31999867
A Novel Classifier with the Immune-Training Based Wavelet Neural Network
After analyzing the classification and training ability of a wavelet neural network (WNN), a novel WNN learning scheme integrating immunity based evolutionary algorithm (IDEA) is proposed, in which, IDEA is an evolutionary algorithm with an embedded immune mechanism. When WNN is used as a classifier, the process of seeking the least mean-square error (LMS) of an optimal problem is equivalent to that of finding the wavelet feature with maximal separability, namely, maximizing its separable division. On the other hand, with the capability of robust learning of its evolutionary process, IDEA is able to eliminate local degenerative phenomenon due to blindfold behaviors of original operators in the existing evolutionary algorithms. In the case of the twin-spiral problem, experimental simulation shows the feasibility of WNN training with the IDEA based learning algorithm.
31834649
A Novel Artificial Immune Network Model and Analysis on Its Dynamic Behavior and Stabilities
A novel model of artificial immune network is presented at first, and then a simulative research work is made on its dynamic behaviors. Simulation results show that the limit cycle and chaos may exist simultaneously when four units are in connection, and the network's characteristic has a close relationship with the intensity of suppressor T-cell's function, B-cell's characteristics and transconductance. Besides this, with Liapunov's method, the sufficient conditions for network's stability is studied, especially for the case of system's characteristics under the condition that the helper T-cells appear as a nonlinear function.
10512188
An immune neural network used for classification
Based on an analysis of immune phenomena in nature and utilizing performances of ANN, a novel network mode, i.e., an immune neural network (INN), is proposed which integrates the immune mechanism and the function of neural information processing. The learning algorithm of an INN selects an excitation function and adaptive algorithm of the network. This model makes it easy for a user to utilize directly the characteristic information of a problem and to simplify the original structure by adjusting the excitation function with prior knowledge, improving the working efficiency and searching accuracy. A theoretical analysis and a simulation test for the twin-spiral problem show that, compared with an artificial neural network, INN is not only effective but also feasible. INN can simplify the structure of the existent model and show good working performance.
3194016
A Novel Model of Artificial Immune Network and Simulations on Its Dynamics
A novel model of artificial immune network is presented at first, and then a simulative research work is made on its dynamic behaviors. In this model, a B cell makes a key role that takes antigens in, so as to generate antibodies as its outputs. Under five different kinds of adjustment by suppressor T cells, number of antibodies will keep to a certain degree through influencing the B cell’s activation. On the other hand, with help T cells, different B cells could cooperate from each other, which makes the system’s dynamic behavior appear more complex, such as phenomena of limit cycle, chaos, etc. Simulative results show that limit cycle and chaos may exist simultaneously when four units are in connection, and the network’s characteristic has a close relationship with the intensity of suppressor T cell’s function.
16539998
Lossy to lossless image compression based on reversible integer DCT
A progressive image compression scheme is investigated using reversible integer discrete cosine transform (RDCT) which is derived from the matrix factorization theory. Previous techniques based on DCT suffer from bad performance in lossy image compression compared with wavelet image codec. And lossless compression methods such as IntDCT, I2I-DCT and so on could not compare with JPEG-LS or integer discrete wavelet transform (DWT) based codec. In this paper, lossy to lossless image compression can be implemented by our proposed scheme which consists of RDCT, coefficients reorganization, bit plane encoding, and reversible integer pre- and post-filters. Simulation results show that our method is competitive against JPEG-LS and JPEG2000 in lossless compression. Moreover, our method outperforms JPEG2000 (reversible 5/3 filter) for lossy compression, and the performance is even comparable with JPEG2000 which adopted irreversible 9/7 floating-point filter (9/7F filter).
15857389
A novel genetic algorithm based on immunity
A novel algorithm, the immune genetic algorithm (IGA), is proposed based on the theory of immunity in biology which mainly constructs an immune operator accomplished by two steps: 1) a vaccination and 2) an immune selection. IGA proves theoretically convergent with probability 1. Strategies and methods of selecting vaccines and constructing an immune operator are also given. IGA is illustrated to be able to restrain the degenerate phenomenon effectively during the evolutionary process with examples of TSP, and can improve the searching ability and adaptability, greatly increase the convergence rate.
16050541
Energy-efficiency bounds for deep submicron VLSI systems in the presence of noise
In this paper, we present an algorithm for computing the bounds on energy-efficiency of digital very large scale integration (VLSI) systems in the presence of deep submicron noise. The proposed algorithm is based on a soft-decision channel model of noisy VLSI systems and employs information-theoretic arguments. Bounds on energy-efficiency are computed for multimodule systems, static gates, dynamic circuits and noise-tolerant dynamic circuits in 0.25-/spl mu/m CMOS technology. As the complexity of the proposed algorithm grows linearly with the size of the system, it is suitable for computing the bounds on energy-efficiency for complex VLSI systems. A key result presented is that noise-tolerant dynamic circuits offer the best trade off between energy-efficiency and noise-immunity when compared to static and domino circuits. Furthermore, employing a 16-bit noise-tolerant Manchester adder in a CDMA receiver, we demonstrate a 31.2%-51.4% energy reduction over conventional systems when operating in the presence of noise. In addition, we compute the lower bounds on energy dissipation for this CDMA receiver and show that these lower bounds are 2.8/spl times/ below the actual energy consumed, and that noise-tolerance reduces the gap between the lower bounds and actual energy dissipation by a factor of 1.9/spl times/.
14600536
An Approach to Provisioning E-Commerce Applications with Commercial Components
Component-based development is a trend towards building e-commerce applications. However, commercial components are rarely used during the development. The reason is that existing approaches to selecting and composing components suffer from the problem that the components retrieved usually do not exactly fit with other components in the system being developed. While formal methods can be used to describe and check semantic characteristics to better match components, there are practical limitations which restrict their adoption. We have proposed a framework to support a semantic description and selection of components. We used Simple Component Interface Language (SCIL) to describe user requirements and pre-built components from the current component sources. Specifications in SCIL can be translated to a variety of models including those that have a formal basis. In this paper, we perform a case study of searching commercial components for a generic e-commerce application. We specify the commercial components in SCIL and use two specific tools: jMocha and Alloy Analyser to identify the correct components that suit a particular task
5641652
Image orientation detection with integrated human perception cues (or which way is up)
In this paper, we propose a set of human perceptual cues used jointly to automatically detect image orientation. The cues used are: orientation of faces, position of the sky, brighter regions, and textured objects, and symmetry. We combine these cues in a Bayesian framework, and the photo acquiring model has been considered carefully as the prior knowledge of the image orientation. Results on more than a thousand different images provide a compelling argument that our approach is a viable one.
15237813
Research on an Immune Mechanism Based Intelligent Spam Filter
With regard to the problem that the traditional antispam filter is usually incapable of recognizing unknown and mutational characters, a novel anti-spam filter based on immune mechanisms is proposed which employs multiple principles and mechanisms of the artificial immune system, such as the self tolerance, immune recognition, immune learning and immune memory. Through combining the idea of intelligent decision support system with the extension of classical rough set theory, a novel algorithm is provided for finding minimal attributes extraction and updating the attributes base. Simulation shows that this technique is able to reduce and improve the availability of the normal anti-spam systems.
14541123
An evolutionary algorithm with population immunity and its application on autonomous robot control
The natural immune system is an important resource full of inspirations for the theory researchers and the engineering developers to design some powerful information processing methods aiming at difficult problems. Based on this consideration, a novel optimal-searching algorithm, the immune mechanism based evolutionary algorithm - IMEA, is proposed for the purpose of finding an optimal/quasi-optimal solution in a multi-dimensional space. Different from the ordinary evolutionary algorithms, on one hand, due to the long-term memory, IMEA has a better capability of learning from its experience, and on the other hand, with the clonal selection, it is able to keep from the premature convergence of population. With the simulation on autonomous robot control, it is proved that IMEA is good at the task of adaptive adjustment (offline), and it can improve the robot's capability of reinforcement learning, so as to make itself able to sense its surrounding dynamic environment.