Nicholas Karcanias

We deal with the study of the Determinantal Assignment Problem when the parameters of the compensator are not entirely free, but some are fixed. The problem is reduced to a restricted form of an exterior algebra problem, which is referred to as Partial Decomposability problem. We study this problem and if there is no solution, we examine the problem of approximate partial decomposability. We treat the problem of exact or partial decomposability into a vector and a multi-vector of lower dimension. If this procedure is repeated then this results in an approximation of the initial multi-vector into a decomposable vector. The approximation of a vector by an optimal decomposable multi-vector is a nonlinear procedure and has been solved completely using the Power Method. The method developed in this paper although it produces a sub-optimal solution, can be used alternatively for the solution of DAP or the Approximate DAP, as a shorter and easier approach, because it is based on known tools as the SVD. We apply these results to treat the Restricted - Approximate Decomposability problem, which leads to approximate solutions to the pole and zero assignment problems.

Cycling is an increasingly popular mode of travel in cities, but its poor safety record currently acts as a hurdle to its wider adoption as a real alternative to the private car. A particular source of hazard appears to originate from the interaction of cyclists with motorized traffic at low speeds in urban areas. But while technological advances in recent years have resulted in numerous attempts at systems for preventing cyclist-vehicle collisions, these have generally encountered the challenge of accurate cyclist localization. This paper addresses this challenge by introducing an innovative bicycle localization algorithm, which is derived from the geometrical relationships and kinematics of bicycles. The algorithm relies on the measurement of a set of kinematic variables (such as yaw, roll, and steering angles) through low-cost on-board sensors. It then employs a set of Kalman filters to predict-correct the direction and position of the bicycle and fuse the measurements in order to improve positioning accuracy. The capabilities of the algorithm are then demonstrated through a real-world field experiment using an instrumented bicycle, called ``iBike'', in an urban environment. The results show that the proposed fusion achieves considerably lower positioning errors than that would be achieved based on dead-reckoning alone, which makes the algorithm a credible basis for the development of future collision warning and avoidance systems.

The paper discusses the outcomes of the conference organized by the InDeal project. The conference took place on 12 December 2018 in Montpellier as part of the EnerGaia energy forum 2018. A holistic interdisciplinary approach for district heating and cooling (DHC) networks is presented that integrates heterogeneous innovative technologies from various scientific sectors. The solution is based on a multi-layer control and modelling framework that has been designed to minimize the total plant production costs and optimize heating/cooling distribution. Artificial intelligence tools are employed to model uncertainties associated with weather and energy demand forecasts, as well as quantify the energy storage capacity. Smart metering devices are utilized to collect information about all the crucial heat substations’ parameters, whereas a web-based platform offers a unique user environment for network operators. Three new technologies have been further developed to improve the efficiency of pipe design of DHC systems: (i) A new sustainable insulation material for reducing heat losses, (ii) a new quick-fit joint for an easy installation, and (iii) a new coating for reducing pressure head losses. The results of a study on the development and optimization of two energy harvesting systems are also provided. The assessment of the environmental, economic and social impact of the proposed holistic approach is performed through a life cycle analysis. The validation methodology of the integrated solution is also described, whereas conclusions and future work are finally given.

This paper presents a holistic innovative solution for the transformation of the current district heating and cooling systems to automated more efficient systems. A variety of technological advancements have been developed and integrated to support the effective energy management of future district heating and cooling sector. First, we identify and discuss the main challenges and needs that are in line with the EU objectives and policy expectations. We give an overview of the main parts that our solution consists of, with emphasis on the forecasting tools and an advanced control system that addresses unit commitment and economic load dispatch problems. The proposed control approach employs distributed and scalable optimisation algorithms for optimising the short-term operations of a district heating and cooling plant subject to technical constraints and uncertainties in the energy demand. To test the performance and validate the proposed control system, a district heating plant with multiple energy generation units and real-life heat load data were used. Simulation experiments were also used to evaluate the benefits of using thermal storage units in district heating systems. The results show that the proposed method could achieve significant cost savings when energy storage is employed. The proposed control strategy can be applied for both operating optimally district heating plants with storage and supporting investment planning for new storage units.

The problem of finding the mapping between unimoduar transformations relating two minimal matrix fraction descriptions (MFDs) of a transfer function, and the similarity transformations relating the respective minimal state-space representations is considered. It is shown that the problem is equivalent to finding the relation of MFDs of the input-state transfer functions of the two systems. This relation turns out to be an equivalence relation involving the unimodular and the similarity matrices relating the MFDs and the state-space systems, respectively. A canonical form for MFDs under this equivalence relation is obtained and it is shown that it leads to a canonical state-space representation, via a realisation procedure.

Heating and cooling consume half of the EU’s energy and much of it is wasted. The lion’s share of heating and cooling is still generated from fossil fuels, mainly natural gas, while only 18% is generated from renewable energy. In order to fulfil the EU’s climate and energy goals, the heating and cooling sector must therefore sharply reduce its energy consumption and cut its use of fossil fuels. To this end the European Commission adopted a heating and cooling strategy in February 2016 as part of the wider Energy Union Package. A number of activities and projects funded by the programmes of European Union are supporting this new EU heating and cooling strategy.