Underground contamination by non-aqueous phase liquids (NAPLs) becomes increasingly serious. Rapid and reliable detection of contaminated zone and degree is the first step to site remediation. In this paper, diesel an...Underground contamination by non-aqueous phase liquids (NAPLs) becomes increasingly serious. Rapid and reliable detection of contaminated zone and degree is the first step to site remediation. In this paper, diesel and fine sand are used as experiment materials to investigate the applicability of using time-domain reflectometry (TDR) to detect LNAPLs contamination. The major work includes: measurement of dielectric constant and electrical conductivity for the diesel-water-air-sand mixtures; measurement of reflection waveform and dielectric constant for specimens with a diesel contaminated layer being sandwiched in sand. The experimental results show the followings: A significant decrease in both dielectric constant and electrical conductivity is observed for the diesel-water-air-sand mixtures when diesel displaces the pore water, and the content of diesel can be calculated by the model; insignificant change in dielectric properties is measured when diesel only displaces the pore gas; when the diesel contaminated sand is sandwiched between two saturated sand layers, the interfaces of the diesel contaminated layer can be identified by analyzing the reflection waveform; for field application, TDR method is valid for the case that LNAPLs seep into saturated sand layer, and the applicability of TDR method in vadose zone depends on the initial saturation of the sand layer. The findings obtained in this paper provide a guidance for the use of TDR for the field investigation of NAPLs contaminated site.展开更多
In this paper, a trust region method for equality constrained optimizationbased on nondifferentiable exact penalty is proposed. In this algorithm, the trail step ischaracterized by computation of its normal component ...In this paper, a trust region method for equality constrained optimizationbased on nondifferentiable exact penalty is proposed. In this algorithm, the trail step ischaracterized by computation of its normal component being separated from computation of itstangential component, i.e., only the tangential component of the trail step is constrained by trustradius while the normal component and trail step itself have no constraints. The other maincharacteristic of the algorithm is the decision of trust region radius. Here, the decision of trustregion radius uses the information of the gradient of objective function and reduced Hessian.However, Maratos effect will occur when we use the nondifferentiable exact penalty function as themerit function. In order to obtain the superlinear convergence of the algorithm, we use the twiceorder correction technique. Because of the speciality of the adaptive trust region method, we usetwice order correction when p = 0 (the definition is as in Section 2) and this is different from thetraditional trust region methods for equality constrained optimization. So the computation of thealgorithm in this paper is reduced. What is more, we can prove that the algorithm is globally andsuperlinearly convergent.展开更多
Named entity recognition （NER） is a core component in many natural language processing applications. Most NER systems rely on supervised machine learning methods, which depend on time-consuming and expensive annotat...Named entity recognition （NER） is a core component in many natural language processing applications. Most NER systems rely on supervised machine learning methods, which depend on time-consuming and expensive annotations in different languages and domains. This paper presents a method for automatically building silver-standard NER corpora from Chinese Wikipedia. We refine novel and language-dependent features by exploiting the text and structure of Chinese Wikipedia. To reduce tagging errors caused by entity classification, we design four types of heuristic rules based on the characteristics of Chinese Wikipedia and train a supervised NE classifier, and a combined method is used to improve the precision and coverage. Then, we realize type identification of implicit mention by using boundary information of outgoing links. By selecting the sentences related with the domains of test data, we can train better NER models. In the experiments, large-scale NER corpora containing 2.3 million sentences are built from Chinese Wikipedia. The results show the effectiveness of automatically annotated corpora, and the trained NER models achieve the best performance when combining our silver-standard corpora with gold-standard corpora.展开更多
基金supported by the National High Technology Research and Development Program of China ("863" Project) (Grant No.2012AA062601)the National Natural Science Foundation of Major International Cooperation Projects (Grant No. 51010008)
文摘Underground contamination by non-aqueous phase liquids (NAPLs) becomes increasingly serious. Rapid and reliable detection of contaminated zone and degree is the first step to site remediation. In this paper, diesel and fine sand are used as experiment materials to investigate the applicability of using time-domain reflectometry (TDR) to detect LNAPLs contamination. The major work includes: measurement of dielectric constant and electrical conductivity for the diesel-water-air-sand mixtures; measurement of reflection waveform and dielectric constant for specimens with a diesel contaminated layer being sandwiched in sand. The experimental results show the followings: A significant decrease in both dielectric constant and electrical conductivity is observed for the diesel-water-air-sand mixtures when diesel displaces the pore water, and the content of diesel can be calculated by the model; insignificant change in dielectric properties is measured when diesel only displaces the pore gas; when the diesel contaminated sand is sandwiched between two saturated sand layers, the interfaces of the diesel contaminated layer can be identified by analyzing the reflection waveform; for field application, TDR method is valid for the case that LNAPLs seep into saturated sand layer, and the applicability of TDR method in vadose zone depends on the initial saturation of the sand layer. The findings obtained in this paper provide a guidance for the use of TDR for the field investigation of NAPLs contaminated site.
基金This research is supported in part by the National Natural Science Foundation of China(Grant No. 39830070,10171055)and China Postdoctoral Science Foundation
文摘In this paper, a trust region method for equality constrained optimizationbased on nondifferentiable exact penalty is proposed. In this algorithm, the trail step ischaracterized by computation of its normal component being separated from computation of itstangential component, i.e., only the tangential component of the trail step is constrained by trustradius while the normal component and trail step itself have no constraints. The other maincharacteristic of the algorithm is the decision of trust region radius. Here, the decision of trustregion radius uses the information of the gradient of objective function and reduced Hessian.However, Maratos effect will occur when we use the nondifferentiable exact penalty function as themerit function. In order to obtain the superlinear convergence of the algorithm, we use the twiceorder correction technique. Because of the speciality of the adaptive trust region method, we usetwice order correction when p = 0 (the definition is as in Section 2) and this is different from thetraditional trust region methods for equality constrained optimization. So the computation of thealgorithm in this paper is reduced. What is more, we can prove that the algorithm is globally andsuperlinearly convergent.
基金Project supported by the National Natural Science Foundation of China(No.14BXW028)
文摘Named entity recognition （NER） is a core component in many natural language processing applications. Most NER systems rely on supervised machine learning methods, which depend on time-consuming and expensive annotations in different languages and domains. This paper presents a method for automatically building silver-standard NER corpora from Chinese Wikipedia. We refine novel and language-dependent features by exploiting the text and structure of Chinese Wikipedia. To reduce tagging errors caused by entity classification, we design four types of heuristic rules based on the characteristics of Chinese Wikipedia and train a supervised NE classifier, and a combined method is used to improve the precision and coverage. Then, we realize type identification of implicit mention by using boundary information of outgoing links. By selecting the sentences related with the domains of test data, we can train better NER models. In the experiments, large-scale NER corpora containing 2.3 million sentences are built from Chinese Wikipedia. The results show the effectiveness of automatically annotated corpora, and the trained NER models achieve the best performance when combining our silver-standard corpora with gold-standard corpora.
