5150797
Optimal Placements of Flexible Objects: Part I: Analytical Results for the Unbounded Case
The authors consider optimal placements of two-dimensional flexible (elastic, deformable) objects. The objects are discs of equal size placed within a rigid boundary. The paper is divided into two parts. In the first part, analytical results for three types of regular, periodic arrangements-the hexagonal, square, and triangular placements-are presented. The regular arrangements are analyzed for rectangular boundaries and radii of discs that are small compared to the area of the placement region, because, in this case, the influence of boundary conditions can be neglected. This situation is called the unbounded case. They show that, for the unbounded case among the three regular placements, the type of hexagonal arrangements provides the largest number of placed units for the same deformation depth. Furthermore, it can be proved that these regular placements are not too far from the truly optimal arrangements. For example, hexagonal placements differ at most by the factor of 1.1 from the largest possible number of generally shaped units in arbitrary arrangements. These analytical results are used as guidances for testing stochastic algorithms optimizing placements of flexible objects. In the second part, mainly two problems are considered: the underlying physical model and a simulated annealing algorithm maximizing the number of flexible discs in equilibrium placements. Along with the physical model, an approximate formula is derived, reflecting the deformation/force relationship for a large range of deformations.
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.
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.
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.
15319623
Inducing Logic Programs With Genetic Algorithms: The Genetic Logic Programming System
The Genetic Logic Programming System (GLPS) combines the learning power of genetic algorithms and the knowledge representation power of logic programming to overcome the shortcomings of existing paradigms. >
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.
5284748
A novel frequency compensation technique for low-voltage low-dropout regulator
A novel frequency compensation technique for low-voltage low-dropout regulator (LDR) is presented. The proposed technique, called pole-control frequency compensation (PCFC), provides faster loop response and does not require a large filtering capacitor. Both theoretical analysis and simulation show that the stability of the LDR using PCFC is independent of the load current, temperature and equivalent series resistance of the filtering capacitor. LDRs are well suited to portable equipment such as cellular phones, pagers and personal digital assistants.
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.
9755080
A 1.2V buck converter with a novel on-chip low-voltage current-sensing scheme
A low-voltage on-chip current-sensing circuit for buck power converter is proposed. The minimum supply voltage of the proposed current-sensing circuit is 1.2V (where V/sub th/ = 0.85V at room temperature), and the sensing accuracy is higher than 94%. The proposed buck converter is able to operate at a 1.2-V supply with higher than 98% power-conversion efficiency and with a maximum output current of 120mA.