1,721,042 research outputs found
Creazione ed implementazione di una Certifcation Authority open-source
No full textQUADERNI DEL DIPARTIMENTO DI SCIENZE ECONOMICHE, MATEMATICHE E STATISTICHE DELL'UNIVERSITA' DEGLI STUDI DI FOGGI
Classification of genomic patterns and protein structures using Clustering and Neural Networks in Mathematica
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Analisi di modelli primari e secondari nella microbiologia predittiva: approccio a reti neurali
No full textLa progettazione di un sistema distribuito orientato alla didattica
No full textQuaderni del Dipartimento di Scienze Economiche, Matematiche e Statistiche – Universita' degli Studi di Foggi
A Neural Network Model for Forecasting Photovoltaic Deployment in Italy
Full text linkThe photovoltaic (PV) industry in Italy has already crossed the threshold of 1 GW of installed capacity. Currently there are approximately 70,000 certified facilities in operation for a power generation of 1,300 GWh/year. With these figures, Italy has become the second country in Europe for PV installed power after Germany. The energy produced would be sufficient to meet the power needs of approximately 1,200,000 people. This leads to some questions: Will this technology continue to grow exponentially even after the recent reduction in rates by the Energy Bill? Will the number of installed PV facilities still grow even with less public support and (probably) a reduction in the technology purchase price? The purpose of this paper is therefore to develop a conceptual model to make a prediction of the PV installed power in Italy through the use of “supervised” artificial neural networks. This model is also applied to the analysis of the spread of this technology in some other European countries
A Wafer Bin Map Clustering Algorithm for Optimizing I.C. Yield Management
No full textThe semiconductor manufacturing process involves long and complex activities, with intensive use of resources. Producers compete through the introduction of new technologies for increasing yield and reducing costs. So, yield improvement is becoming increasingly important as advanced production technologies are complex and interrelated.
In particular, Wafer Bin Maps (WBMs) presenting specific fault models provide crucial information to keep track of process problems in semiconductor manufacturing: production control is often based on the “judgment” of expert engineers who, however, carry out the analysis of map templates through simple visual exploration. In this way, existing studies are subjective, time consuming, and are also limited by the capacity of human recognition.
This study proposes a data mining approach derived from the analysis of social networks, which integrates correlation graphs with clustering analysis to quickly extract patterns from WBMs and then bind them to manufacturing defects. An empirical study of wafer clustering was conducted on real production data for validating the proposed clustering algorithm
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