In motor electromagnetic noise analysis, the main focus is the radial electromagnetic force and other electromagnetic force components are ignored, which causes the analysis to be inaccurate. The electromagnetic simulation was conducted aiming at the problem to analyze the magnetic field and electromagnetic force distribution of 8-pole 24-slot permanent magnet synchronous drive motor. Radial electromagnetic force alone and radial, tangential and axial electromagnetic forces were respectively applied on the stator model of the motor in order to analyze the electromagnetic noise. The electromagnetic noise of the motor was calculated by the boundary element method, and the accuracy of the simulation analysis was verified by experiments. The parameterized structural model of the motor was established, and the influence of different stator slot widths and magnet fillet radii on radial, and tangential electromagnetic force and electromagnetic noise was analyzed. The results show that the tangential electromagnetic force has a certain effect on the electromagnetic noise, and the motor stator model loaded with radial, tangential and axial electromagnetic forces is more reliable. Reasonable reduction of stator slot width and magnet fillet radius can effectively reduce electromagnetic noise of the motor.

An automatic garbage classification system was designed based on machine vision in order to improve the efficiency of front-end collection in garbage classification process. The hardware device of the garbage classification system was designed and manufactured, which mainly included two boxes, the recyclable box and the non-recyclable box. A method of garbage type recognition was proposed based on Inception v3 feature extraction network structure and migration learning aiming at the data lacking problem caused by small garbage data sets. The method was trained and tested on the constructed garbage data set. The test results show that the method can accurately identify garbage types with an average accuracy of 0.99. The trained model was deployed on the raspberry pi 3B+, and tested on the real garbage bin. When the whole system was running stably, the average time for the system to complete the classification of one garbage was 0.95 second. The experimental results show that the automatic garbage classification system can effectively identify the types of garbage and complete the classification and recycling of the garbage.

An improved spatio-temporal graph convolutional networks traffic prediction algorithm, named free-flow reachable matrix-based spatio-temporal graph convolutional networks (FAST-GCN), was proposed, in order to predict real-time traffic flows accurately and improve the sensing and prediction of citywide traffic situation. The characteristics of urban complex road network structure were expressed effectively by the graph convolutional neural network, and the spatio-temporal dependency in complex road networks was explored by introducing free-flow reachable matrices. Thus the accuracy of traffic situation prediction was improved. First, preprocess traffic speeds and sensors location data. Second, with the existing spatio-temporal graph convolutional networks, the graph convolution module based on free flow reachable matrix was integrated to effectively capture the unique spatial characteristics of the urban traffic road networks. Finally, the prediction results were generated through a fully connected output layer. The proposed model was evaluated on a real-world traffic dataset PeMS. The experimental results show that this model could capture physical characteristics of road network and spatio-temporal dependency, and outperform the baselines such as spatio-temporal graph convolutional networks (STGCN), and the prediction accuracy in 45 minutes was improved by up to 5.656%. In addition, compared with baselines, the proposed model can adapt to traffic flow prediction in large-scale road networks and has superior scalability.

A novel method based on health index similarity in multiple time scales with autoencoder (AE MTS-HI) was proposed aiming at the shortage of the traditional similarity-based method in extracting health index and similarity matching. Autoencoder was applied to construct the health index based on monitoring data, which can minimize the loss of nonlinear information. The health index in multiple time scales was developed for similarity matching by considering the fluctuation of the length of test degradation trajectories. The method can remove the accuracy limitation caused by fixed time scales and enhance the prediction robustness. Performance of the proposed method was evaluated on public turbofan engines datasets. Results demonstrate that the method can improve the remaining useful life (RUL) prediction accuracy and provide stable support for predictive maintenance.

A front multi-vehicle target tracking algorithm optimized by DeepSort was proposed in order to improve the awareness of autonomous vehicles to the surrounding environment. Gaussian YOLO v3 model was adopted as the front-end target detector, and training was based on DarkNet-53 backbone network. Gaussian YOLO v3-Vehicle, a detector specially designed for vehicles was obtained, which improved the vehicle detection accuracy by 3%. The augmented VeRi data set was proposed to conduct the re-recognition pre-training in order to overcome the shortcomings that the traditional pre-training model doesn't target vehicles. A new loss function combining the central loss function and the cross entropy loss function was proposed, which can make the target features extracted by the network become better in-class aggregation and inter-class resolution. Actual road videos in different environments were collected in the test part, and CLEAR MOT evaluation index was used for performance evaluation. Results showed a 1% increase in tracking accuracy and a 4% reduction in identity switching times compared with the benchmark DeepSort YOLO v3.

The real-time prediction model that can predict the onset of paroxysmal atrial fibrillation (PAF) 45 min in advance on the one minute electrocardiogram (ECG) segment with 8 Hz sampling frequency was proposed, for real-time and data-intensive application scenarios such as long-term ECG monitoring and intensive care units (ICU). The probabilistic symbolic pattern recognition method was used to extract the pattern transition features within one minute window of down sampled ECG sequence, reducing the calculation complexity of the model and the demand for storage space, so as to ensure the effect of real-time prediction. A hybrid model (CNN-LSTM) of the convolutional neural network (CNN) and the long short-term memory (LSTM) was proposed to extract local spatial features and time-dependent features implied in pattern transition features. An ensemble classifier based on CNN-LSTM was constructed to improve the generalization ability of the model. Spark Streaming technology was used to read, write and calculate ECG streaming data, and low latency communication between data and model was realized. The accuracy, sensitivity, and specificity of the proposed model were 91.26%, 82.21%, and 95.79% respectively. The average delay of model processing was 2 s, which can meet the real-time PAF prediction demand.

The data is easy to be tampered and the access control of data is not flexible in the Internet of Vehicles (IoV). A secure data sharing scheme based on blockchain and ciphertext-policy weighted attribute-based encryption was proposed aming at the above problem. In this scheme, roadside units jointly maintain the generation, verification and storage blocks to achieve distributed storage of data, which ensures the data from being tampered. Attribute-based access control ensures that only authorized entities can access the content of data on the blockchian. A hierarchical access policy formulation method based on multi-attribute was proposed to reduce the complexity of access control policy aiming at the data sharing requirements among the multiple entities and roles in the IoV, by mining the association of attributes in the roles for data access. Experimental results show that the proposed scheme can realize the secure storage and flexible access control of the data in the IoV, and the hierarchical access policy formulation method can effectively reduce the calculation and transmission overhead of vehicles, and meet the access requirements of multiple entities and roles in the IoV.

A new vehicle motion trajectory prediction algorithm was proposed by using the attention mechanism based on the classic convolutional social long-short term memory (LSTM) trajectory prediction algorithm. Firstly, the lateral attention mechanism was introduced to assign different weights to neighboring vehicles. The features obtained from the historical trajectory of the vehicle via LSTM were taken as global features, and the trajectory features were extracted as local features through convolution pooling. The two features were fused as the overall neighbor feature information for trajectory prediction. Secondly, the Encoder-Decoder framework of traditional trajectory prediction was improved, and a vertical attention mechanism on historical position was introduced, so that each moment of prediction could use the historical information, which was most relevant to the current moment. The improved model was verified on the US101 and I80 datasets provided by NGSIM, and the results show that the proposed trajectory prediction algorithm can obtain more accurate future trajectories than other algorithms.

Traditional and new geotechnical engineering problems such as compressed air energy storage, intercepting water with compressed air, carbon dioxide sequestration and oil and gas underground reserve project are all involving air-water two-phase flow and stress coupling problems. For this engineering reality, based on the weak coupling theory of gas-water two-phase seepage and stress in unsaturated soil, a air-water two-phase percolation-stress coupling calculation program based on coupled TOUGH2 and FLAC3D was developed. The calculation program can simulate real air-water two phase flow, and can investigate the gas-water interaction of seepage process. The calculation program considers the direct interaction between gas-water two-phase seepage and soil skeleton deformation, reflects the process of porosity, permeability, capillary pressure and the change of soil physical and mechanical parameters, and achieve a more perfect gas-water two-phase seepage-stress coupling analysis. Furthermore, by comparing with classical drainage test and model test, it is verified that the program can accurately simulate the gas-water two-phase flow-stress interaction.

There have been some research in different image background modeling methods to detect abandoned objects in highway scenes. However, traditional fixed background modeling methods easily generate foreground noises because of the environmental changes, and dynamic background modeling methods quickly integrate the motionless foreground abandoned objects into the background model. A dynamic background modeling method was proposed based on background separation Gaussian mixture model (BS-GMM) for detecting abandoned objects in highway to solve this problem. Background division method and model matching method were improved in traditional Gaussian mixture model. The weight attenuation of the Gaussian distribution models per pixel was utilized to dynamically model and update image background model. The background update frequency of the traditional Gaussian mixture model method was retained, and the stationary target was continuously detected by the method of background separation. The method can reduce the impact of environmental noises easily generated in the open environment of highway, and effectively detect the long-time motionless abandoned objects. The method can achieve the effect of real-time detection in terms of computing performance. The experimental results show that our BS-GMM method produces less foreground noises than other methods, and detects abandoned objects which are motionless for more than 20 seconds. BS-GMM method can be effectively applied to detect abandoned objects in highway.

An improved single-shot-multibox-detector (SSD) algorithm was proposed. Referring to the feature pyramid networks (FPN) algorithm, the features of the Conv4-3 layer were merged with the features of Conv7 and Conv3-3 layers, and the number of default boxes at each location in merged feature map was increased. The squeeze-and-excitation networks (SENet) was added to the network structure; the feature channels of each layer were weighted, in order to enhance the useful feature weights and suppress the invalid feature weights. A series of enhancements were performed on the training data to enhance the generalization performance of the network. The experimental results show that the improved algorithm has a better performance on the VOC (07+12) dataset; the mean average precision (mAP) value of the improved algorithm is 80.4%, which is 2.7% higher than that of the original algorithm; the mAP value of the improved algorithm on COCO dataset (2017) is 42.5%, which is 2.3% higher than that of the original algorithm. Thus, the proposed algorithm can accurately detect the target with a size of at least 16×16 pixels.

A surface water quality prediction model based on graph neural network (GNN) was proposed to solve the problem that water quality data has complex dependencies in both temporal and spatial dimensions. GNN was utilized to model the complex spatial dependencies of monitoring stations, and long short-term memory (LSTM) was used to model the complex temporal dependencies of historical water quality sequences. Then the encoded vector was input into the decoder to get the water quality prediction output. The experimental results show that the model can achieve 23.3%, 26.6% and 14.8% performance improvements compared with time series analysis methods, general regression methods and existing deep learning methods.

A fast simultaneous localization and mapping (SLAM) algorithm based on point and line features was proposed in order to improve the localization accuracy and the robustness of SLAM system under RGB-D cameras in low-textured scenes. During the tracking of non-keyframes, point feature matching was performed based on descriptors, and line feature matching was performed based on geometric constraints. When a new keyframe was inserted, the descriptors of the line features were calculated to complete the line feature matching between the keyframes, and the line feature triangulation algorithm was used to generate map lines. The real-time performance of the SLAM system was improved by reducing the amount of calculation in the line feature matching process. In addition, virtual right-eye lines were constructed using the depth measurement information of line features, and a new method for calculating reprojection errors of line features was proposed. Experimental results on public datasets showed that compared with mainstream methods such as ORB-SLAM2, the proposed algorithm improved the localization accuracy of the RGB-D SLAM system in low-textured scenes. The time efficiency of the proposed algorithm was improved by about 20% compared with traditional SLAM method combining point and line features.

The tracking control strategy of follow-up visual tracking and the method of obtaining high precision relative pose of UAV based on vision and magnetic guidance were proposed in order to solve the problem that UAV can easily lose the target when tracking the ground dynamic target through vision and the positioning accuracy is poor due to serious imaging distortion and unstable picture during landing. A new beacon pattern was designed for UAV visual recognition in order to obtain the target orientation in the tracking process. The recognition speed can reach 5 ms/frame, and real-time tracking is completed by follow-up visual tracking. The magnetic source was set on the dynamic target in the process of landing. The magnetic field characteristics were detected by UAV and the relative position was calculated by BP neural network. A parallel line feature was set in the beacon pattern to assist the visual calculation of the relative angle when the camera was close to the target. The landing can be completed by corresponding motion control after obtaining the relative pose of UAV. The experimental results show that the method can achieve a stable and reliable track and high anti-jamming ability, and can reach high precision with less than 2 cm during landing.

Helical milling tool wear monitoring methods based on traditional machine learning require complex feature extraction and rich experience, and different wear stages have the same misclassification cost. A new method based on the one-dimensional convolutional neural network (1D CNN) and cost-sensitive learning was introduced, aiming at the above problems and considering the characteristics of one dimension of current signals. Current signals are acquired through spindle, revolution shaft and feed shaft of robotic helical milling end-effector respectively as monitoring signals, and samples are divided using sliding window method to reduce network capacity and increase sample numbers and diversity at the same time. The cost matrix is introduced into the network loss function and the misclassification cost of severe wear stage is increased to make 1D CNN cost sensitive. The time domain current signals are input into 1D CNN directly, and the network can extract tool wear features automatically and unify feature extraction and classification of different wear stages together. Experiment results demonstrate that the tool wear monitoring accuracy of the proposed method is 99.29% and the recall of severe wear stage is 99.60% in the robotic helical milling system.

A deep convolutional neural network model was proposed aiming at the problem that the current data and features determine the upper limit of the classification accuracy of the sleep staging model. The parallel convolutional neural network automatically learns the time-domain and frequency-domain features of the original signals in terms of model construction. The feature fusion neural network fuses multi-features through dilated convolution and residual connection. The classification neural network recognizes the sleep stages based on fused features. Synthetic minority oversampling technique (SMOTE) method was applied to enhance data in order to reduce the effect of classification imbalance on classification effect, and two-step training method was applied to optimize the model. The original single-channel electroencephalogram (Fpz-Cz channel) of the Sleep-EDF data set was used to evaluate the proposed model by the 20-fold cross-validate scheme. The overall accuracy and macro-averaging F₁-score were 86.73% and 81.70% respectively. The proposed deep convolution neural network was an end-to-end deep learning model without any prior knowledge. The experimental results showed that the classification accuracy of the proposed model was better than traditional deep learning models.

A garbage image classification method based on improved MobileNet v2 was proposed aiming at the problems of poor real-time performance and low classification accuracy of existing garbage image classification models. A lightweight feature extraction network based on MobileNet v2 was constructed. The parameter numbers of the model were reduced by adjusting its width factor, channel and spatial attention modules were embedded in the model to enhance the network's ability to refine features, a multi-scale feature fusion structure was designed to enhance the adaptability of the network to scale, and transfer learning was used to optimize the model parameters to further improve the model accuracy. Experimental results show that the average accuracy of the algorithm on the self built dataset was 94.6%, which was 2.0%, 3.4%, 3.2%, 2.3% and 1.2% higher than that of MobileNet v2, VGG16, GoogleNet, ResNet50 and ResNet101 models, respectively. The proposed algorithm achieved good performance in two public image classification datasets, CIFAR-100 and tiny-ImageNet. The parameter numbers of the model was only 0.83 M, which was about 2/5 of the basic model. The single inference on edge device JETSON TX2 took 68 ms, which proved the improvement of inference speed and prediction accuracy.

A Poisson noise image denoising method based on Bayesian probability model was proposed. An image denoising model was constructed based on Bayesian maximum a posteriori probability model and with combination of Poisson probability distribution. Considering that Markov random fields cannot represent complex natural images effectively, a higher-order Markov fields of experts was introduced as a prior regular term of the model to represent the probability distribution of the image. The quadratic penalty function was used to optimize the denoising model and restore clear images. The proposed method was compared with other denoising algorithms; the denoising effect was evaluated objectively by using two evaluation indexes: peak signal-to-noise ratio and structural similarity. The experimental results show that, compared with the traditional denoising methods, the peak signal-to-noise ratio of this method increased by at least 0.18 dB, and the denoising performance is significantly better than that of other methods. Thus, the details of the image can be retained better by using this mothed.

As the carrier transport layer in planar perovskite solar cells, metal oxide films have important influence on device properties. The requirements of metal oxide films for planar solar cells in the respect of the morphology, electrical, optical, chemical and thermal properties were systematically overviewed. Worthwhile, the materials characteristic and representative work involving the most promising metal oxide film work as electron transport layer or hole transport layer material were summarized. Research progress of adopting methods such as element doping of metal oxides, surface modification of film and design of composite metal oxide film to improving film mobility, minimizing surface defects and adjusting energy level were proposed. Moreover, the future requirement and the improvement direction of metal oxide thin film deposition technology were discussed after summarizing the advantages and disadvantages of the deposition technology. Finally, the application of low-temperature deposited metal oxide films in flexible devices was expected.