An Efficient Algorithm for Processing Multi-Relation Queries in Relational Databases

After a relation scheme R is decomposed into the set of schemes ρ={R1，…，Rn},we may pose queries as if Rexisted in the database,taking a join of Ri‘s,when it is necessary to implement the query,Suppos a query involves a set of attributes S R，we want to find the smallest subset of ρ whose union includes.S.We prove that the problem is NP-complete and present a polynomial-bounded approximation algorithm.A subset of ρ whose union includes S and has a decomposition into 3NF with a lossless join and preservation of dependencies in given in the paper.     

Feedback stabilization of $N$-dimensional stochastic quantum systems based on bang-bang control

For an $N$-dimensional quantum system under the influence of continuous measurement, this paper presents a switching control scheme where the control law is of bang-bang type and achieves asymptotic preparation of an arbitrarily given eigenstate of a non-degenerate and degenerate measurement operator, respectively. In the switching control strategy, we divide the state space into two parts: a set containing a target state, and its complementary set. By analyzing the stability of the stochastic system model under consideration, we design a constant control law and give some conditions that the control Hamiltonian satisfies so that the system trajectories in the complementary set converge to the set which contains the target state. Further, for the case of a non-degenerate measurement operator, we show that the system trajectories in the set containing the target state will automatically converge to the target state via quantum continuous measurement theory; while for the case of a degenerate measurement operator, the corresponding system trajectories will also converge to the target state via the construction of the control Hamiltonians. The convergence of the whole closed-loop systems under the cases of a non-degenerate and a degenerate measurement operator is strictly proved. The effectiveness of the proposed switching control scheme is verified by the simulation experiments on a finite-dimensional angular momentum system and a two-qubit system.     

DNA-chip-based dynamic broadcast encryption scheme with constant-size ciphertexts and decryption keys

In this paper we present DNA-DBE,a DNA-chip-based dynamic broadcast encryption scheme.In our scheme,new users can join dynamically without modification of other users’decryption keys.Either the ciphertext or the decryption key is of constant-size.Backward secrecy is achieved in DNA-DBE:if new users join the system dynamically,they will not be able to retrieve past data.The security of our scheme relies on hard biological problems,which are immune to attacks of new computing technologies in the future.There exists a special feature in DNA–based cryptosystems,i.e.the set of encryption keys and the set of decryption keys have a many-to-many relationship.The implementation of more complicated DNA cryptosystems taking advantage of this special feature has been previously left as an open problem.Our DNA-DBE system is a solution to this open problem,which is also the first exploration of DNA based group-oriented encryption system.     

An Operational Approach to Belief Revision

A deduction system,called RE-proof system,is constructed for generating the revisions of first order belief sets.When a belief set is rejected by a given fact,all maximal subsets of the belief set consistent with the fact can be deduced from the proof system.The soundness and completeness of the RE-proof system are proved,which imply that there exists a resolution method to decide whether a revision retains a maximal subset of a belief set.     

Movement primitives as a robotic tool to interpret trajectories through learning-by-doing

Articulated movements are fundamental in many human and robotic tasks.While humans can learn and generalise arbitrarily long sequences of movements,and particularly can optimise them to ft the constraints and features of their body,robots are often programmed to execute point-to-point precise but fxed patterns.This study proposes a new approach to interpreting and reproducing articulated and complex trajectories as a set of known robot-based primitives.Instead of achieving accurate reproductions,the proposed approach aims at interpreting data in an agent-centred fashion,according to an agent s primitive movements.The method improves the accuracy of a reproduction with an incremental process that seeks frst a rough approximation by capturing the most essential features of a demonstrated trajectory.Observing the discrepancy between the demonstrated and reproduced trajectories,the process then proceeds with incremental decompositions and new searches in sub-optimal parts of the trajectory.The aim is to achieve an agent-centred interpretation and progressive learning that fts in the frst place the robots capability,as opposed to a data-centred decomposition analysis.Tests on both geometric and human generated trajectories reveal that the use of own primitives results in remarkable robustness and generalisation properties of the method.In particular,because trajectories are understood and abstracted by means of agent-optimised primitives,the method has two main features： 1） Reproduced trajectories are general and represent an abstraction of the data.2） The algorithm is capable of reconstructing highly noisy or corrupted data without pre-processing thanks to an implicit and emergent noise suppression and feature detection.This study suggests a novel bio-inspired approach to interpreting,learning and reproducing articulated movements and trajectories.Possible applications include drawing,writing,movement generation,object manipulation,and other tasks where the performance requires human-like interpretation and generalisation capabilities.     

Design of switched linear systems in the presence of actuator saturation and L-infinity disturbances

This paper considers the problem of disturbance tolerance/rejection of a switched system resulting from a family of linear systems subject to actuator saturation and L-infinity disturbances. For a given set of linear feedback gains, a given switching scheme and a given bound on the L-infinity norm of the disturbances, conditions are established, in terms of linear or bilinear matrix inequalities, under which a set of a certain form is invariant for a given switched linear system in the presence of actuator saturation and L-infinity disturbances, and the closed-loop system possesses a certain level of disturbance rejection capability. With these conditions, the design of feedback gains and switching scheme can be formulated and solved as constrained optimization problems. Disturbance tolerance is measured by the largest bound on the disturbances for which the trajectories starting from a given set remain bounded. Disturbance rejection is measured either by the L-infinity norm of the system output or by the system’s ability to steer its state into and/or keep it within a small neighborhood of the origin. In the event that all systems in the family are identical, the switched system reduces to a single system under a switching feedback law. Simulation results show that such a single system under a switching feedback law could have stronger disturbance tolerance/rejection capability than a single linear feedback law can.     

An Autonomous Navigation Methodology for a Pioneer 3DX Robot

Autonomous navigation is a complex challenge that involves the interpretation and analysis of information about the scenario to facilitate the cognitive processes of a robot to perform free trajectories in dynamic environments. To solve this, the paper introduces a Case-Based Reasoning methodology to endow robots with an efficient decision structure aiming of selecting the best maneuver to avoid collisions. In particular, Manhattan Distance was implemented to perform the retrieval process in CBR method. Four scenarios were depicted to run a set of experiments in order to validate the functionality of the implemented work. Finally, conclusions emphasize the advantages of CBR methodology to perform autonomous navigation in unknown and uncertain environments.     

A General Approach for Dexterous Manipulation Planning Based on Configuration Space Structuring

In this paper, we propose a new method for the dexterous manipulation planning problem, under quasi-static movement assumption. This method computes both object and finger trajectories as well as finger relocation sequence and applies to every object shape and hand geometry. It relies on the exploration of the particular subspaces GS k that are the subspaces of all the grasps that can be achieved for a given set of k grasping fingers. The originality is to use continuous paths in these subspaces to directly link two configurations. The proposed approach captures the GS k connectivity in a graph structure. The answer of the manipulation planning query is then given by searching a path in the computed graph. Another specificity of our technique is that it considers manipulated object and hand as an only system, unlike most existing methods that first compute object trajectory then fingers trajectories and thus can not find a solution in all situations. Simulation experiments were conducted for different dexterous manipulation task examples to validate the proposed method.     

An Improvement of Herbrands Theorem and Its Application to Model Generation Theorem Proving

This paper presents an improvement of Herbrand's theorem.We propose a method for specifying a sub- universe of the Herbrand universe of a clause set S for each argument of predicate symbols and function symbols in S. We prove that a clause set S is unsatisfiable if and only if there is a finite unsatisfiable set of ground instances of clauses of S that are derived by only instantiating each variable,which appears as an argument of predicate symbols or function symbols,in S over its corresponding argument's sub-universe of the Herbrand universe of S.Because such sub-universes are usually smaller(sometimes considerably)than the Herbrand universe of S,the number of ground instances may decrease considerably in many cases.We present an algorithm for automatically deriving the sub-universes for arguments in a given clause set,and show the correctness of our improvement.Moreover,we introduce an application of our approach to model generation theorem proving for non-range-restricted problems,show the range-restriction transformation algorithm based on our improvement and provide examples on benchmark problems to demonstrate the power of our approach.     

An Improvement of Herbrand＇s Theorem and Its Application to Model Generation Theorem Proving

This paper presents an improvement of Herbrand＇s theorem. We propose a method for specifying a subuniverse of the Herbrand universe of a clause set S for each argument of predicate symbols and function symbols in S. We prove that a clause set S is unsatisfiable if and only if there is a finite unsatisfiable set of ground instances of clauses of S that are derived by only instantiating each variable, which appears as an argument of predicate symbols or function symbols, in S over its corresponding argument＇s sub-universe of the Herbrand universe of S. Because such sub-universes are usually smaller （sometimes considerably） than the Herbrand universe of S, the number of ground instances may decrease considerably in many cases. We present an algorithm for automatically deriving the sub-universes for arguments in a given clause set, and show the correctness of our improvement. Moreover, we introduce an application of our approach to model generation theorem proving for non-range-restricted problems, show the range-restriction transformation algorithm based on our improvement and provide examples on benchmark problems to demonstrate the power of our approach.     

A general approach to attribute reduction in rough set theory

The concept of a consistent approximation representation space is introduced. Many types of information systems can be treated and unified as consistent ap- proximation representation spaces. At the same time, under the framework of this space, the judgment theorem for determining consistent attribute set is established, from which we can obtain the approach to attribute reductions in information systems. Also, the characterizations of three important types of attribute sets (the core attribute set, the relative necessary attribute set and the unnecessary attribute set) are examined.     

Filter-based iterative learning control for linear large-scale industrial processes

In the procedure of the steady-state hierarchical optimization with feedback for large-scale industrial processes, a sequence of set-point changes with different magnitudes is carried out on the optimization layer. To improve the dynamic performance of transient response driven by the set-point changes, a filter-based iterative leaning control strategy is proposed. In the proposed updating law, a local-symmetric-integral operator is adopted for eliminating the measurement noise of output information, a set of desired trajectories are specified according to the set-point changes sequence, the current control input is iteratively achieved by utilizing smoothed output error to modify its control input at previous iteration, to which the amplified coefficients related to the different magnitudes of set-point changes are introduced. The convergence of the algorithm is conducted by incorporating frequency-domain technique into time-domain analysis. Numerical simulation demonstrates the effectiveness of the proposed strategy.     

Generalized Moment Theorem and Its Application to Distributed-Parameter Control Systems

In this paper, we generalize the original moment theorem to the problem of existence of a linear operator with minimum norm under some constraint conditions in an abstract normed space. The results are applied to a class of largest controllable set and optimal control problems for distributed parameter systems.     

A Fuzzy Approach to Classification of Text Documents

This paper discusses the classification problems of text documents. Based on the concept of the proximity degree, the set of words is partitioned into some equivalence classes.Particularly, the concepts of the semantic field and association degree are given in this paper.Based on the above concepts, this paper presents a fuzzy classification approach for document categorization. Furthermore, applying the concept of the entropy of information, the approaches to select key words from the set of words covering the classification of documents and to construct the hierarchical structure of key words are obtained.     

The Optimization of Distributed Join in C-POREL System

C-POREL is a distributed relational data base management system, with full distributiontransparency. A relation can be horizontally fragmented into several subrelations allocated todifferent sites. When two or more fragmented relations appear in a join query, many-to-many semijoins between correspondent relations are usually needed in order to fully reducerelations involved. Distribution dependent attributes of a relational schema are defined in thispaper. An optimization approach of distributed join which decreases the amount of trans-mission data by avoiding many-to-many semijoins is given.     

A New Fuzzy Set Theory Satisfying All Classical Set Formulas

A new fuzzy set theory,C-fuzzy set theory,is introduced in this paper.It is a particular case of the classical set theory and satisfies all formulas of the classical set theory.To add a limitation to C-fuzzy set system,in which all fuzzy sets must be"non-uniform inclusive"to each other,then it forms a family of sub-systems,the Z-fuzzy set family.It can be proved that the Zo-fuzzy set system,one of Z-fuzzy set systems,is equivalent to Zadeh's fuzzy set system.Analysis shows that 1) Zadeh's fuzzy set syste...     

Handwritten Chinese Trajectories Prediction with an Improved Flat Functional-link Neural Networks and Kalman Filter

This paper introduces the application of the Flat Functional-link Neural Network （FFNN） to predict handwritten Chinese moving trajectories. To solve the prediction problem of a non-stationary time series, conventional neural networks need a lot of time and samples to train, but FFNN can solve this problem very well. Considering the structure of Chinese characters, the paper makes some improvement for FFNN and chooses the appropriate training samples, and promising experimental results have been obtained. Furthermore a comparison is performed between the predictions of the Flat NN and a Kalman filter. Experiments suggest that the improved FFNN predictor works better for the prediction of trajectories of handwritten Chinese characters.     

A Rapid Parallel Hierarchical Aggregation Algorithm on High Dimensional OLAP

On-Line Analytical Processing （OLAP） refers to the technologies that allow users to efficiently retrieve data from the data warehouse for decision support purposes. OLAP queries tend to be complex, often requiting computationally expensive operations such as multi-table joint and aggregation. In the high dimensional DW, we fully materialize the data cube impossibly. In this paper, we propose a novel aggregation algorithm, DHEPHA, to vertically partition a＇high dimensional dataset into a set of disjoint low dimensional datasets called shell Cube fragments. Using inverted hierarchical encoding indices and pre-aggregated results, OLAP queries are computed online by dynamically constructing cuboids from these Cube fragments. DHEPHA uses the small and their prefix, so it can rapidly retrieve the matching dimension member hierarchical encoding and evaluate the set of query ranges for each dimension, so that it can drastically reduce the multi-table joining effort and even could completely remove one or more join operations. As a result, the method we propose in this paper can greatly reduce the disk I/Os and highly improve the efficiency of OLAP qucrics.     

A Multiscale Approach to Automatic Medical Image Segmentation Using Self-Organizing Map

In this paper,a new medical image classification scheme is proposed using selforganizing map(SOM)combined with multiscale technique.It addresses the problem of the handling of edge pixels in the traditional multiscale SOM classifiers.First,to solve the difficulty in manual selection of edge pixels,a multiscale edge detection algorithm based on wavelet transform is proposed.Edge pixels detected are then selected into the training set as a new class and a multiscale SOM classifier is trained using this training set.In this new scheme,the SOM classifier can perform both the classification on the entire image and the edge detection simultaneously.On the other hand,the misclassification of the traditional multiscale SOM classifier in regions near edges is graeatly reduced and the correct classification is improved at the same time.     

An algorithm to compute maximal contractions for Horn clauses

In the theory of belief revision, the computation of all maximal subsets (maximal contractions) of a formula set with respect to a set of facts is one of the key problems. In this paper, we try to solve this problem by studying the algorithm to compute all maximal contractions for Horn clauses. First, we point out and prove the conversion relationship between minimal inconsistent subsets of union of the formula set and the set of facts and maximal contractions of the formula set with respect to th...