Today, the quantity of data continues to increase, furthermore, the data are heterogeneous, from multiple sources (structured, semi-structured and unstructured) and with different levels of quality. Therefore, it is v...Today, the quantity of data continues to increase, furthermore, the data are heterogeneous, from multiple sources (structured, semi-structured and unstructured) and with different levels of quality. Therefore, it is very likely to manipulate data without knowledge about their structures and their semantics. In fact, the meta-data may be insufficient or totally absent. Data Anomalies may be due to the poverty of their semantic descriptions, or even the absence of their description. In this paper, we propose an approach to better understand the semantics and the structure of the data. Our approach helps to correct automatically the intra-column anomalies and the inter-col- umns ones. We aim to improve the quality of data by processing the null values and the semantic dependencies between columns.展开更多
This paper presents a simple method of forming sum and difference patterns with adaptivenulls. The effects on the sidelobe level and the pointing null of difference pattern by adaptive null areanalyzed. The result tha...This paper presents a simple method of forming sum and difference patterns with adaptivenulls. The effects on the sidelobe level and the pointing null of difference pattern by adaptive null areanalyzed. The result that the increment value of the envelope of the sidelobe level under the effect ofa null is less than l.6dB is proved. The formula about shift value of the pointing null which is thefunction of the jammer direction and array parameters is given in the paper.展开更多
In this paper, for multiple attribute decision-making problem in which attribute values are interval grey numbers and some of them are null values, a decision model based on grey rough sets integration with incomplete...In this paper, for multiple attribute decision-making problem in which attribute values are interval grey numbers and some of them are null values, a decision model based on grey rough sets integration with incomplete information is proposed. We put forward incidence degree coefficient formula for grey interval, by information entropy theory and analysis technique, the method and principle is presented to fill up null values. We also establish the method of grey interval incidence cluster. Because grey system theory and Rough set theory are complementary each other, decision table with preference information is obtained by the result of grey incidence cluster. An algorithm for inducing decision rules based on rough set theory and the dominance relationship is presented. In some extent, this algorithm can deal with decision-making problem in which the attribute values are interval grey numbers and some of them are null values. Contrasted with classical model of cluster decision-making, the algorithm has an advantage of flexibility and compatibility to new information.展开更多
In this paper, several kinds of multivalued logic for relational database and their developing process are presented on the basis of null value's semantics. A new 5 valued logic is led into relational database con...In this paper, several kinds of multivalued logic for relational database and their developing process are presented on the basis of null value's semantics. A new 5 valued logic is led into relational database containing null talue. The feasibility and necessity of using 5 valued logic are expounded. Comparative calculation and logical calculation under 5 valued logic are defined at the end of the paper.展开更多
文摘Today, the quantity of data continues to increase, furthermore, the data are heterogeneous, from multiple sources (structured, semi-structured and unstructured) and with different levels of quality. Therefore, it is very likely to manipulate data without knowledge about their structures and their semantics. In fact, the meta-data may be insufficient or totally absent. Data Anomalies may be due to the poverty of their semantic descriptions, or even the absence of their description. In this paper, we propose an approach to better understand the semantics and the structure of the data. Our approach helps to correct automatically the intra-column anomalies and the inter-col- umns ones. We aim to improve the quality of data by processing the null values and the semantic dependencies between columns.
文摘This paper presents a simple method of forming sum and difference patterns with adaptivenulls. The effects on the sidelobe level and the pointing null of difference pattern by adaptive null areanalyzed. The result that the increment value of the envelope of the sidelobe level under the effect ofa null is less than l.6dB is proved. The formula about shift value of the pointing null which is thefunction of the jammer direction and array parameters is given in the paper.
基金Supported by the NSF of Henan Province(082300410040)Supported by the NSF of Zhumadian City(087006)
文摘In this paper, for multiple attribute decision-making problem in which attribute values are interval grey numbers and some of them are null values, a decision model based on grey rough sets integration with incomplete information is proposed. We put forward incidence degree coefficient formula for grey interval, by information entropy theory and analysis technique, the method and principle is presented to fill up null values. We also establish the method of grey interval incidence cluster. Because grey system theory and Rough set theory are complementary each other, decision table with preference information is obtained by the result of grey incidence cluster. An algorithm for inducing decision rules based on rough set theory and the dominance relationship is presented. In some extent, this algorithm can deal with decision-making problem in which the attribute values are interval grey numbers and some of them are null values. Contrasted with classical model of cluster decision-making, the algorithm has an advantage of flexibility and compatibility to new information.
文摘In this paper, several kinds of multivalued logic for relational database and their developing process are presented on the basis of null value's semantics. A new 5 valued logic is led into relational database containing null talue. The feasibility and necessity of using 5 valued logic are expounded. Comparative calculation and logical calculation under 5 valued logic are defined at the end of the paper.
