LF(LADLE FURNACE) refining technology is the key process to regulate the temperature in steelmaking process. To predict the end temperature of molten steel in LF, this paper proposes a new data preprocessing technique...
详细信息
LF(LADLE FURNACE) refining technology is the key process to regulate the temperature in steelmaking process. To predict the end temperature of molten steel in LF, this paper proposes a new data preprocessing technique based on feature extraction and clustering. Firstly, random forest algorithm was used to predict the temperature, the predictive hit rate of error within ± 10°C was 73.18%. The lasso algorithm and K-means algorithm was used for feature extraction and clustering. After improvement, the prediction accuracy of the LF end temperature of error within ± 10°C was about 88.16%. The results show that this improvement has high prediction accuracy in the prediction about the end temperature of molten steel in LF refining.
We evaluate the feasibility of quantifying surface soil properties over large areas and at a fine spatial resolution using high-resolution airborne imaging spectroscopy. Airborne Visible Infrared Imaging Spectrometer ...
详细信息
We evaluate the feasibility of quantifying surface soil properties over large areas and at a fine spatial resolution using high-resolution airborne imaging spectroscopy. Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data collected by the National Aeronautics and Space Administration immediately after the large 2011 Mississippi River flood at the Birds Point New Madrid (BPNM, approximate to 700 km(2)) floodway in Missouri, USA, was used in a data mining lasso framework for mapping of soil textural properties such as percentages of sand, silt, clay, soil-organic matter, and many other soil chemicals constituents. The modeling results show that the approach is feasible and provide insights in the accuracy and uncertainty of the approach for both soil textural properties and chemical constituents. These models were further used for a pixel-by-pixel prediction of each the soil constituent, resulting in high-resolution (7.6 m) quantitative spatial maps in the entire floodway. These maps reveal coherent spatial correlations with historical meander patterns of Mississippi River and fine-scale features such as erosional gullies, represented by difference in constituent concentration, e. g., low soil organic matter, with the underlying topography immediately disturbed by the large flooding event. Further, we have argued and established that the independent validation results are better represented as a probability density function as compared with a single calibration-validation set. It is also found that modeled soil constituents are sensitive to NDVI and the calibration sample sizes, and the results improve with stricter (lower) NDVI thresholds and larger calibration sets.
This paper deals with the problem of estimating the directions of arrival (DOA) of multiple source signals from a single observation vector of an array data. In particular, four estimation algorithms based on the theo...
详细信息
This paper deals with the problem of estimating the directions of arrival (DOA) of multiple source signals from a single observation vector of an array data. In particular, four estimation algorithms based on the theory of compressed sensing (CS), i.e., the classical a"" (1) minimization (or Least Absolute Shrinkage and Selection Operator, lasso), the fast smooth a"" (0) minimization, and the Sparse Iterative Covariance-Based Estimator, SPICE and the Iterative Adaptive Approach for Amplitude and Phase Estimation, IAA-APES algorithms, are analyzed, and their statistical properties are investigated and compared with the classical Fourier beamformer (FB) in different simulated scenarios. We show that unlike the classical FB, a CS-based beamformer (CSB) has some desirable properties typical of the adaptive algorithms (e.g., Capon and MUSIC) even in the single snapshot case. Particular attention is devoted to the super-resolution property. Theoretical arguments and simulation analysis provide evidence that a CS-based beamformer can achieve resolution beyond the classical Rayleigh limit. Finally, the theoretical findings are validated by processing a real sonar dataset.
The objectives of this Perspective paper are to review some recent advances in sparse feature selection for regression and classification, as well as compressed sensing, and to discuss how these might be used to devel...
详细信息
The objectives of this Perspective paper are to review some recent advances in sparse feature selection for regression and classification, as well as compressed sensing, and to discuss how these might be used to develop tools to advance personalized cancer therapy. As an illustration of the possibilities, a new algorithm for sparse regression is presented and is applied to predict the time to tumour recurrence in ovarian cancer. A new algorithm for sparse feature selection in classification problems is presented, and its validation in endometrial cancer is briefly discussed. Some open problems are also presented.
Sparsity preserving projection（SPP） is a popular graph-based dimensionality reduction（DR） method, which has been successfully applied to solve face recognition recently. SPP contains natural discriminating informa...
详细信息
Sparsity preserving projection（SPP） is a popular graph-based dimensionality reduction（DR） method, which has been successfully applied to solve face recognition recently. SPP contains natural discriminating information by preserving sparse reconstruction relationship of data sets. However, SPP suffers from the fact that every new feature learned from data sets is linear combinations of all the original features, which often makes it difficult to interpret the results. To address this issue, a novel DR method called dual-sparsity preserving projection （DSPP） is proposed to further impose sparsity constraints on the projection directions of SPP. Specifically, the proposed method casts the projection function learning of SPP into a regression-type optimization problem, and then the sparse projections can be efficiently computed by the related lasso algorithm. Experimental results from face databases demonstrate the effectiveness of the proposed algorithm.
To determine if candidate cancer biomarkers have utility in a clinical setting, validation using immunohistochemical methods is typically done. Most analyses of such data have not incorporated the multivariate nature ...
详细信息
To determine if candidate cancer biomarkers have utility in a clinical setting, validation using immunohistochemical methods is typically done. Most analyses of such data have not incorporated the multivariate nature of the staining profiles. In this article, we consider modelling such data using recently developed ideas from the machine learning community. In particular, we consider the joint goals of feature selection and classification. We develop estimation procedures for the analysis of immunohistochemical profiles using the least absolute selection and shrinkage operator. These lead to novel and flexible models and algorithms for the analysis of compositional data. The techniques are illustrated using data from a cancer biomarker study.
Tato práce navrhuje zlepšení výkonu testování programů použitím technik dolování z dat a genetických algoritmů při testování paralelních programů. Para...
详细信息
Tato práce navrhuje zlepšení výkonu testování programů použitím technik dolování z dat a genetických algoritmů při testování paralelních programů. Paralelní programování se v posledních letech stává velmi populárním i přesto, že toto programování je mnohem náročnějsí než jednodušší sekvenční a proto jeho zvýšené používání vede k podstatně vyššímu počtu chyb. Tyto chyby se vyskytují v důsledku chyb v synchronizaci jednotlivých procesů programu. Nalezení takových chyb tradičním způsobem je složité a navíc opakované spouštění těchto testů ve stejném prostředí typicky vede pouze k prohledávání stejných prokládání. V práci se využívá metody vstřikování šumu, která vystresuje program tak, že se mohou objevit některá nová chování. Pro účinnost této metody je nutné zvolit vhodné heuristiky a též i hodnoty jejich parametrů, což není snadné. V práci se využívá metod dolování z dat, genetických algoritmů a jejich kombinace pro nalezení těchto heuristik a hodnot parametrů. V práci je vedle výsledků výzkumu uveden stručný přehled dalších Technik testování paralelních programů.
暂无评论