Control optimisatlion of CO(2) cycles for medium temperature retail food refrigeration systems

This is the post-print version of the final paper published in International Journal of Refrigeration. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2009 Elsevier B.V.This paper describes a detailed procedure into the investigation of optimised control strategies for CO2 cycles in medium temperature retail food refrigeration systems. To achieve this objective, an integrated model was developed composing of a detailed condenser/gas cooler model, a simplified compressor model, an isenthalpic expansion process and constant evaporating temperature and superheating. The CO2 system can operate subcritically or transcritically depending on the ambient temperature. For a transcritical operation, a prediction can be made for optimised refrigerant discharge pressures from thermodynamic cycle calculations. When the system operates in the subcritical cycle, a floating discharge pressure control strategy is employed and the effect of different transitional ambient temperatures separating subcritical and transcritical cycles on system performance is investigated. The control strategy assumes variable compressor speed and adjustable air flow for the gas cooler/condenser to be modulated to achieve the constant cooling load requirement at different ambient conditions.DEFR

Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs

Precision farming robots, which target to reduce the amount of herbicides that need to be brought out in the fields, must have the ability to identify crops and weeds in real time to trigger weeding actions. In this paper, we address the problem of CNN-based semantic segmentation of crop fields separating sugar beet plants, weeds, and background solely based on RGB data. We propose a CNN that exploits existing vegetation indexes and provides a classification in real time. Furthermore, it can be effectively re-trained to so far unseen fields with a comparably small amount of training data. We implemented and thoroughly evaluated our system on a real agricultural robot operating in different fields in Germany and Switzerland. The results show that our system generalizes well, can operate at around 20Hz, and is suitable for online operation in the fields.Comment: Accepted for publication at IEEE International Conference on Robotics and Automation 2018 (ICRA 2018

Swarm potential fields with internal agent states and collective behaviour

Swarm robotics is a new and promising approach to the design and control of multi-agent robotic systems. In this paper we use a model for a system of self-propelled agents interacting via pairwise attractive and repulsive potentials. We develop a new potential field method using dynamic agent internal states, allowing the swarm agents' internal states to manipulate the potential field. This new method successfully solves a reactive path planning problem that cannot be solved using static potential fields due to local minima formation. Simulation results demonstrate the ability of a swarm of agents that use the model to perform reactive problem solving effectively using the collective behaviour of the entire swarm in a way that matches studies based on real animal group behaviour

Applications of Model Predictive Controllers in a Sugar Factory

INTERNATIONAL SYMPOSIUM ON ADVANCED CONTROL OF CHEMICAL PROCESSThis paper presents two applications of Model Predictive Control in a sugar factory: temperature control in the diffusion process and density control in the wastewater treatment plant. The implementation is done using a Generalized Predictive Controller (GPC) designed for a wide class of industrial process, with the same computational requirements as a PID routine and embedded in the existing control system. The processes have in common the existence of long and uncertain dead times, therefore the original GPC algorithm is improved by the use of the T polynomial, which increases the stability robustness by filtering the predictions.Comisión Interministerial de Ciencia y Tecnología (CICYT) TAP 96-0884Comisión Interministerial de Ciencia y Tecnología (CICYT) TAP 98-0541Comisión Interministerial de Ciencia y Tecnología (CICYT) 1FD97-083

When separation is not the answer : breastfeeding mothers and infants affected by COVID-19

The World Health Organisation (WHO) has provided detailed guidance on the care of infants of women who are a person under investigation (PUI) or confirmed to have COVID-19, which supports immediate postpartum mother-infant contact and breastfeeding with appropriate respiratory precautions. Although many countries have followed WHO guidance, others have implemented infection prevention and control policies (IPC) that impose varying levels of postpartum separation and discourage or prohibit breastfeeding or provision of expressed breastmilk. These policies aim to protect infants from the potential harm of infection from their mothers, yet they may fail to fully account for the impact of separation. Global COVID-19 data are suggestive of potentially lower susceptibility and a typically milder course of disease among children, although the potential for severe disease in infancy remains. Separation causes cumulative harms, including disrupting breastfeeding and limiting its protection against infectious disease, which has disproportionate impacts on vulnerable infants. Separation also presumes the replaceability of breastfeeding – a risk that is magnified in emergencies. Moreover, separation does not ensure lower viral exposure during hospitalizations and post-discharge, and contributes to the burden on overwhelmed health systems. Finally, separation magnifies maternal health consequences of insufficient breastfeeding and compounds trauma in communities who have experienced long-standing inequities and violence, including family separation. Taken together, separating PUI/confirmed SARS-CoV-2 positive mothers and their infants may lead to excess preventable illnesses and deaths among infants and women around the world. Health services must consider the short-and-long-term impacts of separating mothers and infants in their policies. This article is protected by copyright

Facing human rights attributes of copyright in Europe in the context of the EU Digital Single Market

The principle of equality as a fundamental norm in law and political philosophy, Jurysprudencja 8., Wojciechowski B., Bekrycht T., Cern K.M., (eds.), Wydawnictwo Uniwersytetu Łódzkiego, Łódź 2017The project was financed by National Science Centre Poland (decision no. DEC-2012/05/B/HS5/01111)

Better abstractions for reusable components & architectures

Software architecture (SA) is a crucial component of Model Driven Engineering (MDE), since it eases the communication and reuse of designs and components. However, existing languages (e.g., UML, AADL, SysML) are lacking many needed features. In particular, they provide rudimentary support for connectors, a first-class element in the components and connectors (C&C) architectural view and one of the most reusable architectural elements. This is unfortunate, since the difficult properties that need to be guaranteed for complex systems are mainly the non-functional properties, like throughput, security and dependability, which are greatly influenced by the employed connectors. This work reviews the basic abstractions of the C&C view of SA and examines extra architectural elements which can support the detailed, explicit and separate description of behaviour, interaction and control logic

Coherence as a feature of real HF signals

Copyright 2005 Society of Photo-Optical Instrumentation Engineers. This paper was published in Noise in Communication Systems, edited by Costas N. Georghiades, Langford B. White, Proc. of SPIE Vol. 5847 and is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.High-frequency (HF) communications is undergoing a resurgence despite advances in long-range satellite communication systems. Defense agencies are using the HF spectrum for backup communications as well as for spectrum surveillance applications. Spectrum management organizations are monitoring the HF spectrum to control and enforce licensing. These activities usually require systems capable of determining the location of a source of transmissions, separating valid signals from interference and noise, and recognizing signal modulation. Our ultimate aim is to develop robust modulation recognition algorithms for real HF signals that propagate by multiple ionospheric modes. One aspect of modulation recognition is the extraction of signal identifying features. The most common features for modulation recognition are instantaneous phase, amplitude, and frequency. Many papers present results based on synthetic data and unproven assumptions. However, this paper continues our previous work by applying the coherence function to noisy real HF groundwave signals; which removes the need for synthesized data and unrealistic assumptions.James E. Giesbrecht, Russell Clarke, and Derek Abbot