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Fumagalli I.,Automata | Piroddi L.,Polytechnic of Milan | Cordone R.,University of Milan
2007 European Control Conference, ECC 2007 | Year: 2015

In Petri nets modeling and control of flexible manufacturing systems, the occurrence of deadlock states must be avoided with suitable design techniques. Many of these are based on siphon control, i.e. they implement generalized mutual exclusion constraints that avoid the emptying of siphons. If all minimal siphons are controlled, an excessive computational load may be required to complete the control sub-net and the latter may turn out to be over-sized. A classification of minimal siphons that selects a minimal number of siphons for the control design is proposed in this work. The classification can be exploited to obtain minimum size maximally permissive controllers. Some examples are provided to demonstrate the feasibility of the approach. © 2007 EUCA.


Fumagalli I.,Automata | Piroddi L.,Polytechnic of Milan | Cordone R.,University of Milan
Proceedings of the 2010 American Control Conference, ACC 2010 | Year: 2010

Siphon control is a widespread methodology for deadlock prevention (DP) in Petri net (PN) models. Besides achieving liveness or DP, control methods should also be evaluated regarding their permissivity (in terms of the number of allowed states) and constraint redundancy. This work introduces a partitioning of the reachability graph based on strongly connected components that nicely and compactly illustrates the PN's evolution behavior, especially regarding liveness, deadlocks and siphon-related properties. The resulting reduced graph is used as a tool for the analysis of DP methods in bounded PNs, to reveal the use of non-maximally permissive constraints and constraint overlapping. © 2010 AACC.


Skrzypczyk K.,Silesian University of Technology | Mellado M.,Automata
Archives of Control Sciences | Year: 2014

This paper addresses the problem of resource division for robotic agents in the framework of Multi-Agent System. Knowledge of the environment represented in the system is uncertain, incomplete and distributed among the individual agents that have both limited sensing and communication abilities. The pick-up-and-collection problem is considered in order to illustrate the idea presented. In this paper a framework for cooperative task assignment to individual agents is discussed. The process of negotiating access to common resources by intercommunicating agents is modeled and solved as a game against Nature. The working of the proposed system was verified by multiple simulations. Selected, exemplary simulations are presented in the paper to illustrate the approach discussed.


Chetpattananondh K.,Prince of Songkla University | Tapoanoi T.,Automata | Phukpattaranont P.,Prince of Songkla University | Jindapetch N.,Prince of Songkla University
Sensors and Actuators, A: Physical | Year: 2014

A water level measurement using an interdigital capacitive sensor with low-cost, low-energy, good repeatability, high linearity, and ease of installation is proposed with a support of experimental results. This sensor comprises a printed circuit board (PCB) with configuration of two interpenetrating comb electrodes. The comb electrode is 70-80 mm width, 300 mm height with 1-2 mm spacing between each comb. This configuration of electrode causes the capacitance between comb electrodes to vary by the water level. Microcontroller is used to calculate the capacitance between comb electrodes in terms of a discharge time correlated to the water level. A practical water level measurement technique using two comb electrodes designated as level and reference sensors is presented. This technique can directly be applied to water with different conditions without recalibration. This sensor is able to measure absolute levels of water with 0.2 cm resolution over 30 cm range. In addition, it is also sensitive enough to trace the variability of water level. A flood monitoring simulation is carried out in wave flume where this sensor is used to detect the rising wave. © 2014 Elsevier B.V.


Grant
Agency: NSF | Branch: Standard Grant | Program: | Phase: SMALL BUSINESS PHASE I | Award Amount: 224.63K | Year: 2016

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to protect consumer privacy while continuing to enable the ad-supported Internet model. Current tracking-based consumer targeting approaches inherently erode consumer privacy, surreptitiously tracking users across many different web sites in an effort to gather demographic and behavioral data. On the flip side of the coin, marketers need to collect such data to successfully reach their audiences, and the revenue that marketers pour into advertising online has become an essential component of the economics of the internet. Today, this delicate balance of competing pros and cons is further threatened by the rise of ad-blocking software, which erodes the value of internet ad placement. The video marketing analytics capability developed in this project will limit marketers need for invasive consumer data, while improving consumer experience. In the commercial realm, marketers would value the opportunity to target their ads in the most emotionally consonant, least disruptive, and most engaging manner possible. This technology will provide marketers with the capability to watch millions of videos algorithmically, thus enabling a more streamlined and customized viewer experience than has ever before been possible on television or on the Internet.

This Small Business Innovation Research Phase I project seeks to develop commercial applications for Perceptual Annotation, a technology developed with NSF funding that allows detailed measurements of human performance to be infused into a machine learning process, allowing the machine learner to both perform better and to perform in a way that is more consistent with humans. By adding this new category of human-derived supervisory signal into a machine learning process, the proposers have demonstrated that it is possible to significantly boost machine vision performance, allowing machines to generalize better to new, previously unseen images. While the companys technology has been rigorously validated on large-scale in the wild academic datasets, a major technical drive in the proposed SBIR Phase I activities will be to shift the companys efforts to the analysis of live, enormous, and ever-expanding data sets such as online videos. A second major drive of the proposed Phase I work will be the construction of second stage machine learning models that take perceptual-annotation-based machine ratings as an input and output actionable marketing decisions.


Grant
Agency: GTR | Branch: Innovate UK | Program: | Phase: Smart - Development of Prototype | Award Amount: 240.40K | Year: 2016

It has been four decades since industrial robots first began to transform assembly lines in Europe, Japan, and the U.S. Yet despite the advent of automation and advances in robotic, 90% of all manufacturing task that could be automated are still done manually (BCG; 2015 - The Robotics Revolution) The reasons for this are simple: economics and capabilities. Even today it is still less expensive to use manual labour than it is to own, operate, and maintain a robotics system, given the tasks that robots can perform. This leads to lower productivity, lower efficiency and lower wages. We will address this with the development of Eva - a lightweight, low cost, collaborative robotic arm and innovative ‘teach-by-example’ programming software. Made of high- strength plastic and controlled by an easy to use app, Eva will be 400% lighter (3.5kg) than other small robotic arms and 500% cheaper, making is possible to automate lower value manufacturing and lab tasks not currently possible. Our ‘teach by example’ software will make tasking, training and redeployment, simple and done in under 3 hours with no training required. We have proved the concept in the lab with a 3D printed arm however significant design and development work is required to create a pre-production prototype capable of prolonged industrial use. This includes improving the robustness of the polymer skeleton and mechanics, designing a new onboard electronics control system, improving the software, developing new arm attachments and extensive technical validation of the prototype made through soft-tooling prior to mass-production. All current robotic arm solution are metallic, expensive, and heavy and designed for precision industrial use not required for several tasks. As a light weight, low cost solution, Eva has the potential to revolutionise the robotics industry - enabling the automation of menial, labour intensive, low costs tasks – potentially saving £16,653pa in human labour costs per installation.


Grant
Agency: National Science Foundation | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 224.63K | Year: 2016

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to protect consumer privacy while continuing to enable the ad-supported Internet model. Current tracking-based consumer targeting approaches inherently erode consumer privacy, surreptitiously tracking users across many different web sites in an effort to gather demographic and behavioral data. On the flip side of the coin, marketers need to collect such data to successfully reach their audiences, and the revenue that marketers pour into advertising online has become an essential component of the economics of the internet. Today, this delicate balance of competing pros and cons is further threatened by the rise of ad-blocking software, which erodes the value of internet ad placement. The video marketing analytics capability developed in this project will limit marketers' need for invasive consumer data, while improving consumer experience. In the commercial realm, marketers would value the opportunity to target their ads in the most emotionally consonant, least disruptive, and most engaging manner possible. This technology will provide marketers with the capability to watch millions of videos algorithmically, thus enabling a more streamlined and customized viewer experience than has ever before been possible on television or on the Internet. This Small Business Innovation Research Phase I project seeks to develop commercial applications for Perceptual Annotation, a technology developed with NSF funding that allows detailed measurements of human performance to be infused into a machine learning process, allowing the machine learner to both perform better and to perform in a way that is more consistent with humans. By adding this new category of human-derived supervisory signal into a machine learning process, the proposers have demonstrated that it is possible to significantly boost machine vision performance, allowing machines to generalize better to new, previously unseen images. While the company's technology has been rigorously validated on large-scale "in the wild" academic datasets, a major technical drive in the proposed SBIR Phase I activities will be to shift the company's efforts to the analysis of "live," enormous, and ever-expanding data sets such as online videos. A second major drive of the proposed Phase I work will be the construction of "second stage" machine learning models that take perceptual-annotation-based machine ratings as an input and output actionable marketing decisions.


Trademark
Automata | Date: 2015-09-14

Downloadable software in the nature of a mobile application for providing information related to the use of medical marijuana.


Patent
Automata | Date: 2013-10-08

An improved system for the recording and display of barometric pressure information, comprising a timekeeper, barometric sensor, memory system, and display, integrated to form an instrument or horological complication for the display of the current barometric pressure and past pressure history by use of an animated display.


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