MINERVA – the Ministerial Network for Valorising Activities in Digitisation Towards an Agreed European Platform for Digitisation of Cultural and Scientific Heritage

MINERVA is a project funded by the European Commission IST Programme within the 5th Framework Programme. It created a network of EU Ministries and other agencies in charge of cultural policies and programmes, which is open to enlargement to new countries and new sectors of the civil society. The network discusses, correlates and harmonises the activities carried out in the field of digitisation of cultural and scientific heritage, aiming at creating a common European platform made up of agreed recommendations, guidelines, standards. The network acts also to foster collaboration between European Commission and Member States, to ensure awareness of European policies at national level, to exchange good practice, to coordinate national programmes in order to embed in national digitisation activities the technical results achieved by the network. Some main outcomes of the activities are presented.

Neural Network Based Approach for Developing the Enterprise Strategy

Modern enterprises work in highly dynamic environment. Thus, the developing of company strategy is of crucial importance. It determines the surviving of the enterprise and its evolution. Adapting the desired management goal in accordance with the environment changes is a complex problem. In the present paper, an approach for solving this problem is suggested. It is based on predictive control philosophy. The enterprise is modelled as a cybernetic system and the future plant response is predicted by a neural network model. The predictions are passed to an optimization routine, which attempts to minimize the quadratic performance criterion.

Performance Comparison of MATLAB and Neuro Solution Software on Estimation of Fuel Economy by Using Artificial Neural Network

In the world, scientific studies increase day by day and computer programs facilitate the human’s life. Scientists examine the human’s brain’s neural structure and they try to be model in the computer and they give the name of artificial neural network. For this reason, they think to develop more complex problem’s solution. The purpose of this study is to estimate fuel economy of an automobile engine by using artificial neural network (ANN) algorithm. Engine characteristics were simulated by using “Neuro Solution” software. The same data is used in MATLAB to compare the performance of MATLAB is such a problem and show its validity. The cylinder, displacement, power, weight, acceleration and vehicle production year are used as input data and miles per gallon (MPG) are used as target data. An Artificial Neural Network model was developed and 70% of data were used as training data, 15% of data were used as testing data and 15% of data is used as validation data. In creating our model, proper neuron number is carefully selected to increase the speed of the network. Since the problem has a nonlinear structure, multi layer are used in our model.