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Morristown, NJ, United States

Honeywell International, Inc. is an American multinational conglomerate company that produces a variety of commercial and consumer products, engineering services, and aerospace systems for a wide variety of customers, from private consumers to major corporations and governments.Honeywell is a Fortune 100 company; in 2012 it was listed as 77th in the Fortune 500 America's ranking. Honeywell has a global workforce of approximately 130,000, of whom approximately 58,000 are employed in the United States. The company is headquartered in Morristown, New Jersey. Its current chief executive officer is David M. Cote. The company and its corporate predecessors were part of the Dow Jones Industrial Average Index from December 7, 1925, until February 9, 2008.The company's current name, Honeywell International Inc., is the product of a merger in which Honeywell Inc. was acquired by the much larger AlliedSignal in 1999. The company headquarters were consolidated with AlliedSignal's headquarters in Morristown, New Jersey; however the combined company chose the name "Honeywell" because of its superior brand recognition.Honeywell has many brands that commercial and retail consumers may recognize, including its line of home thermostats and Garrett turbochargers. Wikipedia.

Salit M.,Honeywell
Optics express | Year: 2011

The most successful Ring Laser Gyroscopes (RLGs) are gas-laser based. It has been recently shown that the type of anomalous dispersion associated with fast light, when present inside an RLG, can increase its scale factor. We evaluate several proposed methods for realizing this appropriate dispersion in gas media, theoretically and experimentally. We find linear gas media in general to be unsuitable for this purpose, with mixed prospects for nonlinear effects.

MacArthur J.W.,Honeywell
Journal of Process Control | Year: 2012

A new gray-box method for nonlinear process identification is presented. Industrial deployment for model predictive control (MPC) is the primary focus of this development. For flexibility, the identification accommodates Hammerstein, Wiener and the more general N-L-N block-oriented structures. Instruments comprised of linear and nonlinear combinations of inputs and outputs are also accommodated. Unique to this approach is the utilization of two sets of bases. One is constructed using an estimate of the process poles and the other is constructed using a predefined set of special cubic splines. An intriguing aspect of this formulation is that nonlinear dynamics are implicitly accommodated. In addition, problems associated with identifying the linear portion of the model in conventional block oriented formulations are removed. Because of the bases formulation, it is possible to solve the identification problem for many supported structures by convex optimization and hence avoid the inherent problems of iterative solutions. To insure open-loop unbiased estimates, any structures using output nonlinearities do require an iterative solution. Two test cases from the open literature are presented as are results from plant step-test data on a problematic air separation unit. © 2011 Elsevier Ltd. All rights reserved.

A mobile wireless device (e.g. smart phone) may be used to remotely control an HVAC system. A program code stored in the memory of the mobile wireless device may cause the mobile wireless device to store geographic information in the memory of the mobile wireless device, monitor a location of the mobile wireless device, and compare the stored geographic information to the location of the mobile wireless device. If the comparison meets predetermined criteria, the program code may cause the mobile wireless device to transmit a command either directly or indirectly to an HVAC controller, causing the HVAC controller to transition from a first operating state having a first temperature setpoint to a second operating state having a second temperature setpoint.

Agency: GTR | Branch: EPSRC | Program: | Phase: Training Grant | Award Amount: 3.94M | Year: 2014

The achievements of modern research and their rapid progress from theory to application are increasingly underpinned by computation. Computational approaches are often hailed as a new third pillar of science - in addition to empirical and theoretical work. While its breadth makes computation almost as ubiquitous as mathematics as a key tool in science and engineering, it is a much younger discipline and stands to benefit enormously from building increased capacity and increased efforts towards integration, standardization, and professionalism. The development of new ideas and techniques in computing is extremely rapid, the progress enabled by these breakthroughs is enormous, and their impact on society is substantial: modern technologies ranging from the Airbus 380, MRI scans and smartphone CPUs could not have been developed without computer simulation; progress on major scientific questions from climate change to astronomy are driven by the results from computational models; major investment decisions are underwritten by computational modelling. Furthermore, simulation modelling is emerging as a key tool within domains experiencing a data revolution such as biomedicine and finance. This progress has been enabled through the rapid increase of computational power, and was based in the past on an increased rate at which computing instructions in the processor can be carried out. However, this clock rate cannot be increased much further and in recent computational architectures (such as GPU, Intel Phi) additional computational power is now provided through having (of the order of) hundreds of computational cores in the same unit. This opens up potential for new order of magnitude performance improvements but requires additional specialist training in parallel programming and computational methods to be able to tap into and exploit this opportunity. Computational advances are enabled by new hardware, and innovations in algorithms, numerical methods and simulation techniques, and application of best practice in scientific computational modelling. The most effective progress and highest impact can be obtained by combining, linking and simultaneously exploiting step changes in hardware, software, methods and skills. However, good computational science training is scarce, especially at post-graduate level. The Centre for Doctoral Training in Next Generation Computational Modelling will develop 55+ graduate students to address this skills gap. Trained as future leaders in Computational Modelling, they will form the core of a community of computational modellers crossing disciplinary boundaries, constantly working to transfer the latest computational advances to related fields. By tackling cutting-edge research from fields such as Computational Engineering, Advanced Materials, Autonomous Systems and Health, whilst communicating their advances and working together with a world-leading group of academic and industrial computational modellers, the students will be perfectly equipped to drive advanced computing over the coming decades.

Agency: Cordis | Branch: H2020 | Program: CSA | Phase: ICT-38-2015 | Award Amount: 1.16M | Year: 2016

The aim of the 30-months PICASSO project is (1) to reinforce EU-US collaboration in ICT research and innovation focusing on the pre-competitive research in key enabling technologies related to societal challenges - 5G Networks, Big Data, Internet of Things and Cyber Physical Systems, and (2) to support the EU-US ICT policy dialogue by contributions related to e.g. privacy, security, internet governance, interoperability, ethics. PICASSO is oriented to industrial needs, provides a forum for ICT communities and involves 24 EU and US prominent specialists in the three technology-oriented ICT Expert Groups and an ICT Policy Expert Group, working closely together to identify policy gaps in the technology domains and to take measures to stimulate the policy dialogue in these areas. A synergy between experts in ICT policies and in ICT technologies is a unique feature of PICASSO. An analysis of the industrial drivers, societal needs, and priorities for EU-US ICT collaboration will be done, and policy gaps will be highlighted. An Opportunity Report will point out new avenues for EU-US research, innovation and policy collaboration. An ICT Industry Toolkit app will support companies and academia in exploiting collaboration opportunities. Policy briefs focusing on specific aspects of identified policy gaps will provide visibility for EU policies and propose ways forward. Strategic initiatives will be investigated and discussed, and a White Paper will be prepared. The outreach campaign will include 30\ events, success stories factsheets, info sessions and webinars. PICASSO will directly contribute to the strengthening of the European industrial leadership in ICT. PICASSOs approach will be integrative, inclusive, industry-driven, societally responsible and beneficial for both EU and US. It is supported by NIST, National Institute of Standard and Technology, US, and the European Cluster Alliance.

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