Data Mining for Source Apportionment of Trace Elements in Water and Solid Matrix

Trace elements migrate among different environment bodies with the natural geochemical reactions, and impacted by human industrial, agricultural, and civil activities. High load of trace elements in water, river and lake sediment, soil and air particle lead to potential to health of human being and ecological system. To control the impact on environment, source apportionment is a meaningful, and also a challenging task. Traditional methods to make source apportionment are usually based on geochemical techniques, or univariate analysis techniques. In recently years, the methods of multivariate analysis, and the related concepts data mining, machine learning, big data, are developing fast, which provide a novel route that combing the geochemical and data mining techniques together. These methods have been proved successful to deal with the source apportionment issue. In this chapter, the data mining methods used on this topic and implementations in recent years are reviewed. The basic method includes principal component analysis, factor analysis, clustering analysis, positive matrix fractionation, decision tree, Bayesian network, artificial neural network, etc. Source apportionment of trace elements in surface water, ground water, river and lake sediment, soil, air particles, dust are discussed

Improving Rolling Bearing Fault Diagnosis by DS Evidence Theory Based Fusion Model

Rolling bearing plays an important role in rotating machinery and its working condition directly affects the equipment efficiency. While dozens of methods have been proposed for real-time bearing fault diagnosis and monitoring, the fault classification accuracy of existing algorithms is still not satisfactory. This work presents a novel algorithm fusion model based on principal component analysis and Dempster-Shafer evidence theory for rolling bearing fault diagnosis. It combines the advantages of the learning vector quantization (LVQ) neural network model and the decision tree model. Experiments under three different spinning bearing speeds and two different crack sizes show that our fusion model has better performance and higher accuracy than either of the base classification models for rolling bearing fault diagnosis, which is achieved via synergic prediction from both types of models

A novel magnetic fluid shock absorber with levitating magnets

The paper presents a shock absorber whose working element includes two magnetic fluid rings around a group of magnets. The damping efficiency of this shock absorber is investigated by the free oscillations of an elastic plate and can be well explained with the classical equations of motion. In the shock absorber, a nonlinear equivalent stiffness is provided by the magnetic repulsion force, which controls the movement of the working element and varies in conformity to a power law. Through the theoretical and experimental study on the magnetic repulsion force, the nonlinear equivalent stiffness is determined and depends on the initial distance between the working element and the repulsion magnet. For an oscillation with the amplitude of 1mm and frequency of 1.1 Hz, the damping efficiency is inversely proportional to the nonlinear equivalent stiffness

Documented Evidence of Agricultural Injury in China

Objective: To describe the documented evidence concerning agricultural injury in China and to identify topics for future research.Method: Literature search and review were conducted to collect publications that were relevant to agricultural injury in China. The process included defining agricultural injury for the purpose of this study, selecting articles according to inclusion criteria and extracting data from each paper. Descriptive methods were used to analyze the contents, research approaches, distribution of authors, and cooperation percentage of agricultural injury studies.Results: After applying the inclusion criteria, 89 articles were included in this study. The author collaboration percentage (number of articles with more than one author divided by number of total articles) and the institutional collaboration percentage (number of articles with more than one organization divided by number of total articles) among the 89 articles were 85.4% and 42.7%, respectively. Most of the authors are affiliated with a Center for Disease Control and Prevention (CDC) or an academic institution located in 10 of the 31 provinces in mainland China. Among the 89 articles, only 6 were on injuries related to agricultural work, the rest (83) dealt with injuries among rural residents with or without clarifying occupations or ongoing activities. Conclusions: Research on agricultural injuries in China is currently in its early stage. More research is needed to obtain evidence that can be used in policy making for agricultural injury control. Our study is the first to describe the documented evidence on agricultural injuries in China and identify topics for future research

An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition

Convolutional neural networks (CNNs) with log-mel audio representation and CNN-based end-to-end learning have both been used for environmental event sound recognition (ESC). However, log-mel features can be complemented by features learned from the raw audio waveform with an effective fusion method. In this paper, we first propose a novel stacked CNN model with multiple convolutional layers of decreasing filter sizes to improve the performance of CNN models with either log-mel feature input or raw waveform input. These two models are then combined using the Dempster–Shafer (DS) evidence theory to build the ensemble DS-CNN model for ESC. Our experiments over three public datasets showed that our method could achieve much higher performance in environmental sound recognition than other CNN models with the same types of input features. This is achieved by exploiting the complementarity of the model based on log-mel feature input and the model based on learning features directly from raw waveforms

Scalable generation of large-scale unstructured meshes by a novel domain decomposition approach

© 2018 Elsevier Ltd A parallel algorithm is proposed for scalable generation of large-scale tetrahedral meshes. The key innovation is the use of a mesh-simplification based domain decomposition approach. This approach works on a background mesh with both its surface and its interior elements much larger than the final elements desired, and decomposes the domain into subdomains containing no undesirable geometric features in the inter-domain interfaces. In this way, the most time-consuming part of domain decomposition can be efficiently parallelized, and other sequential parts consume reasonably limited computing time since they treat a very coarse background mesh. Meanwhile, the subsequent parallel procedures of mesh generation and improvement are most efficient because they can treat individual subdomains without compromising element quality. Compared with published state-of-the-art parallel algorithms, the developed parallel algorithm can reduce the clock time required by the creation of one billion elements on 512 computer cores from roughly half an hour to less than 4 minutes

An improved local remeshing algorithm for moving boundary problems

© 2016 The Author(s). Three issues are tackled in this study to improve the robustness of local remeshing techniques. Firstly, the local remeshing region (hereafter referred to as ‘hole’) is initialized by removing low-quality elements and then continuously expanded until a certain element quality is reached after remeshing. The effect of the number of the expansion cycle on the hole size and element quality after remeshing is experimentally analyzed. Secondly, the grid sources for element size control are attached to moving bodies and will move along with their host bodies to ensure reasonable grid resolution inside the hole. Thirdly, the boundary recovery procedure of a Delaunay grid generation approach is enhanced by a new grid topology transformation technique (namely shell transformation) so that the new grid created inside the hole is therefore free of elements of extremely deformed/skewed shape, whilst also respecting the hole boundary. The proposed local remeshing algorithm has been integrated with an in-house unstructured grid-based simulation system for solving moving boundary problems. The robustness and accuracy of the developed local remeshing technique are successfully demonstrated via industry-scale applications for complex flow simulations