A population dynamics model to describe gene frequencies in evolutionary algorithms

The performance of evolutionary algorithms (EAs) may heavily depend severely on a suitable choice of parameters such as mutation and crossover rates. Several methods to adjust those parameters have been developed in order to enhance EA performance. For this purpose, it is important to understand the EA dynamics, i.e., to appreciate the behavior of the population. Hence, this paper presents a new model of population dynamics to describe and predict the diversity in any particular generation. The formulation is based on selecting the probability density function of each individual. The population dynamics proposed is modeled for a generational population. The model was tested in several case studies of different population sizes. The results suggest that the prediction error decreases as the population size increases.     

Parameter uncertainty and temporal dynamics of sensitivity for hydrologic models: A hybrid sequential data assimilation and probabilistic collocation method

In this study, a hybrid sequential data assimilation and probabilistic collocation (HSDAPC) approach is proposed for analyzing uncertainty propagation and parameter sensitivity of hydrologic models. In HSDAPC, the posterior probability distributions of model parameters are first estimated through a particle filter method based on streamflow discharge data. A probabilistic collocation method (PCM) is further employed to show uncertainty propagation from model parameters to model outputs. The temporal dynamics of parameter sensitivities are then generated based on the polynomial chaos expansion (PCE) generated by PCM, which can reveal the dominant model components for different catchment conditions. The maximal information coefficient (MIC) is finally employed to characterize the correlation/association between model parameter sensitivity and catchment precipitation, potential evapotranspiration and observed discharge. The proposed method is applied to the Xiangxi River located in the Three Gorges Reservoir area. The results show that: (i) the proposed HSDAPC approach can generate effective 2nd and 3rd PCE models which provide accuracy predictions; (ii) 2^nd-order PCE, which can run nearly ten time faster than the hydrologic model, can capably represent the original hydrological model to show the uncertainty propagation in a hydrologic simulation; (iii) the slow (R_s) and quick flows (R_q) in Hymod show significant sensitivities during the simulation periods but the distribution factor (α) shows a least sensitivity to model performance; (iv) the model parameter sensitivities show significant correlation with the catchment hydro-meteorological conditions, especially during the rainy period with MIC values larger than 0.5. Overall, the results in this paper indicate that uncertainty propagation and temporal sensitivities of parameters can be effectively characterized through the proposed HSDAPC approach.     

A hardware-in-the-loop dynamics simulator for motorcycle rapid controller prototyping

The purpose of this research is to develop an innovative hardware-in-the-loop simulator for the purpose of motorcycle power train rapid controller prototyping. Proposed control algorithms can be validated using the developed setup. Such an in-lab validation saves time and development cost and thus is preferable during power train controller development process. The developed simulator includes an engine, a transmission, and a rear wheel from a real motorcycle. A powder brake is rigidly coupled to the rear wheel for road loading generation. A central computer is used to control the operation of the system and the measurement of system dynamics variables. An xPC system is also integrated in the system to provide the feasibility of power train control algorithm rapid prototyping. Comparison between field tests and simulation results show that the simulator can be used to evaluate the performance of different control algorithms for controller rapid prototyping in the laboratory. An example of power train control algorithm development featuring engine fuel injection control using the above rapid prototyping setup is also described.     

A three-dimensional probabilistic fuzzy control system for network queue management

A novel probabilistic fuzzy control system is proposed to treat the congestion avoidance problem in transmission control protocol （TCP） networks. Studies on traffic measurement of TCP networks have shown that the packet traffic exhibits long range dependent properties called self-similarity, which degrades the network performance greatly. The probabilistic fuzzy control （PFC） system is used to handle the complex stochastic features of self-similar traffic and the modeling uncertainties in the network system. A three-dimensional （3-D） membership function （MF） is embedded in the PFC to express and describe the stochastic feature of network traffic. The 3-D MF has extended the traditional fuzzy planar mapping and further provides a spatial mapping among ＂fuzziness-randomness-state＂. The additional stochastic expression of 3-D MF provides the PFC an additional freedom to handle the stochastic features of self-similar traffic. Simulation experiments show that the proposed control method achieves superior performance compared to traditional control schemes in a stochastic environment.     

A probabilistic semantic model for image annotation and multi-modal image retrieval

This paper addresses automatic image annotation problem and its application to multi-modal image retrieval. The contribution of our work is three-fold. (1) We propose a probabilistic semantic model in which the visual features and the textual words are connected via a hidden layer which constitutes the semantic concepts to be discovered to explicitly exploit the synergy among the modalities. (2) The association of visual features and textual words is determined in a Bayesian framework such that the confidence of the association can be provided. (3) Extensive evaluation on a large-scale, visually and semantically diverse image collection crawled from Web is reported to evaluate the prototype system based on the model. In the proposed probabilistic model, a hidden concept layer which connects the visual feature and the word layer is discovered by fitting a generative model to the training image and annotation words through an Expectation-Maximization (EM) based iterative learning procedure. The evaluation of the prototype system on 17,000 images and 7736 automatically extracted annotation words from crawled Web pages for multi-modal image retrieval has indicated that the proposed semantic model and the developed Bayesian framework are superior to a state-of-the-art peer system in the literature.     

A unified view of class-selection with probabilistic classifiers

The possibility of selecting a subset of classes instead of one unique class for assignation is of great interest in many decision making systems. Selecting a subset of classes instead of singleton allows to reduce the error rate and to propose a reduced set to another classifier or an expert. This second step provides additional information, and therefore increases the quality of the result. In this paper, a unified view of the problem of class-selection with probabilistic classifiers is presented. The proposed framework, based on the evaluation of the probabilistic equivalence, allows to retrieve class-selective frameworks that have been proposed in the literature. We also describe an approach in which the decision rules are compared by the help of a normalized area under the error/selection curve. It allows to get a relative independence of the performance of a classifier without reject option, and thus a reliable class-selection decision rule evaluation. The power of this generic proposition is demonstrated by evaluating and comparing it to several state of the art methods on nine real world datasets, and four different probabilistic classifiers.     

Phonon dispersion measured directly from molecular dynamics simulations

A method to measure the phonon dispersion of a crystal based on molecular dynamics simulation is proposed and implemented as an extension to an open source classical molecular dynamics simulation code LAMMPS. In the proposed method, the dynamical matrix is constructed by observing the displacements of atoms during molecular dynamics simulation, making use of the fluctuation–dissipation theory. The dynamical matrix can then be employed to compute the phonon spectra by evaluating its eigenvalues. It is found that the proposed method is capable of yielding the phonon dispersion accurately, while taking into account the anharmonic effect on phonons simultaneously. The implementation is done in the style of fix of LAMMPS, which is designed to run in parallel and to exploit the functions provided by LAMMPS; the measured dynamical matrices could be passed to an auxiliary postprocessing code to evaluate the phonons.

Program summary

Program title: FixPhonon, version 1.0Catalogue identifier: AEJB_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEJB_v1_0.htmlProgram obtainable from: CPC Program Library, Queen?s University, Belfast, N. IrelandLicensing provisions: GNU General Public licenseNo. of lines in distributed program, including test data, etc.: 105 393No. of bytes in distributed program, including test data, etc.: 3 231 800Distribution format: tar.gzProgramming language: C++Computer: AllOperating system: LinuxHas the code been vectorized or parallelized?: Yes. 1 to N processors may be usedRAM: Depends on problem, ≈1 kB to several MBClassification: 7.8External routines: MPI, FFT, LAMMPS version 15, January 2010 (http://lammps.sandia.gov/)Nature of problem: Atoms in solids make ceaseless vibrations about their equilibrium positions, and a collective vibration forms a wave of allowed wavelength and amplitude. The quantum of such lattice vibration is called the phonon, and the so-called “lattice dynamics” is the field of study to find the normal modes of these vibrations. In other words, lattice dynamics examines the relationship between the frequencies of phonons and the wave vectors, i.e., the phonon dispersion. The evaluation of the phonon dispersion requires the construction of the dynamical matrix. In atomic scale modeling, the dynamical matrices are usually constructed by deriving the derivatives of the force field employed, which cannot account for the effect of temperature on phonons, with an exception of the tedious “quasi-harmonic” procedure.Solution method: We propose here a method to construct the dynamical matrix directly from molecular dynamics simulations, simply by observing the displacements of atoms in the system thus making the constructing of the dynamical matrix a straightforward task. Moreover, the anharmonic effect was taken into account in molecular dynamics simulations naturally, the resultant phonons therefore reflect the finite temperature effect simultaneously.Restrictions: A well defined lattice is necessary to employ the proposed method as well as the implemented code to evaluate the phonon dispersion. In other words, the system under study should be in solid state where atoms vibrate about their equilibrium positions. Besides, no drifting of the lattice is expected. The method is best suited for periodic systems, although non-periodic system with a supercell approach is also possible, it will however become inefficient when the unit cell contains too many atoms.Additional comments: The readers are encouraged to visit http://code.google.com/p/fix-phonon for subsequent update of the code as well as the associated postprocessing code, so as to keep up with the latest version of LAMMPS.Running time: Running time depends on the system size, the numbers of processors used, and the complexity of the force field, like a typical molecular dynamics simulation. For the third example shown in this paper, it took about 2.5 hours on an Intel Xeon X3220 architecture (2.4G, quadcore).References:

• [1] 

  C. Campañá, M.H. Müser, Phys. Rev. B 74 (2006) 075420.

• [2] 

  L.T. Kong, G. Bartels, C. Campañá, C. Denniston, M.H. Müser, Comp. Phys. Commun. 180 (6) (2009) 1004–1010.

     

Branching bisimulation for probabilistic systems: Characteristics and decidability

We address the concept of abstraction in the setting of probabilistic reactive systems, and study its formal underpinnings for the strictly alternating model of Hansson. In particular, we define the notion of branching bisimilarity and study its properties by studying two other equivalence relations, viz. coloured trace equivalence and branching bisimilarity using maximal probabilities. We show that both alternatives coincide with branching bisimilarity. The alternative characterisations have their own merits and focus on different aspects of branching bisimilarity. Coloured trace equivalence can be understood without knowledge of probability theory and is independent of the notion of a scheduler. Branching bisimilarity, rephrased in terms of maximal probabilities gives rise to an algorithm of polynomial complexity for deciding the equivalence. Together they give a better understanding of branching bisimilarity. Furthermore, we show that the notions of branching bisimilarity in the alternating model of Hansson and in the non-alternating model of Segala differ: branching bisimilarity in the latter setting turns out to discriminate between systems that are intuitively branching bisimilar.     

A population dynamics approach for the water distribution problem

The water distribution system (WDS) is composed of several elements, where flow control is one of the most important components needed in order to provide a satisfactory level of service. In order to achieve an adequate level of water in the distribution tanks, we need to dynamically control the flow. Here, we propose a population dynamics approach in order to control tanks, by allocating in them the maximum uniform volume. The feedback interconnected systems reach a stable equilibrium point for both approaches presented (i.e. replicator and supply dynamics), and more specific an asymptotically stable equilibrium point for the replicator dynamics case. The stability analysis uses some passivity concepts and classic Lyapunov theory for a closed-loop system that combines the population dynamics (controller), and the WDS (process). We show via simulations the operation under different scenarios.     

A local-motion-based probabilistic model for visual tracking

Color-based tracking is prone to failure in situations where visually similar targets are moving in a close proximity or occlude each other. To deal with the ambiguities in the visual information, we propose an additional color-independent visual model based on the target's local motion. This model is calculated from the optical flow induced by the target in consecutive images. By modifying a color-based particle filter to account for the target's local motion, the combined color/local-motion-based tracker is constructed. We compare the combined tracker to a purely color-based tracker on a challenging dataset from hand tracking, surveillance and sports. The experiments show that the proposed local-motion model largely resolves situations when the target is occluded by, or moves in front of, a visually similar object.     

A probabilistic algorithm for vertex connectivity of graphs

A probabilistic algorithm is presented which computes the vertex connectivity of an undirected graph G = (V,E) in expected time $\text{O((-log ε|V|}{}^{\mathrm{<mtext>3</mtext><mtext>2</mtext>}}\text{|E|)}$ with error probability at most e provided that |E|<frcase|1/2d|V|² for some universal constant d<1.     

Multibody system dynamics simulator for process simulation of ships and offshore plants in shipyards

Since various existing simulation tools based on multibody system dynamics focus on conventional mechanical systems, such as machinery, cars, and spacecraft, there are some problems with the application of such simulation tools to shipbuilding domains due to the absence of specific items in the field of naval architecture and ocean engineering, such as hydrostatics, hydrodynamics, and mooring forces. Thus, in this study, we developed a multibody system dynamics simulator for the process simulation of ships and offshore structures. We based the simulator on six kernels: the multibody system dynamics kernel, the force calculation kernel, the numerical analysis kernel, the hybrid simulation kernel, the scenario management kernel, and the collision detection kernel. Based on these kernels, we implemented a simulator that had the following Graphic User Interfaces (GUIs): the modeling, visualization, and report GUIs. In addition, the geometric properties of blocks and facilities in shipyards are needed to configure the simulation for the production of ships and offshore plants, so these are managed in a database and connected to a specific commercial CAD system in shipyards. We used the simulator we developed in various cases of the process simulation of ships and offshore plants. The results show that the simulator is useful for various simulations of operations in shipyards and offshore industries.     

Probabilistic filters: A stream protocol for continuous probabilistic queries

Pervasive applications, such as natural habitat monitoring and location-based services, have attracted plenty of research interest. These applications, which deploy a lot of sensor devices to collect data from external environments, often have limited network bandwidth and battery resources. The sensors also cannot record accurate values. The uncertainty of data captured by a sensor should thus be considered for query evaluation. To this end, probabilistic queries, which consider data impreciseness and provide statistical guarantees in answers, have been recently studied.     

Rollout strategy-based probabilistic causal model approach for the multiple fault diagnosis

Multiple fault diagnosis (MFD) is used as an effective measure to tackle the problems of real-shop floor environment for reducing the total lifetime maintenance cost of the system. It is a well-known computationally complex problem, where computational complexity increases exponentially as the number of faults increases. Thus, warrants the application of heuristic techniques or AI-based optimization tools to diagnose the exact faults in real time. In this research, rollout strategy-based probabilistic causal model (RSPCM) has been proposed to solve graph-based multiple fault diagnosis problems. Rollout strategy is a single-step iterative process, implemented in this research to improve the efficiency and robustness of probabilistic causal model. In RSPCM instead of finding all possible combinations of faults, collect the faults corresponding to each observed manifestations that can give the best possible result in compared to other methods. Intensive computational experiments on well-known data sets witness the superiority of the proposed heuristic over earlier approaches existing in the literature. From experimental results it can easily inferred that proposed methodology can diagnosed the exact fault in the minimum fault isolation time as compared to other approaches.     

A logical duality for underspecified probabilistic systems

This paper establishes a Stone-type duality between specifications and infLMPs. An infLMP is a probabilistic process whose transitions satisfy super-additivity instead of additivity. Interestingly, its simple structure can encode a mix of probabilistic and non-deterministic behavior, which, as we show, is strongly related to another well-known such model: probabilistic automata. Our duality puts in relation the category of infLMPs and a category of abstract representations of them based on properties only. We exhibit a Galois connection between these categories and show that we have an adjunct pair of functors when restricted to LMPs only. Our duality also shows that an infLMP can be considered as a demonic representative of a system’s information. Moreover, it carries forward a view where states are less important, and events, or properties, become the main characters, as it should be in probability theory. Along the way, we show that bisimulation and simulation are naturally interpreted in this setting, and we exhibit the interesting relationship between infLMPs and the usual probabilistic modal logics. This paper is an extended version of a Concur ’09 paper [13]; in particular, the comparison of infLMPs with probabilistic automata and the Galois connection are new.     

A probabilistic solution discovery algorithm for solving 0-1 knapsack problem

Abstract

In this paper, a probabilistic solution discovery algorithm is developed to solve the NP-hard 0-1 knapsack problem. The proposed method consists of three steps: strategy development, strategy analysis, and solution discovery. In the first step, Monte Carlo simulation is used to generate the strategies based on a vector defining the probability that each item is included in the knapsack. In the second step, we analyse the capacity imposed by each strategy previously generated and penalise the objective value for those strategies exceeding the capacity of the knapsack. At the last step, a subset of ordered strategies is used to update the vector that defines the probability of choosing each item. Two numerical examples are used to demonstrate the efficiency and the performance of the proposed method.     

Performance analysis of probabilistic timed automata using digital clocks

Probabilistic timed automata, a variant of timed automata extended with discrete probability distributions, is a modelling formalism suitable for describing formally both nondeterministic and probabilistic aspects of real-time systems, and is amenable to model checking against probabilistic timed temporal logic properties. However, the previously developed verification algorithms either suffer from high complexity, give only approximate results, or are restricted to a limited class of properties. In the case of classical (non-probabilistic) timed automata it has been shown that for a large class of real-time verification problems correctness can be established using an integral model of time (digital clocks) as opposed to a dense model of time. Based on these results we address the question of under what conditions digital clocks are sufficient for the performance analysis of probabilistic timed automata and show that this reduction is possible for an important class of systems and properties including probabilistic reachability and expected reachability. We demonstrate the utility of this approach by applying the method to the performance analysis of three probabilistic real-time protocols: the dynamic configuration protocol for IPv4 link-local addresses, the IEEE 802.11 wireless local area network protocol and the IEEE 1394 FireWire root contention protocol.

A probabilistic reasoning-based decision support system for selection of remediation technologies for petroleum-contaminated sites

Selection of remediation technologies for petroleum-contaminated sites is difficult given the large number of technologies available and inherent uncertainties involved in the selection process. In this paper, we explore the use of an inexact algorithm for probability reasoning for dealing with the uncertainties involved in the problem. By incorporating domain knowledge as well as the stochastic uncertainty, a probabilistic rule-based decision support system (PDSS) has been developed to support the decision making process. The system has been applied to two case studies, in which the best option of remediation technology can be determined according to calculated probability values. In comparison to deterministic and fuzzy decision support systems, the PDSS can provide a recommendation together with a measure on the reliability or degree to which the recommended decision can be trusted.     

Using probabilistic model checking for dynamic power management

Dynamic power management (DPM) refers to the use of runtime strategies in order to achieve a tradeoff between the performance and power consumption of a system and its components. We present an approach to analysing stochastic DPM strategies using probabilistic model checking as the formal framework. This is a novel application of probabilistic model checking to the area of system design. This approach allows us to obtain performance measures of strategies by automated analytical means without expensive simulations. Moreover, one can formally establish various probabilistically quantified properties pertaining to buffer sizes, delays, energy usage etc., for each derived strategy.Received November 2003Revised September 2004Accepted December 2004 by M. Leuschel and D. J. Cooke     

A probabilistic task scheduling method for grid environments

This paper presents a probabilistic task scheduling method to minimize the overall mean response time of the tasks submitted to the grid computing environments. Minimum mean response time of a given task can be obtained by finding a subset of appropriate computational resources to service the task. To achieve this, a discrete time Markov chain (DTMC) representing the task scheduling process within the grid environment is constructed. The connection probabilities between the nodes representing the grid managers and resources can be considered as transition probabilities of the obtained DTMC. Knowing the mean response times of the managers and resources, and finding fundamental matrix of the DTMC, the mean response time related to each of the absorbing DTMCs existing inside the overall DTMC can be computed. Minimizing the obtained mean response times and taking into account the probability constraints in each of the absorbing DTMCs, a nonlinear programming (NLP) problem is defined. Solving the NLP problem, the connection probabilities between the managers and resources are obtained. Finally, using the connection probabilities, the best scheduling path within the environment and the minimum mean response time of a particular task can be achieved. In a case in which there is only one optimal scheduling choice within the environment, the proposed method can deterministically find such scheduling by assigning zero or one to the connection probabilities. Results obtained from evaluating the proposed method on the hypothesis and real grid environments show the preference of the proposed method compared to the other methods in minimizing both the overall mean response time of the tasks and total makespan of the environment.