On Supervisor Synthesis via Active Automata Learning

Our society\u27s reliance on computer-controlled systems is rapidly growing. Such systems are found in various devices, ranging from simple light switches to safety-critical systems like autonomous vehicles. In the context of safety-critical systems, safety and correctness are of utmost importance. Faults and errors could have catastrophic consequences. Thus, there is a need for rigorous methodologies that help provide guarantees of safety and correctness. Supervisor synthesis, the concept of being able to mathematically synthesize a supervisor that ensures that the closed-loop system behaves in accordance with known requirements, can indeed help.This thesis introduces supervisor learning, an approach to help automate the learning of supervisors in the absence of plant models. Traditionally, supervisor synthesis makes use of plant models and specification models to obtain a supervisor. Industrial adoption of this method is limited due to, among other things, the difficulty in obtaining usable plant models. Manually creating these plant models is an error-prone and time-consuming process. Thus, supervisor learning intends to improve the industrial adoption of supervisory control by automating the process of generating supervisors in the absence of plant models.The idea here is to learn a supervisor for the system under learning (SUL) by active interaction and experimentation. To this end, we present two algorithms, SupL*, and MSL, that directly learn supervisors when provided with a simulator of the SUL and its corresponding specifications. SupL* is a language-based learner that learns one supervisor for the entire system. MSL, on the other hand, learns a modular supervisor, that is, several smaller supervisors, one for each specification. Additionally, a third algorithm, MPL, is introduced for learning a modular plant model.The approach is realized in the tool MIDES and has been used to learn supervisors in a virtual manufacturing setting for the Machine Buffer Machine example, as well as learning a model of the Lateral State Manager, a sub-component of a self-driving car. These case studies show the feasibility and applicability of the proposed approach, in addition to helping identify future directions for research

Pro-poor intervention strategies in irrigated agriculture in Asia: poverty in irrigated agriculture: issues and options: Pakistan

Irrigated farming / Poverty / Irrigation management / Irrigation systems / Water policy / Water rights / Water law / Irrigation scheduling / Organizations / Social aspects / Households / Economic aspects / Expenditure / Irrigation programs / Performance evaluation / Water delivery / Equity / Models / Crop production / Productivity / Wheat / Pakistan

Modular Supervisory Synthesis for Unknown Plant Models Using Active Learning

This paper proposes an approach to synthesize a modular discrete-event supervisor to control a plant, the behavior model of which is unknown, so as to satisfy given specifications. To this end, the Modular Supervisor Learner (MSL) is presented that based on the known specifications and the structure of the system defines the configuration of the supervisors to learn. Then, by actively querying the simulation and interacting with the specification it explores the state-space of the system to learn a set of maximally permissive controllable supervisors

Active Learning of Modular Plant Models

Model-based techniques are these days being embraced by the industry in their development frameworks. While model-based approaches allow for offline verification and validation of the system, and have other advantages over existing methods, they do have their own challenges. One of the challenges is to obtain a model describing the behavior of the system. In this paper we present the Modular Plant Learner (MPL), an algorithm that explores the state-space and constructs a discrete model of a system. The MPL takes as input a hypothesis structure of the system - called the PSH - and using this information, interacts with a simulation of the system to construct a modular discrete-event model. Using an example we show how the algorithm uses the structural information provided - the PSH - to search the state-space in a smart manner, mitigating the state-space explosion problem

Association of long term sodium valproate monotherapy and vitamin D3 levels in epileptic children

Objective: To determine the association of long term sodium valproate monotherapy and vitamin D3 levels in epileptic children Methods: This cross-sectional study was conducted in the Department of Pediatrics, Children Hospital, Pakistan Institute of Medical Sciences, Islamabad for six months from 15th February 2019 to 14th August 2019. A total of one hundred and thirty (n=130) children and adolescents of either gender between age 3-18 years who had a history of two seizures at least 24 hours apart in their life and were on sodium valproate monotherapy for more than one year were enrolled in this study through non-probability, consecutive sampling. Serum vitamin D3 (25-hydroxy vitamin D) levels were measured in all the patients at the time of enrolment into the study. All the demographic data and laboratory investigations were entered on the predesigned proforma and analyzed through SPSS version 17. Results: Vitamin D3 deficiency was found in 47 (36.2%) children which were significantly higher among patients with older age and longer duration of treatment (P<0.05) while gender and BMI of the patients did not show any significant difference (P>0.05). Conclusion: Significant percentage of epileptic children on sodium valproate monotherapy was found to have vitamin D3 deficiency. Therefore we recommend routine screening of vitamin D3 deficiency in all the epileptic children on long-term sodium valproate therapy followed by vitamin D supplementation in deficient patients. &nbsp

Structural and functional analysis of seeligeriolysin O by homology modeling

Seeligeriolysin O (LSO) is a cholesterol-dependant cytolysin of Listeria seeligeri. These toxins are produced by various species of Gram-positive bacteria, including members of the genera Streptococcus, Clostridium, and Listeria. Apart from the cytolytic, LSO has been reported to perform cytokine-inducing activity as well. The present study deals with the prediction of three dimensional model, as well as structural and functional analysis of Seeligeriolysin O. MODELLER9 v8 was used for building the homology model. These predicted 3-dimensional models were evaluated with ProSa and PROCHECK software, and the best 3-dimensional models were selected. Multiple alignment was performed with CLUSTALX. Based on the similarity of predicted three dimensional structure of seeligeriolysin O with perfringolysin O, the seeligeriolysin might have similar structure and function with the later. The predicted three dimensional model of seeligeriolysin O had extended rod shaped structure, having ample beta sheets arranged in four domains. The C-terminal region of seeligeriolysin O might have function similar to perfringolysin O. It has been predicted that seeligeriolysin O insertion occurs more readily in an environment having loosely packed lipid.Key words: Bacterial toxins, tryptophan, Perfringolysin, Listeria seeligeri, cholesterol-dependant cytolysins and domain 4, target protein, template protein

Microencapsulation: A taste and odour masking approach for garlic (Allium sativum) powder

The purpose of this study was to prepare a convenient formulation of garlic powder which has enhanced user compliance by suppressing its characteristic odour and masking the taste. Microcapsules of garlic powder were formulated by solvent evaporation method. Results show >80% yield, >70% drug loading efficiency and slow release profile (promising sustained action for more than 2hrs) and a significant reduction in the aromatic smell and taste evaluated through a single blind cross-over study conducted using ten healthy human volunteers

Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations

Published by Informa UK Limited, trading as Taylor & Francis Group. The technological advancements of recent years have increased the complexity of manufacturing systems, and the ongoing transformation to Industry\ua04.0 will further aggravate the situation. This is leading to a point where existing systems on the factory floor get outdated, increasing the gap between existing technologies and state-of-the-art systems, making them incompatible. This paper presents an event-based data pipeline architecture, that can be applied to legacy systems as well as new state-of-the-art systems, to collect data from the factory floor. In the presented architecture, actions executed by the resources are converted to event streams, which are then transformed into an abstraction called operations. These operations correspond to the tasks performed in the manufacturing station. A sequence of these operations recount the task performed by the station. We demonstrate the usability of the collected data by using conformance analysis to detect when the manufacturing system has deviated from its defined model. The described architecture is developed in Sequence Planner–a tool for modelling and analysing production systems–and is currently implemented at an automotive company as a pilot project