Jos L. M. Vrancken

User requirements modeling and analysis of software-intensive systems

The increasing complexity of software systems makes Requirements Engineering activities both more important and more difficult. This article is about user requirements development, mainly the activities of documenting and analyzing user requirements for software-intensive systems. These are modeling activities that are useful for further Requirements Engineering activities. Current techniques for requirements modeling present a number of problems and limitations. Based on these shortcomings, a list of requirements for requirements modeling languages is proposed. The proposal of this article is to show how some extensions to SysML diagrams and tables can fulfill most of these requirements. The approach is illustrated by a list of user requirements for a Road Traffic Management System.

Real-time scheduling of batch systems using Petri nets and linear logic

This paper presents an approach to model, design and verify scenarios of real-time systems used in the scheduling and global coordination of batch systems. The initial requirements of a system specified with sequence diagrams are translated into a single p-time Petri net model representing the global behavior of the system. For the Petri net fragments involved in conflicts, symbolic production and consumption dates assigned to tokens are calculated based on the sequent calculus of linear logic. These dates are then used for off-line conflict resolution within a token player algorithm used for scenario verification of real-time specifications and which can be seen as a simulation tool for UML interaction diagrams.

Requirements specification and modeling through SysML

Use case diagrams are well-known for their use to specify and describe system requirements. From initial system requirements documents, use cases can be derived representing several scenarios. These scenarios can later be detailed in different ways, as for example, through informal descriptions. In this paper, system requirements are first specified using the SysML requirements diagram and later by use cases. The main goal is to fill the gap between documents written in natural language and use cases by modeling requirements in a graphical and tabular way, which can improve the requirements representation. Also, the relationship between requirements is enhanced. An example of a real time distributed system is given to illustrate the approach.

Intelligent Control in Networks: The Case of Road Traffic Management

This paper illustrates intelligent control in complex networks by the example of road traffic management. An important goal in road traffic management, which as yet has been achieved only very partially, is to make the move from local traffic management to network level traffic management. The approach to this goal described here is a combination of top-down control and several bottom-up control mechanisms: top-down control consists of off-line preparation of control scenarios that apply to certain traffic patterns. The bottom-up mechanisms rely on emergent behavior of multi-agent systems: one set of agents is formed by the vehicles, a second set is formed by the nodes and links of the road network. The approach can to a certain extent be generalized to other types of complex networks

Motorway ramp-metering control with queuing consideration using Q-learning

The standard reinforcement learning algorithms have proven to be effective tools for letting an agent learn from its experiences generated by its interaction with an environment. Among others, reinforcement learning algorithms are of interest because they require no explicit model of the environment beforehand and learning happens through trial and error. This property makes them suitable for real control problems like traffic control. Especially when considering the performance of a network where for instance a local ramp-metering controller needs to consider the performance of the network, since limitations needs to be considered, like the maximum permissible queue length, reinforcement learning algorithms are of interest. Here, a local ramp-metering control problem with queuing consideration is taken up and the performance of standard Q-learning algorithm as well as a newly proposed multi-criterion reinforcement learning algorithm is investigated. The experimental analysis confirms that the proposed multi-criterion control approach has the capability to decrease the state-space size and increase the learning speed of controller while improving the quality of solution.

Freeway traffic state estimation using extended Kalman filter for first-order traffic model in Lagrangian coordinates

Freeway traffic state estimation is one of the central components in real-time traffic management and information applications. Recent studies show that the classic kinematic wave model can be formulated and solved more efficiently and accurately in Lagrangian (vehicle number-time) coordinates. This paper investigates the opportunities of the Lagrangian form for state estimation. The main advantage for state estimation is that in Lagrangian coordinates, the numerical solution scheme is reduced to an upwind scheme. We propose a new model-based extended Kalman filter (EKF) state estimator where the discretized Lagrangian model is used as the model equation. This state estimator is applied to freeway traffic state estimation and validated using synthetic data. Different filter design specifications with respect to measurement aspects are considered. The achieved results are very promising for subsequent studies.

Inverse infrastructures: Coordination in self-organizing systems

The paper argues that a new category of infrastructures is emerging, user-driven, self-organizing and with de-centralized control: inverse infrastructures (IIs). IIs are not well-understood. Moreover, they represent a paradigm shift in infrastructure development. Their bottom-up development shows tension with the current socio-institutional framework for infrastructures. Internationally infrastructure laws and policies are based on a top-down and centralized view of infrastructures. Regulation is based on a control paradigm that does not fit the characteristics of inverse infrastructures and has no ways to deal with them. Policy (re)design is needed in the face of inverse infrastructure emergence.

Prediction of Traffic Flow at the Boundary of a Motorway Network

For online traffic control at traffic control centers, there is a need for predictions of the traffic flow during a short horizon, for example, 30 min ahead. For this effort, predictions are needed of the traffic inflow into the network at motorways on the network boundary and at on-ramps. This paper presents an adaptive prediction algorithm for the inflows into the network in regular traffic situations based on stochastic control theory. The prediction algorithm is based on an adaptive prediction algorithm of T. Bohlin. The algorithm is designed and tested on traffic flow data of the ring road of Amsterdam. The results show that the algorithm provides robust predictions of traffic demand with relatively small errors for the next 30 min in a large-scale real-time environment.

A Fitness Granulation Approach for Large-Scale Structural Design Optimization

The complexity of large-scale mechanical optimization problems is partially due to the presence of high-dimensional design variables, the nature of the design variables, and the high computational cost of the finite element simulations needed to evaluate the fitness of candidate solutions. Evolutionary algorithms are ruled by competitive games of survival and not merely by absolute measures of fitness. They can also exploit the robustness of evolution against uncertainties in the fitness function evaluations. This chapter takes up the complexity challenge of mechanical optimization problems by proposing a new fitness granulation approach that attempts to cope with several difficulties of fitness approximation methods that have been reported in the specialized literature. The approach is based on adaptive fuzzy fitness granulation having as its main aim to strike a balance between the accuracy and the utility of the computations. The adaptation algorithm adjusts the number and size of the granules according to the perceived performance and level of convergence attained. Experimental results show that the proposed method accelerates the convergence towards solutions when compared to the performance of other, more popular approaches. This suggests its applicability to other complex finite element-based engineering design problems.

Multi-level control of networks: the case of road traffic control

In this paper we illustrate multi-level network control by means of road traffic control. There is a natural and very common way of reducing the complexity of a network It consists of two steps: dividing the network into parts and introducing levels in the control of a network. Dividing a network into parts entails the introduction of levels: the internal control within each part is one level and the overall control of the parts is a second level. Applying these two steps recursively introduces a number of levels and a recursive decomposition of a network into a tree structure of parts. Each level has its own view on the network, its own control measures and objectives. A levels view on the network can often be represented by a simpler network. In two case studies we show how the levels and the subdivisions per level can be determined, which control measures and control objectives each level has and how the different levels interoperate. Moreover, we show why it is highly desirable that the simplified networks, representing the views on the network at each of the various levels, should be derived in an automated way, and how this can be done.