Series Of Novel Algorithms Research Articles

A fully automatic-image-based approach to quantifying the geological strength index of underground rock mass

The conventional discontinuity survey process in the mining industry is known to be a time-consuming one and it is technically challenging due to the limited accessibility of fresh rock exposures. A rapid and robust rock mass property quantification system is desirable for rock structure design during mining operations. In this work we develop an image-based and fully automatic rock mass Geological Strength Index (GSI) rating system. The proposed method involves a series of novel algorithms to quantify the GSI rating based on data recovered from digital images. The proposed GSI system includes both Structure Rating (SR) and Joint Condition Digital Imaging (JCDI) to represent the bulk rock and discontinuity surface conditions of the rock mass. Local histogram equalization and self-adaptive gamma correction were introduced into the pre-processing of the rock face images. Compared to conventional histogram equalization, local histogram equalization can effectively restrain the uneven distribution of brightness often present. Self-adaptive gamma correction based on the image properties automatically enlarges the difference between discontinuity areas and intact rock. A novel algorithm combining region growing and the Hough transform is proposed for the automatic extraction of discontinuity areas. Laboratory and field tests demonstrated that the algorithm possesses an advantage over existing methods with regard to better noise suppression and that it can yield reasonable results for images taken in poor photography conditions. Discontinuities were characterized using a novel algorithm comprising area thinning, skeleton linking, spurs removal, and sampling. Using this algorithm, four parameters of the discontinuity can be quantified: length, orientation, separation width, and JRC value. The proposed approach was validated by two field-based case studies.

Computationally fast morphological descriptor-based microstructure reconstruction algorithms for particulate composites

In this paper, we propose a series of novel algorithms for microstructure reconstruction based on constituent morphologies. The proposed algorithm can generate statistically equivalent representative volume elements (RVE) from shape repository consisting of the actual particles from micro-computed tomography (CT) images. The reconstruction algorithm is also general in that it can adopt any morphological descriptors. CDFs errors of the morphological descriptors between the targeted and reconstructed RVEs are minimized within the proposed algorithmic framework. High reliability of the microstructure reconstructions stems from unique and efficient design of the algorithmic framework. One of the novelties of the proposed algorithm is that it does not rely on time-consuming two-point correlation function (TPCF) calculations while it can replicate distributions keeping realistic morphologies of particles. To verify the proposed reconstruction algorithm, the TPCF and homogenized effective properties were compared between targeted and reconstructed RVEs. Moreover, mechanical responses of the targeted and reconstructed RVEs are evaluated and compared by 3D finite element analyses.

Efficient finer-grained incremental processing with MapReduce for big data

With the continuous development of the Internet and information technology, more and more mobile terminals, wear equipment etc. contribute to the tremendous data. Thanks to the distributed computing, we can analyze the big data with quite high speed. However, many kinds of big data have an obvious common character that the datasets grow incrementally overtime, which means the distributed computing should focus on incremental processing. A number of systems for incremental data processing are available, such as Google’s Percolator and Yahoo’s CBP. However, in order to utilize these mature framework, one needs to make a troublesome change for their program to adapt to the environment requirement.In this paper, we introduce a MapReduce framework, named HadInc, for efficient incremental computations. HadInc is designed for offline scenes, in which real-time is needless and in-memory cluster computing is invalid. HadInc takes the advantages of finer-grained computing and Content-defined Chunking(CDC) to make sure that the system can still reuse the results which we have computed before, even if the split data has been changed seriously. Instead of re-computing the changed data entirely, HadInc can quickly find out the difference between the new split and the old one, and then merge the delta and old results into the latest result of the new datasets. Meanwhile, the dividing stability of the datasets is a key factor for reusing the results. In order to guarantee the stability of the dataset’s division, we propose a series of novel algorithms based on CDC.We implemented HadInc by extending the Hadoop framework, and evaluated it with many experiments including three specific cases and a practical case. From the comparing results it can be seen that the proposed HadInc is very efficient.

An automated and rapid method for identifying dam wall locations and estimating reservoir yield over large areas

Global demand for water, food and energy has seen the construction and planning of large dams continue at a steady pace in many parts of the world. However, the process of ‘exhaustively’ examining all potential dams sites within an area as part of an initial scoping study is still largely undertaken using manual methods. This paper describes DamSite, a series of novel algorithms that construct ‘virtual’ dam walls at every pixel along every channel within a catchment, including saddle dams where required by the terrain. By repetitively calculating dam and reservoir dimensions, reservoir yield and dam costs along a river network and for incrementally higher dam walls at each location it is possible to identify both optimal dam wall locations and optimal dam wall height at a given location. The DamSite model was tested in two catchments in northern Australia and accurately pin-pointed previously identified potential dam locations.

Efficient and scalable scaffolding using optical restriction maps.

In the next generation sequencing techniques millions of short reads are produced from a genomic sequence at a single run. The chances of low read coverage to some regions of the sequence are very high. The reads are short and very large in number. Due to erroneous base calling, there could be errors in the reads. As a consequence, sequence assemblers often fail to sequence an entire DNA molecule and instead output a set of overlapping segments that together represent a consensus region of the DNA. This set of overlapping segments are collectively called contigs in the literature. The final step of the sequencing process, called scaffolding, is to assemble the contigs into a correct order. Scaffolding techniques typically exploit additional information such as mate-pairs, pair-ends, or optical restriction maps. In this paper we introduce a series of novel algorithms for scaffolding that exploit optical restriction maps (ORMs). Simulation results show that our algorithms are indeed reliable, scalable, and efficient compared to the best known algorithms in the literature.

GPLEXUS: enabling genome-scale gene association network reconstruction and analysis for very large-scale expression data

The accurate construction and interpretation of gene association networks (GANs) is challenging, but crucial, to the understanding of gene function, interaction and cellular behavior at the genome level. Most current state-of-the-art computational methods for genome-wide GAN reconstruction require high-performance computational resources. However, even high-performance computing cannot fully address the complexity involved with constructing GANs from very large-scale expression profile datasets, especially for the organisms with medium to large size of genomes, such as those of most plant species. Here, we present a new approach, GPLEXUS (http://plantgrn.noble.org/GPLEXUS/), which integrates a series of novel algorithms in a parallel-computing environment to construct and analyze genome-wide GANs. GPLEXUS adopts an ultra-fast estimation for pairwise mutual information computing that is similar in accuracy and sensitivity to the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE) method and runs ∼1000 times faster. GPLEXUS integrates Markov Clustering Algorithm to effectively identify functional subnetworks. Furthermore, GPLEXUS includes a novel ‘condition-removing’ method to identify the major experimental conditions in which each subnetwork operates from very large-scale gene expression datasets across several experimental conditions, which allows users to annotate the various subnetworks with experiment-specific conditions. We demonstrate GPLEXUS’s capabilities by construing global GANs and analyzing subnetworks related to defense against biotic and abiotic stress, cell cycle growth and division in Arabidopsis thaliana.

Fast Position and Accurate Segmentation Algorithms for Detecting Surface Defects of the Thermal-State Heavy Rail Based on Machine Vision

Color imaging in the hot rolled condition provides the better reaction of heavy rail on surface defects. In this paper, it proposes a series of novel algorithms of accurate position and segmentation of surface defects of heavy rail. An image acquisition device is designed on the adjustable camera bracket with the linear array CCD, and based on the correlation among pixels at the image level, a fast positioning method is developed for searching the Region Of Interesting (ROI) of the surface defects. Using the Mean-Shift image filtering algorithm which features multi-parameter kernel function, amendments to the sampling point weights and regional search with the nearest neighbor sampling points, accurate segmentation of the identification character is easily achieved by K-means clustering. Experiments show that this algorithm for the identification of the heavy rail surface defects is proven to be more rapid in testing the inclusions, cracks and oxide skin defects with a good promotional value.

New methodologies for precise building boundary extraction from LiDAR data and high resolution image

PurposeLight Detection and Ranging (LiDAR) offers a fast and effective way to acquire DSM and extract ground objects such as building, trees and so on. However, it is difficult to extract sharp and precise building boundary from LiDAR data, because its ground sample distance (GSD) is often worse than that of high resolution image. Recently, fusion of LiDAR and high resolution image becomes a promising approach to extract precise boundary. To find the correct and precise boundary, the aim of this paper is to present a series of novel algorithms to improve the quality.Design/methodology/approachTo find the correct and precise boundary, this paper presents a series of novel algorithms to improve the quality. At first, a progressive algorithm is presented to register LiDAR data and images; second, a modified adaptive TIN algorithm is presented to filter ground point, where a region growth method is applied in the adaptive TIN algorithm; third, a novel criterion based on the density, connectivity and distribution of point cluster is developed to distinguish trees point; fourth, a novel method based on the height difference between neighbor points is employed to extract coarse boundaries; at last, a knowledge based rule is put forward to identify correct building boundary from parallel edges.FindingsThorough experiments, it is conducted that: the registration results are accurate and reliable; filtered ground points has good quality, without missing or redundancy; all tree clusters bigger than one grid are detected, and points of walls and edges are eliminated with the new criterion; detected edges exactly locate at real building boundaries, and statistics show the detection correctness is 98 percent, and the detection completeness is 95 percent.Originality/valueAll results prove that precise boundary can be extracted with fusion of LiDAR and high resolution image.

EasySpin, a comprehensive software package for spectral simulation and analysis in EPR

EasySpin, a computational package for spectral simulation and analysis in EPR, is described. It is based on Matlab, a commercial technical computation software. EasySpin provides extensive EPR-related functionality, ranging from elementary spin physics to data analysis. In addition, it provides routines for the simulation of liquid- and solid-state EPR and ENDOR spectra. These simulation functions are built on a series of novel algorithms that enhance scope, speed and accuracy of spectral simulations. Spin systems with an arbitrary number of electron and nuclear spins are supported. The structure of the toolbox as well as the theoretical background underlying its simulation functionality are presented, and some illustrative examples are given.