The melting temperature of Z coal ash was reduced by adding calcium–magnesium compound flux(WCaO/WMgO=1). In the process of simulated coal gasification, the coal ash and slag were prepared. The transformation of mine...The melting temperature of Z coal ash was reduced by adding calcium–magnesium compound flux(WCaO/WMgO=1). In the process of simulated coal gasification, the coal ash and slag were prepared. The transformation of minerals in coal ash and slag upon the change of temperature was studied by using X-ray diffraction(XRD). With the increase of temperatures, forsterite in the ash disappears, while the diffraction peak strength of magnesium spinel increases,and the content of the calcium feldspar increases, then the content of the amorphous phase in the ash increases obviously. The species and evolution process of oxygen, silicon, aluminum, calcium, magnesium at different temperatures were analyzed by X-ray photoelectron spectroscopy(XPS). The decrease of the ash melting point mainly affects the structural changes of silicon, aluminum and oxygen. The coordination of aluminum and oxygen in the aluminum element structure, e.g., tetracoordinated aluminum oxide, was changed. Tetrahedral [AlO4] and hexacoordinated aluminoxy octahedral [AlO6] change with the temperature changing. The addition of Ca2+ and Mg2+ destroys silica chain, making bridge oxide silicon change into non-bridge oxysilicon;and bridge oxygen bond was broken and non-bridge oxygen bond was produced in the oxygen element structure. The addition of calcium and magnesium compound flux reacts with aluminum oxide tetrahedron, aluminum oxide octahedron and silicon tetrahedron to promote the breakage of the bridge oxygen bond. Ca2+ and Mg2+ are easily combined with silicon oxide and aluminum oxide tetrahedron and aluminum. Oxygen octahedrons combine with non-oxygen bonds to generate low-melting temperature feldspars and magnesite minerals, thereby reducing the coal ash melting temperatures. The structure of kaolinite and mullite was simulated by quantum chemistry calculation, and kaolinite molecule has a stable structure.展开更多
Intrusion detection can be essentially regarded as a classification problem,namely,dis-tinguishing normal profiles from intrusive behaviors. This paper introduces boosting classification algorithm into the area of int...Intrusion detection can be essentially regarded as a classification problem,namely,dis-tinguishing normal profiles from intrusive behaviors. This paper introduces boosting classification algorithm into the area of intrusion detection to learn attack signatures. Decision tree algorithm is used as simple base learner of boosting algorithm. Furthermore,this paper employs the Principle Com-ponent Analysis (PCA) approach,an effective data reduction approach,to extract the key attribute set from the original high-dimensional network traffic data. KDD CUP 99 data set is used in these ex-periments to demonstrate that boosting algorithm can greatly improve the classification accuracy of weak learners by combining a number of simple “weak learners”. In our experiments,the error rate of training phase of boosting algorithm is reduced from 30.2% to 8% after 10 iterations. Besides,this paper also compares boosting algorithm with Support Vector Machine (SVM) algorithm and shows that the classification accuracy of boosting algorithm is little better than SVM algorithm’s. However,the generalization ability of SVM algorithm is better than boosting algorithm.展开更多
基金Supported partially by the Major Science and Technology Special Projects Foundation of Anhui Province(15czz02045)the Natural Science Foundation of Anhui Province(1508085MB41)the China Postdoctoral Science Foundation(2015M571915)
文摘The melting temperature of Z coal ash was reduced by adding calcium–magnesium compound flux(WCaO/WMgO=1). In the process of simulated coal gasification, the coal ash and slag were prepared. The transformation of minerals in coal ash and slag upon the change of temperature was studied by using X-ray diffraction(XRD). With the increase of temperatures, forsterite in the ash disappears, while the diffraction peak strength of magnesium spinel increases,and the content of the calcium feldspar increases, then the content of the amorphous phase in the ash increases obviously. The species and evolution process of oxygen, silicon, aluminum, calcium, magnesium at different temperatures were analyzed by X-ray photoelectron spectroscopy(XPS). The decrease of the ash melting point mainly affects the structural changes of silicon, aluminum and oxygen. The coordination of aluminum and oxygen in the aluminum element structure, e.g., tetracoordinated aluminum oxide, was changed. Tetrahedral [AlO4] and hexacoordinated aluminoxy octahedral [AlO6] change with the temperature changing. The addition of Ca2+ and Mg2+ destroys silica chain, making bridge oxide silicon change into non-bridge oxysilicon;and bridge oxygen bond was broken and non-bridge oxygen bond was produced in the oxygen element structure. The addition of calcium and magnesium compound flux reacts with aluminum oxide tetrahedron, aluminum oxide octahedron and silicon tetrahedron to promote the breakage of the bridge oxygen bond. Ca2+ and Mg2+ are easily combined with silicon oxide and aluminum oxide tetrahedron and aluminum. Oxygen octahedrons combine with non-oxygen bonds to generate low-melting temperature feldspars and magnesite minerals, thereby reducing the coal ash melting temperatures. The structure of kaolinite and mullite was simulated by quantum chemistry calculation, and kaolinite molecule has a stable structure.
基金National High-tech R&D Program of China (2003AA142060)National Basic Research Program of China (2001CB09403).
文摘Intrusion detection can be essentially regarded as a classification problem,namely,dis-tinguishing normal profiles from intrusive behaviors. This paper introduces boosting classification algorithm into the area of intrusion detection to learn attack signatures. Decision tree algorithm is used as simple base learner of boosting algorithm. Furthermore,this paper employs the Principle Com-ponent Analysis (PCA) approach,an effective data reduction approach,to extract the key attribute set from the original high-dimensional network traffic data. KDD CUP 99 data set is used in these ex-periments to demonstrate that boosting algorithm can greatly improve the classification accuracy of weak learners by combining a number of simple “weak learners”. In our experiments,the error rate of training phase of boosting algorithm is reduced from 30.2% to 8% after 10 iterations. Besides,this paper also compares boosting algorithm with Support Vector Machine (SVM) algorithm and shows that the classification accuracy of boosting algorithm is little better than SVM algorithm’s. However,the generalization ability of SVM algorithm is better than boosting algorithm.
