Transient liquid phase sintering of high density Transient liquid phase sintering of high density Fe₃Al using Fe and Fe₂Al₅/FeAl₂ powders Part 1: Experimentation and results

High density Fe[sub 3]Al was produced through transient liquid phase sintering, using rapid heating rates of greater than 150 K min[sup -1] and a mixture of prealloyed and elemental powders. Prealloyed Fe[sub 2]Al[sub 5]/FeAl[sub 2] (50Fe/50Al, wt-%) powder was added to elemental iron powder in a ratio appropriate for producing an overall Fe[sub 3]Al (13•87 wt-%) ratio. The heating rate, sintering time, sintering temperature, green density and powder particle size were controlled during the study. Heating rate, sintering time and powder particle size had the most significant influence upon the sintered density of the compacts. The highest sintered density of 6•12 Mg m[sup -3] (92% of the theoretical density for Fe3Al) was achieved after 15 minutes of sintering at 1350°C, using a 250 K min[sup - 1] heating rate, 1-6 μm Fe powders and 5•66 μm alloy powders. SEM microscopy suggests that agglomerated Fe[sub 2]Al[sub 5]/ FeAl[sub 2] particles, which form a liquid during sintering, are responsible for a significant portion of the remaining porosity in high sintered density compacts, creating stable pores, larger than 100 μm diameter, after melting. High density was achieved by minimising the Kirkendall porosity formed during heating by unbalanced diffusion and solubility between the iron and Fe[sub 2]Al[sub 5]/FeAl[sub 2] components. The lower diffusion rate of aluminium in the prealloyed powder into the iron compared with elemental aluminium in iron, coupled with a fast heating rate, is expected to permit minimal iron-aluminium interdiffusion during heating so that when a liquid forms the aluminium dissolves in the iron to promote solidification at a lower aluminium content. This leads to a further reduction in porosity

Simulation of electric vehicle driver behaviour in road transport and electric power networks

The integration of electric vehicles (EVs) will affect both electricity and transport systems and research is needed on finding possible ways to make a smooth transition to the electrification of the road transport. To fully understand the EV integration consequences, the behaviour of the EV drivers and its impact on these two systems should be studied. This paper describes an integrated simulation-based approach, modelling the EV and its interactions in both road transport and electric power systems. The main components of both systems have been considered, and the EV driver behaviour was modelled using a multi-agent simulation platform. Considering a fleet of 1000 EV agents, two behavioural profiles were studied (Unaware/Aware) to model EV driver behaviour. The two behavioural profiles represent the EV driver in different stages of EV adoption starting with Unaware EV drivers when the public acceptance of EVs is limited, and developing to Aware EV drivers as the electrification of road transport is promoted in an overall context. The EV agents were modelled to follow a realistic activity-based trip pattern, and the impact of EV driver behaviour was simulated on a road transport and electricity grid. It was found that the EV agents’ behaviour has direct and indirect impact on both the road transport network and the electricity grid, affecting the traffic of the roads, the stress of the distribution network and the utilization of the charging infrastructure

A multi-agent based scheduling algorithm for adaptive electric vehicles charging

This paper presents a decentralized scheduling algorithm for electric vehicles charging. The charging control model follows the architecture of a Multi-Agent System (MAS). The MAS consists of an Electric Vehicle (EV)/Distributed Generation (DG) aggregator agent and “Responsive” or “Unresponsive” EV agents. The EV/DG aggregator agent is responsible to maximize the aggregator’s profit by designing the appropriate virtual pricing policy according to accurate power demand and generation forecasts. “Responsive” EV agents are the ones that respond rationally to the virtual pricing signals, whereas “Unresponsive” EV agents define their charging schedule regardless the virtual cost. The performance of the control model is experimentally demonstrated through different case studies at the micro-grid laboratory of the National Technical University of Athens (NTUA) using Real Time Digital Simulator. The results highlighted the adaptive behaviour of “Responsive” EV agents and proved their ability to charge preferentially from renewable energy sources

Controlling a mobile robot with a biological brain

The intelligent controlling mechanism of a typical mobile robot is usually a computer system. Some recent research is ongoing in which biological neurons are being cultured and trained to act as the brain of an interactive real world robot�thereby either completely replacing, or operating in a cooperative fashion with, a computer system. Studying such hybrid systems can provide distinct insights into the operation of biological neural structures, and therefore, such research has immediate medical implications as well as enormous potential in robotics. The main aim of the research is to assess the computational and learning capacity of dissociated cultured neuronal networks. A hybrid system incorporating closed-loop control of a mobile robot by a dissociated culture of neurons has been created. The system is flexible and allows for closed-loop operation, either with hardware robot or its software simulation. The paper provides an overview of the problem area, gives an idea of the breadth of present ongoing research, establises a new system architecture and, as an example, reports on the results of conducted experiments with real-life robots

Smart management of the charging of electric vehicles

The objective of this thesis was to investigate the management of electric vehicles (EVs) battery charging in distribution networks. Real EVs charging event data were used to investigate their charging demand profiles in a geographical area. A model was developed to analyse their charging demand characteristics and calculate their potential medium term operating risk level for the distribution network of the corresponding geographical area. A case study with real charging and weather data from three counties in UK was presented to demonstrate the modelling framework. The effectiveness of a charging control algorithm is dependent on the early knowledge of future EVs charging demand and local generation. To this end, two models were developed to provide this knowledge. The first model utilised data mining principles to forecast the day ahead EVs charging demand based on historical charging event data. The performance of four data mining methods in forecasting the charging demand of an EVs fleet was evaluated using real charging data from USA and France. The second model utilised a data fitting approach to produce stochastic generation forecast scenarios based only on the historical data. A case study was presented to evaluate the performance of the model based on real data from wind generators in UK. An agent-based control algorithm was developed to manage the EVs battery charging, according to the vehicles’ owner preferences, distribution network technical constraints and local distributed generation. Three agent classes were considered, a EVs/DG aggregator and “Responsive” or “Unresponsive” EVs. The real-time operation of the control system was experimentally demonstrated at the Electric Energy Systems Laboratory hosted at the National Technical University of Athens. A series of experiments demonstrated the adaptive behaviour of “Responsive” EVs agents and proved their ability to charge preferentially from renewable energy sources

Revealing ensemble state transition patterns in multi-electrode neuronal recordings using hidden Markov models

In order to harness the computational capacity of dissociated cultured neuronal networks, it is necessary to understand neuronal dynamics and connectivity on a mesoscopic scale. To this end, this paper uncovers dynamic spatiotemporal patterns emerging from electrically stimulated neuronal cultures using hidden Markov models (HMMs) to characterize multi-channel spike trains as a progression of patterns of underlying states of neuronal activity. However, experimentation aimed at optimal choice of parameters for such models is essential and results are reported in detail. Results derived from ensemble neuronal data revealed highly repeatable patterns of state transitions in the order of milliseconds in response to probing stimuli

Verbalising Query Results to Text

Η δημοκρατικοποίηση των βάσεων δεδομένων επικεντρώνεται στο να γίνουν οι βάσεις δεδομένων διαθέσιμες σε χρήστες που δεν έχουν γνώσεις ή επαφή με γλώσσες επερωτήσεων. Προς αυτή την κατεύθυνση έχει δοθεί ήδη πολύ προσπάθεια στην επίλυση 2 προβλημάτων: Text-to-SQL που επικεντρώνεται στην μετάφραση φυσικής γλώσσας σε SQL και SQL- to-Text που επιλύει το αντίστροφο πρόβλημα. Όμως, ένα πρόβλημα για το οποίο δεν έχει προταθεί λύση ακόμα είναι η εξήγηση των αποτελεσμάτων των επερωτήσεων σε φυσική γλώσσα. Πρώτα ορίζουμε το πρόβλημα Query Results-to-Text ως: δωσμένων των αποτελεσμάτων ενός επερωτήματος, παράγουμε φυσική γλώσσα που περιγράφει τα αποτελέσματα. Προτείνουμε την χρήση προ-εκπαίδευσης του μοντέλου χρησιμοποιώντας δεδομένα πινάκων από τον πραγματικό κόσμο με σκοπό την κατανόηση των πινάκων. Δεύτερον, προτείνουμε ένα βήμα προεπεξεργασίας του επερωτήματος ώστε τα αποτελέσματα, που είναι η είσοδος του μοντέλου μας, να περιέχουν επιπρόσθετα χαρακτηριστικά και οδηγούν σε πιο πλούσιες λεξικοποιήσεις. Τέλος δημιουργούμε δύο Query Results-to-Text Benchmarks, τα οποίο έιναι τα πρώτα σύνολα δεδομένων που περιέχουν αποτελέσματα επερωτήσεων για τελική εκπαίδευση αλλά και για αξιολόγηση του μοντέλου.Database democratization focuses on making databases accessible to non-expert users that are not familiar with database query languages like SQL. In this direction, a lot of effort has already been put into two problems: Text-to-SQL, which focuses on translating a natural language query to SQL, and SQL-to-Text, which is the inverse problem. However, work has lagged behind in explaining query results in natural language. We first define the Query Results-to-Text problem as: given the results of a query, produce a natural language verbalisation describing these results. Then we attempt solving Query Results-to-Text by defining a model, namely QR2T. We propose pretraining QR2T using real-world table datasets focusing on table understanding. We use a preprocessing step that transforms the query so that the query results, which are the input of our model, include additional information, which QR2T can utilize leading to a more informative verbalisation. Finally, we create two Query Results-to-Text benchmarks, which are the first datasets that contain query result verbalisations for both fine-tuning and evaluation

Probabilistic wind power forecasting and its application in the scheduling of gas-fired generators

Accurate information regarding the uncertainty of short-term forecast for aggregate wind power is a key to efficient and cost effective integration of wind farms into power systems. This paper presents a methodology for producing wind power forecast scenarios. Using historical wind power time series data and the Kernel Density Estimator (KDE), probabilistic wind power forecast scenarios were generated according to a rolling process. The improvement achieved in the accuracy of forecasts through frequent updating of the forecasts taking into account the latest realized wind power was quantified. The forecast scenarios produced by the proposed method were used as inputs to a unit commitment and optimal dispatch model in order to investigate how the uncertainty in wind forecast affect the operation of power system and in particular gas-fired generators