34094601
DNA-Based Algorithm for 0-1 Planning Problem
Biochemical reaction theory based DNA computation is of much better performance in solving a class of intractable computational problems such as NP-complete problems, it is important to study the DNA computation. A novel algorithm based on DNA computation is proposed, which solves a special category of 0-1 planning problem by using the surface-based fluorescence labeling technique. The analysis show that our new algorithm is of significant advantages such as simple encoding, low cost and short operating time, etc.
41889531
Research on Pairwise Key Establishment Model and Algorithm for Sensor Networks
Security schemes of pairwise key establishment, which enable sensors to communicate with each other securely, play a fundamental role in research on security issue in wireless sensor networks. A new kind of pairwise key pre-distribution model for sensor networks is proposed. And in addition, based on which, an efficient dynamic key path establishment algorithm is designed. Theoretic analysis and experimental figures show that the new algorithm has better performance than those previous related works.
6464793
Ringed Petersen spheres connected hypercube interconnection networks
On the basis of the short diameter of Petersen graph, and high connectivity of hypercube, a novel interconnection networks, RPSCH(n) (ringed Petersen spheres connected hypercube) is proposed, and whose characteristics are studied simultaneously. It is proved that RPSCH(n) is not only of regularity and good extensibility, but also of shorter diameter and better connectivity than those previous well-known interconnection networks such as Q/sub n/, RP(n) and HP(n). In addition, the unicasting and broadcasting routing algorithms are designed for RPSCH(n), whose communication efficiency are proved to be n-2 and n-2 respectively.
16518214
Key Distribution for Group-based Sensor Deployment Using a Novel Interconnection Graph
In this paper, we propose a pairwise key distribution scheme based on a novel interconnection graph termed Hierarchical Hypercube. Motivated by the fact that sensor nodes are often deployed in groups (for example, dropped from an airplane at different locations) and hence the whole network is composed of multiple such groups, we design Hierarchical Hypercube as a two layer topology, where each group is modeled by an inner hypercube and connections between the groups are modeled using an outer hypercube. we propose a topology termed Hierarchy Hypercube. While retaining the desirable properties already shown by existing pairwise schemes, by using Hierarchical Hypercube, direct communication from a node to any other nodes is not required, either within a group or among groups, and hence this topology can effectively and realistically reflect the connectivity of the physical sensor network deployed in groups. Furthermore, we propose key pre-distribution scheme based on this novel topology and show that the new scheme possesses high probability of direct key establishment, low memory overhead, and resilience in the presence of broken communication links and compromised nodes, and thus still retain the desirable properties even when the ideal logical connection are distorted in the real deployment.
38163613
Bayesian Learning for Image Retrieval Using Multiple Features
Image retrieval using multiple features often uses explicit weights that represent the importance of the features in their similarity metrics. In this paper, a novel retrieval method based on Bayesian Learning is presented. Instead of giving every feature a weight explicitly, the importance of a feature is regulated implicitly by learning a user's perception. Thus, the process of feature combination is adaptive and approximate to a user's perception. Experimental results demonstrate the significance of this method for improving the retrieval efficiency.
119668212
Impulsive optimal control model for the trajectory of horizontal wells
This paper presents an impulsive optimal control model for solving the optimal designing problem of the trajectory of horizontal wells. We take fully into account the effect of unknown disturbances in drilling. The optimal control problem can be converted into a nonlinear parametric optimization by integrating the state equation. We discuss here that the locally optimal solution depends in a continuous way on the parameters (disturbances) and utilize this property to propose a revised Hooke-Jeeves algorithm. The uniform design technique is incorporated into the revised Hooke-Jeeves algorithm to handle the multimodal objective function. The numerical simulation is in accordance with theoretical results. The numerical results illustrate the validity of the model and efficiency of the algorithm.
17672081
A Sub-Vector Weighting Scheme for Image Retrieval with Relevance Feedback
In image retrieval with relevance feedback, feature components are weighted to reflect the high-level concepts, and a user's subjective perception, embodied in the images labelled by the user in the feedback. However, the number of labelled images is often small and the covariance matrix needed for weighting will be singular. For this reason, the commonly used methods discard the mutual correlation among the feature components completely and use a diagonal covariance matrix. In this paper, a sub-vector weighting scheme is proposed. This scheme partitions a multi-dimensional visual feature vector into multiple low-dimensional sub-vectors. The singularity of the covariance matrix for each sub-vector can be avoided due to the lower dimensionality of the sub-vectors. Thus, the mutual correlation in each sub-vector can be retained for weighting and an optimally weighted similarity metric can be applied on each sub-vector. The similarity scores obtained from different sub-vectors are combined, as the final scor...
1252619
A fast approximate algorithm for training L1-SVMs in primal space
We propose a novel and fast algorithm to train support vector machines (SVMs) in primal space, which solves an approximate optimization of SVMs with the properties of unconstraint, continuity and twice differentiability by utilizing the Newton optimization technique. Further, we devise a special pre-extracting procedure to speed up the convergence of the algorithm by resorting to a high-quality initial solution. Theoretical studies show that the proposed algorithm produces an @?-approximate solution to standard SVMs and maintains low computational complexity. Experimental results on benchmark data sets demonstrate that our algorithm is much faster than the dual based method such as SVM^l^i^g^h^t while it achieves the similar test accuracy.
205393463
A new algorithm for solving classical Blasius equation
In this paper, a reliable algorithm is employed to investigate the classical Blasius equation. The algorithm is based mainly on applying the Adomian decomposition method to the transformation of the Blasius equation. The results demonstrate reliability and efficiency of the proposed algorithm.