Abstract:
This article designs a pole-zero compensation proportional-integral controller for the sense mode closed-loop control of MEMS ring vibration gyroscope. The relationships between the mechanical sensitivity and bandwidth of the ring gyroscope and the difference of resonant frequencies and the quality factor are analyzed. The effectiveness of pole-zero on the gyroscope bandwidth is analyzed by the bode diagram. A pole-zero compensation proportional-integral controller (PZ-PIC) is designed. By using this controller in the sense mode closed-loop circuit, the bandwidth of the gyroscope can be expanded. In this way, the contradiction between the mechanical bandwidth and mechanical sensitivity of the ring gyroscope is addressed. The proposed new controller has the advantages of few parameters and simple parameter determination, which is suitable for the mass production using MEMS gyroscopes. After experimental evaluation, the bandwidth of the three prototypes is effectively expanded, with the expanded bandwidth exceeding 90 Hz.
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Study on the magnetic fluid inclination sensor based on magnetization-eddy current effect
Yu Jun, Li Decai, Wang Deyi, He Xinzhi
Abstract:
The stable levitation of permanent magnets in magnetic fluid has a wide range of applications in tilt sensors. In this article, a magnetic fluid inclination sensor using ring permanent magnets, soft magnetic metals, and non-magnetic metals as inertial mass is proposed. The magnetic fluid levitation force received by the inertial mass is used as restoring force. The magnetization effect of soft magnetic metals and the eddy current effect of non-magnetic metals are used together to increase the sensitivity of the inclination sensor. The soft magnetic and non-magnetic metals are used to wrap the permanent magnet to prevent damage under severe impact. The calculation formula is derived to calculate magnetic fluid levitation force, the influence of the axial length of inertial mass on the levitation force and coil inductance has been studied, and the effect of signal source frequency on the output voltage of the sensor is demonstrated. The magnetic fluid inclination sensor is used for the measurement of inclination. When the range of inclination angle is 0~42°, the linearity between the output voltage and the inclination angle is about 6.5%, the sensitivity is 5 mV/°, and the hysteresis is less than 1%.
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Diamagnetic levitation inclinometer based on electromagnetic induction
Xu Yuanping, Yuan Gaozhan, Zhou Jin, Yu Min
Abstract:
As an important angle measurement device, the inclinometer is widely applied in aerospace attitude monitoring and other fields. High-resolution and high-sensitivity inclinometer is a prerequisite for accurate angle acquisition. However, existing inclinometers are often affected by friction-induced accuracy limitations, hindering their applications in precision measurement fields. This article proposes a diamagnetic levitation inclinometer based on electromagnetic induction, characterized by high resolution and non-contact measurement. By utilizing diamagnetic materials as a sensitive element and constructing a permanent magnet array with a potential energy well, stable levitation of the sensitive element is achieved. The measurement principle of the inclinometer is proposed based on the electromagnetic induction effect between the measurement unit and the sensitive element, the levitation position of the sensitive element changes with the input angle. An experimental platform is established to test the sensor′s performance. Experimental results show that the inclinometer can achieve dual-axis angle measurements within a range of ±1.3°, resolution of 0.005°, and linearity of 0.18%. It has significant potential for applications in the precision measurement of small tilt angles.
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LiDAR/IMU matching localization algorithm based on implicit neural map
Gao Wang, Zhao Heng, Liu Hong, Pan Shuguo, Huang Feixuan
Abstract:
Aiming at the problem that existing matching localization algorithms rely on high-memory and dense point cloud map, a light detection and ranging (LiDAR)/inertial measurement unit (IMU) matching localization algorithm based on an implicit neural map is proposed. Firstly, a lightweight and high-resolution implicit neural map is constructed using a shallow perceptron to predict the signed distance field. Secondly, low-frequency state estimation based on the lightweight implicit neural map is realized through a point and implicit neural model registration method. Meanwhile, to address the challenge of handling aggressive motion during single LiDAR implicit registration, an IMU pre-integration method is introduced. This provides predictive state estimation for implicit registration, reducing the number of iterations required during the registration process. Finally, robust high-frequency state estimation is realized by fusing the LiDAR odometry factor and IMU pre-integration factor based on the factor graph. Experimental results in the KITTI dataset, as well as in real-world indoor corridor and outdoor campus environments, demonstrate the effectiveness of the proposed algorithm. The memory usage of the implicit neural map is reduced by 87% compared to traditional point cloud maps, enabling a more lightweight map representation. In the KITTI dataset, the proposed LiDAR implicit registration algorithm improves the positioning accuracy by 43.4% compared to the traditional normal distributions transform (NDT) algorithm. Furthermore, the fusion algorithm, which incorporates IMU data, achieves a 60% improvement in positioning accuracy compared to the NDT-IMU algorithm. The centimeter-level real-time positioning capability of the proposed algorithm in small scenes is also verified in both practical indoor and outdoor campus. Meanwhile, analysis reveals that the integration of IMU significantly reduces the computational time required for implicit neural map registration, effectively enhancing real-time localization performance.
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High power factor control of wide AC input frequency rangecurrent-source PWM rectifiers for aerospace applications
Guo Qiang, Yang Xinyu, Li Shan, Dai Yunlong, Zhang Xiaocheng
Abstract:
When three-phase current rectifiers are used in the active front end of the aviation power supply system, the wide input AC frequency (up to 800 Hz) causes a surge of reactive power in the AC filter capacitor, which leads to the reduction of the system power factor. To effectively reduce the filter capacitor current, a mathematical model in the synchronous rotating coordinate system is formulated, which is based on the feed-forward decoupling control strategy. The capacitor current is compensated by the phase quantity method. In addition, to further improve the power factor, a power factor current vector coordinated control strategy is proposed to realize the control of reactive current. The control loop and parameter sensitivity are analyzed by using MATLAB, and the zero-pole configuration and gain design scheme of the control loop are given. The model-based development and automatic code generation are adopted to realize the high efficiency of the software part development. Finally, the proposed control method is evaluated by simulation and experiment for its correctness and effectiveness. Experimental results show that the total harmonic distortion rate of the grid-side current is less than 5% and the power factor of the system is always above 0.99 throughout the very wide input frequency.
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Research on the anomaly detection method based on EMD in the high-speed data acquisition system
Li Chengyang, Tian Shulin, Yang Kuojun, Ye Peng, Zhao Yu
Abstract:
When high-speed acquisition systems handle data streams reaching tens of GSa/s, the limitations of real-time processing speed prevent the system from detecting occasional abnormal signals in real time, leading to signal omissions. Traditional anomaly detection relies on the a priori characteristics of the signal. However, these methods have low capture efficiency for episodic abnormal signals with unclear characteristics and irregular morphology. Thus, this article proposes a real-time anomaly detection method using empirical mode decomposition (EMD) to improve the system′s ability to capture anomalous signals. Firstly, the feature point extracted based on edge features is used as the start point of EMD, reducing the complexity of anomaly detection. Secondly, the non-noise intrinsic mode functions obtained from the EMD are used to reconstruct the normal signal template, and anomaly detection is carried out based on the degree of match between the test signal and the normal signal template. Finally, a parallel EMD is implemented in hardware to improve anomaly detection efficiency. By detecting anomalies in the modulated signals, the real-time anomaly capture rate of the proposed method is 95%, which represents a significant improvement over the traditional method.
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Study of passive detection methods for dynamic electrochemical impedance spectroscopy of batteries
Wu Xu, Yang Lijun, Xiao Yanlin, Wang Ping, Li Siquan
Abstract:
Electrochemical impedance spectroscopy (EIS), as a non-destructive testing method, is widely used to study the internal electrochemical processes of batteries. Dynamic impedance spectroscopy (DEIS) testing during charging and discharging is more capable of truly reflecting the health status of batteries. At present, DEIS detection mainly relies on electrochemical workstations. Due to the limitation of the excitation source power of the test instrument, it mostly stays in the detection and research of laboratory cells, and cannot be applied to the field detection of high-power module batteries yet. In this article, we propose a battery DEIS detection method without additional excitation, and design a static and dynamic EIS detection system applicable to battery cells and modules. Taking lithium iron phosphate batteries and standard RC resistor cells as testing objects, the proposed testing effects of traditional electrochemical workstations and the testing system are compared and analyzed. The results show that the proposed scheme not only has small testing error and good stability, but also has the significant advantage of not being limited by the power of the external excitation source, which has a good application prospect in the future evaluation of the operating state of large-scale energy storage batteries.
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Reliability assessment of low-voltage DC circuit breaker based on morphological characteristics of contacts
Li Kui, Zhang Yue, Jiang Hui, Hou Tianhang, Guo Qingbin
Abstract:
In this article, a reliability assessment model based on the morphology characteristics of the ablative region of the contacts is proposed for the change of surface morphology of the contacts. Firstly, the contact morphology evolution process and ablation mechanism are analyzed. The contact images are segmented using the adaptive multi-threshold segmentation algorithm based on the improvement of the image block weight, and the melting and splashing part of the arc-guiding plate is extracted as the region of interest. The area and center of mass of the binary map of the region of interest are analyzed as the two key features of the degradation of the contact performance. The change characteristics of the contact morphology are analyzed, and the change points of different degradation stages of circuit breakers are obtained by the Gath-Geva (GG) fuzzy clustering algorithm, and the multi-stage Wiener degradation model with binary feature correlation is formulated by Copula function. The goodness-of-fit of the KS test is less than 0.3, and the model is evaluated by the electric life experiments of low-voltage DC circuit breakers.
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Signal initial phase jitter compensation method based on quantum voltage and digital sampling technology
Chang Zhou, Shi Zhaomin, Wang Lei, Lu Zuliang, Xu Suan
Abstract:
When the AC voltage is measured precisely based on programmable Josephson voltage standard and differential sampling technology, the amplitude error of sinusoidal signal after multi-period averaging is caused by the unstable phase lock of AC voltage source and incomplete clock synchronization. This paper proposes an error quantitatively evaluation method based on the Monte Carlo method to quantitatively evaluate the amplitude error distribution caused by random jitter of the initial phase within the range of Δφ. The findings reveal that the amplitude error primarily depends on the range of phase jitter and is minimally influenced by the number of averaging cycles. For a jitter range of 5 mrad, the amplitude error can reach a magnitude of 10-6. To address this, a compensation method based on the phase jitter range is proposed. By calculating the compensation coefficient and applying the correction, the amplitude error caused by phase jitter is significantly reduced. The compensation effect improves with an increasing number of averaging cycles when the jitter range is fixed. A verification experiment was conducted to validate the proposed compensation method. For a jitter range of 50 mrad, the amplitude error exceeded 1×10-4 V/V after multi-period averaging but was reduced to a magnitude of 10-6 magnitude. These results confirm the effectiveness of the proposed method.
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Mechanism and experimental study of beam director sealing based on magnetorheological fluid
Shen Yurui, Wang Qiyu, Peng Lai, Hua Dezheng, Liu Xinhua
Abstract:
The beam director is the core component of the optical system, and the gas sealing of the internal optical transmission channel plays a decisive role in the overall performance of the system. To address the issues of easy wear and significant relaxation in traditional packing seals, this paper designs a beam director sealing structure based on magnetorheological fluid and studies the working principle and theoretical model of magnetorheological fluid sealing (MRFS) technology. Firstly, a MRFS structure with permanent magnet array and anti-centrifugal type is designed to meet the specific requirements of large diameter seal of beam directors. Secondly, the observation experiment of the rheological properties of the micro-magnetorheological fluid is carried out to analyze the mechanism of MRFS at the micro-scale. At the same time, the macroscopic rheological properties are tested based on rheometer, the yield model of magnetorheological fluid based on magnetorheological complex field is fitted, and the modified MRFS pressure resistance formula and friction moment model are further established. Finally, the equivalent experimental platform of the MRFS of the optical path of the beam director is built, and the pressure resistance test and friction torque test were carried out under different temperature conditions, rain and salt spray simulation conditions. The experimental results show that the MRFS can fully meet the requirements of the optical path seal of the beam director, and the modified model can effectively reflect the influence of temperature on the performance of MRFS. It provides an important theoretical basis and practical guidance for the innovation of the optical path seal technology of the beam director shafting.
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Research on precision measurement method of pneumatic drive for radial run-outof bearing
Wang Junfeng, Zhao Wenhui, Wang Fei, Chen Jiuxu, Mao Li
Abstract:
In order to achieve the online automatic measurement of the radial runout of the outer ring of P4/P2 angular contact ball bearings, we proposed a relative Pneumatic driven precision measurement method and have developed an accompanying measuring device. Through finite element analysis, we assessed the influence of both motor drive and pneumatic drive on the measurement accuracy. The results show that pneumatic drive exerts a lesser influence on accuracy compared to continuous motor drive, leading us to adopt pneumatic actuation for our device. A nonlinear mathematical model of the axial vertical deviation angle, offset and radial vertical deviation angle of the bearing, as well as the measurement error, is established based on the geometric relationship between the aforementioned variables and the axis deviation of the trigonometric function transformation. The results of the numerical analysis demonstrate that when the axial vertical deviation angle is within the range of 0°~0.1°, the offset is within the range of 0~0.1 mm, and the radial vertical deviation angle is also within the aforementioned range. The maximum value of comprehensive measurement error is 7.5×10-6 times the radial runout of the outer ring. Finally, two groups of 7008C/P4 angular contact ball bearings (factory-verified outer ring radial run-out of 2, 3 μm) were employed for accuracy verification. The results indicate that the measurement repeatability is 0.3 μm.
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Research on calibration methods for tool coordinate systems in vision-guided robotic grinding systems
Song Anyu, Deng Huiming, Yang Liang, Zhou Zhongchao, Zhang Lieshan
Abstract:
The calibration of the tool-robot end coordinate system position relationship in the vision-guided robot grinding system is the key to determine the grinding accuracy. In order to solve the problem of poor calibration accuracy and low efficiency in the calibration of the robot tool coordinate system, this paper proposes a calibration scheme based on the grinding contour deviation correction of the test block. The proposed method begins with high-precision hand-eye calibration using a combined optimization algorithm. Next, a line-structured optical sensor scans the standard test block before and after grinding to establish the ideal and actual positional relationship between the tool and the robot base coordinate system. A deviation matrix is introduced to compensate for discrepancies in the tool-to-robot end coordinate system, effectively achieving tool coordinate system calibration. Simulation and experimental results show that the position deviation of the tool coordinate system is within 0.25 mm, and the attitude deviation of the three axes of the coordinate system is less than 0.01°. Furthermore, pipe seam grinding experiments reveal that the residual height of the weld seam after grinding is within 0.2 mm, meeting most industrial requirements.
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Inertial navigation algorithm for quadruped robotassisted by foot-end inertial information
Lu Yongle, Su Sheng, Luo Yi, Xu Xiaodong, Che Yi
Abstract:
To address the problem of rapid decline in positioning accuracy for quadruped robots when satellite signals are unavailable and environmental perception degrades, this paper proposes an inertial navigation algorithm for quadruped robots assisted by foot-end inertial information. Firstly, a leg odometry observation model is constructed based on foot-end inertial data and joint encoder readings to compensate for velocity loss caused by the stationary contact assumption. Subsequently, a temporal convolutional network (TCN) and bidirectional gated recurrent unit (BiGRU) are employed to extract long-term and short-term features from the foot end inertial and joint data, enabling robust estimation of stationary contact intervals. The proposed odometry observation model is employed as measurement input for an Invariant Extended Kalman Filter (InEKF) to correct inertial navigation errors during stationary intervals. Long-distance outdoor localization experiments demonstrated the effectiveness of the algorithm, achieving over 96% accuracy in stationary interval estimation. In open-loop tests, the endpoint error was only 0.93% of the total traveled distance. In mixed-terrain closed-loop experiments, the average eastward and northward errors were 1.07 m and 0.74 m, respectively, highlighting the proposed method's ability to maintain high positioning accuracy over extended periods without relying on external data.
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Camera pose estimation based on hybrid frequency domain and Transformer
Yang Aolei, Gan Shaoying, Yang Banghua, Miao Zhonghua, Xu Yulin
Abstract:
To address the challenges of camera pose estimation and mobile robot localization, a camera pose estimation method is proposed based on a hybrid frequency domain Transformer to predict the position and orientation of a camera from RGB images. Firstly, a camera pose estimation dataset, RotIndoor, is constructed based on indoor scenes, with each sample containing an RGB image of the scene and the ground truth camera poses obtained from a VICON system. Secondly, a pose regression network model, CamPose, is introduced, which effectively integrates spatial and frequency domain information to enhance the representation capability of image features, ultimately achieving higher accuracy in camera pose estimation. Specifically, CamPose incorporates a feature enhancement module based on differential convolution networks to capture fine-grained features within the images. Additionally, a frequency domain encoding layer is designed that applies Fourier transformation to extract frequency characteristics while integrating a frequency domain attention module, enabling the model to sensitively perceive the importance of different frequency components. Finally, experiments are implemented on the public datasets 7Scenes and RotIndoor. The experimental results show that the pose estimation error on the 7Scenes dataset is reduced to 0.17 m/7.85°, and the positioning accuracy on RotIndoor is improved by 23% compared to other methods.
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Fusion extraction of road drivable areas for unmanned sweepers
Li Sichun, Wang Jianjun, Song Weirun, Chen Bo, Wang Wenxin
Abstract:
Real and effective detection of the drivable area of roads is crucial for the path planning, real-time navigation, and obstacle avoidance of unmanned sweeping machines. After obtaining the laser point cloud of the road environment using 3D LiDAR, an improved ground segmentation algorithm is first used to segment the ground point cloud and non-ground point cloud. Then, for structured roads, candidate road boundary points and obstacles are determined based on the geometric features of the point cloud. The random sampling consistency algorithm is combined with the least squares method to extract the curb boundary lines and the isolation lines that remove obstacles on the road surface; For unstructured roads, laser reflectivity clustering is used to extract the road surface, and the sliding window method is used to determine the boundary points and extract the boundary curves using B-spline. In turn, by integrating the boundary lines obtained from the two algorithms using distance criteria, the drivable area of the road is obtained. Finally, an unmanned driving experimental system is used to extract drivable areas. The experimental results show that the fusion algorithm proposed has an accuracy of 96.5% and a recall rate of 92.7% for extracting the drivable area of laser point cloud data on mixed roads, with an average processing time of only 29 milliseconds. The accuracy of measuring the width of the real drivable area can reach 97.1%, proving that the road drivable area fusion algorithm has high accuracy and efficiency.
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Active disturbance rejection control for target tracking of wheeled mobile robot based on hyperbolic-tangent line-of-sight guidance
Liu Xiaosong, Zhu Huanhai, Shan Zebiao, Su Chengzhi
Abstract:
To enhance the target tracking capability of wheeled mobile robots (WMR) in complex environments, addressing disturbances from sideslip and slippage, this paper proposes a self-adaptive disturbance rejection control method based on a hyperbolic tangent line-of-sight guidance strategy. First, by analyzing the sideslip and slippage phenomena of WMR, a kinematic and dynamic model of WMR under disturbances is established. Then, a hyperbolic tangent line-of-sight guidance strategy based on a partition switching mechanism is proposed to improve the dynamic response capability of tracking. Subsequently, a self -adaptive disturbance rejection control algorithm is designed based on the dynamics of the WMR, utilizing an extended state observer to estimate unknown disturbances such as sideslip and slippage, achieving the target tracking task. Simulation and real-world experimental results show that the proposed method offers higher tracking accuracy and stronger disturbance rejection capability in complex environments. In actual tests, WMR quickly returned to the target tracking path after simulated slippage tests, with tracking errors stabilized within 0.025 meters, verifying the effectiveness of the proposed method.
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Research on motion planning of dual-arm service robot based on improved RRT algorithm
Guo Junfeng, Yuan Junping, Zhu Hongxia
Abstract:
To address the problems of end-effectors navigation efficiency, real-time performance, robustness, and path global optimization of a dual-arm service robot, a dual-arm service robot end-effectors path planning method is proposed based on the improved rapidly-exploring random trees algorithm. The method uses random sampling of two random tree parent node links, combines the target deviation angle, and random values to change the fixed-step search strategy, and introduces the artificial potential field method to locally optimize the random sampling, effectively balancing the randomness and blindness of the original algorithm, thus improving the path quality and shortening the planning time. After that, the path redundant points are removed and the path is smoothed by a cubic B-spline curve to optimize the end motion of the dual arms and reduce jitter. The master-slave planning method is used to plan the obstacle avoidance of the master arm first. Then, the slave arm plans the obstacle avoidance and collision avoidance paths according to the path of the master arm. Through MATLAB simulation and a real experimental platform, it evaluates that the algorithm outperforms the traditional RRT and other improved algorithms in terms of the number of iterations, the planning time, and the final path length under an environment of the same complexity. It significantly improves the efficiency and quality of the path planning of the dual-arm service robot.
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Fine-grained detection of ship objects by unmanned surface vehicles
Zuo Zhen, Guo Runze, Sun Bei, Su Shaojing, Sun Xiaoyong
Abstract:
In real sea scenes, the appearance of ship targets is similar and the edge information is blurred. The existing algorithms cannot meet the demands for fine-grained and real-time detection at sea. Therefore, a fine-grained detection method is proposed for ship objects based on multi-scale coordinate attention and multi-network self-supervised learning. First, a multi-scale coordinate attention and multi-network self-supervised learning module is designed. Feature enhancement is carried out on the basis of the original feature pyramid network and path aggregation network to improve fine-grained detection accuracy. Secondly, an unmanned surface vehicle (USV) sensing platform based on pods and electronic compass is constructed, and a dataset containing different ship objects such as fishing boats, speedboats, and merchant ships is prepared. Finally, the algorithm is tested and integrated into public and self-made datasets. The results show that the proposed algorithm has high detection accuracy for ship targets. The mAP50 reaches 94.6% in the real sea scene, which is 1.1% higher than that before the improvement. The operation speed is 27 fps, which verifies the robust and real-time fine-grained detection capability of USVs.
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Research on crack identification algorithm of anti-corrosion layer of air coupling ultrasonic pipeline
Lyu Ruihong, Xie Hailong, Wang Chenli, Liu Jianhua, Huang Ping
Abstract:
The integrity of the corrosion protection layer in natural gas pipelines is crucial for pipeline safety, as cracks are a major hidden hazard that can lead to leaks and accelerate corrosion. This paper employs an air-coupled ultrasonic intra-resonance detection technique, processing the ultrasonic detection signals using ensemble empirical mode decomposition (EEMD) to separate the intrinsic mode function (IMF) components. These components are then analyzed for their correlation with the original signal and subsequently processed using wavelet threshold denoising technique. The denoised signal is reorganized to improve clarity, and six dimensional feature quantities are extracted as inputs to the extreme gradient boosting (XGBoost) model. To further optimize the model′s performance, key parameters such as the number of iterations, tree depth and learning rate of XGBoost are optimized using the Newton-Raphson (NRBO) algorithm to achieve the best recognition efficiency and accuracy. The results show that the method achieves a damage recognition accuracy of 95.83% for the corrosion protection layer, with an average relative error of only 6.3% in crack length prediction. This demonstrates excellent anti-interference ability and high accuracy. The method provides a new idea for natural gas pipeline anti-corrosion layer detection, contributing to improve pipeline safety, reduce maintenance costs and extend pipeline service life.
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Total focusing method for highly attenuated thick-walled structures based on improved sparse representation
Zhou Zhiwei, Rao Jing, Niu Wei
Abstract:
To address the challenges of extracting defect features and low signal-to-noise ratio (SNR) of conventional ultrasonic phased array in the detection of highly attenuated thick-walled structures, an ultrasonic signal processing method based on the improved sparse representation is proposed to solve the problems in this article. Firstly, the ultrasonic full matrix capture is preprocessed to obtain segmented signals and their corresponding time-frequency parameters. Building upon the parameters, the adaptive Gabor sub-dictionaries are constructed. Then, the segmented signals are sparsely decomposed and reconstructed by the improved support matching pursuit algorithm. Compared with the conventional orthogonal matching pursuit algorithm, the improved support matching pursuit algorithm combines the adaptive Gabor sub-dictionaries and lp-norm(0
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Research on ultrasonic defect recognition method based on deep transfer learning
Wei Xinyuan, Zhou Jinghuan, Zhang Nan, Li Dan, Gu Haoran
Abstract:
Data-driven ultrasonic defect identification is widely used in aerospace and industrial manufacturing. However, obtaining a large amount of experimental data remains challenging. While software-generated simulation data is easier to acquire, it differs significantly from experimental data, leading to suboptimal performance when applied directly. In this paper, the ultrasonic defect recognition method based on deep transfer learning is proposed. Firstly, ultrasonic testing experiments and ultrasonic testing simulations were carried out for defects of different shapes, sizes and depths in the specimen at the same time, generating both experimental and simulation data. Furthermore, a deep learning model for ultrasonic defect recognition was established based on simulation data. Then, a small amount of experimental data was employed to fine-tune the pre-trained model through transfer learning. So as to established a model that can achieve accurate defect recognition on the experimental data. Finally, the prediction effect of the built model was verified through experiments. The results show that after transfer learning, the accuracy and precision of the ultrasonic defect recognition model significantly improved, both achieving a value of 0.956.
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Plastic deformation damage detection of TA1 industrial pure titanium by laser ultrasonics
Xia Zhenxin, Chen Dan, Yuan Peilong, Liu Tao, Yin Anmin
Abstract:
Laser ultrasonics longitudinal wave is used to detect the uniform and non-uniform plastic deformation of TA1 industrial pure titanium samples, and CEEMD is used to denoise the signal, obtain the laser ultrasonics eigenvalue and imaging, and obtain the microstructure information by EBSD experiment. Firstly, the imaging analysis of uniform plastic deformation samples shows that with the increase of deformation, the longitudinal wave sound velocity and attenuation coefficient in the frequency domain decrease, while the attenuation coefficient in the time domain does not change much. Then, the correlation between the microstructure and laser ultrasonics eigenvalue is analyzed, and multi-feature fusion parameters (MFFP) imaging is processed by the weighted average method. The overall distribution of MFFP shows a decreasing trend, which corresponds to the evolution of microstructure. Finally, the analysis of non-uniform plastic deformation shows that the laser ultrasonics longitudinal wave velocity and MFFP can distinguish different deformation regions, which is similar to the microstructure distinction regions, confirming that laser ultrasonics MFFP can be used to evaluate the plastic deformation damage of TA1 industrial pure titanium.
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Investigation of exciting SH-type welded guided waves based on electromagnetic acoustic transducers
Liu Bohan, Chen Zongze, Yu Jianxin, Yu Xudong, Deng Mingxi
Abstract:
When exciting SH-type guided ultrasonic waves in plate-like structures, traditionally utilized piezoelectric transducers often exhibit relatively poor ultrasonic beam directivity, thereby affecting the efficiency and accuracy of defect detection and localization. To address this limitation, the present study proposes an excitation approach based on Lorentz force electromagnetic acoustic transducers (EMAT) for generating highly directive feature guided waves (FGW), which are firmly confined to the welded joints of the plate. The configuration of periodic permanent magnets and a repulsion coil is used for fabrication of the EMAT. The propagation and directivity characteristics of far-field ultrasonic beam of SH-type guided waves in flat aluminum plate are analyzed using multiphysics finite element (FE) simulations in both the frequency and time domains, and are validated experimentally. The results have manifested that the proposed excitation method effectively generates SH-type FGW centered at 440 kHz in 4-mm-thick aluminum friction stir welded joints, demonstrating high mode purity and the presence of energy trapping effect localized to the weld seam. Compared to piezoelectric excitation of SH-type weld guided waves, the proposed method effectively suppresses the generation of lateral Lamb waves, providing solid support for the EMAT aided capabilities of long-distance propagation and defect detection.
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Sound quality prediction of vehicle interior noise based on physiological structure of human ear
Liu Zhaohai, Zhang Bo, He Zhiheng, Zhao Yu, Liu Houguang
Abstract:
A new sound quality prediction model for vehicle interior noise, based on the physiological structure of the human ear, is proposed to address the limitations of existing models that fail to effectively analyze differences in interior noise perception among individuals with varying hearing states. The model begins with the collection of interior noise samples from three cars, followed by subjective evaluation experiments to obtain subjective noise ratings. An auditory peripheral module is then constructed, incorporating an outer ear filter model, a middle ear lumped parameter model, a cochlear lumped parameter model, and a hair cell ciliary fluid coupling model to simulate the physiological structure of the human ear. An imitative auditory center module, designed to generate physiological loudness, sharpness, and roughness, is built using the Leakage Integral-and-Fire neuron model to simulate auditory nerve excitation, and deep neural networks to replicate the auditory center’s sound perception. The sound quality decision module is developed by integrating the psychoacoustic parameters generated by the auditory center module into a TabNet model to predict sound quality. Together, the auditory peripheral module, the imitative auditory center module, and the sound quality decision module form the complete sound quality prediction model. Finally, the model′s predictions are compared with those of existing models. Experimental results demonstrate that the proposed model accurately predicts vehicle interior noise quality, with an average prediction error of just 3.3%, outperforming the 6.4% error of artificial neural network-based models and the 7.7% error using the Zwicker model for psychoacoustic parameter calculation. This model offers a novel approach for studying the sound quality of in-vehicle noise for individuals with varying hearing states.
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Frequency extraction of tri-stable stochastic resonance system for dynamic measurement of steering drilling tool
Yang Yi, Zhou Kexin, Zhang Nan, Guo An
Abstract:
In the process of measurement while drilling, the dynamic measurement signal of the drilling tool is often submerged in a complex noise environment. This noise is primarily caused by the strong vibrations of the bottom drilling tool, which result from the drill bit cutting through rock layers and the collisions between the drill string and the borehole wall. Such interference severely affects the attitude measurement of the drilling tool, leading to ineffective guidance and control. In order to solve this problem, this paper uses the dynamic characteristics generated by the tri-stable nonlinear system, that is, the random resonance effect, to extract the frequency of the dynamic measurement signal of the steering drilling tool. Firstly, a random resonance frequency identification method based on the scale transformation tri-stable system is proposed to solve the application limitation of the dynamic measurement signal of the drilling tool. Then, the influence of the characteristic parameters of the potential function on the resonance output of the system is studied, and the corresponding adjustment scheme of the characteristic parameters of the potential function is designed to realize the frequency extraction of the dynamic measurement signal of the drilling tool under any noise intensity. The experimental results of simulation and real drilling data show that the SNR threshold of the signal extracted by the proposed method is as low as -19.5 dB, and the stability is better than that of bis-table systems, which proves the effectiveness and stability of the proposed method.
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Novel method for oscillation data denoising in process industry
Lang Yumin, Lang Xun, Wu Jiande, Liu Yan, Li Peng
Abstract:
Oscillation is a significant indicator of performance degradation in industrial process control loops. Therefore, an effective oscillation monitoring mechanism is essential for ensuring the safe and stable operation of these processes. However, random noise and external disturbances are prevalent in process oscillation data, leading to a low signal-to-noise ratio (SNR), which severely affects the accuracy of oscillation detection and diagnosis. To address this, this paper proposes a novel denoising technique for industrial process data, combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), detrended fluctuation analysis (DFA), and canonical correlation analysis (CCA). First, CEEMDAN is used to decompose the signal into a series of intrinsic mode functions (IMFs). Next, DFA is employed to classify the IMF components into information-dominated and noise-dominated categories. Then, CCA is applied to the noise-dominated IMF components to remove noise. Finally, the outputs of CCA are combined with the information dominated IMF components to reconstruct the denoised oscillation signal. Simulation and experimental results using actual industrial oscillation data show that, compared to existing denoising techniques, this method achieves the lowest root mean squared relative error (RMSE) and the highest correlation in the denoised signals, demonstrating excellent denoising accuracy and robustness.
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Design of adaptive vector tracking loop in spinning vehicle based on M-estimation
Chen Xiyuan, Wang Yuetong, Gao Ning
Abstract:
To solve the problem of unstable tracking satellite signals and inaccurate navigation positioning of the spinning vehicle in the rotating state, a spinning signal model is constructed and an adaptive vector tracking loop based on M-estimation is proposed. On the basis of the traditional tracking loop, a cascade vector tracking loop based on KF+EKF is constructed. The filter is realized by coupling the observed quantities of each signal channel and the output of the tracking loop is used for the bandwidth calculation and the fault detection of the M-estimation to realize the navigation solutions. Simultaneously, the navigation results are fed back to the carrier NCO and code NCO to realize the closed-loop control. The semi physical simulation results show that the proposed vector tracking loop reduces the position error by 67.6% and the velocity error by 67.8% in three-dimensional space compared to the traditional tracking loop. This effectively enhances the stability of signal tracking and the accuracy of navigation positioning for spinning vehicle navigation modules.
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Indoor localization based on semi-tensor product compression sensing
Pu Qiaolin, Zhou Longcan, Zhou Mu, Jiang Fengyi, Li Yunhai
Abstract:
When using the compression sensing (CS) localization algorithm for large scene environments based on the wireless local area network (WLAN), there are two challenges: reduced positioning accuracy and increased computational complexity. To address them, this paper first introduces an improved clustering algorithm for coarse localization to reduce the search range. Specifically, for the singular value problem of wireless signals, we innovatively propose the adaptive intuitionistic fuzzy c-ordered mean clustering algorithm. Secondly, to overcome the high storage pressure brought by the high-dimensional observation matrix, a semi-tensor product compression sensing (STP-CS) technique is proposed. Compared with the traditional CS method, this method can accommodate more access points while maintaining the same dimensionality. Experimental results show that the algorithm proposed in this paper significantly reduces the storage space required by the observation matrix and the computational overhead under the premise of ensuring positioning accuracy. These advantages make it particularly well-suited for large-scale applications.
传感器技术
电子测量技术与仪器
机器人感知与人工智能
声学传感与仪器
Information Processing Technology
About
Organizer：China Association for Science and Technology
Governing Body：China Instrument and Control Society
Chief editorial unitf：Zhang Zhonghua
Address：23rd Floor, Building A, Horizon International Tower,No.6 Zhichun Road, Haidian District,Beijing, China
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Email：cjsi@cis.org.cn
ISSN：11-2179/TH