Merging Procedure Research Articles

Evaluating Scientific Domain Ontologies for the Electromagnetic Knowledge Domain: A General Methodology

The adoption of ontologies as a formalized approach to information codification is a constant-growing phenomenon in scientific research. Moreover, knowledge sharing and reuse can be improved by adopting hierarchical and modular frameworks and therein embedding available ontologies. Unfortunately, merging procedures may bring about severe, time-consuming problems if a careful selection process is not carried out. Based on these considerations, we propose a methodology for evaluating and selecting higher-level ontologies, given the lower-level ones. Our proposal is based on the computation of ad-hoc metrics which take into account structural and semantic aspects and on the adoption of a multi-decisional analysis procedure. The methodology has been applied to identify ontologies suitable for providing a scientific domain high-level codification to Electromagnetism. Two well-known scientific domain ontologies have been selected and evaluated with the proposed methodology.

Application of Stereo‐Digital Image Correlation to Full‐Field 3‐D Deformation Measurement of Intervertebral Disc

Abstract: This paper presents the application of a Stereo‐Digital Image Correlation (Stereo‐DIC) based approach for full‐field deformation measurements on a porcine intervertebral disc (IVD) under in‐vitro loading. Full‐field capabilities of Stereo‐DIC provide useful information on the IVD structure‐function relationship needed for designing novel disc replacement devices on the basis of biomimetic concepts. In this work, the use of a multi‐view Stereo‐DIC system allows full‐field measurement over more than 180° of the IVD surface. This is achieved by sequentially moving a single camera through seven fixed positions in order to cover the desired angle of vision. Ad hoc data processing and merging procedures are calibrated for a cylindrical sample. Strain maps are determined for a rubber cylinder subjected to rigid‐body motions and then to uniform compression. These preliminary procedures serve to evaluate strain‐mapping errors as well as to assess repeatability and accuracy of camera re‐positioning. Once the entire procedure is verified and calibrated, a fresh porcine functional spine unit is loaded under anterior, posterior and lateral compression. From displacement maps obtained with the multi‐view set‐up, it is possible to gather information on both IVD bulging and surface strains. Results are consistent with the IVD global behaviour under standard testing protocols reported in the recent literature. Furthermore, detailed information on local variations in structural response and stiffness properties occurring when load is applied in different regions of the IVD are obtained. The present approach allows to correlate structural response with the entire 3‐D deformation and strain field and not just with a single displacement/strain component and thus it could be effectively used for explaining in‐homogeneity observed in human discs degenerative patterns.

On-line elimination of local redundancies in evolving fuzzy systems

In this paper, we examine approaches for reducing the complexity of evolving fuzzy systems (EFSs) by eliminating local redundancies during training, evolving the models on on-line data streams. Thus, the complexity reduction steps should support fast incremental single-pass processing steps. In EFSs, such reduction steps are important due to several reasons: (1) originally distinct rules representing distinct local regions in the input/output data space may move together over time and get significantly over-lapping as data samples are filling up the gaps in-between these, (2) two or several fuzzy sets in the fuzzy partitions may become redundant because of projecting high-dimensional clusters onto the single axes, (3) they can be also seen as a first step towards a better readability and interpretability of fuzzy systems, as unnecessary information is discarded and the models are made more transparent. One technique is performing a new rule merging approach directly in the product cluster space using a novel concept for calculating the similarity degree between an updated rule and the remaining ones. Inconsistent rules elicited by comparing the similarity of two redundant rule antecedent parts with the similarity of their consequents are specifically handled in the merging procedure. The second one is operating directly in the fuzzy partition space, where redundant fuzzy sets are merged based on their joint α-cut levels. Redundancy is measured by a novel kernel-based similarity measure. The complexity reduction approaches are evaluated based on high-dimensional noisy real-world measurements and an artificially generated data stream containing 1.2 million samples. Based on this empirical comparison, it will be shown that the novel techniques are (1) fast enough in order to cope with on-line demands and (2) produce fuzzy systems with less structural components while at the same time achieving accuracies similar to EFS not integrating any reduction steps.

Assessing uncertainty in estimates of atmospheric temperature changes from MSU and AMSU using a Monte-Carlo estimation technique

[1] Measurements made by the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit (AMSU) provide a multidecadal record of global atmospheric temperature change, which have been used by several groups to produce long-term temperature records of thick layers of the atmosphere from the lower troposphere to the lower stratosphere. Here we present an internal uncertainty estimate for the Remote Sensing Systems data sets made using a Monte Carlo approach that includes contributions to the total uncertainty from sampling error, premerge adjustments to each individual satellite, and the merging procedure. The results can be used to estimate uncertainties in this product at all space and time scales of interest to any specific application. On small space and time scales sampling effects dominate. On the longer time scales intersatellite merging is important at all levels and the diurnal adjustment is a critical uncertainty for the two layers that have a significant surface component, particularly over land. A comparison of trends for the globe, tropics, and extratropics between the best estimate data set along with these error estimates and homogenized radiosonde estimates and available MSU/AMSU estimates from other groups is undertaken. This shows consistency between our product and those produced by others within the stated uncertainty for many regions and layers. In almost as many cases, however, the interdata set differences of the estimated trends are too large be accounted for by the internal uncertainty estimates derived herein.

Color image segmentation by edge linking and region grouping

A novel method toward color image segmentation is proposed based on edge linking and region grouping. Firstly, the edges extracted by the Canny detector are linked to form regions. Each of the end points of edges is connected by a direct line to the nearest pixel on another edge segment within a sub-window. A new distance is defined based on the feature that the edge tends to preserve its original direction. By sampling the lines to the image, the image is over-segmented to labeled regions. Secondly, the labeled regions are grouped both locally and globally. A decision tree is constructed to decide the importance of properties that affect the merging procedure. Finally, the result is refined by user’s selection of regions that compose the desired object. Experiments show that the method can effectively segment the object and is much faster than the state-of-the-art color image segmentation methods.

An optimal merging technique for high-resolution precipitation products

Precipitation products are currently available from various sources at higher spatial and temporal resolution than any time in the past. Each of the precipitation products has its strengths and weaknesses in availability, accuracy, resolution, retrieval techniques and quality control. By merging the precipitation data obtained from multiple sources, one can improve its information content by minimizing these issues. However, precipitation data merging poses challenges of scale-mismatch, and accurate error and bias assessment. In this paper we present Optimal Merging of Precipitation (OMP), a new method to merge precipitation data from multiple sources that are of different spatial and temporal resolutions and accuracies. This method is a combination of scale conversion and merging weight optimization, involving performance-tracing based on Bayesian statistics and trend-analysis, which yields merging weights for each precipitation data source. The weights are optimized at multiple scales to facilitate multiscale merging and better precipitation downscaling. Precipitation data used in the experiment include products from the 12-km resolution North American Land Data Assimilation (NLDAS) system, the 8-km resolution CMORPH and the 4-km resolution National Stage-IV QPE. The test cases demonstrate that the OMP method is capable of identifying a better data source and allocating a higher priority for them inmore » the merging procedure, dynamically over the region and time period. This method is also effective in filtering out poor quality data introduced into the merging process.« less

Parallelized Evolutionary Learning for Detection of Biclusters in Gene Expression Data

The analysis of gene expression data obtained from microarray experiments is important for discovering the biological process of genes. Biclustering algorithms have been proven to be able to group the genes with similar expression patterns under a number of experimental conditions. In this paper, we propose a new biclustering algorithm based on evolutionary learning. By converting the biclustering problem into a common clustering problem, the algorithm can be applied in a search space constructed by the conditions. To further reduce the size of the search space, we randomly separate the full conditions into a number of condition subsets (subspaces), each of which has a smaller number of conditions. The algorithm is applied to each subspace and is able to discover bicluster seeds within a limited computing time. Finally, an expanding and merging procedure is employed to combine the bicluster seeds into larger biclusters according to a homogeneity criterion. We test the performance of the proposed algorithm using synthetic and real microarray data sets. Compared with several previously developed biclustering algorithms, our algorithm demonstrates a significant improvement in discovering additive biclusters.

A fast algorithm for muon track reconstruction and its application to the ANTARES neutrino telescope

An algorithm is presented, that provides a fast and robust reconstruction of neutrino induced upward-going muons and a discrimination of these events from downward-going atmospheric muon background in data collected by the ANTARES neutrino telescope. The algorithm consists of a hit merging and hit selection procedure followed by fitting steps for a track hypothesis and a point-like light source. It is particularly well-suited for real time applications such as online monitoring and fast triggering of optical follow-up observations for multi-messenger studies. The performance of the algorithm is evaluated with Monte Carlo simulations and various distributions are compared with that obtained in ANTARES data.

Regional Objective Analysis for Merging High-Resolution MERIS, MODIS/Aqua, and SeaWiFS Chlorophyll- a Data From 1998 to 2008 on the European Atlantic Shelf

In this paper, we define the method that is used to merge high-resolution multisensor chlorophyll-a (chl-a) data on the Ireland-Biscay-Iberia Regional Ocean Observing System area from 1998 to the present at a resolution of 1.1 km. The method is based on geostatistics and is known as kriging. The merged variable is the daily anomaly of chl-a, with the anomaly being defined as the difference between the daily image and the mean historical field for the considered day. For each day, the continuous anomaly image is generated using the kriging method, and the mean historical field is then added to obtain the cloudless field of chl- a. The initial satellite chl-a data set used in the merging procedure is derived from the daily level-2 water leaving radiances of three ocean color sensors: the Sea-Viewing Wide Field of View Sensor on the Orbview platform, the Moderate Resolution Imaging Spectroradiometer on the Aqua platform, and the Medium Resolution Imaging Spectrometer Instrument on the ENVISAT platform. The chl-a concentration is obtained using a specific algorithm developed by Ifremer, known as OC5 product. Before merging, each satellite-derived chl-a product has been compared to in situ data and has been validated using a matchup data set. After this validation against in situ data, intercomparisons between the satellite data sets have been performed. As the chl-a anomaly variability depends, in this region, on the season and the distance from the shore, local space-time semivariograms have been calculated to estimate the spatiotemporal dependence or covariance of the chlorophyll anomalies. The semivariograms, used in the estimation of the kriged anomaly, are defined by their nuggets (noise), their spatial and temporal range (maximum distance for a nonnull covariance between the anomalies), and their sill (maximum variance). The spatial range of the semivariogram has been approximated locally on a regular grid. The nuggets and the sills have been deduced from the square of the mean of the chl-a concentration (the climatological reference) as we have observed a classical proportionality effect between the square of the chl-a mean, the variance of the distribution, and the parameters of the semivariograms. Compared to each original product, the analysis shows a complete coverage and differences with the in situ data that are statistically equivalent to those observed with the initial satellite data set. The merged product offers a number of applications for environmental monitoring such as the monitoring of the eutrophication risk required by the Water Framework Directive of the European Union.

An Enhanced Procedure for the Merging of Atmospheric Particle Size Distribution Data Measured Using Electrical Mobility and Time-of-Flight Analysers

Current merging algorithms for particle size spectral data collected with electrical mobility and aerodynamic time-of-flight instruments either require a priori knowledge of densities and shape factors, or use alignment of the number spectra alone to determine an optimal fit and effective density. In this work, an enhanced algorithm is described in which the best fit between the two instrument datasets is achieved for the number, surface area, and volume spectra, also yielding estimated values of transition-regime effective density. When applied to data collected at a kerbside site, integrated aerosol mass calculated from the merged data correlates highly with independently measured PM10 mass data. Typical merged data from the site are shown and used to examine the diurnal and wind direction dependence of the estimated values of transition-regime effective density derived from the merging procedure.

Pattern-matching-based X-architecture zero-skew clock tree construction with X-Flip technique and via delay consideration

As IC fabrication technologies get into nanometer era, clock routing gradually dominates chip performance indicated by delay, cost, and power consumption. X-architecture can be applied for routing metal wires in diagonal and rectilinear directions to overcome the above challenges due to wirelength reduction. In this paper, we present a clock routing algorithm, called PMXF, to construct an X-architecture zero-skew clock tree with minimum delay. An X-pattern library is defined for simplifying the merging procedure of the DME approach, an X-Flip technique is proposed for reducing the wirelength between the paired points, and a wire sizing technique is applied for achieving zero skew. In terms of clock delay, wirelength, power consumption, and via count listed in the experimental results on benchmarks, the proposed PMXF algorithm can respectively achieve more reductions compared with other previous X-architecture clock routing algorithms.

Quantized correlation coefficient for measuring reproducibility of ChIP-chip data

BackgroundChromatin immunoprecipitation followed by microarray hybridization (ChIP-chip) is used to study protein-DNA interactions and histone modifications on a genome-scale. To ensure data quality, these experiments are usually performed in replicates, and a correlation coefficient between replicates is used often to assess reproducibility. However, the correlation coefficient can be misleading because it is affected not only by the reproducibility of the signal but also by the amount of binding signal present in the data.ResultsWe develop the Quantized correlation coefficient (QCC) that is much less dependent on the amount of signal. This involves discretization of data into set of quantiles (quantization), a merging procedure to group the background probes, and recalculation of the Pearson correlation coefficient. This procedure reduces the influence of the background noise on the statistic, which then properly focuses more on the reproducibility of the signal. The performance of this procedure is tested in both simulated and real ChIP-chip data. For replicates with different levels of enrichment over background and coverage, we find that QCC reflects reproducibility more accurately and is more robust than the standard Pearson or Spearman correlation coefficients. The quantization and the merging procedure can also suggest a proper quantile threshold for separating signal from background for further analysis.ConclusionsTo measure reproducibility of ChIP-chip data correctly, a correlation coefficient that is robust to the amount of signal present should be used. QCC is one such measure. The QCC statistic can also be applied in a variety of other contexts for measuring reproducibility, including analysis of array CGH data for DNA copy number and gene expression data.

An online predictor model as adaptive habitually linear and transiently nonlinear model

In this paper, we propose a new online predictor model for complex nonlinear processes. The proposed adaptive habitually linear and transiently nonlinear model (AHLTNM) can follow fast and significant structural variations in the process, which is caused by various sources of uncertainty. The proposed AHLTNM learning method tends to keep the model as simple as possible. While the developed model can be as complex as a TS fuzzy model with flexible precedents, it habitually tends to shrink to an adaptive linear model. The adaptive linear model evolves gradually and expands transiently to a nonlinear TS-type model, when the linear model cannot follow the variations of the process. This expansion starts and continues as long as the modeling error is higher than an adaptive threshold. The model habitually and gradually shrinks to an adaptive linear model when the modeling error becomes lower than the adaptive threshold. Evolving from a linear model to a nonlinear one and then returning to a linear model is performed through specially designed split and merge procedures, which are embedded into AHLTNM learning method. The performance of our proposed online predictor model is examined and compared with that of two well-known TS-type online identification methods in a benchmark example and two other case studies: short term prediction of electrical load time series and prediction of daily minimum temperature time series.

An agglomerative segmentation framework for non-convex regions within uterine cervix images

The National Cancer Institute has collected a large database of uterine cervix images termed “cervigrams”, for cervical cancer screening research. Tissues of interest within the cervigram, in particular the lesions, are of varying sizes and of complexnon-convex shapes. The tissues possess similar color features and their boundaries are not always clear. The main objective of the current work is to provide a segmentation framework for tissues of interest within the cervix, that can cope with these difficulties in an unsupervised manner and with a minimal number of parameters. The proposed framework transitions from pixels to a set of small coherent regions (superpixels), which are grouped bottom-up into larger, non-convex, perceptually similar regions. The merging process is performed utilizing a new graph-cut criterion termed the normalized-mean cut (NMCut) and an agglomerative clustering framework. Superpixels similarity is computed via a locally scaled similarity measure that combines region and edge information. Segmentation quality is evaluated by measuring the overlap accuracy of the generated segments and tissues that were manually marked by medical experts. Experiments are conducted on two sets of cervigrams and lead to the following set of observations and conclusions: 1) The generated superpixels provide an accurate decomposition of the different tissues; 2) The local scaling process improves the clustering results; 3) The influence of different graph-cut criterions on the segmentation accuracy is evaluated and the NMCut criterion is shown to provide the best results; 4) A comparison between several modifications to the agglomerative clustering process is conducted. The results are shown to be strongly influenced by the merging procedure; 5) The agglomerative clustering framework is shown to outperform a state-of-the-art spectral clustering algorithm.

Detection of Systematic Errors in Travel-Time Data Using a Minimum 1D Model: Application to Costa Rica Seismic Tomography

Many seismological studies depend on the accuracy of timing of seis- mological data. In seismic tomography, travel-time residuals defined as differences between the observed and calculated arrival times of seismic phases are minimized to constrain 3D velocity structure. Inconsistencies and large errors in data sets that result from incorrect station coordinates, errors in the timing acquisition system, errors in the merging procedure, inconsistency in the picking and phase misidentification can also generate travel-time residuals, and because of their systematic nature, these errors cannot be treated as random noise even by exploiting a large number of travel times. While the inverse problem is perfectly set up to deal with random noise, systematic errors lead to significant artifacts in the solution, but may not be detected by a poste- rior error assessment. For this reason, detecting and removing systematic travel-time errors from data sets before inversion is crucial for seismic tomography studies. We present a methodology based on the use of a minimum 1D model to detect and remove systematic errors in travel-time data by detailed analysis of station delays and observation residuals and apply it to a local earthquake data set from Costa Rica. The determination of the exact nature of detected inconsistencies needs further investiga- tions in each individual case. If the cause of detected systematic errors cannot be determined beyond any doubt and the afflicted data may not be corrected, they must be deleted from the data set. To assess the extent of influence of systematic errors on hypocenter locations and their uncertainties, we present two examples showing the effects of station mislocation.

Adaptive Encoding Algorithm for Multispectral Images

A new adaptive multispectral image compression technique based on the regions identified is proposed. The algorithm is adaptive in the sense that according to the data type class of the region, appropriate encoding technique is chosen. The image is first segmented by means of Region splitting and merging procedure based on the statistical characteristic of the image. Then class adaptive hotelling transform or Karhunen Loeve transform (KLT) in the spectral domain and the shape adaptive wavelet transform in the spatial domain are adopted in the image by considering the spatial, spectral and statistical properties which are unique to the multispectral images. The quadtree is used for determining the transform block size and a single KLT matrix is used for the regions of same class ie., class adaptive KLT is applied and the transformation is followed by shape adaptive wavelet transform(SAWT) incorporating the spatial and structural properties of the multispectral image. After transformation, based on the regions identified, if the region is relatively uniform or smooth, the SPIHT (Set Partitioning in Hierarchical Trees) algorithm is adopted. If not, that is, if the region is highly textured in nature, then object based wavelet method is used for compression. Thus the advantages of both SPIHT algorithm and object based wavelet encoding method, both in terms of visual quality and PSNR values, are incorporated in a single compression technique.

Hierarchical agglomerative sub-clustering technique for particles management in PIC simulations

The effectiveness of Particle-In-Cell (PIC) codes lies mainly in the robustness of the methods implemented, under the fundamental assumption that a sufficient number of pseudo-particles is concerned for a correct representation of the system. The consequent drawback is the huge increase of computational time required to run a simulation, to what concerns the particles charge assignment to the grid and the motion of the former through the latter. Moreover the coupling of such methods with Monte-Carlo-Collisional (MCC) modules causes another expensive computational cost to simulate particle multiple collisions with background gas and domain boundaries. Particles management techniques are therefore often introduced in PIC-MCC codes in order to improve the distribution of pseudo-particles in the simulation domain: as a matter of facts, the aim at managing the number of samples according to the importance of the considered region is a main question for codes simulating a local phenomenon in a larger domain or a strongly collisional system (e.g.: a ionizing plasma, where the number of particles increases exponentially). A clustering procedure based on the distribution function sampling applied to the 5D phase space (2D in space, 3D in velocity) is here proposed, representing the leading criterion for particles merging and splitting procedures guaranteeing the second order charge moments conservation. Applied to the study of the electrical breakdown in the early discharge phase of a Plasma Focus device, this technique is shown to increase performances of both PIC kernel and MCC module preserving the solution of the electric field and increasing samples representativeness in stochastic calculations (with respect to more traditional merging and splitting procedures).

Aeromagnetic map compilation: procedures for merging and an example from Washington

Rocks in Antarctica and offshore have widely diverse magnetic properties. Consequently, aeromagnetic data collected there can improve knowledge of the geologic, tectonic and geothermal characteristics of the region. Aeromagnetic data can map concealed structures such as faults, folds and dikes, ascertain basin thickness and locate buried volcanic, as well as some intrusive and metamorphic rocks. Gridded, composite data sets allow a view of continental-scale trends that individual data sets do not provide and link widely-separated areas of outcrop and disparate geologic studies. Individual magnetic surveys must be processed so that they match adjacent surveys prior to merging. A consistent representation of the Earth's magnetic field (International Geomagnetic Reference Field (IGRF)) must be removed from each data set. All data sets need to be analytically continued to the same flight elevation with their datums shifted to match adjacent data. I advocate minimal processing to best represent the individual surveys in the merged compilation. An example of a compilation of aeromagnetic surveys from Washington illustrates the utility of aeromagnetic maps for providing synoptic views of regional tectonic features.

Bias Adjustment of Satellite Precipitation Estimation Using Ground-Based Measurement: A Case Study Evaluation over the Southwestern United States

Abstract Reliable precipitation measurement is a crucial component in hydrologic studies. Although satellite-based observation is able to provide spatial and temporal distribution of precipitation, the measurements tend to show systematic bias. This paper introduces a grid-based precipitation merging procedure in which satellite estimates from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN–CCS) are adjusted based on the Climate Prediction Center (CPC) daily rain gauge analysis. To remove the bias, the hourly CCS estimates were spatially and temporally accumulated to the daily 1° × 1° scale, the resolution of CPC rain gauge analysis. The daily CCS bias was then downscaled to the hourly temporal scale to correct hourly CCS estimates. The bias corrected CCS estimates are called the adjusted CCS (CCSA) product. With the adjustment from the gauge measurement, CCSA data have been generated to provide more reliable high temporal/spatial-resolution precipitation estimates. In the case study, the CCSA precipitation estimates from the proposed approach are compared against ground-based measurements in high-density gauge networks located in the southwestern United States.

Clustering Based on Multiple Criteria for LVQ and K-Means Algorithm

When classifying linearly separable data by learning vector quantization (LVQ) or K-Means algorithm (KMA), we cannot necessarily obtain satisfactory classification results for bad selections of initial cluster centers and differences among the distributions of class data. In this paper, to realize reliable classification, clustering based on multiple criteria for LVQ and KMA is proposed, and its performance is provided. To obtain suitable cluster centers, KMA with the split and merge procedure proposed by Kaukoranta et al. is introduced to minimize the squared-error distortion. LVQ using those cluster centers as initial ones is applied to the data, andΚclusters are produced. Introducing a criterion of whether each cluster reveals unimodality, subclusters split by KMA for clusters having no unimodality are merged into appropriate neighboring clusters except one subcluster, and the validity of the classification result is checked.