Polynomial Delay Algorithm for Listing Minimal Edge Dominating sets in Graphs

The Transversal problem, i.e, the enumeration of all the minimal transversals of a hypergraph in output-polynomial time, i.e, in time polynomial in its size and the cumulated size of all its minimal transversals, is a fifty years old open problem, and up to now there are few examples of hypergraph classes where the problem is solved. A minimal dominating set in a graph is a subset of its vertex set that has a non empty intersection with the closed neighborhood of every vertex. It is proved in [M. M. Kant\'e, V. Limouzy, A. Mary, L. Nourine, On the Enumeration of Minimal Dominating Sets and Related Notions, In Revision 2014] that the enumeration of minimal dominating sets in graphs and the enumeration of minimal transversals in hypergraphs are two equivalent problems. Hoping this equivalence can help to get new insights in the Transversal problem, it is natural to look inside graph classes. It is proved independently and with different techniques in [Golovach et al. - ICALP 2013] and [Kant\'e et al. - ISAAC 2012] that minimal edge dominating sets in graphs (i.e, minimal dominating sets in line graphs) can be enumerated in incremental output-polynomial time. We provide the first polynomial delay and polynomial space algorithm that lists all the minimal edge dominating sets in graphs, answering an open problem of [Golovach et al. - ICALP 2013]. Besides the result, we hope the used techniques that are a mix of a modification of the well-known Berge's algorithm and a strong use of the structure of line graphs, are of great interest and could be used to get new output-polynomial time algorithms.Comment: proofs simplified from previous version, 12 pages, 2 figure

Συσχέτιση Σεισμικών Παραμέτρων και Ολικών Δεικτών Βλάβης σε Κατασκευές Οπλισμένου Σκυροδέματος

In the present work, a correlation is made between seismic intensity parameters and total damage indicators of reinforced concrete structures. For this purpose, natural ground motions recordings were used, from which intensity measures were calculated and alternatives were proposed. Then, through dynamic inelastic time history analyzes, damage indices were evaluated for 1st and 2nd order theory (P-Delta effects). The correlation between each seismic parameter and the corresponding damage index was made through polynomial regressions. Seismic parameters that do not take into account the characteristics of the building, showed a minimal or moderate correlation, while spectral parameters of velocities and energies explain the change of damage indices in percentages above 80 or even 90 per cent.Στην παρούσα εργασία πραγματοποιείται συσχέτιση μεταξύ σεισμικών παραμέτρων έντα-σης και ολικών δεικτών βλάβης σε κατασκευές οπλισμένου σκυροδέματος. Για αυτόν τον σκοπό, χρησιμοποιήθηκαν καταγραφές πραγματικών σεισμών, από τις οποίες υπολογίστηκαν παράμετροι, που χαρακτηρίζουν ένα επιταχυνσιογράφημα και προτάθηκαν εναλλακτικές τους. Στη συνέχεια μέσω δυναμικών ανελαστικών αναλύσεων χρονοϊστορίας αποτιμήθηκαν δείκτες βλάβης για θεωρία 1ης και 2ης τάξης. Η συσχέτιση μεταξύ του εκάστοτε ζεύγους σεισμικής παραμέτρου και δείκτη βλάβης έγινε μέσω πολυωνυμικών παλινδρομήσεων. Σεισμικές παρά-μετροι που δεν λαμβάνουν υπόψη τους τα χαρακτηριστικά του ταλαντωτή, παρουσίασαν ελάχιστη ή μέτρια συσχέτιση, ενώ φασματικές παράμετροι ταχυτήτων και ενεργειών ερμη-νεύουν τη μεταβολή των δεικτών βλάβης σε ποσοστά άνω του 80 ή και 90 τοις εκατό

Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning Algorithms

Advanced machine learning algorithms have the potential to be successfully applied to many areas of system modelling. In the present study, the capability of ten machine learning algorithms to predict the structural damage of an 8-storey reinforced concrete frame building subjected to single and successive ground motions is examined. From this point of view, the initial damage state of the structural system, as well as 16 well-known ground motion intensity measures, are adopted as the features of the machine-learning algorithms that aim to predict the structural damage after each seismic event. The structural analyses are performed considering both real and artificial ground motion sequences, while the structural damage is expressed in terms of two overall damage indices. The comparative study results in the most efficient damage index, as well as the most promising machine learning algorithm in predicting the structural response of a reinforced concrete building under single or multiple seismic events. Finally, the configured methodology is deployed in a user-friendly web application.Keywords: seismic sequence; machine learning algorithms; repeated earthquakes; structural damage prediction; intensity measures; damage accumulation; machine learning; artificial neural networ

Structural Damage Prediction Under Seismic Sequence Using Neural Networks

Advanced machine learning algorithms, such as neural networks, have the potential to be successfully applied to many areas of system modelling. Several studies have been already conducted on forecasting structural damage due to individual earthquakes, ignoring the influence of seismic sequences, using neural networks. In the present study, an ensemble neural network approach is applied to predict the final structural damage of an 8-storey reinforced concrete frame under real and artificial ground motion sequences. Successive earthquakes consisted of two seismic events are utilised. We considered 16 well-known ground motion intensity measures and the structural damage that occurred by the first earthquake as the features of the machine-learning problem, while the final structural damage was the target. After the first seismic events and after the seismic sequences, both actual values of damage indices are calculated through nonlinear time history analysis. The machine-learning model is trained using the dataset generated from artificial sequences. Finally, the predictive capacity of the fitted neural network is accessed using the natural seismic sequences as a test set

Comparing energy system optimization models and integrated assessment models: Relevance for energy policy advice

Background: The transition to a climate neutral society such as that envisaged in the European Union Green Deal requires careful and comprehensive planning. Integrated assessment models (IAMs) and energy system optimisation models (ESOMs) are both commonly used for policy advice and in the process of policy design. In Europe, a vast landscape of these models has emerged and both kinds of models have been part of numerous model comparison and model linking exercises. However, IAMs and ESOMs have rarely been compared or linked with one another. Methods: This study conducts an explorative comparison and identifies possible flows of information between 11 of the integrated assessment and energy system models in the European Climate and Energy Modelling Forum. The study identifies and compares regional aggregations and commonly reported variables. We define harmonised regions and a subset of shared result variables that enable the comparison of scenario results across the models. Results: The results highlight how power generation and demand development are related and driven by regional and sectoral drivers. They also show that demand developments like for hydrogen can be linked with power generation potentials such as onshore wind power. Lastly, the results show that the role of nuclear power is related to the availability of wind resources. Conclusions: This comparison and analysis of modelling results across model type boundaries provides modellers and policymakers with a better understanding of how to interpret both IAM and ESOM results. It also highlights the need for community standards for region definitions and information about reported variables to facilitate future comparisons of this kind. The comparison shows that regional aggregations might conceal differences within regions that are potentially of interest for national policy makers thereby indicating a need for national-level analysis

Increasing the Operation Efficiency of Air Conditioning System for Integrated Power Plant on the Base of Its Monitoring

Increasing the Operation Efficiency of Air Conditioning System for Integrated Power Plant on the Base of Its Monitoring / E. Trushliakov, A. Radchenko, S. Forduy, A. Zubarev, A. Hrych // Advances in intelligent systems and computing. – 2020. – Т. 1113 AISC . – P. 351–360Abstract. The efficiency of reciprocating gas engines of integrated energy systems (IES) for combined electricity, heat and refrigeration generation is strictly influenced by their cyclic air temperatures. To evaluate the effect of gas engine cyclic air deep cooling, compared with conventional its cooling, the data on dependence of fuel consumption and power output of gas engine JMS 420 GS-N.L on its inlet air temperature at varying ambient air temperatures at the entrance of the radiator for scavenge air cooling were received. The results of treatment of gas engine efficiency monitoring proved non-effective operation of conventional chilling all the ambient air, coming into the engine room, because of increased air temperature at the inlet of turbocharger (TC), caused by heat influx from surroundings in the engine room. A new method of gas engine inlet air two-stage cooling at increased ambient air temperatures and advanced cyclic air cooling system with absorption lithium-bromide chiller and refrigerant ejector chiller was proposed. With this chilled water from absorption lithiumbromide chiller is used as a coolant in the first high-temperature stage of engine inlet air cooler and boiling refrigerant of ejector chiller in the second lowtemperature stage

Efficient Looping Units for FPGAs

∗ Special thanks to Grigoris Dimitroulakos for presenting this paper at the ISVLSI 2010 venu