Abstract:Welding defects can cause serious damage to the working operation of the steel welded structure. In order to study the influence of welding defects on the damage evolution of Q245R steel under bending load, bending experiments were carried out on Q245R steel specimens without defects and slag inclusion defects, and real-time monitoring was carried out using acoustic emission technology and digital image correlation methods. The results show that compared with the non-defect specimens, the characteristic parameters of the acoustic emission signal amplitude, the cumulative number of impacts and the energy of the slag inclusion specimens are greatly increased, and the amplitude and energy of the slag inclusion specimen are as high as 80.5 dB and 231.2 mV·ms, respectively. Under a load of 3.3 kN, the maximum displacement field of the slag inclusion specimen is increased by 21.093 mm. The experimental results can provide a reference for the health monitoring of steel welded structures.

Abstract:In view of the actual project needs, a set of state monitoring system is designed for an innovative multi-functional catenary operation vehicle, which can realize the real-time monitoring and motion attitude model tracking such as reinforcing bar detection, drilling anchor, drawing test, hanging column installation, maintenance and so on. The system is designed and implemented based on LabVIEW and CAN bus. In the system, sensors and other lower computer devices are mounted on five CAN buses. After the information is integrated by the controller, it is transmitted to the upper computer software for display through the CAN box. In addition, the software stores all data in the local database created by Access, and important data in the base central database. Finally, the interface display and model attitude tracking function are tested in the field, and the attitude data are used to perform capsizing calculation. The results show that the system program runs stably, the display function is normal, and the model motion synchronization is good. The minimum sum moment of AB overturning line is about 210000, much greater than 0. This indicates that the system can complete real-time monitoring and attitude tracking tasks, and there is no danger of overturning in test conditions.

Abstract:In order to study the influence of the high voltage transmission lines near the airport on the reception of VOR(Very High Frequency Omnidirectional Range) and DME(Distance Measuring Equipment) signal, test the electromagnetic radiation generated by the high voltage transmission lines near the airport. By controlling and changing the parameters in the calculation model, study the influence law of the transmission line on the electromagnetic interference of aircraft airborne signals, and obtain the maximum height of the aircraft subjected to high-voltage wire EMI(Electromagnetic Interference). According to the radio wave propagation theory and the relevant provisions of national standard, analyze the influence of electromagnetic interference on the airborne signals at this position .The SNR(Signal to Noise Ratio)of the VOR signal received by the aircraft at the maximum height of EMI is 42.7dB, Comply with the requirements of the national standard minimum which is 20dB. DME signal signal-to-noise ratio is 97.3dB, in line with the national standard.On this basis, calculate the protective distance of the high voltage transmission line to the airport VOR/DME station , and propose the suggestion of active interference protection distance between the range of the high voltage transmission line and the airport VOR/DME station .The research results in this paper can provide a basis for the electromagnetic compatibility design of high voltage transmission lines and radio equipment such as airport communication, navigation and surveillance.

Abstract:Aiming at the difficulty of analog circuit fault feature extraction, an analog circuit fault feature extraction method based on wavelet packet energy spectrum and independent component analysis is proposed. Firstly, the fault output signal of the circuit is obtained through simulation, the output signal is decomposed and reconstructed by wavelet packet analysis, and the energy of each frequency band is obtained as the fault eigenvalue through the reconstruction coefficient. Then the independent component analysis algorithm is used to optimize the fault eigenvalues, so as to construct the eigenvector reflecting the circuit fault. Finally, the support vector machine is constructed, the fault feature vector is input for training and testing, and the accuracy of circuit fault diagnosis is obtained. Simulation results show that this method can effectively extract the feature parameters that can feature circuit faults, and the diagnosis accuracy can reach 95.7%.

Abstract:In order to solve the problem of few researchers on cybersickness and most of them are subjective, an objective method of cybersickness based on eye movement data was proposed. Used eye-tracking technology, we established an evaluation model based on the self-evaluation questionnaire scores and movement parameters of VR system, the impact of movement factors in the VR environment was explored. Then analyze the relationship between visually induced motion sickness level and the change of blink rate. Results show that the blink rate was significantly in different degrees of cybersickness, and histogram analysis showed that the absolute growth rate could describe the degree of symptoms caused by virtual motion mechanism. Experiments prove that the blink rate can be used as an objective evaluation index to quantify cybersickness by objective mathematics. In addition, according to the linear following of the data changes and the significance of the evaluation model, it is show that eye movement data can also be used as a real-time indicator, as feedback to guide the variation of motion parameters, this information provide reference for design of VR system.

Abstract:In order to solve the problem of many iterations in traditional model predictive current control, improve the flexibility of two degrees of freedom about direction and amplitude in voltage vector synthesis of dual three-phase motor, and reduce the output torque ripple and current ripple, a dual second-order model predictive control algorithm based on current loop optimization is proposed. Compared with the conventional speed loop using the PI control algorithm and current loop using the traditional MPC control algorithm, this algorithm adopts the second-order MPC control mode in the speed loop, which reduces the speed regulation time and increases the motor immunity; The second-order MPC control mode is adopted in the current loop, and the traditional iterative calculation method is improved to calculate the direction and amplitude of the output voltage vector through the analytic function, which increases the flexibility of the output voltage vector. Four vector SVPWM is used to modulate the voltage vector to reduce the current amplitude in the harmonic subspace. The simulation results show that the algorithm can significantly reduce the torque ripple and current ripple, and increase the transient performance of the motor.

Abstract:Solar tracking; Solar position algorithm; Embedded system; Altitude angle; Azimuth angle; Contrastive analysis

Abstract:Aiming at the environmental differences and plant adaptability problems in the ex situ conservation of rare and endangered plants, this paper proposes a framework for ex situ conservation of plants constructed using BeiDou's high-precision spatiotemporal technology. The framework is based on precision agriculture technology and parallel system theory. Through the construction of artificial ecosystems and parallel planting, the problem of differences between ex situ conservation and the natural ecological environment, as well as the adaptability of natural return of plants, is solved. The framework proposed in this paper is conducive to improving the success rate of ex situ conservation, thereby enhancing the benefits of ex situ conservation of rare and endangered plants.

Abstract:Aiming at the problem of low prediction accuracy caused by randomness, fluctuation and nonlinear factors of power load, a short-term load prediction model based on least squares support vector machine (LSSVM) optimized by variational mode decomposition (VMD) and Sparrow search algorithm (SSA) was proposed. In this method, the original load time series was decomposed into the intrinsic mode function (IMF) and residual component (Res) of different frequencies by VMD. Then, different LSSVM prediction models were established for each component and parameters were optimized by SSA. Finally, the final prediction results were obtained by combining the predicted values of each component. Taking two groups of real data from The University of Mons in Belgium and a certain area of Henan Province in China as examples, the prediction results were compared with the predicted values of LSSVM, VMD-LSSVM and SSA-LSSVM models, and the MAPE values of the two groups of data proposed in this paper were 1.5016% and 4.765% respectively, far lower than those of other models. The results show that the combined prediction model in this paper has some advantages in prediction accuracy.

Abstract:In view of the large measurement error introduced by the non-verticality of the wire, which still exists in the Magnetic sensor arrays, a 3-D tunnel magnetoresistance (TMR) sensor array scheme is proposed to precisely measure the current when the wire is eccentric and tilted. Then the current measurement error of the single-axis, two-axis and three-axis TMR sensor array is simulated and compared, where the maximum current errors are 25%, 20.5% and 1.39%. It is obvious that the 3-D TMR sensor array can significantly improve the measurement accuracy and overcome the influence of eccentricity and tilt, so the 3-D TMR sensor array has great advantages in the field of current measurement.

Abstract:For the deviation between the actual wavelength and the output wavelength of the tunable wavelength infrared light source, an infrared wavelength calibrator based on photoacoustic spectroscopy technology was designed in this paper. Firstly, the gaseous small molecules are equipped in a photoacoustic cavity, with the tunable laser to be calibrated irradiated in the photoacoustic cavity, the vibration-rotational fine absorption spectrum of the molecule is obtained. Then, the known and accurate vibrational rotation spectrum peak positions of these simple gaseous molecules are compared with the obtained photoacoustic spectrum. Finally, the output light emitted by the infrared laser is corrected by the comparison. With this method, the existing infrared parametric oscillator (OPO/OPA,) in our laboratory has been calibrated in the range of 2800-3600 cm-1. Using a photoacoustic cavity containing gaseous methane and ammonia, the calibration fitting curve of the output wavelength of the OPO/OPA was obtained. This infrared light wavelength calibrator can be used in a wide infrared range. It is a complement to the existing laser wavelength calibrator.

Abstract:This paper proposes a license plate recognition algorithm based on LSTM theory and algorithm in complex environment, aiming at the problem that the recognition accuracy and response time are reduced due to natural light and rainy weather. In order to enhance the contrast of license plate area and reduce the difficulty of location, the license plate image is preprocessed with corrosion algorithm, gray scale and binarization. Secondly, image recognition processing algorithm is used for license plate location, character segmentation, character recognition and other operations. The characters obtained after the license plate segmentation are normalized and the appropriate size is unified as the input of the long and short duration memory network (LSTM), and the corresponding Chinese characters, numbers and alphabetic characters are as the output. The model of license plate recognition system is realized through the TRAINED LSTM network. After a lot of data collection and training, compared with the existing license plate recognition system, the algorithm proposed in this paper has a recognition accuracy of 98.90% Chinese characters, 99.40% alphanumeric characters and a single image recognition speed of 2.65ms.

Abstract:To improve the depth estimation accuracy and efficiency of stereo vision, a stereo matching method based on binary disparity segmentation and 3D convolution is proposed. Firstly, features are extracted from the stereo image and fed into the segmentation module. Then, for each parallax plane, the 3D convolution module is run separately to detect whether the target is closer than the given distance, or to estimate the depth according to any rough order. Finally, 3D convolution layer is used to estimate the output of binary segmentation module, and the final disparity map is obtained after fine processing. Experimental results show that the 3-px error of the proposed method is 4.37% and the EPE error is 1.06 pixels in SceneFlow dataset. The small error approximation depth guided aggregation network (GA-Net) method on KITTI2015 dataset. And the proposed method has the highest efficiency in different depth quantization levels.

Abstract:At present, the smart home system based on wireless technology has brought comfort and convenience to people, but the system is vulnerable to attack, and there are more private data to be protected. For the data security problem in wireless communication, a kind of smart home system and its secure communication method is designed, namely, the networking structure of the system is designed, external network and internal network are planned. The home device nodes in internal collect information and apply ZigBee to communicate mode, while other devices apply WiFi to communicate. The router is deployed as MQTT server, mobile phones and other control devices are MQTT clients, and SSL communication is configured on server, client program is designed. While the MQTT client in the external network accesses the server, it uses first secure intranet penetration scheme of Zerotier to connect to the internal network, and then generates public keys and certificates by SSL protocol for secure communication. The cross diffusions method of L-P chaotic system is proposed to generate AES initial round key, and applied to ZigBee secure communication. The experimental results are that MQTT client in internet and server can connect to a ZeroTier network and communicate securely through SSL protocol can communicate securely; ZigBee communication mode achieves the encryption effect, compared with the standard AES algorithm, it is shorter encryption time and shorter communication delay, which are reduced by 3.77%, and 28.5% respectively, and less energy consumption, the average residual energy of nodes is increased by 30.22%. Therefore, the experimental results show that the communication method proposed in this paper is secure and applicable.

Abstract:The prediction of AQI in the future by using machine learning algorithm is helpful to analyze the trend of air quality change in the future from a macro perspective. When a single machine learning model is traditionally used to predict air quality, it is difficult to obtain good prediction results under different AQI fluctuation trends. In order to effectively solve this problem, the prediction method is improved. When using random forest model and long and short-term memory model based on convolution neural network and attention mechanism to predict the AQI data in Chengdu, a difference fusion analysis model is designed according to the characteristics of different prediction accuracy under different AQI fluctuation trends. The experimental results show that the MSE of the proposed difference fusion analysis model is 5.8% lower than that of the random forest model, and 6.3% lower than that of the long-term and short-term memory model based on convolutional neural network and attention mechanism.

Abstract:In response to the increasingly wide-ranging requirements for high-precision time synchronization systems, a high-precision time synchronization method for BeiDou-3 navigation satellite system (BDS-3) receivers is proposed. This time synchronization method runs on a BDS-3 navigation chip developed by our company. A closed loop time synchronization method similar to the digital carrier loop is used, in which PVT is equivalent to a phase discriminator and a frequency discriminator、a pulse per second(PPS) output control module is equivalent to a numerically controlled oscillator（NCO） and third-order phase-locked loop assisted by second-order frequency-locked loop is applied. By the experience in this paper, the time synchronization accuracies of one hour and one day are 2.64ns and 3.83ns. The experimental results show that the time synchronization accuracy of the method is improved without increasing hardware resources.

Abstract:Focusing on the high-performance speed sensorless drive control of six-phase induction motor (IM), a second-order sliding mode (SOSM) model reference adaptive system (MRAS) estimator is designed to realize the optimized speed sensorless direct torque control (DTC) for six-phase IM. The super twisting algorithm is used in the new control scheme to propose a compensated flux observer, which is inherently an SOSM strategy. This observer is adopted as the reference model for MRAS speed estimation. In addition, a SOSM controller based on the upper twisting algorithm is designed for the speed outer loop in the DTC to improve the robustness to external load disturbances. Finally, the test results verified the SOMO MRAS speed sensorless DTC strategy can overcome the inherent chattering problem of classical sliding mode control, and has strong robustness to parameter uncertainties and dc bias.

Abstract:Aiming at the problems of low positioning accuracy and complex system equipment of two-dimensional direction finding time difference of single station and three station time difference location model based on external satellite radiation positioning. In this paper , we design a limit constraints time difference of two station location model. using a priori knowledge about the target height information is introduced into the earth position equation, instead of positioning time difference in the model, the three station time difference equation dimension ,solve the two station is conditioned by measuring condition unable to establish an effective positioning equation problem, using Newton iterative method to solve the target position. The high precision target position is obtained under limited conditions. The experimental results show that the parameters such as time difference, distance between station and satellite orbit error have little influence on the positioning accuracy, and the relative positioning accuracy of each parameter is better 0.2%R. This positioning method has good application value in engineering.

Abstract:Ultra-wide Band (UWB) ranging indoor positioning is widely used in shopping malls, residents, hospitals and other places, improving the competitiveness of indoor positioning is positioning accuracy. In order to achieve accurate ranging under various interference conditions in the actual measurement process, Kalman filter algorithm is adopted to reduce the influence of external interference on ranging as much as possible, so as to reduce the measurement error. Aiming at the problem of UWB ranging accuracy improvement, a comparative experiment using Kalman filter as processing algorithm is designed. The results show that the ranging accuracy has been greatly improved, and the error has been reduced from 5.8% to 2.44% in the experimental environment. In the distance where the accuracy is most obviously improved, the data reliability has been improved and the ranging accuracy has been improved by 58% through drawing analysis. Experimental results show that Kalman filter can effectively improve the ranging accuracy of UWB.

Abstract:Sequence time domain reflectometry (STDR) is a common cable fault detection method, but the traditional STDR method can only identify the basic short-circuit and open-circuit faults, and cannot identify other types of faults with high or low resistance loads, which is limited in the application of accurate cable fault diagnosis. This paper proposes a normalized STDR analysis method for cable fault identification, which firstly determines the cable fault location according to the time difference between the autocorrelation peak of the input signal (m-sequence) and the cross-correlation peak of the input and reflected signals, then establishes a cable load impedance estimation method based on the normalization of cross-correlation peaks, and ultimately the fault type can be accurately identified through the estimated load impedance at the fault point. Cable fault experiments show that the error of cable fault location is less than 0.25%, and the correlation coefficient between the estimated cable load impedance and the true load impedance is more than 98.80%, which verifies the effectiveness of the proposed method. The proposed normalized STDR analysis method not only ensures the accuracy of fault location, but also realizes the accurate estimation of load impedance at fault point, so as to realize the accurate identification of different fault types such as short circuit, open circuit, high-resistance load and low resistance load, and breaks through the limitation of the traditional STDR method which can only identify the short circuit and open circuit faults.

Abstract:Using the gas (DGA) generated when a power transformer fails to diagnose the transformer fault has become an important diagnostic method at home and abroad. This paper chooses to use Convolutional Neural Network (CNN) as the transformer fault diagnosis model to diagnose the power transformer. However, the diagnostic performance of CNN largely depends on its structure, and there is a problem that it is difficult to manually select model hyperparameters. Aiming at this problem, in order to improve the diagnostic accuracy of the model, an improved particle swarm optimization algorithm (IPSO) is designed to automatically optimize the hyperparameters of CNN. By improving the inertia weight W and the learning factors C1 and C2 in the PSO algorithm, the optimization ability of the particles is improved, thereby constructing a diagnostic model with better performance and achieving the purpose of improving the accuracy of the diagnosis. The experimental results show that the IPSO algorithm has better global and local optimization capabilities than PSO, and the CNN built based on the IPSO algorithm has a higher diagnostic accuracy than the CNN built by human experience, and the accuracy rate is increased by 5.84%.

Abstract:Coverage control is a fundamental and critical problem in many applications of wireless sensor networks. Aiming at the high dimensional optimization of sensor node deployment and the complexity of coverage area, a coverage optimization method based on cuckoo search algorithm with principal component analysis is proposed for wireless sensor network. Based on the standard cuckoo search (CS) algorithm, this algorithm adds the principal component analysis method to reduce the correlation between cuckoo individual position information and improve the exploration ability of the algorithm. Simulation results show that when the contribution rate is greater than 0.5, the PCA cuckoo search algorithm not only outperforms the standard CS algorithm in six benchmark test functions, but also can effectively improve the coverage area of nodes in wireless sensor network.

Abstract:The size measurement and defect detection of chip pins are of great significance in intelligent manufacturing activities. In order to achieve high-quality and high-precision detection of chip pin width, spacing and length, and pin defects, the author uses the HALCON vision software platform and adopts shape matching Perform detection experiments with one-dimensional measurement algorithms. First, the upper computer controls the camera to collect pictures, and uses the shape-based template matching method combined with the pyramid search algorithm to match and locate the chip. Secondly, the chip pin area is obtained by applying affine transformation. Finally, the extracted pin area is used The one-dimensional measurement algorithm realizes the size measurement and defect detection of the chip. The experimental results show that the detection time of a single image is 56ms, the measurement error is ±0.01mm, and the defect detection accuracy rate is 100%. Therefore, using machine vision online detection can not only ensure the accuracy of measurement, but also ensure the accuracy, and the requirements of high precision and real-time in the detection industry have been fully met.

Abstract:The detection accuracy of the gap in the internal threaded joint of a rocket engine is an important indicator of its quality. Due to the complex internal surface of the engine shell, the quality of the internal gap is not only low in efficiency but also in poor reliability by manual inspection. Proposing a visual inspection method for inward seams based on FNN network. The feature parameters of the image are constructed with gray-level co-occurrence matrix and PCA algorithm, and the FNN network is trained to classify and classify the rough and finished surfaces of the internal seams of rocket engine shells. The recognition rate is 98.8%; then, different image processing is performed for the two types of situations, and the Sobel operator is used to find the edge of the gap; finally, the system error of the algorithm (collecting the original image error, the straight line fitting error) is corrected through calibration, and the internal slit is completed Width is accurately measured. Experiments show that the method is stable and reliable, and can achieve a recognition accuracy of ±0.02mm in the range of 0.1mm-0.6mm. This method realizes the high-precision measurement of the threaded joints inside the rocket engine shell, and provides technical guarantee for the realization of high-efficiency automatic production and quality inspection of products.

Abstract:Most face images in real life are occluded, which often leads to the loss of key information of the face to be detected. Aiming at the problem of difficulty in extracting facial features due to occlusion in the process of face recognition, this paper designs a occluded face detection algorithm based on VGGNet and multi-feature point fusion. This method uses the VGG-16 framework as the backbone network for feature extraction, and adds the occlusion processing unit OCC-Net (Occlusion-Net) before the input of the fully connected layer of the traditional VGG network. In this layer, the method of multi-feature fusion is first adopted to enhance the network's extraction of facial features; then the scale-invariant feature transformation (SIFT) algorithm is used to expand the small-scale feature maps in the network to obtain richer complementary information and improve The traditional VGG network has a serious problem of small-scale feature loss caused by multiple convolution and pooling operations; finally, the regression box parameters are improved to reduce the sensitivity of the loss function to the occluded area, and the position information of the occluded area is obtained through border regression. Improved the accuracy of face detection in the presence of occlusion. The experimental results show that compared with commonly used algorithms such as PCANet, Faster RCNN, and traditional VGGNet without OCCNet, the algorithm in this paper can more accurately locate the occluded face on the commonly used occlusion data sets such as FDDB and RMFD, which confirms The effectiveness and robustness of the algorithm.

Abstract:In view of the high dynamic topology of ad hoc network and the busy degree of nodes, which leads to the increase of packet transmission delay and packet loss rate, the paper presents a protocol which is based on the idea of dispersed routing to avoid the delay and packet loss of trigger and retransmission, and adds delay algorithm to avoid the busy node being selected as the nodes of main path .Through simulation, different node rates are set, and the average delay and packet loss rate of AOMDV protocol and the improved protocol are compared. It shows that the improved routing protocol is better than the AOMDV protocol in terms of end-to-end delay and packet loss rate. Especially when the node moves faster than 30m/s, the performance of network transmission is significantly improved.

Abstract:With the development of 5g、WiFi communication technology and the wide application of ultra wideband (UWB) high precision positioning system, the mutual interference between orthogonal frequency division multiplexing (OFDM) signal and UWB signal cannot be ignored in indoor space. In this paper, the analysis method of system equivalent carrier to noise ratio is adopted, combined with the physical characteristics of the two systems, and the physical layer model of the two systems is built by MATLAB. According to the spectral separation coefficient, the amount of noise caused by the interference signal entering the receiver is calculated, and then the BER of OFDM communication system and the positioning accuracy of UWB system are obtained by the equivalent signal to noise ratio of the receiver in the case of interference. Simulation results show that there is signal interference between OFDM signal and UWB signal. The positioning error of UWB signal at the interference center is amplified by more than 12 times, and the bit error rate of OFDM communication signal rises to about 10-3. In other words, the same frequency mutual interference of signals will degrade the performance of 5G, WiFi and other communication systems and seriously affect the positioning accuracy of UWB system. Through the proposed system deployment optimization scheme based on distributed low-power micro-base station, the positioning performance is significantly improved in the case of almost constant communication performance in the experimental area, and the decimeter positioning accuracy can be guaranteed in the whole experimental area. The research results can provide reference for the integrated design and equipment deployment of 5G communication system and UWB positioning system.

Abstract:There are many similar vehicles in city monitoring, which brings great challenges to vehicle re-identification. Local features such as front, window and roof are the subtle differences of similar vehicles. According to the attention characteristics of the thermal map of the vehicle detection algorithm, a MCRF-SSD algorithm is proposed to detect the local feature area of the vehicle, and combines it with GMM-EM clustering algorithm. The detection performance is better than the current mainstream algorithm on the open data set.At the same time, in order to increase the inter-instance and reduce the intra-instance, the Arcface loss function is introduced into the feature extraction stage. In order to improve the performance of vehicle re recognition, in the stage of global feature and local feature fusion, a focus fusion structure (FFS) method is proposed, which can preserve the spatial distribution of feature graph, and a learnable parameter is introduced to improve the efficiency of feature fusion. Experimental results show that the performance of the proposed algorithm is better than that of the current best performance scheme in public VehicleID and VeRi datasets.