Study on Vehicle Operating Safe State Monitoring Parameter and Measurement Model

AbstractAiming to the present problems of vehicle operating safe state monitoring technology, study on vehicle operating safe state monitoring parameter and measurement model is proposed. Vehicle operating safe state monitoring parameters including monitoring vehicle's motion attitude parameter(MAP), dynamic load parameter(DLP) and braking performance parameter(BPP) of three key parameters are put forward, which is more comprehensive and scientific than ever. By establishing measurement model using WEIS to realize vehicle operating safe state parameter monitoring. Analyzed the connection among MAP, DLP and BPP information in reducing the unnecessary, repetitive physical sensing the amount of cases, some parameters that other traditional systems cannot measure can be obtained. The model forms a relative integrative and independent vehicle safety early warning monitoring platform, and has a great promotional value if information terminals access to Internet of Things

Child neglect in one-child families from Suzhou City of mainland China

Background The one-child policy introduced in China in 1979 has led to far-reaching changes in socio-demographic characteristics. Under this policy regime, each household has few children. This study aims to describe the prevalence of child neglect in one-child families in China and to examine the correlates of child neglect. Methods A cross-sectional study of 2044 children aged 6 to 9 years and recruited from four primary schools in Suzhou City, China was conducted. Neglect subtypes were determined using a validated indigenous measurement scale reported by parents. Child, parental and family characteristics were obtained by questionnaires and review of social security records. Linear regression analyses were performed to estimate the associations between these factors and the subtypes of child neglect. Results The prevalence of child any neglect was 32.0% in one child families in Suzhou City, China. Supervisory (20.3%) neglect was the most prevalent type of child neglect, followed by emotional (15.2%), physical (11.1%), and educational (6.0%) neglect After simultaneous adjustment to child and family characteristics and the school factor, boys, children with physical health issues and cognitive impairment, younger and unemployed mother, were positively associated with neglect subtypes. We also found that parents with higher education and three-generation families were negatively associated with neglect. Conclusion The rates of child neglect subtypes vary across different regions in China probably due to the different policy implementation and socio-economic levels, with a lower level of physical and educational neglect and a higher level of emotional neglect in this study. The three-generation family structure was correlates of neglect which may be unique in one child families. This indicates that future intervention programs in one-child families should target these factorsBioMed Central open acces

Chassis Assembly Detection and Identification Based on Deep Learning Component Instance Segmentation

Chassis assembly quality is a necessary step to improve product quality and yield. In recent years, with the continuous expansion of deep learning method, its application in product quality detection is increasingly extensive. The current limitations and shortcomings of existing quality detection methods and the feasibility of improving the deep learning method in quality detection are presented and discussed in this paper. According to the characteristics of numerous parts and complex types of chassis assembly components, a method for chassis assembly detection and identification based on deep learning component segmentation is proposed. In the proposed method, assembly quality detection is first performed using the Mask regional convolutional neural network component instance segmentation method, which reduces the influence of complex illumination conditions and background detection. Next, a standard dictionary of chassis assembly is built, which is connected with Mask R-CNN in a cascading way. The component mask is obtained through the detection result, and the component category and assembly quality information is extracted to realize chassis assembly detection and identification. To evaluate the proposed method, an industrial assembly chassis was used to create datasets, and the method is effective in limited data sets of industrial assembly chassis. The experimental results indicate that the accuracy of the proposed method can reach 93.7%. Overall, the deep learning method realizes complete automation of chassis assembly detection

Pressure vessel-oriented visual inspection method based on deep learning.

The detection of surface parameters of pressure vessel welds guarantees safe operation. To address the problems of low efficiency and poor accuracy of traditional manual inspection methods, a method for welding morphological parameters combined with vision and structured light is proposed in this study. First, a feature point extraction algorithm for weld parameters based on deep convolution was proposed. An accurate extraction method of weld image feature point coordinates was designed based on the combination of the loss function via seam undercut feature recognition and weld feature point extraction network structure. Second, a training data enhancement method based on the third-order non-uniform rational B-spline (NURBS) curve was proposed to reduce the amount of data collection for training. Finally, a pressure vessel measurement device was designed, and the feature point extraction performance of the deep network and common feature point extraction networks, DeepLabCut and HR-net, proposed in this study were compared to analyze the theoretical accuracy of the surface parameter measurement. The results indicated that the theoretical accuracy of the parameter measurements was within 0.065 mm

Simulation study of local thermal runaway of 18650 lithium battery module under multi-point thermal trigger

The thermal runaway of lithium power battery is the key problem of battery safety, according to the standard SAE J2464–2009 single point heating key position, the proposed multi-point trigger based thermal runaway of lithium power battery module simulation method and battery module thermal runaway battery monomer ratio PN, thermal runaway trigger time tn two indicators, the thermal runaway rule of 18650 lithium power battery module under different trigger position, trigger points. The results show that considering the external thermal insulation conditions of the power lithium battery module, the geometric angular position in the module is the most dangerous position, and the number of thermal trigger points is positively correlated with PN

Research on an Electromagnetic Interference Test Method Based on Fast Fourier Transform and Dot Frequency Scanning for New Energy Vehicles under Dynamic Conditions

In recent years, electromagnetic interference (EMI) of new energy vehicles, including difference mode symmetric interference and common mode asymmetry interference, has attracted the attention of many scholars. So far, EMI tests for new energy vehicles under steady conditions cannot reflect the actual EMI of the running vehicle. The results of EMI test methods based on fast Fourier transform (FFT) under dynamic conditions have worse frequency resolutions, and frequency/amplitude accuracy has low precision. Therefore, this paper proposes an EMI test method based on FFT and dot frequency scanning (DFS) for new energy vehicles under dynamic conditions. The identification method for accelerating, sliding, and braking conditions is studied. A comprehensive EMI key evaluation index system for new energy vehicles is built, including characteristic points with maximum amplitude, area, ratio, and density coefficients for high-amplitude characteristic points. Among them, the maximum amplitude is an index to evaluate extreme values. The ratio of high-amplitude characteristic points is a comprehensive index to evaluate the overall region. The density coefficient is an index to evaluate the local region. Finally, this method is applied to three vehicles. With the same instruments, by reducing the FFT frequency span, the frequency resolution and frequency accuracy increase. The results indicate that the EMI of new energy vehicles can be tested under dynamic conditions with high accuracy according to the operable evaluation indexes

Blind Deblurring of Saturated Images Based on Optimization and Deep Learning for Dynamic Visual Inspection on the Assembly Line

Image deblurring can improve visual quality and mitigates motion blur for dynamic visual inspection. We propose a method to deblur saturated images for dynamic visual inspection by applying blur kernel estimation and deconvolution modeling. The blur kernel is estimated in a transform domain, whereas the deconvolution model is decoupled into deblurring and denoising stages via variable splitting. Deblurring predicts the mask specifying saturated pixels, which are then discarded, and denoising is learned via the fast and flexible denoising network (FFDNet) convolutional neural network (CNN) at a wide range of noise levels. Hence, the proposed deconvolution model provides the benefits of both model optimization and deep learning. Experiments demonstrate that the proposed method suitably restores visual quality and outperforms existing approaches with good score improvements

SPWD Based IEEE 1451.2 Smart Sensor Self-Recognition Mechanism and Realization

AbstractIn order to improve the self-recognition capability of the IEEE 1451 smart sensor and enhance the level of sensor's intelligence and application flexibility, this paper presents a SPWD (sorting pulse width difference) based IEEE 1451.2 smart sensor self-recognition mechanism. The mechanism realizes baud rate self-adaption of IEEE 1451.2 serial interface first adopting the SPWD method. It also utilizes TEDS (transducer electronic data sheet) definition and configuration technique and virtual TEDS parsing algorithm to achieve smart sensor self-recognition. Then, an IEEE 1451 smart weighing sensor system is constructed using this mechanism and its self-recognition properties are tested. The experiment results show that, when the baud rate is 28800 bit/s, SPWD based IEEE 1451.2 smart sensor's recognition rate is 99.07% and its average recognition time is 1.20s