Abstract:As a key equipment for continuous, long-distance, and high-volume transportation of various discrete materials, intelligent belt conveyors have drawn high attention in recent years. Research on their health monitoring technology is of great significance in promoting deep intelligence of belt conveyors, ensuring stable and efficient operation, and achieving comprehensive intelligent protection and safe production. This article first takes the key components of belt conveyors, such as conveyor belts, idlers, drums, and reducers as research objects. The research progress of related health monitoring methods is analyzed, which are mainly divided into three aspects: Information perception and processing, fault diagnosis, and status evaluation and prediction. Then, the advantages and disadvantages of existing health monitoring technologies for intelligent belt conveyors are summarized. The existing problems and potential challenges are analyzed. Finally, from the aspects of multi-source state perception and preprocessing technology, early fault diagnosis technology, health assessment and prediction technology, full state networked monitoring technology, and digital twin monitoring technology, the potential directions with research value for the future are given.

Abstract:Lithium-ion batteries are widely used in portable electronic devices, electric vehicles, energy storage systems, and other fields owing to their advantages of high energy density and long cycle life. However, in the process of repeatedly charging and discharging the lithium-ion battery, irreversible expansion due to reversible lithium insertion expansion, over-charge, or over-discharge might occur. The irreversible expansion is an important factor affecting the cycle life of lithium-ion batteries. Therefore, the study of the expansion characteristics of lithium-ion batteries is helpful to further understanding the aging mechanism of batteries. Then, the safety design of the battery system is given, which has great application value in the field of new energy vehicles and energy storage. Based on this, this article summarizes the present situation and progress of the research on the expansion characteristics of lithium-ion batteries using external sensors, internal sensors, and digital imaging technology. Meanwhile, the main technical characteristics of the various methods are discussed and analyzed. The development trend and future prospects of the measurement technology for the expansion characteristics of lithium-ion batteries are prospected.

Abstract:The linear motor feed drive axis has the advantages of fast velocity, large acceleration, and short response time. However, the large heat generated by the primary coil easily causes thermal deformation of the external components and affects the positioning accuracy of the feed axis. To solve the problem, a thermal error modeling method for the direct-drive feed axis considering the multi-field coupling of “electromagnetic-thermal-fluid” is proposed. The electromagnetic, thermal, and flow fields of the direct-drive feed axis are analyzed, and the control equation of the temperature field is established under multi-field coupling. A simplified method for solving the control equation is proposed, which separates the equation into three stages, including heat generation, steady-state heat convection, and heat conduction. Then, the three stages are recoupled to obtain an explicit analytical model of the temperature field and the deformation field. The experimental results show that this model reveals the mechanism of the thermal error of direct-drive feed axes. It can accurately predict the thermal error, and automatically adjust the linear and nonlinear intervals, which is of great significance for improving the motion accuracy of direct-drive machine tools.

Abstract:The fault signals of rolling bearings in mechanical systems are usually nonlinear, non-stationary, and accompanied by strong background noise, which makes it difficult to realize early warning of bearing fault. An intelligent early-warning method for rolling bearings based on beta distribution and filter denoising algorithm is proposed in this article. First, the early warning threshold value interval of the monitoring data is calculated by the threshold determination method based on the beta distribution. Then, the average filtering algorithm is used to reduce the noise of the collected data to eliminate the data monitoring noise. Meanwhile, the noise reduction effects of moving average filtering, H-P filtering, and morphological filtering are compared and analyzed. Finally, the calculated warning threshold interval is compared with the filtered data, and the early warning is made according to whether the monitoring data exceed the threshold interval. The XJTU-SY dataset and bearing experimental data are used to evaluate the accuracy of the algorithm. The results show that the proposed method can accurately calculate the early warning threshold interval of rolling bearings in smooth operation and effectively warn the bearings of early failures. The fastest early warning response time is 56. 76 s and the slowest early warning response time is 778. 20 s. Furthermore, the comparative analysis results show that the effect of moving average filtering is better than those of H-P filtering and morphological filtering when filtering the original data.

Abstract:With the rapid development of the new energy industry, how to deal with a large number of retired batteries is problem. The secondary utilization scenarios of retired batteries need to be determined based on the state of health (SOH). However, the traditional method of obtaining SOH is time-consuming and energy-consuming. Therefore, the study of fast SOH estimation is very meaningful. The unavailability of historical working condition information and the unknown state of charge at the time of detection make fast SOH estimation very difficult. For this reason, this article proposes a fast SOH acquisition strategy for retired batteries based on the difference in state of charges. In this article, the state of charge′s differences of different SOH retired batteries are used to generate multiple health features. Meanwhile, to select suitable hyperparameters for the random forest algorithm, the genetic optimization random forest regression algorithm is proposed to be applied for SOH estimation. Through experiments, the proposed strategy substantially reduces the estimation time of SOH for retired batteries. Through multiple strategies to avoid contact resistance and wire resistance during measurement, the error of health state estimation of 10 retired batteries is lower than 3% .

Abstract:Existing data-driven methods for predicting the remaining useful life of bearings often rely on data from a specific operating condition to train the corresponding prediction model. The valuable degradation features present in data from other conditions are disregarded. To effectively capture and utilize degradation features across diverse operating conditions, this article proposes a personalized federated learning-based method for bearing remaining useful life prediction. In this method, monitoring data from bearings under different conditions are distributed among multiple clients, while a central server collaborates with these clients to develop personalized prediction models by model transfer, combination, and local updates. To integrate the global model aggregated by the central server with the local model, an adaptive local combination algorithm is introduced, which preserves useful degradation features that aid in initializing the client′ s model and enhancing prediction performance. The proposed method is evaluated by using two datasets of bearings. The results show its ability to construct high-performance prediction models for bearings operating under different operating conditions. In comparison to local training method, this method manifests a minimum decrease of 13% in root mean square error.

Abstract:To improve the accuracy of tidal prediction further enhance the adaptability of the prediction model, and address a series of problems, including the difficulties of intelligent and adaptive extraction of low-frequency tidal components, weak ability to dynamically process tidal information, limitations of a single prediction model for overall tidal prediction, this paper proposes an improving tidal prediction model based on adaptive optimal variational modal decomposition of energy entropy and GRU recurrent neural networks. Firstly, the tidal data are normalized, and the VMD method is utilized for adaptive variational modal decomposition. Then, the optimal decomposition level is confirmed based on the energy entropy of the components. Finally, each component of the optimal decomposition is standardized and separately predicted and synthesized by GRU. The final prediction data are formed through reverse normalization. Through verification and analysis, compared with LSTM and BiLSTM models, the GRU model has better performance in terms of tidal prediction. The RMSE values are increased by 53% and 96. 8% , respectively. However, compared with a single GRU model, the proposed prediction model has RMSE increase 81. 3% again, and the accuracy improvement effect is more obvious. The method in this paper has high promotion and application value for tidal analysis and prediction.

Abstract:Ocean observation instruments and equipment are essential manifestations of the national ocean comprehensive strength, which are important ways to ensure the effective operation of an operational ocean observation network. Although marine observation instruments and equipment in China have played important roles in the development of the ocean observation network in recent years, there remains a gap compared with the advanced countries. Based on the needs of marine development, a multi-level ocean observation instruments and equipment system framework of space-based, shore-based, sea-based, and seabed-based platform and equipment are proposed in this article. The present status of various marine observation instruments and equipment in China is reviewed, and the problems are expounded which exist in the development of China′ s marine observation instruments and equipment in the aspects of system construction, sensor construction, test and verification platform, standard, etc. Some suggestions are provided, such as increasing the research and development of equipment localization, establishing evaluation standards and methods, carrying out on-site comparative measurement, and strengthening the application of high-tech achievements ,which will provide a reference for the construction and development of China′s operational ocean observation network.

Abstract:Current cooperative spacecraft relative pose measurement methods face the struggles of balancing field of view and precision as well as lacking straightforward precision metrics. We propose a method based on lighthouse tracking system, which can balance both field of view and precision and introduce a precision assessment metrics. The lighthouse tracking system comprising of laser emitters and multiple light sensors realizes angular measurements to achieve relative pose measurement. Leveraging the angular measurement principles of lighthouse tracking system, we present a PnP pose estimation algorithm. To address the precision analysis of pose measurement, we establish an angular error propagation model for PnP problem, and introduces dilution of precision for attitude and position as a precision assessment metric. Experimental results indicate that the lighthouse tracking system achieves attitude and position measurement precision at the order of 0. 1° and millimeters, respectively. The dilution of precision for attitude and position demonstrates a consistent pattern in estimating precision relative to experimental results, which can be utilized for a rapid assessment of measurement precision and guides the optimization of sensor installation layout. Moreover, the angular error propagation model is applicable to measurement systems involving PnP problems involved in the monocular vision.

Abstract:The thermal accuracy of the spindle is the main reason for the decline of precision CNC machine tools. The traditional datadriven thermal accuracy modeling method emphasizes the optimization of modeling algorithms and ignores the analysis of thermal accuracy characteristics, resulting in low robustness, poor interpretation, and complex model structure. In this regard, the thermal accuracy characteristics of the spindle are analyzed from the perspective of data mechanism, and a thermal accuracy modeling method is proposed. Temperature-sensitive points (TSPs) need to be selected before thermal accuracy modeling, and the LASSO algorithm is used to realize the adaptive TSPs selection. Based on quantile regression analysis, it is proved that the TSPs have double variability, and the compound quantile regression algorithm is used to improve modeling accuracy. The variable operating conditions tend to reduce the generalization ability of the model. The L2 regularization algorithm is used to improve the robustness of the model. Therefore, the thermal precision modeling method of spindles based on composite quantile regression and elastic network regularization is proposed. The experiments show that the thermal error of the machine tool after compensation using the proposed modeling method fluctuates within ±2 μm, an increase of 93. 3% compared to before compensation. The proposed modeling method has advantages in prediction accuracy, robustness, adaptability, and interpretation.

Abstract:Facing gears are widely used in aero-engine and helicopter equipment in the form of cross-shaft or intersecting-axis transmission. However, its unique face structure makes it difficult and inefficient to machine, and offline inspection leads to micrometrescale machining accuracy that is difficult to converge quickly. To address this problem, this article develops an on-machine measurement system for face gears. To solve the problems of low reconstruction accuracy of tooth face and difficulty in constructing and slow solving coordinate system in the on-machine measurement system, a mathematical model of tooth face is formulated, which is based on the principle of gear insertion processing. The evaluation method of tooth face error with segmentation approximation and the fast solution method of measurement coordinate system with normal distance constraints are proposed. The accurate solution of tooth surface error and the fast solution of measuring coordinate system are realized. Finally, the on-machine measurement of face gears is completed on the YK7280 CNC grinding machine to evaluate the accuracy of the proposed algorithms as well as to validate the accuracy of the system with the Klinberg Gear Measuring Center. The experiments show that the developed on-machine measurement system has a tooth shape error of 6 μm, which meets the machining and inspection requirements of grade 5 face gears.

Abstract:In order to achieve low-cost, non-contact, and all-weather power supply for sensors on cable based underwater sensing platforms, this paper proposes an underwater single wire power transfer system that utilizing mooring cable and open seawater as the power transmission link. Firstly, the propagation mechanism of electromagnetic waves in the mooring cable and seawater is analyzed, and the Maxwell equations of system are established. It’ s found that electromagnetic beams propagate on the surface of the single wire according to analysis. Then, a model of the mooring cable in seawater is established using Ansys/ HFSS software and the electromagnetic field distribution as well as Poynting vector distribution are simulated, which verifies the correctness of theoretical analysis. Finally, a 50 meter single wire power transfer experimental prototype is built and corresponding experiment is conducted in seawater. The experimental results show that the proposed underwater single wire power transfer system can transfer 187 W to the load, and the maximum efficiency of system can reach 49. 2% . The feasible of single wire power transfer system for non-contact power supply of sensors on cable based underwater sensing platforms is verified.

Abstract:The aerospace, mechanical, acoustic, and equipment manufacturing industries demand extremely high observation dynamic indicators for detection instruments, which even exceed the limits of existing commercially available high-resolution analog-to-digital converter devices. In this article, a dual-ADC vertical synchronous sampling mechanism is proposed to extend the total dynamic range through data stream splicing. A data correction method based on the Lagrange interpolation Farrow structure filter is introduced to address the quantization data delay error introduced by hardware differences between the acquisition ADC branches. Simulation and hardware experiments show that the proposed dual ADC vertical synchronous sampling architecture increases the dynamic range of the experimental object by 13. 318 dB. Meanwhile, the proposed error correction method can effectively reduce the delay error between vertical synchronous sampling channels. Therefore, the dual ADC dynamic range expansion has the practical value.

Abstract:The non-contact electrocardiogram (ECG) sensing technology has a promising application in the field of long-term sleeping ECG monitoring. The non-contact ECG sensing technology based on array electrodes has made good progress. However, the current noncontact ECG sensing electrodes based on array electrodes are mostly manufactured by using the hard printed circuit board technology, which has low comfort. Meanwhile, the relationship between the human body pressure distribution and the signal quality of non-contact ECG sensing based on array electrodes is not studied in this aspect based on our best knowledge. To address those issues, this study develops an arrayed non-contact ECG sensing electrode based on flexible printed circuit (FPC). The experimental results show that the ECG signals obtained from the proposed system are high, and the averaged signal-to-noise ratio is larger than 38 dB. Moreover, the effect of pressure distribution of human back on the ECG signal quality is analyzed by conducting the human trials. The results demonstrate that the obtained ECG signal quality is relatively low when the electrode unit with human body pressure is lower than 9 mmHg used as one of the positive or negative ECG signal input units. The probability for obtaining ECG signals with lower quality is higher than 54. 17% . When the unit pressure is higher than 9 mmHg, the probability for obtaining ECG signals with lower quality is lower than 5. 94% . It proves that the unit pressure is one of the key factors that can affect the quality of capacitive ECG signals.

Abstract:When the magnetic coupling mechanism of the wireless power transfer system is deviated and deflected, there are problems of a drastic decrease in coupling coefficient and efficiency. In this article, a single flux loop grid flat spiral pad (GFSP) magnetic coupling mechanism is designed for medium or small power wireless charging applications. The uniform distribution of the magnetic field makes it both anti-misalignment and anti-deflection. In addition, a single-switch inverter P#LCC-S resonant network topology is proposed for the compensation topology, which effectively simplifies the circuit structure while retaining the performance of the traditional LCC-S compensation network. Firstly, a magnetomotive force model is formulated to reveal the magnetic field distribution law at different spatial positions of the mechanism and optimize the parameters of the mechanism. Secondly, an equivalent model of the dual P # LCC-S resonant network circuit is established to derive the parameter configurations of the resonant elements under the constant excitation current of the transmitting coil and the constant output voltage. Finally, a 300 W experimental prototype is constructed, and the coupling coefficient retention rate of the prototype is always larger than 50% and the transmission efficiency is greater than 80% within the ranges of ±120 mm of XOY plane misalignment, 50 ~ 100 mm of Z-axis misalignment, and 0° ~ 90° of Z-axis deflection, respectively. Therefore, the validity and feasibility of the proposed system are evaluated.

Abstract:For mobile robot clusters, a ground-end Lidar localization system needs a small number of Lidar sensors. However, it has several technical problems, such as severe feature loss, difficult target segmentation, etc. Hence, a multi-target tracking method is proposed, which is based on hierarchical segmentation and clustering of multi-perspective Lidar point clouds. Firstly, a dynamic threshold segmentation technique is used to convert multi-source Lidar point clouds into point groups. A surface area heuristic-bounding volume hierarchy method is devised to distribute these point groups into point cloud space, and a spatial hierarchical tree is established using the recursive algorithm. Secondly, the loss function of distance-intersection over union (D-IoU) is improved, and a relevant point group clustering method based on the undirected graph is proposed to generate the target point cloud for each mobile robot. Thirdly, the Kalman filter is used to track the target point cloud of each robot. A depth-first searching technique based on the spatial hierarchical tree is proposed to track the target point cloud. Finally, a multi-perspective ground-end Lidar tracking system is developed. Target tracking experiments are implemented for a group of mobile robots, which evaluates the effectiveness and efficiency of the proposed method.

Abstract:Inspired by the stiffness adjustment principle of the cam and slope-slider mechanisms, a novel reconfigurable variable stiffness joint with linear displacement based on the slope-cam mechanisms is proposed to meet the dynamic trans-interval stiffness adjustment requirement of orthopedic robots during the correction process. Stiffness reconstruction is achieved by adjusting the number of slope-slider mechanisms, spring stiffness, and slope displacement, while the corresponding working principle is illustrated in detail. The joint is designed comprehensively. The active and passive stiffness adjustment mechanism are revealed, and the equivalent stiffness model is formulated. Resorting to numerical analysis software, the mathematical model of stiffness adjustment is established, and the distribution characteristics of equivalent output stiffness and output forces are studied. The virtual prototype model of the joint is established, and the performance simulation analysis is carried out to evaluate the correctness of the theoretical stiffness model. The proposed variable stiffness joint is applied to an orthopedic robot. The equivalent output stiffness of the joint can be adjusted between 34. 08 to 2 762. 64 N·mm -1 , and the corresponding stiffness of the orthopedic robot can be controlled within the range of 689. 94~ 6 250. 41 N·mm -1 , which meets the requirement of deformity correction.

Abstract:A method for robot demonstration learning based on forward hidden Markov models is proposed to address the problems of low parameter estimation efficiency and poor trajectory reconstruction accuracy in the process of robot demonstration trajectory encoding. The method identifies key points of multiple collected demonstration trajectories using the Linde-Buzo-Gray ( LBG) algorithm, selects appropriate trajectory calculation model initialization parameters using the minimum distortion criterion, and completes model parameter estimation by combining the Baum-Welch algorithm. On this basis, the Viterbi algorithm is used to calculate the most likely attribution state of each sample point, and the maximum likelihood estimation is utilized to recalculate the state parameters of each sample point attributed to each state. Finally, the reconstructed trajectory is obtained through Gaussian mixture regression. To evaluate the effectiveness of the algorithm, a handwritten letter trajectory dataset and a demonstration learning experiment of wafer mechanical arm automatic feeding trajectory are designed. The average Fr􀆧chet distance is introduced to quantitatively evaluate the trajectory reconstruction accuracy. The experimental results show that the proposed method improves the trajectory reconstruction accuracy by 15. 15% compared to traditional methods, with an average Fr􀆧chet distance of 5. 49 in the automatic wafer loading trajectory of the robotic arm,which indicates promising application prospects.

Abstract:With the deployment of the national deep-earth energy strategy and underground infrastructure projects in China, the demand for autonomous mobile robots in underground mines, engineering, and pipelines is growing rapidly. Underground autonomous robots have to bear troubles like satellite positioning signal denial and scene degradation which easily lead to serious error drift in robot pose estimation and distortion in environmental map construction. To address the problem of incomplete state estimation of underground degraded environment robots, an accurate and robust LiDAR-inertial SLAM framework and method is proposed. It combines the inertial odometer and the LiDAR-inertial odometer by the cascade optimization process. In addition, the intensity feature is introduced into LiDAR point cloud feature matching to reduce the matching error caused by sparse point cloud geometric features, and correct constraint direction is introduced through degradation detection to ensure the robustness and accuracy of pose estimation. The experimental results on public datasets and field tunnels show that the proposed method has excellent performance both in accuracy and robustness. The positioning accuracy in the degraded roadway reaches 0. 03 m, which can provide reliable state estimation and environment description for robots in underground degraded environments.

Abstract:The accuracy requirements of industrial robots are increasingly higher. Firstly, to improve the accuracy performance of industrial robots, this article proposes a full pose kinematic error model based on Modified Denavit-Hartenberg (M-DH), which can better describe the error of industrial robots. Secondly, this article constructs the error fitness function of position and attitude respectively. The multiple objective particle swarm optimization (MOPSO) algorithm is combined to achieve accurate identification of kinematic parameters. Thus, the problems of the position error and attitude error with different scales and magnitudes are solved. Finally, the effectiveness of the MOPSO algorithm is evaluated through experiments. The experimental results show that the average position error and the average attitude error of the Staubli TX60 industrial robot are reduced by 81. 04% and 66. 64% , respectively. Compared with the single-objective optimization algorithms based on the Levenberg-Marquardt ( LM) algorithm, the Beetle antennae search swarm optimization (BSO) algorithm, and the particle swarm optimization (PSO) algorithm, the MOPSO optimization algorithm presented in this article is the best method in terms of the generalization in kinematic parameters identification and the maximum pose error optimization.

Abstract:As one of the important vital signs, blood pressure is a risk assessment indicator for cardiovascular disease. Central arterial pressure has a strong pathophysiological relationship with target organ damage and cardiovascular disease. Arterial blood pressure monitoring has high predictive value for cardiovascular and cerebrovascular events, which can provide a basis for clinical diagnosis, such as human cardiac function and arterial elastic characterization. The proposed ultrasonic piezoelectric transducer array has the characteristics of flexibility and being able to fit the human neck compared to traditional rigid ultrasonic probes. The 1 × 16 linear ultrasonic transducer array with low frequency (2. 5 MHz) and high frequency (4. 5 MHz) is produced and compared. Both arrays are imaged on ultrasonic phantom, with penetration depths of 90 and 40 mm, respectively. The transducer array has achieved continuous and repeated blood pressure detection of the carotid artery in the phased array-focused emission mode. Compared with the measurement results of the medical central arterial pressure detector, the average relative error of the blood pressure waveform measured by the flexible array and the medical detector in the resting state is 3. 83% , and the average relative error after intense exercise is 5. 47% . The measurement results of the arterial blood pressure waveform are accurate and the medical value is rich.

Abstract:To address the poor minimum detection performance and low precision of traditional non-modulated turbidity detection technology in low turbidity measurements, a turbidity detection technology based on orthogonal phase-sensitive demodulation is proposed. Based on the principle of turbidity detection, the propagation mechanism of scattered light in water is analyzed, and the response characteristic relationship between turbidity and scattered light intensity is established. To extract weak photoelectric signals, an orthogonal phase-sensitive demodulation circuit consisting of dual-channel analog switches is used. The characteristics of frequency selection and phase identification of the orthogonal phase-sensitive demodulation principle are derived in detail. By selecting the signal spectrum and correcting phase deviation influences, the suppression of noise that is neither same-frequency nor same-phase is achieved. Therefore, the signal-to-noise ratio of the system is enhanced. A turbidity sensor is developed, the corresponding test apparatus is constructed, and the performance is evaluated. Experimental results show that, within a 0 ~ 5 NTU measurement range, the turbidity sensor has good linearity, accuracy, and stability. The limit of detection can reach as low as 0. 004 9 NTU. This method significantly improves the minimum detection performance and measurement accuracy and methodologically avoids the impact of DC drift noise and broadband noise on turbidity measurements at low signal-to-noise ratios.

Abstract:To meet the requirements of high-precision and large-distance digital leveling measurement, a new two-dimensional composite encoding rule for the digital level ruler is proposed in this article. The leveling ruler consists of two column-coded bars, including an absolute-coded bar and a relative-coded bar. The absolute coded column consists of black and white stripes of different widths, and several coded bars along the ruler are combined to form a code segment adopting a binary-decimal coding rule, which uniquely determines the absolute position of the code segment on the ruler. The relative coded column consists of black and white stripes of equal width and relatively large size. The position on the ruler is represented by reference codes that exist simultaneously in the relative coded column and the absolute coded column. According to the proposed coding rules, an accurate reading method for the sight line is designed. The experiments of height difference ,repeatavility and long visual distonce are carned out on teh designed level rkuler. The results show that the measurement time of the 2-D composite code is less than 1. 57 s within 30 m, the barcode recognition rate is 100% , and the maximum deviation is less than 0. 09 mm, which has the advantages of fast, accurate recognition and stability.

Abstract:A vibration sensor based on hexagonal and compact fiber Bragg grating is designed, which consists of elastomer, mass block, fiber Bragg grating (FBG) and shell. Firstly, the hexagonal elastomer structure is analyzed and simulated theoretically. The measurement ranges of sensitivity and frequency are changed by designing hairpin turns structure, so that the designed FBG sensor can meet the vibration monitoring requirements of different mechanical equipment. The performance of sensor is tested with the vibration exciter and the application in vacuum pump vibration monitoring is also demonstrated. The experimental results show that the designed FBG vibration sensor possesses good responses. The first-order natural frequency of conventional hexagonal elastomer FBG sensor is as high as 1 481 Hz, and the sensitivity is 4. 93 pm/ g at the vibration frequency of 100 Hz. For comparison, the first-order natural frequency of hexagonal elastomer FBG sensor with hairpin turns structure is only 185 Hz, and the sensitivity is 105. 73 pm/ g when the vibration frequency is 100 Hz. The transverse interference of both sensors is less than 5% , which possesses a good ability to resist lateral interference.

Abstract:In order to solve the problem that the trajectory tracking control of small unmanned helicopters is affected by internal and external disturbances such as the accuracy of system mathematical model, the attitude measurement error caused by the low-cost MEMS and the interference of flight environment, a linear/ nonlinear hybrid active disturbance rejection control (ADRC-LADRC) method was designed for the unmanned helicopter trajectory tracking controller. Furthermore, a controller parameter tuning method based on bacterial foraging optimization-flower pollination algorithm (BFO-FPA) was proposed, which improved the convergence speed of BFO algorithm and enhanced the global search capability of FPA algorithm. Finally, with the ALIGN unmanned helicopter′s tail-locking spiral climb flight test, it was verified that the optimized active-disturbance rejection trajectory tracking controller based on BFO-FPA algorithm can effectively overcome the influence of internal and external disturbances on the unmanned helicopter and improve the trajectory tracking accuracy and robustness of controller.

Abstract:The sensor tag in this article uses passive wireless sensing technology to transmit data and energy. It uses the built-in sensing module to detect the magnetic field intensity around the cable, realizing cable current measurement without battery maintenance and cable connection. However, due to the limitation of microwatt power supplied by radio frequency waves, the number of sampling points of the sensor tag is restricted, the sampling frequency is low and the nonlinearity of the magnetic field sensitive element makes the mapping relationship between the sensor data and the cable current complicated. Hence, it is difficult to calibrate and measure the sensor tag. To solve this problem, this article proposes an optimization algorithm combining particle swarm optimization and interior point method to extract the characteristic values (frequency and effective value) from the sensing data by Fourier series fitting and calibration. During the measurement, the characteristic values are obtained by Fourier series fitting of sensor data groups. Then, the characteristic values are substituted into the sensor model established during calibration. The constant amplitude current measurement with the frequency of 47~ 60 Hz and an effective value of 5~ 45 A is realized, and the relative errors of current frequency and effective value are less than 0. 4% and 1. 9% , respectively. The experimental results show that the measurement system equipped with the sensor tag can not only realize passive wireless measurement of cable current, but also can realize the maximum current fluctuation measurement allowed by domestic power system standards.

Abstract:Molten iron temperature is an important index to analyze the quality of molten iron, judge the furnace temperature state, and evaluate the energy consumption level of blast furnaces. The molten iron temperature measurement is a mandatory demand of the iron and steel industry. According to the working principle, the existing measurement technology of molten iron temperature is classified and analyzed. Combining with the environmental characteristics of blast furnace casting field and molten iron, the advantages and disadvantages of the existing temperature measurement technology are discussed from the aspects of temperature measuring principle, measuring position, and technical characteristics. To realize the real-time online and accurate measurement of molten iron temperature in blast furnaces, the research progress and application potential of the infrared visual temperature measurement method are introduced. Based on summarizing the existing measurement techniques of molten iron temperature, the main problems and future research directions of infrared visual temperature measurement are analyzed. The objective is to provide some references for researchers in academia and industry on temperature measurement of molten iron.

Abstract:A fusion information enhanced method based on Transformer is proposed to address the issue of misalignment when the current 3D object detection methods fuse different modal data, which mitigates the disruption of correlation between data and data loss. Firstly, a region proposal network of dual fusion feature module based on transformer is designed, which utilizes the deformable attention mechanism to fuse the extracted lidar point cloud features and image features into dual domain features and generate pre-selected boxes. Then, the refinement of box is designed by using a feature information enhancement module, which utilizes a deep completion mechanism to complement the dense depth and feature semantic information. Finally, a multimodal feature cross attention module is designed, which uses a dynamic cross attention mechanism to obtain correlations between different modalities, thereby aligning and fusing feature information effectively. The experimental results based on the Kitti, Nucences, and Waymo datasets demonstrate the effectiveness of method. A large number of ablation experiments have proven the effectiveness and efficiency of each module in the algorithm. The experimental results based on a real vehicle platform show that the algorithm possesses strong robustness in complex practical environments.

Abstract:To solve the problem of total focusing imaging of double-layer media under blind measurement conditions, this article proposes a total focusing method ( TFM) based on virtual source ( VS). Firstly, a series of virtual sources are established on the irregular interface of the double-layer media by using the first echo time-of-flight of the diagonal signals of full matrix capture data. Then, the irregular interface of the double-layer media is reconstructed by interpolating or fitting the virtual sources on the interface. Finally, the reconstructed interface is used for possible refraction points to achieve the TFM imaging condition for the irregular interface of doublelayer media. The results show that the position of the surface and flaws are both located correctly in TFM images under blind measurement conditions. Compared with the synthetic aperture focusing technique ( SAFT) imaging technology based on virtual source (VS), the signal-to-noise ratio of the VS-TFM images is improved by 4. 66~ 13. 31 dB in the convex specimen, and by 4. 74~ 12. 8 dB in the concave specimen, with nearly the same API values and minimal increase in imaging time. Therefore, the proposed method extends TFM imaging to double-layer media with irregular surfaces in blind measurement conditions.