Damage identification of bridge structure based on frequency domain decomposition and strain mode

Because it is difficult to determine the degree and location of damage to bridge structures under environmental excitation, a new method combining frequency domain decomposition and strain mode identification for bridge structure damage assessment is proposed and the related identification principle is derived. General variations in the strain mode parameters of different reinforced concrete beam damage states are accordingly studied under ambient excitation. The results show that the proposed strain mode identification method based on frequency domain decomposition has good anti-noise performance and can identify the strain mode parameters of a structure relying solely on the strain response information of the structure, even under strong background noise. The mutation of the strain mode was effectively used to determine the damage condition of reinforced concrete beams, and the adaptability, feasibility, and reliability of the proposed method for modal parameter and damage identification of reinforced concrete beams under environmental excitation were verified

Research on Precipitation Prediction Model Based on Extreme Learning Machine Ensemble

Precipitation is a significant index to measure the degree of drought and flood in a region, which directly reflects the local natural changes and ecological environment. It is very important to grasp the change characteristics and law of precipitation accurately for effectively reducing disaster loss and maintaining the stable development of a social economy. In order to accurately predict precipitation, a new precipitation prediction model based on extreme learning machine ensemble (ELME) is proposed. The integrated model is based on the extreme learning machine (ELM) with different kernel functions and supporting parameters, and the submodel with the minimum root mean square error (RMSE) is found to fit the test data. Due to the complex mechanism and factors affecting precipitation change, the data have strong uncertainty and significant nonlinear variation characteristics. The mean generating function (MGF) is used to generate the continuation factor matrix, and the principal component analysis technique is employed to reduce the dimension of the continuation matrix, and the effective data features are extracted. Finally, the ELME prediction model is established by using the precipitation data of Liuzhou city from 1951 to 2021 in June, July and August, and a comparative experiment is carried out by using ELM, long-term and short-term memory neural network (LSTM) and back propagation neural network based on genetic algorithm (GA-BP). The experimental results show that the prediction accuracy of the proposed method is significantly higher than that of other models, and it has high stability and reliability, which provides a reliable method for precipitation prediction

Baseline-free damage identification based on asymmetrical energy consumption

In order to solve the problem of obtaining accurate data for an intact beam, a baseline-free damage identification approach, based on the difference in the energy consumption of a beam, has been presented in this paper. An energy model was established in order to illustrate that the difference in the energy consumption is mainly due to the respiration effect of cracks, and that the energy consumption of a beam bending downward can be utilized as a replacement for the baseline data. Thus, the standard data and the comparative data can be separated from the measurement data. Based on this data, a statistical damage factor that can be used to locate and quantify the damage in a beam has been defined. Finally, an identification algorithm was established and has been experimentally verified for use with pre-damaged reinforced concrete beams. The experimental results have illustrated that the location and singularity of a singular point in the damage indicator sequence can locate the damage and quantify the severity of the damage in a beam, respectively

Spatio-temporal variations and influencing factors of polycyclic aromatic hydrocarbons in atmospheric bulk deposition along a plain-mountain transect in western China

Ten atmospheric bulk deposition (the sum of wet and dry deposition) samplers for polycyclic aromatic hydrocarbons (PAHs) were deployed at a plain-mountain transect (namely PMT transect, from Daying to Qingping) in Chengdu Plain, West China from June 2007 to June 2008 in four consecutive seasons (about every three months). The bulk deposition fluxes of ∑15-PAHs ranged from 169.19 μg m−2 yr−1 to 978.58 μg m−2 yr−1 with geometric mean of 354.22 μg m−2 yr−1. The most prevalent PAHs were 4-ring (39.65%) and 3-ring (35.56%) PAHs. The flux values were comparable to those in rural areas. Higher fluxes of total PAHs were observed in the middle of PMT transect (SL, YX and JY, which were more urbanized than other sites). The seasonal deposition fluxes in the sampling profile indicated seasonality of the contaminant source was an important factor in controlling deposition fluxes. PAHs bulk deposition was negatively correlated with meteorological parameters (temperature, wind speed, humidity, and precipitation). No significant correlations between soil concentrations and atmospheric deposition were found along this transect. PAHs in soil samples had combined sources of coal, wood and petroleum combustion, while a simple source of coal, wood and grass combustion for bulk deposition. There were significant positive correlation relationship (p < 0.05) between annual atmospheric bulk deposition and local PAHs emission, with biomass burning as the major contribution to the total emission of PAHs. This transect acts as an important PAHs source rather than being a sink according to the ratio of deposition/emission. Mountain cold trap effect existed in this transect where the altitude was higher than 1000 m. Long-range transport had an impact on the bulk deposition in summer. And this transect was a source to Tibetan only in summer. The forward trajectory analysis showed most air masses did not undergo long-range transport due to the blocking effect of surrounding mountains. Only a few air masses (<10%) arrived at the eastern and northern region of China or farther regions via long-range transport

Review of Industrial Robot Stiffness Identification and Modelling

Due to their high flexibility, large workspace, and high repeatability, industrial robots are widely used in roughing and semifinishing fields. However, their low machining accuracy and low stability limit further development of industrial robots in the machining field, with low stiffness being the most significant factor. The stiffness of industrial robots is affected by the joint deformation, transmission mechanism, friction, environment, and coupling of these factors. Moreover, the stiffness of a robot has a nonlinear distribution throughout the workspace, and external forces during processing cause irregular deviations of the robot, thereby affecting the machining accuracy and surface quality of the workpiece. Many scholars have researched identifying the stiffness of industrial robots and have proposed methods for improving the performance of industrial robots, mainly by optimizing the body structure of the robot and compensating for deformation errors with stiffness models. This paper reviews recent research on the stiffness modelling of industrial robots, which can be broadly classified as finite element analysis (FEA), matrix structure analysis (MSA), and virtual joint modelling (VJM) methods. Each method is studied from three aspects: algorithms, implementation, and limitations. In addition, common measurement techniques have been introduced for measuring deformation. Further research directions are also discussed

Compressed sensing MRI via fast linearized preconditioned alternating direction method of multipliers

Abstract Background The challenge of reconstructing a sparse medical magnetic resonance image based on compressed sensing from undersampled k-space data has been investigated within recent years. As total variation (TV) performs well in preserving edge, one type of approach considers TV-regularization as a sparse structure to solve a convex optimization problem. Nevertheless, this convex optimization problem is both nonlinear and nonsmooth, and thus difficult to handle, especially for a large-scale problem. Therefore, it is essential to develop efficient algorithms to solve a very broad class of TV-regularized problems. Methods In this paper, we propose an efficient algorithm referred to as the fast linearized preconditioned alternating direction method of multipliers (FLPADMM), to solve an augmented TV-regularized model that adds a quadratic term to enforce image smoothness. Because of the separable structure of this model, FLPADMM decomposes the convex problem into two subproblems. Each subproblem can be alternatively minimized by augmented Lagrangian function. Furthermore, a linearized strategy and multistep weighted scheme can be easily combined for more effective image recovery. Results The method of the present study showed improved accuracy and efficiency, in comparison to other methods. Furthermore, the experiments conducted on in vivo data showed that our algorithm achieved a higher signal-to-noise ratio (SNR), lower relative error (Rel.Err), and better structural similarity (SSIM) index in comparison to other state-of-the-art algorithms. Conclusions Extensive experiments demonstrate that the proposed algorithm exhibits superior performance in accuracy and efficiency than conventional compressed sensing MRI algorithms

Research on the Influence of Drone Countermeasure Equipment on the Surveillance System Used in Civil Aviation

Due to the lack of perfect regulations and standards in civilian drones field, drones sometimes fly illegally. When illegally flying drones invade airports, which causes flights to be diverted and even airports to be paralyzed. In order to protect the flight safety, drone countermeasure equipment is need to use for driving away illegally flying drones. But drone countermeasure equipment is a new active interference source. When it is used at or around airports, which may cause electromagnetic interference to the surveillance system, and it will not work correctly. Therefore, it is necessary to study protective distances of the surveillance system. In this paper, a method for analyzing the electromagnetic interference influence of drone countermeasure equipment on the surveillance system is proposed. Firstly the radiated field strength of drone countermeasure equipment is measured in an anechoic chamber, and then stray radiation field strengths at frequency bands of a secondary surveillance radar (SSR) and an automatic dependent surveillance-broadcast (ADS-B) also are acquired. Meanwhile, test results are compared with protection requirements of electromagnetic environment of SSR and ADS-B. Moreover, electromagnetic interference effects of drone countermeasure equipment on SSR and ADS-B are analyzed according to the electromagnetic compatibility theory and protection ratios of SSR and ADS-B. At last, protective distances of SSR and ADS-B are proposed separately. The results can provide some technical supports for the safe use of drone countermeasure equipment at airports