A review of wildland fire spread modelling, 1990-present 3: Mathematical analogues and simulation models

In recent years, advances in computational power and spatial data analysis (GIS, remote sensing, etc) have led to an increase in attempts to model the spread and behvaiour of wildland fires across the landscape. This series of review papers endeavours to critically and comprehensively review all types of surface fire spread models developed since 1990. This paper reviews models of a simulation or mathematical analogue nature. Most simulation models are implementations of existing empirical or quasi-empirical models and their primary function is to convert these generally one dimensional models to two dimensions and then propagate a fire perimeter across a modelled landscape. Mathematical analogue models are those that are based on some mathematical conceit (rather than a physical representation of fire spread) that coincidentally simulates the spread of fire. Other papers in the series review models of an physical or quasi-physical nature and empirical or quasi-empirical nature. Many models are extensions or refinements of models developed before 1990. Where this is the case, these models are also discussed but much less comprehensively.Comment: 20 pages + 9 pages references + 1 page figures. Submitted to the International Journal of Wildland Fir

Workshop on Verification and Theorem Proving for Continuous Systems (NetCA Workshop 2005)

Oxford, UK, 26 August 200

IST Austria Technical Report

As hybrid systems involve continuous behaviors, they should be evaluated by quantitative methods, rather than qualitative methods. In this paper we adapt a quantitative framework, called model measuring, to the hybrid systems domain. The model-measuring problem asks, given a model M and a specification, what is the maximal distance such that all models within that distance from M satisfy (or violate) the specification. A distance function on models is given as part of the input of the problem. Distances, especially related to continuous behaviors are more natural in the hybrid case than the discrete case. We are interested in distances represented by monotonic hybrid automata, a hybrid counterpart of (discrete) weighted automata, whose recognized timed languages are monotone (w.r.t. inclusion) in the values of parameters.The contributions of this paper are twofold. First, we give sufficient conditions under which the model-measuring problem can be solved. Second, we discuss the modeling of distances and applications of the model-measuring problem

Simulating Land Use Land Cover Change Using Data Mining and Machine Learning Algorithms

The objectives of this dissertation are to: (1) review the breadth and depth of land use land cover (LUCC) issues that are being addressed by the land change science community by discussing how an existing model, Purdue\u27s Land Transformation Model (LTM), has been used to better understand these very important issues; (2) summarize the current state-of-the-art in LUCC modeling in an attempt to provide a context for the advances in LUCC modeling presented here; (3) use a variety of statistical, data mining and machine learning algorithms to model single LUCC transitions in diverse regions of the world (e.g. United States and Africa) in order to determine which tools are most effective in modeling common LUCC patterns that are nonlinear; (4) develop new techniques for modeling multiple class (MC) transitions at the same time using existing LUCC models as these models are rare and in great demand; (5) reconfigure the existing LTM for urban growth boundary (UGB) simulation because UGB modeling has been ignored by the LUCC modeling community, and (6) compare two rule based models for urban growth boundary simulation for use in UGB land use planning. The review of LTM applications during the last decade indicates that a model like the LTM has addressed a majority of land change science issues although it has not explicitly been used to study terrestrial biodiversity issues. The review of the existing LUCC models indicates that there is no unique typology to differentiate between LUCC model structures and no models exist for UGB. Simulations designed to compare multiple models show that ANN-based LTM results are similar to Multivariate Adaptive Regression Spline (MARS)-based models and both ANN and MARS-based models outperform Classification and Regression Tree (CART)-based models for modeling single LULC transition; however, for modeling MC, an ANN-based LTM-MC is similar in goodness of fit to CART and both models outperform MARS in different regions of the world. In simulations across three regions (two in United States and one in Africa), the LTM had better goodness of fit measures while the outcome of CART and MARS were more interpretable and understandable than the ANN-based LTM. Modeling MC LUCC require the examination of several class separation rules and is thus more complicated than single LULC transition modeling; more research is clearly needed in this area. One of the greatest challenges identified with MC modeling is evaluating error distributions and map accuracies for multiple classes. A modified ANN-based LTM and a simple rule based UGBM outperformed a null model in all cardinal directions. For UGBM model to be useful for planning, other factors need to be considered including a separate routine that would determine urban quantity over time

Qualitative Fault Isolation of Hybrid Systems: A Structural Model Decomposition-Based Approach

Quick and robust fault diagnosis is critical to ensuring safe operation of complex engineering systems. A large number of techniques are available to provide fault diagnosis in systems with continuous dynamics. However, many systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete behavioral modes, each with its own continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task computationally more complex due to the large number of possible system modes and the existence of autonomous mode transitions. This paper presents a qualitative fault isolation framework for hybrid systems based on structural model decomposition. The fault isolation is performed by analyzing the qualitative information of the residual deviations. However, in hybrid systems this process becomes complex due to possible existence of observation delays, which can cause observed deviations to be inconsistent with the expected deviations for the current mode in the system. The great advantage of structural model decomposition is that (i) it allows to design residuals that respond to only a subset of the faults, and (ii) every time a mode change occurs, only a subset of the residuals will need to be reconfigured, thus reducing the complexity of the reasoning process for isolation purposes. To demonstrate and test the validity of our approach, we use an electric circuit simulation as the case study

Computer Aided Verification

This open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency

Beyond Moore's technologies: operation principles of a superconductor alternative

The predictions of Moore's law are considered by experts to be valid until 2020 giving rise to "post-Moore's" technologies afterwards. Energy efficiency is one of the major challenges in high-performance computing that should be answered. Superconductor digital technology is a promising post-Moore's alternative for the development of supercomputers. In this paper, we consider operation principles of an energy-efficient superconductor logic and memory circuits with a short retrospective review of their evolution. We analyze their shortcomings in respect to computer circuits design. Possible ways of further research are outlined.Comment: OPEN ACCES

Evolutionary Optimization of ZIP60: A Controlled Explosion in Hyperspace

The “ZIP” adaptive trading algorithm has been demonstrated to out-perform human traders in experimental studies of continuous double auction (CDA) markets. The original ZIP algorithm requires the values of eight control parameters to be set correctly. A new extension of the ZIP algorithm, called ZIP60, requires the values of 60 parameters to be set correctly. ZIP60 is shown here to produce significantly better results than the original ZIP (called “ZIP8” hereafter), for negligable additional computational costs. A genetic algorithm (GA) is used to search the 60-dimensional ZIP60 parameter space, and it finds parameter vectors that yield ZIP60 traders with mean scores significantly better than those of ZIP8s. This paper shows that the optimizing evolutionary search works best when the GA itself controls the dimensionality of the search-space, so that the search commences in an 8-d space and thereafter the dimensionality of the search-space is gradually increased by the GA until it is exploring a 60-d space. Furthermore, the results from ZIP60 cast some doubt on prior ZIP8 results concerning the evolution of new ‘hybrid’ auction mechanisms that appeared to be better than the CDA

Predicting vertical urban growth using genetic evolutionary algorithms in Tokyo’s minato ward

[Abstract] This article explores the use of evolutionary genetic algorithms to predict scenarios of urban vertical growth in large urban centers. Tokyo’s Minato Ward is used as a case study because it has been one of the fastest growing skylines over the last 20 years. This study uses a genetic algorithm that simulates the vertical urban growth of Minato Ward to make predictions from pre-established inputted parameters. The algorithm estimates not only the number of future high-rise buildings but also the specific areas in the ward that are more likely to accommodate new high-rise developments in the future. The evolutionary model results are compared with ongoing high-rise developments in order to evaluate the accuracy of the genetic algorithm in simulating future vertical urban growth. The results of this study show that the use of genetic evolutionary computation is a promising way to predict scenarios of vertical urban growth in terms of location as well as the number of future buildings