Cone-beam Computerized Tomography (CBCT) has the advantages of high ray utilization and detection efficiency, short scan time, high spatial and isotropic resolution. However, the X-rays emitted by ...CBCT examination are harmful to the human body, so reducing the radiation dose without damaging the reconstruction quality is the key to the reconstruction of CBCT. In this paper, we propose a sparse angle CBCT reconstruction algorithm based on Guided Image FilteringGIF, which combines the classic Simultaneous Algebra Reconstruction Technique(SART) and the Total p-Variation (TpV) minimization. Due to the good edge-preserving ability of SART and noise suppression ability of TpV minimization, the proposed method can suppress noise and artifacts while preserving edge and texture information in reconstructed images. Experimental results based on simulated and real-measured CBCT datasets show the advantages of the proposed method.
With the continuous development of information technology and the rapid increase in new users of social networking sites, recommendation technology is becoming more and more important. After ...research, it was found that the behavior of users on social networking sites has a great correlation with their personalities. The five characteristics of the OCEAN personality model can cover all aspects of a user’s personality. In this research, a micro-directional propagation model based on the OCEAN personality model and a Stacked Denoising Auto Encoder (SDAE) was built through the application of deep learning to a collaborative filtering technique. Firstly, the dimension of the user and item feature matrices was lowered using SDAE in order to extract deeper information. The user OCEAN personality model matrix and the reduced user feature matrix were integrated to create a new user feature matrix. Finally, the multiple linear regression approach was used to predict user-unrated goods and generate recommendations. This approach allowed us to leverage the relationships between various factors to deliver personalized recommendations. This experiment evaluated the RMSE and MAE of the model. The evaluation results show that the stacked denoising auto encoder collaborative filtering algorithm can improve the accuracy of recommendations, and the user’s OCEAN personality model improves the accuracy of the model to a certain extent.
In this study, earthquakes with magnitudes above 3.0 on the Richter scale in southwest China from 1900 to 2013 are taken as research objects. The spatio-temporal migration characteristics of seismic ...activity in the area of concern are studied by using geostatistics method to explore the rules of seismic activity. From the perspective of geostatistics and based on
-value, eight major seismic zones in southwest China were studied zone-wise by using the geostatistics tool ArcGIS and the seismic analysis tool ZMAP to explore the spatial and temporal migration characteristics of earthquakes. It was found that each seismic zone had its own quiet period with relatively inactive seismic activity. Simultaneously, through the study of the time-varying characteristics of the seismic belt, it was found that the time-varying regularity of the seismic frequency of the fault belt with the same or related geological structure background is roughly consistent. In conclusion, the seismic activity in southwest China will still be active in the future.
Recently, all kinds of geological disasters happen frequently on the earth. In China, there are countless earthquakes every year, which greatly affect the country’s economic level and development as ...well as the people’s life and health. The analysis of seismic activity is becoming more and more significant. In this article, the spatial distribution of China’s seismic activities was analyzed by using the provincial seismic data from 1970 to 2013. On the basis of spatial autocorrelation analysis theory, Global Moran’s
, Local Moran’s
, and the Local Indicators of Spatial Association are used to measure the geospatial distribution characteristics of China’s seismic activities. The research results show that earthquakes in mainland China have significant global autocorrelation characteristics as a whole, and the global autocorrelation coefficients are all positive. And the
-value test (
< 0.05) shows that earthquakes in mainland China present a spatial agglomeration pattern. Furthermore, we observed a reduction trend in disparities of seismic activity among regions in China.
In this article, the integrity of the seismic catalog obtained (1970–2014,
> 2.8) was verified according to the Gutenberg–Richter relation, the appropriate minimum magnitude was determined, and the ...whole region was divided into five areas according to the geological structure background of the whole research object and the trend of the historical seismic zone. We applied multifractal analysis in each partition. The results showed that although in different geological backgrounds, before major earthquakes, the earthquake time series information dimension had different degrees of growth, and the parameter after the flame had different degrees of decline. To a certain extent, this reflected the seismic energy accumulation and release process. In addition, the variation of fractal parameters in scale-free regions of time series and spatial distribution series also indicated that these two kinds of sequences in different regions show the characteristics of a multifractal structure rather than a single and uniform fractal structure.
In recent years, the occurrence and frequency of haze are constantly increasing, severely threatening people’s daily lives and health and bringing enormous losses to the economy. To this end, we used ...cluster analysis and spatial autocorrelation methods to discuss the spatial and temporal distribution characteristics of severe haze in China and to classify regions of China. Furthermore, we analyzed the interaction between haze pollution and the influence of economy and energy structure in 31 provinces in China, providing references for the prevention and treatment of haze pollution. The processed data mainly include API, meteorological station data, and PM 2.5 concentration distribution vector graph. The results show the yearly haze pattern from 2008 to 2012, and present a strong pattern of pollution concentrated around Beijing–Tianjin, the Yangtze River Delta, southwest China, and central China. The overall spatial pattern of decreasing from north to south is relatively constant over the study period.
In the field of visual reasoning, image features are widely used as the input of neural networks to get answers. However, image features are too redundant to learn accurate characterizations for ...regular networks. While in human reasoning, abstract description is usually constructed to avoid irrelevant details. Inspired by this, a higher-level representation named semantic representation is introduced in this paper to make visual reasoning more efficient. The idea of the Gram matrix used in the neural style transfer research is transferred here to build a relation matrix which enables the related information between objects to be better represented. The model using semantic representation as input outperforms the same model using image features as input which verifies that more accurate results can be obtained through the introduction of high-level semantic representation in the field of visual reasoning.
As an air pollution phenomenon, haze has become one of the focuses of social discussion. Research into the causes and concentration prediction of haze is significant, forming the basis of haze ...prevention. The inversion of Aerosol Optical Depth (AOD) based on remote sensing satellite imagery can provide a reference for the concentration of major pollutants in a haze, such as PM2.5 concentration and PM10 concentration. This paper used satellite imagery to study haze problems and chose PM2.5, one of the primary haze pollutants, as the research object. First, we used conventional methods to perform the inversion of AOD on remote sensing images, verifying the correlation between AOD and PM2.5. Subsequently, to simplify the parameter complexity of the traditional inversion method, we proposed using the convolutional neural network instead of the traditional inversion method and constructing a haze level prediction model. Compared with traditional aerosol depth inversion, we found that convolutional neural networks can provide a higher correlation between PM2.5 concentration and satellite imagery through a more simplified satellite image processing process. Thus, it offers the possibility of researching and managing haze problems based on neural networks.
After the completion of the Three Gorges Dam, it increases the risk of inducing an earthquake. We use the GRACE Gravity Field Model to analyze the relationship between the operation of the Three ...Gorges Reservoir and the change of gravity field in western Sichuan. The research results indicate that the reservoir water level and the western Sichuan gravitational field are positively correlated. In the early stage of rising water level, the change of gravity field is not apparent, and the change of gravity field gradually increases with time. Therefore, the change of reservoir water level affects the gravity field in western Sichuan. The dynamic changes of the gravity field can reflect the Earth’s material change and deformation process and are closely related to earthquakes. Consequently, the Three Gorges Dam will indirectly affect the seismicity in western Sichuan by affecting the gravity field. The research provides valuable information for studying regional reservoir earthquake disasters and supports related policy decisions.
Nowadays, cities meet numerous sustainable development challenges in facing growing urban populations and expanding urban areas. The monitoring and simulation of land use and land-cover change have ...become essential tools for understanding and managing urbanization. This paper interprets and predicts the expansion of seven different land use types in the study area, using the PLUS model, which combines the Land use Expansion Analysis Strategy (LEAS) and the CA model, based on the multi-class random patch seed (CARS) model. By choosing a variety of driving factors, the PLUS model simulates urban expansion in the metropolitan area of Hangzhou. The accuracy of the simulation, manifested as the kappa coefficient of urban land, increased to more than 84%, and the kappa coefficient of other land use types was more than 90%. To a certain extent, the PLUS model used in this study solves the CA model’s deficiencies in conversion rule mining strategy and landscape dynamic change simulation strategy. The results show that various types of land use changes obtained using this method have a high degree of accuracy and can be used to simulate urban expansion, especially over short periods.
PDF
