This paper studies error formulas for Lagrange projectors determined by Cartesian sets. Cartesian sets are properly subgrids of tensor product grids. Given interpolated functions with all order continuous partial deri...
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This paper studies error formulas for Lagrange projectors determined by Cartesian sets. Cartesian sets are properly subgrids of tensor product grids. Given interpolated functions with all order continuous partial derivatives, the authors directly construct the good error formulas for Lagrange projectors determined by Cartesian sets. Owing to the special algebraic structure, such a good error formula is useful for error estimate.
In this paper, a novel method is proposed for judging whether a component set is a consistency-based diagnostic set, using SAT solv- ers. Firstly, the model of the system to be diagnosed and all the observations are d...
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In this paper, a novel method is proposed for judging whether a component set is a consistency-based diagnostic set, using SAT solv- ers. Firstly, the model of the system to be diagnosed and all the observations are described with conjunctive normal forms (CNF). Then, all the related clauses in the CNF files to the components other than the considered ones are extracted, to be used for satisfiability checking by SAT solvers. Next, all the minimal consistency-based diagnostic sets are derived by the CSSE-tree or by other similar algorithms. We have implemented four related algorithms, by calling the gold medal SAT solver in SAT07 competition – RSAT. Experimental results show that all the minimal consistency-based diagnostic sets can be quickly computed. Especially our CSSE-tree has the best effciency for the singleor double-fault diagnosis.
In this paper, we present a brand new dataset named cellphone buttery defects in X-ray(CBDx). CBDx consists of 300 X-ray images and 250 of them are anomaly free. We name them 'good'. Others have some defects i...
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1Introduction and main contributions In the field of social networks and knowledge graphs,semi-supervised learning models based on graph convolutional networks have achieved great success in node classification[1],ind...
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1Introduction and main contributions In the field of social networks and knowledge graphs,semi-supervised learning models based on graph convolutional networks have achieved great success in node classification[1],inductive node embedding[2],link prediction[3],and *** semi-supervised models based on graph convolutional network(GCN)[4]expect to obtain more feature information of a graph or accelerate the training.
1 Introduction Local search method is a rising star for solving combinatorial optimization problems in recent years,and the state-of-the-art local search-based incomplete Maximum Satisfiability(MaxSAT)solversshowpromi...
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1 Introduction Local search method is a rising star for solving combinatorial optimization problems in recent years,and the state-of-the-art local search-based incomplete Maximum Satisfiability(MaxSAT)solversshowpromisingperformance even competitive to many complete solvers in recent MaxSAT Evaluations.
Semantic segmentation of 3D point clouds is often limited by the challenge of obtaining labeled data. Few-shot point cloud segmentation methods, which can learn previously unseen categories, help reduce reliance on la...
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Image transmission is one of the biggest challenges in wireless sensor networks because of the limited resource on sensor nodes. We proposed two image transmission schemes driven by reliability and real time considera...
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Image transmission is one of the biggest challenges in wireless sensor networks because of the limited resource on sensor nodes. We proposed two image transmission schemes driven by reliability and real time considerations in order to transfer JPEG images over Zigbee-based sensor networks. By adding two bytes counter in the header of data packet, we can easily solve the repeated data reception problem caused by retransmission mechanism in traditional Zigbees network layer. We proposed an efficient retransmission and acknowledgment mechanism in Zigbees application layer. By classifying different data reception response events, we can provide data packets with differential responses and ensure that image packets can be transferred quickly even with large maximum number of retransmission. Practical results show the effectiveness of our solutions to make image transmission over Zigbee-based sensor networks efficient.
Ontology evolution in the Model Driven Semantic Web can be looked as a process of model transformations. A model-transformation based conceptual framework for ontology evolution is presented in the paper. Applications...
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ISBN:
(纸本)3885793989
Ontology evolution in the Model Driven Semantic Web can be looked as a process of model transformations. A model-transformation based conceptual framework for ontology evolution is presented in the paper. Applications of model transformations in every phase of ontology evolution process are described. The framework combines technologies of ontology evolution, Ontology Definition Metamodel and model transformations, and it can be looked as a method for ontology evolution in the Model Driven Semantic Web.
Prenatal depression,which can affect pregnant women’s physical and psychological health and cause postpartum depression,is increasing ***,it is essential to detect prenatal depression early and conduct an attribution...
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Prenatal depression,which can affect pregnant women’s physical and psychological health and cause postpartum depression,is increasing ***,it is essential to detect prenatal depression early and conduct an attribution *** studies have used questionnaires to screen for prenatal depression,but the existing methods lack *** diagnose the early signs of prenatal depression and identify the key factors that may lead to prenatal depression from questionnaires,we present the semantically enhanced option embedding(SEOE)model to represent questionnaire *** can quantitatively determine the relationship and patterns between options and *** first quantifies options and resorts them,gathering options with little difference,since Word2Vec is highly dependent on *** resort task is transformed into an optimization problem involving the traveling salesman ***,all questionnaire samples are used to train the options’vector using ***,an LSTM and GRU fused model incorporating the cycle learning rate is constructed to detect whether a pregnant woman is suffering from *** verify the model,we compare it with other deep learning and traditional machine learning *** experiment results show that our proposed model can accurately identify pregnant women with depression and reach an F1 score of *** most relevant factors of depression found by SEOE are also verified in the *** addition,our model is of low computational complexity and strong generalization,which can be widely applied to other questionnaire analyses of psychiatric disorders.
The paper is concerned with the improvement of the rational representation theory for solving positive-dimensional polynomial systems. The authors simplify the expression of rational representation set proposed by Tan...
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The paper is concerned with the improvement of the rational representation theory for solving positive-dimensional polynomial systems. The authors simplify the expression of rational representation set proposed by Tan and Zhang(2010), obtain the simplified rational representation with less rational representation sets, and hence reduce the complexity for representing the variety of a positive-dimensional ideal. As an application, the authors compute a "nearly" parametric solution for the SHEPWM problem with a fixed number of switching angles.
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