A case study of scheduling storage tanks using a hybrid geneticalgorithm

This paper proposes the application of a hybrid genetic algorithm (GA) for scheduling storage tanks. The proposed approach integrates GAs and heuristic rule-based techniques, decomposing the complex mixed-integer optimization problem into integer and real-number subproblems. The GA string considers the integer problem and the heuristic approach solves the real-number problems within the GA framework. The algorithm is demonstrated for three test scenarios of a water treatment facility at a port and has been found to be robust and to give a significantly better schedule than those generated using a random search and a heuristic-based approach     

Large-scale,high-resolution agricultural systems modeling using a hybrid approach combining grid computing and parallel processing

The solution of complex global challenges in the land system, such as food and energy security, requires information on the management of agricultural systems at a high spatial and temporal resolution over continental or global extents. However, computing capacity remains a barrier to large-scale, high-resolution agricultural modeling. To model wheat production, soil carbon, and nitrogen dynamics in Australia's cropping regions at a high resolution, we developed a hybrid computing approach combining parallel processing and grid computing. The hybrid approach distributes tasks across a heterogeneous grid computing pool and fully utilizes all the resources of computers within the pool. We simulated 325 management scenarios (nitrogen application rates and stubble management) at a daily time step over 122 years, for 12,707 climate–soil zones using the Windows-based Agricultural Production Systems SIMulator (APSIM). These simulations would have taken over 30 years on a single computer. Our hybrid high performance computing (HPC) approach completed the modeling within 10.5 days—a speed-up of over 1000 times—with most jobs finishing within the first few days. The approach utilizes existing idle organization-wide computing resources and eliminates the need to translate Windows-based models to other operating systems for implementation on computing clusters. There are however, numerous computing challenges that need to be addressed for the effective use of these techniques and there remain several potential areas for further performance improvement. The results demonstrate the effectiveness of the approach in making high-resolution modeling of agricultural systems possible over continental and global scales.     

A Bayesian approach to identification of hybrid systems

In this paper, we present a novel procedure for the identification of hybrid systems in the class of piecewise ARX systems. The presented method facilitates the use of available a priori knowledge on the system to be identified, but can also be used as a black-box method. We treat the unknown parameters as random variables, described by their probability density functions. The identification problem is posed as the problem of computing the a posteriori probability density function of the model parameters, and subsequently relaxed until a practically implementable method is obtained. A particle filtering method is used for a numerical implementation of the proposed procedure. A modified version of the multicategory robust linear programming classification procedure, which uses the information derived in the previous steps of the identification algorithm, is used for estimating the partition of the piecewise ARX map. The proposed procedure is applied for the identification of a component placement process in pick-and-place machines.     

A structural approach to detectability for a class of hybrid systems

We address detectability of linear switching systems. We show that detectability of a linear switching system reduces to asymptotic stability of a suitable switching system with guards extracted from it. A condition for checking asymptotic stability of linear switching systems with guards is also derived.     

A formal approach to modeling office information systems

This paper formally specifies a document model for office information systems, including formal definitions of document types (frame templates), a document type hierarchy, folders, and folder organizations. Folder Organizations are defined using predicates and directed graphs. AReconstruction Problem for folder organizations is then formulated; viz., under what circumstances it is possible to reconstruct a folder organization from its folder level predicates. The Reconstruction Problem is solved in terms of such graph-theoretic concepts as Associated Digraphs, transitive closure, and redundant/nonredundant filing paths. A Transitive Closure Inversion algorithm is then presented which efficiently recovers a Folder Organization digraph from its Associated Digraph.This work was supported in part by the National Science Foundation under Grant No. IRI-9224602, by the New Jersey Institute of Technology undre Grant No. 421280 and by a grant from AT&T Foundation.     

A probabilistic fuzzy approach to modeling nonlinear systems

Recently, the study of incorporating probability theory and fuzzy logic has received much interest. To endow the traditional fuzzy rule-based systems (FRBs) with probabilistic features to handle randomness, this paper presents a probabilistic fuzzy neural network (ProFNN) by introducing the probability of input linguistic terms and providing linguistic meaning into the connectionist architecture. ProFNN integrates the probabilistic information of fuzzy rules into the antecedent parts and quantifies the impacts of the rules on the consequent parts using mutual subsethood, which work in conjunction with volume defuzzification in a gradient descent learning frame work. Despite the increase in the number of parameters, ProFNN provides a promising solution to deal with randomness and fuzziness in a single frame. To evaluate the performance and applicability of the proposed approach, ProFNN is carried out on various benchmarking problems and compared with other existing models with a performance better than most of them.     

A digital twin emulator of a modular production system using a data-driven hybrid modeling and simulation approach

Virtual commissioning is a key technology in Industry 4.0 that can address issues faced by engineers during early design phases. The process of virtual commissioning involves the creation of a Digital Twin—a dynamic, virtual representation of a corresponding physical system. The digital twin model can be used for testing and verifying the control system in a simulated virtual environment to achieve rapid set-up and optimization prior to physical commissioning. Additionally, the modular production control systems, can be integrated and tested during or prior to the construction of the physical system. This paper describes the implementation of a digital twin emulator of an automated mechatronic modular production system that is linked with the running programmable logic controllers and allow for exchanging near real-time information with the physical system. The development and deployment of the digital twin emulator involves a novel hybrid simulation- and data-driven modeling approach that combines Discrete Event Simulation and Agent Based Modeling paradigms. The Digital Twin Emulator can support design decisions, test what-if system configurations, verify and validate the actual behavior of the complete system off-line, test realistic reactions, and provide statistics on the system’s performance.

     

A computational approach to synthesizing guards for hybrid systems

We propose a technique for synthesizing switching guards for hybrid systems to satisfy a given state-based safety constraint. Using techniques from sum of squares (SOS) optimization, we design guards defined by semialgebraic sets that trigger mode switches, and we guarantee that the synthesized switching policy does not allow Zeno executions. We demonstrate our approach on an example of switched affine systems and on an application to traffic ramp metering.     

A new approach to mathematical modeling of host-parasite systems

We propose a new approach to mathematical modeling of host-parasite systems by using partial differential equations where the degree of parasitism in a host is represented by a continuous variable p. This contrasts with the standard approach found in the literature of using a countable number of ordinary differential equations, one for each nonnegative integer, corresponding to the class of hosts having exactly that number of parasites. The new model bears some similarity with size-structured models of population dynamics. We specialize the model to a specific pair of helminth macroparasites infecting sea bass. We show that the model is well posed and we study its asymptotic behavior. Finally, we present results from some simulations.     

A genetic approach to modeling fuzzy systems based on information granulation and successive generation-based evolution method

In this paper, we introduce and investigate a new category of fuzzy inference systems based on information granulation and genetic optimization used to system identification. We show the applications of such systems to identification of nonlinear systems. The formal framework of information granulation and resulting information granules themselves become an important design facet of the fuzzy models. By embracing fuzzy sets, the model is geared towards capturing essential relationship between information granules rather than concentrating on plain numeric data. Information granulation realized with the use of the commonly exploited C-Means clustering helps determine the initial values of the parameters of the fuzzy models. This in particular concerns such essential components of the rules as the initial apexes of the membership functions standing in the premise part of the fuzzy rules and the points of the polynomial functions standing in the consequence part. The initial apexes (center points) of the membership functions based on C-Means algorithm are tuned with the aid of the genetic algorithm (GA), while the tuned apexes are also used to adjust the points of the consequent polynomials (conclusions) of the rules. In particular, the initial apexes of the membership functions and the initial points of the consequent polynomials are adjusted and updated every time through successive evolution process. The overall design methodology involves a hybrid structural and parametric optimization. Genetic algorithms and C-Means clustering are used to optimize the model with respect to its structure and parameters. To determine the structure and estimate the values of the parameters of the fuzzy model we consider the successive tuning method with generation-based evolution by means of genetic algorithms. The model is evaluated with the use of numerical experimentation and its quality is compared with respect to some other fuzzy models already encountered in the literature.     

A new approach to fuzzy modeling and control of discrete-time systems

We present a new approach to fuzzy modeling and control of discrete-time systems which is based on the formulation of a novel state-space representation using the hyperbolic tangent function. The new representation, designated the hyperbolic model, combines the advantages of fuzzy system theory and classical control theory. On the one hand, the hyperbolic model is easily derived from a set of Mamdani-type fuzzy rules. On the other hand, classical control theory can be applied to design controllers for the hyperbolic model that not only guarantee stability and robustness but are themselves equivalent to a set of Mamdani-type fuzzy rules. Thus, this new approach combines the best of two worlds. It enables linguistic interpretability of both the model and the controller, and guarantees closed-loop stability and robustness.     

Identification and modeling of nonlinear dynamical systems using a novel self-organizing RBF-based approach

In this paper, a novel self-organizing radial basis function (SORBF) neural network is proposed for nonlinear identification and modeling. The proposed SORBF consists of simultaneous network construction and parameter optimization. It offers two important advantages. First, the hidden neurons in the SORBF neural network can be added or removed, based on the neuron activity and mutual information (MI), to achieve the appropriate network complexity and maintain overall computational efficiency for identification and modeling. Second, the model performance can be significantly improved through the parameter optimization. The proposed parameter-adjustment-based optimization algorithm, utilizing the forward-only computation (FOC) algorithm instead of the traditionally forward-and-backward computation, simplifies neural network training, and thereby significantly reduces computational complexity. Additionally, the convergence of the SORBF is analyzed in both the structure organizing process phase and the phase following the modification. Lastly, the proposed approach is applied to model and identify the nonlinear dynamical systems. Simulation results demonstrate its effectiveness.     

Complementarity modeling of hybrid systems

A complementarity framework is described for the modeling of certain classes of mixed continuous/discrete dynamical systems. The use of such a framework is well known for mechanical systems with inequality constraints, but we give a more general formulation which also applies, for instance, to switching control systems. The main theoretical results in the paper are concerned with uniqueness of smooth continuations; the solution of this problem requires the construction of a map from the continuous state to the discrete state. A crucial technical tool is the so-called linear complementarity problem from mathematical programming, and we introduce various generalizations of this problem     

Environmental risk assessment of offshore produced water discharges using a hybrid fuzzy-stochastic modeling approach

In recent years, regulators and environmental groups have identified the large volumes of wastewater discharged through offshore petroleum production activities as an issue of concern. In this paper, fuzzy set theory coupled with Monte Carlo analysis have provided a stochastic simulation of pollutant dispersion for the prediction of the environmental risks associated with produced water discharges. With the application of the fuzzy set to data drawn from previous risk-assessment studies, the model allowed the evaluation of various existing pollution standards for marine environments. The present modeling method was validated against data for lead (Pb) levels in the area adjacent to an offshore petroleum facility located on the Grand Banks of Newfoundland, Canada through a multi-year field expedition. The proposed risk-assessment approach contributes to the implementation of effective assessment and management of produced water discharges in the offshore water environment.     

A new approach to robust control of hybrid systems over infinitetime

The paper presents a new approach to output feedback robust control synthesis problems for hybrid dynamical systems. The hybrid system under consideration is a composite of a continuous plant and a discrete-event controller. An output feedback H^∞-control problem on an infinite time interval is considered. The main results are given in terms of the existence of suitable solutions to a dynamic programming equation and a Riccati algebraic equation of the H^∞-filtering type. These results show a connection between the theories of hybrid dynamical systems and robust and nonlinear control     

A formal mathematical framework for modeling probabilistic hybrid systems

The development of autonomous agents, such as mobile robots and software agents, has generated considerable research in recent years. Robotic systems, which are usually built from a mixture of continuous (analog) and discrete (digital) components, are often referred to as hybrid dynamical systems. Traditional approaches to real-time hybrid systems usually define behaviors purely in terms of determinism or sometimes non-determinism. However, this is insufficient as real-time dynamical systems very often exhibit uncertain behavior. To address this issue, we develop a semantic model, Probabilistic Constraint Nets (PCN), for probabilistic hybrid systems. PCN captures the most general structure of dynamic systems, allowing systems with discrete and continuous time/variables, synchronous as well as asynchronous event structures and uncertain dynamics to be modeled in a unitary framework. Based on a formal mathematical paradigm exploiting abstract algebra, topology and measure theory, PCN provides a rigorous formal programming semantics for the design of hybrid real-time embedded systems exhibiting uncertainty.      

Compositional and uniform modeling of hybrid systems

Discusses fundamentals of hybrid system modeling. Emphasis is put on compositionality and the use of multiform time. Compositionality refers to the ability of freely composing hybrid systems. Since hybrid systems are considered, different time indexes occur, and modularity calls for considering “time” as local to each module, this is what is called multiform time. The proposed framework is behavioral, hence a way is provided to automatically synthesize proper scheduling constraints for the joint simulation of discrete-time, event-based, and continuous-time components. Finally, the relations of this model with the more traditional state-based point of view are discussed     

A meta modeling approach to workflow management systems supporting exception handling

Workflow Management Systems (WFMSs) facilitate the definition of structure and decomposition of business processes and assists in management of coordinating, scheduling, executing and monitoring of such activities. Most of the current WFMSs are built on traditional relational database systems and/or using an object-oriented database system for storing the definition and run time data about the workflows. However, a WFMS requires advanced modeling functionalities to support adaptive features, such as on-line exception handling. This article describes our advanced meta-modeling approach using various enabling technologies (such as object orientation, roles, rules, active capabilities) supported by an integrated environment, the ADOME, as a solid basis for a flexible WFMS involving dynamic match making, migrating workflows and exception handling.