Retrieval of atmospheric environmental parameters and identification of new particle formation events from images with deep learning

Air pollution and climate change pose threats to human health and the environment. Gathering climatic and environmental data, such as visibility, temperature, relative humidity, and mass concentration of aerosol particles with diameter of 2.5 or 10 micrometers or less (PM2.5, PM10, respectively), is the first step in understanding the processes that contribute to air pollution and climate change. Ground-based in situ stations are sparsely and unevenly distributed, meaning that these stations cannot offer spatially seamless coverage for parameters. Satellite observations provide global coverage. Clouds, however, lead to missing values in some satellite products, like the land surface temperature (LST) datasets derived from thermal infrared bands. Finding a method that can automatically produce consistent identification results is essential since new particle formation (NPF), a significant source of atmospheric aerosols, is also tied to the environment and climate. By developing deep learning techniques and utilizing images that contain climatic and environmental information, this thesis seeks to answer three research questions about climate and the environment. The first study focuses on retrieving parameters from Red-Green-Blue (RGB) and hyperspectral images captured near the ground. The second study seeks to retrieve spatially seamless air temperature from satellite-derived LST products that contain missing values. The last research aims to automatically identify the new particle formation (NPF) events and obtain the related parameters such as growth rate, start time, and end time of each event. In summary, the main findings of this thesis are as follows. (1) A model was proposed to simultaneously retrieve a suite of parameters, including visibility, temperature, relative humidity, PM2.5, and PM10, from images captured near the ground. The proposed model achieves generally better retrieval results compared with three classic deep learning models. RGB images are more cost-efficient than hyperspectral images for retrieving parameters. Images with relatively lower spatial resolution can also be used for retrieval. Retrieving multiple parameters is not only possible for images captured at a fixed location but for images captured on a continental scale. (2) By leveraging a UNet model and an image-to-image training approach, it is possible to extract spatially seamless air temperature by filling the gaps in satellite-derived LST with a specified constant value. The retrieval results are with a higher spatial resolution and generally better retrieval accuracy compared with the fifth generation reanalysis for the global climate and weather (ERA5) of the European Center for Medium-Range Weather Forecasts (ECMWF). (3) Leveraging the typical features of image objects, instance segmentation methods such as Mask R-CNN can be applied to detect NPF events from aerosol number size distribution datasets. Other derivatives, such as growth rates, start times, and end times, can also be determined automatically. The proposed method improves the automatic level for analyzing the NPF events and obtains consistent results for NPF datasets collected in different sites.Tiivistelmä ei saatavilla

Quantum-Inspired Keyword Search on Multi-model Databases

With the rising applications implemented in different domains, it is inevitable to require databases to adopt corresponding appropriate data models to store and exchange data derived from various sources. To handle these data models in a single platform, the community of databases introduces a multi-model database. And many vendors are improving their products from supporting a single data model to being multi-model databases. Although this brings benefits, spending lots of enthusiasm to master one of the multi-model query languages for exploring a database is unfriendly to most users. Therefore, we study using keyword searches as an alternative way to explore and query multi-model databases. In this paper, we attempt to utilize quantum physics's probabilistic formalism to bring the problem into vector spaces and represent events (e.g., words) as subspaces. Then we employ a density matrix to encapsulate all the information over these subspaces and use density matrices to measure the divergence between query and candidate answers for finding top-k the most relevant results. In this process, we propose using pattern mining to identify compounds for improving accuracy and using dimensionality reduction for reducing complexity. Finally, empirical experiments demonstrate the performance superiority of our approaches over the state-of-the-art approaches.Peer reviewe

Image-to-Image Training for Spatially Seamless Air Temperature Estimation with Satellite Images and Station Data

Air temperature at approximately 2 m above the ground (T-a) is one of the most important environmental and biophysical parameters to study various earth surface processes. T-a measured from meteorological stations is inadequate to study its spatio-temporal patterns since the stations are unevenly and sparsely distributed. Satellite-derived land surface temperature (LST) provides global coverage, and is generally utilized to estimate T-a due to the close relationship between LST and T-a. However, LST products are sensitive to cloud contamination, resulting in missing values in LST and leading to the estimated T-a being spatially incomplete. To solve the missing data problem, we propose a deep learning method to estimate spatially seamless T-a from LST that contains missing values. Experimental results on 5-year data of mainland China illustrate that the image-to-image training strategy alleviates the missing data problem and fills the gaps in LST implicitly. Plus, the strong linear relationships between observed daily mean T-a (T-mean), daily minimum T-a (T-min), and daily maximum T-a (T-max) make the estimation of T-mean, T-min, and T(max )simultaneously possible. For mainland China, the proposed method achieves results with R-2 of 0.962, 0.953, 0.944, mean absolute error (MAE) of 1.793 degrees C, 2.143 degrees C, and 2.125 degrees C, and root-mean-square error (RMSE) of 2.376 degrees C, 2.808 degrees C, and 2.823 degrees C for T-mean, T-min, and T-max, respectively. OPeer reviewe

Band Ranking via Extended Coefficient of Variation for Hyperspectral Band Selection

Hundreds of narrow bands over a continuous spectral range make hyperspectral imagery rich in information about objects, while at the same time causing the neighboring bands to be highly correlated. Band selection is a technique that provides clear physical-meaning results for hyperspectral dimensional reduction, alleviating the difficulty for transferring and processing hyperspectral images caused by a property of hyperspectral images: large data volumes. In this study, a simple and efficient band ranking via extended coefficient of variation (BRECV) is proposed for unsupervised hyperspectral band selection. The naive idea of the BRECV algorithm is to select bands with relatively smaller means and lager standard deviations compared to their adjacent bands. To make this simple idea into an algorithm, and inspired by coefficient of variation (CV), we constructed an extended CV matrix for every three adjacent bands to study the changes of means and standard deviations, and accordingly propose a criterion to allocate values to each band for ranking. A derived unsupervised band selection based on the same idea while using entropy is also presented. Though the underlying idea is quite simple, and both cluster and optimization methods are not used, the BRECV method acquires qualitatively the same level of classification accuracy, compared with some state-of-the-art band selection methodsPeer reviewe

Porosity Prediction of Granular Materials through Discrete element method and back propagation neural network algorithm

Granular materials are used directly or as the primary ingredients of the mixtures in industrial manufacturing, agricultural production and civil engineering. It has been a challenging task to compute the porosity of a granular material which contains a wide range of particle sizes or shapes. Against this background, this paper presents a newly developed method for the porosity prediction of granular materials through Discrete Element Modeling (DEM) and the Back Propagation Neural Network algorithm (BPNN). In DEM, ball elements were used to simulate particles in granular materials. According to the Chinese specifications, a total of 400 specimens in different gradations were built and compacted under the static pressure of 600 kPa. The porosity values of those specimens were recorded and applied to train the BPNN model. The primary parameters of the BPNN model were recommended for predicting the porosity of a granular material. Verification was performed by a self-designed experimental test and it was found that the prediction accuracy could reach 98%. Meanwhile, considering the influence of particle shape, a shape reduction factor was proposed to achieve the porosity reduction from sphere to real particle shape

Retrieval of Multiple Atmospheric Environmental Parameters From Images With Deep Learning

Retrieving atmospheric environmental parameters such as atmospheric horizontal visibility and mass concentration of aerosol particles with a diameter of 2.5 or 10 μm or less (PM 2.5 , PM 10 , respectively) from digital images provides new tools for horizontal environmental monitoring. In this study, we propose a new end-to-end convolutional neural network (CNN) for the retrieval of multiple atmospheric environmental parameters (RMEPs) from images. In contrast to other retrieval models, RMEP can retrieve a suite of atmospheric environmental parameters including atmospheric horizontal visibility, relative humidity (RH), ambient temperature, PM 2.5 , and PM 10 simultaneously from a single image. Experimental results demonstrate that: 1) it is possible to simultaneously retrieve multiple atmospheric environmental parameters; 2) spatial and spectral resolutions of images are not the key factors for the retrieval on the horizontal scale; and 3) RMEP achieves the best overall retrieval performance compared with several classic CNNs such as AlexNet, ResNet-50, and DenseNet-121, and the results are based on experiments on images extracted from webcams located in different continents (test R2 values are 0.63, 0.72, and 0.82 for atmospheric horizontal visibility, RH, and ambient temperature, respectively). Experimental results show the potential of utilizing webcams to help monitor the environment. Code and more results are available at https://github.com/cvvsu/RMEP .Peer reviewe

λ-Density Functional Valence Bond: A Valence Bond-Based Multiconfigurational Density Functional Theory With a Single Variable Hybrid Parameter

A new valence bond (VB)-based multireference density functional theory (MRDFT) method, named λ-DFVB, is presented in this paper. The method follows the idea of the hybrid multireference density functional method theory proposed by Sharkas et al. (2012). λ-DFVB combines the valence bond self-consistent field (VBSCF) method with Kohn–Sham density functional theory (KS-DFT) by decomposing the electron–electron interactions with a hybrid parameter λ. Different from the Toulouse's scheme, the hybrid parameter λ in λ-DFVB is variable, defined as a function of a multireference character of a molecular system. Furthermore, the EC correlation energy of a leading determinant is introduced to ensure size consistency at the dissociation limit. Satisfactory results of test calculations, including potential energy surfaces, bond dissociation energies, reaction barriers, and singlet–triplet energy gaps, show the potential capability of λ-DFVB for molecular systems with strong correlation

How to Achieve Efficiency and Accuracy in Discrete Element Simulation of Asphalt Mixture: A DRF-Based Equivalent Model for Asphalt Sand Mortar

The clump-based discrete element model is one of the asphalt mixture simulation methods, which has the potential to not only predict mixture performance but also simulate particle movement during compaction, transporting, and other situations. However, modelling of asphalt sand mortar in this method remains to be a problem due to computing capacity. Larger-sized balls (generally 2.0-2.36 mm) were usually used to model the smaller particles and asphalt binder, but this replacement may result in the mixture\u27s unrealistic volumetric features. More specifically, replacing original elements with equal volume but larger size particles will increase in buck volume and then different particle contacting states. The major objective of this research is to provide a solution to the dilemma situation through an improved equivalent model of the smaller particles and asphalt binders. The key parameter of the equivalent model is the diameter reduction factor (DRF), which was proposed in this research to minimize the effects of asphalt mortar\u27s particle replacement modelling. To determine DRF, the DEM-based analysis was conducted to evaluate several mixture features, including element overlap ratio, ball-wall contact number, and the average wall stress. Through this study, it was observed that when the original glued ball diameters are ranging from 2.00 mm and 2.36 mm, the diameter reduction factor changes from 0.82 to 0.86 for AC mixtures and 0.80 to 0.84 for SMA mixtures. The modelling method presented in this research is suitable not only for asphalt mixtures but also for the other particulate mix with multisize particles

Intelligent and Scalable Air Quality Monitoring with 5G Edge

Air pollution introduces a major challenge for societies, where it leads to the premature deaths of millions of people each year globally. Massive deployment of air quality sensing devices and data analysis for the resultant data will pave the way for the development of real-time intelligent applications and services, e.g., minimization of exposure to poor air quality either on an individual or city scale. 5G and edge computing supports dense deployments of sensors at high resolution with ubiquitous connectivity, high bandwidth, high-speed gigabit connections, and ultralow latency analysis. This article conceptualizes AI-powered scalable air quality monitoring and presents two systems of calibrating low-cost air quality sensors and the image processing of pictures captured by hyperspectral cameras to better detect air quality. We develop and deploy different AI algorithms in these two systems on a 5G edge testbed and perform a detailed analytics regarding to 1) the performance of AI algorithms and 2) the required communication and computation resources.Peer reviewe

Superconductivity in the cobalt-doped V3Si A15 intermetallic compound

The A15 structure of superconductors is a prototypical type-II superconductor that has generated considerable interest since the early history of superconducting materials. This paper discusses the superconducting properties of previously unreported V3-xCoxSi alloys. It is found that the lattice parameter decreases with increasing cobalt-doped content and leads to an increased residual resistivity ratio (RRR) value of the V3-xCoxSi system. Meanwhile, the superconducting transition temperature (Tc) cobalt-doped content. Furthermore, the fitted data show that the increase of cobalt-doped content also reduces the lower/upper critical fields of the V3-xCoxSi system. Type-II superconductivity is demonstrated on all V3-xCoxSi samples. With higher Co-doped content, V3-xCoxSi alloys may have superconducting and structural phase transitions at low-temperature regions. As the electron/atom (e/a) ratio increases, the Tc variation trend of V3Si is as pronounced as in crystalline alloys and monotonically follows the trend observed for amorphous superconductors.Comment: 20 pages, 7 figure