Search

During multi-layer single-pass additive manufacture based on gas metal arc welding, the formation of product surface is highly affected by the robot welding process status. The thermal history of the manufacturing process was recorded by a thermocouple, and the arc current was collected by a current sensor to investigate the relationship between arc current stability and manufacture workpiece surface formation. The empirical mode decomposition energy entropy of different Zas, which are related to the current stability and utilization of arc power, were calculated. The 3D geometric dimension information of the workpiece was scanned by a laser vision sensing system. Experimental results show the current signal of the arc can be used to assess process stability, current stability caused the temperature of the basement, and surface formation changed. The energy entropy of arc current increased with the increase of Zas. The unstable arc can take away much of the heat generated by the arc heat source leading to the increase of deposition height and side surface roughness.

Under the excitation of alternating magnetic field, the disadvantage of Magneto-optical Imaging (MOI) Non-destructive Testing (NDT) is no obvious difference between the light and shade of micro defects (sub-surface cracks) image. It will lead to great difficulties in feature extraction and recognition of welding defects. In response to this problem, the magneto-optical image (induced magnetic field) characteristics of welding defects under an alternating magnetic field are examined in this study. The variation of induced magnetic fields of different welding defects (cracks, pits, unfused, etc.) is studied by simulation and MOI to optimize the nondestructive detection of MOI under alternating excitation. Both the simulation experiment and the MOI detection experiment are conducted to study the variation of the induced magnetic field under the excitation frequency of 50 Hz and sampling frequency of 75 frames/s. First, the variation law of the induced magnetic field for the welding defect is analyzed by simulation. Then, by studying the color moments of different defect magneto-optical image data sets, the periodic variation of the same defect magneto-optical image under alternating excitation is analyzed. To prove the correctness of the periodic law summarized by the color moment, the cosine similarity is used to test the periodic variation of the image. The results show that the contrast of welding defects on the magneto-optical image changes periodically. According to this rule, the image with the strongest contrast is selected in magneto-optical image data set for defect feature extraction and recognition to overcome its shortcomings.

Safety inspection nowadays is an effective means to safeguard public security, which mainly relies on professional security personnel to carry out inspections. In order to detect automatically contraband in X-ray images, a new prohibited item detection method on the strength of the modified YOLOv7 algorithm is present. The spatial attention constructed by large kernel attention was introduced into the lower layer of the YOLOv7 backbone network to extract the remote dependence information and texture information of the lower layer feature map. The proposed method was tested on public X-ray data set for a safety inspection, and the result showed that the improved means can advance the detection accuracy of the model.

Nanfeng Zhang,1,* Tingting Qian,2,* Shan Sun,2,* Wei Cao,1 Zhixian Wang,1 Danling Liu,2 Peifan Li,2 Jingfang Wu,2 Huawei Li,1,2 Jianming Yang1 1Department of ENT, Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230031, People’s Republic of China; 2Department of ENT Institute and Otorhinolaryngology, Eye & ENT Hospital, State Key Laboratory of Medical Neurobiology, NHC Key Laboratory of Hearing Medicine Research, Fudan University, Shanghai, 200032, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jianming Yang; Huawei Li, Email JMyang88@163.com; hwli@shmu.edu.cnBackground: Otitis media with effusion (OME) is a non-suppurative inflammation of the middle ear that is characterized by middle ear effusion and hearing loss. However, the mechanisms of OME are not fully understood. The aim of this study was to determine the function and the mechanism of the IL-17 cytokine in the pathogenesis of OME and to investigate IL-17 as a potential strategy for the treatment of OME.Methods: In this study, the OME rat model was induced by ovalbumin (OVA) as previously described. The severity of OME was determined with an oto-endoscope, by histochemical analysis, and by acoustic immittance. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of RNA-sequencing (RNA-seq) data was carried out to analyze the signaling pathways related to the pathogenesis of OME, which indicated that IL-17 is involved in OME. The anti-IL-17A monoclonal antibody was administrated by nasal drip to block IL-17 to treat OME in the rat model. The rats were finally injected intraperitoneally with the inhibitor of Notch signaling pathway to study the mechanisms of IL-17-induced inflammation. Serum and lavage fluid were collected for the detection of related cytokines, and middle ear tissue was collected for Western blot, quantitative real-time PCR (qRT-PCR), and immunohistochemical and immunofluorescence analysis.Results: KEGG analysis of RNA-seq data suggested that the IL-17 signaling pathway might be involved in the onset of OME. IL-17 expression was confirmed to be increased in both the serum and the middle ear of the rat model. The monoclonal antibody against IL-17 neutralized IL-17, inhibited the inflammation in the middle ear, and reduced the overall severity of OME in vivo. Furthermore, the Notch signaling pathway was activated upon IL-17 upregulation in OME and was suppressed by IL-17 blockage. However, there was no change in IL-17 expression after Notch inhibitor treatment, which reduced the severity of OME in the rat middle ear.Conclusion: IL-17 plays a key role in the pathogenesis of the OVA-induced OME rat model. IL-17 induced inflammatory responses via the Notch signaling pathway and targeting IL-17 might be an effective approach for OME therapy.Keywords: otitis media with effusion, IL-17, anti-IL-17A monoclonal antibody, notch

This paper studies the magneto-optical image (obtained by different sampling frequencies) features of weld defects under alternating magnetic field excitation. Weld cracks with different gaps—0.03 mm, 0.05 mm, 0.07 mm, and 0.1 mm—were detected through the magneto-optical imaging method. In order to study the effect of sampling frequency on magneto-optical imaging, different sampling frequencies, such as 5 fps, 15 fps, 25 fps, 35 fps, 45 fps, 55 fps, 65 fps, and 75 fps, were used under the excitation of an alternating magnetic field. The distribution range of the gray-scale value for the magneto-optical image obtained at each sampling frequency was analyzed. The effect of the sampling frequency on the magneto-optical imaging was analyzed by comparing the magneto-optical images of weld defects and their gray-value curves at different sampling frequencies. Experimental results show that the sampling frequency of the magneto-optic sensor affects the distribution range of the gray-scale value of the magneto-optic image. Under the same experimental conditions, as the sampling frequency increases, the whole magneto-optical image becomes dark, and the span of the gray value becomes larger.

Alternating current field measurement (ACFM) is a technology for detecting magnetic flux leakage (MFL) using a magnetic sensor and is often used to detect welding defects. An alternating magnetic field was generated under the excitation of an alternating electric field. The magnetic field direction of an alternating magnetic field changes with time and the size and direction of the leakage magnetic field on the weldment also change periodically. In order to verify this rule, the distribution of the leakage magnetic field in different magnetisation directions was analysed through simulation and experiment. The simulation results show that when sinusoidal excitation is applied to the coil, the direction of magnetisation changes with time and the magnetic field density (B) gradually changes periodically on the weldment. This was verified through related experiments. When the surface of the weldment had welding defects, the dynamic magneto-optical image was captured in different magnetisation directions. Welding defects have different magneto-optical images in different frames, which can present more information for the same defect. In addition, in the experiment, the correlation between the grey-level co-occurrence matrix (GLCM) of magneto-optical images in different magnetisation directions and that of the leakage magnetic field in the corresponding direction was analysed. Simulations and experiments were performed on different types of welding defect. It was found that the trend of characteristics for the leakage magnetic fields was similar to that of the characteristics for the grey-level co-occurrence matrix of the magneto-optical images. The results show that the characteristics of the leakage magnetic field can reflect the texture features of the welding defect.

The spectrometer is applied in this paper to get the in-process information of welding defects during the high-power disk laser welding process. The high-dimensional signal captured by the spectrometer is fed into a data-driven framework based on stacked auto-encoder (SAE) to automatically extract salient features for performing real-time welding defects detection. The particle swarm optimization (PSO) algorithm is employed to optimize the proposed data-driven framework by acquiring the global optimal parameters to strengthen its capability in extracting the representative features from the original high-dimensional signal. The extracted features are classified by a softmax classifier to get the real-time identifications of the welding defects. The proposed framework is compared with the conventional shallow artificial intelligent methods, such as back-propagation (BP) neural network and support vector machine (SVM), and reveals better performance. The actual welding experiments under different welding parameters are implemented to validate the detection accuracy of our proposed data-driven framework. This paper provides an effective framework for the detection of the high-power disk laser welding status in real-time.

Customized bus services are conducive to improving urban traffic and environment, and have attracted widespread attention. However, the problems encountered in the new customized bus mode include the large difference between the basis of customized bus passenger flow data analysis and the basis of the traditional bus passenger flow data analysis, and the difficulty in different vehicle scheduling caused by the combination of traditional and customized bus modes. We propose a customized bus passenger flow analysis algorithm and multi-destination customized bus line capacity scheduling algorithm, and display them in an intuitive way. The experimental results show that the algorithm model established in this paper can basically meet the data requirements of operation and management, and can provide decision support for customized bus line planning.

Welding statuses monitoring is crucial to quality control during high power disk laser welding of thick plates. A multiple-sensor-system is designed to capture the features of the keyhole, plume, spatter, optical and spectrum information of the welding area. These signals comprehensively depict the welding statuses, and are used to on-line monitor the welding statuses. The correlations between the captured multiple signals are analyzed by the correlation analysis method and Linear discriminative analysis (LDA) analysis and Stacked Auto-Encoder (SAE) are implemented, and the dimension-reduced non-linear transformation of the original features acquired by SAE shows better discriminative and representative capacity than the linear combination of the original features acquired by the LDA. This research not only investigates the correlations between the signals of the keyhole, plume, photodiode and spectrum information during high power disc laser welding but also provides a novel method to conduct online welding statuses monitoring. Three different welding experiments under different parameters were conducted, and these experiments include blowout, humping and undercut defects, respectively.

Because of the continuous progress of vehicle hardware, the condition where the vehicle cannot load a complex algorithm no longer exists. At the same time, with the progress of vehicle hardware, the number of texts shows exponential growth in actual operation. In order to optimize the efficiency of mass data transmission in actual operation, this paper presented the text information (including position information) of the maximum entropy principle of a neural network probability prediction model combined with the optimized Huffman encoding algorithm, optimization from the exchange of data to data compression, transmission, and decompression of the whole process. The test results show that the text type vehicle information based on compressed algorithm to optimize the algorithm of data compression and transmission can effectively realize data compression. It can also achieve a higher compression rate and data transmission integrity, and after decompression it can basically guarantee no distortion. The method proposed in this paper is of great significance for improving the transmission efficiency of vehicle text information, improving the interpretability and integrity of text information, realizing vehicle monitoring, and grasping real-time traffic conditions.

A multi-sensor system, including an auxiliary illumination (AI) visual sensor system, an UVV band visual sensor system, a spectrometer, and two photodiodes, is established to capture signals of the welding status during high-power disk laser welding. The features of visible light and reflected laser light signal were extracted by decomposing the originally captured signals into different frequency bands by wavelet packet decomposition method (WPD). The captured signal of the spectrometer mainly covers the optical wavelength from 400 nm to 900 nm, which was divided into 25 sub-bands to extract the spectrum features of the spectrometer signal by statistical methods. The features of the plume are acquired by the UVV band visual sensor system, and the features of keyhole are extracted captured from the images captured by AI visual sensor system through the digital image processing method. Based on these quantified real-time features of the welding process, a deep learning algorithm based on convolutional neural network (CNN) was developed to detect three different welding defects during high-power disk laser welding. The established deep learning model is compared with the backpropagation neural network (BP) model, and it shows higher accuracy and robustness in detecting welding defects with the multi-sensor system. Its effectiveness was also validated by four welding experiments with different welding parameters.

The respiratory tract microbiota are deemed as the gatekeeper to health. Consequently, microbiota dysbiosis can lead to the development of diseases. To identify the exact origins of the localized pathogenic bacteria, we investigated bacterial composition in the upper airway tract. Separate mucosal swabs were collected from nostril or oropharynx of each participant. Meanwhile, the lymphoid tissues including adenoids and tonsils were collected during operation. DNAs were exacted from all the samples for the following 16S rRNA analysis. At the phylum level, the basic bacterial structures in the adenoids, tonsils, oropharynx, and nostrils were generally similar: five main phyla Firmicutes, Proteobacteria, Bacteroidetes, Actinobacteria, and Fusobacteria form the majority of the microbiota. However, across these four sites, the microbiota composition differed. More specifically, the bacterial composition in the nostrils was unique. There, Firmicutes and Actinobacteria were the most abundant phyla, while Bacteroides and Fusobacteria were the least abundant. At the genus level, Staphylococcus, Dolosigranulum, Corynebacterium, and Moraxella were the most plentiful, while Fusobacteria was the least ample. Across all sites, Streptococcus displayed similar abundances. Fusobacteria exhibited higher abundances in the lymphoid tissues and oropharynx. Haemophilus and Neisseria were more plentiful in the tonsils and oropharynx. Notably, Klebsiella, which is normally localized to the gut, was abundant in the adenoids and tonsils. Our data indicate that promising pathogenic bacteria originate from all sites in the upper airway. The upper tract lymphoid tissues, normally considered as immune organs, may also serve as reservoirs for pathogenic bacteria.

With the rapid development of sensor technology for automated driving applications, the fusion, analysis, and application of multimodal data have become the main focus of different scenarios, especially in the development of mobile edge computing technology that provides more efficient algorithms for realizing the various application scenarios. In the present paper, the vehicle status and operation data were acquired by vehicle-borne and roadside units of electronic registration identification of motor vehicles. In addition, a motion model and an identification system for the single-vehicle lane-change process were established by mobile edge computing and self-organizing feature mapping. Two scenarios were modeled and tested: lane change with no vehicles in the target lane and lane change with vehicles in the target lane. It was found that the proposed method successfully identified the stochastic parameters in the process of driving trajectory simulation, and the standard deviation between simulation and the measured results obeyed a normal distribution. The proposed methods can provide significant practical information for improving the data processing efficiency in automated driving applications, for solving single-vehicle lane-change applications, and for promoting the formation of a closed loop from sensing to service.

It is of great significance to detect the weld surface to ensure welding quality. Aiming at the problem of weld surface defect detection and 3D reconstruction algorithm, a method of defect detection and three - dimensional reconstruction based on laser scanning 3D point cloud has been proposed. The method of BP neural network is used to filter the point cloud data obtained by laser displacement sensor scanning, and the result is qualitatively and quantitatively analyzed. The three-dimensional reconstruction model of pit defects on weld surface is established. The reconstructed error is within the allowable range. The experimental results show that applying BP neural network to weld defect detection can effectively reduce the complexity of 3D reconstruction and improve the accuracy of 3D reconstruction of weld surface.

Magneto-optical imaging is based on the theory of leakage magnetic field and the Faraday magneto-optical effect. A magnetic dipole model of pit was established and verified by comparing with gray value of magneto-optical image of pit. The sag, pit, non-melting and non-defect of welded pieces made in the detection of magneto-optical were simulated by the two-dimensional finite element models. The magnetic induction intensity distributions of defects in the lift strength of 1mm were obtained in the simulations. Experiments on detecting the defects referred above by magneto-optical detection were carried out and magneto-optical images of the defects were acquired. The simulations were proved reliable when compared with magnetic field distribution of models and gray curves of magneto-optical images of the corresponding defects. Also, these models analyzed magnetic induction intensity distributions of different defects in different strengths of lift. Furthermore, it provided the theoretical and experimental basis for further study of the mechanism of magneto-optic imaging.

This paper researches the magneto-optical (MO) imaging law of weld cracks under alternating magnetic field excitation. Weld surface and subsurface cracks are detected by a MO sensor, and the relationship between the MO images’ characteristics and the magnetic field strength is analyzed based on the Faraday MO effect. A magnetic dipole model is proposed to study the magnetic field distribution over the weld crack. A finite element analysis (FEA) model of the weld crack is established, and the relationship between the magnetic flux leakage signal and the crack width is analyzed, which is useful for identifying cracks either on the surface or on the subsurface of the weld. A MO imaging nondestructive testing (NDT) experiment is carried out to detect weld cracks under alternating magnetic field excitation, and the difference among weld cracks is obtained by analyzing the gray values of the weld cracks’ MO images. Research results show that the magnetic flux leakage signals of weld surface and subsurface cracks can be clearly distinguished, the magnetic field intensity of the surface cracks is larger than that of the subsurface cracks at the same width, and the MO image of the weld cracks can reflect the intensity of the magnetic field through varied brightness, that is, the gray value of the MO image can match the corresponding magnetic field intensity.

The heating characteristic of electric heating element has great influence on the drying performance of a clothes dryer. In this paper, a self-designed condenser was applied in a condensing tumbler clothes dryer to enhance heat transfer. Performance parameters, such as average moisture evaporation rate, final moisture content and moisture extraction rate (MER), were used to study the effect of three different types of electric heating elements (PTC, resistance wire and modified resistance wire) on drying performance of the dryer. The results indicated that a resistance wire did not enhance the drying performance and the energy efficiency of the dryer significantly, compared with PTC. So a modified resistance wire with aluminum splints was developed. It was observed that the average moisture evaporation rate increased obviously. Besides, both the moisture extraction rate and the final moisture content were reduced in different degrees. Even more gratifying was that the drying time decreased evidently. Therefore, the modified resistance wire in a tumbler clothes dryer might have great potential to reduce the residential energy consumption after further research.

This paper discusses the effect of parameters in MIG based wire and arc additive manufacturing (WAAM) using stainless-steel wire and Q235 as substrate. The response surface methodology (RSM) and ANOVA were used to determine the effects of current, voltage, and welding velocity on the responses of bead height and width. It was found that voltage had the largest effect on the bead width, followed by welding velocity and current. But on the bead height, welding velocity is the most significant parameter, followed by current and voltage. The genetic algorithm (GA) was utilized to optimize the parameters to obtain the maximum ratio of weld bead width to height for multi-layer single-pass arc additive deposition.

In laser-MIG hybrid welding, the visual information can be used to monitor the stability of the process and estimate the weld formation. Taking the hybrid welding of ER308 stainless steel as an example, a high-speed camera is used to simultaneously obtain visual information of top and bottom of the weldment during the welding process. Combined with the visual characteristics of arc light, molten hump and spatter at the bottom, the influence of various welding state characteristics on weld formation was analyzed. The comparison between the welding process of good weld and root humps illustrated that the time series curves of the area and centroid coordinates of the molten hump at the bottom can reflect the formation process of the root humps. The main cause of root humps is the lack of heat input. The results support a basis for monitoring and controlling the laser–MIG hybrid welding process status.

Laser welding of 1 mm-thick steel and PMMA was employed via beam oscillating. Three modes of beam oscillation, transverse, longitudinal and circular were explored. Due to the stirring effect, the beam oscillation improves the weld formation and promotes the flow of bubbles in the molten layer. The circular oscillation gives the strongest weld homogeneous without overheat discoloration defect. The bonding strength was 38% more than the weldment without beam oscillation. The weld strength improvement was attributed to the variation of the oscillating speed varies the time-frequency distribution of the beam energy. This technology reduces the morphological defects and increases the uniformity of the weld. The improvement mechanism of beam oscillation on bubble behavior and melt flow behavior is also discussed.

The image of laser-MIG hybrid welding process was collected by high-speed camera, the arc area and the barycenter of droplet were extracted by image processing method, the weld width and weld height were extracted from the 3D point cloud of weldment surface, and the relationship between droplet transition features, droplet velocity and weld formation were analyzed. Results show that the extremum of arc area and droplet transition period have the same change trend. The change of droplet velocity can effectively reflect the change of weld width. The decrease of arc length can increase the droplet transition frequency, speed up the droplet velocity and increase the weld width. The abrupt increase of arc length will increase the ratio of height to width, which reduce the welding quality.

Security inspection is an important measure to ensure public safety. At present, X-ray security inspection equipment is widely used in security checkpoints. However, it is not efficient to recognize prohibited items in X-ray images manually. Automated security inspection system has become the development trend of security field. In this paper, the Yolov4 detector was used to detect prohibited items in X-ray images. In order to improve the detection performance, we added CBAM attention modules to different parts of Yolov4. A public dataset is used for simulation experiments, which shows that the addition of CBAM can effectively improve the detection performance of the detector.

At present, the surface defects of car door seals are generally detected by manual operation, which is inefficient and costly. This paper presents a new detection method to find the surface defects of car door seals with the application of modified YOLO V3 algorithm. Firstly, the K-means clustering algorithm was employed to analyze the number and size of the candidate box of the target detection. Then, the feature pyramid was constructed in the first module of the trunk feature extraction network. Finally, the attention mechanism SE module was added in the output part of darknet53 and two feature gold towers extraction network of the trunk feature. The improved YOLO V3 algorithm was carried out on the defect data set of automobile door seals. The results indicate that compared with the original YOLO V3 algorithm, the improved YOLO V3 mAP reaches 52.71%, increasing by 3.28%.

In consideration of imaging principle and sampling process of the X-ray imager, the collected images often appear blurry and noisy. We propose to add image processing in the reconstruction process to optimize the 3D reconstruction surface results of X-ray images in this paper. This paper proposes an automatic image data acquisition method to acquire images. According to the characteristics of X-ray images, an image processing method is drawn up. In this paper, the original image and the processed X-ray image are divided into two groups, and three-dimensional reconstruction is performed respectively, and the visualization effect and reconstruction time of the reconstruction result is compared. Experiments show that the running speed of the program is improved, and the visualization results show that the surface texture refinement and the reconstruction structure of the reconstruction effect after image processing have also been well optimized.

Prohibited articles detection is an important way to ensure public safety. In order to improve the flexibility of security inspections in complex public places, this paper uses three different convolutional neural networks, YOLOv3, MobileNet-SSD, and YOLOv3-Tiny, to detect the prohibited articles on the security inspection image data. Two performance indicators, mean Average Precision and Frames Per Second, were used to test the effect of the propose model. Finally, this trained model was deployed to the Raspberry Pi mobile device. Experimental results show that using YOLOv3-Tiny model for prohibited articles detection can improve the detection speed by about 10 times and the high detection precision is also maintained. This experiment provides a research idea for the fast detection of prohibited articles on mobile devices.

In the development of technology for smart cities, the installation and deployment of electronic motor vehicle registration identification have attracted great attention in terms of smart transportation in recent years. Vehicle velocity measurement is one of the fundamental data collection efforts for motor vehicles. The velocity detection using electronic registration identification of motor vehicles is constrained by the detection algorithm, the material of the automobile windshield, the placement of the decals, the installation method of the signal reader, and the angle of the antenna. The software and hardware for electronic motor vehicle registration identification produced in the standard manner cannot meet the accuracy of velocity detection for all scenarios. Based on the actual application requirements, we propose a calibration method for the numerical output of the automobile velocity detector based on edge computing of the optimized multiple reader/writer velocity values and based on a particle swarm-optimized radial basis function (RBF) neural network. The proposed method was tested on a two-way eight-lane road, and the test results showed that it can effectively improve the accuracy of velocity detection using electronic registration identification of motor vehicles. Compared with the actual velocity, 87.12% of all the data samples had an error less than 5%, and 91.76% of the data samples for vehicles in the center lane had an error less than 5%. By calibrating the electronic vehicle velocity based on the registration identification, the accuracy of velocity detection in different application environments can be improved. Moreover, the method can establish an accurate foundation for application in traffic flow management, environmental protection, traffic congestion fee collection, and special vehicle traffic management.

Magneto-optical (MO) imaging non-destructive testing (NDT) system excited by rotating magnetic field is proposed for feature extraction and detection classification of invisible arbitrary-angle weld defects. Based on Faraday rotation effect, the relationship between the imaging characteristics of weld defect MO image and the leakage magnetic field intensity is analyzed. The gray-level co-occurrence matrix (GLCM) method is used to extract texture features of the weld defect MO images, and the texture features of the images can reflect the leakage magnetic field characteristics of the defects. These texture features of the weld defect MO images are used as the input vector of the defect classification model based on support vector machine (SVM). The effectiveness and feasibility of the classification model are verified by the weld defect detection experiment. Experimental results show that the established recognition model can accurately classify invisible arbitrary-angle weld defects.

Dissimilar joining of 304 stainless steel to PMMA is accomplished by a Nd: YAG pulsed laser. The light and process were represented by laser peak power, welding speed, defocus, pulse duration, pulse frequency and Ar gas flow rate, as these experiment parameters have significant effects on joints strength, weld width, joints quality and appearance. The welding parameters were optimized by an orthogonal experiment design, then the validation and optimization experiments were carried out to obtain the best combination of process parameters. A maximum weld strength of the joints of 974 N was achieved (the joints efficiency reached 70% of the base PMMA), much larger than ever reported. Bead profiles such as bead width and mechanical strength were investigated, and five types of 304 stainless steel-PMMA welded joints and three kinds of fracture model have been proposed.

Seam tracking is important for butt joint laser welding. A magneto-optical imaging approach is proposed to detect the micro-gap weld whose width is less than 0.2 mm. The symmetry of the magnetic field above the weld joint is an important precondition to ensure the detection accuracy of the magneto-optical imaging method. However, in actual complex industrial scene, it is difficult to guarantee complete symmetry of the magnetic field. This paper proposes an effective method for solving the problem of magneto-optical imaging deviation under an asymmetric magnetic field. Two possible factors causing the asymmetry of magnetic field above weld joint are firstly investigated using finite element analysis. By analyzing the characteristics of the magneto-optic medium used in the sensor and measuring the magnetic field distribution of weld joint at different lift-off height and different excitation voltage, the prediction model of deviation between the weld position detected by magneto-optical imaging and the actual weld position is built by back propagation (BP) neural network. The experimental result of weld seam tracking based on magneto-optical imaging shows that the change of the lift-off height will affect the detection accuracy of the weld position, and sufficient accuracy can be ensured after correcting the deviation according to the prediction model of magneto-optical imaging deviation.

Traffic congestion is a common problem in many countries, especially in big cities. At present, China’s urban road traffic accidents occur frequently, the occurrence frequency is high, the accident causes traffic congestion, and accidents cause traffic congestion and vice versa. The occurrence of traffic accidents usually leads to the reduction of road traffic capacity and the formation of traffic bottlenecks, causing the traffic congestion. In this paper, the formation and propagation of traffic congestion are simulated by using the improved medium traffic model, and the control strategy of congestion dissipation is studied. From the point of view of quantitative traffic congestion, the paper provides the fact that the simulation platform of urban traffic integration is constructed, and a feasible data analysis, learning, and parameter calibration method based on RBF neural network is proposed, which is used to determine the corresponding decision support system. The simulation results prove that the control strategy proposed in this paper is effective and feasible. According to the temporal and spatial evolution of the paper, we can see that the network has been improved on the whole.

In order to explore the relationship between the welding process and welded quality, a multiple sensor fusion system was built to obtain the photodiode and visible light information during the welding. Features of keyhole, plasma and spatters were extracted from five sensors, including two photodiode sensors, one spectrometer sensor, one ultraviolet and visible light sensing camera and one auxiliary illumination sensing camera, 15 features were analyzed by normalization and principle component analysis, and principle component numbers was chosen as input parameters of support vector machine classification, Three weld quality types were defined according to the weld seam width and weld depth. The overall accuracy of training data was 98%, and the overall accuracy of testing data was 91%, respectively. Experimental results showed that the estimation on welding status was accurate and effective, thus providing an experimental example of monitoring high-power disk laser welding quality.

With the extensive application of the concept of green traffic, the relationship between the driving characteristics of different drivers and energy consumption and traffic performance conditions, etc. is gradually becoming a research hotspot. Based on bus status data recorded by travel data recorders with a vehicle-mounted satellite positioning function and in view of external bus behaviours and driver’s performance, a bus driving characteristic model of drivers is established. A time-space analysis model of the driving characteristics of different drivers based on traffic performance index is also established through fuzzy association rules and a type-2 fuzzy set prediction algorithm. Test results show that the prediction algorithm can accurately describe the time-space relationship between the traffic congestion index and bus driving characteristic model and achieve relatively high prediction accuracy. The problem of the lagging release of traffic performance index caused by massive calculation for floating vehicle data can be effectively solved through this algorithm, which can serve as an important reference for analyzing traffic performance conditions, as well as the energy conservation and emission reduction of buses.

Due to the continuous progress in the field of vehicle hardware, the condition that a vehicle cannot load a complex algorithm no longer exists. At the same time, with the progress in the field of vehicle hardware, a number of studies have reported exponential growth in the actual operation. To solve the problem for a large number of data transmissions in an actual operation, wireless transmission is proposed for text information (including position information) on the basis of the principles of the maximum entropy probability and the neural network prediction model combined with the optimization of the Huffman encoding algorithm, from the exchange of data to the entire data extraction process. The test results showed that the text-type vehicle information based on a compressed algorithm to optimize the algorithm of data compression and transmission could effectively realize the data compression, achieve a higher compression rate and data transmission integrity, and after decompression guarantee no distortion. Therefore, it is important to improve the efficiency of vehicle information transmission, to ensure the integrity of information, to realize the vehicle monitoring and control, and to grasp the traffic situation in real time.