39116801
Integration on finite sets
Various types of integrals with respect to signed fuzzy measures on finite sets with cardinality n can be presented as corresponding rules for partitioning the integrand. The partition can be expressed as an n-dimensional vector, whereas the signed fuzzy measure is also an n-dimensional vector. Thus, the integration value is the inner product of these two vectors. Two pairs of extremes, the Lebesgue-like integral versus the Choquet integral and the upper integral versus the lower integral, are discussed in detail. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 1073–1092, 2006.
18141920
Learning functional dependency networks based on genetic programming
Bayesian Network (BN) is a powerful network model, which represents a set of variables in the domain and provides the probabilistic relationships among them. But BN can handle discrete values only; it cannot handle continuous, interval and ordinal ones, which must be converted to discrete values and the order information is lost. Thus, BN tends to have higher network complexity and lower understandability. In this paper, we present a novel dependency network which can handle discrete, continuous, interval and ordinal values through functions; it has lower network complexity and stronger expressive power; it can represent any kind of relationships; and it can incorporate a-priori knowledge though user-defined functions. We also propose a novel Genetic Programming (GP) to learn dependency networks. The novel GP does not use any knowledge-guided nor application-oriented operator, thus it is robust and easy to replicate. The experimental results demonstrate that the novel GP can successfully discover the target novel dependency networks, which have the highest accuracy and the lowest network complexity.
18388949
A probabilistic cooperative-competitive hierarchical model for global optimization
Abstract Stochastic searching methods have been applied widely to areas such as continuous and combinatorial optimization problems in a number of disciplines. Many existing methods solve these problems by navigating on the surface of the possibly rugged landscape. This kind of navigation is not very effective because the property of the landscape at different resolutions can be very different. Time spent at the beginning of the search on the detailed part of the landscape is often useless. Appropriate searching strategies should be adopted at different resolutions. In this paper, we propose a new probabilistic searching model for global optimization. The main contributions of the model are (1) to provide a basis for resolution control and smoothing of search space and (2) to introduce continuous memory into stochastic search. The basis of resolution control is achieved by dividing the search space into a finite number of n -dimensional partitions structurally. The number of partitions governs the resolution of the search space. The more the partitions, the finer is the search space and the more detailed and rugged is the landscape. The benefits are twofold. First, the rugged landscape problem can be smoothed, because the ruggedness is a matter of the number of partitions. Hence, the difficulty in search due to the ruggedness of the landscape can be controlled. Second, it provides a basis to implement algorithms that may change the ‘view’ of the landscape during the search process because we can dynamically divide the search space accordingly. Another important feature that we use is continuous memory . Throughout the search process, searching experience is continuously accumulated in order to shape the global picture of the search space guiding the future searching direction. We present results on the algorithm performance in handling numerical function optimization. The empirical results show that our new model is comparable to, and in many cases performs better than, that of the other advanced methods in terms of solution quality and computation required.
6017611
Balancing Samples' Contributions on GA Learning
A main branch in Evolutionary Computation is learning a system directly from input/output samples without investigating thternal behaviors of the system. Input/output samples captured from a real system are usually incomplete, biased and noisy. In order to evolve a precise system, the sample set should include a complete set of samples. Thus, a large number of samples should be used. Fitness functions being used in Evohitionary Algorithms usually based on the matched ratio of samples. Unfortunately, some of these samples may be exactly or semantically duplicated. These duplicated samples cannot be identified simply because we do not know the internal behavior of the system being evolved. This paper proposes a method to overcome this problem by using a dynamic fitness function that incorporates the contribution of each sample in the evolutionary process. Experiments on evolving Finite State Machines with Genetic Algorithms are presented to demonstrate the effect on improving the successful rate and convergent speed of the proposed method.
17795196
Scalable model-based clustering by working on data summaries
The scalability problem in data mining involves the development of methods for handling large databases with limited computational resources. We present a two-phase scalable model-based clustering framework: first, a large data set is summed up into subclusters; Then, clusters are directly generated from the summary statistics of subclusters by a specifically designed expectation-maximization (EM) algorithm. Taking example for Gaussian mixture models, we establish a provably convergent EM algorithm, EMADS, which embodies cardinality, mean, and covariance information of each subcluster explicitly. Combining with different data summarization procedures, EMADS is used to construct two clustering systems: gEMADS and bEMADS. The experimental results demonstrate that they run several orders of magnitude faster than the classic EM algorithm with little loss of accuracy. They generate significantly better results than other model-based clustering systems using similar computational resources.
18293423
Using Evolutionary Programming and Minimum Description Length Principle for Data Mining of Bayesian Networks
We have developed a new approach to learning Bayesian network structures based on the minimum description length (MDL) principle and evolutionary programming. It employs a MDL metric, which is founded on information theory, and integrates a knowledge-guided genetic operator for the optimization in the search process.
28941391
Optimum nested Miller compensation for low-voltage low-power CMOS amplifier design
An optimum structure of nested Miller compensation (NMC) is developed. The developed structure using a feedforward transconductance stage and null resistor on NMC, called NMCFNR, solves the bandwidth reduction and poor transient response problems of low-voltage low-power NMC amplifiers. For comparison, amplifiers compensated by NMC and NMCFNR have been fabricated. Experimental results show that NMCFNR improves the gain-bandwidth product, slew rate and settling time by more than three times with 8/spl deg/ increase on the phase margin and no increase of the power consumption. Moreover, it improves the negative power supply rejection ratio by at least 54 dB. The die area of the NMCFNR amplifier is about half of that of the NMC amplifier.
43168296
Traffic models for wireless communication networks
The authors introduce a deterministic fluid model and two stochastic traffic models for wireless networks. The setting is a highway with multiple entrances and exits. Vehicles are classified as calling or non-calling, depending on whether they have calls in progress. The deterministic model ignores the behavior of individual vehicles and treats them as a continuous fluid, whereas the stochastic traffic models consider the random behavior of each vehicle. However, all three model's use the same two coupled partial (or ordinary) differential equations to describe the system evolution. The call density and call handoff rate (or their expected values in the stochastic models) are readily computable by solving these equations. Numerical examples are presented to illustrate how the models can be used to investigate various aspects of time and space dynamics in wireless networks. These examples also show that the models can serve as useful tools for system engineering and planning.<<ETX>>
1231926
A Kalman-filter method for power control in broadband wireless networks
A Kalman-filter method for power control is proposed for broadband, packet-switched TDMA wireless networks. By observing the temporal correlation of cochannel interference when transmitters can send data contiguously, a Kalman filter is used to predict interference power in the future. Based on the predicted interference and estimated path gain between the transmitter and receiver, the transmission power is determined to achieve a desired signal-to-interference-plus-noise ratio (SINR). Performance results reveal that the Kalman-filter method for power control provides a significant performance improvement. Specifically, when a message consists of 10 packets on average, the 90 and 95 percentile of the SINR by the new method are 3.94 and 5.53 dB above those when no power control is in use, and lie just 0.73 and 1.04 dB below the upper-bound performance of the optimal power control, respectively, in a system with 4-sector cells and an interleaved frequency assignment of a reuse factor of 2/8. As a by-product, these results show that the cell layout and assignment scheme combined with the new method for power control can be used to support high-speed data services.
15996223
A low-voltage CMOS low-dropout regulator with enhanced loop response
A 1.5-V 100-mA CMOS low-dropout regulator with a double pole-zero cancellation scheme and a linearly operated power PMOS transistor at dropout to enhance the loop-gain response, is presented. The circuit realization is well-studied and developed with respect to the loop-gain response, the transient response, the output noise and the output accuracy, as well as the standby power consumption. Implemented in a 0.6 /spl mu/m CMOS process, experimental results show that the regulator provides a full load transient response of less than 1-/spl mu/s settling time and less than 50-mV overshoots and undershoots. Moreover, it provides a ripple rejection of better than -26 dB and an output noise of 0.07 /spl mu/V/sqrt Hz at 100 kHz.
320235
Handling signature purposes in workflow systems
Abstract In paper-based workflow systems, signatures of individuals or groups of people have been used extensively for different purposes. Currently there are numerous studies on computerizing workflow systems. Also there are studies on implementing digital signatures in electronic media. But the diversified purposes of a signature in workflow makes a straightforward implementation of digital signature schemes inadequate. There are few studies on the implication of different signature purposes on electronic workflow systems. The purposes of signature are closely associated with the two modes of decision making, namely single and group. These two modes of decision making in turn lead to single and group signatures. This paper reports our comprehensive studies on signature purposes. These include the analysis of common signature purposes in workflow, classification of these purposes, classification of modes of decision making associated with these signature purposes, signing and validation requirements for handling these signature purposes, and finally an architecture to be incorporated in workflow engines for handling these signature purposes. In summary, this paper addresses a commonly neglected problem in information systems research: management control by signature in workflow.