Analysis of the introduction of a business process mapping system in Rosetti Marino spa

After a first theoric introduction about Business Process Re-engineering (BPR), are considered in particular the possible options found in literature regarding the following three macro-elements: the methodologies, the modelling notations and the tools employed for process mapping. The theoric section is the base for the analysis of the same elements into the specific case of Rosetti Marino S.p.A., an EPC contractor, operating in the Oil&Gas industry. Rosetti Marino implemented a tool developped internally in order to satisfy its needs in the most suitable way possible and buit a Map of all business processes,navigable on the Company Intranet. Moreover it adopted a methodology based upon participation, interfunctional communication and sharing. The GIGA introduction is analysed from a structural, human resources, political and symbolic point of view.

Tecniche automatiche di mapping e localizzazione in ambienti indoor

Il mapping di grandezze fisiche risulta estremamente importante, essendo in grado di fornire un adeguato supporto per la localizzazione e il monitoraggio di parametri ambientali sensibili. Nel caso indoor, in assenza di un sistema di localizzazione di riferimento analogo al GPS per il caso outdoor, sfruttando appieno le potenzialità della sensoristica a bordo degli smartphone, si è fatto progressivamente strada il mapping di grandezze fisiche quali, ad esempio, il segnale Wi-Fi e il campo magnetico terrestre. In questo caso il mapping, senza richiedere alcuna infrastruttura e coadiuvato dall'utilizzo di dispositivi portatili largamente diffusi ad uso quotidiano, rappresenta una soluzione relativamente recente ridefinibile come Mobile Crowd Sensing. Il MCS rappresenta un nuovo paradigma di servizio, volto a sfruttare l'interconnettività tra dispositivi portatili per effettuare misurazioni di caratteristiche ambientali in maniera automatizzata, aggregandole in un sistema cloud usufruibile ad una vasta comunità. Tuttavia , il considerevole flusso di dati generato, la variabilità temporale delle grandezze di interesse e il rumore insito nelle misurazioni costituiscono problematiche fondamentali per l'utilizzo e la gestione delle misurazioni effettuate. Per tali motivi l'attività di tesi ha previsto i seguenti obiettivi: (i) fornire una panoramica delle principali tecniche e tecnologie di localizzazione volta a motivare l'importanza del mapping di grandezze fisiche ambientali; (ii) individuazione di grandezze fisiche appetibili per la creazione di mappe affidabili e realizzabili nei contesti applicativi più disparati, sfruttando risorse già presenti nell'ambiente; (iii) sviluppo di un algoritmo statistico in grado di fornire una stima accurata dell'andamento spaziale della grandezza di interesse attraverso un numero limitato di misurazioni, mantenendo la compatibilità con processi MCS e una bassa complessità computazionale. L’algoritmo sviluppato è stato validato attraverso simulazioni e misurazioni svolte in ambienti reali. In particolare, prove sperimentali sono state effettuate nell’arena Vicon nei laboratori DEI dell’Università di Bologna, sede Cesena, concepita dal gruppo di ricerca Casy.

Noise mapping of highly trafficked roads of the province of Bologna according to D.Lgs. 194/2005

The European Community has stressed the importance of achieving a common understanding to deal with the environmental noise through community actions of the Member States. This implies the use of harmonized indicators and specific information regarding the values of indicators, the exceedance of limits and the number of people and dwellings exposed to noise. The D.Lgs. 149/2005 in compliance with the European Directive 2002/49/EC defines methodologies, noise indicators and types of outputs required. In this dissertation the work done for the noise mapping of highly trafficked roads of the Province of Bologna will be reported. The study accounts for the environmental noise generated by the road infrastructure outside the urban agglomeration of Bologna. Roads characterized by an annual traffic greater than three millions of vehicles will be considered. The process of data collection and validation will be reported, as long as the implementation of the calculation method in the software and the procedure to create and calibrate the calculation model. Results will be provided as required by the legislation, in forms of maps and tables. Moreover results regarding each road section accounted will be combined to gain a general understanding of the situation of the overall studied area. Although the understanding of the noise levels and the number of people exposed is paramount, it is not sufficient to develop strategies of noise abatement interventions. Thus a further step will be addressed: the creation of priority maps as the basis of action plans for organizing and prioritizing solutions for noise reduction and abatement. Noise reduction measures are reported in a qualitative way in the annex and constitute a preliminary research.

Machine Learning algorithm on seat comfort level measurement, mapping pressure values with body regions

The comfort level of the seat has a major effect on the usage of a vehicle; thus, car manufacturers have been working on elevating car seat comfort as much as possible. However, still, the testing and evaluation of comfort are done using exhaustive trial and error testing and evaluation of data. In this thesis, we resort to machine learning and Artificial Neural Networks (ANN) to develop a fully automated approach. Even though this approach has its advantages in minimizing time and using a large set of data, it takes away the degree of freedom of the engineer on making decisions. The focus of this study is on filling the gap in a two-step comfort level evaluation which used pressure mapping with body regions to evaluate the average pressure supported by specific body parts and the Self-Assessment Exam (SAE) questions on evaluation of the person’s interest. This study has created a machine learning algorithm that works on giving a degree of freedom to the engineer in making a decision when mapping pressure values with body regions using ANN. The mapping is done with 92% accuracy and with the help of a Graphical User Interface (GUI) that facilitates the process during the testing time of comfort level evaluation of the car seat, which decreases the duration of the test analysis from days to hours.

Private sensors and crowdsourced rainfall data: accuracy and effectiveness for monitoring severe events and mapping pluvial flooding in urban areas

The increasing number of extreme rainfall events, combined with the high population density and the imperviousness of the land surface, makes urban areas particularly vulnerable to pluvial flooding. In order to design and manage cities to be able to deal with this issue, the reconstruction of weather phenomena is essential. Among the most interesting data sources which show great potential are the observational networks of private sensors managed by citizens (crowdsourcing). The number of these personal weather stations is consistently increasing, and the spatial distribution roughly follows population density. Precisely for this reason, they perfectly suit this detailed study on the modelling of pluvial flood in urban environments. The uncertainty associated with these measurements of precipitation is still a matter of research. In order to characterise the accuracy and precision of the crowdsourced data, we carried out exploratory data analyses. A comparison between Netatmo hourly precipitation amounts and observations of the same quantity from weather stations managed by national weather services is presented. The crowdsourced stations have very good skills in rain detection but tend to underestimate the reference value. In detail, the accuracy and precision of crowd- sourced data change as precipitation increases, improving the spread going to the extreme values. Then, the ability of this kind of observation to improve the prediction of pluvial flooding is tested. To this aim, the simplified raster-based inundation model incorporated in the Saferplaces web platform is used for simulating pluvial flooding. Different precipitation fields have been produced and tested as input in the model. Two different case studies are analysed over the most densely populated Norwegian city: Oslo. The crowdsourced weather station observations, bias-corrected (i.e. increased by 25%), showed very good skills in detecting flooded areas.