High-Resolution PM2.5 Estimation Based on the Distributed Perception Deep Neural Network Model

The accurate measurement of the PM2.5 individual exposure level is a key issue in the study of health effects. However, the lack of historical data and the minute coverage of ground monitoring points are obstacles to the study of such issues. Based on the aerosol optical depth provided by NASA, combined with ground monitoring data and meteorological data, it is an effective method to estimate the near-ground concentration of PM2.5. With the deepening of related research, the models used have developed from univariate and multivariate linear models to nonlinear models such as support vector machine, random forest model, and deep learning neural network model. Among them, the depth neural network model has better performance. However, in the existing research, the variables used are input into the same neural network together, that is, the complex relationship caused by the lag effect of features and the correlation and partial correlation between features have not been considered. The above neural network framework can not be well applied to the complex situation of atmospheric systems and the estimation accuracy of the model needs to be improved. This is the first problem that we need to be overcome. Secondly, in the missing data value processing, the existing studies mostly use single interpolation methods such as linear fitting and Kriging interpolation. However, because the time and place of data missing are complex and changeable, a single method is difficult to deal with a large area of strip and block missing data. Moreover, the linear fitting method is easy to smooth out the special data in bad weather. This is the second problem that we need to overcome. Therefore, we construct a distributed perception deep neural network model (DP-DNN) and spatiotemporal multiview interpolation module to overcome problems 1 and 2. In empirical research, based on the Beijing–Tianjin–Hebei–Shandong region in 2018, we introduce 50 features such as meteorology, NDVI, spatial-temporal feature to analyze the relationship between AOD and PM2.5, and test the performance of DP-DNN and spatiotemporal multiview interpolation module. The results show that after applying the spatiotemporal multiview interpolation module, the average proportion of missing data decreases from 52.1% to 4.84%, and the relative error of the results is 27.5%. Compared with the single interpolation method, this module has obvious advantages in accuracy and level of completion. The mean absolute error, relative error, mean square error, and root mean square error of DP-DNN in time prediction are 17.7 μg/m3, 46.8%, 766.2 g2/m6, and 26.9 μg/m3, respectively, and in space prediction, they are 16.6 μg/m3, 41.8%, 691.5 μg2/m6, and 26.6 μg/m3. DP-DNN has higher accuracy and generalization ability. At the same time, the estimation method in this paper can estimate the PM2.5 of the selected longitude and latitude, which can effectively solve the problem of insufficient coverage of China’s meteorological environmental quality monitoring stations.

Experimental Study on Mudstone’s Strength Characteristics in Deep-Buried Coal-Measure Formation: A Case Study of Permian Longtan Formation

To investigate the strength characteristics of mudstone in deep-buried coal-measure formation, four types of experiments have been conducted: (i) the X-ray diffraction (XRD) test; (ii) the scanning electron microscope (SEM) scanning test; (iii) the point load strength index test; and (iv) the uniaxial compressive strength test. It was concluded that the mudstone of the deep-buried coal measures in the Longtan Formation is dominated by chlorite, quartz, and albite using the XRD test, of which chlorite is primary, accounting for 74.3%. It was found that the three minerals in the mudstone are unevenly distributed using the SEM scanning test, albite is irregularly distributed in chlorite, and quartz is present in the albite and chlorite. Sixty-five specimens were tested for the point load strength index. After processing the data using the method suggested by the International Society for Rock Mechanics and Rock Engineering(ISRM), it was found that the maximum value of Is(50) was 6.10 MPa, the minimum is 0.14 MPa, and 53% of the specimens’ Is(50) values are below 2.0 MPa. The RMT-150C rock mechanics testing machine was used to conduct uniaxial compression tests on six specimens. The maximum uniaxial compressive strength (UCS) value is 59.26 MPa, the minimum value is 31.77 MPa, and the average is 45.64 MPa. Linear fitting and logarithmic fitting are carried out for the correlation between UCS and Is(50). The goodness of fit R2 of the linear fitting is 0.863, and that of the logarithmic fitting is 0.919, indicating a strong correlation between them. When it is challenging to make standard specimens, Is (50) can be used to estimate UCS.

Sensitivity calibration of a three-axis accelerometer under different temperature conditions using the hybrid GA–PSO–BPNN algorithm

Purpose Three-axis accelerometers play a vital role in monitoring the vibrations in aircraft machinery, especially in variable flight temperature environments. The sensitivity of a three-axis accelerometer under different temperature conditions needs to be calibrated before the flight test. Hence, the authors investigated the efficiency and sensitivity calibration of three-axis accelerometers under different conditions. This paper aims to propose the novel calibration algorithm for the three-axis accelerometers or the similar accelerometers. Design/methodology/approach The authors propose a hybrid genetic algorithm–particle swarm optimisation–back-propagation neural network (GA–PSO–BPNN) algorithm. This method has high global search ability, fast convergence speed and strong non-linear fitting capability; it follows the rules of natural selection and survival of the fittest. The authors describe the experimental setup for the calibration of the three-axis accelerometer using a three-comprehensive electrodynamic vibration test box, which provides different temperatures. Furthermore, to evaluate the performance of the hybrid GA–PSO–BPNN algorithm for sensitivity calibration, the authors performed a detailed comparative experimental analysis of the BPNN, GA–BPNN, PSO–BPNN and GA–PSO–BPNN algorithms under different temperatures (−55, 0 , 25 and 70 °C). Findings It has been showed that the prediction error of three-axis accelerometer under the hybrid GA–PSO–BPNN algorithm is the least (approximately ±0.1), which proved that the proposed GA–PSO–BPNN algorithm performed well on the sensitivity calibration of the three-axis accelerometer under different temperatures conditions. Originality/value The designed GA–PSO–BPNN algorithm with high global search ability, fast convergence speed and strong non-linear fitting capability has been proposed to decrease the sensitivity calibration error of three-axis accelerometer, and the hybrid algorithm could reach the global optimal solution rapidly and accurately.

Modeling of Sea Surface Temperature through Fitting Linear Model with Interaction

Sea surface temperature (SST) is one of the attributes of the world climate system and global warming. The relationship between SST and other climate parameters can be represented in a linearity approach. Through this approach, SST variability shows monthly and yearly effects. Information on these two time effects is important for knowing the period of peak effect as well as other statistical measures in the linear fitting model. The models used include transformation and without covariate transformation, interaction and without covariate interaction, and with centering and with the addition of time covariates in the model. The linear fitting model chosen as the basis for construction is a model with a combination effect of covariate interaction and transformation giving an increase in the magnitude of multiple R2 (56.62%) and adjusted R2 (56.13%) respectively 0.31% and 0.43%. This indicates that the time covariate has a very strong significant effect on the model compared to the continuous covariate. In general, the model has a statistical significance of p-value < 2.2e-16, as well as for the time covariate. However, because the model has an autocorrelation and a large AIC value, this effect is removed by means of an autoregressive moving average. The obtained linear fitting model for SST data is the model with AIC 403.2987.

DSCope: Development of Automatic Program for Detecting Fractures and Measuring Dip Angles

Changes in underground environments have been predicted by investigating underground bedrock conditions and analyzing the shapes of discontinuities in the rocks. The most commonly used method is to drill a borehole, insert a camera inside and capture the wall of the borehole in a photograph to investigate the discontinuities. However, if the images of the borehole cannot be captured, the characteristics of the discontinuities in the bedrock are analyzed by capturing the drilling cores in photographs. In this case, considerable time is required to analyze the drilling cores with the naked eye and measure the attitudes of the discontinuities developed in the cores in detail. Moreover, the results may vary depending on the researcher’s perspective. To overcome these limitations, this study develops a program for analyzing photographs of drilling cores. The program can automatically identify discontinuities in drilling cores and measure the attitudes through linear fitting using only drilling core photographs. In addition, we apply the program to practical field data to verify its applicability. We found that the program could provide more accurate and objective information on drilling cores than the currently used method and could more effectively organize the characteristics of fractures in the study area.

Pengukuran Indeks Bias Larutan untuk Mengetahui Kadar Gula dalam Tebu dengan Menggunakan Metode Difraksi Fraunhofer Celah Tunggal

Refractive index measurements of sugar solutions have been carried out in several concentrations. The method used is fraunhofer diffraction. The equation of relationship between the concentration of the sugar solution and the refractive index based on the results of the linear fitting can be written as, Where n is the refractive index of the sugar solution and x is the solution concentration. From this equation, a fairly small gradient value of 1.59145 is obtained. This shows that a 1% increase in the concentration of the sugar solution will cause the refractive index to decrease by 1.59145. The refractive index of the solution changes when the concentration changes. The refractive index of the solution increases with increasing concentration of the solution. This is because along with the increase in concentration, more glucose is found in the sugar solution. As a result, the solution will be more concentrated because the glucose molecules that are arranged are getting denser. The above equation is used to calculate the concentration of sugar in sugar cane. It was found that brown sugar cane had a concentration of 33.38% compared to 26.34% in yellow sugarcane. While the measurement of the concentration of sugar cane in different planting areas shows almost the same results, this is because the level of accuracy of the tool is still large when compared to the difference in the concentration of the sugar solution.