Trying to control multiple computers in live performances is a challenging task. Often computers intercommunicate using fixed or manual parameters. However when projects expand across many devices this is hard to maintain. Especially in situations where the parameters tend to change. We propose a new protocol which facilitates flexibility and autonomous setups in an orchestrated environment.
Electrification of transportation, communication, working and living continues worldwide. Televisions, telephones, servers are an important part of everyday life. These loads and most sustainable sources as well, have one thing in common: Direct Current. The Dutch research and educational programme ‘DC – road to its full potential’ studies the impact of feeding these appliances from a DC grid. An improvement in energy efficiency is expected, other benefits are unknown and practical considerations are needed to come to a proper comparison with an AC grid. This paper starts with a brief introduction of the programme and its first stages. These stages encompass firstly the commissioning, selection and implementation of a safe and user friendly testing facility, to compare performance of domestic appliances when powered with AC and DC. Secondly, the relationship between the DC-testing facility and existing modeling and simulation assignments is explained. Thirdly, first results are discussed in a broad sense. An improved energy efficiency of 3% to 5% is already demonstrated for domestic appliances. That opens up questions for the performance of a domestic DC system as a whole. The paper then ends with proposed minor changes in the programme and guidelines for future projects. These changes encompass further studying of domestic appliances for product-development purposes, leaving less means for new and costly high-power testing facilities. Possible gains are 1) material and component savings 2) simpler and cheaper exteriors 3) stable and safe in-house infrastructure 4) whilst combined with local sustainable generation. That is the road ahead. 10.1109/DUE.2014.6827758
This paper discusses an Induction Generator (IG) system that provides regulated voltage at any load condition. It utilizes the classical self-excitation principle but, in addition to that, it makes use of a current regulated PWM inverter to control the output voltage magnitude. It is primarily intended for micro hydro plants to be used in rural areas, where the cost of conventional distribution system is high, and the water resources are available to drive an unregulated low hear turbine. The proposed topology is presented, followed by an analysis of the control structure. The methodology is validated via simulation studies.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
Organ-on-a-chip technology holds great promise to revolutionize pharmaceutical drug discovery and development which nowadays is a tremendously expensive and inefficient process. It will enable faster, cheaper, physiologically relevant, and more reliable (standardized) assays for biomedical science and drug testing. In particular, it is anticipated that organ-on-a-chip technology can substantially replace animal drug testing with using the by far better models of true human cells. Despite this great potential and progress in the field, the technology still lacks standardized protocols and robust chip devices, which are absolutely needed for this technology to bring the abovementioned potential to fruition. Of particular interest is heart-on-a-chip for drug and cardiotoxicity screening. There is presently no preclinical test system predicting the most important features of cardiac safety accurately and cost-effectively. The main goal of this project is to fabricate standardized, robust generic heart-on-a-chip demonstrator devices that will be validated and further optimized to generate new physiologically relevant models to study cardiotoxicity in vitro. To achieve this goal various aspects will be considered, including (i) the search for alternative chip materials to replace PDMS, (ii) inner chip surface modification and treatment (chemistry and topology), (iii) achieving 2D/3D cardiomyocyte (long term) cell culture and cellular alignment within the chip device, (iv) the possibility of integrating in-line sensors in the devices and, finally, (v) the overall chip design. The achieved standardized heart-on-a-chip technology will be adopted by pharmaceutical industry. This proposed project offers a unique opportunity for the Netherlands, and Twente in particular, which has relevant expertise, potential, and future perspective in this field as it hosts world-leading companies pioneering various core aspects of the technology that are relevant for organs-on-chips, combined with two world-leading research institutes within the University of Twente.
