Fuzzy Ensembles for Embedding Adaptive Behaviours in Semi-Autonomous Avatars in 3D Virtual Worlds

Semi-autonomous avatars should be both realistic and believable. The goal is to learn from and reproduce the behaviours of the user-controlled input to enable semi-autonomous avatars to plausibly interact with their human-controlled counterparts. A powerful tool for embedding autonomous behaviour is learning by imitation. Hence, in this paper an ensemble of fuzzy inference systems cluster the user input data to identify natural groupings within the data to describe the users movement and actions in a more abstract way. Multiple clustering algorithms are investigated along with a neuro-fuzzy classifier; and an ensemble of fuzzy systems are evaluated.

A 30-Day-Ahead Forecast Model for Grass Pollen in North London, United Kingdom

A 30-day ahead forecast method has been developed for grass pollen at north London. The total period of the grass pollen season is covered by eight multiple regression models, each covering a 10-day period running consecutively from 21st May to 8th August. This means that three models were used for each 30-day forecast. The forecast models were produced using grass pollen and environmental data from 1961-1999 and tested on data from 2000 and 2002. Model accuracy was judged in two ways: the number of times the forecast model was able to successfully predict the severity (relative to the 1961-1999 dataset as a whole) of grass pollen counts in each of the eight forecast periods on a scale of one to four; and the number of times the forecast model was able to predict whether grass pollen counts were higher or lower than the mean. The models achieved 62.5% accuracy in both assessment years when predicting the relative severity of grass pollen counts on a scale of one to four, which equates to six of the eight 10-day periods being forecast correctly. The models attained 87.5% and 100% accuracy in 2000 and 2002 respectively when predicting whether grass pollen counts would be higher or lower than the mean. Attempting to predict pollen counts during distinct 10-day periods throughout the grass pollen season is a novel approach. The models also employed original methodology in the use of winter averages of the North Atlantic Oscillation to forecast 10-day means of allergenic pollen counts.

Constructing a 7-day Ahead Forecast Model for Grass Pollen at North London, United Kingdom

A number of media outlets now issue medium-range (~7 day) weather forecasts on a regular basis. It is therefore logical that aerobiologists should attempt to produce medium-range forecasts for allergenic pollen that cover the same time period as the weather forecasts. The objective of this study is to construct a medium-range (< 7 day) forecast model for grass pollen at north London. The forecast models were produced using regression analysis based on grass pollen and meteorological data from 1990-1999 and tested on data from 2000 and 2002. The modelling process was improved by dividing the grass pollen season into three periods; the pre-peak, peak and post peak periods of grass pollen release. The forecast consisted of five regression models. Two simple linear regression models predicting the start and end date of the peak period, and three multiple regression models forecasting daily average grass pollen counts in the pre-peak, peak and post-peak periods. Overall the forecast models achieved 62% accuracy in 2000 and 47% in 2002, reflecting the fact that the 2002 grass pollen season was of a higher magnitude than any of the other seasons included in the analysis. This study has the potential to make a notable contribution to the field of aerobiology. Winter averages of the North Atlantic Oscillation were used to predict certain characteristics of the grass pollen season, which presents an important advance in aerobiological work. The ability to predict allergenic pollen counts for a period between five and seven days will benefit allergy sufferers. Furthermore, medium-range forecasts for allergenic pollen will be of assistance to the medical profession, including allergists planning treatment and physicians scheduling clinical trials.

Blog on virtual worlds for children

A series of blog posts giving ideas on virtual worlds for children and emerging industry activity and practices.

Short-term scheduling considering five-minute and hour-ahead energy resource management

The introduction of new distributed energy resources, based on natural intermittent power sources, in power systems imposes the development of new adequate operation management and control methods. This paper proposes a short-term Energy Resource Management (ERM) methodology performed in two phases. The first one addresses the hour-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. Both phases consider the day-ahead resource scheduling solution. The ERM scheduling is formulated as an optimization problem that aims to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixed-integer non-linear programming approach and by a heuristic approach based on genetic algorithms. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units has been implemented in a PSCADbased simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior.

Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints

Energy resource scheduling becomes increasingly important, as the use of distributed resources is intensified and massive gridable vehicle use is envisaged. The present paper proposes a methodology for dayahead energy resource scheduling for smart grids considering the intensive use of distributed generation and of gridable vehicles, usually referred as Vehicle- o-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with V2G owners. It takes into account these contracts, the user´s requirements subjected to the VPP, and several discharge price steps. Full AC power flow calculation included in the model allows taking into account network constraints. The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33 bus distribution network and V2G is used to illustrate the good performance of the proposed method.

ANN based day-ahead spinning reserve forecast for electricity market simulation

Electricity market players operating in a liberalized environment require adequate decision support tools, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. This paper deals with short-term predication of day-ahead spinning reserve (SR) requirement that helps the ISO to make effective and timely decisions. Based on these forecasted information, market participants can use strategic bidding for day-ahead SR market. The proposed concepts and methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.

ANN based day-ahead ancillary services forecast for electricity market simulation

Adequate decision support tools are required by electricity market players operating in a liberalized environment, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services (AS) represent a good negotiation opportunity that must be considered by market players. Based on the ancillary services forecasting, market participants can use strategic bidding for day-ahead ancillary services markets. For this reason, ancillary services market simulation is being included in MASCEM, a multi-agent based electricity market simulator that can be used by market players to test and enhance their bidding strategies. The paper presents the methodology used to undertake ancillary services forecasting, based on an Artificial Neural Network (ANN) approach. ANNs are used to day-ahead prediction of non-spinning reserve (NS), regulation-up (RU), and regulation down (RD). Spinning reserve (SR) is mentioned as past work for comparative analysis. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.

Price forecasting in the day-ahead Iberian electricity market using a conjectural variations Arima model

Price forecast is a matter of concern for all participants in electricity markets, from suppliers to consumers through policy makers, which are interested in the accurate forecast of day-ahead electricity prices either for better decisions making or for an improved evaluation of the effectiveness of market rules and structure. This paper describes a methodology to forecast market prices in an electricity market using an ARIMA model applied to the conjectural variations of the firms acting in an electricity market. This methodology is applied to the Iberian electricity market to forecast market prices in the 24 hours of a working day. The methodology was then compared with two other methodologies, one called naive and the other a direct forecast of market prices using also an ARIMA model. Results show that the conjectural variations price forecast performs better than the naive and that it performs slightly better than the direct price forecast.

Day-ahead Resource Scheduling Including Demand Response for Electric Vehicles

The energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and massive gridable vehicle (V2G) use is envisaged. This paper presents a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and V2G. The main focus is the comparison of different EV management approaches in the day-ahead energy resources management, namely uncontrolled charging, smart charging, V2G and Demand Response (DR) programs i n the V2G approach. Three different DR programs are designed and tested (trip reduce, shifting reduce and reduce+shifting). Othe r important contribution of the paper is the comparison between deterministic and computational intelligence techniques to reduce the execution time. The proposed scheduling is solved with a modified particle swarm optimization. Mixed integer non-linear programming is also used for comparison purposes. Full ac power flow calculation is included to allow taking into account the network constraints. A case study with a 33-bus distribution network and 2000 V2G resources is used to illustrate the performance of the proposed method.

Stochastic Framework for Strategic Decision-making of Load-serving Entities for Day-ahead Market

The deregulation of electricity markets has diversified the range of financial transaction modes between independent system operator (ISO), generation companies (GENCO) and load-serving entities (LSE) as the main interacting players of a day-ahead market (DAM). LSEs sell electricity to end-users and retail customers. The LSE that owns distributed generation (DG) or energy storage units can supply part of its serving loads when the nodal price of electricity rises. This opportunity stimulates them to have storage or generation facilities at the buses with higher locational marginal prices (LMP). The short-term advantage of this model is reducing the risk of financial losses for LSEs in DAMs and its long-term benefit for the LSEs and the whole system is market power mitigation by virtually increasing the price elasticity of demand. This model also enables the LSEs to manage the financial risks with a stochastic programming framework.

Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Being ahead of its time the consumer resistance phenomenon: A comparative case study research

Double Degree.

Tetrathiafulvalene and 2,2'-Bipyridine: Bridging both Worlds in the Pursuit of Novel Molecular Materials

The preparation and characterization of two families of building blocks for molecule-based magnetic and conducting materials are described in three projects. In the first project the synthesis and characterization of three bis-imine ligands LI - L3 is reported. Coordination of LI to a series of metal salts afforded the five novel coordination complexes Sn(L4)C4 (I), [Mn(L4)(u-CI)(CI)(EtOH)h (II), [CU(L4)(u-sal) h(CI04)2 (sal = salicylaldehyde anion) (III), [Fe(Ls)2]CI (IV) and [Fe(LI)h(u-O) (V). All complexes have been structurally and magnetically characterized. X-ray diffraction studies revealed that, upon coordination to Lewis acidic metal salts, the imine bonds of LI are susceptible to nucleophilic attack. As a consequence, the coordination complexes (I) - (IV) contain either the cyclised ligand L4 or hydrolysed ligand Ls. In contrast, the dimeric Fe3+ complex (V) comprises two intact ligand LI molecules. In. this complex, the ligand chelates two Fe(III) centres in a bis-bidentate manner through the lone pairs of a phenoxy oxygen and an imine nitrogen atom. Magnetic studies of complexes (II-V) indicate that the dominant interactions between neighbouring metal centres in all of the complexes are antiferromagnetic. In the second project the synthesis and characterization two families of TTF donors, namely the cyano aryl compounds (VI) - (XI) and the his-aryl TTF derivatives (XII) - (XIV) are reported. The crystal structures of compounds (VI), (VII), (IX) and (XII) exhibit regular stacks comprising of neutral donors. The UV -Vis spectra of compounds (VI) - (XIV) present an leT band, indicative of the transfer of electron density from the TTF donors to the aryl acceptor molecules. Chemical oxidation of donors (VI), (VII), (IX) and (XII) with iodine afforded a series of CT salts that where possible have been characterized by single crystal X -ray diffraction. Structural studies showed that the radical cations in these salts are organized in stacks comprising of dimers of oxidized TTF donors. All four salts behave as semiconductors, displaying room temperature conductivities ranging from 1.852 x 10-7 to 9.620 X 10-3 Scm-I. A second series of CT salts were successfully prepared via the technique of electrocrystallization. Following this methodology, single crystals of two CT salts were obtained. The single crystal X-ray structures of both salts are isostructural, displaying stacks formed by trimers of oxidized donors. Variable temperature conductivity measurements carried out on this series of CT salts reveal they also are semiconductors with conductivities ranging from 2.94 x 10-7 to 1.960 X 10-3 S em-I at room temperature. In the third project the synthesis and characterization of a series of MII(hfac)2 coordination complexes of donor ligand (XII) where M2+ = Co2+, Cu2+, Ni2+ and Zn2+ are reported. These complexes crystallize in a head-to-tail arrangement of TTF donor and bipyridine moieties, placing the metal centres and hfac ligands are located outside the stacks. Magnetic studies of the complexes (XV) - (XVIII) indicate that the bulky hfac ligands prevent neighbouring metal centres from assembling in close proximity, and thus they are magnetically isolated.

Comparative Lessons on Corporate law and CSR: Hard Law as Solution? Do the French offer the way ahead?

This paper provides a comparative analysis of corporate law and CSR and asks whether there are lessons for Australia from corporate law and CSR developments in France. This presentation presents a summary of the provisions of the new French Act Number 2010-788 passed on 12 July 2010 – called “Grenelle 2” –. Firstly, article 225 of Law’s Grenelle 2 changes the Commercial Code to extend the reach of non-financial reporting and to ensure its pertinence. Secondly, article 227 Law’s Grenelle 2 amends certain provisions of the Commercial and Environmental Codes and incorporates into substantive law the liability of parent companies for their subsidiaries. In fine, article 224 of Law’s Grenelle 2 reinforces the pressure on the market to act in a responsible manner. It modifies article 214-12 of the Monetary and Financial Code in order to compel institutional investors (mutual funds and fund management companies) to take social, environmental and governance criteria into account in their investment policy.