Supremica – An integrated environment for verification, synthesis and simulation of discrete event systems

An integrated environment, Supremica, for verification, synthesis and simulation of discrete event systems is presented. The basic model in Supremica is finite automata where the transitions have an associated event together with a guard condition and an action function that updates automata variables. Supremica uses two main approaches to handle large state-spaces. The first approach exploits modularity in order to divide the original problem into many smaller problems that together solve the original problem. The second approach uses an efficient data structure, a binary decision diagram, to symbolically represent the reachable states. Models in Supremica may be simulated in the environment. It is also possible to generate code that implements the behavior of the model using both the IEC 61131 and the IEC 61499 standard

Learning Visual Reasoning Without Strong Priors

Achieving artificial visual reasoning - the ability to answer image-related questions which require a multi-step, high-level process - is an important step towards artificial general intelligence. This multi-modal task requires learning a question-dependent, structured reasoning process over images from language. Standard deep learning approaches tend to exploit biases in the data rather than learn this underlying structure, while leading methods learn to visually reason successfully but are hand-crafted for reasoning. We show that a general-purpose, Conditional Batch Normalization approach achieves state-of-the-art results on the CLEVR Visual Reasoning benchmark with a 2.4% error rate. We outperform the next best end-to-end method (4.5%) and even methods that use extra supervision (3.1%). We probe our model to shed light on how it reasons, showing it has learned a question-dependent, multi-step process. Previous work has operated under the assumption that visual reasoning calls for a specialized architecture, but we show that a general architecture with proper conditioning can learn to visually reason effectively.Comment: Full AAAI 2018 paper is at arXiv:1709.07871. Presented at ICML 2017's Machine Learning in Speech and Language Processing Workshop. Code is at http://github.com/ethanjperez/fil

DESIGN OF OPTIMAL PROCEDURAL CONTROLLERS FOR CHEMICAL PROCESSES MODELLED AS STOCHASTIC DISCRETE EVENT SYSTEMS

This thesis presents a formal method for the the design of optimal and provably correct procedural controllers for chemical processes modelled as Stochastic Discrete Event Systems (SDESs). The thesis extends previous work on Procedural Control Theory (PCT) [1], which used formal techniques for the design of automation Discrete Event Systems (DESs). Many dynamic processes for example, batch operations and the start-up and shut down of continuous plants, can be modelled as DESs. Controllers for these systems are typically of the sequential type. Most prior work on characterizing the behaviour of DESs has been restricted to deterministic systems. However, DESs consisting of concurrent interacting processes present a broad spectrum of uncertainty such as uncertainty in the occurrence of events. The formalism of weighted probabilistic Finite State Machine (wp-FSM) is introduced for modelling SDESs and pre-de ned failure models are embedded in wp-FSM to describe and control the abnormal behaviour of systems. The thesis presents e cient algorithms and procedures for synthesising optimal procedural controllers for such SDESs. The synthesised optimal controllers for such stochastic systems will take into consideration probabilities of events occurrence, operation costs and failure costs of events in making optimal choices in the design of control sequences. The controllers will force the system from an initial state to one or more goal states with an optimal expected cost and when feasible drive the system from any state reached after a failure to goal states. On the practical side, recognising the importance of the needs of the target end user, the design of a suitable software implementation is completed. The potential of both the approach and the supporting software are demonstrated by two industry case studies. Furthermore, the simulation environment gPROMS was used to test whether the operating speci cations thus designed were met in a combined discrete/continuous environment

Model predictive control techniques for hybrid systems

This paper describes the main issues encountered when applying model predictive control to hybrid processes. Hybrid model predictive control (HMPC) is a research field non-fully developed with many open challenges. The paper describes some of the techniques proposed by the research community to overcome the main problems encountered. Issues related to the stability and the solution of the optimization problem are also discussed. The paper ends by describing the results of a benchmark exercise in which several HMPC schemes were applied to a solar air conditioning plant.Ministerio de Eduación y Ciencia DPI2007-66718-C04-01Ministerio de Eduación y Ciencia DPI2008-0581

The ISIS Project: Real Experience with a Fault Tolerant Programming System

The ISIS project has developed a distributed programming toolkit and a collection of higher level applications based on these tools. ISIS is now in use at more than 300 locations world-wise. The lessons (and surprises) gained from this experience with the real world are discussed

Towards an online mitigation strategy for N2O emissions through principal components analysis and clustering techniques

Emission of N2O represents an increasing concern in wastewater treatment, in particular for its large contribution to the plant's carbon footprint (CFP). In view of the potential introduction of more stringent regulations regarding wastewater treatment plants' CFP, there is a growing need for advanced monitoring with online implementation of mitigation strategies for N2O emissions. Mechanistic kinetic modelling in full-scale applications, are often represented by a very detailed representation of the biological mechanisms resulting in an elevated uncertainty on the many parameters used while limited by a poor representation of hydrodynamics. This is particularly true for current N2O kinetic models. In this paper, a possible full-scale implementation of a data mining approach linking plant-specific dynamics to N2O production is proposed. A data mining approach was tested on full-scale data along with different clustering techniques to identify process criticalities. The algorithm was designed to provide an applicable solution for full-scale plants' control logics aimed at online N2O emission mitigation. Results show the ability of the algorithm to isolate specific N2O emission pathways, and highlight possible solutions towards emission control

The middle manager: Friend or foe of employee involvement

Middle management resistance has been frequently identified as a significant barrier to the success of employee involvement practices. This paper reviews evidence from the literature and from 12 case studies on the role played by middle managers in employee involvement initiatives. There is evidence that middle management resistance often acts as a significant impediment to employee involvement. However, there is also evidence that this resistance is often a symptom of inconsistency between organisational systems and the goals of employee involvement and of inadequate training and support for middle managers. Employee involvement initiatives should pay attention to aligning organisational systems with the goals of employee involvement and treat middle managers as the targets as well as the implementors of employee involvement