19656381
Target Recognition of FLIR Images on Radial Basis Function Neural Network
The study of small target recognition in low SNR (Signal Noise Ratio) is the key problem about processing of forward-looking infrared (FLIR) images information. Eight features of objects based on IR radiation characteristics and wavelet-based are presented. These features are used to a radial basis function (RBF) network as input for learning and classification. The propose recognition algorithm is invariant to the translation, rotation, and scale channel of a shape. Experiments by real infrared images and noisy images are performed, and recognition results show that the method is very effective.
38431053
An Optimization Method Based on Chaotic Immune Evolutionary Algorithm
Immune Evolutionary Algorithm (IEA) is proposed on the shortages of evolution algorithm and biological immune mechanism. According to the characteristics of chaos, a novel Chaotic Immune Evolutionary Algorithm (CIEA) is presented which introduces chaos to IEA. The algorithm has the merits of chaos, immunity and evolutionary algorithm. It can ensure the ability of global search and local search and enhance the performances of the algorithm. At last, we analyze the efficiency of the algorithm with two typical optimization problems. The analysis result shows that CIEA converges quickly and effectively avoids the inherent problem that the evolution algorithm traps in immature convergence, so CIEA is an effective way to solve complex optimization problem.
28565174
Trust Evolution Policies for Security in Collaborative Ad Hoc Applications
The vision of pervasive computing has introduced the notion of a vast, networked infrastructure of heterogeneous entities interact through collaborative applications, e.g., playing a multi-player online game on the way to work. This will require interactions between users who may be marginally known or completely unknown to each other, or in situations where complete information is unavailable. This introduces the problem of assigning access rights to such marginally known or unknown entities. Explicit trust management has emerged as a solution to the problem of dealing with partial information about other users and the context in which the interaction takes place. We have implemented an access control mechanism based on the human notion of trust, where recommendations or initial participation in low risk interactions will allow entities to slowly build trust in each other. As the trust between two entities grows, interactions that entail a higher degree of risk may be allowed to proceed. We have used this mechanism in a simple role-based access control mechanism that uses trust to assign roles to users in a distributed blackjack card game application. This application allows us to experiment with different policies for trust-based admission control and trust evolution. In this paper we present an evaluation of policies specifying trust dynamics, which shows that our prototype reacts appropriately to the behaviour of other users and that the system updates trust values and implements admission policies in a manner similar to what would be expected from human trust assessment. This indicates that trust evolution policies can replace explicit human intervention in application scenarios that are similar to the evaluated prototype.
37352290
Comparison of Two Fitness Functions for GA-Based Path-Oriented Test Data Generation
Automatic path-oriented test data generation is not only a crucial problem but also a hot issue in the research area of software testing today. As a robust metaheuritstic search method in complex spaces, genetic algorithm (GA) has been used to path-oriented test data generation since 1992 and outperforms other approaches. A fitness function based on branch distance (BDBFF) and another based on normalized extended Hamming distance (SIMILARITY) are both applied in GA-based path-oriented test data generation. To compare performance of these two fitness functions, a triangle classification program was chosen as the example. Experimental results show that BDBFF-based approach can generate path-oriented test data more effectively and efficiently than SIMILARITY- based approach does.
43007315
The impact of smart disks and spatial reuse property on RAID-5 storage systems
RAID storage systems using SCSI and traditional disk drives cannot meet the growing demand of new I/O intensive applications. New technologies such as smart disks and SSA are being proposed to improve the performance, scalability, and reliability of RAID systems. The objective of this work is to investigate the impact of smart disks and SSA's spatial reuse property on the performance of RAID-5 storage systems. In order to perform this work, we have enhanced CMU's RAIDframe simulator so that it accurately models the overhead of current SCSI and SSA protocols, and we have created smart disk models which include on-drive XOR logic and on-drive caches. We observe that neither smart disks alone nor spatial reuse property alone has much impact on RAID system performance. However, if smart disks and spatial reuse property are properly used in combination, RAID-5 system performance and scalability can be further enhanced. In particular, we propose a parity caching approach to improve the performance of small writes.
28086202
A new elliptic equation rational expansion method and its application to the shallow long wave approximate equations
A new elliptic equation rational expansion method is presented by a new general ansatz, which is a direct and unified algebraic method for constructing multiple and more general travelling wave solution for nonlinear partial differential equation and implemented in a computer algebraic system. The proposed method is applied to consider the shallow long wave approximate equation and obtains rich new families of the exact solutions, including rational form solitary wave, rational form triangular periodic, rational form Jacobi and Weierstrass doubly periodic solutions.
15834568
An Artificial Neural Network Based on CIEA
Chaotic Immune evolutionary algorithm (CIEA) is proposed on the shortage of current algorithms training artificial neural network, biological immune mechanism and the characteristics of evolution. A novel artificial neural network based on chaotic immune evolutionary algorithm (CIEANN) is presented in this paper. The algorithm has the merits of chaos, immunity and evolutionary algorithm. It can ensure the ability of global search and local search and enhance the performances of the algorithm. At last, we analyzed the performance of CIEANN with a typical XOR problem and compared it with several common ANNs. The analysis results show that CIEANN converges quickly and avoids immature convergence and is an effective way to solve optimization problem.